International Review of Neurobiology Volume 67 Neuroimaging Part B Distinguishing Neural Substrates of Heterogeneity among Anxiety Disorders

DISTINGUISHING NEURAL SUBSTRATES OF HETEROGENEITY AMONG ANXIETY DISORDERS

Jack B. Nitschke* and Wendy Hellery *Waisman Laboratory for Brain Imaging and Behavior, Departments of Psychiatry and Psychology, University of Wisconsin, Madison, Wisconsin 53705 y Psychology Department and the Beckman Institute for Advanced Science and Technology University of Illinois, Champaign‐Urbana, Illinois 61820

I. Obsessive-Compulsive Disorder A. Cognitive Studies B. Neuroimaging Studies II. Posttraumatic Stress Disorder A. Cognitive Studies B. Neuroimaging Studies III. Panic Disorder A. Cognitive Studies B. Neuroimaging Studies IV. Specific Phobia (Simple Phobia) A. Cognitive Studies B. Neuroimaging Studies V. Social Phobia (Social Anxiety Disorder) A. Cognitive Studies B. Neuroimaging Studies VI. Generalized Anxiety Disorder A. Cognitive Studies B. Neuroimaging Studies VII. Discussion References

The cognitive and brain correlates of anxiety disorders are active areas of investigation that contribute importantly to the accruing knowledge base on pathological processes associated with anxiety. This chapter reviews cognitive and neuroimaging findings for each of six anxiety disorders: obsessive‐compulsive disorder, posttraumatic stress disorder, panic disorder, specific phobia, social phobia, and generalized anxiety disorder. Cognitive biases toward threat are common to all six disorders and may correspond to hyperactivation of right hemisphere regions dedicated to threat. Brain structures subserving anxiety pathology include orbital frontal, prefrontal, anterior cingulate, and right parietal Note: This chapter is reprinted with permission from Oxford University Press. It also appears in Cognitive and AVective Neuroscience of Psychopathology. INTERNATIONAL REVIEW OF NEUROBIOLOGY, VOL. 67 DOI: 10.1016/S0074-7742(05)67001-8

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cortices, as well as the amygdala, hippocampus, and caudate. Neuropsychological data from extant research on cognitive, aVective, and brain processes indicate that anxiety is not a homogenous entity and that attempts to map its neural circuitry must consider symptom variability and comorbidity with other types of psychopathology. Numerous areas of research and an expansive corpus of literature contribute to current neuroscientific understanding of anxiety pathology. Basic neuroscience work explaining some of the key brain mechanisms and circuitry of fear and anxiety in nonhuman animals has served as a critical foundation for research on humans with anxiety disorders (e.g., Davis, 1999; Davis and Lee, 1998; Gray and McNaughton, 2000; LeDoux, 1996). Research with people who have brain damage has provided further links between anxiety and the brain (Nitschke et al., 2000). The relatively new fields of cognitive neuroscience and aVective neuroscience are concerned with very similar questions regarding brain– behavior relationships as were fundamental to the older field of neuropsychology. The neuroimaging tools central to those disciplines are important supplements to the traditional neuropsychological test batteries and cognitive/behavioral paradigms. Thus, this review of the neuroscience findings in anxiety disorders covers a wide array of methods that together inform knowledge of the brain mechanisms involved in the circuitry governing pathological forms of anxiety. Cognitive science is another relevant domain of research that is often overlooked in discussions about brain substrates in anxiety (Nitschke and Heller, 2002; Nitschke et al., 2000). Cognitive research over the past two decades has contributed substantially to knowledge about brain function in anxiety. A large body of work demonstrates that anxiety disorders are characterized by cognitive biases, indicating a heightened response to the possibility of threat (for reviews, see Heinrichs and Hofmann, 2001; McNally, 1998; Nitschke and Heller, 2002; Nitschke et al., 2000). Attentional biases have been elicited very reliably across a variety of paradigms in which potentially threatening information is associated with greater attentional capture in individuals with anxiety disorders than in controls. The interference of this attentional capture with other cognitive processing serves as the operationalization of this bias in research studies. Furthermore, attentional biases have been found to disappear on remission (for reviews, see McNally, 1998; Nitschke and Heller, 2002), suggesting that such biases are state‐ dependent. Cognitive biases have also been observed in the form of interpretation and memory biases. Across a number of diVerent paradigms involving ambiguous stimuli that can be interpreted as threatening or neutral, anxious people choose the threatening meaning. Accruing evidence suggests that anxiety disorders are also accompanied by enhanced memory for negative or threatening information under certain conditions. These cognitive data suggest dysfunctional

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activation of a right hemisphere system involved in threat perception (for review, see Nitschke et al., 2000; see also Compton et al., 2000, 2003). In addition to these cognitive biases, cognitive deficits have been documented in anxiety disorders. One is a tendency to do more poorly on tasks that require selective attention and concentration. This deficit has been suggested to reflect a general problem of preoccupation and distraction because of worry or rumination that interferes with other mental processes (for review, see Nitschke et al., 2000). Compromised visual–spatial functioning has also been reported. In addition, individuals with posttraumatic stress disorder often exhibit deficits in explicit memory. Taken together, these cognitive deficits suggest aberrant frontal, anterior cingulate, right parietal, and hippocampal functioning. Building on this cognitive research, as well as on behavioral and electroencephalographic (EEG) findings (for review, see Nitschke et al., 2000) and an extensive literature in nonhuman animals examining fear and anxiety (for reviews, see LeDoux, 1996; Davis and Lee, 1998), hemodynamic neuroimaging research has implicated a number of the suggested regions (for reviews, see Martis et al., 2002; Nitschke and Heller, 2002; Nitschke et al., 2000). Despite substantial evidence for abnormalities in cognitive processing and brain activation and for consistency across emotional, cognitive, and neural domains, the diversity of findings also warrants the importance of respecting unique patterns and heterogeneity both among and within the various anxiety disorders. An observation that has become increasingly salient in the burgeoning neurobiological literature on anxiety and its disorders is the lack of clarity and specificity about what anxiety is. Views of anxiety range from its use in contemporary clinical research as a rubric term that encompasses fear, panic, worry, and all the anxiety disorders listed in the Diagnostic and Statistical Manual of Mental Disorders (DSM‐IV; APA, 1994) to its very specific operationalization referring to context conditioning and long‐term sensitization (e.g., Davis and Lee, 1998) to a more generic personality dimension closely linked to neuroticism (e.g., Gray, 1982; Gray and McNaughton, 2000). Furthermore, the heterogeneity within each of the diVerent anxiety disorders has become increasingly apparent and represents a major problem for investigators attempting to uncover the neurobiological correlates of individual anxiety disorders. Inconsistencies across studies may be explained by the fact that anxiety is not a unitary phenomenon and that diVerent types and symptoms of anxiety are associated with particular cognitive patterns (Heller and Nitschke, 1998; Nitschke et al., 2000). An important mission of neuroscience research in this area is to help unravel the inchoate notions of anxiety that currently exist. Thus, although it is important to look for generalizations regarding the neural mechanisms of anxiety, it is also necessary to consider the possibility of heterogeneity by being as specific as possible regarding the disorder or type of anxiety under investigation.

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The aim of this chapter is to assess what is known about the neural circuitry of anxiety disorders by examining the relevant cognitive research and structural and functional neuroimaging data, including morphometric magnetic resonance imaging (MRI), functional MRI (f MRI), and positron emission tomography (PET), using various radiotracers such as [18F ]fluorodeoxyglucose (FDG) for glucose metabolism and 15O‐labeled water for blood flow, single‐photon emission computed tomography (SPECT) with 133Xenon or 99mTc‐HMPAO, and scalp‐ recorded EEG. This review of the cognitive and human neuroimaging literatures reveals that the anxiety disorders engage brain regions involved in threat perception (e.g., right prefrontal cortex [PFC], parietal, and temporal regions; Compton et al., 2000, 2003; Davidson et al., 2000; Nitschke and Heller, 2002; Nitschke et al., 2000), anxious arousal (right parietal and temporal cortices; Nitschke et al., 2000), fear (e.g., amygdala; LeDoux, 1996), vigilance for motivationally salient events (e. g., amygdala; Davis and Whalen, 2001; Whalen, 1998), decoding of motivationally relevant emotional information such as the reward and punishment value of a stimulus (e.g., orbital frontal cortex [OFC]; Rolls, 1999a,b), worry (e.g., left PFC, parietal, and temporal regions; Nitschke et al., 2000), response conflict (e.g., anterior cingulate cortex [ACC]; Carter et al., 1999, 2000; Davidson et al., 2002), and memory (e.g., hippocampus; Squire, 1992). The aforementioned heterogeneity should also lead to some diverse findings for the diVerent anxiety disorders. The focus here is on the anxiety disorders as defined by the DSM‐IV, although consistent patterns have emerged in studies using nonclinical and brain‐lesioned human populations (for review, see Nitschke et al., 2000).

I. Obsessive-Compulsive Disorder

The most widely investigated anxiety disorder from a neuropsychological perspective has been obsessive‐compulsive disorder (OCD). The emphasis on obsessions and compulsions in connection to the experienced anxiety and distress reported by individuals with OCD is unique among the anxiety disorders and can be linked to a number of neuropsychological abnormalities.

A. COGNITIVE STUDIES An extensive cognitive literature on OCD points most strongly to nonverbal memory and other visual–spatial deficits (e.g., Boone et al., 1991; Christensen et al., 1992; Cohen et al., 1996; Kim et al., 2002; Kwon et al., 2003; Purcell et al., 1998; Savage et al., 1996, 1999; Zielinski et al., 1991; see also Constans et al., 1995; McNally and Kohlbeck, 1993). No evidence of a verbal memory deficit has been

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found (Foa et al., 1997). Ample documentation of impaired executive functions including verbal fluency also exists (e.g., Abbruzzese et al., 1997; Head et al., 1989; Kim et al., 2002; Kwon et al., 2003; Moritz et al., 2001; Purcell et al., 1998; Veale et al., 1996), with trends reported by Cohen et al. (1996) for several neuropsychological tests. Recent data suggest that these cognitive deficits are for the most part not due to medication (serotonin reuptake inhibitors, benzodiazepines), although there was some evidence for benzodiazepines aVecting verbal fluency (Mataix‐Cols et al., 2002). It is possible that problems in executive function could account for at least some of the visual–spatial deficits found. For example, Savage et al. (1999) found that poor organizational strategies for copying a figure mediated the nonverbal memory deficit for reproducing a figure among patients with OCD. In addition, that laboratory found worse nonverbal memory using the same task to be associated with larger right PFC volumes in patients with OCD (Grachev et al., 1998). Should these findings be replicated, one possible explanation is that such morphometry diVerences may be a manifestation of heightened threat perception and negative aVect occupying right PFC resources normally dedicated to nonverbal memory. Two very recent studies (Cavedini et al., 2002; Nielen et al., 2002) examined decision‐making function in patients with OCD using a standard gambling task found to be sensitive to PFC damage, especially ventromedial regions (Bechara et al., 1994). Cavedini et al. (2002) reported poorer performance in patients with OCD, especially treatment nonresponders, than patients with panic disorder or nonpsychiatric controls. Using the identical task, Nielen et al. (2002) found a similar pattern with patients with severe OCD performing more disadvantageously than nonpsychiatric controls and patients with OCD scoring below the mean on measures of symptom severity (pairwise comparisons were not reported). Of additional relevance to cognitive functioning in OCD, Foa et al. (1993) documented that the attentional bias toward threat‐related material seen across all the anxiety disorders for the emotional Stroop paradigm also emerges in OCD. In this paradigm, subjects are asked to name the color of words varying in emotional content while ignoring their meanings. Foa et al. (1993) found that patients with OCD with washing rituals took longer to name the color for contamination words than for neutral words, suggesting that the threatening nature of the contamination words interfered with the task of naming the color. They also had longer response latencies to contamination words than did OCD nonwashers or nonpsychiatric controls. On the other hand, OCD nonwashers had longer latencies to negative than neutral words, whereas the opposite pattern was seen in controls. In similar studies in which the OCD‐relevant words did not necessarily reflect the primary concerns of the patients with OCD, no interference eVects were observed (Kampman et al., 2002; McNally et al., 1990). Another

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possible explanation for these null findings is that emotional Stroop eVect sizes are very small (Koven et al., 2003). These and other inconsistencies in the literature on attention bias in anxiety disorders may, therefore, be due to a lack of power. These attentional findings implicate the involvement of right hemisphere regions important for threat perception (Compton et al., 2000, 2003; Nitschke and Heller, 2002; Nitschke et al., 2000). With regard to memory biases, Foa et al. (1997) found no bias for contamination sentences for either explicit or implicit memory. However, they did replicate the finding that patients with OCD are less confident than nonpsychiatric controls about memory‐related judgments (Constans et al., 1995; McNally and Kohlbeck, 1993). Thus, the cognitive literature is fairly conclusive in demonstrating that the memory concerns frequently voiced by patients with OCD (e.g., ‘‘Did I lock the door?) are not the result of a memory deficit or a memory bias but rather a lack of confidence in their memory. This lack of confidence is likely to be related to the characteristic fear of forgetting some activity that has become a target for compulsive behavior, and thus is a reflection of the underlying anxiety in OCD. As such, the degree to which confidence is lacking might correlate with activity in neural structures associated with fear and other anxiety‐related features.

B. NEUROIMAGING STUDIES The most common finding to emerge in morphometric MRI studies to date is a reduction in caudate volume (Robinson et al., 1995; Rosenberg et al., 1997), with a trend also reported by Jenike et al. (1996). However, Aylward et al. (1996) found no caudate diVerences, and Scarone et al. (1992) reported an increase in right caudate volume. Similar inconsistencies for the caudate have emerged in functional imaging studies examining resting states using PET and SPECT to measure glucose metabolism and blood flow. Increases were reported in three samples (Baxter et al., 1987, 1988; Rubin et al., 1992), with Perani et al. (1995) reporting a trend in the same direction. However, Lucey et al. (1997a,b) found a reduction, and others observed no diVerences from nonpsychiatric controls (e.g., Swedo et al., 1989). In contrast, symptom provocation paradigms using PET (McGuire et al., 1994; Rauch et al., 1994) and fMRI (Breiter et al., 1996) have consistently shown caudate activation. The corticostriatal model of OCD proposed by Rauch et al. (1998) posits that pathology within the caudate results in OFC and ACC hyperactivity by means of ineYcient thalamic gating. An OFC–caudate loop may comprise much of the neural circuitry associated with the repetitive and perseverative nature of obsessions and compulsions (see also Alexander et al., 1991). Further pursuing the evidence of caudate abnormalities, Rauch et al. used PET and fMRI while

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patients with OCD performed an implicit learning task shown to be dependent on striatal function in nonpsychiatric volunteers (Rauch et al., 1995b, 1997c). The striatum was not activated in subjects with OCD (Rauch et al., 1997a), suggesting that OCD symptoms pertinent to perseveration occupy the resources normally allocated to implicit learning. The caudate activation observed in the symptom provocation studies suggests that inconsistencies in other reported findings may be due to heterogeneity in the degree of symptom severity among OCD patient samples. Taken together, these data suggest that augmented caudate activation is associated with the perseverative nature of obsessions and compulsions, which also may serve to enlarge that structure. Hemodynamic studies of OCD have implicated a number of other regions, most consistently OFC and ventral ACC, areas of the brain involved in various aVective and cognitive processes. PET and SPECT studies using protocols not involving a task have revealed that patients with OCD have more blood flow or glucose metabolism than nonpsychiatric controls in OFC (Baxter et al., 1987, 1988; Kwon et al., 2003; Rubin et al., 1992; Swedo et al., 1989; but see Machlin et al., 1991; but see Busatto et al., 2001) and regions of interest (ROIs) including the ventral ACC (Machlin et al., 1991; Perani et al., 1995; but see Busatto et al., 2001). Similar findings for the OFC have been observed during an auditory continuous performance task using PET to measure glucose metabolism (Nordahl et al., 1989). OFC and ventral ACC activations have also been reported in fMRI (Adler et al., 2000; Breiter et al., 1996) and PET (Rauch et al., 1994) studies using symptom provocation paradigms with actual obsessional stimuli. In another study using symptom provocation by means of presentation of idiographically selected contaminants in patients with OCD, McGuire et al. (1994) found symptom intensity to be correlated with right inferior frontal/OFC but not ACC activation. Busatto et al. (2001) found that obsessive‐compulsive symptoms correlated positively with left OFC blood flow. A less potent experimental elicitation of symptoms by means of auditory presentation of obsessional material did not induce blood flow changes in these areas using PET (Cottraux et al., 1996). Further evidence of frontal and ACC dysfunction in OCD can be inferred from two EEG studies examining event‐related potentials (ERPs) in a Go‐NoGo task (Malloy et al., 1989) and a selective attention task (Towey et al., 1994). With the amygdala often highlighted in models of the neural circuitry of fear, anxiety, and emotion (e.g., Charney et al., 1998; LeDoux, 1996), it is worth noting that amygdala activation has not been routinely documented in human neuroimaging research on OCD. In fact, only Breiter et al. (1996) reported amygdala activation in subjects with OCD, who were exposed to stimuli highly relevant to their obsessions. One of the subjects studied by McGuire et al. (1994) also showed amygdala activation, as did two of the seven patients with OCD examined by Adler et al. (2000). Because the documented rapid habituation of the amygdala and the vulnerability of the amygdala to susceptibility artifact in fMRI research

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may have precluded detection of amygdala activation in previous research, studies addressing these issues (e.g., analysis of early trials, event‐related fMRI, attention to data acquisition parameters to maximize signal in the amygdala) are needed to further assess whether the amygdala plays a role in OCD. Treatment studies further inform the neural circuitry characterizing OCD. Both cognitive‐behavioral and pharmacological therapies have been associated with normalized (i.e., decreased) glucose metabolism in the caudate nucleus (Baxter et al., 1992; Benkelfat et al., 1990; Saxena et al., 1999, 2002; Schwartz et al., 1996; but see Baxter et al., 1987; Swedo et al., 1992), and OFC (Benkelfat et al., 1990; Saxena et al., 1999, 2002; Swedo et al., 1992; but see Baxter et al., 1987, 1992; Schwartz et al., 1996). Results for the ventral ACC are less certain, because relevant reports have either not examined that area or have not distinguished ventral and dorsal sectors. A recent report explicitly delineating a ventral ACC region found no treatment eVect (Saxena et al., 2002). Some studies examining ROIs including the ventral ACC have found reductions in glucose metabolism (Baxter et al., 1992; Perani et al., 1995), whereas others have not (Brody et al., 1998; Saxena et al., 1999; Swedo et al., 1992). Similar findings have emerged for blood flow measured by SPECT in the OFC (Rubin et al., 1995) and ACC (Hoehn‐Saric et al., 1991). Baxter et al. have reported that pretreatment correlations between caudate and orbital regions ranging from 0.44 to 0.74 decreased significantly after eVective treatment (Baxter et al., 1992; Schwartz et al., 1996). In addition, lower pretreatment OFC glucose metabolism may be associated with better response to medications, whereas the converse may be true for psychotherapy (Brody et al., 1998; Saxena et al., 1999; Swedo et al., 1989). Response to pharmacotherapy has also been predicted by glucose metabolic reductions in the ACC (Swedo et al., 1989) and left caudate (Benkelfat et al., 1990); however, Brody et al. (1998) did not replicate those findings (Saxena et al., 1999 only reported conducting tests for the OFC). Overall, treatment studies further implicate the caudate, OFC, and ACC in OCD. They suggest that the hyperactivity of these structures in OCD is state‐dependent and that pretreatment levels of activity may have prognostic value. The inconsistencies in findings remain to be addressed in further research. The cognitive data implicating right hemisphere regions suggest the importance of threat perception and evaluation in OCD. The functional significance of the caudate, OFC, and ACC hyperactivity often reported before treatment are consistent with their roles in the perseverative nature of obsessions and compulsions, in decoding reward and punishment values of perceived and real events (c.f. Rolls, 1999; Rauch, 2003), and in response conflict about whether to perform some mental activity or compulsive behavior. These abnormalities may disrupt decision‐making, consistent with recent work in that area (Cavedini et al., 2002; Nielen et al., 2002). As noted previously, the cognitive data imply the

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involvement of right hemisphere regions involved in threat perception. The absence of more right‐sided eVects in the imaging data should be interpreted with caution, because it may be due to the diYculty of conducting adequate tests of asymmetry (Davidson and Irwin, 1999). A final important consideration is the high level of comorbid depression in people with OCD. Visual–spatial (including nonverbal memory) and executive deficits in depression are well established and are congruent with the reduced activity in right parietal and bilateral frontal regions often reported for depression (Heller and Nitschke, 1997, 1998). The extent to which the nonverbal memory and executive deficits in OCD can be attributed to depression, anxiety, obsessions, or compulsions has not been determined, in part because the co‐occurrence of these various symptoms makes disentangling their eVects exceedingly diYcult. Moritz et al. (2001) reported recent evidence for the importance of depression in their study of 36 patients with OCD using a median split according to Hamilton Rating Scale for Depression (HRSD) scores. Patients with OCD with high HRSD scores performed more poorly than nonpsychiatric controls on six measures of executive function and more poorly than those with low scores on four of those measures. Furthermore, the pronounced brain abnormalities accompanying depression (for reviews, see Davidson et al., 2002; Mohanty and Heller, 2002) certainly have consequences for neuroimaging research on OCD. For example, Martinot et al. (1990) reported a bilateral diminution of PFC glucose metabolism in 16 patients with OCD compared with 8 nonpsychiatric controls and no eVects for OFC; however, despite not meeting criteria for DSM‐III, current major depressive episode, these patients likely were characterized by significantly higher levels of depression than the controls.

II. Posttraumatic Stress Disorder

The past decade has witnessed an explosion of research examining the neurobiological mechanisms and neuropsychological, behavioral, and cognitive concomitants of posttraumatic stress disorder (PTSD). The diagnostic requirement of exposure to a traumatic event makes this disorder an ideal candidate for testing etiological hypotheses based on the rich conditioning literature, including classical cue conditioning, operant conditioning, and context conditioning. However, the array of reexperiencing, avoidance, and arousal symptoms and the common comorbidity with depression (and substance abuse in war veterans) add layers of complexity that make unraveling the neural circuitry of PTSD seem an intractable enterprise. Moreover, classification of PTSD remains a highly controversial topic, not only with regard to prototypic symptoms and

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subtyping but also with regard to whether it should be considered an anxiety disorder. Despite these obstacles, the emerging body of research is contributing to understanding this elusive condition.

A. COGNITIVE STUDIES As with OCD, a commonly reported cognitive abnormality in PTSD is an attentional bias toward threat‐related stimuli on tasks such as the emotional Stroop test. This eVect has been reported for rape victims (e.g., Foa et al., 1991), combat veterans (e.g., Kaspi et al., 1995; McNally et al., 1990, 1993, 1996; Vrana et al., 1995), motor vehicle accident victims (Bryant and Harvey, 1995; Buckley et al., 2002), crime victims (Paunovic et al., 2002), and people involved in a ferry disaster (Thrasher et al., 1994). Recovery from PTSD has been shown to eliminate the attentional bias (Foa et al., 1991), whereas patients with PTSD who have not recovered continue to show the bias toward threat cues when retested (McNally et al., 1993). A memory bias toward trauma‐relevant material has also been found in patients with PTSD for explicit memory (Paunovic et al., 2002; Vrana et al., 1995; but see Bremner et al., 2003) and conceptual implicit memory dependent on the meaning of the words used (Amir et al., 1996c), suggesting a more pervasive proclivity toward threat‐related material that is not confined to the frequently reported attentional eVect. No bias was found on tasks of implicit memory depending on physical, perceptual features of the words rather than on their meaning (McNally and Amir, 1996; Paunovic et al., 2002). Recent evidence suggests that individuals with PTSD also show an interpretation bias for threat meanings of homographs (Amir et al., 2002). Consistent with these cognitive data, a recent ERP study using threat words as the low‐probability stimulus type in an oddball paradigm reported that patients with PTSD had larger P3 amplitudes than nonpsychiatric controls for trauma‐ relevant but not trauma‐irrelevant threat words (Stanford et al., 2001). The oddball paradigm is composed of frequent presentations of one stimulus type and infrequent presentations of a second stimulus type, which typically elicits an enlarged ERP component known as P3 or P300. Taken together, these data are suggestive of right hemisphere abnormalities pertinent to threat perception. The other salient cognitive finding in PTSD is an explicit memory deficit. Compromised memory performance has been observed in combat veterans (e.g., Bremner et al., 1993; McNally et al., 1994, 1995; Uddo et al., 1993; Yehuda et al., 1995), rape victims ( Jenkins et al., 1998), and adult survivors of childhood abuse (e.g., Bremner et al., 1995b; but see Stein et al., 1997). A recent study evaluating the relationship between PTSD symptoms and cognitive functioning within 10 days of traumatic events (primarily motor vehicle accidents and terror attacks) found converging evidence for an association with nonverbal but not verbal

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memory (Brandes et al., 2002). These data corroborate the reports of reduced hippocampal volume in PTSD to be reviewed next.

B. NEUROIMAGING STUDIES Most studies examining structural abnormalities in PTSD have implicated the hippocampi, with reduced volume ranging from 8–30% (Bremner et al., 1995a, 1997; Gilbertson et al., 2002; Gurvits et al., 1996; Villarreal et al., 2002; but see Fennema‐Notestine et al., 2002). Similarly, SchuV et al. (1997) reported a trend for a 6% right hippocampal reduction in combat veterans, and Stein et al. (1997) observed a 5% reduction on the left in adult survivors of childhood abuse, most of whom met DSM‐IV criteria for PTSD. It is not known whether smaller hippocampal size is due to cell loss, cell atrophy, or to some other cause (Rajkowska, 2000; Sapolsky, 2000; Sheline, 2000). Controversy persists with regard to the role of cortisol as a causative factor in the hippocampal reductions observed in PTSD (Yehuda, 1997). A recent study with monozygotic twins discordant for combat exposure suggests that smaller hippocampi may be a predisposing factor for PTSD (Gilbertson et al., 2002), although data on pediatric PTSD are not in line with this. In two independent samples, De Bellis et al. (1999, 2002a) observed no hippocampal volumetric diVerences in children and adolescents with PTSD (ages 4–17), a finding replicated in another pediatric sample composed of individuals either meeting criteria for PTSD or with subthreshold PTSD symptoms (Carrion et al., 2001). Regardless, hippocampal abnormalities are likely critical for the aforementioned explicit memory deficit in PTSD. Indeed, Bremner et al. (1995a) reported a strong correlation (r ¼ 0.64) between explicit verbal memory and right hippocampal volume in combat veterans with PTSD. On the other hand, Stein et al. (1997) found no relationship between hippocampal volume and explicit verbal memory in women with a history of childhood sexual abuse, although there was an association (r ¼ –0.73) between the left hippocampus and dissociative symptom severity. Further research is needed on factors (e.g., chronicity of PTSD) contributing to relations among PTSD symptoms, hippocampal volume, and memory. In contrast to the preceding morphometric data, functional neuroimaging studies examining PTSD have implicated a host of structures. Two recent symptom provocation studies used script‐driven imagery in conjunction with PET in adult female victims of childhood sexual abuse with and without PTSD (Bremner et al., 1999a; Shin et al., 1999). Bremner et al. found that personalized traumatic scripts were associated with less blood flow in the right hippocampus and more blood flow in ventral ACC, PFC, insula, posterior cingulate, and motor cortex for women with PTSD than those without. Shin et al. reported more blood

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flow in the ventral ACC, OFC, and insula for childhood abuse victims with than those without PTSD. Two more recent studies using script‐driven imagery with fMRI are characterized by substantial methodological diVerences in the samples and paradigm used from the preceding PET studies that may explain the absence of activation in the regions observed by Bremner et al. and Shin et al. (Lanius et al., 2001, 2002). In other relevant research, combat‐related pictures and sounds activated the ventral ACC in combat veterans with PTSD and combat controls without PTSD (Bremner et al., 1999b; Liberzon et al., 1999). Using SPECT, Liberzon et al. observed activation of the ventral ACC/ medial PFC in nonpsychiatric controls as well. Another report from this group indicated that only subjects with PTSD showed more blood flow in the medial PFC, whereas both subjects with PTSD and nonpsychiatric controls showed a trend for increased blood flow in the ventral ACC (Zubieta et al., 1999). Using PET, Bremner et al. (1999b) also found PTSD to be associated with increased blood flow in parietal, posterior cingulate, and motor areas. In a more recent study of women with PTSD related to severe childhood sexual abuse (rape before the age of 13), Bremner et al. (2003) found more activation in those same three brain areas along with less activation of a large anterior area spanning the OFC, ACC, and medial PFC during retrieval of trauma‐related than neutral word‐ pairs. It remains to be seen whether activation in some of these regions (e.g., ventral ACC) is specific to PTSD or has more to do with task demands or other phenomena (e.g., mood, comorbid depression, the presence of other types of anxiety). Several symptom provocation studies of PTSD have reported amygdala activation (Liberzon et al., 1999; Rauch et al., 1996; Shin et al., 1997). Other areas implicated by Rauch et al. in a PET study using script‐driven imagery were the ventral ACC and right OFC, insula, and temporal cortex. In an independent sample, they found increased ventral ACC blood flow in combat veterans with PTSD when generating a mental image of a previously studied combat picture (Shin et al., 1997). Both those studies also reported a blood flow decrease in Broca’s area in response to trauma‐related stimuli (see also Fischer et al., 1996), perhaps indicative of downregulation of this verbal generation region in the service of more eVective recruitment of phylogenetically older structures more appropriate for the extreme fear and horrific traumas experienced by people who go on to develop PTSD. The importance of the amygdala and OFC for the circuitry implicated in PTSD is further underscored by research not targeting symptom‐related stimuli. Using fMRI and a backward masking paradigm previously shown to activate the amygdala in nonpsychiatric volunteers (Whalen et al., 1998), Rauch et al. (2000) found that combat veterans with PTSD had larger right amygdala responses to fearful faces masked by neutral faces than did combat controls without PTSD. These responses to fear expressions are consistent with cognitive biases toward

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threat discussed earlier for patients with PTSD. An older study conducted by Semple et al. (1993) reported more OFC blood flow as measured by PET during an auditory CPT and a word‐generation task in combat veterans with PTSD and substance abuse than nonpsychiatric controls. Less parietal blood flow during the continuous performance task was also observed (Semple et al., 1996). A newer study from that group found that a similar sample of patients with PTSD had more right amygdalar and left parahippocampal blood flow during the same continuous performance task than nonpsychiatric controls (Semple et al., 2000), adding further support to the symptom provocation findings in the preceding. In sum, both cognitive and neuroimaging findings suggest the engagement of several right hemisphere regions, consistent with evidence that these areas are diVerentially involved in responding to threat. In addition, the neuroimaging data highlight a distributed array of structures not clearly lateralized, including the OFC, ACC, amygdala, and hippocampus, regions associated with decoding motivationally salient material, response conflict, fear, vigilance for motivationally salient events, and memory. As with OCD, the OFC and ventral ACC seem to be involved in the brain circuitry associated with the pathogenesis and expression of PTSD. Important points of divergence between the two disorders emerge in the subcortex, with the caudate specific to OCD and the amygdala and hippocampus implicated in numerous studies examining PTSD. It is unclear whether the decrease in Broca’s area is unique to PTSD, in part because deactivations often are not reported. As with OCD, the rate of depressive disorders in PTSD populations is extremely high, which again warrants attention to the known cognitive and neurobiological correlates of depression in any discussion of the brain circuitry central to PTSD.

III. Panic Disorder

Characterized by recurrent unexpected panic attacks that share many features with basic fear responses, panic disorder has been viewed as the preeminent candidate condition for postulating dysfunction of the fear circuitry identified in research with nonhuman animals. However, the literature has shown this to be a disappointing enterprise, and the neural machinery aVected in panic disorder remains largely a mystery. It is important to note that even in most individuals experiencing frequent panic attacks (once or more per day), more time is spent worrying about having future attacks or about the implications of those attacks than having actual attacks. Addressing this issue, we have previously described the importance of anxious apprehension and anxious arousal as distinct dimensions of anxiety that are manifest to varying degrees at diVerent times both within and across individuals with panic disorder, as well as other anxiety disorders

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(Nitschke et al., 2000). Central to worry, anxious apprehension is characterized by a concern for the future and verbal rumination about negative expectations and fears, whereas anxious arousal is defined by panic symptoms and an immediate fear response. For obvious reasons, animal models are not particularly conducive to tracking the circuitry associated with worry, although research on context conditioning, long‐term sensitization, and anticipatory anxiety is certainly relevant (e.g., Davis and Lee, 1998; Nitschke et al., 2001). In addition to the need for careful dissection of the aVective processes involved, the various neuroscience research tools now available with humans hold considerable promise for identifying the neural correlates of panic disorder.

A. COGNITIVE STUDIES Cognitive reports in the literature on panic disorder have been sparser than for OCD or for PTSD. The most common finding is a bias for panic‐relevant words on implicit and explicit memory tasks (e.g., Amir et al., 1996b; Becker et al., 1994, 1999; Cloitre and Liebowitz, 1991; Cloitre et al., 1994; McNally et al., 1989), although negative findings have been reported (Otto et al., 1994; Rapee, 1994). Perceptual asymmetry on a dichotic listening task suggestive of more left than right hemisphere activity was associated with better memory for threat words in patients with panic disorder but not in nonpsychiatric controls (Otto et al., 1994). These results suggest a pattern akin to the increased left hemisphere activity characterizing generalized anxiety disorder (see later), anxious apprehension, and worry (for review, see Nitschke et al., 2000). There is also evidence of a bias toward threatening words in a priming task involving lexical and nonlexical word pairs, one presented above the other (McNally et al., 1997). Patients with panic showed faster reaction times in naming the threat targets after the threat prime but only when the target was in the bottom position. With regard to other forms of cognitive bias, emotional Stroop interference has been observed in patients with panic disorder (Buckley et al., 2002; Ehlers et al., 1988; McNally et al., 1990, 1994; but see Kampman et al., 2002). Interpretation bias in the form of a bias toward catastrophic interpretation of panic‐ relevant stimuli in ambiguous scenarios was first documented in individuals with agoraphobia (McNally and Foa, 1987) and subsequently in panic disorder (Clark et al., 1997; Harvey et al., 1993). A recent study found such a bias for ambiguous scenarios in children of individuals with panic disorder after viewing a video clip of a woman with panic disorder describing a severe panic attack, suggesting that the increase in panic interpretations for these children may serve as a vulnerability factor for the development of panic disorder (Schneider et al., 2002). These memory, attentional, and interpretation biases again point to the involvement of right hemisphere systems that mediate anxious arousal in response to threat,

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with dichotic listening data suggesting left hemisphere engagement, perhaps reflecting anxious apprehension.

B. NEUROIMAGING STUDIES The one known quantitative morphometric study found that patients with panic disorder had smaller temporal lobes than nonpsychiatric controls but no hippocampal diVerences (Vythilingam et al., 2000). Evidence for temporal lobe aberrations has also been documented by use of qualitative grading methods (Fontaine et al., 1990). Eleven patients exhibited abnormal signal activity in the temporal lobes, which was most prominent at the interface of the right medial temporal lobe and parahippocampal cortex. Consistent with these data, hemodynamic imaging studies have repeatedly implicated abnormalities in hippocampal and parahippocampal regions. The first report was a PET study finding more right than left parahippocampal blood flow in patients with panic disorder who responded to lactate infusion (Reiman et al., 1984). This finding held for the full sample, with right‐sided parahippocampal asymmetries also observed for blood volume and oxygen metabolism (Reiman et al., 1986). DiVerential hippocampal asymmetries in the same direction were found for glucose metabolism in patients with panic disorder while engaged in an auditory continuous performance task (Nordahl et al., 1990, 1998). Other investigators have reported diVerent hippocampal and parahippocampal eVects. Bisaga et al. (1998) found that patients with panic disorder exhibited more glucose metabolism in the left hippocampus and parahippocampal area than nonpsychiatric controls. Those patients also exhibited less glucose metabolism in right inferior parietal and right superior temporal regions, which could be due to comorbid depression (Heller and Nitschke, 1998). Patients with panic disorder showed more left hippocampal blood flow measured by PET than nonpsychiatric controls in anticipation of a pentagastrin challenge and subsequently when its eVects had subsided (Boshuisen et al., 2002). In a SPECT study, De Cristofaro et al. (1993) found no diVerences in hippocampal asymmetry, but rather patients showed bilateral decreases. In light of hippocampal involvement in explicit memory, these findings suggest that the hippocampi and surrounding parahippocampal areas may play a role in the explicit memory bias toward threat emerging in the cognitive literature. The PFC has also figured importantly in the neuroimaging data published to date. In addition to the hippocampal asymmetries noted previously, Nordahl et al. (1990, 1998) observed an inferior frontal asymmetry with more right than left metabolism in both patient samples with panic disorder. In the 1990 study, patients also exhibited more right frontal and occipital metabolism and less left parietal metabolism than nonpsychiatric controls. Similar group diVerences

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in inferior PFC asymmetry (right > left), right frontal (marginally significant), and occipital cortex were reported by De Cristofaro et al. (1993). Anticipation of and rest after pentagastrin challenge were both accompanied by less left inferior frontal activation in patients with panic disorder than controls; asymmetry was not formally tested (Boshuisen et al., 2002). Consistent with findings for OCD and PTSD reviewed previously, patients in that study also showed more ventral ACC and bilateral OFC activation than controls; however, bilateral insula activation showed the converse pattern. The first quantitative EEG study on panic disorder documented abnormal patterns of asymmetry in both frontal and parietal regions, with patients exhibiting relatively more right‐sided activity than nonpsychiatric controls (Wiedemann et al., 1999). More right than left frontal activity was documented for the patients but not the controls, whereas the patients did not exhibit the parietal left > right asymmetry observed in controls. Furthermore, the same frontal asymmetry was also present while the patients viewed a spider, an erotic, and an emergency picture, but not a mushroom. Symptom provocation studies of panic disorder using hemodynamic methods have assumed the form of pharmacological challenges. Using SPECT during sodium lactate infusion that induced global blood flow increases, Stewart et al. (1988) found that patients who panicked after infusion exhibited larger occipital increases, especially on the right, than nonpanicking subjects, whereas the nonpanicking subjects showed larger global increases, especially over the left hemisphere. In a PET study, Reiman et al. (1989) found no blood flow increases after sodium lactate infusion among nonpanicking subjects, whereas the patients with panic disorder who had panic attacks exhibited increased blood flow in anterior temporal, insula/claustrum/putamen, superior colliculus/periaqueductal gray, and cerebellar vermis regions. Of note, the anterior temporal findings may be an artifact of muscular contraction of the jaw (Benkelfat et al., 1995; Drevets et al., 1992), such that more recent imaging studies on anxiety often use teeth‐clenching control conditions (e.g., Javanmard et al., 1999; Rauch et al., 1996; Reiman, 1997). The parallel between the most frequently observed cognitive and neuroimaging findings is noteworthy. As the only anxiety disorder with a memory bias toward threat just as reliable as an attentional bias, if not more so, panic disorder also is unique with regard to frequently reported hippocampal findings in functional imaging studies. With the hippocampus known to be the critical structure for explicit memory function, these findings suggest that the commitment of certain right hemisphere regions to threat may extend to the hippocampus. Consistent with the argument forwarded for OCD and PTSD, the involvement of broader right hemisphere systems encompassing various territories governing threat perception corresponds to findings of memory and attentional biases. The PFC asymmetry observed in four studies using diVerent

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technologies is in concordance with that position. The issue of comorbidity with depression again deserves mention, because the explicit memory bias and the PFC asymmetry are commonly seen in depression. The OFC, ACC, and caudate regions highlighted in the preceding sections for OCD and PTSD have not emerged with any consistency in research on patients with panic disorder, notwithstanding the recent OFC and ACC findings reported by Boshuisen et al. (2002).

IV. Specific Phobia (Simple Phobia)

Characterized by a persistent, excessive, and unreasonable fear of a specific object or situation, this disorder is very amenable to research investigation both with regard to experimental designs (e.g., presenting subjects with phobic stimuli) and subject sampling because of the prevalence of specific phobias and the relatively low rates of comorbidity with other mental disorders. However, studies with phobics are few, perhaps because of minimal public health interest in specific phobias, because they generally do not compromise the occupational or social functioning of aVected individuals to the same extent as do other anxiety disorders. The preponderance of physiological research to date has focused on peripheral psychophysiological measures such as skin conductance, cardiovascular, and neuroendocrine activity (for review, see Fyer, 1998). No structural imaging data are available for specific phobias, and other neuropsychological research has been quite limited.

A. COGNITIVE STUDIES The handful of studies investigating cognitive function in phobic individuals has documented the presence of an attentional bias but no memory bias. In women with spider phobia, Van Den Hout et al. (1997) documented interference for both masked and unmasked words associated with spiders on a modified Stroop task similar to those used in the OCD, PTSD, and panic disorder studies previously. Using Stroop tests involving spider, general negative, and neutral words, Watts et al. (1986) and Lavy et al. (1993) found larger interference for the spider words in spider phobics than matched nonanxious controls. No Stroop interference eVects were observed in driving phobics for motor vehicle accident words; however, the words did not reflect their primary concerns but rather were designed for accident victims who had PTSD (Bryant and Harvey, 1995). Other cognitive findings for specific phobia include the absence of memory (Watts and Coyle, 1993) and interpretation (Schneider et al., 2002) biases.

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B. NEUROIMAGING STUDIES Functional neuroimaging data have been inconclusive as to the brain circuitry of specific phobias. When small animal phobics were exposed to containers housing the feared animal, Rauch et al. (1995a) found blood flow increases using PET in a number of regions implicated in the preceding studies for OCD and PTSD (see Rauch et al., 1997b), including the left OFC, right ACC, and left insula. Conversely, two earlier PET studies by Fredrikson et al. using film clips of the feared stimuli with snake and spider phobics did not find blood flow increases in any region except the secondary visual cortex (Fredrikson et al., 1993, 1995; Wik et al., 1993). The only other PET study conducted with specific phobics found that confronting animal phobics with their feared animal did not elicit blood flow changes in any region of the brain, although significant cardiovascular and self‐reported anxiety changes were observed (Mountz et al., 1989). They also reported no resting baseline diVerences between the phobics and nonpsychiatric controls. In a SPECT study of women with spider phobia, those reporting panic while watching a video of spiders exhibited less frontal blood flow, especially on the right side, than during a neutral film ( Johanson et al., 1998). The remaining phobic women who reported anxiety but did not panic showed more right frontal blood flow to the spider film (although significance level was not reported). The sole published EEG study of specific phobia found more right than left parietal activity to be associated with higher pretreatment spider phobia scores, whereas frontal activity was not related to pretreatment or posttreatment clinical measures (Merckelbach et al., 1998). There are exciting new data in the first two fMRI studies—both event‐ related—of specific phobia. Larson et al. (2002) collected data using five slices centered on the amygdala to examine the time course of amygdala activation in spider phobia because of its clear relevance for the fear response evoked by confronting phobic stimuli. With time points every 300 ms, they found that phobics displayed faster bilateral amygdalae responses to spider pictures than did nonpsychiatric controls. The amygdala activation was also more rapid to spider pictures than to nonphobogenic negative and neutral slides. No diVerences in the magnitude of activation were observed, whereas a subsequent study found that 1‐s video clips of striking snakes elicited more amygdala activation than crawling snakes, which in turn elicited more than swimming fish in questionnaire‐defined snake phobics but not in nonphobic controls (Schaefer et al., 2002). A number of other regions diVerentiated the phobic and nonphobic subjects when both sets of snake stimuli were compared with fish stimuli. Phobics had more activation than controls to the snakes in the insula, precentral gyrus, thalamus, and right hippocampus (image distortion compromised the reliability of images from PFC, OFC, and ventral ACC). These data suggest that video clips presenting movement may be more potent elicitors of amygdala response than

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still photographs and that the finer temporal resolution provided by fMRI than PET assists in explaining brain responses that are short‐lived. Because of the dearth of cognitive and neuroimaging research investigating specific phobias, little is known about the neurobiology accompanying such intense, long‐standing fear of an object that is often harmless. The findings of an attentional bias toward threat suggest the involvement of right hemisphere regions oriented toward threat; however, the neuroimaging studies are too few and equivocal to provide a consistent picture of the critical brain regions. The recent event‐related fMRI studies of specific phobia point to the importance of the amygdala and suggest that chronometry is an often overlooked but key factor to consider in neuroimaging research. Data from the laboratories of Rauch and Davidson suggest further commonality with brain areas implicated in other anxiety disorders, including the OFC, ACC, and insula. It may be that the circuitry implicated is much less pronounced than seems to be the case for the other anxiety disorders, just as the impact on everyday functioning is on average far less than for the others.

V. Social Phobia (Social Anxiety Disorder)

Now often referred to as social anxiety disorder, social phobia can be viewed as a variant of specific phobia that pertains to social or performance situations. Individuals with social phobia fear that they will act in a humiliating or embarrassing way when in the presence of other people. Recent epidemiological studies have identified it as the third most prevalent psychological disorder in the United States, after depression and alcoholism. Accordingly, the past 5 years have witnessed an explosion of research interest in the disorder, with eVorts to identify the aVected neural circuitry very much in their infancy.

A. COGNITIVE STUDIES Cognitive research has implicated a number of abnormalities in social phobia, most of which are consistent with the information‐processing biases described in other anxiety disorders. The numerous studies examining attention, interpretation, and memory biases in social phobia have recently been reviewed (Heinrichs and Hofmann, 2001). This literature abounds in evidence of attention and interpretation biases toward social threat across multiple diVerent paradigms. Moreover, successful treatment has resulted in the diminution of attention (e.g., Mattia et al., 1993) and interpretation (e.g., Foa et al., 1996) biases toward threat.

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One relevant study not reviewed by Heinrichs and Hofmann (2001) examined attention bias for facial expressions in generalized social phobia (Gilboa‐ Schechtman et al., 1999). Consistent with the dot probe and Stroop studies reviewed, phobic subjects showed an attentional bias toward angry faces as measured by several metrics using the face‐in‐the‐crowd paradigm, whereas nonanxious controls did not. Convergent evidence was reported for social anxiety in a nonclinical sample using a modified version of the dot probe task pairing masked threat and neutral faces and using letters instead of dots (Mogg and Bradley, 2002). For a dot probe paradigm pairing unmasked faces and household objects, social phobics directed their attention away from faces regardless of facial expression (neutral, happy, or negative—the negative pictures contained equal numbers of anger, sadness, fear, and disgust), whereas nonpsychiatric controls did not exhibit an attentional preference (Chen et al., 2002). These data suggest that competing stimuli may serve to distract social phobics and attenuate the attentional capture by threatening cues. A similar distraction process may be at work in explaining the findings of Stroop interference suppression to social‐threat words in social phobics but not nonpsychiatric controls before giving a speech (see Mathews and Sebastian, 1993, for comparable findings in snake‐fearful subjects when in the presence of a snake they are told they will have to approach on completion of the Stroop task). In this case, the right hemisphere resources devoted to threat might all be allocated to the situation surrounding the impending social performance, such that the threat words no longer are perceived as threatening (relative to the impending speech) to the same degree as they are under nonanxious experimental conditions. The authors oVered a diVerent interpretation, suggesting that subjects use strategic processes to inhibit the competing meaning of the words when anxious. They found results for social phobia consistent with this line of reasoning in their subsequent study using a Stroop task that used diVerent ratios of words to nonwords in an attempt to experimentally manipulate the degree to which such strategic processes are used (Amir et al., 2002). Research eliciting anticipatory anxiety in interpretation/judgment bias paradigms is needed to determine whether these findings for attentional bias extend to other domains of information processing. Until very recently, it was widely accepted that social phobia was not accompanied by a memory bias (e.g., Rapee et al., 1994). However, recent evidence suggests otherwise (Amir et al., 2000, 2001; but see Wenzel and Holt, 2002). Two reports using face stimuli provide further evidence for an explicit memory bias in ¨ st (1996) first documented the eVect in a paradigm in social phobia. Lu¨ ndh and O which social phobics and nonpsychiatric controls were asked to judge faces as either critical or accepting. Unlike the controls, the phobics showed a memory bias for faces they had previously judged as critical. In two elegant experiments ¨ st (1996), Foa et al. (2000) found following up the seminal report by Lu¨ ndh and O

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that social phobics recognized more angry and disgust faces than happy or neutral ones, whereas no diVerences were observed for nonanxious controls. The same pattern was seen for reaction time data, with social phobics showing longer latencies in making a decision about the negative than the nonnegative facial expressions. Furthermore, phobic subjects had longer latencies for angry than disgust faces, whereas controls did not. Similar specificity was observed in the attentional face‐in‐the‐crowd paradigm using faces mentioned earlier, with social phobics detecting anger faces faster than disgust ones, whereas controls showed no diVerence (Gilboa‐Schechtman et al., 1999). Taken together, data from these face paradigms suggest a general negativity bias (e.g., all negative emotion expressions) that is amplified by faces connoting threat (e.g., anger expressions), again consistent with the engagement of right hemisphere regions implicated in threat perception. Consistent with cognitive findings for OCD, visual–spatial impairment including nonverbal memory deficits has been documented in social phobia (Cohen et al., 1996; Hollander et al., 1996) as has executive dysfunction (Cohen et al., 1996). Along with other findings of left‐sided neurological soft signs (Hollander et al., 1996), these visual–spatial deficits are consistent with right hemisphere dysfunction in social phobia. Possibly, these deficits are produced by the augmented engagement of the right hemisphere in threat perception, as indicated by the reviewed literature on cognitive bias, with a consequent lack of resources for other processes lateralized to the right hemisphere such as visual–spatial functions.

B. NEUROIMAGING STUDIES Structural abnormalities of the brain have not been observed in social phobics (Potts et al., 1994); however, a set of recent functional neuroimaging studies point to several critical regions. Surveying EEG at the scalp, Davidson et al. (2000) found that social phobics exhibited a larger anterior temporal right > left asymmetry (marginally significant for lateral frontal and parietal sites) during anticipation of making a public speech than nonpsychiatric controls. Using PET to measure blood flow in social phobics, Reiman (1997) reported that singing in front of observers activated a number cortical and subcortical regions, including lateral PFC, anterior temporal, and posterior cingulate regions, with trends noted in the ACC, medial PFC, amygdala, and hippocampus. Another PET study found that social phobics exhibited increased blood flow in the right dorsolateral PFC, left inferior temporal cortex, and left amygdaloid complex (extending into the hippocampus) during anticipation of a public speaking task (Tillfors et al., 2002). Those subjects also exhibited larger blood flow increases than nonphobic controls in the right amygdaloid complex (again extending into the hippocampus) while speaking in front of an audience compared with speaking alone (Tillfors

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et al., 2001). Increased blood flow in a number of regions including the OFC and insula were apparent for the controls but not the phobics. In that same sample of social phobics, patients treated with citalopram and those treated with cognitive‐ behavioral therapy showed a greater blood flow reduction during public speaking than wait‐list controls in anterior and medial temporal cortex (including the amygdala and hippocampus), especially on the right (Furmark et al., 2002). This same pattern was observed when comparing responders (four of six in each treatment group, one of six controls) to nonresponders. In addition, responders also showed larger blood flow decreases than nonresponders in the right dorsolateral PFC and in both ventral and dorsal ACC. Three additional neuroimaging reports presented pilot or preliminary SPECT and PET data with mixed results for the structures implicated in the preceding studies on social phobia (Stein and Leslie, 1996; Van Ameringen et al., 1998; Van der Linden et al., 2000). The emphasis on public speaking in this area of research has proven fruitful in highlighting the right PFC, ACC, and amygdala as important structures in the circuitry aVected by social anxiety. The first of several fMRI studies on social phobia to be published found that social phobics showed greater amygdala activation bilaterally to neutral faces than did nonpsychiatric controls despite no diVerences in subjective ratings of the faces, whereas both groups showed the expected activation of the amygdala to aversive odors (Birbaumer et al., 1998). However, it seems that this eVect for the amygdala did not maintain for the full sample (Schneider et al., 1999), with social phobics only exhibiting greater amygdalar and hippocampal activation than controls when the neutral faces were paired with aversive odors. A subsequent study by the same group with a smaller sample (four social phobics, seven nonpsychiatric controls) did not replicate this eVect in a similar conditioning paradigm using painful pressure as the unconditioned stimulus (Veit et al., 2002). However, they did find right amygdala and bilateral OFC activation to the neutral faces before acquisition in the social phobics. In another fMRI study examining brain responses to human facial stimuli, individuals with social phobia showed greater left medial temporal cortex (including amygdala) activation than nonpsychiatric controls for angry and for contemptuous faces compared with happy faces (Stein et al., 2002). No diVerences were observed for fearful or nonexpressive faces compared with happy faces. The fMRI research conducted to date provides further evidence of amygdala involvement in social phobia even in settings in which the threat of social evaluation is not as salient as in public speaking. Overall, these cognitive and neuroimaging data point most strongly to right cortical regions and the amygdala, especially in paradigms involving methods that are ecologically relevant to social phobia such as face stimuli and social performance. The concordance of the cognitive findings with the right‐sided

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brain activation reported by Davidson et al. (2000) suggests that the circuitry of social phobia includes right hemisphere regions involved in threat perception. The recruitment of the amygdala in anticipation of and during public speaking and in response to human face stimuli with diVerent facial expressions is consistent with amygdalar function in vigilance for motivationally salient events (Davis and Whalen, 2001).

VI. Generalized Anxiety Disorder

The salience of worry and verbal rumination in generalized anxiety disorder (GAD) suggests the involvement of left‐hemisphere structures dedicated to language. In contrast to the other anxiety disorders that may involve varying degrees of worry about disorder‐specific content, worry is the hallmark of GAD. Although worry about everyday problems is not pathological in itself, the person with GAD worries excessively, has diYculty controlling the worry, and experiences significant distress and impaired social and occupational functioning as a result. The exceedingly high rates of comorbidity with depression have made it very diYcult to isolate brain abnormalities in GAD. Both cognitive and neuroimaging studies have, therefore, often been quite compromised in terms of diagnostic specificity.

A. COGNITIVE STUDIES As with the other anxiety disorders covered previously, GAD is characterized by an attentional bias toward threat in Stroop (Bradley et al., 1995; Martin et al., 1991; Mathews and MacLeod, 1985; Mathews et al., 1995; Mogg et al., 1989, 1993), dot probe (MacLeod et al., 1986), distractor (Mathews et al., 1990, 1995), and dichotic listening (Mathews and MacLeod, 1986) paradigms. Consistent findings have emerged in two newer paradigms using emotional faces. Using a variant of the dot probe task, Bradley et al. (1999) reported that patients with GAD had slower reaction times for threatening than neutral faces compared with controls. Using a similar probe detection task, Mogg et al. (2000) measured eye movements and found that subjects with GAD showed a bias toward threat faces for the two eye‐movement metrics used, but they did not replicate the reaction time diVerences documented by Bradley et al. (1999). Several of these studies reported the absence of an attentional bias in comparison groups with clinical depression (Mogg et al., 1993, 2000) or with comorbid GAD and depression (Bradley et al., 1995). Evidence for general rather than threat‐specific distractibility

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has also been found (Bradley et al., 1999; see also Mathews et al., 1990, 1995), although even these studies found results for threat conditions to be more robust than for nonthreat conditions. Despite earlier evidence to the contrary (Mathews et al., 1990), recovery from GAD does not seem to be accompanied by a residual attentional bias (Mathews et al., 1995; see also Mogg et al., 1992), consistent with findings reviewed previously for other anxiety disorders. Findings of a memory bias in GAD have been mixed. A bias toward threat has generally not been observed for explicit memory tasks (Becker et al., 1999; MacLeod and McLaughlin, 1995; Mathews et al., 1989a; Mogg et al., 1987; Otto et al., 1994). However, Friedman et al. (2000) found an explicit memory bias in two separate GAD samples with extremely low rates of comorbid depression (although comorbidity with social phobia was 60%). Several important methodological diVerences from earlier studies (e.g., incidental learning task, no imagery instructions, longer stimulus exposure) suggest the presence of an explicit memory bias in GAD under conditions optimal for detecting memory biases in clinical anxiety (see Becker et al., 1999). In addition, Otto et al. (1994) documented the same relationship between auditory perceptual asymmetry and memory bias toward threat discussed previously for panic disorder in a sample of patients with GAD. The inferred pattern of more left than right hemisphere activity was associated with better memory for threat words, consistent with left‐sided neuroimaging findings for GAD reviewed in the following. Implicit memory bias has emerged for GAD under some conditions (MacLeod and McLaughlin, 1995; Mathews et al., 1989a) but not others (Mathews et al., 1995), a discrepancy that cannot be explained by the type of implicit memory tested (see preceding discussion contrasting conceptual and perceptual implicit memory in PTSD). There is also evidence that patients with GAD have a bias to interpret ambiguous stimuli as threatening (Eysenck et al., 1991; Mathews et al., 1989b). Recovered patients do not show implicit memory or interpretive biases (Eysenck et al., 1991; Mathews et al., 1989b). Again, the cognitive bias literature suggests state‐dependent recruitment of right hemisphere regions involved in threat perception, perhaps superimposed on the left‐sided perceptual asymmetry and neuroimaging findings also observed in patients with GAD. There is some indication of mild cognitive deficits in GAD that are consistent with the notion that worry occupies cognitive resources that otherwise might be deployed for various experimental tasks and everyday functions. Wolski and Maj (1998) documented performance deficits on a modified Sternberg memory task in a group of 87 patients with anxiety, 77 of whom had GAD. The general distractibility eVects reviewed earlier (e.g., Bradley et al., 1999) provide further support for this position. However, overall performance deficits are generally not seen on attention and memory tasks (e.g., Mathews et al., 1990; Otto et al., 1994).

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B. NEUROIMAGING STUDIES The sole morphometric MRI study on GAD was conducted with children and adolescents (De Bellis et al., 2000, 2002b). Right amygdala and superior temporal gyrus volumes were larger in patients than in matched nonpsychiatric controls. No diVerences were found in the hippocampi, PFC, corpus callosum, thalamus, or basal ganglia or for total intracranial or total cerebral volumes. In contrast to the other anxiety disorders covered here, functional neuroimaging studies are older, with almost no work published in the past decade. Wu et al. (1991) found that patients had less glucose metabolism in the basal ganglia (composed of caudate, putamen, and globus pallidus) and more in left inferior frontal, left inferior occipital, right posterior temporal, and right precentral regions than nonpsychiatric controls during a passive viewing task. The left inferior frontal finding and concomitant greater left than right frontal metabolism are in line with the hypothesis that language centers involved in worry (e.g., Broca’s area) are activated. During a visual CPT using degraded stimuli performed only by the patients, basal ganglia and right parietal metabolism increased, whereas decreases were seen in right temporal and occipital lobes. Consistent with their earlier report (Buchsbaum et al., 1987) claimed to be on the same GAD sample (the gender breakdown was slightly diVerent), benzodiazepine therapy resulted in decreased occipital, basal ganglia, and limbic system (composed of the amygdala, hippocampus, and cingulate) metabolism. In a SPECT study, patients with GAD showed increased left orbital frontal blood flow when asked to freely associate about threatening pictures presented before blood flow measurement ( Johanson et al., 1992). The specificity of the eVects to GAD in the latter two studies is not clear, because neither one included a control group. Several recent fMRI studies have been undertaken to examine the neural substrates of aVective processing in GAD. Thomas et al. (2001) reported that a pediatric sample predominantly composed of individuals with GAD showed a larger right amygdala response to fearful than neutral faces, a pattern not present for either comparison group (nonpsychiatric control children, girls with major depressive disorder). In another study using a similar face paradigm, adult patients with GAD exhibited more left amygdala activation to fearful than to neutral or happy expressions, whereas nonpsychiatric controls did not (Johnstone et al., 2002). In that same sample using a paradigm that used warning cues that predict subsequent aversive or neutral pictures, Nitschke et al. (2002) found that patients with GAD showed more activation than controls in the left inferior PFC during anticipation of aversive pictures and in left OFC, bilateral PFC, and bilateral insula after those aversive pictures. As such, the recent fMRI work on GAD has reported findings for some of the same structures implicated in neuroimaging research on other anxiety disorders.

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Involvement of diVerent brain areas in GAD can also be gleaned from several EEG studies. EEG topography from 32 sites revealed no baseline diVerences between patients with GAD and nonpsychiatric controls (Grillon and Buchsbaum, 1987). When presented with neutral lights in a basic orienting response paradigm, those patients showed less alpha suppression (presumably reflecting decreased activity) than controls, especially over the occipital lobe, perhaps reflecting a diminution of attention to external stimulation because of competing processes devoted to worry. An earlier EEG study by the same group examined benzodiazepine treatment eVects in patients with random assignment to placebo or drug group and in nonpsychiatric controls (Buchsbaum et al., 1985). Using 16 midline and left‐hemisphere sites, they found that patients had less delta and alpha (more activity) than controls, especially over the left posterior temporal cortex. Correlational analyses revealed that increased left frontal alpha (decreased activity) was associated with clinical improvement for patients in the drug group, consistent with left PFC findings in GAD and worry reviewed previously. Of relevance to imaging research despite only recording from three midline electrodes, a recent treatment study of GAD explored frontal midline theta activity, which is thought to reflect reduction of anxiety during task performance (Suetsugi et al., 2000). Criteria for frontal midline theta at the midfrontal site were not met for any of the 28 patients at the initial visit. The 26 patients for whom frontal midline theta appeared after psychotherapy or pharmacotherapy showed dramatic clinical improvement, whereas the remaining two individuals continued to exhibit high levels of anxiety. Although further research is needed to examine the reliability of these findings, one potentially noteworthy interpretation is that worry interferes with the production of frontal midline theta. The dearth of recent neuroimaging data for GAD is striking compared with the proliferation of such research conducted with the other five anxiety disorders covered in this review. The recent fMRI work is consistent with neuroimaging work on those anxiety disorders documenting the hyperresponsivity of the amygdala to motivationally salient stimuli. The handful of other studies, along with the more extensive cognitive science literature examining GAD, point to several brain regions deserving further investigation. On the basis of the cognitive deficit and left‐sided neuroimaging findings, the circuitry involved in worry and the structures overlapping with attention and working memory (e.g., PFC, parietal regions, particularly left hemisphere) are conspicuous candidates for uncovering brain aberrations in GAD. In addition, the right hemisphere territories implicated by the cognitive biases accompanying GAD are also likely constituents of the brain circuitry involved in the pathophysiology of GAD.

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VII. Discussion

Across the many cognitive and neuroimaging studies reviewed here, cognitive bias toward threat is the one attribute common to all six anxiety disorders covered. Attentional biases have been observed in all disorders, whereas data for explicit and implicit memory biases have been mixed. Findings of a memory bias have been replicated most consistently for panic disorder, with substantial evidence also reported for PTSD, social phobia, and GAD. On the other hand, no studies have found a memory bias in OCD or specific phobia. Interpretation (i.e., judgment) biases have not been extensively examined among clinical populations, although there is ample evidence of such a bias in OCD, social phobia, and GAD. This orientation toward threat in anxiety disorder populations suggests the involvement of particular anterior and posterior right hemisphere regions (for reviews, see Compton et al., 2000, 2003; Nitschke et al., 2000; Nitschke and Heller, 2002). As described by Nitschke et al. (2000), these biases may be related to an emotion surveillance system of the right hemisphere designed to evaluate the presence of a threat in the external environment. This right hemisphere system may correspond to the cortical processes that McNally (1998) postulated to accompany a subcortical circuit involved in attentional biases toward threat. The hyperactivation of this right hemisphere system may interfere with visual spatial functions for which right posterior regions are specialized, as seen in OCD and social phobia. The right‐sided increases in activation reported in many of the neuroimaging studies examining anxiety disorders—with the notable exception of GAD, which likely invokes left hemisphere regions devoted to verbal processes needed for worry—may be a manifestation of the heightened reliance on this emotion surveillance system governing threat perception and evaluation. The anxiety disorders covered here are further characterized by a number of divergent neuropsychological patterns. In contrast to the morphometric and functional studies on OCD, the caudate nucleus is not implicated in any of the other anxiety disorders. PTSD is the only disorder to be accompanied by memory deficits and by reduced hippocampal volume. Findings of hippocampal asymmetries have been reported exclusively for panic disorder. Unlike the other disorders, the preponderance of imaging findings for GAD implicates left hemisphere regions. Amygdala activation has not been observed with any inconsistency, except in PTSD and social phobia, although amygdala involvement is likely underestimated because of the concerns about habituation and susceptibility artifact noted previously. OFC and ventral ACC activation has been reliably found only in OCD and PTSD. And, finally, visual spatial deficits have been observed for OCD and social phobia but not the others. This

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summary of the findings points to the substantial heterogeneity among the anxiety disorders. Although anxiety is often referred to as a homogenous construct, neuroscience findings for anxiety disorders clearly indicate the importance of noting distinctions and variable symptom expression both across and within diagnoses. Several useful neurobiological models have been proposed, including one for OCD (Rauch et al., 1998) and another concentrating primarily on PTSD (Charney et al., 1998). We have proposed a neuropsychological framework positing a distinction between two types of anxiety (e.g., Nitschke et al., 2000). As noted previously, anxious apprehension is characterized primarily by worry and relies on left hemisphere processes, whereas anxious arousal is characterized by immediate fear and panic symptoms and is closely aligned with the emotion surveillance system of the right hemisphere. In general, GAD is characterized more strongly by anxious apprehension than are the other disorders, whereas panic disorder is likely accompanied by the highest levels of anxious arousal. However, it is important to note that these two forms of anxiety are not mutually exclusive and likely exist in all individuals with anxiety disorders to varying degrees. Pronounced individual diVerences within a disorder in the expression of both forms of anxiety are also likely, as are intraindividual diVerences across time. Although several models explain some of the variability in the cognitive and neuroimaging findings for anxiety disorders, no current formulation can account for all the heterogeneity. Attending to psychological and biological mechanisms should inform this heterogeneity, which impedes attempts to unravel the neural circuitry of clinical anxiety. One means of accomplishing this is research with clinical populations that rigorously examines the brain correlates of specific anxiety symptoms, such as worry, contamination obsessions, and avoidance of feared objects or situations. Another approach is to appeal to knowledge about which brain regions govern specific functions relevant to anxiety pathology (c.f. Davidson et al., 2002). Basic research with humans and nonhuman animals has uncovered some of the circuitry involved in those psychological phenomena central to anxiety disorders and showcased in this review (e.g., threat evaluation, fear, response conflict). This emphasis on mechanisms is also promising for research examining the interface with other neurobiological systems shown to be critical for the expression of fear and to manifest irregularities in anxiety disorders, such as cortisol, corticotropin‐releasing factor (CRF), cholecystokinin (CCK), tachykinins, neuropeptide‐Y, serotonin, norepinephrine, gamma‐aminobutyric acid (GABA), and N‐methyl‐D‐aspartate (NMDA). These are some of the areas that await synthesis with the neural correlates of anxiety that have been identified in the large corpus of cognitive and neuroimaging research examining anxiety disorders.

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Acknowledgments

We gratefully acknowledge the assistance of Krystal Cleven and Kristen Mackiewicz with the preparation of this chapter. Portions of this chapter are adapted by permission from Nitschke and Heller (2002). Copyright 2002 by John Wiley and Sons, Ltd.

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NEUROIMAGING IN DEMENTIA

K. P. Ebmeier, C. Donaghey, and N. J. Dougall Division of Psychiatry, University of Edinburgh, Kennedy Tower, Morningside Park Edinburgh EH10 5HF, United Kingdom

I. Introduction II. Dementia A. Definition B. Diagnosis III. Imaging Modes A. Magnetic Resonance Imaging (MRI) B. Emission Tomographies (SPECT, PET) IV. Diagnostic Imaging A. Use of Functional Imaging in Clinical Practice B. Diagnostic Accuracy of Clinical Criteria Compared with Neuropathology C. Diagnostic Accuracy of Functional Imaging D. Structural MRI in Clinical Practice E. Diagnostic Patterns of Functional Imaging for AD F. MRI Diagnostic Patterns for AD G. Diagnostic Patterns DiVerentiating AD from VD H. Diagnostic Patterns DiVerentiating AD from FTD I. Diagnostic Patterns DiVerentiating AD from DLB J. New Image Analysis Techniques K. Summary V. Functional Imaging A. Correlations of Function with Baseline Scan B. Functional Activation Studies C. Summary VI. Pharmacological Imaging A. Introduction B. Amyloid Imaging C. Acetylcholine Receptors D. Dopamine Transporter VII. Conclusion References

Dementia is the global and acquired impairment of mental function and is a major health problem, particularly in the elderly. Its most common forms with substantial imaging data are dementia of the Alzheimer’s type (AD), frontotemporal dementia, vascular dementia, and Lewy body dementia. The most commonly used imaging modalities are magnetic resonance imaging and emission tomography in their various forms. INTERNATIONAL REVIEW OF NEUROBIOLOGY, VOL. 67 DOI: 10.1016/S0074-7742(05)67002-X

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Imaging can be used for diagnostic purposes. Single‐photon emission tomography patterns of temporal‐parietal lobe hypoperfusion, for example, diVerentiate AD from non‐AD dementia, with sensitivity between 63% and 86% and specificity between 73% and 93%. Similar data for positron emission tomography are sensitivity of 94% and specificity of 73%. There seem to be typical patterns of brain appearance in each type of dementia, which change over the natural course of the disease, thus giving systematic and potentially complete information that is only available on an accidental basis for pathological data. Neuropsychological function is specifically correlated with particularly functional imaging data, such as brain perfusion and metabolism measures. Combining functional imaging with neuropsychological techniques can help researchers and clinicians to identify regions associated with particular cognitive functions essential for performing a given cognitive task. Imaging of brain receptors has been importantly used to explore the distribution of amyloid, cholinergic, and dopaminergic receptors in patients, thus contributing to the in vivo pharmacology and potentially to the treatment of the dementias. I. Introduction

Dementia is one of the most quickly expanding topics in psychiatric research, while remaining the most frustrating condition to treat. Over the past decade, the genetics and pathophysiology of certain dementias, especially dementia of the Alzheimer type, have been increasingly clarified. However, the treatment of this condition lags far behind its basic research. Although a number of approaches have been explored, from acetylcholinesterase inhibitors to amyloid vaccinations, the results have been disappointing (Broytman and Malter, 2004; Courtney et al., 2004; Ritchie et al., 2004; Robinson et al., 2004). Neuroimaging has been used in basic clinical research, for diagnostic purposes, or using dementia as a paradigm for local brain lesions to examine normal and disturbed cognitive performance and brain function in parallel. This review will try to give a brief summary of each of these approaches. II. Dementia

A. DEFINITION Dementia of the Alzheimer’s type is defined by DSM IV (APA, 1994) as the development of multiple cognitive deficits manifested by both memory impairment and one or more of the following: aphasia, apraxia, agnosia, or

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FIG. 1. Course of intellectual performance in individuals with normal intelligence and subjects with learning disability with and without dementia. Note early onset of dementia in individual with low IQ.

disturbance in executive functioning. In addition, the cognitive deficits of each cause significant impairment in social or occupational functioning and represent a significant decline from a previous level of functioning. In other words, dementia is not just defined by poor memory but usually by a multitude of symptoms that can by related to localized (cortical) brain changes. It follows that dementia can be more diYcult to diagnose in subjects with high premorbid functioning. At the other end of the spectrum, it is possible to diagnose dementia in people with low premorbid levels of intellectual functioning. The determination of premorbid intellectual function in general terms is, therefore, often necessary for a diagnosis of dementia (Fig. 1). B. DIAGNOSIS The diagnosis of dementia is clinical and, apart from some rare conditions with identifiable organic changes during certain investigations, so is the diVerential diagnosis of dementia. In other words, there is no unambiguous or pathognomonic test for a diagnosis of, for example, Alzheimer’s dementia or vascular dementia. There are postmortem pathological criteria (Geddes et al., 1997; Mirra et al., 1991; Wilcock et al., 1989), but in vivo histological findings are rarely feasible.

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Dementia of the Alzheimer’s type is defined (APA, 1994) as described previously, with gradual onset and continuing cognitive decline. Exclusion criteria are other central nervous system (CNS) conditions that cause progressive deficits in memory and cognition (e.g., cerebrovascular disease, Parkinson’s disease, Huntington’s disease, subdural hematoma, normal‐pressure hydrocephalus, brain tumor), systemic conditions that are known to cause dementia (e.g., hypothyroidism, vitamin B or folic acid deficiency, niacin deficiency, hypercalcemia, neurosyphilis, HIV infection), and substance‐induced conditions. The disturbances are also not caused by delirium (acute organic confusional states) or depression and schizophrenia. Associated with the disease, particularly in its later stages, are neurological symptoms and signs, such as dysarthria or involuntary movements, seizures, muscular contractures, and behavioral changes, such as increased or reduced activity, personality change, depressed mood, disinhibited behavior such as sexual deviancy, addictions, aggression, and psychotic symptoms such as hallucinations or delusions. Vascular dementia, in particular multiinfarct dementia (MID), often develops in a stepwise manner, as small strokes cause increasing impairment, often with a sudden deterioration and subsequent slight improvement above the previous level of functioning. In general, symptoms are as described in II.A, but with a number of more typical features. These have been summarized in the Ischemic Scale (Hachinski et al., 1975; Zekry et al., 2002). Patients accumulate points (1–2) if they have an abrupt onset of symptoms (2); stepwise deterioration (1); fluctuating course (2); nocturnal confusion (1); relative preservation of personality (1); depression (1); somatic complaints (1); sudden outbursts of aggression, anxiety, crying, or laughing (‘‘emotional incontinence’’ ¼ 1); as well as physical signs and symptoms of cerebrovascular disease, such as past or present hypertension (1), atherosclerosis (1), history of stroke (2), focal neurological signs (2), or symptoms (2). Approximately 20% of patients with dementia have a mixed vascular– Alzheimer pathology, so that the predictive validity of clinical diagnosis using these criteria is not impressive (Zekry et al., 2002). Lewy body dementia (LBD) is characterized by fluctuating cognition with pronounced variation in attention and alertness, recurrent visual hallucinations that are typically well formed and detailed, as well as spontaneous motor features of Parkinsonism. Patients often have repeated falls, syncopes, transient loss of consciousness, neuroleptic (dopamine‐D2 blocker) sensitivity, systematized delusions, and nonvisual hallucinations. Lewy body dementia is less likely in the presence of stroke if there are focal neurological signs or brain imaging evidence, physical illness, or other brain disorder suYcient to account for the clinical picture (McKeith et al., 1996). There are obviously many more possible etiologies of dementia, but to concentrate on types of dementia with substantive scientific imaging literature, we will focus on the preceding three.

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III. Imaging Modes

A. MAGNETIC RESONANCE IMAGING (MRI) Imaging modes are traditionally divided into structural and functional imaging. Reflection shows that distinction of methods is often diYcult along these lines: functional scans always incorporate structural information; otherwise, no anatomical associations could be made. Structural imaging modes can now be used to examine brain function. Magnetic resonance imaging with paramagnetic tracers (Gadolinium), functional magnetic resonance imaging (fMRI), and magnetic resonance spectroscopy (MRS) are examples. The blurring of structural and functional information is particularly important in the study of dementias, because here we often find both cell loss and alteration of function at the same time. In other words, partial volume eVects because of tissue loss have to be controlled for before it is possible to stipulate functional changes in the brains of patients with dementia. Although computed tomography (CT) is still widely used clinically, most research is now conducted using MRI for its greater resolution, its greater soft tissue contrast in the brain, and its absence of ionizing radiation. The method of MRI has been described in Volume 1 of this review. In relation to its use in dementia, it is important to remember that lying in the gantry tunnel of an MRI machine is stressful even to healthy subjects, about 2% of patients cannot complete their scan because of inordinate anxiety, and between 4% and 30% have anxiety symptoms of any severity (Melendez and McCrank, 1993). Patients with dementia have the additional problem that the purpose of the investigation may escape them after a while, so that reminders are necessary to maintain their cooperation with the procedure. It is often helpful to have a carer available for the scan who knows the patient and who can play a reassuring role. In assessing imaging studies, in particular MRI studies in dementia, it has to be remembered that patients have been selected for their ability to cooperate, even to give informed consent, whereas the quality of scans tends to be inferior to that achieved with healthy volunteers. Movement artifacts and positioning problems are relatively common in this group. Short acquisition times are more important than for other subject groups. B. EMISSION TOMOGRAPHIES (SPECT, PET) A short description of emission tomographic methods is necessary, because there is no summary in the review. Two modalities are in use, single‐photon emission computed (SPECT) and positron emission tomography (PET). Table I summarizes the characteristics of several radiotracers used, which are of importance for imaging patients with dementia.

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TABLE I SOME RADIOTRACERS USED IN THE STUDY OF DEMENTIA (RECEPTOR LIGANDS ARE IN ITALICS) Tracer

Isotope‐mode

HMPAO ECD Iodo‐QNB 5‐I‐A‐85380 MK‐801 Water Oxygen FDG

99m

Glucose Nicotine MP4A

11

T‐SPECT Tc‐SPECT 123 I‐SPECT 123 I‐SPECT 123 I‐SPECT 15 O‐PET 15 O‐PET 18 F‐PET 99m

C‐PET C‐PET 11 C‐PET 11

Physiology Perfusion Perfusion Muscarinic receptor Nicotinic receptor NMDA receptor Blood flow Oxygen uptake Glucose uptake (aerobic þ anaerobic) Glucose uptake (aerobic) Nicotinic receptor AChE activity

Tracers of brain perfusion and brain metabolism are distinct among other things in their time of uptake into the brain (i.e., the time over which brain activity is integrated in the final image). This can range from hundreds of milliseconds for BOLD fMRI to 2 min for SPECT tracers, such as HMPAO and ECD, as well as PET with 15O‐labeled water, to 30–45 min for the uptake of 18 F‐deoxyglucose. The information required determines which method is most advantageous. Examining brain activity over 30 min with FDG may be useful if the interest is in baseline brain states, but to sustain a diYcult task over this period will lead to variable performance and habituation phenomena, which may confound the task eVect. Short half‐life radioisotopes are more suitable for repeat examinations for the comparison of several tasks, both for statistical and radiation safety reasons. Application of the radiotracer outside the scanner (18F‐FDG, HMPAO, ECD) may be more practicable in ill and uncooperative patients. Less invasive techniques (e.g., measuring the input activity curve with detectors over the patient’s neck) may also be more practical than invasive methods that require, for example, arterial cannulation. IV. Diagnostic Imaging

A. USE

OF

FUNCTIONAL IMAGING

IN

CLINICAL PRACTICE

Routine functional imaging for dementia is controversial. The frequency of use varies considerably across clinics because of the considerable expense of routine testing, and its quality is often suboptimal for a variety of reasons: Reported study measures vary widely with the diVerent characteristics of imaging

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systems, varying sophistication of image analysis, diVerent diagnostic cutoV criteria, heterogeneous study populations, and study designs. Although single imaging studies often conclude that ‘‘PET or SPECT is useful,’’ diagnostic eVect sizes reported vary considerably. Further research will have to identify the parameters that increase the diagnostic usefulness of imaging tests and to quantify the error associated with method bias. To date, guidelines have generally not supported the routine use of functional imaging in the diagnostic evaluation for dementia; general clinical endorsement will only follow when these tests consistently increase diagnostic accuracy over and above a competent clinical diagnosis. Consequently, functional imaging techniques are recognized as having an important role to play in dementia assessment (Reisberg et al., 1997), but because of the insuYcient data on prognostic validity for treatment response or pathological diagnosis (Knopman et al., 2001; Patterson et al., 1999), functional imaging is often reserved for the more diYcult‐to‐diagnose cases (BraVman et al., 2000). B. DIAGNOSTIC ACCURACY OF CLINICAL CRITERIA COMPARED WITH NEUROPATHOLOGY As described earlier in this chapter, a diagnosis of ‘‘probable’’ or ‘‘possible’’ AD by clinical criteria (NINCDS‐ADRDA) is achieved to a large extent by ruling out other possible causes of dementia, whereas a ‘‘definite’’ diagnosis of AD is ascertainable only by postmortem evidence in conjunction with a clinical history of dementia. Studies comparing clinical criteria with the neuropathological ‘‘gold standard’’ report average values for sensitivity (81%) and specificity (70%) for ‘‘probable’’ AD and a good sensitivity (93%) and poor specificity (48%) for ‘‘possible’’ AD (Knopman et al., 2001). Because clinical and pathological diagnoses of dementia often diVer, comparing the accuracy of imaging studies against a neuropathological diagnosis will yield results diVerent from a comparison with clinical diagnosis. The latter can be improved by following patients up to confirm the original clinical assessment. On the other hand, longer duration of the disease between scan and clinical follow‐up or postmortem examination increases the possibility of confounding comorbidity and medication. C. DIAGNOSTIC ACCURACY

OF

FUNCTIONAL IMAGING

Studies comparing the accuracy of in vivo functional imaging with postmortem neuropathology have found that the SPECT pattern of temporal‐parietal lobe hypoperfusion does diVerentiate AD from non‐AD dementia; one study found a sensitivity of 86% and specificity of 73% (Bonte et al., 1997), whereas another

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study found 63% sensitivity and 93% specificity for SPECT; comparing the clinical criteria against neuropathology in the same group of subjects for a diagnosis of ‘‘probable’’ AD achieved only 59% sensitivity but a specificity of 95% ( Jagust et al., 2001). Against a benchmark of postmortem pathology, functional imaging alone seems to classify dementia as well as, but not superior to, clinical criteria. Considering SPECT and clinical criteria together, a positive SPECT result was found to significantly increase the odds of having AD by an additional factor of five over clinical diagnosis alone (Jagust et al., 2001). Comparing PET with neuropathological diagnosis, a sensitivity of 93% and a specificity of 63% were reported for separating patients with AD from patients without AD who had an illness that was ‘‘diagnostically challenging or diYcult to characterize by clinical criteria’’ (HoVman et al., 2000). Clinical diagnosis of probable AD in the same group of patients achieved sensitivity and specificity of 63% and 100%, respectively. In a multicenter study with 3 years of clinical follow‐up, pathologically confirmed diagnosis of AD was also predicted by PET with a sensitivity of 94% and a specificity of 73% in comparison with other patients initially seen with dementia (Silverman et al., 2001). Direct comparisons of PET and SPECT have found PET to be of higher diagnostic accuracy than SPECT in the diVerentiation of AD from VD (Mielke and Heiss, 1998) and AD from healthy volunteers (Messa et al., 1994). The best correspondence between SPECT perfusion and PET metabolic patterns for AD has been observed in temporoparietal and posterior cingulate association cortices in mild to moderate AD (Herholz et al., 2002). Systematic reviews and meta‐analysis of functional imaging studies using clinical criteria as the ‘‘gold standard’’ have found good discrimination between AD and healthy control subjects, at least at a group level. Overall pooled sensitivities derived from the literature for PET and SPECT were 86% and 77% against specificities of 86% and 89%, respectively (Dougall et al., 2004; Patwardhan et al., 2004). This suggests that PET is more sensitive but does not have superior specificity to SPECT. Although these evidence‐based reviews conclude that the clinical value of functional imaging is uncertain at this time, another review concluded that there is little evidence to support a role for PET (Gill et al., 2003). Longitudinal follow‐up studies are urgently required to explore the predictive validity of imaging with respect to final diagnosis, rate of decline, and treatment response. Apart from numerical values for diagnostic accuracy, the possible consequences of clinical diagnosis will play a part in deciding whether functional brain scanning will be used on a routine basis. As long as treatments are low risk and of moderate eYcacy, it may be preferable to treat all patients. If the treatments become more eVective and greater potential risks are incurred with their use, it may become more likely that additional diagnostic procedures need to be used (Kulasingam et al., 2003).

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D. STRUCTURAL MRI

IN

CLINICAL PRACTICE

MRI has been introduced into clinical practice relatively recently, steadily replacing CT as a means of examining brain structure. The sensitivity of MRI to detect AD is low, because it is impeded by cell atrophy that is also observed in age‐matched healthy volunteers. However, the presence and degree of atrophy can help determine diagnosis; whereas generalized atrophy may be indicative of AD; focal atrophy in discrete brain areas may point to other diagnoses. MRI also allows for the assessment of periventricular and subcortical white matter hyperintensities. Whereas white matter abnormalities on MRI are not demonstrably increased in those patients with AD without cerebrovascular risk factors compared with healthy volunteers (Kozachuk et al., 1990), cortical and lacunar infarcts are indicative of vascular dementia or multiinfarct dementia. Guidelines recommend the use of structural imaging such as MRI or CT at the time of initial dementia assessment to rule out pathological structural lesions such as brain neoplasms or subdural hematomas (Knopman et al., 2001). Other than routine structural scanning at initial assessment to exclude other possible treatable diagnoses, MRI studies have largely set out to quantify atrophy in demented patients and other comparison diagnostic groups using volumetric measurements of specific brain regions, for example medial temporal lobes. The resolution of MRI is such that atrophy of the amygdala, hippocampus, and parahippocampal gyrus has also been estimated; increased atrophy has been observed in patients with AD (Kesslak et al., 1991). Atrophy ratings by MRI visual assessment have succeeded in providing separation between AD and other comparison groups; for instance, using anterior hippocampal atrophy as a diagnostic marker obtained a sensitivity to detect AD of 83% and a specificity of 80% for controls (O’Brien et al., 1997).

E. DIAGNOSTIC PATTERNS

OF

FUNCTIONAL IMAGING

FOR

AD

Resting state cerebral activity has been investigated largely by imaging regional glucose metabolism with PET or regional cerebral blood flow (rCBF) perfusion with SPECT. Whereas hypometabolism refers to a reduction in metabolism, usually measured as glucose uptake, hypoperfusion refers to a reduction of perfusion within brain areas where deficits have occurred. Both techniques of SPECT and PET are capable of mapping radionuclide distribution in three dimensions, reflecting brain biochemical and physiological processes ( Jagust, 2004). Early studies demonstrated regional changes in blood flow, oxygen metabolism, and glucose metabolism with SPECT and PET (Benson et al., 1983; Frackowiak et al., 1981).

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In the diagnosis of clinically ‘‘probable’’ AD, dysfunction observed by hypometabolism or hypoperfusion in temporoparietal association cortices and later in frontal association cortices has been well documented (Alexander et al., 2002; HoVman et al., 2000; Masterman et al., 1997; Nestor et al., 2004; Volkow et al., 2002). Patient age, gender, and dementia severity have moderating eVects on this diagnostic pattern. Patients with younger onset AD have relative deficits in the parietal lobe and posterior cingulate (Hanyu et al., 2003; Kaneko et al., 2004), whereas older patients have deficits in medial temporal areas (Kemp et al., 2003) and increased image heterogeneity, making diagnostic interpretation more challenging. Conversely, relatively higher perfusion and metabolism are found in elderly than younger AD patients, even when dementia severity is accounted for as a confounding covariate (Mielke et al., 1992; Salmon et al., 2000). Men with AD are more likely to have bilateral parietotemporal deficits, whereas women with AD are more likely than men to have unilateral deficits, most often left‐sided (Nitrini et al., 2000; Ott et al., 2000). Metabolic and perfusion deficits are proportional to dementia severity of AD, and brain areas aVected diVer accordingly. Using pathological staging together with perfusion changes in AD, reductions in tissue and perfusion are first observed in the anterior medial temporal lobe, posterior cingulate, and precuneus before appearing in temporoparietal areas and finally frontal lobes (Bradley et al., 2002). Posterior cingulate abnormalities in early AD have been frequently reported (Minoshima et al., 1997; Rossor et al., 1996), as have hippocampal reductions (Elgh et al., 2002; Stein et al., 1998; Villa et al., 1995). A meta‐analysis of all brain areas found to be involved in AD has confirmed the pathological findings: blood flow and metabolism are first reduced in the posterior cingulate and precuneus before advancing to the medial temporal structures and the parietotemporal association cortex (Zakzanis et al., 2003). Primary visual and sensorimotor cortex, basal ganglia, and cerebellum are relatively well preserved in AD (Herholz, 2003). Longitudinal functional imaging studies suggest that observed impairment at initial dementia assessment is predictive of subsequent clinical deterioration; diagnostic patterns of patients presenting with mild cognitive impairment (MCI) may be predictive of conversion to dementia (Herholz et al., 1999). Hypoperfusion in the posterior cingulate cortex has been reported at initial assessment of individuals with MCI who subsequently converted to AD (Kogure et al., 2000), but conversely is not present in patients with MCI who remain stable without decline to AD (Huang et al., 2002). The earliest area of cortical hypometabolism in MCI is the retrosplenial cortex, a subregion of posterior cingulate cortex (Nestor et al., 2003). Although this proposition has been successfully tested at the group level, to be useful clinically, it requires further research and validation.

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F. MRI DIAGNOSTIC PATTERNS

FOR

AD

Medial temporal lobe width atrophy in AD has been well documented in MRI studies (Erkinjuntti et al., 1993; Killiany et al., 1993), whereas studies investigating brain volume loss in mild AD have found reductions in temporal lobe volumes (Murphy, 1993). Significant clusters of gray matter loss aVecting areas other than medial temporal cortex occur (in decreasing order) in posterior cingulate gyrus, precuneus, and temporoparietal association cortex, (Baron et al., 2001). Atrophy of the amygdala and hippocampus has also been regularly reported in AD (O’Brien et al., 1997). EVects of gender on age‐related volume loss in healthy volunteers have been detected with MRI; whereas men seem to have significantly greater atrophy than women in entire brain, frontal, and temporal lobes, women have greater atrophy in the hippocampus and parietal lobes (Murphy et al., 1996). With respect to AD, greater gray matter loss occurs in the anterior cingulate for men than women and also relative to controls (Ballmaier et al., 2004). Comparing PET with MRI, PET is more diagnostically accurate than volume measures with MRI (De Santi et al., 2001). Reduced metabolism found with PET for AD is, therefore, likely to be a real eVect and not just the result of underlying atrophy as observed by MRI: Metabolic diVerences in parietal, temporal, frontal, posterior cingulate, and precuneus between AD and healthy volunteer groups remain after correcting PET images for partial volumes eVects of atrophy (Ibanez et al., 1998; Meltzer et al., 1996). Correcting functional images for partial volume eVects increases diagnostic accuracy (Kanetaka et al., 2003). MRI volume measures have a relatively higher sensitivity and lower specificity than SPECT in the detection of early AD; taking the independent information from both MRI and SPECT in combination increased accuracy further (El Fakhri et al., 2003). Longitudinal studies of serial MRI measurements have demonstrated that volume loss in the hippocampus is positively correlated with increasing dementia severity; patients with AD have more atrophy than those with MCI and healthy volunteers in turn. Hippocampal atrophy in patients with MCI at initial assessment seems to be predictive of subsequent conversion to AD ( Jack et al., 1999).

G. DIAGNOSTIC PATTERNS DIFFERENTIATING AD

FROM

VD

Whereas Alzheimer’s disease has been typically characterized by reduced perfusion and metabolism in temporoparietal areas of the brain, diVuse multifocal ‘‘patchy’’ abnormalities extending over cortical and subcortical structures have been observed in vascular dementia (VD) (Mielke and Heiss, 1998). High diagnostic accuracies have been obtained for SPECT using the typical AD

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pattern, sensitivities for AD as high as 82–90% and specificities against VD of 77–82% have been obtained (Butler et al., 1995; deFigueiredo et al., 1995; Lee et al., 2001). Global and frontal reductions in blood perfusion are found in VD compared with healthy volunteers (Yoshikawa et al., 2003). The earliest published functional imaging results consisted mainly of well‐ defined case–control severe dementia SPECT studies; reasonable classification was achieved despite imaging systems with limited resolution (Battistin et al., 1990; Launes et al., 1991). Mild to moderate dementia has been more diYcult to diVerentiate; recent studies have often achieved accurate classification only through the use of sophisticated multivariate statistical analysis of image data rather than observer‐dependent visual assessment (Burdette et al., 1996; Honda et al., 2003; Lee et al., 2003). Studies of early‐onset dementia have reported hippocampal deficits for AD compared with VD, whereas frontal lobes, including the cingulate and superior frontal gyri, have been reported to be more aVected in VD than AD (Lee et al., 2001; Nagata et al., 2000). Particularly for early VD, basal ganglia deficits can be observed (Starkstein et al., 1996). The occipital cortex seems to be preserved in both AD and VD, (Nagata et al., 2000). SPECT seems more eVective (77% correct classification) than structural MRI in diVerentiating AD from MID (50% correct) (Butler et al., 1995). For vascular dementia, MRI typically shows characteristic multiple areas of hyperintensity in white matter, basal ganglia, and/or thalamus, although a diagnosis of dementia with these abnormalities is not implicit. One study using MRI to detect periventricular white matter lesions did not successfully discriminate between AD and VD groups; subcortical white‐matter changes were also of questionable diagnostic importance, because they were found to be prevalent in 100% of VD and as much as 60% of AD patients (Bowen et al., 1990). Considering MID, the frequency of lacunar infarcts, especially in thalamus and basal ganglia, has been reported to be diagnostic of VD or AD with cerebrovascular disease (Lechner and Bertha, 1991). When functional imaging deficits correspond with MRI abnormalities, a diagnosis of VD is more likely, particularly for asymmetrical patterns and when deficits occur in basal ganglia and thalamus. Because AD and VD often overlap in a mixed presentation of dementia, imaging cannot be used independently of clinical information to obtain a diagnosis.

H. DIAGNOSTIC PATTERNS DIFFERENTIATING AD

FROM

FTD

Bilateral frontal deficits have been widely reported as diagnostic of FTD (Diehl et al., 2004; Pickut et al., 1997). A SPECT study with pathological confirmation found bilateral frontal reductions in 88% of patients with FTD compared with none in the comparison group of patients with AD (Read et al., 1995).

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Apart from frontotemporal areas, limbic areas, basal ganglia, and thalamus are aVected in FTD compared with healthy volunteers (Ishii et al., 1998). Medial temporal lobes are relatively spared in FTD; this has been proposed as a diagnostic marker to separate AD from FTD (Sjogren et al., 2000). An absence of reduced perfusion in the posterior cingulate cortex is a diagnostic marker of FTD as opposed to AD (Bonte et al., 2004), whereas anterior cingulate and caudate nucleus deficits are indicative of FTD compared with normal volunteers (Pagani et al., 2001).

I. DIAGNOSTIC PATTERNS DIFFERENTIATING AD

FROM

DLB

Reductions in perfusion or metabolism in the occipital cortex observed with PET and SPECT have been reported to diVerentiate DLB from AD, irrespective of the clinical severity of disease (Lobotesis et al., 2001; Okamura et al., 2001). In contrast to occipital reductions, medial temporal areas of the brain are well preserved in DLB but not in AD (Ishii et al., 1999). Reductions in cingulate areas have also been observed (Imamura et al., 2001). Occipital metabolic reductions as a potential diagnostic marker distinguish DLB from AD and have been confirmed pathologically, with a PET study achieving sensitivity against postmortem of 90% and a specificity of 80% (Minoshima et al., 2001). Because VD can present with similar symptoms to DLB, structural imaging with MRI is clinically indicated; patients with DLB do not exhibit as many white matter lesions associated with VD (Barber et al., 1999). MRI research studies have found less temporal lobe, hippocampal, and amygdala atrophy with DLB compared with AD (Barber et al., 2000) and less gray matter loss in orbitofrontal and temporal cortices (Ballmaier et al., 2004). Volumetric diVerences between DLB and VD have not been observed (Barber et al., 2000). These research techniques are promising but have yet to be adopted into routine clinical practice.

J. NEW IMAGE ANALYSIS TECHNIQUES To improve the diagnosis of early dementia, sophisticated techniques, such as multivariate statistical analysis, are increasingly used to detect subtle changes associated with early disease. The earliest published studies used visual assessment of images that is observer‐dependent; quantitative objective approaches such as regions of interest analysis with SPECT and PET images were then adopted by research studies, either independently or in comparison with visual assessments. However these methods do not use the entire data set. Multivariate statistical approaches such as statistical parametric mapping (SPM) (Friston et al., 1995; Soonawala et al., 2002), three‐dimensional (3D)

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stereotactic surface projections (3D‐SSP) (Burdette et al., 1996; Hanyu et al., 2003), 3D fractal analysis (3D‐FA) (Yoshikawa et al., 2003), neural networks (Dawson et al., 1994; Kippenhan et al., 1994), and discriminant function analysis (Mahony et al., 1994; O’Brien et al., 2001), have demonstrated increased accuracy. Functional imaging will hopefully increase in value as more of these methods cross over from research into clinical practice. Combinations of imaging modalities can improve accuracy of diagnostic classification. Traditionally, MRI imaging has been recommended in guidelines specifically as a means of excluding the small proportion of treatable causes of dementia. Richer information could be derived by the use of volumetric measurements of MRI and by adding in the independent information of PET or SPECT. Coregistering functional images to the patients own structural MRI scan is highly discriminative of AD compared with healthy volunteers (Callen et al., 2002). MRI and functional imaging give independent information, which, when used together, improves classification rates (O’Brien et al., 2001; Varma et al., 2002).

K. SUMMARY The current ‘‘gold standard’’ of clinical diagnosis can be improved by following patients up over a period of time to confirm the diagnosis. Unfortunately, the frequency of postmortem diagnosis will further decrease, except in very special settings. The best available ‘‘gold standard’’ should be used in clinical cohorts of patients to arrive at a realistic estimate of the clinical usefulness of neuroimaging. Improving diagnostic accuracy is possible by increased use of functional imaging, coregistered to MRI structural scans when possible and then corrected for partial volume eVects. Furthermore, quantitative image assessment by sophisticated multivariate statistical analysis will also enable an accurate diagnosis as early as possible and, by implication, facilitate early treatment intervention.

V. Functional Imaging

Over the past decade, there has been a considerable increase in research eVectively integrating functional imaging and neuropsychology. Functional imaging modes, such as PET, SPECT, and fMRI, can be combined with neuropsychological information and used to investigate the neural correlates of cognitive and behavioral processes. The use of imaging techniques has led to improvements in the understanding of the dementias and their longitudinal course and may lead to further developments, for example, in evaluating potential

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treatments. Such developments relating to functional imaging techniques in collaboration with neuropsychology will hopefully contribute to the advances in the early diagnosis of the dementias. Mild cognitive impairment is the amnesic prodrome of AD in which patients have progressive memory symptoms, confirmed on formal neuropsychological tests, in the absence of the pervasive cognitive deficits indicative of dementia (Petersen et al., 2001). As mentioned earlier, the earliest changes associated with MCI identified with functional imaging are deficits in the posterior cingulate cortex. As the disease advances, the occipital and frontal association cortices are progressively aVected (Alexander et al., 2002). In neuropsychological terms, the classic presentation for the progression of AD begins with episodic memory deficits, impairments in semantic memory become apparent, followed by ‘‘executive’’ impairment. Once the disease develops into the latter stages, language, both expressive and receptive, visuospatial/constructional, praxis, and, finally, behavioral problems occur. Functional neuroimaging techniques can identify regions associated with a certain cognitive task; they cannot determine which of these regions are essential for performing the task. This information can be provided by neuropsychological studies (Cabeza and Nyberg, 2000). Thus, to obtain informative findings relating to cognitive function, neuroimaging techniques should be complemented by neuropsychological methods.

A. CORRELATIONS

OF

FUNCTION

WITH

BASELINE SCAN

Functional brain imaging oVers potential insights into all of the main pathological features of AD. Neuroimaging methods, combined with neuropsychological data, suggest that reductions in blood flow first seen in temporal and parietal regions are related to the pronounced episodic memory impairment in AD (Grady, 2000). There has been considerable evidence in support of the correlation between the temporal and parietal regions in relation to episodic memory. A functional neuroimaging study dissociated the roles of the anterior and posterior hippocampus. Specifically, the left anterior hippocampus responded to novelty during learning of behaviorally relevant and irrelevant items, whereas a bilateral posterior hippocampal response to novelty was observed only during learning of behaviorally relevant items (Strange et al., 1999). Desgranges et al. (2002) conducted an interesting study investigating the neural substrates of episodic memory impairment in AD. The correlations observed involved principally the limbic structures, namely the hippocampal regions (as mentioned previously), including the rhinal cortices and the bilateral posterior cingulate and retrosplenial cortices, consistent with previous studies (Desgranges et al., 1998). Furthermore, this study showed that the sites of significant correlations between memory scores and

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resting metabolism vary according to the severity of cognitive impairment, suggesting that in AD the brain areas that subserve residual episodic memory shift from the limbic to the neocortical association structures with increasing impairment and disease severity. There is ongoing debate regarding whether semantic memory or executive functioning is the next cognitive function in AD to decline after episodic memory. Here, we will first look at semantic memory. Neuropsychological assessment has shown that tests of confrontational naming and semantic/category fluency performance are shown to be relatively sensitive to changes in the early stages of AD, specifically, greater vulnerability of person‐specific as opposed to object‐based semantic knowledge (Thompson et al., 2002). It is thought that this occurs when neurodegenerative changes extend from the posterior cingulate cortex to the adjacent temporal neocortex ( Jobst et al., 1992) together with a generalized loss of neocortical synapses and long corticocortical projection systems. In a PET study researching the neural substrates of semantic memory in AD, it was demonstrated that regional cerebral glucose metabolism in the left inferior temporal region correlated consistently with the scores of verbal semantic memory tests, suggesting that dysfunction of this region contributes to verbal semantic memory breakdown in AD (Hirono et al., 2001). Executive functioning refers to mental activity that is involved in the planning, initiation, and regulation of behavior (Lezak, 1983, 1995). One aspect of executive functioning is referred to as ‘‘working memory.’’ Working memory, a type of on‐ line information processing involved in the short‐term maintenance and transformation of information (Goldberg et al., 1998), generally has been shown to correlate with metabolism in the temporoparietal association cortex, with the left hemisphere for verbal and right hemisphere for spatial memory respectively (Trollor and Valenzuela, 2001). In addition to activation of the temporoparietal association cortex, prefrontal activations have been consistently correlated with working memory. An fMRI study researching spatial and nonspatial working memory further supported prefrontal activation (D’Esposito et al., 1998). Once they analyzed their results, they demonstrated that there was not a dorsal/ventral subdivision of the prefrontal cortex, but rather a hemispheric organization; left, non‐spatial working memory, and right, spatial working memory. Attention generally has been shown to correlate with metabolism in the anterior cingulate; a particular correlation has been shown between divided attention and anterior cingulate activity (Perry et al., 2000). Functional imaging has provided support for the role of the anterior cingulate gyrus in the attentional resolution of competing stimulus and response paradigms such as the Stroop test (Bench et al., 1993). Other anterior brain regions, notably the inferior prefrontal cortex, have shown activation on functional imaging studies using paradigms, similar to the Wisconsin Card Sorting Test, that require subjects to shift cognitive sets (Carter et al., 1995).

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B. FUNCTIONAL ACTIVATION STUDIES Functional neuroimaging has made possible the identification of large‐scale activation patterns associated with higher‐order cognitive processes. One such activation study by Backman et al. (1999) using PET looked at the brain regions associated with episodic retrieval in normal aging and those with AD. The study found some interesting patterns of brain activation during episodic retrieval revealing several similarities between the normal elderly subjects and the patients with AD. Both groups showed bilateral increases of activity in the orbital and dorsolateral prefrontal cortex in cued recall relative to baseline. Most PET activation studies show a unilateral right prefrontal activation during episodic retrieval (Cabeza and Nyberg, 1997). The authors continue by suggesting that the current data on orbital and dorsolateral prefrontal activation imply that patients with early AD attempt to retrieve information and monitor the products of retrieval similar to that in healthy elderly people. This study also demonstrated that both groups exhibited increased activity in the left precuneus and right cerebellum during episodic retrieval. These findings are in line with previous research reporting activation of precuneus and cerebellum (Backman et al., 1997; Cabeza and Nyberg, 1997). In a study using fMRI, Prvulovic et al. (2002) looked at visuospatial processing in AD. They were able to demonstrate that patients with AD showed less activation in the frontal regions, basal ganglia, thalamus, and left and right superior parietal cortex, whereas they revealed more task‐related activity in the left occipitotemporal cortical regions than controls. Furthermore, these activation patterns were obtained while the two groups did not diVer significantly in their task performance. The observed increase in activity in the occipitotemporal cortical regions in patients with AD may reflect a compensatory response when trying to complete the task (Prvulovic et al., 2002). This suggestion has been supported by recent functional activation studies indicating that the AD brain attempts to compensate for the damage to multiple functional systems by reorganizing the spatial and temporal patterns of functional circuits (Bookheimer et al., 2000; Smith et al., 2002). Another important finding from this study was a double dissociation between patients and controls concerning their diVerential activation of the dorsal superior parietal lobe and the ventral fusiform gyrus stream. Although patients showed significantly less activation in the dorsal stream, they revealed higher task‐related activity in the left fusiform gyrus than controls. This shows that in AD, ventral and dorsal visual pathways are not only diVerently damaged at the input side as demonstrated during passive visual stimulation (Mentis et al., 1996) but also these diVerences remain during active engagement of these regions (Prvulovic et al., 2002). Compared with the amount of functional activation studies compiled to research AD, the research looking at other dementias is relatively sparse. An

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exception to this is a study by Mummery et al. (1999) that looked at the disrupted temporal lobe connections in semantic dementia. They used PET to examine changes caused by focal disease to the operation of the network of regions activated by a semantic task. Subjects diagnosed as patients with semantic dementia where scanned while performing both a semantic task and a nonsemantic task using similar stimulus materials. Similar to previous studies, they were able to show that patients activated some areas consistently with the age‐ matched controls; these included the left inferior frontal regions, the left posterior middle temporal gyrus, left temporo‐occipito‐parietal junction, left superior occipital gyrus, anterior cingulated, and right cerebellum. The main area to show substantially reduced activity was particularly in the left posterior inferior temporal gyrus. Furthermore, the authors suggest that the lack of activation in this area is consistent with the observation that all the patients in the study were anomic (i.e., they had an impairment relating to recalling names and extreme word‐finding diYculty).

C. SUMMARY The combination of functional imaging modes with neuropsychology techniques are providing researchers and clinicians alike with the means of identifying regions that are associated with a cognitive function and essential for performing a given cognitive task. Such information is invaluable in the detection and identification of the course of progression in the dementias. Furthermore, the results from such collaborations will have important implications for our understanding of the natural course of AD and other dementias and will be of value in identifying diVerent rates of progression that may represent possible clinical subgroups.

VI. Pharmacological Imaging

A. INTRODUCTION The scope for pharmacological imaging in the dementias is limited. In Alzheimer’s disease, the eVectiveness of acetylcholinesterase inhibitors suggests muscarinic (Claus et al., 1997) or nicotinic receptors (Nordberg, 2004) as possible targets for pharmacological imaging. The typical pathological appearance of amyloid plaques makes markers of intracerebral amyloid potentially useful imaging agents (Nordberg, 2004). Finally, in dementia of the Lewy‐body type, there is a reduction in dopaminergic cells that is reflected in a reduction of presynaptic

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receptors (i.e., the dopamine transporter site; O’Brien et al., 2004; Walker et al., 2004). A useful pharmacological imaging agent must enter the brain in suYcient amounts, must be stable in vivo, and be moderately lipophilic; there should be no metabolites that are taken up into the brain and label the same or similar targets. The tracer should be retained in the brain (have high aYnity to the binding site), be sensitive so that it detects small numbers of target sites, should be specific to the target site, and should have little nonspecific binding (Nordberg, 2004). In addition, adding the radioactive label should not change its pharmacological behavior, or, indeed, the pharmacological behavior described previously should be tested with the label already attached. This is a tall order and explains why progress with in vivo imaging has been frustratingly slow.

B. AMYLOID IMAGING Over the past decade, a number of ligands have been developed that are able to highlight amyloid in vivo by use of diverse imaging methods such as SPECT, PET, MRI, and multiphoton microscopy (Table II; Nordberg, 2004). Attempts to label monoclonal antibodies for human in vivo imaging have been hampered by poor brain penetration (Friedland et al., 1997), but attempts using smaller molecule ligands have demonstrated increased binding in patients with Alzheimer’s disease (Klunk et al., 2004; Shoghi‐Jadid et al., 2002). Shoghi‐Jadid et al. (2002) used 18F‐FDDNP PET in nine patients with AD and seven age‐matched controls to detect in vivo abnormal amyloid deposition in the brain. The retention of 18F‐FDDNP in cortical regions of the patients was

TABLE II COMPOUNDS USED FOR IMAGING OF AMYLOID IN THE BRAINS OF LIVING ORGANISMS Reference Friedland et al., 1997 Shoghi‐Jadid et al., 2002 Klunk et al., 2004 Mathis et al., 2003 Poduslo et al., 2002 Poduslo et al., 2002 Wadghiri et al., 2003 Mathis et al., 2002 Bacskai et al., 2001 Bacskai et al., 2003

Imaging compound 99

Tc‐10H3 F‐FDDNP 11 C‐PIB 11 C‐BTA‐1 MION‐ 1–40 PUT‐Gd‐ Gd‐DTPA‐ 1–40 BTA‐1 Thioflavin‐S PIB 18

Nordberg 2004; permission applied for.

Imaging technique

Study

SPECT PET PET PET MRI MRI mMRI Multiphoton Multiphoton Multiphoton

Patients with AD Patients with AD Patients with AD Baboons Mice PS Mice PS Mice APP/PS Mice APP/PS Mice APP Mice APP

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FIG. 2. 99mTc‐labeled HMPAO (left) and 123I‐labeled 5‐I‐A‐85380 (right) SPECT scans, maximum intensity projections (‘‘glass brain’’) rotated from coronal to sagittal view, illustrating perfusion in cerebral/cerebellar cortex and nicotinic receptor binding in cerebellum, brainstem, thalami, and selected cortical regions. Note that in contrast to a surface rendering of the brain, high activity areas from the front and the back of the brain are equally visible. (Scans acquired on a Strichman 12‐detrector head scanner with NeuroFocus (ß3.36/1) software and rendered with OsiriX version 1.4 on Apple Mac.)

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10–15% higher than in the pons (Shoghi‐Jadid et al., 2002). Klunk et al. (2004) used 11C‐PIB PET in 16 patients with mild AD and nine healthy volunteers. Patients showed a slower washout of 11C‐PIB from brain regions than controls, but the compound did not show any significant correlation between amyloid load and cognitive impairment as measured by MMSE (Klunk et al., 2004; Nordberg, 2004). Table II summarizes the compounds that have been used for in vivo labeling of amyloid in humans and animal models.

C. ACETYLCHOLINE RECEPTORS There is evidence of a reduction in cholinergic transmission in patients with Alzheimer’s disease (Perry et al., 1978). Muscarinic receptor binding has been examined using 123I‐QNB (Weinberger et al., 1991; Wyper et al., 1993) and 123I‐4 iododexetemide (Claus et al., 1997). There seems to be a reduction in binding even in early dementia and after correction for partial volume eVects (Claus et al., 1997). Nicotinic receptors are equally reduced in number, most notably in early Alzheimer’s disease (Nordberg, 2001). Fig. 2 shows the distribution of nicotinic receptors in the brainstem, cerebellum, thalamus, and certain cortical regions from SPECT images with the ligand 123I‐5IA.

D. DOPAMINE TRANSPORTER Presynaptic dopamine transporter ligands, such as FP‐CIT, can give an index of reduced dopaminergic activity, such as in Parkinson’s disease and dementia with Lewy bodies (DLB) (O’Brien et al., 2004; Walker et al., 2004). Transporter loss in Parkinson’s disease and Lewy body dementia seems to be equal, with a flatter caudate‐putamen gradient in the former (Walker et al., 2004). However, DLB can be distinguished from Alzheimer‐type dementia with a sensitivity of 78% and a specificity of 94% (O’Brien et al., 2004).

VII. Conclusion

Dementias are a fertile field for the application of neuroimaging. The organic changes of dementia, which often involve cell loss, are easier to identify than abnormalities in the so‐called functional psychiatric conditions, such as mood disorder or schizophrenia. On the other hand, reductions in tracer uptake tend to be due to a combination of anatomical and functional pathology, which means

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that the investigation of functional changes requires a careful correction of cell‐ loss–induced partial volume eVects. This will require a structural scan (e.g., MRI) to be coregistered with the relevant functional scan. Anatomical changes also mean that automatic algorithms involving spatial transformation into a standard space, such as statistical parametric mapping, require particular care to avoid artifacts. It is hoped that the near future will bring the development of specific tracers that shed light on very specific pharmacological or pathological mechanisms that clarify the etiology and possibly support the therapy of dementia.

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PREFRONTAL AND ANTERIOR CINGULATE CONTRIBUTIONS TO VOLITION IN DEPRESSION

Jack B. Nitschke and Kristen L. Mackiewicz Waisman Laboratory for Brain Imaging and Behavior Departments of Psychiatry and Psychology, University of Wisconsin Madison, Wisconsin 53705

I. II. III. IV. V. VI.

Defining Volition and Outlining its Relationship to Depression DLPFC and Volition DLPFC and Depression ACC and Volition ACC and Depression DLPFC and ACC in Volition: Synthesis and Future Directions for Depression Research References

Lack of volition is a key feature of depression. Integrally tied to other symptoms of depression, avolition importantly contributes to the symptom heterogeneity, variations in disease course, and diVerential treatment response that characterize mood disorders. The dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC) have prominent and distinct roles in volition, particularly in the formation and implementation of action plans. Cognitive control and conflict monitoring of internal and external cues are central domains of DLPFC and ACC function that are recruited during volition. Depressed individuals frequently exhibit hypoactivity in these two cortical regions, which may explain the lack of volition that frequently accompanies depression. Accordingly, cognitive deficits in the organization and allocation of resources, as well as decreased engagement in goal‐directed behaviors, likely reflect deficient functioning of the DLPFC and ACC. Increased attention to volition in research on depression may further inform eVorts to uncover the neurobiology and pathophysiology of the disorder and, in turn, improve on current treatment options. The heterogeneity of symptoms in depression has been a critical confound in our attempts to understand and successfully treat individuals with it. As a result, the etiology and pathophysiology of depression remains elusive. The cardinal features of sad mood and loss of interest or pleasure have not been particularly Note: This chapter is reprinted here with permission from MIT Press. It also appears in Disorders of Volition. INTERNATIONAL REVIEW OF NEUROBIOLOGY, VOL. 67 DOI: 10.1016/S0074-7742(05)67003-1

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useful in explaining our understanding of the disorder. Despite the advent of sophisticated techniques for imaging the brains of people with depression, the neuroimaging data accrued have not implicated a clear neural signature. Indeed, the lack of a clear consensus in the relevant neuroscience literature may reflect the symptom heterogeneity across individuals experiencing depression. Viewing depression as a disorder of volition provides a promising line of inquiry for answering questions about the nature of depression. Volition is a multifaceted construct of high relevance to the dysfunction accompanying depression. Cognitive neuroscience research has uncovered the neural substrates of some of the key features of volition, which direct our attention to two brain territories in particular—the dorsolateral prefrontal cortex (DLPFC) and the anterior cingulate cortex (ACC), illustrated in Fig. 1. Neuroimaging and other neuropsychological investigations of depression have repeatedly reported abnormalities in those two cortical regions. This chapter reviews the neuroimaging findings demonstrating the recruitment of the DLPFC and ACC in functions central to volition, as well as the large literature implicating those two structures in depression. A synthesis follows that highlights the importance of volition for understanding the phenomenology and neural circuitry of depression and points to innovative research designs to further probe how volition operates in depression.

I. Defining Volition and Outlining its Relationship to Depression

In this chapter, volition is defined as the conscious implementation of an intention to act, either physically or mentally. Volition requires an individual to focus and properly allocate attention and resources for the initiation and implementation of a definite action plan. Actual execution of the action is viewed beyond the scope of volition. There are two crucial components involved in volition: the desire to act and the formation and implementation of an action plan. The desire to act demands that an individual wants to execute an action or action plan. This desire is a conscious intention and subserves the formation of an action plan. It is the underlying motivational force driving the implementation of an action plan and, therefore, must be present before the formation of an action plan can even occur. The second component focuses on an individual’s ability to make a firm commitment to an action plan and organize the resources necessary to implement it. This commitment entails taking all the prerequisite steps necessary to commence an action. The fulfillment of such preparations central to the formation of a strategy for action requires the coordination of multimodal resources. Individuals rely on environmental clues and various cognitive (e.g., memory retrieval) and aVective processes (e.g., threat detection) to organize a representation of the

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FIG. 1. Key brain regions involved in volition and depression. (A) Dorsolateral prefrontal cortex shown in blue. (B) Anterior cingulate cortex shown in yellow.

relevant information and to generate a plausible strategy for action. The implementation of an action plan requires an individual to appropriately recruit and delegate both mental and physical resources to prepare for an action. Without the formation and implementation of a feasible action plan, it is unlikely an individual will execute an action. This is especially relevant to depression, because many individuals with depression often have a desire to act but lack the ability to implement an action plan. They fulfill the first component of volition, namely desire, but are unable to execute the second component, formation and implementation of an action plan. Research by Hertel and colleagues provides direct empirical support for this contention. They have found that memory deficits in depressed participants are eliminated when they receive

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instructions before the start of a task to use specific organizational strategies requiring a memory‐based action plan (for reviews, see Hertel, 1994, 1997, 2000). On the basis of these and other data, Hertel posited a cognitive‐initiative account that depressed individuals show diminished initiative in allocating available attentional resources for using strategies that improve memory performance. This lack of initiative is highly pertinent to the compromised volition observed in depressed patients and suggests a deficit in the capacity to implement an action plan. Hertel and colleagues did not study individuals with very severe, treatment‐ refractory forms of depression, and people with this type of depression may lack both the desire to act and the ability to implement an action plan. Biological constraints, such as anatomical connectivity, limit the candidate brain regions that can provide the convergence and coordination required by volition. As illustrated in ensuing sections of this chapter, the DLPFC and ACC meet these anatomical criteria, perform functions of relevance to volition, and have been repeatedly implicated in research on depression.

II. DLPFC and Volition

Volition requires delicate balance and coordination between external sensory information and internal cognitive and aVective processes. Cognitive control is the ability to synthesize information from the environment, usually gained through experience, with thoughts and aVect to produce goal‐oriented responses. Given the overlap between the functions of volition and cognitive control, cognitive control is a vital constituent of volition. The DLPFC serves a highly important executive role within the brain and subsequently is the main site of cognitive control in both humans and monkeys (Badre and Wagner, 2004; Miller, 2000; Miller and Cohen, 2001; Koechlin et al., 2003; c.f., Garavan et al., 2002). Recent neuroimaging data have demonstrated that tasks requiring high levels of cognitive control activate the DLPFC (Baker et al., 1996; Banich et al., 2000; Cohen et al., 1994, 1997; Frith et al., 1991; MacDonald et al., 2000; Smith and Jonides, 1999). Given that cognitive control facilitates the culmination of sensory and motor information, the DLPFC is an appropriate gateway for directing cognitive control because of its vast neural connections to virtually all sensory and motor systems and subcortical structures (Miller, 2000). A growing body of research has demonstrated that cognitive control in the DLPFC engages top‐down processing, namely behavior that is guided by aVect, thoughts, and experience, as opposed to bottom‐up processing, which is behavior directed by sensory stimulation (Cohen and Servan‐Schreiber, 1992; Miller, 1999, 2000; Passingham, 1993; Wise et al., 1996). Volition requires the engagement of top‐down processing because of the eVortful organization of resources

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implemented during the formation of an action plan. Furthermore, volition implies that an individual wants to eradicate established maladaptive behaviors and replace them with new responses to the same situation, event, or cognition. The DLPFC plays a critical role in acquiring novel behavioral responses corresponding to associations and respective neural connections that are weaker or less established than those for habitual responses. Rather than being fundamentally involved in automatic behaviors or the activation of established neural pathways, the DLPFC subserves the acquisition of new associations and the establishment and strengthening of new neural pathways driven by desired goals (Miller, 2000; Miller and Cohen, 2001). On the basis of the formation of new associations, the DLPFC is the likely sector of the prefrontal cortex (PFC) that derives rules used to direct ensuing behaviors (Dehaene and Changeux, 1991; Miller, 1999; Shimamura, 2000). This aspect of cognitive control is absolutely critical to volition, because it requires an individual to make attentional shifts and to implement novel actions. Such top‐down processing aids in the organization and allocation of cognitive resources to successfully ignore competing alternatives and distractions. Miller and Cohen (2001) have proposed a model of top‐down processing, in which the PFC represents goals and directs the use of resources necessary to achieve them. Integral to volition, goals are the crux of convergence between desire and the formation of an action plan. Goal‐directed behavior is initiated and maintained through PFC biases, which are complex neural connections of varying strengths between areas of the PFC and other areas of cortex, such as the visual cortex, somatosensory cortex, thalamus, and basal ganglia. These pathways guide attentional shifts and goals on the basis of established rules or patterns of behavior. Cues from the environment activate established neural pathways, and repeated activation strengthens the neural connections of these pathways. Goal‐directed behavior requires the formation of new neural and behavioral associations. Activation of these new neural pathways in situations in which they produce a desired behavior reinforces and strengthens the connections, perhaps by means of dopaminergic projections from the midbrain ventral tegmental area (Miller and Cohen, 2001; Mirenowicz and Schultz, 1994, 1996; Montague et al., 1996; Schultz, 1998; Schultz et al., 1993). This plasticity of the PFC is crucial for the implementation of desired goal‐oriented associations between a circumstance and new behavior. An important aspect of the PFC’s involvement in goal‐oriented behaviors is its role in reward processing, consistent with the aforementioned links to the dopamine system. If goals were not rewarding, then we would not desire to pursue them or implement actions that would lead to subsequent goal attainment. Neurons in the PFC show increased activity as the quality and quantity of a reward increases, especially in response to behavioral changes (Miller, 2000; Miller and Cohen, 2001; Wallis and Miller, 2003). By willfully changing behavior and establishing

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new behavioral trajectories, an individual can use the rewards reaped from these changes as a type of circular reinforcement for continued changes, an idea of obvious applicability in the psychotherapeutic treatment of depression. Considering volition warrants an appreciation of the numerous subdivisions of the PFC. The emphasis in this chapter is on the DLPFC, which is primarily responsible for the constituents of volition outlined previously, including the representation and selection of goals and the implementation of action plans and behavioral change. Indeed, the PFC findings for the recent elegant studies addressing cognitive control have a dorsolateral focus (Badre and Wagner, 2004; Kerns et al., 2004; Koechlin et al., 2003; Matsumoto and Tanaka, 2004; Miller, 2000; Miller and Cohen, 2001). However, other sectors of the PFC may also contribute to the instantiation of volition. An impressive number of studies have reported dorsomedial PFC activation in a range of paradigms, suggesting a role in monitoring thoughts and emotions relating to the desired goals and plans for behavioral change (Gusnard and Raichle, 2001). More ventral sectors of the medial PFC have been implicated in aVective regulation processes (Davidson et al., 2000b; Milad and Quirk, 2002; Morgan and LeDoux, 1995; Morgan et al., 1993, 2003; Pine, 2003; Quirk et al., 2000; but Gewirtz et al., 1997; Myers and Davis, 2002) and may serve to dampen emotions that are not conducive with goals and initiating action plans. The established role of the ventrolateral PFC in response inhibition (Aron et al., 2004; Garavan et al., 1999; Konishi et al., 1998a,b, 1999; Nielson et al., 2002) would be needed for inhibiting behavioral responses that contradict new goals. Directly below the ventrolateral PFC and lateral to the ventromedial PFC on the ventral surface of the frontal lobe, the orbitofrontal cortex (OFC) encodes the reward and punishment value of stimuli (Elliott et al., 2000; Nitschke et al., 2004b, in press; O’Doherty et al., 2001; Rolls, 1999, 2000; Wallis and Miller, 2003). That information may reinforce or contradict one’s goals, thereby enhancing or impeding subsequent initiation of an action plan. The varied functions performed by these other PFC sectors provide important information by means of direct connections with the DLPFC that is used by the DLPFC in the volitional processes of forming, coordinating, and implementing action plans (e.g., Wallis and Miller, 2003). In summary, the DLPFC subserves an executive role in cognitive control, which is exercised through top‐down processing. Specifically, the DLPFC is a critical component in overriding established behaviors and creating new behavioral responses by means of neural associations. In addition, the DLPFC facilitates reward processing, especially in response to behavioral changes. These functions of the DLPFC overlap to a high degree with the constituent of volition corresponding to the formation and implementation of action plans. Although the emphasis in this chapter is on the DLPFC, other sectors of the PFC subserve specific functions required for volition.

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III. DLPFC and Depression

As currently conceptualized, depression is composed of a constellation of negative symptoms that result in reduced goal‐directed behavior and decreased engagement in potentially rewarding activities. Such symptoms include a persistent depressed mood, a loss of interest or pleasure in activities that were once enjoyable, feelings of guilt and hopelessness, decreased movement and speech, slowness of action, reduced verbal and nonverbal expression, flat aVect, and loss of drive (American Psychological Association, 1994). Cognition is frequently disturbed in people with depression, particularly in the domain of maladaptive cognitive representations and processes (Abramson et al., 1978, 1989; Beck, 1967, 1987; Blaney, 1986; Davis and Nolen‐Hoeksema, 2000; Hankin et al., 2001; Hertel, 1994, 2000; Nitschke et al., 2004a; Nolen‐Hoeksema, 1987, 1990; Watkins, 2002), which may be fundamental to compromised volition. These cognitive aberrations and the demonstrated link between the DLPFC and cognitive control reviewed in the preceding section suggest that depressed individuals should show abnormalities in this brain region. Research examining the neural substrates of depression has identified aberrant activity patterns in the DLPFC during resting states. A number of positron emission tomography (PET) and single‐photon emission computerized tomography (SPECT) studies have reported bilateral DLPFC decreases in blood flow and glucose metabolism (for reviews, see Davidson and Henriques, 2000; Davidson et al., 2002; Dougherty and Rauch, 1997; Heller and Nitschke, 1997, 1998). Moreover, successful treatment is often accompanied by normalization of DLPFC activity (Bench et al., 1995; Buchsbaum et al., 1997; Kennedy et al., 2001; Mayberg et al., 1999, 2000, 2002; but see Brody et al., 1999; Goldapple et al., 2004; Martin et al., 2001). Early structural neuroimaging studies suggested that these functional findings might be accompanied by anatomical diVerences in the DLPFC (for review, see Davidson et al., 2002). However, recent morphometric studies using more sophisticated procedures for parcellation of the frontal lobe into its constituent sectors indicated reduced volume in the OFC and ventromedial PFC but not in the DLPFC (for review, see Davidson et al., in press). Consistent with a general deficit in goal‐oriented behavior, depressed individuals demonstrate impairments in diVerent types of eVortful processing, including memory (for reviews, see Burt et al., 1995; Hartlage et al., 1993; Heller and Nitschke, 1997). In an elegant set of studies, Hertel and her colleagues have shown that memory deficits in depressed participants are eliminated when organizational strategies are provided before the start of a task (for reviews, see Hertel, 1994, 1997, 2000). Her cognitive‐initiative account posits that depressed individuals show diminished initiative in allocating available attentional resources for using strategies that improve memory performance. As such, compromised

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memory performance in depressed people is not due to a lack of eVort or insuYcient cognitive resources but rather an inability to initiate or deploy organizing strategies. Consistent with Hertel’s model, we recently found that depressed individuals did not show the association between DLPFC activity, as measured by electroencephalography (EEG), and memory performance observed for nondepressed controls (Nitschke et al., 2004a). In a PET study providing further support for Hertel’s cognitive‐initiative framework (1994, 2000), depressed patients showed less DLPFC blood flow bilaterally on a complex planning task than nonpsychiatric controls (Elliott et al., 1997). Similarly, patients with DLPFC lesions were impaired in using organization strategies during episodic memory tasks and benefitted from instruction in the use of such strategies (Gershberg and Shimamura, 1995; Incisa della Rocchetta and Milner, 1993). Taken together, these data implicate the DLPFC in the impaired cognitive control, initiative, and volition characterizing depression, all of which are needed for selecting goals and implementing action plans. Other cognitive characteristics of depression contribute less directly to avolition in depression. Depression is typically accompanied by a ruminative cognitive style characterized by automatic negative thoughts that interfere with other cognitive processes important for volition, such as cognitive control (Davis and Nolen‐Hoeksema, 2000; Nolen‐Hoeksema and Morrow, 1993; Watkins and Brown, 2002). In research on cognitive biases for valanced information, depression is accompanied by better recall for negative material and worse recall for positive material (for reviews, see Blaney, 1986; Watkins, 2002). Such biases have been observed for explicit memory tasks and some conceptually driven implicit memory tasks, but not for perceptually driven implicit memory tasks. Two recent reports found that processing biases toward unpleasant stimuli in depression were associated with more right DLPFC activity (Elliott et al., 2002; Nitschke et al., 2004a). These right‐sided findings are consistent with a large amount of literature documenting asymmetrical patterns of frontal activity (right greater than left) in depression (for reviews, see Davidson and Henriques, 2000; Davidson et al., 2002; Heller and Nitschke, 1997, 1998). On the basis of these and other data, we have proposed that right PFC mechanisms represent withdrawal‐related negative emotions and threat perception (Nitschke and Heller, 2002, 2005; see also Davidson et al., 2000a; Nitschke et al., in press), which impede volitional processes such as the selection of goals and the implementation of action plans. The diminished left DLPFC activity contributing to the asymmetry findings frequently observed in depression may be directly involved in avolition as well. Davidson has proposed that the left DLPFC is involved in goal‐directed approach tendencies (for reviews, see Davidson, 2000; Davidson et al., 2002, 2003b; see also Nitschke et al., 2004a; Urry et al., 2004). Accordingly, reduced left DLPFC activity in people with depression may explain their inability to override established behaviors and initiate new goal‐directed behaviors. A recursive

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dysfunctional loop may ensue, with deficient approach tendencies leading to the preservation of negative aVect and cognitions, which in turn hinder the willful formation and pursuit of action plans for desired behaviors. In general, many of the negative symptoms and cognitive deficits associated with depression may be linked to DLPFC hypoactivity. In particular, memory impairment in depression stems from an inability to initiate organizational strategies corresponding to reductions in DLPFC activity. Deficits in implementing organizational strategies parallel the formation and implementation of action plans apparent in volition. Links are also made between impaired volition and other cognitive abnormalities in depression, such as cognitive biases toward negative material. Moreover, the left and right DLPFC may contribute somewhat independently to volition, given their respective involvement in goal‐directed approach and threat‐related withdrawal tendencies.

IV. ACC and Volition

Volition requires an individual to focus and allocate attention and energy, organize resources, and commit to an action plan. Furthermore, an individual must integrate clues from the environment with cognitive and aVective processes to generate a plausible strategy for action. An important precursor of implementing an action plan is selecting a desired action from among competing alternatives. Conflict monitoring entails the mediation of internal and external incongruencies, thus informing the consequent selection of one action plan over another (Matsumoto and Tanaka, 2004). Furthermore, conflict monitoring is central to detecting and subsequently responding to conditions that require increased cognitive control (Badre and Wagner, 2004). Up‐regulation of cognitive control is crucial for the volitional processes of successfully instituting and implementing new goal‐directed behaviors in place of established behavior patterns. Providing evidence for the ACC as the ‘‘executor’’ in signaling this up‐regulation of cognitive processes, numerous cognitive neuroscience reports have implicated the ACC in monitoring and detecting response conflict in information processing (Badre and Wagner, 2004; Banich et al., 2001; Barch et al., 2000, 2001; Botvinick et al., 1999, 2001; Braver et al., 2001; Carter et al., 1998, 2000; Dehaene et al., 2003; Kerns et al., 2004; MacDonald et al., 2000; Milham et al., 2001; van Veen and Carter, 2002). This line of research indicates that the ACC detects crosstalk relating to conflict and interference between diVerent brain regions and subsequently issues a call for further processing and increased cognitive control to help resolve the conflict (Badre and Wagner, 2004; Barch et al., 2001; Carter et al., 1998, 2000; Kerns et al., 2004; Miller and Cohen, 2001). The primary center called on is the

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DLPFC because of its involvement in maintaining necessary task demands and altering behavioral plans (Davidson et al., 2002; Miller and Cohen, 2001). Research in this area has now documented interplay between the ACC and DLPFC (Badre and Wagner, 2004; Botvinick et al., 2001; Kerns et al., 2004; MacDonald et al., 2000; Matsumoto and Tanaka, 2004). Particularly noteworthy is the recent evidence of a direct relationship between ACC activity on high‐ conflict trials and subsequent DLPFC activity and behavioral adjustments (Kerns et al., 2004), supporting the claim that the ACC detects conflict and leads to the recruitment of the DLPFC to initiate cognitive control (Botvinick et al., 2001; Cohen et al., 2000; Kerns et al., 2004; van Veen and Carter, 2002). However, causal ambiguity remains as to whether the cognitive control implemented by the DLPFC is a consequence of the conflict detected by the ACC or serves to prevent future conflicts (Matsumoto and Tanaka, 2004). Nonetheless, these two key brain regions work together to orchestrate the implementation of volitional action plans. The plethora of neuroimaging reports finding DLPFC and ACC abnormalities in depressed patients suggests a disruption of this system that corresponds with the cognitive deficits that accompany depression (Hartlage et al., 1993; Heller and Nitschke, 1997; Hertel, 1994, 2000; Nitschke et al., 2004a). As with the PFC, the ACC can be parceled into subdivisions serving distinct functions (Bush et al., 2000; Davidson et al., 2002; Devinsky et al., 1995; Mayberg, 1997; Mayberg et al., 1999; Nitschke et al., in press; Pizzagalli et al., 2001; Vogt et al., 1995). For example, a wealth of data points to the utility of a distinction between dorsal ACC as a cognitive sector and ventral ACC as an aVective sector (Bush et al., 2000; Davidson et al., 2002; Devinsky et al., 1995; Pizzagalli et al., 2001). The aforementioned research on conflict monitoring has used various cognitive paradigms and has consistently implicated the dorsal ACC. It is connected with the DLPFC, posterior cingulate, parietal cortex, and supplementary motor areas, which all play important roles in response selection and relevant cognitive processing (Davidson et al., 2002). As such, the dorsal ACC is of obvious relevance to the implementation of action plans required for volition. Encompassing the rostral and ventral areas of the ACC, the aVective sector may be instrumental in conflict monitoring for content that is aVective in nature (Davidson et al., 2002; Nitschke et al., in press). Implicated in the regulation of visceral and autonomic responses to stressful events, emotional expression, and social behavior (Davidson et al., 2002; Pine, 2003), the ventral ACC is connected with a host of brain regions involved in emotion, such as the amygdala, nucleus accumbens, insula, periaqueductal gray, hypothalamus, and multiple sectors of the PFC, including the DLPFC (Devinsky et al., 1995; Pandya et al., 1981; Vogt and Pandya, 1987). The ventral ACC likely performs functions similar to the dorsal ACC in detecting conflict and recruiting the DLPFC—either directly or by way of the dorsal ACC—for cognitive control. The primary diVerence between the two subdivisions of the ACC in this regard may be that the

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comparator in the ventral ACC fields aVective input, such as signals representing depressed mood or other unpleasant emotions. In addition to the up‐regulation of cognitive control, volition requires down‐regulation of unpleasant aVect that interferes with goal‐directed behavior. A growing body of evidence indicates that the ventral ACC and adjacent ventromedial PFC are directly responsible for these regulatory functions by means of projections to the amygdala (Nitschke et al., in press; Pine, 2003) following from the detection of conflict involving emotion. The functions outlined here may be central to ACC involvement in various forms of normal and abnormal aVective processing, including pain (Craig, 2003; Craig et al., 2000; Ploghaus et al., 1999; Rainville et al., 1997; Sawamoto et al., 2000; Wager et al., 2004), aversion (Nitschke et al., in press; Small et al., 2003), sad mood (Mayberg et al., 1999), major depressive disorder (Brody et al., 2001; Mayberg et al., 2000), obsessive compulsive disorder (Breiter et al., 1996; Rauch et al., 1997), simple phobia (Rauch et al., 1995), and posttraumatic stress disorder (Rauch et al., 1996; Shin et al., 1997). In fact, psychosurgical lesions of the ACC have been used as a means of treatment for patients with mood and anxiety disorders (for review, see Binder and Iskandar, 2000). Patients with ACC lesions are generally apathetic and unconcerned with significant events (Devinsky et al., 1995; Luu and Posner, 2003), which may reflect an inability to detect conflict among competing response options. To summarize, the ACC is heavily involved in conflict monitoring, with the dorsal sector serving as a cognitive subdivision and the ventral sector as an aVective subdivision. The ACC is responsible for detecting both internal crosstalk and external conflict, and subsequently recruiting brain regions for assistance with resolving conflict and responding appropriately. For example, direct projections from the ACC to the DLPFC provide the call for the cognitive control and top‐down processing needed for volition. Elements of volition executed by the ACC include selection of appropriate action plans from competing alternatives and the recruitment of neural components for the implementation of goal‐directed behaviors.

V. ACC and Depression

Depression involves the disturbance of volitional processes dependent on the ACC, such as conflict monitoring and the selection of an action from among available alternatives. There is sometimes a conflict between an individual’s mood and the behavior expected in a particular role or social context. For example, depressed mood hampers goal setting and intentional action, yet demands of the environment may include expectations to act in specific ways. In an individual with normal levels of ACC activity, the ACC would signal a call

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to other brain regions, particularly the DLPFC, to resolve the conflict and engage in the appropriate goal‐directed behavior. However, in an individual with abnormally low levels of ACC activity, the conflict between one’s mood and perceived expectations would not be eVectively monitored, and thus the usual call for further processing would not be issued. The data on ACC function in depression most consistently reveal a pattern of decreased activity in both dorsal and ventral regions of the ACC (Beauregard et al., 1998; Bench et al., 1992; Curran et al., 1993; Davidson et al., 2003a; Drevets et al., 1997; George et al., 1997; Ito et al., 1996; Kumar et al., 1993; Kumari et al., 2003; Mayberg et al., 1994, 2000). Most of these findings were for the dorsal ACC (e.g., Bench et al., 1992; George et al., 1997; Mayberg et al., 1994), suggesting disturbances in cognitive function of relevance to conflict monitoring and the ensuing selection and implementation of action plans. Whereas the state of being depressed is associated with reduced dorsal ACC activity, remission has been characterized by increased activity in the same region (Bench et al., 1995; Buchsbaum et al., 1997; Goldapple et al., 2004; Kennedy et al., 2001; Mayberg et al., 1999, 2000, 2002). Other studies on depression have reported ventral ACC hypoactivity (Drevets et al., 1997; Ito et al., 1996) and reduced ventral ACC responses to diVerent tasks (Bremner et al., 2004; Davidson et al., 2003a; Kumari et al., 2003). Disengagement of the ventral ACC in depression may reflect hindered volitional processing related to detecting conflict between unpleasant emotions and desired goal states and to down‐regulating such emotions. Indeed, depressed mood and feelings of failure often conflict with desires, goals, and societal expectations, as noted previously. Hyperactivity in this area at baseline in eventual treatment responders (Buchsbaum et al., 1997; Davidson et al., 2003a; Ebert and Ebmeier, 1996; Mayberg et al., 1997; Pizzagalli et al., 2001; Wu et al., 1992, 1999; see also Kumari et al., 2003) suggests that this important function of the ventral ACC is intact in a subgroup of depressed patients, with preserved volition perhaps playing a key role in treatment response. The rostral, pregenual aspect of the ventral ACC has been the locus identified in these treatment studies. Heavily connected with both dorsal and more ventral sectors of the ACC (Devinsky et al., 1995; Pandya et al., 1981; Vogt and Pandya, 1987), the rostral ACC is strategically located for facilitating the integration of aVective and cognitive information (Mayberg et al., 1999; Pizzagalli et al., 2001). In an influential model, Mayberg and colleagues (Mayberg, 1997; Mayberg et al., 1999) have purported that the diVerent regions of the ACC are responsible for the formation and eventual maintenance of some depressive symptoms. The dorsal region of the ACC is responsible for modulating cognitive symptoms, such as attentional and executive deficits, whereas the ventral region of the ACC is postulated to be involved in vegetative and somatic symptoms. Following from the recent evidence that the dorsal ACC signals the DLPFC when cognitive

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control is needed (Kerns et al., 2004; Matsumoto and Tanaka, 2004), the reduced dorsal ACC activity observed in depression might result in deactivation of the DLPFC and corresponding deficits in cognitive control, initiative, and executive function (Davidson et al., 2002; Heller and Nitschke, 1997; Hertel, 1994, 2000; Nitschke et al., 2004a). Recent morphometric studies provide indications of structural diVerences in the ACC as well. Some reports documented reductions in volume, glial cell density, and neuronal size (Ballmaier et al., 2004; Botteron et al., 2002; Cotter et al., 2001; Drevets et al., 1997; Hirayasu et al., 1999), whereas others found no diVerences (Brambilla et al., 2002; Bremner et al., 2002). The contradictory findings in this nascent literature occurred in studies examining the entire ACC as well as those focusing on the ventral, subgenual sector, which has received particular attention as a result of the initial finding in this area reported by Drevets et al. If upheld in future research, reductions in the size of the ACC in depressed individuals may contribute importantly to compromised functioning in that area as it bears on volitional processing. In sum, aVective and cognitive deficits are closely linked with one another in depression, and the ACC is ideally situated anatomically to mediate this convergence. Optimal selection of action plans to be implemented depends on the integration of relevant aVective and cognitive information, as well as the detection and resolution of conflict. The inability of depressed people to properly detect conflict and organize resources to respond may lead to decreased goal‐oriented behavior and implementation of action plans; hence, volition is hampered because cognitive and aVective information is not integrated into an organized plan of action. Interestingly, treatment studies have indicated that depressed patients with increased baseline activity in the rostral ACC, the region of the ACC implicated in the convergence of cognitive and aVective information, demonstrate a better response to treatment.

VI. DLPFC and ACC in Volition: Synthesis and Future Directions for Depression Research

In this chapter, we have outlined two constituent components in the neural circuitry of volition: the DLPFC and the ACC. These structures have distinct roles that contribute to the selection and implementation of action plans. Subserving cognitive control, the DLPFC is involved in the representation and selection of goals and in the implementation of action plans and behavioral change. The ACC has been implicated in monitoring conflict among external and internal cues, with the dorsal ACC modulating cognitive aspects and the ventral ACC more involved in aVect. It plays a central role in signaling and recruiting additional brain regions, particularly the DLPFC, to resolve the

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conflict and initiate the appropriate action. Working in concert, these two key regions form the cornerstones of the neural signature of volition, especially with regard to the implementation of volitional action plans. The cascade of events that occurs allows for the eventual selection of new goal‐directed behaviors that override previously established behavior patterns. The DLPFC and ACC hypoactivity observed in depressed individuals reflects the lack of volition that frequently accompanies depression. Avolition is a constituent of depression that is tightly linked to cognitive deficits in the organization and allocation of resources and attention. These cognitive deficits are highly aVected by the functions of the DLPFC and the ACC and their respective roles in cognitive control and conflict monitoring. Deficient functioning of these two key regions leads to decreased goal setting and subsequent decreases in the formation of organizational strategies for action in people with depression. An important line of inquiry is how the DLPFC and ACC with their respective roles in volition are linked to the broader circuitry and symptomatology of depression. A number of other brain areas have been implicated in depression, including the hippocampus, amygdala, and parietal cortex (Davidson et al., 2002, in press; Heller and Nitschke, 1997, 1998). As noted previously, volition is likely crucial for the expression of other common symptoms of depression such as psychomotor retardation and anhedonia. Finally, the emphasis on the brain instantiation of volition as it relates to depression may inform the heterogeneity in symptom expression that is so prominent in depression. For example, we have suggested that brain‐based subtyping (e.g., PFC‐based versus ACC‐based depression subtypes) may prove to be superior to symptom‐based subtyping in addressing heterogeneity (Davidson et al., 2002). This chapter showcases volition as a symptom worthy of study in the depression literature. Further understanding of the operation of volition in depression will depend on the use of novel paradigms in neuroimaging research that target specific symptoms and cognitive irregularities in depression. As this review demonstrates, most neuroimaging studies conducted to date have concentrated on resting baseline data. In addition to potential confounds, such as falling asleep and the variability in mental processes engaged within and across research participants, little is known about the nature and meaning of a default mode of regional brain activity in the absence of a task (Gusnard and Raichle, 2001; Raichle et al., 2001). Future research employing well‐characterized paradigms used in the literature on cognitive control and conflict monitoring reviewed here and tailored to include content pertinent to depression are particularly appealing, given the theoretical rationale for expecting compromised function in these domains among depressed individuals. An appreciation of volition as a central feature of depression has implications for research on volition as well. Severe depression provides the opportunity to investigate avolition in an extreme form. As one manifestation of symptom

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heterogeneity in depression, aVected individuals experience lack of volition to varying degrees, which may correspond to the replicated finding that a subgroup of depressed patients actually show increased ventral ACC activity before treatment and that the amount of pretreatment ventral ACC activity is directly proportional to the degree of eventual treatment response. Thus, individuals with depression are an excellent research population for surveying individual diVerences in volition across a broad range of its expression. Increased integration of depression and volition research may further inform the pathophysiology of the disorder and viable treatment options. Avolition as a symptom of depression could be directly attacked using biological or psychological interventions that target the DLPFC and the ACC and their respective functions in cognitive control and conflict monitoring. In addition, mental health care providers could capitalize on the preserved volition that exists in some patients, an issue that is not widely appreciated. In sum, serious consideration of volition could contribute significantly to mitigating the tremendous personal suVering and societal burden associated with depression.

Acknowledgments

We gratefully acknowledge the assistance of Krystal Cleven and Sid Sarinopoulos with preparation of this chapter.

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Further Reading

Hirayasu, Y., and Shenton, M. E. (1999). Subgenual cingulate cortex volume in first‐episode psychosis. Am. J. Psychiatry 156, 1091–1093.

FUNCTIONAL IMAGING RESEARCH IN SCHIZOPHRENIA

H. Tost,* G. Ende,* M. Ruf,* F. A. Henn,* and A. Meyer‐Lindenbergy *Central Institute of Mental Health, NMR‐Research in Psychiatry, Faculty of Clinical Medicine Mannheim, University of Heidelberg, 68072 Mannheim, Germany, and y Neuroimaging Core Facility and Unit on Integrative Neuroimaging, Genes, Cognition and Psychosis Program, National Institute of Mental Health, Bethesda, Maryland 20892

I. II. III. IV. V. VI. VII.

Psychomotor Disturbances Early Visual Processing Deficits Auditory System Selective Attention Working Memory Dysfunction Antipsychotic Drug EVects Neuroimaging Genomics References

In the preceding decade, functional neuroimaging has emerged as a pivotal tool for psychiatric research. Techniques such as magnetic resonance imaging (MRI) and positron emission tomography (PET) help bridge the gap between genetic and molecular mechanisms and psychological and behavioral phenomena by characterizing brain dysfunction underlying psychiatric disorders on the neural systems level. This has been of particular relevance for schizophrenia research. This chapter reviews important fMRI studies in neurocognitive domains relevant for schizophrenia, such as motor, visual, auditory, attentional, and working memory function, as well as advances in the visualization of medication eVects and the functional characterization of susceptibility genes. The evolution of our understanding about the nature and treatment of disease is often linked to technological advances providing access to otherwise unobservable structures and processes. A case in point is the enormous benefit medicine as a whole has derived from the development and further improvement of imaging techniques (e.g., microscopy, sonography, computed tomography). Arguably, however, the discipline where imaging has had the largest impact on The views expressed by this author do not necessarily represent those of NIMH or NIH or the Federal Government. INTERNATIONAL REVIEW OF NEUROBIOLOGY, VOL. 67 DOI: 10.1016/S0074-7742(05)67004-3

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our understanding of the pathophysiology, but also the very concepts of the disease entities under study, is psychiatry. Psychiatry’s challenge is unique in that it must provide a testable scientific account that spans levels of description leading from genes and elementary biological processes to disturbed behavior and social adaptation. Modern imaging techniques such as magnetic resonance imaging (MRI) and positron emission tomography (PET) provide access to a systems‐level description of the relevant neurobiology that allows for relating the underlying cellular and genetic processes to the neurocognitive and psychopathological domain. This has contributed enormously to establish and anchor psychiatric research firmly in the broader neuroscience community. In consequence, our current understanding of psychiatric disorders is a neuroscientific one, characterized by the interpretation of disease states in the context of functional, biochemical, and microstructural alterations of the brain. Without the insights provided by noninvasive medical imaging techniques, the progress made in psychiatric neuroscience in the past decade seems unthinkable. Even notions about the pathogenesis and treatment of psychiatric disorders that were regarded as polar opposites have begun to be understood in a unified framework of a neurobiologically founded diathesis‐stress model. For example, the classical dichotomy of somatotherapy and psychotherapy is becoming obsolete as our understanding of functional brain alterations during these therapeutic modalities evolves and shows important commonalities (Goldapple et al., 2004). Current evidence‐based etiological models of schizophrenia point toward the key importance of interactions between predisposing vulnerability, mainly because of genetic susceptibility conferred by multiple risk genes, and environmental factors. The neurodevelopmental hypothesis proposes that schizophrenia emerges from intrauterine disturbances in temporolimbic–prefrontal interactions that manifest as clinical illness after adolescence (Weinberger, 1987). According to this hypothesis, the disturbed neural interaction leads to an impairment of prefrontal function manifesting as negative symptoms (e.g., blunted speech, lack of drive) and cognitive deficits, especially in the executive domain (e.g., working memory, selective attention). Because of deficient prefrontal control exerted on phylogenetically older brain areas, subcortical dopamine release in the basal ganglia is thought to become disinhibited, a phenomenon linked, possibly by the relevance of dopamine for the stabilization of cortical neural assemblies, to the emergence of positive symptoms like hallucinations and delusions (Meyer‐Lindenberg et al., 2002). Since the early 1990s, physiological alterations of brain function have been investigated with functional magnetic resonance imaging (f MRI). In the beginning, the experimental procedures were rather simple, usually using a blockwise alternation of diVerent stimulation conditions. In the following years, the methodological spectrum expanded to event‐related task designs, which allow the analysis of brain responses to brief stimuli under conditions that

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can rapidly change. Advances in the analysis of connectivity between brain regions allow for the characterization of dynamic network interactions. Further technical developments, including those in computational power and data storage, led to the development of scanners with ultra‐fast gradient systems. Today, multichannel RF coils (array coils) can decrease acquisition time and/or increase signal‐to‐noise and spatial resolution substantially by simultaneous measurement of partial volumes. By using advanced acquisition schemes, whole brain data collection with highly resolved slices is now routinely done within a few seconds. In the past 4 years, the broad availability of clinical scanners has given rise to an enormous amount of f MRI studies in psychiatric research. Focussing on selected neurocognitive domains in schizophrenic patients, this chapter reviews important f MRI studies of the past decade. Because of the sheer volume of published results, our review cannot aim for all‐inclusiveness and should best be read as a partial and necessarily subjective view of a vital and still expanding field.

I. Psychomotor Disturbances

Patients with schizophrenia frequently exhibit psychomotor disturbances. Manifestations range from involuntary motor acts, neurological soft signs (e.g., coordination deficits) to complex disorders of behavioral control and catatonic symptoms (Schroeder et al., 1991; Vrtunski et al., 1986). Although quite a lot of f MRI research was performed in this domain, the neurofunctional basis of the disturbances is still only incompletely known. Most studies used simple repetitive motor activities (e.g., sequential finger opposition) alternating with resting conditions in a block‐design approach. Early investigations [e.g., the work of Wenz (1994) or Schro¨ der and colleagues (1995)] reported hypoactivation of primary sensorimotor and supplementary motor cortices in schizophrenia, a finding not consistently replicated by subsequent studies (Braus et al., 1999; Buckley et al., 1997; Schro¨ der et al., 1995, 1999; Wenz et al., 1994). In addition, data indicating altered functional asymmetry of the cortical hemispheres during motor tasks have been published [e.g., recently by the group of Yurgelun‐Todd (2004); Bertolino et al., 2004a; Mattay et al., 1997; Rogowska et al., 2004]. One emerging finding is that patients with schizophrenia may be characterized by a reduced lateralization index during motor performance. In light of the usually pronounced lateralization of cortical activation during motor function, this indicates an abnormal situation in terms of reduced contralateral recruitment or deficient ipsilateral inhibition of motor areas, respectively. However, a substantial number of contradictory findings, as well as some empirical data (Bertolino et al., 2004a; Braus et al., 1999; 2000b), show that further studies in

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these areas will benefit from controlling for confounding factors such as medication eVects (see also pharmacological section of this review).

II. Early Visual Processing Deficits

Neuropsychological research has repeatedly confirmed the presence of visual information‐processing deficits in schizophrenia (BraV and Saccuzzo, 1981, 1985; Keri et al., 2000; Moritz et al., 2001). Among others, patients exhibit a significantly increased error rate during performance of so‐called backward masking tasks, which use contiguous distractor presentations to disturb the sensory processing of target stimuli (BraV and Saccuzzo, 1981, 1985). Another aVected visual domain is deficient perceptual discrimination of target velocities, a research area extensively investigated by Holzman and coworkers (Chen et al., 1999a,b,c). Because some studies indicate that visual‐processing abnormalities may be observable in asymptomatic relatives of patients with schizophrenia (Chen et al., 1999b; Green et al., 1997), they may be valuable as a trait marker of disease vulnerability. Delineation of the underlying neural‐processing deficit may, therefore, be valuable as an endophenotype. Consequently, much research has been directed at the characterization of visual information‐processing deficits in behavioral experiments. Here, high error rates during processing of stimuli of higher spatial frequency, or moving stimuli, suggest a pathophysiological involvement of the dorsal visual‐processing stream in patients with schizophrenia (Cadenhead et al., 1998; O’Donnell et al., 1996; Schwartz et al., 1999). The so‐called magnocellular network comprises cortical areas specialized for the handling of motion and depth cues (e.g., the motion‐ sensitive field V5 [hMT], posterior‐parietal cortex [PPC], and frontal eye fields [FEF]; Ungerleider and Mishkin, 1982; Ungerleider et al., 1998). The exact location of the presumed dorsal stream dysfunction, however, cannot be identified by use of a behavioral approach. Prior empirical data were, therefore, interpreted in manifold ways [e.g., as a sign of a deficient prefrontal control of lower visual areas or a thalamic filter dysfunction (Keri et al., 2000; Levin, 1984a,b)]. Among others (Chen et al., 1999a; Stuve et al., 1997; Tek et al., 2002), the group surrounding Holzman (Chen et al., 1999a,b,c) assumes a ‘‘bottom‐up’’ processing of motion signals in V5 as being responsible for visual processing dysfunctions and executive deficits observable in patients with schizophrenia (e.g., eye‐tracking dysfunction, spatial working memory deficits). Only relatively few research groups have used f MRI to study early visual‐ information processing in schizophrenia to date. One of our own studies (Braus et al., 2002) investigated visuoacoustic integration in 12 neuroleptic‐naive patients with a passive stimulation paradigm involving the simultaneous presentation of a

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visual 6‐Hz checkerboard and an auditory drumbeat stimulus. Compared with healthy controls, the patient group displayed a significant activation decrease in both the thalamic geniculate body and higher order areas of the dorsal processing stream (PPC, FEF, and DLPFC). The results indicate a fundamental visual‐ processing deficit of the dorsal stream network that is already noticeable at disease onset, even in the absence of marked cognitive demands (Braus et al., 2002). Subsequent f MRI studies of our group examined the pathophysiological model supposed by Holzman and colleagues, proposing a circumscribed functional deficiency of V5 during visual motion perception. In a first step, we examined brain functional correlates of patients with schizophrenia and healthy controls during the passive perception of moving visual targets (Tost et al., 2003a). The stimulation paradigm consisted of a pseudo‐randomized presentation of tilted and moving sinusoidal gratings, permitting the identification of V5 in the occipitotemporal association cortex (see Fig. 1). Data analysis confirmed a strong recruitment of the dorsal processing network in both groups. Furthermore, group comparison verified a significantly enhanced activation of controls in posterior‐parietal areas, whereas activation diVerences in V5 were absent (see Fig. 2). The assumption of deficient processing of motion signals in V5 is largely based on behavioral experiments indicating a significantly lower contrast sensitivity for the discrimination of small‐velocity diVerences in schizophrenia (Chen et al., 1999c). Thus, in a second step, we examined the neurobiological background of this phenomenon with f MRI (Tost et al., 2003b, 2004). The block design f MRI paradigm included the sequential presentation of moving sinusoidal gratings with varying velocity diVerences, presented in a pseudo‐randomized manner (easy task condition: 11
/s vs. 5
/s; diYcult task condition: 8
/s vs. 6
/s). Patients with schizophrenia and healthy controls were instructed to indicate the faster grating of each stimulus pair during the scan. In both groups, task performance yielded a significant activation enhancement of a highly distributed visuomotor network, including subcortical parts of the visual system (lateral geniculate nucleus), primary and extrastriate visual cortices (V1–V5), and higher order areas of the dorsal visual processing stream (PPC, SMA, lateral premotor cortex, DLPFC, see Fig. 3). Direct comparison of the easy and diYcult target discrimination revealed a load‐dependent activity enhancement in posterior‐ parietal and prefrontal cortices but not V5. Interaction analysis disclosed a significantly decreased activation of PPC and DLPFC in the patient group; activation diVerences in V5, however, could not be verified. In summary, our own functional imaging results do not support a popular hypothesis deduced from behavioral data, suggesting a circumscribed processing deficit of the visual motion area V5 in schizophrenia. Instead, our results point to a deficient processing of motion cues at a higher level of the dorsal visual network usually associated with executive functioning, the control of eye movements,

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FIG. 1. Visual motion perception paradigm. Statistical comparison of the diVerent stimulation conditions in a general linear model analysis (moving vs. stationary gratings) allows the identification of the motion‐sensitive processing area V5 (hMT) in the occipital temporal association cortex.

and the ‘‘top‐down’’ control of lower visual cortices (Kastner et al., 1998, 1999; Ungerleider et al., 1998).

III. Auditory System

The perception of voices in the absence of external stimuli (auditory hallucinations) is one of the cardinal symptoms of schizophrenia. Cognitive models first suggested underlying abnormalities in the processing of inner speech, a notion not supported by functional imaging studies. Instead, in the past 15 years, empirical evidence repeatedly indicated structural and functional disturbances of the superior temporal gyrus (STG), a crucial part of the network controlling the perception and production of speech. A close relationship between the

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FIG. 2. Comparison of striate and extrastriate visual processing areas V1–V5 (contrast a: stationary þ moving visual stimuli > baseline) and the motion‐selective processing area V5 (contrast b: moving stimuli > stationary stimuli) in healthy controls (1) and schizophrenic patients (2). No significant group diVerences are evident in the lower parts of the dorsal visual network (interaction analysis p  0.0001, uncorrected).

severity of auditory hallucinations and the extent of STG volume reduction, for instance, was already found by Bartha and colleagues in 1990 (Barta et al., 1990). Functional imaging results provided by the groups of Schnorr (1995), Murray (1993, 1995), and WoodruV (1995, 1997) demonstrated a pronounced activity enhancement of auditory‐ and speech‐processing cortices during hallucinatory experiences (Heschl’s gyrus, Broca and Wernicke area) (McGuire et al., 1993, 1995; Silbersweig et al., 1995; WoodruV et al., 1995, 1997). A particularly convincing study was conducted by Dierks et al. (1999), which demonstrated the potential of event‐related f MRI study designs for psychiatric research. From a neuroscientific point of view, these results yield a plausible explanation for the fact that patients accept the internally generated voices as real. Consistent with the proposal of a regional disconnection syndrome contributing to the symptomatology of schizophrenia, current f MRI, DTI, and morphometric imaging data indicate a correlation of hallucination severity with the extent of the functional and structural connectivity abnormalities of the STG

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FIG. 3. Visuomotor activation associated with the discrimination of target velocities. Compared with the healthy controls (1), schizophrenic patients (2) display a significant activation decrease in higher order areas of the dorsal visual processing stream (premotor cortex, SMA, PPC, insular cortex, ACG; interaction analysis p  0.0001, uncorrected).

(Gaser et al., 2004; Hubl et al., 2004; Lawrie et al., 2002). Furthermore, these alterations have been shown to interfere with the cortical processing of regular auditory stimuli in schizophrenia as well. An f MRI study of Wible and coworkers (2001), for example, provided evidence for a dysfunctional processing of mismatch stimuli (a descriptive term for the presentation of diVering tones embedded in a series of standard tones) in the primary auditory cortex (Wible et al., 2001). Other f MRI studies point to a diminished response of the temporal lobes to external speech during hallucinatory experiences (David et al., 1996; WoodruV et al., 1997). This phenomenon is usually explained as the competition of physiological and pathological processes for limited neural processing capacity. IV. Selective Attention

The neuropsychological term attention describes the selection and integration of relevant information units from the perceptual stream, requiring the complex interplay of diVerent brain regions and functions. In schizophrenia

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research, scientific descriptions of attentional dysfunction can be traced back to the initial descriptions by Kraepelin and Bleuler. These disturbances are considered by some as promising cognitive endophenotypes of disease vulnerability, because they precede disease onset, persist during remissions, and are also found in asymptomatic relatives (Cornblatt and Malhotra, 2001; Egan et al., 2000; Gold and Thaker, 2002). The continuous performance test (CPT)—rather a terminological label than a standardized test device—is one of the most popular neuropsychological measures in schizophrenia research. The term encompasses a variety of tasks best summarized as requiring selective attention (typical task requirements: selective responses to certain targets, inhibition of inadequate reactions to nontargets, high rate of stimuli over a period of less than 10 minutes). Apart from simple choice reaction tasks (involving the selection of a certain target from an assortment of stimuli, CPT‐X) more complex CPT versions with additional cognitive requirements can be distinguished. So‐called degraded CPTs use blurred visual presentations to manipulate the perceptual requirements of the task (e.g., Siegel et al., 1995). Appropriate handling of contingent CPTs requires the additional monitoring of preceding task conditions. Because of their strong resemblance to 1‐back tasks, these cognitive tests extend into the working memory domain (e.g,. CPT‐AX, CPT‐IP, and CPT‐double‐T). Other CPT variants use interspersed distractors to assay impulse control; the resulting task demands are similar to classic cognitive interference tasks (e.g., Stroop). Thus, any assessment of functional imaging findings in this domain needs to carefully take the specific task arrangements into account. So far, most functional imaging studies have used contingent CPTs to examine selective attention dysfunction in schizophrenia. Dorsolateral prefrontal hypoactivation of the patient group is a widely replicated finding, likely because of the moderate working memory load of the tasks (MacDonald and Carter, 2003; Volz et al., 1999). Barch and coworkers (2001) observed a comparable DLPFC dysfunction in neuroleptic‐naive patients as well, arguing against medication eVects (Barch et al., 2001). Simple CPT choice‐reaction tasks, however, were rarely investigated with fMRI. Only one study by Eyler and colleagues (2004) used a simple CPT paradigm, providing evidence for a right inferior frontal activation decrease in the patient group. The authors hypothesized that the unusual ventral lateral location of the group diVerence may be a consequence of the lower executive demands of their task (Eyler et al., 2004). An important neural interface of cognition, emotion, and behavioral control, the dorsal anterior cingulate gyrus (ACG) is prominently activated during the performance of cognitive interference tasks (Cohen et al., 2000). Early PET studies already showed ACG hypoperfusion during interference in schizophrenia (Carter et al., 1997). According to Yu¨ cel and colleagues (2002), the activation loss may coincide with the absence of a morphologically diVerentiated paracingulate

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gyrus in patients (Yu¨ cel et al., 2002). Several studies conducted by Carter, Barch, Cohen, and colleagues demonstrated a comparatively specific (performance correlated) ACG dysfunction in schizophrenia (Carter et al., 1999, 2001); the authors extended their results into a framework encompassing computational models of prefrontal dopamine function (Braver et al., 1999). An f MRI study conducted by Heckers and coworkers (2004) confirmed, even under comparable task performance conditions, an absence or abnormal localization of dorsal ACG activation in patients with schizophrenia (Heckers et al., 2004). The described functional ACG results are supplemented by growing DTI evidence indicating disturbed integrity of the cingulate bundle (Kubicki et al., 2003; Sun et al., 2003). Although the total number of studies on this topic is still limited to date, current evidence for a structural and functional disturbance of the anterior cingulate gyrus in schizophrenia is convincing (Weiss et al., 2003).

V. Working Memory Dysfunction

The institution and flexible adaptation of behavioral patterns depending on environmental demands is one of the main functions of prefrontal cortex. The high rate of so‐called executive dysfunction (e.g., working memory abnormalities) thus argues for involvement of the prefrontal regions in the pathogenesis of schizophrenia (Glahn et al., 2000; Gold et al., 1997; Goldman‐Rakic, 1994; Silver et al., 2003). Unlike short‐term memory, the working memory concept is aimed at the active storage of information necessary for the performance of cognitive operations but not available from the environment. So‐called ‘‘n‐back’’ tasks are a popular neuropsychological instrument for the assessment of working memory dysfunction. Here, participants are required to constantly monitor a sequence of stimulus presentations and react to items that match the one presented ‘‘n’’ stimuli previously. These tasks are popular, because working memory load can be increased parametrically by increasing the parameter ‘‘n’’ (1‐back, 2‐back, etc.) while keeping stimulus and response conditions constant. Another popular measure of executive function is the Wisconsin card sorting test (WCST), a complex task requiring abstract reasoning and cognitive flexibility in addition to working memory. Both instruments have been used extensively in imaging research to examine the neurobiological correlates of frontal lobe dysfunction in schizophrenia. Patients with schizophrenia display irregular activation patterns during working memory tasks regardless of performance level (Honey et al., 2002), motivation (Berman et al., 1988), or the particular stimulus material used (Spindler et al., 1997; Stevens et al., 1998; Tek et al., 2002; Thermenos et al., 2004). Comparable diVerences can also be observed in healthy siblings of patients with schizophrenia

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(Callicott et al., 2004). The precise mechanism of the prefrontal functional deficit, however, is still a matter of some debate. Most early functional imaging studies indicated a DLPFC hypoactivation, both at rest and during working memory performance (Andreasen et al., 1997; Paulman et al., 1990; Volz et al., 1999). Discrepant results, however, accumulated in the past several years have made it necessary that the theory of a pure ‘‘hypofrontality’’ in schizophrenia had to be revised, or at least amended (Manoach et al., 1999, 2000; Ramsey et al., 2002). Several lines of evidence support the contention that the simple descriptive term of a hypoactivation or hyperactivation underestimates the real complexity of the issue (Callicott et al., 2003). Even in healthy subjects, for instance, DLPFC activation follows a complex and load‐dependent course similar to an inverted U function. According to this, prefrontal activation level increases with task demands until a capacity limit is reached, followed by DLPFC activation decrease concomitant with behavioral decompensation (as indicated by the corresponding increase of performance errors) (Callicott et al., 1999). A comparable relationship between working memory eVort and amount of prefrontal neural discharge was observed in animal studies (Goldman‐Rakic et al., 2000). Second, some groups have observed an increase of activation as subjects exceed their capacity limit (Mattay et al., 2003), arguing for an ‘‘(in)eYciency’’ concept in which increased activation may be indicative of excessive and task‐inadequate neuronal recruitment. A further complication is derived from the fact that neuroimaging data typically reflect a mapping of statistical significance levels representing composite measures of ‘‘signal’’ and ‘‘noise’’ (as measured by the mean shift in BOLD eVect and the residual variance, respectively) that may correspond to diVering neuronal phenomena in the context of working memory. Recent reviews have attempted to reconcile discrepant findings in this domain in the context of more complex functional models (Callicott et al., 2000; Manoach, 2003). Current pathophysiological theories, therefore, assume deficient neural processing in patients with schizophrenia that may, depending on the current capacity reserve, manifest as prefrontal hyperactivation or hypoactivation, respectively. The course of the DLPFC activation level may thus correspond to a pathological left shift in the inverted U load‐response curve described previously: patients may display a relatively enhanced prefrontal activation level under low cognitive load (hyperactivity subsequent to the ineYcient use of neural resources), whereas the reverse may be found under increasing working memory demands (hypoactivity as sign of neural capacity constraints) (Jansma et al., 2004; Manoach et al., 2000). A recent article by Callicott and colleagues (2003) found group diVerences in DLPFC activation, supporting the pathophysiological model of a shifted inverted U function in schizophrenia (Callicott et al., 2003). However, findings not compatible with this model were noted, as well. The functional correlates of DLPFC dysfunction thus seem to manifest as a highly complex,

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capacity‐dependent pattern of coincident hyperactivity and hypoactivity states. The main commonality of most studies seems to be less the directionality than the location of the abnormality, namely, the middle frontal gyrus and the corresponding Brodmann areas 46 and 9. More work will be necessary before a theoretical account can be reached that encompasses the current empirical data while remaining predictive enough to be potentially falsifiable.

VI. Antipsychotic Drug Effects

The psychopharmacology of schizophrenia has progressed in the past 10 years, focusing on the development of novel antipsychotic drugs with an atypical eVect profile (e.g., clozapine, amisulpride, olanzapine). Some studies indicate that, compared with typical neuroleptic drugs (e.g., haloperidol), these substances may be superior with regard to the treatment of negative symptoms and cognitive deficits (Meltzer and McGurk, 1999; Meltzer et al., 1994). Although other studies have shown no such advantage, the absence of substantial extrapyramidal side eVects is a definite improvement in patient quality of life in many cases. Until the mid‐nineties, research on antipsychotic drug eVects was mainly limited to behavioral experiments (Lieberman et al., 1994; Nestor et al., 1991; Zahn et al., 1994). MRI studies examining structural, functional, and metabolic correlates of antipsychotic drug treatment emerged at the turn of the last century (Arango et al., 2003; Bertolino et al., 2001; Braus et al., 2001; Ende, 2000; Ende et al., 2000; Heitmiller et al., 2004). To date, most functional MRI studies in this field have been aimed at drug‐ induced changes of voluntary motor control and executive functioning. In this context, favorable eVects of atypical antipsychotics on putative functional disturbances in schizophrenia have been repeatedly reported (Ramsey et al., 2002). A recent study by Bertolino and coworkers (2004), for instance, shows a normalization of sensorimotor hypoactivation in the course of olanzapine treatment (Bertolino et al., 2004a). Another longitudinal study conducted by the group of Andreasen (2001) showed normalized functional connectivity of cortico‐talamic‐ cerebellar‐cortical circuits with the same agent (Stephan et al., 2001). Furthermore, several older studies suggest at least partially beneficial treatment eVects. Especially prefrontal functions showed some degree of normalization with atypical (but not typical) antipsychotic drug treatment (Braus et al., 1999; 2000a,b,c; Honey et al., 1999). This notion is supported by MR spectroscopy data indicating a higher level of the neuronal viability marker N‐acetylaspartate (NAA) in patients receiving atypical treatment (Bertolino et al., 2001) as opposed to patients with typical antipsychotics (Ende et al., 2000).

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A conclusive picture does not currently emerge from imaging results on antipsychotic drug eVects conducted to date. The amount of scientific publications on this topic is still small, and methodologically necessary study designs (e.g., double‐blind) are almost completely lacking. Given the cross‐sectional design of most of the studies, the conclusion of a ‘‘normalizing’’ or ‘‘restoring’’ drug eVect—drawn by some authors from reductions or absence of fMRI group diVerences—must remain tentative. Furthermore, even in longitudinal study designs, a demonstration of functional recovery is conditional on the reliable and valid characterization of the underlying pathology. As reviewed, however, for most of the studied domains, the functional correlate of the schizophrenic deficit syndrome is not yet precisely delineated. This may account for some drug eVect inconsistencies reported (e.g., compare the data provided by Ramsey et al. and Honey et al. in Table I: drug‐induced restoration of executive functions may manifest as enhanced activation subsequent to a pathological hypoactivity or reduced activation after a pathological hyperactivity). Future f MRI studies with more complex study designs will certainly be capable of dissolving this apparent heterogeneity (e.g., double‐blind investigation of genetically defined responder groups). Convergent observations indicating an association of functional and clinical improvement with the COMT genotype are major steps in this direction (Bertolino et al., 2004b).

VII. Neuroimaging Genomics

The completion of the draft sequence of the human genome was a pivotal achievement that profoundly changed all aspects of medicine and is beginning to transform neuroimaging in psychiatry as well. The characterization of the eVects of genomic variation on neural systems level function using neuroimaging promises to yield decisive insights into both normal and dysfunctional processes important for protective and risk factors for mental illness (Gould and Husseini, 2004). In the case of schizophrenia, it seems overwhelmingly likely that the substantial genetic component of the disease risk is conferred by multiple, interacting, individually small‐risk or susceptibility genes. A promising strategy is, therefore, the characterization of convergent pathways involved in the eVects of genomic variation in such risk genes (e.g., dysbindin, neuroregulin 1, catechol‐ O‐methyltransferase COMT, BDNF) on neural function. This work will usually commence after gene identification by linkage, association, or candidacy (Harrison and Weinberger, 2004). Even further in the future may be the opposite strategy, whereby systems‐level endophenotypes, such as those defined by neuroimaging, may become useful in gene‐finding eVorts.

TABLE I RECENT f MRI FINDINGS IN SCHIZOPHRENIA RESEARCH

Voluntary motor control

Author (year)

Study results

Rogowska et al. (2004)

Reduced activation of sensorimotor cortices and altered hemispherical asymmetry during sequential finger opposition (SFO) (Rogowska et al., 2004). Reduced activation level and disturbed functional connectivity of the thalamus and lentiform nucleus (Menon et al., 2001). Hypoactivation of sensorimotor cortices and highly variable task performance during pronation‐supination (Schro¨ der et al., 1999). Significant activation decrease of PPC and DLPFC during the discrimination of diVerent target velocities (Tost et al., 2004). Passive motion perception: significant hypoactivity of PPC, no significant group diVerences in the motion‐sensitive visual area V5 (Tost et al., 2003a). Neuroleptic‐naive patients: hypoactivity of the thalamus and higher areas of the dorsal processing network under visuoacoustic stimulation (Braus et al., 2002). Frontotemporal connectivity decrease is correlated with severity of auditory hallucinations (Lawrie et al., 2002). Reduced STG activation during auditory mismatch points to an early central processing deficit in the auditory system (Wible et al., 2001). Hallucination experiences are associated with an activation enhancement of the primary auditory cortex (Dierks et al., 1999). Limited response of speech processing areas to external stimulation during auditory hallucinations (WoodruV et al., 1997). Simple choice reaction: significant activation decrease of the right inferior‐frontal cortex despite comparable task performance (CPT‐X) (Eyler et al., 2004) Cognitive interference: dislocated or absent activation of the dorsal ACG, same task performance rate and accuracy (Heckers et al., 2004). Cognitive interference: additional recruitment of DLPFC and ACG resources, comparable task accuracy (Weiss et al., 2003).

Menon et al. (2001) Schro¨ der et al. (1999) Visual system

Tost et al. (2004) Tost et al. (2003)

108 Braus et al. (2002) Auditory system

Laurie et al. (2002) Wible et al. (2001) Dierks et al. (1999) WoodruV et al. (1997)

Selective attention

Eyler et al. (2004) Heckers et al. (2004) Weiss et al. (2003)

Barch et al. (2001) Volz et al. (1999) Working memory

Callicott et al. (2004) Schlosser et al. (2003) Callicott et al. (2003)

Manoach et al. (2000) Callicott et al. (1999) 109

Callicott et al. (1998)

Medication eVects

Volz et al. (1997) Bertolino et al. (2004) Ramsey et al. (2002) Stephan et al. (2001) Braus et al. (2000) Braus et al. (1999, 2000) Honey et al. (1999)

New York1‐back: deficient DLPFC activation in neuroleptic‐naive patients; unobtrusive inferior frontal activation pattern (CPT‐AX) (Barch et al., 2001). 1‐back: significant hypoactivation of mesial frontal and cingulate areas during CPT performance (CPT‐TT) (Volz et al., 1999). Significantly enhanced recruitment of prefrontal resources in healthy siblings (n‐back) (Callicott et al., 2004). Altered eVective connectivity of cerebellum‐thalamus (#), cerebellum‐frontal lobe (#), and thalamus‐cortex (") (n‐back) (Schlosser et al., 2003). Deficient neural processing strategy: advanced hypofrontality with higher working memory demands, preservation of task performance leads to a functional overload of DLPFC resources (n‐back) (Callicott et al., 2003). Patients show a left prefrontal hyperactivity and enhanced spatial heterogeneity of prefrontal activation patterns (Manoach et al., 2000). Load‐dependent course of the BOLD response in healthy subjects: inverted U‐shaped function with increasing task demands (n‐back) (Callicott et al., 1999). DLPFC hypoactivation in the patient group, not attributable to motion artifacts (n‐back) (Callicott et al., 1998). Decreased activation of the right prefrontal cortex (WCST) (Volz et al., 1997). Motor control (L*): improvement of sensorimotor hypoactivation with olanzapine, unchanged lateralization disturbance (Bertolino et al., 2004a). Abstract reasoning (X*): neuroleptic‐naive patients excessively recruit frontal areas, regular activation level in atypically medicated patients (Ramsey et al., 2002). Motor control (L*): normalization of cerebellar functional connectivity after olanzapine administration (Stephan et al., 2001). Visual information processing (X*): selective prefrontal BOLD‐attenuation in typically (but not atypically) medicated patients (Braus et al., 2000b). Motor control (X*): selective sensorimotor BOLD‐attenuation in patients under typical neuroleptics. Regular activation patterns in neuroleptic‐naive first episode‐ and atypically medicated patients, respectively (Braus et al., 2000b; Braus et al., 1999). Working memory (L*): medication switch from typical neuroleptics to risperidone induces an activation enhancement of PPC and DLPFC (Honey et al., 1999). (Continued )

TABLE I (Continued ) Author (year) Molecular brain imaging

Egan et al. (2004)

110 Egan et al. (2001)

* X, cross‐sectional design; L, longitudinal design.

Study results GRM3 metabotropic glutamate receptor variation is associated with an enhanced risk for schizophrenia, ineYcient activation of DLPFC (working memory), hippocampal activation decrease (episodic memory), attenuated prefrontal NAA‐levels, and executive cognitive deficits (Egan et al., 2004). Dopamine catabolism: COMT val‐allele is associated with an enhanced risk for schizophrenia, ineYcient activation of DLPFC (working memory), and executive cognitive deficits (Egan et al., 2001).

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Current work has largely focused on functional polymorphisms with at least partially characterized eVects on their gene products. A common Val108/ 158Met substitution in the gene for COMT, for example, leads to a substantial decrease in the activity of this major enzyme in dopamine catabolism (Chen et al., 2004). Another well‐studied example outside the domain of schizophrenia is the 5‐HTTPLR polymorphism in the promoter region of the serotonin transporter (Hariri et al., 2002). Hariri and Weinberger (2003) enumerate requirements for experimental design in neuroimaging genomics (Hariri and Weinberger, 2003): use of well‐characterized behavioral probes; control of confounding variables such as age, performance, IQ ; and control of genomic confounds. The often‐ small eVects referable to genetic variation in susceptibility genes require large sample sizes and convergent evidence from multimodal imaging (structural, functional, neurochemical) combined with cognitive and clinical data (Egan et al., 2004). As in the field of psychiatric genetics as a whole, the definition and validation of useful statistical standards guiding work in this area is still in flux. The first example of this approach in schizophrenia was the characterization of the eVect of the COMT polymorphism on cognition, prefrontal function, and risk for schizophrenia by Egan and coworkers (Egan et al., 2001). This finding was subsequently independently replicated (Bilder et al., 2004; Goldberg et al., 2003) and extended to other psychiatric conditions as well (Tiihonen et al., 1999; Zubieta et al., 2003). A similar multimodal approach was recently used by the same group to characterize a risk haplotype in a gene encoding a metabotropic glutamate receptor (GRM3), demonstrating ineYcient prefrontal response, reduced neuronal integrity in prefrontal cortex, hypoactivation of the hippocampus during episodic memory, as well as impaired cognitive performance during verbal memory associated with an identified genetic variation conferring increased risk for schizophrenia. This work represents a major advance, because the risk haplotype as such did not have a direct functional correlate on the genetic/molecular level because it was composed of noncoding single nucleotide polymorphisms. Rather, the imaging work itself provided crucial convergent evidence that this risk haplotype has functional eVects (Egan et al., 2004). The characterization of susceptibility gene mechanisms using multimodal neuroimaging is likely to increase in importance in the coming years and substantially enrich our understanding of the pathophysiology of schizophrenia. The first studies in this emerging field attest to the unexpectedly high power of imaging approaches to delineate genomic variation. It is to be hoped that the detection of convergent functional pathways of diverse risk genes will lead not only to better understanding of the illness but also to the discovery of novel treatment targets. In any case, functional neuroimaging will likely retain its pivotal role in the characterization of systems‐level mechanisms linking the genetic‐molecular level to mental and social phenomena.

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Ungerleider, L. G., and Mishkin, M. (1982). Two cortical visual systems. In ‘‘Analysis of visual behavior’’ (R. J. Mansfield, Ed.), pp. 549–586. MIT Press, Cambridge, MA. Volz, H., Gaser, C., Hager, F., Rzanny, R., Ponisch, J., Mentzel, H., Kaiser, W. A., and Sauer, H. (1999). Decreased frontal activation in schizophrenics during stimulation with the continuous performance test—a functional magnetic resonance imaging study. Eur. Psychiatry 14, 17–24. Volz, H. P., Gaser, C., Hager, F., Rzanny, R., Mentzel, H. J., Kreitschmann‐Andermahr, I., Kaiser, W. A., and Sauer, H. (1997). Brain activation during cognitive stimulation with the Wisconsin Card Sorting Test—a functional MRI study on healthy volunteers and schizophrenics. Psychiatry Res. 75, 145–157. Vrtunski, P. B., Simpson, D. M., Weiss, K. M., and Davis, G. C. (1986). Abnormalities of fine motor control in schizophrenia. Psychiatry Res. 18, 275–284. Weinberger, D. R. (1987). Implications of normal brain development for the pathogenesis of schizophrenia. Arch. Gen. Psychiatry 44, 660–669. Weiss, E. M., Golaszewski, S., Mottaghy, F. M., Hofer, A., Hausmann, A., Kemmler, G., Kremser, C., BrinkhoV, C., Felber, S. R., and Wolfgang Fleischhacker, W. (2003). Brain activation patterns during a selective attention test – a functional MRI study in healthy volunteers and patients with schizophrenia. Psychiatry Res. 123, 1–15. Wenz, F., Schad, L. R., Knopp, M. V., Baudendistel, K. T., Flomer, F., Schroder, J., and van Kaick, G. (1994). Functional magnetic resonance imaging at 1.5 T: Activation pattern in schizophrenic patients receiving neuroleptic medication. Magn. Reson. Imaging 12, 975–982. Wible, C. G., Kubicki, M., Yoo, S. S., Kacher, D. F., Salisbury, D. F., Anderson, M. C., Shenton, M. E., Hirayasu, Y., Kikinis, R., Jolesz, F. A., and McCarley, R. W. (2001). A functional magnetic resonance imaging study of auditory mismatch in schizophrenia. Am. J. Psychiatry 158, 938–943. WoodruV, P., Brammer, M., Mellers, J., Wright, I., Bullmore, E., and Williams, S. (1995). Auditory hallucinations and perception of external speech. Lancet 346, 1035. WoodruV, P. W., Wright, I. C., Bullmore, E. T., Brammer, M., Howard, R. J., Williams, S. C., Shapleske, J., Rossell, S., David, A. S., McGuire, P. K., and Murray, R. M. (1997). Auditory hallucinations and the temporal cortical response to speech in schizophrenia: A functional magnetic resonance imaging study. Am. J. Psychiatry 154, 1676–1682. Yu¨ cel, M., Pantelis, C., Stuart, G. W., Wood, S. J., MaruV, P., Velakoulis, D., Pipingas, A., Crowe, S. F., Tochon‐Danguy, H. J., and Egan, G. F. (2002). Anterior cingulate activation during Stroop task performance: A PET to MRI coregistration study of individual patients with schizophrenia. Am. J. Psychiatry 159, 251–254. Zahn, T. P., Pickar, D., and Haier, R. J. (1994). EVects of clozapine, fluphenazine, and placebo on reaction time measures of attention and sensory dominance in schizophrenia. Schizophr. Res. 13, 133–144. Zubieta, J. K., Heitzeg, M. M., Smith, Y. R., Bueller, J. A., Xu, K., Xu, Y., Koeppe, R. A., Stohler, C. S., and Goldman, D. (2003). COMT val158met genotype aVects mu‐opioid neurotransmitter responses to a pain stressor. Science 299, 1240–1243.

Further Reading

Harrison, P. J., and Weinberger, D. R. (2005). Schizophrenia genes, gene expression, and neuropathology: On the matter of their convergence. Mol. Psychiatry 10, 40–68.

NEUROIMAGING IN FUNCTIONAL SOMATIC SYNDROMES

Patrick B. Wood Department of Family Medicine, Louisiana State University Health Sciences Center, Shreveport, Louisiana 71103

I. Introduction II. The Disorders A. Chronic Fatigue Syndrome B. Fibromyalgia Syndrome C. Irritable Bowel Syndrome D. Miscellaneous Disorders III. Discussion and Conclusions References

A number of conditions are common in the clinical environment for which no readily demonstrable pathologies exist to explain the symptoms that plague aZicted individuals. A list of these ‘‘functional somatic syndromes’’ includes such entities as chronic fatigue syndrome, fibromyalgia syndrome, and irritable bowel syndrome. Given the enduring lack of peripheral pathology, research into the underlying abnormalities that might characterize these disorders has increasingly focused on the central nervous system as the potential source of the problem. Accordingly, a variety of central imaging techniques have been used in the search for abnormalities that might explain these conditions, including both functional and structural magnetic resonance imaging, magnetic resonance spectroscopy, and positron emission tomography. This chapter is a review of these findings, which include changes in the function and structure of key brain areas. A discussion of potential mechanisms that may explain these phenomena is also presented, along with suggestions for future investigations.

I. Introduction

In the context of clinical medicine, a number of disorders lack readily demonstrable pathology to explain the variety of symptoms that aZicted individuals have. A list of such disorders might include such entities as chronic fatigue syndrome

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(CFS),* fibromyalgia syndrome (FMS), irritable bowel syndrome (IBS), chronic low back pain (CLBP), interstitial cystitis, temporomandibular joint dysfunction (TMD), Gulf War syndrome (GWS), premenstrual syndrome (PMS), tension headache, and multiple chemical sensitivities (MCS). Various categorizations have been articulated in an attempt to capture this spectrum of disorders, including medically unexplained symptoms, somatoform disorders, aVective spectrum disorders, central sensitivity spectrum disorders, and functional somatic syndromes (Hudson and Pope, 1989; Katon and Walker, 1998; Sharpe et al., 1995). Given the enduring lack of peripheral pathology to characterize such disorders, researchers have sought alternative explanations for their phenomena, an eVort that has historically produced two divergent lines of inquiry. The first approach, broadly termed psychosomatics, is based on the presupposition that, in the absence of bodily dysfunction, the patient’s underlying pathology (according to the Cartesian formula) must, therefore, be contained within the machinations of the mind, implying that the symptoms these patients so often complain of may represent a somatized manifestation of occult neuroticism. Needless to say, this perspective is widely resented among patient populations. A second line of inquiry has maintained a more biological perspective and sought to investigate alterations within the central nervous system (CNS) as the potential seat of pathology. In so doing, a variety of modalities have been used to distinguish the aZicted from the well, including such methods as neuropsychiatric testing, measurement of diVerences in evoked potentials (as recorded by electroencephalograms), pain threshold testing, and a variety of neuroimaging techniques. The latter will form the focus of this chapter. Among the facets of this spectrum of disorders that fuel debate concerning the nature of the underlying pathology (whether psychological, biological, or imaginary) is the frequent association of such conditions with both elevated stress levels and neuropsychiatric comorbidity. It is, therefore, noteworthy that basic science has demonstrated that the experience of a variety of chronic stressors has the capacity to produce a broad range of physiological perturbations, including alteration in the function of the hypothalamic‐pituitary‐adrenal (HPA) axis, degradation of various immunological parameters, and atrophic change in the very substance of the spinal cord and brain. The latter are perhaps best described in the hippocampus, wherein exposure to psychosocial stress produces atrophic changes generally related to decreased adult granule cell proliferation (i.e., neurogenesis) within the dentate gyrus, retraction of apical dendrites of CA3 pyramidal neurons, and frank neurotoxicity in both rodent and primate models (Fuchs et al., 2001; McEwen, 2000; Uno et al., 1989). These changes are believed to be mediated in part by glucocorticoid hormones working in concert with excitatory amino acids and N‐methyl‐D‐aspartate (NMDA) subtype glutamate *Chronic fatigue syndrome is closely allied (identical?) to a condition referred to as myalgic encephalomyelitis (ME), largely in the United Kingdom. The CFS convention is used in this chapter.

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receptors, along with a variety of other stress‐related neurotransmitters. In the rat, stress‐related changes at the cellular and molecular level have also been described in the medial prefrontal cortex (Cook et al., 2004; Kuipers et al., 2003; Radley et al., 2004; Wellman, 2001), amygdala (Vyas et al., 2002), and piriform cortex (Nacher et al., 2004). Bearing the notion in mind, then, that stress produces demonstrable changes to the CNS may lend new perspective on the variety of findings reviewed herein. II. The Disorders

A. CHRONIC FATIGUE SYNDROME Chronic fatigue syndrome (CFS) is a severely disabling illness of uncertain etiology characterized by a chronic, sustained, or fluctuating sense of debilitating fatigue without any other known underlying medical conditions. CFS is also associated with flulike complaints and such neurological signs and symptoms as persistent headache, impaired cognitive abilities, neuropsychological symptoms, and sensorimotor disturbances (Manu and Matthews, 1998). Both physical and laboratory findings are generally unremarkable. Historically speaking, the neurologist George Beard first introduced the term neurasthenia to describe a condition very much reminiscent of the current construct (Beard, 1869). More recently, CFS was thought to represent the chronic sequelae of viral infection, because of its proposed relationship to the Epstein‐Barr virus in the 1980s, giving rise to the derogatory term ‘‘yuppie flu.’’ However, the etiological relationship between viral infection and CFS has not been convincingly confirmed. Although the exact etiology of the disorder remains a mystery, the onset and course of this illness are exacerbated by physical and emotional stressors (Demitrack and CroVord, 1998; Hatcher and House, 2003; Theorell et al., 1999; Van Houdenhove et al., 2001). In 1988, the U. S. Centers for Disease Control and Prevention commissioned a working case definition of the disorder (Holmes et al., 1988). This was later revised by Fukuda et al. (1994), who defined CFS as a condition requiring the presence of persistent or relapsing fatigue that: (1) is of new or definite onset; (2) remains unexplained after clinical evaluation; (3) is not the result of ongoing exertion; (4) is not substantially relieved by rest; and (5) produces at least a 50% reduction in prior levels of occupational or social functioning. In addition, a number of other symptoms must be present, many of which are neurological, including impairment in short‐term memory and concentration; headache of a new type, pattern, or severity; and sleep disturbances. Other definitions also exist, including the Oxford (Sharpe et al., 1991) and Australian (Lloyd et al., 1988) definitions; the latter emphasizes the immunological dysfunction associated with the disorder (for review, see Gerrity et al. [2004]).

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1. Functional Analysis: Single Positron Emission Computed Tomography (SPECT) Troughton et al. (1992) were the first to report the use of neuroimaging to evaluate for potential brain abnormalities in CFS, in which 40 patients with CFS were scanned using technicium‐99m‐hexamethylpropyleneamine oxime (Tc‐99m‐HMPAO) brain SPECT. Nineteen abnormal scans were obtained, with hypoperfusion within posterior parietal areas as the most common pattern and frontal defects being second. Focal perfusion abnormalities were generally asymmetrical in pattern and similar to those previously reported in Alzheimer’s disease. Ichise et al. (1992) also used Tc‐99m‐HMPAO brain SPECT to compare 60 clinically defined patients with CFS with 14 healthy normal controls (HNC) and reported that patients with CFS showed significantly lower cortical/cerebellar rCBF ratios throughout multiple brain regions compared. Eighty percent of subjects with CFS showed at least one or more areas in which rCBF ratios were significantly less than normal values, including frontal (63%), temporal (35%), parietal (53%), and occipital lobes (38%). The rCBF ratios of basal ganglia were also reduced in 40% of subjects. Schwartz et al. (1994a) compared both MR imaging and SPECT findings in patients with CFS versus HNC. Patients with CFS had a significantly higher incidence of SPECT abnormalities than HNC (7.31 versus 0.43), occurring in 81% of patients versus 21% of HNC. The authors also reported MR abnormalities consisting of foci of T2‐bright signal in the periventricular and subcortical white matter and in the centrum semiovale occurring at a rate of 2.06 foci per patient in CFS versus 0.80 foci in HNC. Of the 16 patients with CFS studied, MR abnormalities were present in 50% compared with 20% of 15 age‐matched HNC. A subset of patients had repeat SPECT and MR studies, and the results suggested that a decrease in the number of SPECT abnormalities correlated with improvement in clinical status, whereas MR changes seemed irreversible. Goldberg et al. (1997) investigated rCBF in 13 pediatric cases of CFS using both 133Xenon (133Xe) and Tc‐99m‐HMPAO brain SPECT. They reported hypoperfusion detectable by 133Xe in bilateral temporal lobes (L > R), bilateral parietal lobes, and in the right frontal lobe, whereas Tc‐99m‐HMPAO scans demonstrated bilateral hypoperfusion in orbitofrontal, anterior temporal, and dorsal aspects of both frontal lobes and parietooccipital lobes. Lewis et al. (2001) evaluated the relationship between rCBF and CFS by conducting a co‐twin control study of 22 monozygotic twins discordant for CFS. Twins underwent a structured psychiatric interview and resting Tc‐99m‐ HMPAO brain SPECT scans. Scans were interpreted independently by two physicians blinded to illness status and then again at a blinded consensus reading. For quantitative image analysis, an rCBF ratio comparing target brain regions with both whole brain and cerebellum was calculated using an imaging fusion program to co‐register and automatically quantify images under comparison.

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Their results demonstrated that rCBF among twins with and without CFS were similar in mean number of abnormalities, leading to the conclusion that no distinctive pattern of resting rCBF abnormalities exists in CFS. Abu‐Judeh et al. (1998) compared rCBF using Tc‐99m‐HMPAO brain SPECT with dual‐head gamma camera using 18F‐fluorodeoxyglucose (FDG) to investigate brain metabolism in CFS. A total of 18 patients with CFS were investigated. Thirteen patients had abnormal SPECT brain perfusion scans, whereas only three demonstrated abnormal glucose metabolism with this technology. The authors concluded that discordance exists between rCBF and FDG brain uptake in CFS, which they interpreted to mean that abnormalities in brain perfusion might exist without corresponding changes in glucose uptake. Given that fatigue in CFS is markedly exacerbated by physical exertion, Peterson et al. (1994) tested the hypothesis that mild exercise might provoke both diVerences in serum cytokine levels and rCBF abnormalities detectable by Tc‐99m‐HMPAO brain SPECT. Ten patients with CFS and 10 HNC were asked to walk on a horizontal treadmill at a speed of 1 mph for a maximum of 30 minutes or until exhaustion. The authors report that interleukin (IL)‐1 beta, IL‐6, and tumor necrosis factor alpha (TNF‐
) were undetectable in sera of either group both before and after exercise, whereas serum transforming growth factor beta (TGF‐) levels were elevated at rest in CFS group. Although not significantly diVerent, elevations in serum TGF‐ and diVerences in rCBF were accentuated after exercise in the CFS group, leading to the conclusion that the eVect of exercise on serum TGF‐ and cerebral blood flow seem to be magnified in patients with CFS. Schmaling et al. (2003) performed SPECT on 15 subjects with CFS and 15 HNC under two conditions: at rest and when performing a cognitive task. No group diVerences were found for performance despite CFS subjects’ perceptions of exerting more mental eVort. Image analysis demonstrated that whereas HNC had relatively more blood flow to the anterior cingulate cortex than patients with CFS during both conditions, patients showed a significantly greater increase within the left but not the right anterior cingulate cortex during task performance. The total volume of brain activation was greater in CFS during task performance in a pattern that suggested diVuse changes in rCBF among patients with CFS versus more focal changes in HNC. Among the challenges facing both patients and clinicians is the tendency of many clinicians to lump subjects with CFS together as a group manifesting a somatized variant of major depressive disorder (MDD). A variety of SPECT studies have been performed with an eye toward comparing and contrasting the two entities. Schwartz et al. (1994b) first used SPECT to investigate four clinical groups: patients with CFS, AIDS dementia complex, MDD, HNC. Those with AIDS dementia complex had the largest number of defects (9.15 per patient), whereas the HNC had the fewest (1.66 per patient). In fact, patients with CFS and MDD had similar numbers of defects per patient (6.53 and 6.43,

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respectively); however, the midcerebral uptake index was found to be significantly lower in CFS (0.667) and in AIDS dementia complex (0.650) than in patients with MDD (0.731) or HNC (0.716). A significant negative correlation was found between the number of defects and midcerebral uptake index in patients with CFS and AIDS dementia complex, but not in MDD or HNC. In all groups, perfusion defects were primarily located in frontal and temporal lobes. The similarities between CFS and AIDS dementia lead to the conclusion that CFS may be related to chronic viral encephalitis, whereas the similar distribution and number of defects in CFS and MDD may explain their clinical similarities. In a two‐part study, Costa et al. (1992) first reported widespread reduction of rCBF in 24 patients with CFS compared with 24 HNC, with hypoperfusion of the brainstem as a marked and constant finding. They then performed confirmatory tests to evaluate brainstem perfusion in CFS compared with HNC, MDD, and epilepsy (Costa et al., 1995). Data from 146 subjects were included in the study consisting of 40 HNC, 67 patients with CFS (40 with no psychiatric disorders, 13 with CFS and MDD, 14 with CFS and other psychiatric disorders), 10 epileptics, 20 young depressed patients, and 9 elderly depressed individuals. Brainstem hypoperfusion was confirmed in all patients with CFS, whereas those with no psychiatric disorders showed significantly lower brainstem perfusion than depressed subjects. Goldstein et al. (1995) compared 133Xe brain SPECT in CFS, MDD, and HNC in subjects aged 45 years and older. Thirty‐three patients with CFS, 26 patients with MDD, and 19 age‐matched HNC were evaluated. They reported that subjects with CFS showed rCBF variation within the bilateral cortical hemispheres at a rate similar to depressed subjects. Fischler et al. (1996) used Tc‐99m‐HMPAO brain SPECT to examine the relationship between CFS symptoms and rCBF and reported significantly positive correlations between frontal blood flow and subjective and objective cognitive impairment, self‐rated physical activity limitations, and total score on the Hamilton Depression Rating Scale. A comparison of rCBF among CFS, MDD, and HNC demonstrated a lower superofrontal perfusion index in MDD with neither a global nor a marked regional hypoperfusion in CFS compared with HNC. Asymmetry (R > L) of tracer uptake at the parietotemporal level was demonstrated in CFS compared with MDD. In the latest study that used SPECT to compare CFS with MDD, MacHale et al. (2000) used statistical parametric mapping (SPM) to compare rCBF in 30 nondepressed patients with CFS, 12 patients with MDD, and 15 HNC. Regional count densities were normalized by proportional scaling to whole‐brain blood flow. They report an increased perfusion in the right thalamus, pallidum, and putamen in CFS and in depression, whereas patients with CFS also had increased perfusion in the left thalamus. Depressed patients diVered from those with CFS in having relatively less perfusion of the left prefrontal cortex. The

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results were similar on region of interest (ROI) analysis. The authors concluded that rCBF patterns in nondepressed subjects with CFS are, in fact, similar, but not identical, to those with MDD. They also postulated that thalamic overactivity might correlate with increased somatic awareness in both populations of patients, whereas reduced prefrontal perfusion in MDD may be associated with the greater neuropsychological deficits of that disorder. 2. Functional Analysis: Positron Emission Tomography (PET) Tirelli et al. (1998) used FDG PET to investigate brain metabolism in CFS using in 18 patients with CFS without psychiatric illness compared with a group of 6 patients aVected by MDD and 6 age‐matched HNC. The patients with CFS were medication free, whereas those with MDD were drug free for at least 1 week before the PET examination. They report that patients with CFS showed significant hypometabolism in the right mediofrontal cortex and brainstem compared with HNC (in agreement with the SPECT findings reported by Costa et al., 1995), whereas those with MDD showed a significant and severe hypometabolism of the medial and upper frontal regions bilaterally while the brainstem was normal. The authors suggested that brainstem hypometabolism might serve as a marker for the in vivo diagnosis of CFS. Siessmeier et al. (2003) likewise used FDG PET to investigate metabolic alterations in CFS and to explore potential correlations with neuropsychological deficits. Twenty‐six patients with CSF (13 women, 13 men) ages 26 to 61 years old underwent extensive psychometric testing. Image analysis was done in two parts: the identification of patterns of abnormal glucose metabolism in individual patients, and group analysis to correlate regional impairment of glucose metabolism with various psychometric indices. Twelve of the 26 patients with CFS (46%) showed no significant abnormalities compared with HNC, whereas 12 of the 14 remaining patients had decreased glucose metabolism in the cingulate gyrus and adjacent mesial cortical areas. Five of these showed additional decreases in the orbitofrontal/frontobasal cortex. Two related patients demonstrated a diVerent pattern centered in the cuneus/precuneus region with no changes in cingulate or orbitofrontal regions. Group comparison revealed areas of significantly decreased metabolism that were restricted to the orbitofrontal cortex, cingulate gyrus, and adjacent mesial cortical areas. No diVerences in brainstem metabolism were reported, in contrast to the findings by Tirelli et al. (1998) and Costa et al. (1995) Although no correlation between regional decreases in glucose metabolism and the severity of perceived fatigue was observed, correlations were detected between: (1) the severity of depression and changes in the right mesial orbitofrontal cortex, temporal superior and medial gyrus, and the anterior cingulate cortex; (2) the severity of anxiety scores and a decrease in glucose metabolism in the bilateral parahippocampal gyri; and (3) for mental components of health‐ related quality of life and decreased glucose metabolism in the orbitofrontal

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cortex, mainly on the left side, and also in the mesial and dorsolateral prefrontal regions and in the mesial and superior temporal cortex. No correlation between the physical function and regional impairment of glucose metabolism was found. 3. Functional Analysis: Functional Magnetic Resonance Imaging (fMRI) The first investigation of CFS that used fMRI was reported by de Lange et al. (2004), who investigated 16 patients with CFS and 16 matched HNCs during both a motor imagery task and a control visual imagery task. Although patients with CFS were considerably slower on performance of both tasks, the increase in reaction time with increasing task load was similar between the groups. Both groups used largely overlapping neural resources; however, during the motor imagery task, patients with CFS evoked stronger responses in visually related structures. Furthermore, there was a marked between‐groups diVerence during erroneous performance. In both groups, the dorsal anterior cingulate cortex was specifically activated during error trials. Conversely, the ventral anterior cingulate cortex was active when healthy controls made an error but remained inactive in patients with CFS when they made errors. The authors concluded that CFS might be associated with dysfunctional motor planning, whereas diVerences between groups observed during erroneous performance were interpreted to suggest motivational disturbances in the context of CFS. 4. Anatomical Analysis: Magnetic Resonance Imaging (MRI) Although functional imaging was first used to investigate functional abnormalities in patients with CFS, others have sought to identify potential anatomical defects that might be associated with the disorder. Natelson et al. (1993) were the first to report such an analysis, in which structural MRI abnormalities were compared between 52 patients with CFS and a control group of 52 age‐ and gender‐matched individuals who had undergone imaging because of a history of head trauma or headache. They report that patients with CFS had significantly more abnormal scans than controls (27% vs. 2%), with abnormalities characterized by foci of increased white matter T2 signal in nine patients with CFS and one control and ventricular or sulcal enlargement in five patients with CFS. As previously noted, Schwartz et al. (1994a) also reported an increased incidence of MR abnormalities in patients with CFS consisting of foci of T2‐bright signal in the periventricular and subcortical white matter and in the centrum semiovale. A follow‐up of patients with subcortical signal hyperintensities in this study revealed three who had symptoms suggestive of other known medical causes of CFS, leading to the conclusion that whereas some patients with CFS may have brain lesions detectable by MRI, some symptoms may pertain to unrelated illness. Lange et al. (1999) also used MRI to evaluate 39 patients with CFS (18 with a DSM‐III‐R Axis I psychiatric diagnosis since illness onset and 21 without) compared to 19 sedentary HNC. Nonpsychiatric patients with CFS showed a

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significantly larger number of brain abnormalities on T2‐weighted images than HNC, as well as patients with CFS with comorbid psychiatric diagnoses. Cerebral changes in the primary group again consisted of mostly punctate subcortical white matter hyperintensities found predominantly in the frontal lobes. No significant diVerence was found when CFS groups were combined and compared with HNC. They concluded that the use of stratification techniques may be an important strategy in understanding the pathophysiology of CFS and that frontal lobe pathology could explain the more severe cognitive impairment previously reported in patients with CFS without psychiatric diagnoses. Cook et al. (2001) examined the relationship between potential MRI abnormalities and self‐reported physical functional status in 48 subjects with CFS. They reported a significant negative correlation between the presence of brain MRI abnormalities and both the physical functioning and physical component summary subscales of the Medical Outcomes Study SF‐36. Patients with CFS with MRI‐identified brain abnormalities scored significantly lower on both measures than subjects with CFS without an identified brain abnormality. They concluded that the presence of brain abnormalities in CFS are significantly related to subjective reports of physical function and that subjects with CFS with MRI brain abnormalities report being more physically impaired than those patients without brain abnormalities. Not all MRI studies report positive results, though. Cope et al. (1995) examined 26 subjects with chronic fatigue, with and without coexisting depression, and 18 age‐matched HNC recruited from primary care clinics after a presumed viral illness. A comparison was also made with 13 psychiatric controls with MDD. They report that no substantial diVerences existed in performance on a variety of psychometric instruments between chronically fatigued subjects, most of whom met the criteria for CFS, and HNC, whereas subjective cognitive dysfunction in the former correlated positively with measures of psychopathology. Regarding MRI, white‐matter lesions were found in a minority of subjects from each group, with no significant diVerences between them. Follow‐up analysis demonstrated that improvement in fatigue and depression coincided with improved performance on cognitive measures, leading to the conclusion that objective cognitive and MRI abnormalities are not prominent features of CFS and that subjective complaints of cognitive impairment likely reflect psychopathology versus a postviral process. Similarly, Greco et al. (1997) studied 43 patients of mixed ages and gender in comparison to 43 age‐ and gender‐matched control subjects. The patient group was subdivided into three subgroups according to the presence of psychiatric comorbidity: CFS (n ¼ 15), CFS þ depression (n ¼ 14), and CFS þ other (i.e., generalized anxiety and somatization disorder; n ¼ 14). They report that MRI findings were abnormal in 13 (32%) of the patients in the study group and in 12 (28%) of HNC. One patient with CFS had multiple areas of demyelination in the supratentorial periventricular white matter. Another patient with

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CFS þ depression had a single focus of probable demyelination in the supratentorial periventricular white matter. In four patients with CFS, MRI findings consisted of one or several punctate hyperintense foci in the corona radiata, centrum ovale, and frontal white matter. The remaining seven patients had frontoparietal subcortical white matter foci of high T2 signal. They concluded that the prevalence of white matter hyperintensities was not diVerent between the patients and the control subjects and that no specific pattern of white matter abnormalities detectable by MRI exists in CFS. Taking a somewhat diVerent approach, Lange et al. (2001) compared the total lateral ventricular volume, as well as the right and left hemisphere subcomponents, in 28 patients with CFS and 15 controls and found that ventricular volumes in the CFS group were larger than in control groups, a diVerence that approached statistical significance. Group diVerences in ventricular asymmetry were not observed. In a related report, Puri et al. (2004) present the case of a female patient with a 6‐year history of unremitting symptoms of CFS in whom a 16‐week regimen of an essential fatty acid supplementation rich in eicosapentaenoic acid (EPA) resulted in a marked clinical improvement in her symptoms. Quantification of the lateral ventricular volumes in the baseline and 16‐week follow‐up registered images of high‐resolution MRI structural scans showed that the treatment was accompanied by a marked reduction in the lateral ventricular volume during this period, from 28,940 mm3 to 23,660 mm3. 5. Anatomical Analysis: Magnetic Resonance Spectroscopy (MRS) Tomoda et al. (2000) reported three pediatric cases of CFS (ages 11, 12, and 13 years) who initially had a low‐grade fever and generalized fatigue develop, followed by sleep disturbance and psychosomatic symptoms, and their performance ability deteriorated. SPECT scanning revealed reduced rCBF in the left temporal and occipital lobes in two cases, whereas one demonstrated a marked elevation in blood flow in the left basal ganglia and thalamus. MRS revealed elevation of the choline (Cho)/creatine (Cr) ratio in the patients with CFS within a voxel placed in primarily frontal white matter. No variation in other metabolites was reported, and none of the three exhibited evidence of focal structural abnormalities on MRI. Brooks et al. (2000) investigated the structural integrity of the hippocampus in seven patients with CFS matched with 10 age‐ (but not gender) matched HNC. Although they detected no diVerences in hippocampal volume, MRS demonstrated a significantly reduced concentration of n‐acetylaspartate (NAA) in the right hippocampus in patients. Puri et al. (2002) tested the hypothesis that CFS may be associated with altered cerebral metabolites in the frontal and occipital cortices. MRS was carried out in eight patients with CFS and eight age‐ and gender‐matched HNC. Spectra were obtained from the dominant motor and occipital cortices, which demonstrated mean Cho/Cr ratio in the occipital cortex in CFS to be significantly higher than in the controls. No

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other metabolite ratios were significantly diVerent between the two groups. There was also a loss of the normal spatial variation of Cho in CFS. Chaudhuri et al. (2003) reasoned that because fatigue is a common symptom of neurological diseases that aVect basal ganglia function, this brain center would be a natural target of inquiry. Accordingly, they measured metabolite concentrations within the basal ganglia in eight nonpsychiatric patients with CFS compared with age‐ and gender‐matched HNC and reported a highly significant increase in Cho content in patients with CFS. 6. Summary A review of the preceding studies would suggest that, broadly speaking, CFS seems to be associated with abnormalities characterized by diVuse punctate T2‐weighted hyperintensities on MRI and by diVuse perfusion abnormalities demonstrated by SPECT, in at least a subset of patients. Brainstem hypofunction is suggested by the demonstration of both hypoperfusion (Costa et al., 1995) and reduced metabolism (Tirelli et al., 1998). Increased perfusion within the thalamus and basilar structures has also been reported (MacHale et al., 2000). There is evidence from MRS that hippocampal neuronal integrity may be challenged as demonstrated by a reduction in hippocampal NAA content (Brooks et al., 2000) and that diVuse changes in brain tissue characterized by an increase in Cho content are present. Functional activation during a cognitive task was associated with both a subjective sense of increased eVort and a diVuse recruitment of cortical centers in patients with CFS versus a more focal pattern in HNC (de Lange et al., 2004). Finally, volumetric analysis has suggested an overall decrease in ventricular volume (Lange et al., 2001), which seems to have responded favorably to dietary supplementation with essential fatty acids in a single case report (Puri et al., 2004).

B. FIBROMYALGIA SYNDROME Fibromyalgia syndrome is a common disorder whose cardinal feature is chronic widespread pain. Other associated symptoms include tenderness to light palpation, sleep disturbance, and chronic fatigue. The current concept of FMS derives from the work of Gowers and Stockman (Gowers, 1904; Stockman, 1904), who together proposed that chronic widespread pain likely resulted from ‘‘fibrositis’’ or diVuse inflammation of fibrous connective tissues (Abeles, 1998). Subsequent analysis of these tissues has failed to demonstrate such changes, and, in fact, no evidence of peripheral pathology has been revealed to date, despite extensive research. Given the lack of inflammation, the name was subsequently changed to ‘‘fibromyalgia.’’ In 1990, the American College of Rheumatology published diagnostic criteria for the disorder (Wolfe et al., 1990), which have been

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widely adopted, although eVorts are currently afoot to elaborate updated criteria to define the disorder. For example, the Canadian Case Definition has recently been published, which places an increased level of emphasis on a variety of neurological phenomena associated with the disorder (Jain et al., 2004). FMS has been characterized as a ‘‘stress‐related disorder’’ because of its frequent onset after acute or chronic stressors and apparent exacerbation of symptoms during periods of physical or emotional stress (Hudson et al., 1992). Factors associated with the onset of FMS include severe infectious illness, physical trauma, and severe emotional distress (Demitrack and CroVord, 1998). The combination of a lack of readily demonstrable pathology to which symptoms might be related and the dependence on subjective patient reporting has fueled much of the criticism of the FMS construct. This has led to large segments of the medical community dismissing such patients as neurotic complainers aspiring to fashionable diagnoses (Ford, 1997). In response, one of the chief goals of research should be to explain objective evidence for the existence of physiological characteristics that distinguish patients with FMS from the unaVected. Accordingly, because investigations into potential peripheral explanations of the disorder have proven largely fruitless, increasing attention has been focused on mechanisms within the CNS (Yunus, 1992). The first objective findings were provided by Moldofsky et al. (1975), who reported an unusually high incidence of the anomalous alpha‐rhythms in the non‐rapid eye movement (NREM) sleep EEG, a phenomenon that has been dubbed ‘‘alpha‐delta sleep.’’ Since then, a growing number of chemical abnormalities have been demonstrated in cerebrospinal fluid, including increased concentrations of substance P and nerve growth factor (Giovengo et al., 1999; Russell et al., 1994; Vaeroy et al., 1988) and decreased serotonin, norepinephrine, and dopamine (Legangneux et al., 2001; Russell et al., 1992). In addition, results from studies examining sensitivity to experimental stimulation have shown that patients with FMS have polymodal sensitivity to pressure, heat, cold, electrical, and chemical stimulation (Gracely et al., 2003; Lautenbacher et al., 1994; Sorensen et al., 1998). Experimental evaluation of pain regulatory systems has also shown patients with FMS display a dysregulation of diVuse noxious inhibitory controls (Kosek and Hansson, 1997; Lautenbacher and Rollman, 1997), an exaggerated wind‐up pain response to repetitive stimulation (Staud et al., 2001), and an absence of exercise‐induced analgesic response (Kosek et al., 1996; Mengshoel et al., 1995). Together, these results point to dysregulation of the nociceptive system at the central level. 1. Functional Analysis: SPECT Using 133Xe brain SPECT, Johansson et al. (1995) were the first to report baseline abnormalities in rCBF, consisting of normal flow level with slight but significant focal flow decreases in dorsolateral frontal cortical areas of both

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hemispheres, also slight to moderate flow decreases in temporal and parietal regions. Mountz et al. (1995) investigated potential brain abnormalities in FMS using Tc‐99m‐HMPAO brain SPECT. Their a priori hypothesis centered on both the thalamus, given its role in perception and integration of pain signals and in regulation of the HPA axis, and the caudate nucleus, which is also involved in pain perception. Accordingly, resting‐state rCBF was analyzed in these regions in 10 untreated female patients with FMS compared with seven age‐, gender‐, and education‐matched HNC. Standard noncontrast CT scans were done for co‐registration of SPECT images and for analysis for potential anatomical variation. They report that measurements of rCBF in the bilateral hemithalami and bilateral heads of the caudate nuclei were significantly lower in subjects with FMS than in HNC, and that patients with FMS were characterized by significantly lower cortical rCBF. No anatomical variation was appreciated, although the sensitivity of manual comparison of CT scans is admittedly fairly low. Although indices of anxiety and depression were significantly higher in subjects with FMS than HNC, there was no correlation between these measures and measures of pain, suggesting that their findings were likely unrelated to a psychiatric burden. The same group (San Pedro et al., 1998) later reported the case of a father and daughter pair with familial restless legs syndrome (RLS) who underwent Tc‐99m‐HMPAO brain SPECT scanning at rest and during the state of increased pain induced by immobility. Scanning in both cases demonstrated decreased rCBF in the caudate nucleus and increased thalamic and cingulate rCBF with increased pain compared with indices from HNC. The association of these two painful disorders and abnormalities within the caudate nucleus are especially intriguing, given the frequent comorbidity of RLS in patients with FMS (Yunus and Aldag, 1996), a phenomenon that seems to suggest a role for the basal ganglia in both disorders. Taking their cue from the previous two studies, Kwiatek et al. (2002) performed a whole‐brain replication SPECT study of FMS to investigate whether rCBF may be abnormal in any cerebral structures. They compared SPECT scans from 17 women with FMS and 22 women HNC. In this case, SPECT images were coregistered to T1‐weighted MRI, and both SPM and manually drawn ROI analyses were used to detect diVerences between groups. They report that both analyses registered significantly reduced rCBF in the right but not left thalamus in patients with FMS, although ROI analysis indicated a nonsignificant trend toward reduced rCBF in the left thalamus. SPM analysis further indicated a reduction of rCBF in the inferior pontine nucleus, which correlated with ROI analysis. Although no reduction was detected in either caudate nucleus, in contradiction to the findings of Mountz et al. (1995), SPM detected a significant reduction in rCBF in another element of the basal ganglia: the right lentiform nucleus. With regard to clinical measures, subjects with FMS had higher indices of depression and anxiety (without meeting criteria for specific diagnoses), but no

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correlation was found between these variables and alterations in rCBF or patient indices of pain. Their findings of involvement within the pons seem to correlate with the findings of two other investigations of the brainstem in CFS (Costa et al., 1995; Tirelli et al., 1998), which is thought to be a closely allied condition. Although the eVective treatment of FMS remains a matter of intense investigation, no pharmacological agent is perhaps better studied that amitriptyline, given its usefulness in improving sleep and decreasing pain levels at relatively low dose (Godfrey, 1996). Accordingly, Adiguzel et al. (2004) sought to determine whether the eVects of amitriptyline treatment and consequent clinical improvement would be reflected in changes in rCBF. Using semiquantitative analysis, they assessed rCBF in 14 patients with primary FMS before and after 3 months of treatment with low‐dose amitriptyline (25 mg/day). They report that subjects experienced a statistically significant improvement in visual analog scale and tender point count after treatment, with a concomitant increase in rCBF in bilateral hemithalami and basal ganglia and decrease in bilateral temporal, left temporooccipital, and right occipital lobes. No structural imaging studies were performed, because it was the investigators’ intent to demonstrate intrasubjective changes in rCBF in response to a specific intervention. 2. Functional Analysis: fMRI Gracely et al. (2002) were the first to use fMRI to evaluate diVerential patterns of cerebral activation during the application of painful pressure in patients with FMS versus HNC. Sixteen right‐handed patients with FMS were matched to 16 right‐handed HNC. Scanning was performed on patients with FMS while receiving pressure applied to the left thumbnail, whereas HNC underwent testing under two conditions: the ‘‘stimulus pressure control’’ condition, during which they received the same amount of pressure delivered to patients with FMS, and the ‘‘subjective pain control’’ condition, during which the intensity of stimulation was increased to deliver a subjective level of pain similar to that reported by patients. As anticipated, the average pressure necessary to evoke painful sensation in patients with FMS was significantly lower than the pressure necessary to evoke the same report of pain in HNC. In response to subjectively equal pain levels, FMS subjects demonstrated relative hyperactivation within the contralateral primary sensory, inferior parietal, insular, anterior and posterior cingulate cortices, and posterior lobe of the cerebellum (see Fig. 1). On the ipsilateral side, subjects demonstrated hyperactivation in the superior temporal cortex and anterior and posterior lobes of the cerebellum and deactivation of the medial frontal gyrus. Although not stated explicitly by the authors, inspection of the images and the information provided on data analysis suggests that patients with FMS also demonstrate a relative hypoactivation of a number of important brain centers during subjectively equal stimulation, including contralateral caudate and putamen; thalamic ventral anterior, anterior and ventral lateral nuclei; the inferior

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FIG. 1. Stimuli and responses during pain scans. Common regions of activation in patients (red) and in the subjective pain control condition (green), in which the eVects of pressure applied to the left thumb suYcient to evoke a pain rating of 11 (moderate) are compared with the eVects of innocuous pressure. Significant increases in the functional magnetic resonance imaging (f MRI) signal (arrows) resulting from increases in regional cerebral blood flow (rCBF) are shown in standard space superimposed on an anatomical image of a standard brain. Images are shown in radiological view, with the right brain shown on the left. Overlapping activations are shown in yellow. The similar pain intensities, produced by significantly less pressure in patients, resulted in overlapping or adjacent activations in the contralateral primary somatosensory cortex (SI), inferior parietal lobule (IPL), secondary somatosensory cortex (SII), superior temporal gyrus (STG), insula, and putamen, and in the ipsilateral cerebellum. The f MRI signal was significantly decreased in a common region in the ipsilateral SI. Compared with stimulation with innocuous pressure, stimulation of healthy controls by the pressure levels used in the patients evoked significantly less pain and two regions of significant increases in rCBF, in the ipsilateral superior temporal gyrus and precentral gyrus (not shown). Neither of these regions coincided with regions of activation in the patient group. The graph shows mean pain rating plotted against stimulus intensity for the experimental conditions. In the fibromyalgia condition, a relatively low stimulus pressure (2.4 kg/cm2) produced a high pain level (mean ± SD, 11.30 ± 0.90). In the stimulus pressure control condition, administration of a similar stimulus pressure (2.33 kg/cm2) to control subjects produced a very low level of rated pain (mean ± SD, 3.05 ± 0.85). In the subjective pain control condition, administration of significantly greater stimulus pressures to the control subjects (4.16 kg/cm2) produced levels of pain (mean ± SD, 11.95 ± 0.94) similar to those produced in patients by lower stimulus pressures. From Gracely R. H., Petzke F., Wolf J. M., Clauw D.J. (2002). Functional magnetic resonance imaging evidence of augmented pain processing in fibromyalgia. Arthritis Rheum. 46, 1333–1343. Copyright # 2002, American College of Rheumatology. Reprinted with permission of Wiley‐Liss, Inc., a subsidiary of John Wiley and Sons, Inc.

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gyrus of the frontal cortex; supplemental motor cortex; and ipsilateral insula, globus pallidus, and thalamic ventral lateral nucleus. Pain catastrophizing, or characterizations of pain as awful, horrible, and unbearable, has been recognized as an important factor in the experience of pain. Therefore, Gracely et al. (2004) investigated the potential association between catastrophizing and brain responses to blunt pressure among 29 subjects with FMS as assessed by functional MRI. Given the hypothetical role of catastrophizing in the augmentation of pain perception, they hypothesized that catastrophizing would be associated with enhanced activation of structures involved in attentional and emotive aspects of nociception. Accordingly, they report that, after controlling for depressive symptoms, residual scores of catastrophizing were significantly correlated with enhanced activation in response to subjectively painful thumb pressure in brain areas related to anticipation of pain (contralateral medial frontal cortex, ipsilateral cerebellum), attention to pain (contralateral dorsal anterior cingulate cortex, bilateral dorsolateral prefrontal cortex), and emotional aspects of pain (ipsilateral claustrum). Catastrophizing also correlated with enhanced activation of the ipsilateral parietal cortex and contralateral lentiform nuclei of the basal ganglia. Analysis of subjects classified as high or low catastrophizers, on the basis of a median split of residual catastrophizing scores, showed that although both groups displayed significant increases in ipsilateral secondary somatosensory cortex, the magnitude of activation was twice as large among high catastrophizers. High catastrophizers also displayed unique activation in the contralateral anterior cingulate cortex and in bilateral lentiform nuclei. The authors suggest that the latter pattern of activation is consistent with previous research indicating that catastrophizing is associated with greater pain behavior and increased emotional expression in response to pain (Sullivan et al., 2001); however, the correlation seems unclear. Cook et al. (2004) used an experimental heat paradigm to investigate the cerebral response to experimental pain in FMS. In this experiment, nine female patients with FMS and comorbid CFS but no psychiatric illnesses were compared with nine gender‐matched HNC. Psychophysical testing revealed patients with FMS to be nearly twice as sensitive to painful heat stimuli as HNC. Patients with FMS also had higher scores on the Beck Depression Inventory and the Kohn Reactivity Scale and a greater degree of posttest anxiety as measured by the State‐Trait Anxiety Inventory. All subjects subsequently underwent fMRI scanning during painful and nonpainful heat stimuli. Scanning occurred over five conditions delivered in counterbalanced order: a practice session (no stimuli); nonpainful warm stimuli; and absolute thermal pain stimulus (47 C); and a perceptually equivalent pain stimulus. In response to nonpainful warm stimuli, subjects with FMS had significantly greater activity in prefrontal, supplemental motor, insular, and anterior cingulate cortices—areas that have been consistently shown by brain imaging to be active in HNC during painful but not non‐noxious

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warm stimuli (Derbyshire et al., 1997; Peyron et al., 2000). In response to absolute painful stimuli, subjects with FMS rated stimuli as equally unpleasant but more intense than did control subjects, which corresponded to greater activation of the contralateral insular cortex, a center consistently implicated in intensity coding. When given stimuli of subjectively equal intensity (i.e., visual analog scale ¼ 5/10), both the distribution and degree of activation were similar. Using fMRI, Derbyshire et al. (2004a) identified brain areas directly involved in the generation of pain using hypnotic suggestion in HNC to create an experience of pain in the absence of noxious stimulus. Analysis revealed significant changes during this hypnotically induced pain experience within the thalamus and anterior cingulate cortex, insula, prefrontal, and parietal cortices, which compared well with the activation patterns during pain from nociceptive sources. They subsequently analyzed 13 highly hypnotizable patients with FMS during hypnotic suggestion to modulate their experience of pain (Derbyshire et al., 2004b). For comparison, 15 highly hypnotizable HNC were tested with identical pain modulation suggestions but with actual painful heat stimulation. SPM analysis revealed bilateral cortical activation in patients with FMS within the midcingulate, primary and secondary sensory, inferior parietal, insula, and prefrontal cortices, as well as bilateral activation of the cerebellum and thalamus, which correlated with increasing pain report facilitated by hypnotic suggestion. In contrast, patterns of activation in HNC involved far fewer regions at lower levels of significance. 3. Functional Analysis: PET Wik et al. (1999) used [15O] butanol PET to measure rCBF in patients with FMS during hypnotically induced analgesia and resting wakefulness. Eight highly hypnotizable female patients with FMS participated in the study in which subjects were instructed to be relaxed and go into a deep trance, to watch a videotape that played for distraction, and not to feel any pain whatsoever. For resting wakefulness, patients simply watched the videotape without hypnotic suggestion. Hypnotic analgesia decreased rCBF bilaterally in a cluster that included posterior cingulate gyrus and the posterior part of the anterior cingulate gyrus, whereas increased rCBF was observed bilaterally in the subcallosal cingulate gyrus, the right thalamus, and left inferior parietal cortex. Imagery from a subset of four patients also demonstrated increased orbitofrontal cortex activation. Lekander et al. (2000) studied the potential relationship between neural and immune activity in patients with chronic pain by correlating rCBF as measured by [15O]butanol brain PET to immune function in five patients with FMS. They report that natural killer cell activity correlated negatively with right hemisphere activity in the secondary somatosensory and motor cortices, as well as the thalamus, which partly replicated previous data in HNC, whereas natural killer cell activity was negatively related to bilateral activity in the posterior cingulate

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cortex. Accordingly, they concluded that, although the data were limited, immune parameters in FMS seemed to be related to activity in brain areas involved in pain perception, emotion, and attention. Wik et al. (2003) compared resting rCBF as measured by PET in eight patients with FMS and eight controls and found higher rCBF for patients with FMS than controls bilaterally in the retrosplenial (i.e., posterior cingulate) cortex (maximum values located in BA 23, extending to BA 29 and 30) and previously shown to be involved during memory‐related evaluations of the environment (Vogt et al., 1992). They also reported lower rCBF in the patients in two left hemisphere clusters, one located in frontotemporal regions and another in the tempo‐ parietooccipital cortex. Yunus et al. (2004) investigated whether changes in regional cerebral glucose metabolism may characterize patients with FMS using FDG PET. They studied 12 patients with FMS with no comorbid psychiatric diagnosis and seven pain‐free HNC in a blinded manner. Count ratios were computed from manually drawn ROIs, visualized by reference to the PET scan image, which were duplicated from controls to patients for consistency of ROI size and positioning. No anatomical scans were collected for reference. Semiquantitative analysis of regional FDG uptake in both cortical and subcortical brain structures failed to demonstrate significant diVerences between patients with FMS and HNC in any brain structures measured. The authors noted that certain limitations might not have provided maximum sensitivity to detect subtle changes in FDG metabolism. For example, their localization of brain structure depended on visual interpretation of PET images because, unfortunately, no anatomical images were collected to provide co‐registration between PET data and brain anatomy, as in previous studies. The authors also cite Abu‐Judeh et al. (1998), who suggested that there is likely discordance between SPECT brain perfusion and glucose metabolism when only 3 of 18 subjects in their study showed metabolic abnormality detectable by dual‐headed gamma camera coincidence imaging (which diVers from PET) compared with 15 of 18 who had rCBF abnormalities detectable by SPECT—a study that also suVered from a lack of anatomical imagery for co‐registration. Although these negative findings lend support to the lead author’s previous contention that FMS diVers considerably from psychiatric illness (Yunus, 1994), further PET studies of FMS are warranted to evaluate both brain glucose metabolism (incorporating MRI for rigorous anatomical correlation and using quantitative data analysis strategies, e.g., SPM) and functional neurotransmitter profiling, as has been elsewhere suggested (Wood, 2004b). 4. Anatomical Analysis: MRI Glabus et al. (2004) applied voxel‐based morphometry to high‐resolution MRI to evaluate for changes in gray matter density in the brains of eight patients with FMS compared with eight age‐ and gender‐matched HNC. They reported

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gray matter concentration reduction occurred bilaterally in the inferior frontal gyrus, limbic system frontal gyrus, right medial temporal gyrus, left thalamus, left anterior cingulate, and left cerebellum (see Fig. 2). In addition, there was a significant negative correlation in gray matter concentration in the right inferior temporal gyrus, left cerebellum, and right parahippocampal gyrus with a composite patient rating of pain, fatigue, rest, and stiVness taken from items on the Fibromyalgia Impact Questionnaire.

FIG. 2. Reductions in gray matter concentration in patients with fibromyalgia versus age‐ and gender‐matched healthy normal controls. Images were spatially normalized to a standard brain atlas using SPM software supplied by the Wellcome Department of Imaging Neuroscience, London, UK (http://www.fil.ion.ucl.ac.uk/spm), and re‐sampled as isotropic voxels of dimensions 1 x 1 x 1 mm. Images were then partitioned into gray, white, and CSF subcompartments by a recursive algorithm using a Bayesian framework that partitions the image by voxel intensity and spatial ‘‘belonging’’ probability. The resultant images were smoothed with an isotropic Gaussian filter (12‐mm FWHM) and then tested statistically for diVerences at the group level at every voxel. Voxel‐ based morphometry was applied to test for diVerences in gray matter concentration. Colorized images represent diVerences in t score value, with lighter (white) scores of highest significance.

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5. Anatomical Analysis: MRS Wood et al. (2004) investigated whether FMS may be associated with hippocampal abnormalities detectable by MRS by scanning 17 female patients with FMS and compared them with 10 age‐ and gender‐matched controls, who underwent single‐voxel proton MRS of the bilateral hippocampi. All participants were premenopausal; patients were divided into two groups based on whether they had a history of major depression. Patients demonstrated significant reduction in NAA/Cr ratio in the right hippocampus, and an increase in the mI/Cr ratio that approached significance. No MRS diVerences were detected between those with or without a history of depression. There was a highly significant negative correlation between the subjects’ Fibromyalgia Impact Questionnaire score and NAA/Cr ratio. They concluded that FMS is associated with brain metabolite abnormalities in the right hippocampus that correlate with a patient’s subjective experience of the disorder. 6. Summary FMS seems to be characterized by baseline perfusion abnormalities involving structures within both the thalamus and pontine nucleus. Functional analysis reveals involvement of multiple cortical centers recruited at lower levels of stimulation than that required for similar activation in controls, whereas equally subjective levels of pain seem to be associated with hypoactivation of a variety of basilar structures. DiVerential involvement of centers involved in cognitive and emotive processing, particularly the cingulate cortex, has been demonstrated by a variety of studies, leading most of the authors to conclude that FMS may involve a form of secondary hyperalgesia characterized by hypervigilance or increased attention to noxious stimuli. Despite evidence for diVerential functional activation, baseline metabolism was reported as normal (Yunus et al., 2004). Despite common patient complaints of cognitive diYculties, no reports of functional analysis using a cognitive paradigm have been reported thus far. Evidence for reduced neuronal integrity within the hippocampus is oVered by preliminary data (Wood et al., 2004), as well as diVuse cortical changes represented by reduced gray matter density and volume (Glabus et al., 2004). C. IRRITABLE BOWEL SYNDROME A variety of functional disorders characterized by variable combinations of chronic or recurrent symptoms of the gastrointestinal tract unrelated to readily demonstrable structural or biochemical abnormalities are common in primary and tertiary care clinics. Irritable bowel syndrome (IBS), defined as the presence of chronic or recurrent abdominal pain or discomfort relieved by defecation and associated with

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alterations in the frequency and character of stool, numbers among the most common reasons for referral to gastrointestinal specialists. Originally the disorder was conceptualized as disorder of excessive mucus production ostensibly related to inflammatory processes, whereas later investigations focused on peripheral mechanisms of disordered motility (Longstreth, 1998). Most recently, the phenomenon of small intestinal bacterial overgrowth has been proposed as a potential etiological mechanism (Lin, 2004), and interventions aimed at correcting this condition seem to have been successful at alleviating at least some of the morbidity associated with the disorder (Pimentel et al., 2000). As with other functional disorders, the consistent lack of peripheral pathology has lead to investigations of potential CNS abnormalities that may underlie the disorder, particularly a dysregulation of brain–gut communication and altered central pain control mechanisms. As such, these investigations have focused primarily on functional rather than anatomical diVerences between patients and controls and on potential gender diVerences given the predominance of female patients. 1. Functional Analysis: PET Silverman et al. (1997) were the first to assess the eVects of rectal pressure stimuli on rCBF by using H2‐15O (water) PET in six HNC and six subjects with IBS. PET scans were obtained at baseline and during both actual and simulated delivery of anticipated stimuli. Changes in rCBF were interpreted using both SPM and ROI analysis. In HNC, perception of pain during actual or simulated delivery of painful stimuli was significantly associated with activity of the anterior cingulate cortex, whereas no anterior cingulate cortex response to perception of nonpainful stimuli was observed. They report that in patients with IBS, the anterior cingulate cortex failed to respond to the same stimuli, whereas significant activation of the left prefrontal cortex was seen. NaliboV et al. (2001) also assessed brain responses to anticipated and delivered rectal balloon distention using water PET in 12 nonconstipated patients with IBS and 12 HNC. Cortical responses to moderate rectal distention and anticipated but undelivered distention were assessed before and after a series of repetitive noxious sigmoid distentions. Although brain regions activated by actual and simulated distentions were similar in both groups, patients with IBS demonstrated relatively lateralized activation of the right prefrontal cortex; reduced activation of perigenual cortex, temporal lobe, and brainstem; and enhanced activation of the rostral anterior and posterior cingulate cortex. Ringel et al. (2003) compared regional brain activity in response to rectal balloon distention in patients with IBS and healthy controls by comparing six patients with IBS and six HNC. PET scans were obtained during rectal balloon distentions. SPM and ROI analyses were performed to identify and compare diVerences in rCBF for each distention pressure within and between the groups of interest. In post‐hoc analyses, patients with a history of sexual or physical abuse were compared with patients without abuse. In response

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to rectal distention, controls exhibit a greater increase in anterior cingulate cortex activity compared with the IBS group. Thalamic activity was higher in the patients with IBS relative to the control group. Increased anterior cingulate cortex activity was observed in patients with IBS with no history of abuse similar to controls, whereas no such increased activity was noticed in the abused group. In conclusion, this study replicates previous findings showing alterations in brain response to rectal distention in patients with IBS. The observations on the eVect of abuse suggest a possible modulating role of abuse history on this brain response. IBS and FMS are frequently comorbid conditions. Chang et al. (2003) hypothesized that one of the mechanisms underlying this comorbidity may be related to increased activation of brain regions concerned with the processing and modulation of visceral and somatic aVerent information, in particular subregions of the anterior cingulate cortex. Using water PET, the group measured rCBF in age‐matched female patients with either IBS or IBS þ FMS during noxious visceral (rectal) and somatic pressure stimulation. Baseline visceral symptom severity was rated significantly higher in the patients with IBS compared with the patients with IBS þ FMS, whereas patients with IBS þ FMS rated somatic pain as more intense than abdominal pain. During testing, the somatic stimulus was less unpleasant than the visceral stimulus for patients with IBS without FMS, whereas somatic and visceral stimuli were equally unpleasant in those with IBS þ FMS. Group diVerences in regional brain activation occurred entirely within the middle subregion of the anterior cingulate cortex, in which there was a greater rCBF increase in response to noxious visceral stimuli in IBS alone and to somatic stimuli in IBS þ FMS. The authors conclude that chronic stimulus‐specific enhancement of anterior cingulate cortex responses to sensory stimuli in both syndromes may be associated with cognitive enhancement of visceral (IBS) or somatic (IBS þ FMS) sensory input and may, therefore, play a key pathophysiologic role in these chronic pain syndromes. Berman et al. (2000) measured regional neural activation by water PET in two experiments evaluating potential gender diVerences in a total of 30 patients with IBS. Although most stimuli were not rated as painful, rectal pressure increased rCBF in areas commonly associated with somatic pain, including the anterior cingulate cortex, insula, prefrontal cortex, thalamus, and cerebellum. Regional activations for men were much stronger despite similar stimulus ratings in male and female patients. Rectal pressure activated the insula bilaterally in men but not in women. Insula activation was associated most strongly with objective visceral pressure, whereas anterior cingulate activation was associated more with correlated ratings of subjective discomfort. NaliboV et al. (2003) also compared gender diVerences in brain responses to visceral and psychological stressors. Measurements of rCBF using water PET were compared across 23 female and 19 male nonconstipated patients with IBS under conditions of moderate rectal

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inflation and anticipation of a visceral stimulus. In response to the visceral stimulus, women showed greater activation in the ventromedial prefrontal cortex, right anterior cingulate cortex, and left amygdala, whereas men showed greater activation of the right dorsolateral prefrontal cortex, insula, and dorsal pons/ periaqueductal gray. Similar diVerences were observed during anticipation. Men also reported higher arousal and lower fatigue. Their results led to the conclusion that male and female patients with IBS diVer in activation of brain networks concerned with cognitive, autonomic, and antinociceptive responses to delivered and anticipated aversive visceral stimuli. The serotonin receptor (5‐HT3R) antagonist alosetron reduces the symptoms of female patients with diarrhea‐predominant IBS; therefore, Berman et al. (2002) investigated its eVect on rCBF in the absence and presence of rectal or sigmoid stimulation. Forty‐nine nonconstipated patients with IBS (26 women) underwent water PET brain scans before a randomized, placebo‐controlled, 3‐week trial with alosetron. Treatment improved IBS symptoms, which correlated with reduced rCBF in 5‐HT3R–containing regions of the emotional motor system (i.e., 5‐HT3R‐rich amygdala, ventral striatum, and dorsal pons) but not in areas activated by pain. Reduction of rCBF seemed greatest in the absence of visceral stimulation and was partially reversed by rectal or sigmoid distention. Mayer et al. (2002) also used water PET to assess the eVect of alosetron treatment on IBS symptoms and on regional brain activation by rectosigmoid distention. They reported that active treatment resulted in clinical improvement and was significantly associated with decreased activity of the amygdala, ventral striatum, hypothalamus, and infragenual cingulate gyrus. Another group (Nakai et al., 2003) investigated potential diVerences in brain 5‐HT metabolism using alpha‐ [11C] methyl‐L‐tryptophan as the PET tracer. Given the hypothesis that IBS represents a form of central sensitization and the fact that serotonergic modulation seems to alleviate some of the symptoms, the group compared diVerences in 5‐HT synthesis between patients with IBS and matched HNC and between male and female patients with IBS. Their results demonstrated a significant diVerence in brain 5‐HT synthesis in female patients with IBS only, in whom synthesis was increased in the right medial temporal gyrus compared with the female HNC, findings that are in accordance with the eYcacy of alosetron in improving IBS symptoms in female patients with diarrhea‐predominant IBS. 2. Functional Analysis: fMRI Mertz et al. (2000) were the first to use fMRI to investigate abnormalities in brain activation in IBS by performing nonpainful and painful rectal distention in 18 patients with IBS and 16 HNC. In this study, rectal stimulation increased the activity of the anterior cingulate cortex, prefrontal cortex, insula, and thalamus in most subjects. In subjects with IBS, but not controls, pain led to greater activation of the anterior cingulate cortex than did nonpainful stimuli. Patients with IBS

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had a greater number of pixels activated in the anterior cingulate cortex and reported greater intensity of pain at 55 mmHg distention than controls. Yuan et al. (2003) also examined the eVect of rectal balloon distention stimulus using fMRI to compare the distribution, extent, and intensity of activated areas between patients with IBS and HNC. Twenty‐six patients with IBS and 11 matched HNC were tested. Low‐volume rectal distention increased the activity of anterior cingulate cortex, insular cortex, prefrontal cortex, and thalamus. At higher levels of distention, the activation area and percentage change in MR signal intensity within the insula, prefrontal cortex, and thalamus were significantly greater in patients with IBS than in controls as were subjective reports of pain and discomfort. Bonaz et al. (2002) induced transient rectal pain in 12 patients with IBS only (11 women) and reported strong variability of the individual with significant activations in only two subjects. Group analysis did not reveal a specific pattern of activation but rather demonstrated significant right‐sided deactivations within the insula, amygdala, and striatum. Verne et al. (2003) investigated potential brain mechanisms of hyperalgesia in patients with IBS using two paradigms: rectal distention and painful cutaneous heat stimuli (i.e., foot immersion in 45 C and 47 C water bath). Brain activation in age‐ and gender‐matched HNC was subtracted from results in patients with IBS to reveal that both stimuli evoked greater neural activity in several brain regions of patients with IBS, including those related to early stages of somatosensory processing (e.g., thalamus, somatosensory cortex) as well as those more related to cognitive and aVective processing (insular, anterior cingulate, posterior cingulate, prefrontal cortex). Bernstein et al. (2002) also used fMRI to identify central loci activated in response to visceral stimuli (stool and pain). In this study, two control groups were used: HNC and a positive control group of patients with inflammatory bowel disorders (IBD). In contrast to Mertz’s group, they reported that a significantly higher percentage of pixels activated in the anterior cingulate cortex over both conditions that was greatest in HNC, followed by IBS and then IBD. Deactivation of left somatosensory cortex was greatest in IBS, followed by IBD and then HNC. Frontal deactivation in all groups bordered on statistical significance. In addition, nonparametric evaluation of the data suggested two patterns of response to pain (i.e., binary on/oV and graded) among pixels in the anterior cingulate cortex. Recently, Sidhu et al. (2004) compared cortical responses between eight female patients with IBS and eight age‐ and gender‐matched HNC in response to three stimulation levels: subliminal, liminal, and nonpainful supraliminal rectal distention. They reasoned that cortical registration of subliminal viscerosensory signals represents cerebral cortical activity induced by stimulation of intestinal sensory neurocircuitry without the influence of perception‐related cortical activity, whereas those associated with perception represent both neural circuitry and cognitive processes. They report that in HNC, there was a direct

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FIG. 3. Pairs of horizontal sections from the severe IBS/psychological distress (top) and clinical recovery (8 months later) (bottom) follow‐up f MRI studies. Images are a subtraction of a BOLD signal generated during baseline rectal distention (15 mmHg) from that at 50 mmHg distention. Each presymptomatic and postsymptomatic pair of sections was selected to show a single cortical area diVerence; that is, there were other significant changes in the same and other sections. The Z scores were matched to the standardized atlas, and the significance of Z scores is shown with standard color coding (red for highest to blue for lowest). Cortical areas with high initial activation during the ‘‘severe IBS’’ state on top are circled in white and include the MCC, primary somatosensory cortex, and prefrontal area 6/44. Areas with initially low levels of activation that were significantly elevated at clinical recovery (8 months later) are highlighted with black‐stroked circles and included areas 40 and 22 and the anterior insula. Reductions in cortical activity that may account for resolution of stimulus‐ evoked pain and psychosocial symptoms are the MCC, primary somatosensory cortex, and prefrontal area 6/44. Reprinted from Drossman D. A., Ringel Y., Vogt B. A., Leserman J., Lin W., Smith J. K., Whitehead W. (2003) Alterations of brain activity associated with resolution of emotional distress and pain in a case of severe irritable bowel syndrome. Gastroenterology 124, 754–761; Copyright #2003, with permission from The American Gastroenterological Association.

relationship between stimulus intensity and cortical activity volumes in response to each stimulation level, whereas in contrast, in patients with IBS this relationship was absent, and fMRI activity volumes for each level of stimulation were similar. The association of psychosocial disturbances with more severe IBS is well recognized. Accordingly, Drossman et al. (2003) report the case of a young woman with functional gastrointestinal complaints and a dense history of sexual

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abuse and psychosocial distress. In this report, psychosocial, clinical, and fMRI assessment was performed when the patient experienced severe symptoms and again 8 months later when clinically improved. During severe illness, the patient demonstrated a low visceral pain threshold associated with significant activation of the mid‐cingulate cortex, prefrontal cortex, and the somatosensory cortex. During this time, the patient experienced elevated life stress and major psychosocial impairment. When clinically improved, there was resolution in activation of these three areas associated with psychosocial improvement and an increased threshold to rectal distention (see Fig. 3). The authors concluded that activation of the MCC and related areas involved with visceral pain encoding are associated with poor clinical status in patients with severe IBS and psychosocial distress, which seem to be responsive to clinical improvement. Finally, Wilder‐Smith et al. (2004) recently explored abnormalities in endogenous pain inhibitory mechanisms in patients with IBS using fMRI. Scanning was performed on 10 female patients with IBS (five with constipation‐predominant and five with diarrhea‐predominant subtypes) and 10 matched HNC during rectal balloon distention alone or during activation of diVuse noxious inhibitory control by painful heterotopic stimulation by placing one foot in an ice‐water bath. Rectal pain was scored with and without heterotopic stimulation. They report that heterotopic stimulation decreased median rectal pain scores significantly in HNC but not in patients with either form of IBS. Brain activation changes during heterotopic stimulation diVered highly significantly between the three groups. The main centers aVected were the amygdala, anterior cingulate cortex, hippocampus, insula, periaqueductal gray, and prefrontal cortex, which form part of the matrix controlling emotional, autonomic, and descending modulatory responses to pain. They concluded that abnormalities in endogenous pain–inhibitory mechanisms involving diVuse noxious inhibitory controls and other supraspinal modulatory pathways are present in IBS, which diVer in patients with constipation and diarrhea subtypes. 3. Summary A review of neuroimaging findings in IBS demonstrates that all studies currently reported in the literature have investigated functional versus anatomical variations in patients versus controls. Various brain centers have been reported as demonstrating diVerential patterns of activation in response to (or in anticipation of) noxious stimuli, including thalamus, cerebellum, amygdala, temporal lobes, insula, prefrontal cortex, and anterior cingulate cortex. DiVerences in activation seem to be mediated in part by patient gender and such factors as emotional distress and history of sexual abuse, as well as predominant subtype (i.e., constipation vs. diarrhea). DiVerential activation of the anterior cingulate cortex seems to be a dominant theme, although there is discordance as to whether this structure is hyperactive or hypoactive in response to stimuli. DiVerences in

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technique likely account for the diVerences in findings. In addition, a role for diVerences in serotonergic neurometabolism is implicated, at least in female patients with IBS with a diarrhea‐predominant subtype.

D. MISCELLANEOUS DISORDERS We have thus far reviewed the neuroimaging findings among some of the better‐known functional somatic syndromes (i.e., CFS, FMS, and IBS). In addition to these, a number of other disorders lack demonstrable pathology to explain patient complaints, which has led to explorations of potential changes within the CNS that might explain the symptoms. The following section oVers a brief review of these. 1. Chronic Low Back Pain Chronic or recurrent low back pain (CLBP) is a common condition that often occurs in the absence of demonstrable pathology as elicited by either clinical investigation or anatomical imaging. It is frequently associated with anxiety and depression, and a growing literature has investigated the role of psychosocial factors in the onset and persistence of symptoms. As in other functional pain disorders, the lack of demonstrable pathology in CLBP has led to the suggestion that the phenomenon may be related to changes in the CNS that would pertain to augmented central pain processing. Accordingly, Derbyshire et al. (2002) used water PET to study cerebral activation in a group of 16 patients with CLBP compared with 16 HNC in response to thermal stimuli characterized as mild and moderate heat pain. Correlation of rCBF with the subjective pain experience revealed similar responses across groups in the cerebellum, midbrain (including the PAG), thalamus, insula, lentiform nucleus, and mid‐cingulate cortex. The authors report that although some small diVerences were observed between the groups, they were not suYcient to suggest abnormal nociceptive processing in patients with CLBP. In contrast, Giesecke et al. (2004) investigated the possibility that CLBP may be characterized by abnormal central pain mechanisms by using fMRI to compare 11 patients with CLBP to 16 patients with FMS and 11 HNC. They reported that, despite low numbers of tender points in the CLBP group, experimental pain testing (i.e., the controlled delivery of pressure to the thumbnail bed) revealed hyperalgesia in CLBP as well as in FMS, inasmuch as the pressure required to produce slightly intense pain was significantly higher in the controls (5.6 kg) than in the patients with CLBP (3.9 kg) or FMS. When equal amounts of low pressure were applied to all three groups, fMRI demonstrated five common regions of neuronal activation in pain‐related cortical areas in the patients with CLBP and FMS (in the contralateral primary and secondary somatosensory cortices, inferior parietal lobule, cerebellum, and ipsilateral

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secondary sensory cortex), whereas this stimulus resulted in only a single activation in controls (in the contralateral secondary sensory somatosensory cortex). When subjects in the three groups received stimuli that evoked subjectively equal pain, fMRI revealed common neuronal activations in all three groups. These findings led to the conclusion that CLBP is, indeed, characterized by augmented central pain processing. In a series of experiments designed to explore CNS involvement in CLBP, investigators used 1H‐MRS to map brain chemistry changes in CLBP and to correlate such changes with levels of anxiety and depression. In their pilot study, Grachev et al. (2000) demonstrated reductions of both NAA and glucose in the dorsolateral prefrontal cortex, whereas cingulate, sensorimotor, and other brain regions showed no diVerences in chemical concentration. Spatial interrelationship between chemicals within and across brain regions was also reported as abnormal in patients with CLBP, with specific relationships reported between regional chemicals and perceptual measures of pain and anxiety. In a series of follow‐up studies (Grachev et al., 2001, 2002), the group reported that in patients with CLBP, NAA levels in the orbitofrontal cortex and dorsolateral prefrontal cortex demonstrated strong negative correlation to pain, whereas anxiety levels were correlated with NAA changes in the orbitofrontal cortex alone. DiVerences in brain chemistry depended on a three‐way interaction among brain regions, subject groups (i.e., CLBP vs. HNC), and anxiety levels. The chemical–perceptual network best related to pain in patients with CLBP was composed of the dorsolateral prefrontal cortex and orbitofrontal cortex, whereas the chemical– anxiety network was best related to orbitofrontal cortex chemistry in HNC and to four regions (dorsolateral prefrontal cortex, anterior cingulate cortex, orbitofrontal cortex, and thalamus) in patients with CLBP. Perhaps not surprisingly, those changes in the cingulate cortex were best related to the aVective component of pain. In their most recent report (Grachev et al., 2003), the group analyzed the relationship between CLBP and depression by examining interrelationships between regional distribution of NAA levels in relationship to measures of both pain and depression. Reduction of NAA levels was again demonstrated in the right dorsolateral prefrontal cortex of patients with CLBP compared with HNC. Depression correlated with NAA levels in the same region and was unrelated to NAA levels elsewhere in either group. The pain levels in CLBP were also associated with NAA changes in the right dorsolateral prefrontal cortex, although these relationships were much weaker compared with levels of depression. DiVerences in regional connectivity patterns were again reported between subjects and controls as measured by regional distribution patterns of NAA and other metabolites. Finally, Apkarian et al. (2004) recently applied volumetric analysis to high‐ resolution MRI scans to compare 26 patients with CLBP with matched HNC. Patients with CLBP were divided into two groups: neuropathic (i.e., those

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exhibiting pain because of sciatic nerve involvement) and nonneuropathic. Patients with CLBP showed 5–11% less neocortical gray matter volume than control subjects. They report that the magnitude of this decrease is equivalent to the gray matter volume lost in 10 to 20 years of normal aging. The decreased volume correlated with pain duration, such that a 1.3 cm3 loss of gray matter occurred for every year of chronic pain. In addition, regional gray matter density in 17 patients was compared with matched HNC using voxel‐based morphometry. Gray matter density was reduced in the bilateral dorsolateral prefrontal cortex and right thalamus and was strongly related to pain characteristics in a pattern distinct for neuropathic and nonneuropathic CBP. 2. Chronic Whiplash Chronic whiplash may be understood as a syndrome characterized by diVuse pain and such neuropsychiatric symptoms of disturbed cognition, subjective fatigue, and deficits in short‐term and declarative memory that occur as the sequelae of whiplash cervical injury. As with other functional somatic syndromes, the existence of the disorder is hotly contested, given the potential financial and judicial implications of such a condition. The search for demonstrable pathology led Otte et al. (1995) to investigate the condition using Tc‐99m‐HMPAO brain SPECT. They subsequently reported that 28 patients with late whiplash syndrome demonstrated perfusion abnormalities consisting of hypoperfusion in a parietooccipital distribution. Anatomical studies (CT scans) were negative in all cases. In a subsequent report, the same group (Otte et al., 1996) performed Tc‐99m bicisate brain SPECT on an additional 10 patients with late whiplash and again reported hypoperfusion abnormalities in the parietooccipital distribution. Their consistent findings of parietooccipital hypoperfusion with SPECT were then correlated with FDG PET, which demonstrated a reduction of glucose metabolism in the posterior parietal occipital region bilaterally (Otte et al., 1997a,b). They hypothesized that parietooccipital hypometabolism may be caused by extended activation (i.e., sensitization) of nociceptive aVerent nerves from the upper cervical spine after trauma. A study by another group (Bicik et al., 1998) using Tc‐99m‐HMPAO in late whiplash syndrome was unable to replicate findings in the parietooccipital region but reported decreased FDG uptake in the frontopolar and lateral temporal cortex and in the putamen, noting that frontopolar abnormalities correlated with scores on the Beck Depression Inventory. Given the anatomical distribution of abnormalities described by Otte’s group, Freitag et al. (2001) investigated whether patients with whiplash may have impairments in visual motion perception. The authors report that in symptomatic patients with late whiplash, a significant reduction in their ability to perceive coherent visual motion existed compared with controls and asymptomatic individuals with history of neck injury. Functional MRI demonstrated similar activation during random dot motion in all three groups, which was significantly

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decreased during coherent dot motion in the symptomatic patients compared with the other two groups. The author concluded that the combination of reduced psychophysical motion performance and reduced fMRI responses in symptomatic patients with late whiplash syndrome both point to a functional impairment in cortical areas sensitive to coherent motion. Finally, Lorberboym et al. (2002) investigated whether there is a correlation between cerebral perfusion findings, P300 recording (an electrophysiological marker of cognitive ability), and neuropsychological tests in patients with whiplash injury and described correlations between SPECT and P300 abnormalities in a subset of patients. 3. Premenstrual Syndrome (PMS) Grant et al. (1988) used MRI to investigate whether premenstrual neurological symptoms are due to cerebral edema and brain swelling. They measured total cranial and lateral ventricular CSF volumes at midcycle and premenstrually in 20 women with a normal menstrual cycle and reported that total cranial CSF volume actually increased premenstrually, thereby ruling out the possibility that that cerebral swelling causes premenstrual neurological symptoms. Later, Epperson et al. (2002) tested the hypothesis that premenstrual dysphoric disorder (PMDD) may be related to changes in brain GABAergic function in correlation with menstrual changes in circulating levels of neuroactive ovarian steroids. They acquired serial 1H‐MRS spectra from the occipital cortex in 14 healthy menstruating women and 9 patients with PMDD and reported a significant (group x phase) interaction, with most findings explained by a reduction in cortical GABA levels during the follicular phase in patients with PMDD compared with HNC. Cortical GABA levels declined across the menstrual cycle in healthy women, whereas women with PMDD experienced an increase in cortical GABA levels from the follicular phase to the mid‐luteal and late luteal phases. Their findings suggest that disturbances in brain GABAergic function may be modulated by neuroactive steroids, thereby contributing to the pathogenesis of PMDD. 4. Gulf War Syndrome (GWS) Gulf War syndrome (GWS) is a fairly controversial diagnosis that apparently results in susceptible individuals deployed to combat theaters in the Persian Gulf. Patients with GWS evidence a variety of central neurological deficits, including neuropsychological evidence of brain dysfunction, greater asymmetry of brainstem auditory evoked potential, greater interocular asymmetry of nystagmic velocity on rotational testing, increased asymmetry of saccadic velocity, prolonged interpeak latency of the lumbar‐to‐cerebral peaks on posterior tibial somatosensory evoked potentials, and diminished nystagmic velocity after caloric stimulation bilaterally (Haley et al., 1997). Various animal studies attempting to replicate conditions experienced by Gulf War veterans have suggested that exposure to a combination of stress and such chemicals as pyridostigmine (used

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as prophylaxis against chemical weapons attacks) and insecticides commonly used by the armed forces results in both a variety of neurological deficits and demonstrable neurotoxicity in such brain centers as the cerebral cortex, including the cingulate cortex, hippocampus and dentate gyrus, thalamus, hypothalamus, and cerebellum (Abdel‐Rahman et al., 2002, 2004; Friedman et al., 1996; Hoy et al., 1996, 2000). Accordingly, Haley et al. (2000a) used 1H‐MRS to test neuronal integrity within the basal ganglia and brainstem in Gulf War veterans with GWS and found the NAA/Cr ratio to be significantly lower in the basal ganglia and brainstem in GWS. The same group (Haley et al., 2004b) investigated the potential relationship between central dopamine activity and changes within the basal ganglia, in which dopamine is an important neurotransmitter. They reported that HVA/MHPG ratio was inversely associated with NAA/Cr ratio in the left basal ganglia but not in the right, leading to the conclusion that reduced NAA/Cr ratio in the left basal ganglia of these veterans with GWS is associated with altered central dopamine production in a lateralized manner, related in part to injury to dopaminergic neurons in the basal ganglia. Menon et al. (2004) tested hippocampal integrity in GWS by acquiring MRS spectra from the bilateral hippocampus in 10 GWS veterans compared with 5 Gulf War veterans without GWS and 6 Vietnam veterans. The hippocampal NAA/Cr ratio of the GWS group was found to be significantly lower than that of the entire control group or the unaVected Gulf War control group, whereas the Cho/Cr ratio of the GWS group was not diVerent from that for either control group. 5. Multiple Chemical Sensitivities (MCS) The operant theory underlying the MCS construct is that after exposure to a variety of chemicals (usually volatile chemicals, such as industrial solvents) aVected individuals go on to have a wide range of subjective symptoms develop in response to repeat exposure to even subthreshold levels. Although the construct remains perhaps the most controversial diagnosis among the various functional somatic syndromes, a number of reports exist that describe the neurological sequelae of long‐term exposure to industrial solvents and pesticides (Baker et al., 1985). In addition, a growing body of evidence suggests that demonstrable neurological changes may even occur from extremely low levels that are below sensory threshold (Ross et al., 1999). Accordingly, a number of investigators have sought to determine whether functional brain changes may be present in persons who evince sensitivity to chemical exposure. Heuser et al. (1985) conducted a combination of 133Xe and Tc‐99m‐HMPAO brain SPECT in 41 patients exposed to either pesticides or industrial solvents and found that, compared with age‐matched controls, patients exposed to chemicals have diminished CBF, worse in the right hemisphere, with random presentation of areas of hypoperfusion, more prevalent in the dorsal frontal and parietal lobes.

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Simon et al. (1992) reported a study involving 43 subjects (three believed to be have neurotoxicity secondary to breast implants, the other 40 because of industrial exposures) in whom 88% demonstrated cortical perfusion abnormalities during Tc‐99m‐HMPAO brain SPECT in contrast to 10 HNC and 30 patients with MDD, in whom no such abnormalities were detected. The same group (Simon et al. 1994) evaluated potential brain abnormalities associated with MCS by using Tc‐99m‐HMPAO brain SPECT to study six male Gulf War veterans with complaints of abnormal chemical sensitivity and both neurological and psychological symptoms compared with six age‐ and gender‐matched controls. The group report that patients demonstrated diversion of tracer into soft tissues, which was absent in controls, lobar perfusion abnormalities, multiple focal abnormalities consisting of punctate areas of both increased and decreased perfusion, and severe mismatch between early and late phase scans (thought to reflect diVerences in rCBF vs. metabolic rates). In a carefully controlled study of 25 HNC and 25 patients who experienced solvent exposure, Fincher et al. (1997) report specific physiological abnormalities related to rCBF were revealed during Tc‐99m‐HMPAO brain SPECT consisting of significantly decreased early phase uptake and a significant increase in uptake diVusely in subjects with mixed organic solvent exposure. Two studies appear in the literature in which PET was used to investigate potential brain changes in MCS. Heuser and Wu (2001) performed FDG PET scans in seven patients with MCS and compared them with scans from a library of 56 HNC. Their results demonstrated significant diVuse cortical hypometabolism, which corresponded to areas of cortical hypoperfusion found in previous SPECT studies reported previously. In contrast, significant hypermetabolism was found in portions of the limbic system (including the extended amygdala region) and also extended into the cerebellum and visual cortex and downward into the brainstem. The authors suggested that areas of hypermetabolism might represent a form of kindling of limbic structures, presumably because of prior exposure to industrial solvents. Bornschein et al. (2002) scanned 12 patients with MCS using FDG PET and reported that no abnormalities were detected compared with established normative values. A variant of MCS, in which multisystem involvement, including neurological sequelae, is thought to develop as a result of exposure to environmental toxins related to enclosed building spaces has been dubbed ‘‘sick building syndrome.’’ In this case, exposure is believed to be related to either industrial toxins present in building materials and recirculated by means of modern construction techniques, which make buildings more airtight, or else by means of exposure to presumably toxic metabolites of various mold strains. Accordingly, Rea et al. (2003) studied 100 patients who were diagnosed with sick building syndrome after exposure to toxic molds in their homes and had evidence of neurological sequelae and respiratory abnormalities. Neuropsychological evaluations of 46 of the patients who exhibited symptoms of neurological impairment showed typical

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abnormalities in short‐term memory, executive function/judgment, concentration, and hand/eye coordination. Subsequent SPECT scanning in a subgroup of 30 of the original 100 patients revealed abnormalities in rCBF in 26 (86%).

III. Discussion and Conclusions

The search for organic diVerences between patients with various functional somatic syndromes and the nonaZicted has led researchers to investigate the CNS as the potential wellspring of pathology. As reviewed herein, most neuroimaging studies conducted to date have demonstrated diVerences within the CNS, whether at baseline or in response to stimulation. Given this involvement, the challenge is then to make meaningful connections between such findings and the symptom complexes with which they are associated. Among the aspects of the functional somatic syndromes that make both research and clinical practice such challenging endeavors is the fact that many of the symptoms are multidimensional and thus nonspecific. A patient’s complaint of fatigue, for example, may indicate that the patient is experiencing insuYcient sleep, physical weakness, mental exhaustion, or suboptimal metabolism. Likewise, the source of a given patient’s complaint of widespread pain may indicate an increase in absolute pain levels, whether an increase in the activity of peripheral nociceptors or increased aVerent processing in pathways ascending to the brain, or rather a decrease in the patient’s capacity to tolerate stimulation, suggesting either a dysfunction of descending antinociceptive processes or flaws in the individual’s psychic constitution insofar as pain tolerance is concerned. With regard to enhanced nociception, baseline perfusion studies in FMS have suggested subcortical involvement (i.e., thalamus and elements of the basal ganglia), whereas functional studies using sensory stimulation have demonstrated diVerential activation of both sensory and cognitive centers in both FMS and IBS. The latter have been interpreted by many authors to be evidence of secondary hyperalgesia variously attributed to increased cognitive processing, hypervigilance, or increased attention to noxious stimulus. Caution must be used with regard to this interpretation, because it seems to imply a volitional component to an individual’s experience of a given disorder and may, therefore, lead to blaming patients for the their own symptoms. The notion of ‘‘catastrophization’’ is a prime example. One interpretation of this phenomenon would suggest that if patients were to simply reframe their perspective on their condition, they would be able to reduce morbidity through force of will. Although there may be some merit to this idea, one is left to wonder what organic substrate may ultimately explain such phenomena. An alternate explanation for the phenomenon of increased activity in brain centers involved in the cognitive aspects of nociception

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takes into account the interplay of various regions in the overall experience of somatosensory perception and the potential impact of stressful experience on these same areas. AVerent signals arriving from the periphery are connected to parallel networks within the thalamus wherein they are subject to modulatory processes before being distributed to higher cortical and limbic brain centers. These higher centers, in turn, provide feedback control over such signal flow, in part by influencing the balance between excitatory and inhibitory mechanisms. As previously noted, basic science has demonstrated that the experience of stressful events impacts such centers (i.e., hippocampus, medial prefrontal cortex, and amygdala) and may, therefore, theoretically contribute to enhanced nociception. The applicability of such findings to functional somatic syndromes is suggested by preliminary data from anatomical studies in FMS (Glabus et al., 2004; Wood et al., 2004). This author has elsewhere proposed that stressful experience increases excitatory neurotransmission through the action of stress‐ related neurohormonal factors interacting with glutamatergic processes within the hippocampus and frontal cortex (Wood, 2004a). These in turn are proposed to suppress dopaminergic reactivity in the basal ganglia and other critical brain centers, thus producing an enhancement of nociceptive processes, which the subject would experience as stress‐induced hyperalgesia/allodynia (Wood, 2004b). Variability in the topology of such organic changes, along with the contribution of distinct peripheral insults, likely accounts for individual variations in the experience of enhanced nociception, whether regional or widespread, as suggested by Chang et al. (2003), who reported group diVerences in brain activity among patients with IBS with or without comorbid FMS were restricted to patterns of activation within the anterior cingulate cortex. Again, taken from this perspective, the findings of Gracely et al. (2002), who reported that low‐level pressure stimulus of patients with FMS results in diVerential cortical activation and an apparent subcortical hypoactivation, are especially intriguing. The findings of Drossman et al. (2003), in which increased psychosocial stress produced parallel changes in anterior cingulate cortex activation and decreased pain thresholds in a subject with IBS, are also noteworthy. Given the role of the anterior cingulate cortex in the attentional aspect of nociception, the authors concluded that their subject’s experience of increased pain might have been due to increased attention focused on unpleasant sensations, as evidenced by the activation of this brain center. Although the inference of a volitional component to this phenomenon is tempting, an alternate possibility might be that stress‐ induced hyperalgesia developed as a result of stress‐induced changes to the very substance of the attentional mechanism (i.e., the subject’s anterior cingulate cortex), wherein an increase in the excitability of this region resulted in her inability not to pay attention to a given stimulus (i.e., a reduced threshold to stimulation). In theory, such a breakdown might involve enhanced excitatory throughput from brain centers that supply aVerent drive to the anterior cingulate

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cortex (e.g., the hippocampus), a disruption of inhibitory influences (e.g., dopaminergic, GABAergic, or
2‐adrenergic modulation), or a combination thereof. With regard to chronic fatigue, demonstrations of brainstem involvement by means of SPECT (Costa et al., 1992, 1995) and PET (Tirelli et al., 1998) are particularly intriguing, given that an infectious etiology to CFS has long been suspected, as justified by such factors as sudden onset and sporadic concentrated outbreaks. The brainstem contains a variety of nuclei supplying activating neurotransmitters to diVuse regions of the brain. The neurologist and author Oliver Sacks, in his book Awakenings (Sacks, 1973), provides the fascinating account of a series of patients struck by a severely debilitating illness (von Economo’s encephalitis), which ostensibly resulted from a viral agent targeting dopaminergic centers, as witnessed by subsequent patient responses to the then novel agent levodopa. Accordingly, one might speculate that an insult to activational nuclei within the brainstem, whether chemical or infectious, might produce the pattern of hypoactivation reported, which in turn might ostensibly account for the both the experience of overwhelming fatigue and the type of diVuse network involvement reported in subsequent functional analysis (e.g., de Lange et al., 2004; Schmaling et al., 2003). Although most investigations reviewed herein describe abnormalities that strongly suggest that diVerences indeed exist within the CNS that may characterize these various disorders, heterogeneity of patient samples likely accounts for much of the variance in these findings. Future studies will likely benefit from subgrouping patient populations on the basis of such factors as patient circumstances at symptom onset, duration of illness, and clinical comorbidities. Crossover analysis, in which imaging modalities successfully demonstrating pathology in one disorder are applied to others, also seems warranted. An example of resonance between diVerent imaging modalities is represented by research into posttraumatic stress disorder (PTSD), which has been characterized by functional changes within the anterior cingulate cortex as revealed by fMRI (Shin et al., 2001), as well as brain metabolite changes demonstrated by 1H‐MRS (De Bellis et al., 2000). On the basis of animal research demonstrating that stressful experience contributes to molecular change within the anterior cingulate cortex, these findings in PTSD, the archetype of stress‐related brain disorders, were predictable. As previously noted, preliminary anatomical studies have suggested organic changes to the hippocampus and other structures in FMS and CFS, which have also been demonstrated to be involved in functional analysis in response to stimulation. Functional analysis of diVerential rCBF responses to rectal stimulation in IBS has strongly suggested involvement of the anterior cingulate cortex (Bernstein et al., 2002; Bonaz et al., 2002; Drossman et al., 2003; Mertz et al., 2000; Sidhu et al., 2004; Silverman et al., 1997; Verne et al., 2003); thus, one would predict that 1H‐MRS investigation of brain metabolite changes in IBS will

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eventually demonstrate metabolite disturbances within the anterior cingulate cortex and likely the hippocampus, thalamus, and basal ganglia as well. Ultimately, the challenge of medical science is to translate demonstrable pathology into meaningful intervention that ultimately has an impact on a patient’s existential condition for the better. Although consistently eVective therapies remain largely undescribed, the studies by both Nakai et al. (2003) and Adiguzel et al. (2004), in which clinical improvements in response to specific pharmacological interventions corresponded to brain changes demonstrable by functional neuroimaging, represent significant landmarks. Future investigations into specific molecular mechanisms involved in the individual syndromes will likely prove most profitable. Such work would most likely gain direction from the field of biological psychiatry, which has successfully used ligand‐based PET analysis to characterize the molecular substrates of such organic brain disorders as PTSD, MDD, obsessive‐compulsive disorder (OCD), and schizophrenia. Regarding FMS, for example, preliminary evidence provided by Holman (2003) suggests the usefulness of dopamine agonists in the treatment of the disorder, a notion that resonates well with a proposed model in which a dysregulation of dopaminergic neurons accounts for most symptoms (Wood, 2004b). Both serotonergic and noradrenergic abnormalities have also been proposed (Russell, 1998). Definitive analysis by way of ligand‐based PET studies has the potential to sort out the contribution of these various factors. In conclusion, the lack of peripheral pathology to explain the symptoms of which patients with functional somatic syndromes complain has led investigators to explore potential changes within the CNS that may characterize a given disorder. As reviewed herein, there indeed seem to be a variety of changes that distinguish the aZicted from the well. The situation resonates in many ways with progress in other fields of medicine, in which unexplained symptoms are initially attributed to either spiritual or psychic forces, only to be ‘‘organified’’ once technological advances are made that enable the exploration of pathologies undetectable by previously existing technology. For example, malaria (from the Latin for ‘‘bad air’’) was once attributed to exposure to miasmal conditions surrounding haunted swamps until the advent of microscopy enabled the identification of an infectious agent. Likewise, asthma, now known to result from inflammatory reactions within the airways, was until quite recently thought to be a manifestation of neuroticism amenable to psychotherapeutic intervention. The discipline of biological psychiatry has demonstrated forcefully that psychological processes are linked to organic substrate. As the physiological eVects of stressful insults (whether infectious, traumatic, or psychosocial) gain further recognition, our medical culture will hopefully transcend the type of philosophical dualism that relegates as yet unexplained patient phenomena to some metaphysical third space. In so doing, we can begin to explore rational interventions to improve the lot of patients to whom medical science has traditionally had little to oVer.

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NEUROIMAGING IN MULTIPLE SCLEROSIS

Alireza Minagar,* Eduardo Gonzalez‐Toledo,y James Pinkston,* and Stephen L. Jaffe* y

*Department of Neurology, and Department of Radiology, Louisiana State University School of Medicine, Shreveport Shreveport, Louisiana 71103

I. Introduction II. Clinical Manifestations III. The Pathology of MS as it Relates to Neuroimaging A. The Acute MS Plaque B. The Chronic MS Plaque IV. MRI in Multiple Sclerosis A. T1‐Weighted Images B. T2‐Weighted Images, FLAIR, DWI, and ADC Map C. MR Spectroscopy D. Magnetization Transfer Imaging E. Functional MRI (fMRI) F. DiVusion Tensor Imaging (DTI) V. SPECT and PET Scanning VI. The Neuroimaging of Neuropsychological Dysfunction in Multiple Sclerosis A. MRI Neuroimaging Techniques B. Measures of Central Atrophy C. Advanced MRI Neuroimaging Techniques D. Psychosocial Manifestations References

The past decade has witnessed remarkable advances in our understanding of multiple sclerosis (MS), including the natural course of MS; the immunopathogenic interactive mechanisms between cerebral endothelial cells, activated CD4 T lymphocytes, macrophages, and other inflammatory mediators; and the development of new therapies that can reduce relapse rate and delay onset of disability. Undoubtedly, these achievements could not have been possible without the application of modern neuroimaging techniques. Magnetic resonance imaging (MRI) studies initiated in the 1980s have provided objective evidence of the dynamic and destructive nature of MS. Indeed, MRI and other MR techniques such as fluid attenuated inversion recovery, diVusion‐weighted imaging/ ADC map, magnetic resonance spectroscopy, magnetization transfer imaging, functional MRI, diVusion tensor imaging, and single photon computed/positron

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emission tomography have altered our conception of MS as a purely demyelinating, immune‐mediated disease of the central nervous system (CNS) into a disease with far more complex eVects on the CNS, including axonal loss and neuronal degeneration. These advanced techniques have enabled monitoring of complicated pathophysiological events, including the development of demyelinating lesions and the associated loss of oligodendrocytes, axons, and neurons. In addition, these imaging techniques have made it possible to better monitor a patient’s response to MS therapies. This chapter presents an extensive review of the latest developments in the neuroimaging of MS.

I. Introduction

Multiple sclerosis (MS) seems to be at least partially a T‐cell–mediated (Th‐1 type pro‐inflammatory bias over Th‐2 antiinflammatory) autoimmune disorder of the central nervous system with the immune cascade directed against the myelin/oligodendrocyte complex, leading not only to demyelination but also axonal degeneration. Environmental and genetic factors may be central to the pathogenesis of MS. For example, individuals with certain HLA genotypes have MS develop with greater frequency, and various environmental factors, including viral (human herpes virus‐6 [HHV‐6]) and bacterial infections, metabolic stressors, superantigens, and reactive metabolites, may be involved in the triggering mechanism producing the immune cascade. The advent of increasingly sophisticated magnetic resonance imaging (MRI) techniques has further complicated our conceptions of MS. Magnetic resonance spectroscopy (MRS) has demonstrated brain neuronal and axonal loss early in the disease and defects in normal‐appearing white matter (NAWM) with minimal, if any, inflammatory activity. Thus, it has become necessary to hypothesize a secondary degenerative/ metabolic or apoptotic process, perhaps resulting from the initial autoimmune inflammatory process or related to the initial environmental triggering mechanism but thereafter independent, with one or the other process becoming dominant (i.e., primary‐progressive vs. relapsing‐remitting MS). MS incidence in the United States is approximately 100 cases per 100,000 individuals per year, with a tendency toward a higher incidence in the temperate climates. Approximately two thirds of new cases of relapsing‐remitting MS (RRMS) occur in women, and the disease is one of the major causes of disability in the younger age groups, producing large economic, and social burdens. The clinical picture of MS is highly variable, probably dependent on the quantity and quality of the immune cascade and location and size of the lesions produced. It attacks all areas of the brain, predominantly the highly myelinated white matter but also myelinated axons within the gray or neuronal areas of the brain. Now,

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the findings of primary neuronal loss further complicate our attempts to understand the pathophysiologic mechanism. Plaques of demyelination are observed in all areas of the brain, including the optic nerves, cerebral hemispheres, brainstem, cerebellum, and the spinal cord, the latter especially significant because lesions in the descending and ascending pathways can produce major disability with minimal pathologic involvement. The most common form of MS is RRMS, which is characterized by episodes of central nervous system (CNS) dysfunction followed by improvement and then recurrent exacerbations of dysfunction (i.e., relapses). This RRMS category comprises the largest number of cases, approximately 85%, but can proceed to a secondary progressive phase with incomplete or no remission phases. The disease can also present a primary progressive (PPMS) course without any obvious relapses or remissions. Acute fulminant varieties include Schilder’s disease, Balo´ ’s concentric sclerosis, Marburg’s variant, and the demyelinating pseudotumor (or tumefactive) variant. Of course, with the lack of pathophysiologic knowledge as to these various subtype presentations and the lack of particular subtype markers at this point, neuroimaging has become primarily important in diagnosing these variants (Figs. 1 and 2). In addition, without a defined pathophysiological mechanism, the disease (or perhaps diseases) is diagnosed on a phenomenal basis most currently using the McDonald Criteria, which not only is based on the clinical presentation but the neuroimaging presentation, specifically the MRI evaluation (Table I). And the disease activity observed with MRI scanning demonstrates a frequency 5 to 10 times that which can be clinically identified (Fazekas et al., 1999). MS neuroimaging began in the 1970s with the use of radioisotope brain scanning, allowing visualization of large MS plaques in which the isotope accumulated as a result of blood–brain barrier (BBB) breakdown (Fig. 3). However, the advent of computerized assisted tomographic imaging (CAT) in the mid to late1970s enabled the visualization of MS lesions in a large number of patients, and the lesion size necessary for visualization was markedly reduced (approximately 10 mm) (Fig. 4). With the noncontrast CAT scan, low‐attenuation lesions were identified in the cerebral white matter; and with high‐dose intravenous iodinated contrast media, many lesions (plaques) were seen to enhance, establishing the fact that the BBB had broken down, and this enhancing plaque became characteristic of the acute clinical relapse (Fig. 5) (Vin˜ uela et al., 1982). In addition, CAT scanning in more advanced cases demonstrated progressive atrophy attributed at that point exclusively to demyelination in the brain’s white matter. With the introduction of MRI, the lesion burden detected in patients improved over CAT scanning by at least a factor of 10. With the addition of paramagnetic gadolinium–containing contrast agents, the MS plaques were more readily visualized, and gadolinium enhancement of the lesion because of BBB breakdown allowed the acute lesion associated with the clinical relapse to be

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FIG. 1. Tumefactive (pseudotumor) form of MS. MRI axial views demonstrate a pseudotumor in the left frontal lobe. (A) FLAIR sequence: A hyperintense left frontal mass with significant mass eVect (white arrows) and linear hyperintensities perpendicular to the wall of the lateral ventricle (black arrows) are observed. (B) T1‐weighted view demonstrates a hypointense lesion with irregular outline involving the white matter. (C) T1‐weighted, postcontrast (Gd‐DTPA) infusion study demonstrates incomplete ring enhancement, which is a characteristic feature of MS plaques.

readily identified. At this point, MRI has completely superseded CAT as an MS evaluation tool. As previously noted, at this time the various acute fulminant subtypes of MS can only be primarily diagnosed by MRI, although secondary

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FIG. 2. Multiple sclerosis variant: Balo´ ’s concentric sclerosis.

biopsy is often still necessary. In addition, the criteria for the MS phenomenal diagnosis rest strongly with MRI as does follow‐up of the clinical course of the disease, especially as related to therapeutic trials. Unfortunately, the disability seen clinically in MS has yet to be reflected by MRI evaluation, but refinement of MRI techniques may establish this correlation. In addition to the routine T1 and T2 images, fluid‐attenuated inversion recovery (FLAIR) images have helped identify the smaller and less advanced lesions. MRS is being used to evaluate loss of axonal and neuronal mass, which may very well correlate better with clinical disability. Magnetic transfer imaging (MTI), diVusion‐weighted imaging (DWI), and the apparent diVusion‐weighted map (ADC map), as well as functional MRI (fMRI), have begun to play very specific roles in understanding the pathophysiological mechanisms of this disease process. MRI tensor imaging is being investigated as a tool for evaluation of white matter tracts that may very specifically follow fiber tract atrophy and correlate more specifically with MS disability, thus enabling clearer delineation of therapeutic interventions. Positron emission tomography (PET) scanning and single photon emission computed tomography (SPECT), a less complicated procedure, allows investigation of the metabolic dysfunction associated with MS pathology. Blood flow, oxygen metabolism, and glucose metabolism can all be studied with these modalities, and, as will be discussed, may further help define axonal, myelin, and neuronal pathology. The expense of testing has decreased recently, and production of ligands by radiopharmacists has markedly improved, as has

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TABLE I REVISED DIAGNOSTIC CRITERIA FOR DIAGNOSIS OF MS (MCDONALD CRITERIA) Clinical Presentation Two or more attacks; objective clinical evidence of 2 or more lesions Two or more attacks; objective clinical evidence of 1 lesion

One attack; objective clinical evidence of 2 or more lesions One attack; objective clinical evidence of 1 Lesion mono‐symptomatic presentation; clinically isolated syndrome)

Insidious neurological progression suggestive of MS

Additional Data Needed for MS Diagnosis Nonea Dissemination in space, demonstrated by MRIb or Two or more MRI‐detected lesions consistent with MS plus positive CSFc or Await further clinical attack implicating a different site Dissemination in time, demonstrated by MRId or Second clinical attack Dissemination in space, demonstrated by MRIb or Two or more MRI‐detected lesions consistent with MS plus positive CSFc and Dissemination in time, demonstrated by MRId or Second clinical attack Positive CSFc and Dissemination in space, demonstrated by 1) Nine or more T2 lesions in brain or 2) 2 or more lesions in spinal cord, or 3) 4–8 brain plus 1 spinal cord lesion or abnormal VEPe associated with 4–8 brain lesions, or with fewer than 4 brain lesions plus 1 spinal cord lesion demonstrated by MRI and Dissemination in time, demonstrated by MRId or Continued progression for 1 year

If criteria indicated are fulfilled, the diagnosis is multiple sclerosis (MS); if the criteria are not completely met, the diagnosis is ‘‘possible MS’’; if the criteria are fully explored and not met, the diagnosis is ‘‘not MS.’’ a No additional tests are required; however, if tests [magnetic resonance imaging (MRI), cerebral spinal fluid (CSF)] are undertaken and are negative, extreme caution should be taken before making a diagnosis of MS. Alternative diagnoses must be considered. There must be no better explanation for the clinical picture. b MRI demonstration of space dissemination. c Positive CSF determined by oligoclonal bands detected by established methods (preferably isoelectric focusing) different from any such bands in serum or by a raised IgG index. d MRI demonstration of time dissemination. e Abnormal visual evoked potential of the type seen in MS (delay with a well‐preserved wave form).

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FIG. 3. Scintillation camera, 99mTc‐DTPA isotope brain scan demonstrating MS plaques: One focal area of increased activity in the left parietal region, and three areas in the right frontal and parietal regions (white arrows). (A) Left, AP view; middle, PA view; and Right, vertex view. (B) Left, Left lateral view; and Right, Right lateral view (Courtesy of Miller and Potsaid, 1974).

FIG. 4. An MS plaque on computed tomography, which appears as a hypodense, oval lesion in the white matter.

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FIG. 5. MS plaques on CT scan, precontrast (A), and postcontrast (white arrows) (B), and post‐ high‐volume–contrast infusion with delayed imaging (C). Plaques appear as hyperdense, oval lesions in both gray (white arrows) and white matter (open arrows) (Courtesy of Vin˜ uela et al., 1982).

the resolution of PET scanning. Radioactive ligands presently being developed for high‐resolution PET may allow study of the actual autoimmune process in vivo.

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II. Clinical Manifestations

Corticospinal tract involvement, which occurs in a large number of patients with MS, manifests with a sense of ‘‘heaviness’’ or ‘‘stiVness’’ of one or more limbs. The lower extremities are more frequently involved than the upper extremities. Initially, the involvement of the lower extremities is unilateral, progressing to bilateral involvement. Spasticity of the lower and/or upper extremities, hyperactive deep tendon reflexes with extensor plantar responses (Babinski’s sign), clonus (which may be sustained and severe) of ankle and knee, and violent myoclonic jerks are the other common clinical manifestations of corticospinal tract demyelination. Somatosensory complaints are among the earliest clinical manifestations of MS and develop in 52–70% of these patients. Patients usually report numbness, tingling, burning, or tightness in their extremities. In cases with transverse myelitis, patients report a bandlike tightness around their trunk. Neurological examination commonly demonstrates reduction of pinprick, vibration, and joint position sense. Reduced perception of temperature and pain are less frequently found. Demyelination of visual pathways is common in MS, and the most significant manifestation is optic neuritis (ON). ON occurs in 14–23% of cases. Patients initially complain of blurring of vision unilaterally or bilaterally accompanied by photophobia and retroocular pain that is aggravated by eye movement. Neuroophthalmologic examination of MS patients with ON discloses diminished visual acuity of varying degrees; presence of a central scotoma; a positive ‘‘swinging‐flashlight test’’ (Gunn pupillary sign) indicative of an aVerent pupillary defect, with swelling of the optic nerve head (papilledema) and/or hemorrhages or exudates (papillitis); or a normal optic disc in retrobulbar optic neuritis. Patients with MS have several distinct painful syndromes. These include trigeminal neuralgia, glossopharyngeal neuralgia (Minagar and Sheremata, 2000), radicular pain, intractable headaches, and occipital neuralgia. Brainstem involvement in patients with MS manifests with variable clinical presentations because of cranial nerve fiber tract and long‐tract demyelination. Impairment of ocular motility is commonly observed. Nystagmus, most often horizontal, occurs in a large number of patients. Other forms of nystagmus, such as rotatory, vertical, upbeat, downbeat, and seesaw, occur less commonly. Minagar et al. (2001) reported an MS patient with brainstem involvement and perverted head‐shaking nystagmus. Unilateral or bilateral internuclear ophthalmoplegia, a common manifestation of MS, results from involvement of the medial longitudinal fasciculus and clinically presents with failure of the eye ipsilateral to the lesion to adduct, whereas the contralateral eye abducts fully with horizontal nystagmus. Other neuroophthalmologic abnormalities in patients with MS consist of horizontal and vertical gaze paresis, slowness of smooth visual

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pursuit, the one‐and‐a‐half syndrome, weakness or paralysis of individual nerves innervating extraocular muscles, and skew deviation. Other clinical manifestations include dysarthria, facial paresis, auditory impairment, and blepharospasm. Neuropsychiatric abnormalities in patients with MS will be discussed separately.

III. The Pathology of MS as it Relates to Neuroimaging

Most MS cases, at least initially, present as RRMS with very specific neuropathology. During the course of RRMS, both acute and chronic plaques are identifiable. These are most often seen in the white matter either adjacent to the ventricles or juxtaposed subcortically. These juxtacortical lesions can aVect the cortex with loss of cortically located myelinated axons and neurons. These so-called plaques are usually well defined, discreet, and circumscribed, ranging from a centimeter in diameter up to a much larger, more amorphous lesion occupying a substantial portion of the hemisphere (i.e., as in demyelinating pseudotumor; Fig. 2). Plaques are primarily localized perivenously with permanent loss of oligodendrocytes and myelin sheaths and secondary astrogliosis. Axonal loss may occur especially in the interior of the lesions.

A. THE ACUTE MS PLAQUE Production of an MS plaque is initiated with changes in the endothelial lining of the microvasculature producing breakdown of the BBB and adherence of inflammatory cells, specifically T lymphocytes, to the endothelium; these lymphocytes then pass through the vessel wall and its associated BBB and attack the oligodendrocyte/myelin complex in the area. Cerebral edema occurs with BBB breakdown; and with the loss of surrounding architecture, the potential for extracellular diVusion of water molecules increases. T‐cell invasion produces destruction of myelin and axons and triggers oligodendrocyte apoptosis. Macrophages enter the lesion clearing the myelin/oligodendrocyte debris, as well as enhancing the inflammatory response. The major component of typical plaque pathology is the loss of myelin in a very demarcated region. Inflammatory cellular infiltrate may extend into the surrounding tissue adjacent to the myelin‐denuded plaque. Quantitative and qualitative variance of this immune‐ mediated phenomenon against the oligodendrocyte/myelin complex would seem to be responsible for the more fulminant variants of MS illustrated previously, involving larger brain areas with greater and more generalized necrotic eVects appearing within the lesions. So‐called normal‐appearing white matter (NAWM)

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seems to undergo biochemical and histochemical changes early in the disease process, and these phenomena are being investigated by MRS.

B. THE CHRONIC MS PLAQUE At autopsy, the most commonly found MS plaque is the chronic plaque, which grossly presents as a firm, grayish brown circumscribed lesion in the various areas of the central nervous system. In chronic cases of MS, the destruction of axons and myelin produces a significant degree of atrophy, which can be serially followed by MRI. As previously discussed, neuronal degeneration/apoptosis with cell loss further adds to this atrophy, either as a result of the original disease‐triggering mechanism or secondarily triggered by the inflammatory cascade. These plaques lack myelin, and very often many axons have been lost as well. Macrophages still often laden with debris can be seen for long periods of time in these chronic plaques. Astrocytosis occurs throughout the plaque, often with increased numbers of oligodendrocytes rimming the edge of the lesions. These chronic plaques are visualized as so called black holes on T1‐weighted MRI, and with increased chronic cellularity (astrogliosis, etc.) the possibility for extracellular molecular diVusion decreases, allowing the lesions to be aged by diVusion‐weighted MRI. At this point, evidence of active inflammation as indicated by the presence of T and B cells, as well as plasma cells and the occasional mast cell, is rare.

IV. MRI in Multiple Sclerosis

A. T1‐WEIGHTED IMAGES Unenhanced T1‐weighted images specifically identify chronic MS lesions and allow sequential monitoring of lesion burden accumulation quantitatively for individual patients. The addition of the paramagnetic contrast material, gadolinium (Gd), shortens the T1‐weighted and T2‐weighted relaxation times of brain tissues. Disruption of the BBB followed by transendothelial migration of activated leukocytes into brain tissue is among the most persistent and earliest abnormalities seen in an MS‐aVected brain (Minagar, 2003; Minagar et al., 2001b). Disruption of the BBB is thought to precede formation of new lesions or plaques (Kermode et al., 1990). Leakage of Gd through the disrupted BBB is regarded as an indicator of the acute inflammatory activity initially involved in the pathogenesis of MS (Yousry, 2000) and produces the contrast‐enhancing lesion that objectifies the acute MS exacerbation. Contrast accumulation by an acute lesion generates an area of

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FIG. 6. MS plaques onT1‐weighted MRI, postcontrast infusion. Nonenhancing, old plaque (arrowhead), nodular enhancement (thin arrow), open ring enhancement (thick arrow).

increased brightness, or ‘‘enhancement’’, on T1‐weighted images. Thus, postcontrast T1‐weighted images are most helpful for detecting acute, active MS lesions associated with BBB disruption (Fig. 6). Contrast‐enhancing lesions are detected most commonly in patients with RRMS, and the enhancement usually continues for 1 month in the aVected areas of brain and spinal cord. Increasing the dose of contrast material (‘‘triple dosing’’ of Gd) and delaying the time from the injection of the contrast to image acquisition can potentially lead to detection of more contrast‐ enhancing lesions. Vascular structures through which Gd may leak and gliotic lesions may sometimes present with a bright ring of intermediate intensity surrounding them and should be diVerentiated from active MS lesions (Fig. 6). Certain studies have attempted to establish a correlation between MRI activity and various inflammatory markers in the blood, cerebrospinal fluid, or urine. A significant positive correlation has been found between contrast‐enhancing lesions and changes in the number of cells secreting interleukin‐2 over a 6‐month period (Calabresi et al., 1998). Minagar et al. (2001b) showed a positive association between the presence of contrast‐enhancing lesions and elevated plasma levels of endothelial microparticles carrying CD31 adhesion molecules from their parent endothelial cells. Jy et al. (2004), in a study involving endothelial microparticle–monocyte complexes, demonstrated that higher plasma levels of these complexes in patients with MS corresponded with the presence of contrast‐enhancing lesions. In addition, an association between contrast‐enhancing lesions and cells that secrete interferon gamma, or lymphotoxin alpha, has been reported (Hartung et al., 1995). Another

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study demonstrated a correlation between contrast‐enhancing lesions and soluble intracellular adhesion molecule I (ICAM‐I) (Khoury, 1999). Because acute contrast‐enhancing lesions present 5 to 10 times more commonly than acute clinical signs of an exacerbation in patients with MS, this MRI parameter has been used in clinical trials to decrease the number of patients and the timeframe needed to explain the therapeutic eVects of the tested medication (Frank et al., 1994; Nauta et al., 1994). Although contrast‐enhancing lesion activity, similar to T2‐weighted hyperintense lesions, demonstrate only a weak correlation with clinical disability (Kappos et al., 1999), a positive correlation exists between the number of enhancing lesions and the onset and severity of clinical exacerbations (Khoury et al., 1994), as well as the future formation 1 to 2 years later of enhancing lesions and T2‐weighted lesions (Molyneux et al., 1998; Simon et al., 1998). However, there are no substantive data as yet demonstrating that the occurrence of enhancing lesions predicts prognosis for patients with MS. The pattern of enhancement in MS can be both solid and ringlike. Incomplete ring enhancement is occasionally observed in large MS lesions, a phenomenon known as the ‘‘open‐ring’’ sign. Open‐ring enhancement may also be observed with infections, neoplastic processes, and adrenoleukodystrophy but is most commonly detected with demyelinating lesions (Masdeu et al., 1996).

B. T2‐WEIGHTED IMAGES, FLAIR, DWI,

AND

ADC MAP

In conventional T2‐weighted images, CSF signal is bright, and MS lesions also appear as hyperintense foci that may be confluent with the lateral ventricles and each other (Fig. 7). Both active and chronic demyelinating lesions of MS present as hyperintense signal abnormalities on T2‐weighted images, and their diVerentiation is not possible with this sequence alone. T2‐weigthed images enable observation of a large number of MS lesions particularly in the periventricular areas and centrum semiovale. Ovoid MS lesions may also present as abnormal hyperintense signals in cerebellar peduncles, cerebral hemispheres, and the midbrain. The disadvantages of using T2‐weighted images include obscuration of posterior fossa lesions by flow artifact and diYculty in distinguishing MS lesions situated adjacent to the lateral borders of the ventricles. Early acute lesions are hyperintense on T2‐weighted sequences but usually have a hypointense ring, probably containing activated macrophages. Late active lesions are hyperintense on T2‐weighted but are hypointense on T1‐weighted images, which probably signifies demyelination and axonal loss. However, using DWI and apparent diVusion coeYcient (ADC) map may assist in diVerentiating active MS lesions from subacute and chronic lesions (Fig. 8). Demyelinating plaques typically aVect periventricular regions, the corpus callosum, and brainstem/cerebellum. The periventricular white matter lesions

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FIG. 7. T2‐weigthed MR axial images demonstrating areas of demyelination and surrounding edema (arrows).

FIG. 8. On the ADC map, active lesions in the early acute phase appear with low values (0.69
10–3 mm2/s F 0.15) (A, arrow); but in the subacute phase, high ADC values are observed (1.21
10–3 mm2/s F 0.19) (B, arrow). After contrast infusion, all active lesions in the acute phase and 66.6% in the subacute phase enhance.

typically make contact with the ependymal surface of the ventricles. The long axis of a periventricular lesion is frequently perpendicular to the long axis of the lateral ventricle because of perivenular demyelination (Dawson’s fingers) (Fig. 9).

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FIG. 9. In 1916, the Scottish pathologist James Walker Dawson characterized the distinctive ventricle‐based MS lesions as ‘‘fingers.’’ In this FLAIR axial view, ‘‘Dawson’s fingers’’ can be observed as ovoid lesions in the periventricular white matter; this configuration is attributed to the extension of the demyelinating process along straight medullary venules that run perpendicular to the ventricular walls.

The lesions in the corpus callosum are characteristically seen on the inner surface adjacent to the lateral ventricles. Posterior fossa involvement in MS typically presents with lesions in the brainstem, middle cerebellar peduncles, and cerebellar white matter. Sagittal images are superior in locating callosal and pericallosal demyelinating lesions, whereas axial images are more sensitive for posterior fossa lesions. T2‐weighted images are advantageous in that a relatively larger number of MS lesions are detected particularly in the centrum semiovale or adjacent to the ventricles. Another commonly used MRI technique for diagnosis and follow‐up of MS lesions is FLAIR, which is perhaps the most sensitive imaging sequence for observation of white matter lesions. FLAIR images overcome the diYculty of conventional T2‐weighted images in which the signal from adjacent plaques, or plaques adjacent to ventricles, can be indistinguishable. White matter lesions of MS present as hyperintense signals on FLAIR images, and their presence supports a diagnosis of MS (Fig. 10).

C. MR SPECTROSCOPY MRS provides a quantitative tool to assess the pathogenesis of MS by focusing primarily on two major pathologic features of MS: Inflammatory demyelination and neuronal/axonal loss (Zivadinov and Bakshi, 2004). In patients

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FIG. 10. FLAIR image. Multiple demyelinating lesions involving the white matter of both centrum semiovali. In one of the plaques, the relationship to a central vessel can be observed (arrow).

with MS, inflammatory demyelination on MRS is characterized by elevations of choline, lactate, and lipids, whereas neuronal and axonal losses are distinguished by decreases in the N‐acetylaspartate (NAA) peak. A decrease of NAA in the white and gray matter most likely indicates alterations of density, size, or metabolic activity of the axons (Fig. 11). Longitudinal MRS studies of MS lesions have indicated that the extent of recovery from tissue damage in the postacute phase of lesion evolution is variable (Arnold et al., 1992; De Stefano et al., 1995).

D. MAGNETIZATION TRANSFER IMAGING MTI is an advanced quantitative MR technique that can be used in the evaluation of patients with MS and is based on interaction and exchange between mobile protons in a free water pool and those bound to macromolecules; but only the mobile protons’ activity contributes to the signal intensity. During the development of demyelinating plaques, tissue damage is reflected by a decrease in this exchange of mobile and bound protons, and thus reduction in the magnetization transfer ratio (MTR). MTR is a quantitative measure enabling MTI. Indeed, MTI is a very sensitive neuroimaging technique for detecting disease activity and monitoring disease progression in patients with MS. Two general methods have been used for MTI in patients with MS. One method includes global measurement of MTR calculated voxel‐to‐voxel throughout the brain or within a particular area of the brain, and these measurements are expressed as a

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FIG. 11. Magnetic resonance spectroscopy (MRS). PRESS sequence, Short TE, 36 ms. With short echo times, some compounds such as myoinositol (myoI) and glutamate (glx) can be seen at better advantage. In this case, there is an increase in lactate (lac), a marker for anaerobic metabolism that is present in the acute stages of MS plaque development. Glutamate, a neuroexcitatory compound, is increased as is myoinositol. Increase in myoinositol is generally an indicator of glial reaction.

histogram (van Buchem et al., 1998). The other method measures changes within lesions, and the results are expressed as changes in the MTI within the region of interest. The MTR histogram is assessed by various measures. The peak height of a histogram reflects the measure of the number of pixels at the most common MTR value. The position of the peak delineates the most frequent MTR value. Preliminary studies using MTR in MS revealed that the height of the histogram peak was decreased in MS, perhaps indicating demyelination and axonal loss. Evidence obtained from animal studies supported this concept that MTR mainly reflects these two processes (Dousset et al., 1992; Kimura et al., 1996). In patients with MS, a relationship between decreased MTR, axonal loss, and the degree of demyelination has been delineated (van Waesberghe et al., 1998). A number of neuroimaging studies have investigated the structural changes of new enhancing lesions for periods ranging from 3–36 months (Dousset et al., 1998; Goodkin et al., 1998; Rocca et al., 1999). The findings of these studies indicate that, on average, the MTR declines when the lesion begins to enhance

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FIG. 12. Magnetization transfer imaging (MTI). A baseline image (A) is compared with an image obtained with an oV‐resonance pulse (B), which enhances the contribution of macromolecules. The average normal value is 46%. When the macromolecules’ pool is decreased, a low MTR value appears. The low value indicates reduced capacity of the macromolecules in the central nervous system to exchange magnetization with the surrounding water molecules reflecting damage to myelin or to the axonal membrane. In this case, the MTR value was 24.49%.

(Fig. 12). A partial or complete recovery of the MTR may occur in the next few months after the initial decline. This recovery of the MTR indicates that demyelination and remyelination are strongly involved in producing the observed MTR changes. MTI has also been applied in MS to study the pathological changes in the areas of white matter that appear normal with conventional MRI techniques. Reductions of MTR have been detected in NAWM before lesion formation (Goodkin et al., 1998; Pike et al., 2000), and these values may provide prognostic data. MTR can also be applied to determine global lesion burden by means of MTR histogram analysis (van Buchem et al., 1996). In general, patients with MS have lower average MTR values than normal controls. MTI has also been studied as a tool for objective correlation with the degree of clinical disability in patients with MS. Santos et al. (2002), in a 5‐year longitudinal study, assessed MRI data, mainly the mean MTR in gray matter, lesions identified in T2‐weighted images, in NAWM, and in a thick central brain slice. A strong significant correlation was found between MTR values measured in NAWM in clinically stable versus worsening subjects. Also reported was a strong correlation between study entry MTR values in NAWM and the subsequent 5‐year change in the expanded disability status scale (EDSS) scores.

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E. FUNCTIONAL MRI (fMRI) Because of its high magnetic susceptibility, oxygen produces a major decrease in the MR signal. When an area of the brain is used, there is an increase in blood flow/oxygen to that area. For example, if a patient is asked to move a finger, the contralateral frontal cortex will be excited with an increase in blood flow to the area. The resultant increased oxygen concentration in this region will change the MR signal, diVerentiating that area from unexcited areas of the brain. These highlighted areas are superimposed on a T2 image for localization purposes, producing a blood oxygen level dependent (BOLD) image (Fig. 13). fMRI can objectify the neuroplasticity that often masks the clinical cognitive decline in the early stages of MS. Compensatory cortical activation is observed on fMRI during cognitive tasks at the earliest stages of MS. Thus larger,

FIG. 13. Functional MRI (fMRI). BOLD images demonstrating the compensatory cortical activation pattern (i.e., plasticity occurring as a result of disease‐produced dysfunction) in patients with MS after a paced visual serial addition task: Brodmann areas 6, 8, and 9 in the right hemisphere and area 39 in the left hemisphere are activated. Healthy controls activated only area 32, part of the cingulate cortex (Courtesy of StaVen et al., 2002).

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compensatory areas are present in gray and NAWM in patients with MS as the disease progresses (Audoin et al., 2003). Because of the presence of subcortical structural damage, patients with MS, when performing a simple motor task, tend to activate regions that are usually activated in healthy control subjects during the performance of more complex tasks (Filippi et al., 2004). This is confirmed in a study demonstrating greater brain activation than controls with recruitment of additional brain areas (Mainero et al., 2004). Patients with MS exhibited greater cortical activity than did healthy controls in regions associated with sensorimotor, attentional, and executive components of verbal working memory. Reduced activity was shown in Broca’s area and bilaterally in the cerebellum (Sweet et al., 2004). BOLD time series data contain spontaneous low‐frequency fluctuations that seem to be synchronous between the right and left primary motor cortices. However, patients with MS exhibited lower functional connectivity between right‐ and left‐hemisphere primary motor cortices when compared with control subjects (Lowe et al., 2002).

F. DIFFUSION TENSOR IMAGING (DTI) Brownian movement of water molecules can be measured with an ADC map (see earlier). DiVusion in the brain is not random because of the presence of nerve fibers, molecular diVusion of water following along these nerve pathways. Measurements of similar ADC values along these fiber tracts can give an accurate representation of nerve pathways. And this technique is designated diVusion tensor imaging. The diVusive properties of an anisotropic medium can be described with a 3
3 symmetric tensor. The eigenvalues of the diVusion tensor are the diVusion coeYcients in the three principal directions of diVusivity; and the eigenvector corresponding to the largest eigenvalue is the main diVusivity direction in the medium. It is then possible to construct image maps from the main diVusivity direction and for various anisotropy indices calculated from the eigenvalues (fractional anisotropy). The anisotropy indices range from 0, in the case of completely isotropic diVusion, to 1, in the case of completely anisotropic diVusion Generally, the tensor orientation map of the principal diVusivity direction for the brain is overlaid on T2‐weighted EPI images. Standard color codes are: red ¼ left‐right, green ¼ posterior‐anterior, blue ¼ superior‐inferior. (Fig. 14). Normal white matter tracts with coherently oriented fibers show a high degree of diVusion anisotropy, whereas decreased anisotropy can be expected in white matter disease states in which myelination or axonal integrity is disrupted (Castriota‐Scandenberg et al., 2003). In NAWM by standard MRI, regions of nerve fiber breakdown can be readily depicted with DTI. Changes in diVusion tensor eigenvalues suggest axonal injury and/or dysfunction induced by Wallerian degeneration.

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FIG. 14. DiVusion tensor imaging (DTI). The tensor orientation map of the principal diVusivity for various brain structures is overlaid on a T2‐weighted EPI image. Standard color codes are: red ¼ left‐right; green ¼ posterior‐anterior; blue ¼ superior‐inferior.

Fractional anisotropy (FA) maps are useful in multiple sclerosis, lesions demonstrating increased water diVusion in otherwise NAWM. The lowest FA values are in plaques, and in ascending order in NAWM and in actual normal white matter. In a study measuring FA in MS plaques, the authors found a mean FA value of 0.251 (0.133–0.436) compared with contralateral NAWM, where the mean value was 0.429 (0.204–0.712). However, the discriminatory power of ADC values to distinguish focal nonactive lesions of diVerent T1 hypointensity may be greater than FA (Oh et al., 2004).

V. SPECT and PET Scanning

Single photon emission computed tomography (SPECT) is a relatively inexpensive and a widely available method for measuring cerebral blood flow (CBF)/perfusion, which seems to reflect cerebral metabolism. However, resolution is very low and thus usefulness in MS imaging is limited. Neuropsychiatric symptoms in MS, which cannot be localized by MRI lesion activity, have been investigated with SPECT CBF studies usually using 99Tc‐labeled ligands, with findings referable to large parts of the cerebral hemispheres or limbic system

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FIG. 15. MRI and SPECT in multiple sclerosis. (A) FLAIR sequence showing periventricular demyelination and Dawson’s fingers. (B) Axial SPECT. Reduction of cerebral blood flow in frontal and parietal cortices (arrows). (Courtesy of Asghar‐Ali et al., 2004).

(Fig. 15) (Asghar‐Ali et al., 2004; Gandelman‐Marton et al., 2003; Iniguez et al., 2000; Sabatini et al., 1996). SPECT has also been used to diVerentiate fulminant tumefactive MS variants from actual brain neoplasms (Terada and Kamata, 2003), but results here are variable, because MS lesions can often be hypermetabolic (Olivero et al., 1995). Positron emission tomography (PET), although more labor‐intensive, expensive, and complicated, gives much better resolution and can be co‐registered with the patient’s MRI for even better localization of the emitting areas of ligand uptake. The primary use of PET in MS revolves around the measurement of metabolic rate either with 15O water for cerebral blood flow or more directly with 18 F‐fluoro deoxyglucose measuring neuronal glucose uptake. By these methods patients with MS have demonstrated reduced glucose metabolism in frontal cortex and basal ganglia with clinical fatigue (Roelcke et al., 1997); hypermetabolism in acute MS plaques (Schiepers et al., 1997); and hypometabolism in frontal/ temporal cortex correlating with progressive MRI lesions in these brain areas when acute mental disorders were present (Fig. 16) (Blinkenberg et al., 1996). Patients with progressive MS lesions demonstrate reorganization of cognitive function (Santa Maria et al., 2004). Reduced metabolism in the cerebral cortex correlates with MRI T2 lesion load and the severity of cognitive disability (Blinkenberg et al., 2000), although progressive metabolic reduction in the frontal

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FIG. 16. PET scan showing reductions of cerebral glucose metabolism in a subject with MS. Global cortical cerebral metabolic rate of glucose (mol/100g/min) is listed below each PET image (Courtesy of Blinkenberg et al., 1999).

and parietal cortical areas over 2 years did not correlate with either changes in the total lesion area or the Expanded Disability Status Scale (Blinkenberg et al., 1999). In addition, hypometabolism has been localized not only to disconnected cortex as a result of adjacent white matter lesions but also in apparently uninvolved cortical and brainstem neuronal areas (Bakshi et al., 1998). With increasing resolution and new ligands, PET has been directed toward the study of MS inflammation in vivo. Initially 55Co was used as a calcium tracer based on the findings that increased Caþþ influx across neuronal membranes was activated by the inflammatory cascade when initiating apoptosis and activating T lymphocytes. By co‐registering MRI and PET, clustered uptake was seen throughout the MS brain in contrast to controls (Jansen et al., 1997), and MRI lesions tended to correspond to areas of 55Co uptake (Jansen et al., 1995). More recently, the 11C–PK11195 ligand has been developed that labels the peripheral benzodiazepine receptor. This receptor undergoes expression induction in activated microglia, the CNS macrophage, and this increased receptor expression correlates with increase in both nitrous oxide and tumor necrosis factor concentrations in vitro (Wilms et al., 2003). Moreover, in normal brain, uptake of this ligand is minimal (Debruyne et al., 2002) By use of MRI/PET co‐registration, increased binding of this ligand has been found inside plaques and in surrounding areas, as well as apparently uninvolved cerebral cortex (Banati et al., 2000) (Fig. 17). Increased uptake of this ligand has been demonstrated in enhancing MRI lesions, and general increase in ligand uptake correlates with both clinical and MRI objectified relapse. Moreover, during MS disease progression, increased uptake of 11C‐PK 11195 has been found in NAWM suggesting microglia activation in these supposedly unaVected areas (Debruyne et al., 2003).

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FIG. 17. MRI and [11C] (R)‐PK1195 PET. (A) Three orthogonal views of [11C] (R)‐PK1195 images co‐registered and overlaid on the MRI showing spinothalamic tract‐associated [11C] (R)‐ PK1195 signals extending through the brainstem and pons into the thalamus. (B–D): T1‐weighted (B) and T2‐weighted MRI (C) and [11C] (R)‐PK1195 PET overlaid onto T1‐weighted MRI (D) show lesions in all diVerent MRI sequences that partially overlap with areas of increased [11C] (R)‐PK1195 binding (red arrow). The white arrow points to a ‘‘black hole’’ in an area that appears strongly

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VI. The Neuroimaging of Neuropsychological Dysfunction in Multiple Sclerosis

Approximately 45–65% of individuals diagnosed with MS have some form of cognitive dysfunction (DeSousa et al., 2002). Cognitive dysfunction may occur at any time during the course of MS, may be accompanied by mild or severe neurological disability, and is a primary cause of disability among individuals with MS (Bagert et al., 2002). Counterintuitively, course and duration of disease do not necessarily predict cognitive functioning at the individual patient level, because patients with significant physical disability may not manifest cognitive deficits. Rao et al. (1991) reported that cognitive impairment is unrelated to depression, illness duration, disease course, or medication use, although it is weakly associated with physical disability. Cognitive deficits in MS are most frequently seen in areas of learning, memory, and information processing (Bagert et al., 2002). Less common, although still often found, are deficits in visuospatial abilities, executive functioning, and, rarely, auditory attention (Fischer, 2003). Impairment in memory functioning is one of the most reliable findings in patients with MS (Rao, 1986). However, significant variability exists between individuals with MS, with some persons demonstrating little or no impairment in memory functioning, and others evidencing significant deficits. Problems with concept formation, set‐shifting, and responding to environmental feedback are also sometimes noted, and these deficits seem to be independent of attentional, memory, or motivational factors (Rao, 1986). Neuropsychological deficits in patients with MS vary considerably, and cognitive impairment is not necessarily inevitable. However, once an individual with MS manifests a specific cognitive deficit, they are unlikely to show significant improvement in that ability, unless the decline is tied to a phase of acute exacerbation (DeSousa et al., 2002). In addition, after a demonstrable decline, the individual’s cognitive functioning in that area may remain stable without further progression over time. The most common course of cognitive functioning in MS consists of a gradual progression of cognitive deficits over time, although hypointense in the T1‐weighted MRI and has little binding of [11C] (R)‐PK1195. Note, however, that a similar ‘‘black hole’’ (yellow arrowhead) adjacent to the right occipital horn of the lateral ventricle shows significant [11C] (R)‐PK1195 binding. (E–F) Demonstration of the definition of the MRI lesion load masks. Purple T1‐weighted lesions excluding black holes; blue‐black hole only; green‐gadolinium enhancing areas; dark grey (in F), T2‐weighted lesions; red, areas of overlap between significantly increased [11C] (R)‐PK1195 binding and MRI‐defined areas of pathology; yellow, areas of increased [11C] ( R)‐PK1195 binding and no overlap with any MRI‐defined pathology. (G) Average percentage volume of the MRI‐defined lesions overlapping with increased [11C] (R)‐PK1195 binding. The red square and the red triangle represent patients in relapse at the time of scans. The yellow diamond represents a patient with secondary progressive multiple sclerosis. T1*, black holes (Courtesy of Banati et al., 2000).

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rapid cognitive decline is seen in some 25% of patients with MS (Fischer, 2003). In general, patients with progressive forms of MS more often show cognitive impairment than those with relapsing‐remitting courses.

A. MRI NEUROIMAGING TECHNIQUES Individuals with MS frequently manifest symptoms of both physical and cognitive decline. However, these two areas of disability have been shown to demonstrate diVerent relationships with neuroimaging findings. Specifically, investigating the relationship between lesion location and cognitive performance in MS, Charil et al. (2003) report an apparent divergence between lesions related to physical disability and cognitive dysfunction. These authors evaluated 452 MRI scans of patients with MS using an automated lesion‐detection algorithm. They noted that lesions within the left internal capsule and the periventricular white matter were more likely to occur in patients with physical disability. Conversely, and not unexpectedly, patients with cognitive dysfunction were more likely to demonstrate lesions at the gray–white junction of association, limbic, and prefrontal cortices. Although the physical manifestations of MS are frequently found to correlate with T2 lesion burden on MRI, cognitive deficits do not always produce the same strength of association with conventional neuroimaging techniques that delineate MRI lesion burden (Bagert et al., 2002; Comi et al., 2001; Rovaris and Filippi, 2000). However, white matter lesion burden as seen on conventional MRI correlates well with neuropsychological performance but not with physical disability. Furthermore, MRI has also been shown to correlate with the expected area of involvement given an individual’s pattern of neuropsychological test diYculties (e.g., frontal lobe), and total lesion load has been found to be a reliable predictor of cognitive dysfunction in measures of language, visuospatial problem solving, abstract/conceptual reasoning, and recent memory (Comi et al., 2001). Despite little correlation between physical disability and lesion burden, MRI imaging in MS reveals correlations between cognitive functioning and lesion burden, as well as diVuse disease of NAWM. Notably, studies assessing tissue atrophy, including whole‐brain atrophy and central atrophy (Fig. 18), report even stronger associations with cognitive impairment (Benedict et al., 2004). Likewise, more reliable and substantial correlations are noted between cognitive dysfunction and MRI, as well as MTI measures of brain atrophy (Bagert et al., 2002). By studying 23 individuals with MS in a longitudinal design over a 4‐year period, Sperling et al. (2001) demonstrated that overall lesion burden on T2‐ weighted MRI was greatest in the frontal and parietal white matter regions and was strongly negatively correlated with patient performance on measures of

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FIG. 18. Chronic multiple sclerosis. Ventricular dilation, thinning of the corpus callosum (A) and axonal loss/black holes (B), (white arrows).

sustained complex attention and working verbal memory. These authors posited that a disruption in frontoparietal subcortical networks might be responsible for the pattern of cognitive deficits typically seen in patients with MS. Piras et al. (2003) also studied the longitudinal relationship between cognitive functioning and neuroimaging in individuals with MS. These authors note that

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lesions within the temporal, occipital, and frontal white matter, as well as cortical atrophy, correlate well with performance on tasks of attention and memory at baseline. At a mean follow‐up of 8.5 years, testing revealed significant correlations between cortical atrophy and attention, and short‐term visuospatial memory. Lesions adjacent to the lateral ventricles in the frontal lobe correlated with spatial long‐term memory. Piras et al. conclude that the cognitive impairment seen in MS seems consistent with reductions in information processing seen over time. However, they note that the increase in lesions over time, as seen on MRI, did not correlate with changes in cognitive functioning or disease course over the same period. Benedict et al. (2002) investigated the correlation between regional cortical atrophy and neuropsychological test performance. Low performances correlated with greater levels of cortical atrophy in the superior frontal and parietal cortices than in other brain regions. Total lesion area and third ventricular width were significantly correlated with each neuropsychological measure. None of the MRI measures correlated with depression. However, a subject’s cortical atrophy, after controlling for total lesion area and third ventricular width, was highly correlated with neuropsychological impairment. Furthermore, the right and left superior frontal lobes were found to be the regions of cortex most susceptible to atrophic changes producing cognitive impairment.

B. MEASURES

OF

CENTRAL ATROPHY

Several studies have evaluated the ability of neuroimaging measures of central atrophy to characterize and predict neuropsychological findings in patients with MS. Authors report a reliable relationship between cognitive functioning and MR measures among 37 individuals with RRMS and secondary progressive SPMS who demonstrate at least mild cognitive impairment. Specifically, the strongest correlations were seen between central (ventricular enlargement) cerebral atrophy and cognition, and the combined product of all MR measures accounted for more than half the variance associated with cognitive performance on neuropsychological testing. The single best cognitive function correlating with neuroimaging results was a measure of complex attention (Christodoulou et al., 2003). Bicaudate ratio (BCR: The minimum intercaudate distance divided by brain width along the same line) has also been investigated as a measure of subcortical brain atrophy related to MS. Bermel et al. (2002) found that BCR was significantly higher among 60 subjects with MS than 50 matched controls, suggesting a reliable measure of subcortical atrophy in patients with MS. Furthermore, regression models selected BCR, but not total brain atrophy, T1 or T2 lesion

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volume, or caudate volume, as significantly predictive of cognitive functioning on a measure of complex attention in subjects with MS. Berg et al. (2000) studied the transverse diameter of the third ventricle, lateral ventricles, and frontal horns in 74 subjects with MS and matched controls. These authors reported that ventricular diameters were significantly larger in subjects with MS than in healthy controls. Ventricular diameters did not correlate significantly with assessed depression, although they were found to be significantly associated with all administered measures of neuropsychological functioning. Finally, only third ventricular diameter correlated significantly with disability as measured by the EDSS. Benedict et al. (2002) studied the relationship between blinded quantitative MRI analysis derived T2‐weighted lesion load and third ventricular width (3VW) and neuropsychological testing. These authors report more prominent cortical atrophy in the frontal and parietal areas compared with other brain regions in their subjects with MS. Furthermore, performance on cognitive testing was significantly correlated with both T2 lesion load and 3VW. MRI was not correlated with measures of depression. Measures of cortical atrophy within the frontal cortex bilaterally were found to be significant predictors of performance on tasks of verbal learning, spatial learning attention, and conceptual reasoning. The authors assert that the bilateral frontal cortices are most prone to atrophy in MS and that this atrophy can be used to predict neuropsychological impairment while accounting for lesion burden as well as 3VW. Benedict et al. (2004) further investigated the relationship between conventional measures of lesion burden and brain atrophy as they relate to cognitive dysfunction, while controlling for depression, premorbid intellectual functioning, and age, among 37 subjects with MS and 27 matched controls. T1 volume, FLAIR volume, 3VW, BCR, and brain parenchymal fraction (BPF) were studied against measures of neuropsychological functioning while controlling for these demographic factors. Results indicated that 3VW was the only imaging variable retained in regression analysis as having significant predictive ability regarding neuropsychological functioning. When 3VW was not included, BPF was found to be a significant predictor. These authors concluded that brain atrophy was more sensitive at predicting neuropsychological dysfunction than lesion burden and that central atrophy (3VW) was especially strongly associated with cognitive impairment.

C. ADVANCED MRI NEUROIMAGING TECHNIQUES Researchers have asserted that one of the strongest predictors of neuropsychological impairment is total lesion area. However, this relationship has been weak with conventional MRI. Advanced MRI neuroimaging techniques (e.g.,

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MTI of NAWM) may reveal better relationships between cognitive dysfunction and anatomical pathology (DeSousa et al., 2002). These newer MRI techniques are more sensitive and reliable and show promise in monitoring the course of MS (Rovaris and Filippi, 2000). These techniques demonstrate increased specificity for the pathology of MS and, therefore, improve assessment of disease evolution. Low magnetization transfer imaging is a technique sensitive to myelin or axonal membrane damage. It allows the researcher to assess the functioning of NAWM for a more accurate assessment of disease eVect and burden. Low MTI has been found to correlate well with cognitive impairment and better than routine MRI sequences when assessing physical disability (Comi et al., 2001). Zivadinov et al. (2001) studied 63 patients with RRMS and found that those individuals with and without cognitive impairment on neuropsychological testing showed no significant diVerences in lesion load as determined by T1‐weighted and T2‐weighted MRI. The only imaging variables found to correlate significantly with cognitive performance were BPF and average magnetization transfer ratio (MTR) of normal‐appearing brain tissue (NABT). The authors assert that parenchymal atrophy was not associated with the severity of microscopic damage of NABT and suggest that these two factors may be independent early in the course of MS. DiVusion tensor imaging (DTI) has also been studied for its ability to predict cognitive diYculties in MS. Rovaris et al. (2002) studied the relationship between DTI and cognitive functioning in nine individuals with RRMS. These authors note moderate correlations between values from many neuroimaging measures and performance on measures of complex attention and verbal fluency (correlations ranging from –0.30 to –0.53). However, significant correlations were not found between any of the cognitive test scores and brain volume, average lesion fractional anisotropy, or whole brain tissue fractional anisotropy. The authors conclude that DTI measures correlate well with deficits in language, attention, and memory, and further state that macroscopic lesions, as well as damage to NAWM and normal appearing gray matter, play roles in the neuropsychological deficits seen in individuals with RRMS. Researchers have further commented on restricted NAA levels as an indication of axonal damage in NAWM of individuals with RRMS (Gadea et al., 2004). Gadea et al. studied samples of NAA normalized to creatine (Cr) from the locus ceruleus nucleus of the pontine ascending reticular activating system and proton MRS of the right and left hemipons of 19 individuals with RRMS soon after diagnosis and manifesting only mild disability. Roughly 47% of the subjects demonstrated impaired attention. Pontine NAA/Cr levels were found to account for 39% of the variability associated with these attentional diYculties but did not correlate with an index of cerebral lateralization. The authors concluded that because patients with RRMS with the lowest scores on attentional measures

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demonstrated the lowest NAA/Cr levels, restricted NAA/Cr likely indicates pathological alterations associated with cognitive dysfunction. Researchers have also investigated the association between lateralizing cognitive performance and NAA levels in subjects with MS. Pan et al. (2001) report that NAA levels in the left periventricular region determined by MRS correlated significantly with verbal memory and in the right periventricular region with performance on a visual problem‐solving task. Mainero et al. (2004) investigated attentional and memory functioning in relation to fMRI activation patterns. The authors studied 22 subjects with RRMS with minimal to no impairment on neuropsychological testing and 22 matched controls with a measure of mental processing eYciency and a memory recall task. They found that during both measures, subjects with MS demonstrated significantly greater brain activation than did controls and that subjects with MS recruited additional brain regions. This finding was more striking among MS patients with performances approximating that of controls than for those with lower scores. These findings were interpreted as suggesting adaptive mechanisms exhibited by subjects with MS that may account for the underlying neural disorganization or disinhibition associated with MS.

D. PSYCHOSOCIAL MANIFESTATIONS Impairment of neuropsychological functioning has been reported to be a major predictor of reduced quality of life, joblessness, and caregiver distress in individuals with MS (Benedict et al., 2004). Neuroimaging studies have also been used to assess the eVects of MS on the quality of life, bladder and sexual functioning, and emotional health of individuals, and it is to this area that we now turn. It has been noted that patients with MS begin manifesting clinical impairment and disease progression after they first experience a critical level of brain atrophy. Furthermore, researchers have reported associations between brain atrophy and psychosocial functioning in MS, and reduced quality of life has been associated with brain atrophy. Janardhan and Bakshi (2000) studied 60 consecutive subjects with MS. These authors reported that self‐reported quality of life is related to MRI brain lesions and atrophy. Specifically, they note that quality of life was more impaired among SPMS than RRMS subjects and that correlations between quality of life were much greater for brain atrophy and hypointense lesions on T1‐weighted measurements, than hyperintense lesions on T2‐weighted measurements. Moreover, the relationship between quality of life and contrast‐enhanced images was not found to be significant. In contrast, the symptom of fatigue so commonly seen in MS does not seem to show the same strength of association with neuroimaging results.

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Impaired urodynamic and sexual functioning are also frequently found among individuals with MS. They exert a significant negative influence on quality of life and have also been associated with brain atrophy (Zivadinov and Bakshi, 2004a). Specifically, bowel and bladder dysfunction have been found to correlate with lesions within the medial frontal lobes, cerebellum, insula, dorsal midbrain, and the pons, all areas known to be associated with the control of these functions (Charil et al., 2003). In addition, Zivadinov et al. (2003) note that pontine lesion load on T1‐ weighted MRI was found to be associated with impaired sexual functioning among 31 subjects with RRMS, although no other neuroimaging variable produced a significant correlation. Likewise, Zorzon et al. (2003) found no diVerences in total brain, frontal, and pontine T1 lesion load or on measures of whole‐brain or frontal atrophy between subjects with MS with and without sexual dysfunction. The only imaging measure diVering significantly between these two groups was pontine BPF. Finally, individuals with MS frequently manifest symptoms of mood disorder and depression (Benedict et al., 2004). Less frequently, individuals with MS evidence symptoms of euphoria and pathological laughter or crying. Both lesion burden and atrophy have been reported as correlating with a major depressive disorder, although stronger associations are reported for lesion burden. Brain atrophy also seems to correlate well with observed euphoria and disinhibition, although lesion burden shows some association with euphoria as well. The authors suggest that euphoria and disinhibition in MS represent the eVects of disease progression with its associated worsening cognition and personality changes. References

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Further Readings

Davie, C. A., Hawkins, C. P., Barker, G. J., Brennan, A., Tofts, P. S., Miller, D. H., and McDonald, W. I. (1994). Serial proton magnetic resonance spectroscopy in acute multiple sclerosis lesions. Brain 117 (Pt 1), 49–58. Kuhlmann, T., Lucchinetti, C., and Zettl, U. K. (1999). Bcl‐2‐expressing oligodendrocytes in multiple sclerosis lesions. Glia. 28, 34–39. Minagar, A., and Alexander, J. S. (2003b). Blood‐brain barrier disruption in multiple sclerosis. Mult. Scler. 9, 540–549. Tan, I. L., van Schijndel, R. A., Pouwels, P. J., van Walderveen, M. A., Reichenbach, J. R., Manoliu, R. A., and Barkhof, F. (2000). MR venography of multiple sclerosis. AJNR Am. J. Neuroradiol. 21, 1039–1042. Trapp, B. D., Peterson, J., RansohoV, R. M., Rudick, R., Mork, S., and Bo, L. (1998). Axonal transection in the lesions of multiple sclerosis. N. Engl. J. Med. 338, 278–285.

STROKE

Roger E. Kelley* and Eduardo Gonzalez-Toledoy *Department of Neurology and Department of Radiology, Louisiana State University Health Sciences Center Shreveport, Louisiana 71103

y

I. Introduction II. Brain Scan in Acute Stroke A. Indications and Interpretation B. Evaluation of Stroke Mechanism C. Reasons for a Follow-up Brain Scan III. Clinical Utility of MRI Brain Scan in Acute Stroke A. Overview B. Newer MR Techniques in Acute Stroke IV. MRI Versus CT Brain Scan in Acute Stroke V. Prognostic and Outcome Information Provided by Routine and Functional Brain Scan A. Acute Ischemic Stroke B. Hemorrhagic Stroke VI. Vascular Imaging A. Occlusive Cerebrovascular Disease B. Cerebral Aneurysms and Arteriovenous Malformations References

Significant developments have occurred in the imaging of acute stroke. First and foremost, one must distinguish primary ischemic from primary hemorrhagic stroke. This is the first tier of the mechanistic approach. For primary ischemic stroke, one must try to sort out embolic from thrombotic or thromboembolic. Various potential mechanisms have to be addressed in terms of mechanism, including hemodynamic-mediated watershed-type infarcts, sinovenous occlusive disease, vasculopathies such as primary central nervous system vasculitis, connective tissue disorders, infectious processes such as syphilis and idiopathic occlusive processes such as moyamoya disease, and vascular dissection. Primary hemorrhagic stroke can include the most common, hypertensive intracerebral hemorrhage, as well as intracerebral bleeding secondary to aneurysms or arteriovenous malformations. There has been a tremendous refinement in the approach toward both primary ischemic and primary hemorrhagic stroke, because newer imaging techniques can provide an in vivo glimpse of metabolic and blood flow status and allow determination of potential salvageable brain tissue. Several noninvasive vascular imaging techniques allow ready evaluation of the integrity of the cerebral vasculature in patients with stroke. Such information can INTERNATIONAL REVIEW OF NEUROBIOLOGY, VOL. 67 DOI: 10.1016/S0074-7742(05)67007-9

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be important in assessing response to interventional therapy and providing prognostic information in terms of functional outcome. However, one must sort through the true value of such newer techniques to prevent unacceptable costs for imaging that do not have an impact on patient management.

I. Introduction

The use of neuroimaging in stroke is vitally important for eVective patient management. The CT brain scan, usually with no contrast enhancement necessary, remains the neuroimaging procedure of first choice at most medical centers. The CT brain scan generally allows ready diVerentiation of primary ischemic from hemorrhagic stroke. It can also provide mechanistic information in terms of the most likely explanation for the vascular insult. For example, the demonstration of multiple infarcts in diVerent vascular territories is most compatible with a cardiogenic embolic mechanism. Acute involvement of the ipsilateral middle cerebral artery (MCA) and anterior cerebral artery (ACA) territories implies internal carotid artery (ICA) occlusion on that same side. The presence of a ‘‘hyperdense’’ MCA, usually in the setting of an acute MCA distribution infarction, implies an acute thrombosis of the vessel (Fig. 1). The CT brain scan can readily identify intracerebral hemorrhage (ICH) in most circumstances. However, its sensitivity for the detection of subarachnoid hemorrhage (SAH) is no greater than 90–95% within the first 24 hours of the ictus and progressively

FIG. 1. Noncontrast CT brain scan demonstrating ‘‘hyperdense MCA sign’’ reflecting acute right middle cerebral artery thrombosis (arrow).

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diminishes beyond this timeframe (Adams et al., 1983; Vermeulen and van Gijn, 1990). A potential limitation of the CT brain scan is its early normality in acute ischemic stroke and limited sensitivity for the detection of ischemic stroke in the brainstem and cerebellum. However, it is important to point out that neuroimaging does not replace the clinical history or the general physical examination or neurological examination in acute stroke. Careful attention to the time of presentation; premonitory symptoms; the symptoms and signs at the time of presentation; medical history, including risk factors for stroke, family history, social history, the vital signs, the neurovascular examination, cardiac auscultation, evaluation for rash, and other manifestations of systemic disease; and a complete neurological examination are mandatory for eVective evaluation and management of the patient. Unfortunately, much of this is curtailed by necessity when evaluating the patient for potential use of thrombolytic therapy where the time window for initiation of therapy remains within 3 hours of onset of symptoms except for medical centers where special protocols are in place in an attempt to extend this window of treatment. MR imaging has special advantages over CT brain scan in certain circumstances. It is now well established that diVusionweighted MR can detect early ischemic changes within several hours of presentation (Singer et al., 1998), whereas the CT brain scan often does not reveal ischemic changes for 6–12 hours from the time of onset. Furthermore, diVusionweighted MR is capable of detecting reversible ischemic changes (Zivin, 1997), where therapeutic intervention has the greatest chance for success. When coupled with perfusion imaging, the so-called perfusion-diVusion mismatch can allow detection of impaired perfusion of brain tissue that is susceptible to infarction if perfusion of this viable tissue is not restored within a finite period of time (Kidwell et al., 2000). This viable, but at-risk, tissue has been termed the ischemic penumbra (Read et al., 1998). The ischemic penumbra is a source of great interest for both thrombolytic therapy and neuroprotective therapy (Baron, 1999), because the smaller the tissue loss associated with cerebral infarction, the greater the prognosis for a good recovery. Newer MR techniques, such as susceptibilityweighted imaging, oVer the potential for MR to readily distinguish primary ischemia from primary hemorrhage in the acute setting and also hold promise for the detection of intravascular clots (Hermier and Nighoghossian, 2004). MR spectroscopy may also have potential in the assessment of brain tissue viability in cerebrovascular disease (Giroud et al., 1999). Vascular imaging used to consist of routine digital-subtraction intraarterial (DSA) angiography. For a period, there was great interest in intravenous DSA, but the images were never of such consistent quality that they could accurately replace intraarterial studies. However, with the advent of MR angiography (MRA), as well as spiral CT angiography (CTA), we now have a noninvasive means of assessing for occlusive cerebrovascular disease related to atherosclerosis,

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vascular anomalies such as aneurysm and arteriovenous malformation (AVM), vascular dissection, cerebral vasculitis, sinovenous occlusive disease, specifically with magnetic resonance venography (MRV), and more esoteric causes of stroke such as moyamoya disease (Lee et al., 2004). However, these vascular imaging techniques have not necessarily replaced duplex ultrasonography of the extracranial vessels or transcranial Doppler ultrasonography (TCD) of the intracranial vessels in assessing for such entities as ICA stenosis, vasospasm associated with aneurysmal SAH, or occlusive intracranial vascular disease associated with sickle cell anemia (Moritani et al., 2004). When discussing neuroimaging in stroke, especially in an increasing environment of cost-consciousness in medicine, it is important for the clinician to ask how the particular neuroimaging modality will impact on management of the patient. One does not need to see a well-defined infarction pattern on a brain scan to diagnose stroke in most instances. For example, a right lateral medullary infarct that manifests as a typical Wallenberg’s syndrome should be deduced on the basis of clinical features and an admission CT brain scan compatible with an infarction by either being negative or by demonstrating a low density in the appropriate vascular territory involved. If demonstration of a right vertebral artery stenosis, dissection, or occlusion, or occlusive disease of the right posterior inferior cerebellar artery will aVect management, a vascular imaging study, such as an MRA, CTA, or routine DSA, will be justified and indicated. However, one has to question the practicality of additional imaging, such as an MRI brain scan, to support the clinician’s acumen or sense of self-worth if it will in no way impact on patient care.

II. Brain Scan in Acute Stroke

A. INDICATIONS

AND INTERPRETATION

The CT brain scan, especially when performed without contrast, remains the most cost-eVective and clinically relevant neuroimaging study in most patients with acute stroke. It is mandatory to rule out the possibility of an alternative explanation for the clinical presentation. For example, a mass-producing lesion, such as a brain tumor, abscess, or subdural hematoma, can present with the sudden onset of focal neurological deficit, including transitory symptoms that can mimic transient ischemic attack (TIA) It is also possible to see contrast enhancement in patients with TIA-like symptoms, and this may correlate with an embolic mechanism of the TIA and more severe symptoms (Kimura et al., 2000). However, one has to be careful about the use of contrast in completed infarction, because this might mimic a mass lesion, such as a tumor (Elster and Moody,

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1990). When there is clinical suspicion for a mass-producing lesion, such as metastatic disease, then contrast enhancement is clearly indicated, as long as there is no contraindication to its use. Because of the potential for a severe allergic reaction or renal toxicity, non-iodine–based contrast with MRI is often the preferred study. In a study of 300 patients seen with TIA or minor stroke 10–23 days from symptom onset (Schulz et al., 2004), DWI provided potentially useful information in terms of patient management in 14% of cases.

B. EVALUATION

OF

STROKE MECHANISM

1. Ischemic Patterns The purpose of the initial brain scan is primarily to diVerentiate primary ischemia from primary hemorrhage, and this remains the primary advantage that acute CT brain scan has over acute MRI. However, the CT brain scan can help in a number of potential ways in determining the mechanism of the vascular insult (Table I). This is in terms of both size and location. For example, a small subcortical infarction (Fig. 2) is most like lacunar type in nature. This implies a small vessel, hypertensive-mediated, occlusive process in most circumstances, possibly related to breakdown of the blood–brain barrier (Wardlaw et al., 2003). This type of infarction pattern is associated with lipohyalinosis of penetrating arteries, usually in the setting of longstanding hypertension. Such an infarction

TABLE I IDENTIFICATION OF POTENTIAL STROKE MECHANISMS BY CT BRAIN SCAN 1. DiVerentiation of primary ischemic versus primary hemorrhagic stroke. 2. Identification of subcortical versus cortical location. 3. Identification of multiple cerebrovascular territory involvement supportive of an embolic mechanism. 4. Identification of hemorrhagic transformation of an ischemic infarct by follow-up CT brain scan that tends to support an embolic mechanism. 5. Identification of a borderzone (watershed) territorial distribution that could be related to hypoperfusion, especially if the presentation is associated with a significant drop in the blood pressure. 6. Finding of a ‘‘hyperdense’’ middle cerebral artery compatible with acute clot formation. 7. Identification of larger intracerebral aneurysms in association with subarachnoid blood. 8. Identification of serpiginous vessels on contrast-enhanced study suggestive of a vascular malformation. 9. Identification of small, usually
1  1 cm, infarction in the capsular region, basal ganglia, or brainstem supportive of lacunar-type stroke. 10. Identification of combined anterior and middle cerebral artery infarction supportive of ipsilateral carotid artery occlusion.

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FIG. 2. T2-weighted MRI brain scan demonstrating a discrete area of increased signal intensity (arrow) representing a lacunar-type infarction aVecting the right thalamus.

pattern does not necessarily indicate the need for evaluation of carotid stenosis, a cardiogenic source of embolus, or a hypercoagulable state (Boiten and Lodder, 1991). It is quite possible that the CT brain scan will be negative in the acute setting, and MRI can be a more sensitive study if neuroimaging confirmation is indicated (von Kummer et al., 2000; Warach et al., 1996). Fairly characteristic clinical syndromes are associated with lacunar-type infarct, such as pure motor hemiparesis, pure sensory stroke, subcortical sensorimotor stroke, clumsy handdysarthria, and hemiparesis-hemiataxia. It is important to emphasize that lacunar-type stroke is subcortical, or brainstem, in location, so the presence of cortical deficit with the stroke (e.g., aphasia, apraxia, agnosia) is not compatible with such an infarction pattern. However, diVerent mechanisms may play a role depending on the size of the infarction (Arauz et al., 2003), and a recent diVusion/ perfusion MRI study raised questions about how reliably lacunar-type infarcts can be diagnosed on clinical grounds (Gerraty et al., 2002). Multiple cerebral infarcts in diVerent vascular territories (Fig. 3) raise suspicion of a cardiogenic source of embolism. Other clues to a potential embolic mechanism include sudden maximal neurological deficit at the onset, vessel branch occlusion seen by vessel imaging, enhanced potential for associated seizure at onset, perhaps an enhanced potential for significant headache at onset, a reported increased risk of hemorrhagic transformation of the infarct (Fig. 4), and it has been reported that embolic stroke is less likely to be associated with premonitory TIA. Of interest, in terms of TIA mechanism, abnormalities on DWI in patients initially seen with transient symptoms of stroke are associated

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FIG. 3. T2-weighted MRI brain scan that demonstrates infarction of the right (and left) anterior cerebral artery, middle cerebral artery, and middle cerebral artery–posterior cerebral artery (watershed) distributions (arrows). This reflects multiple vascular territory involvement that is highly suspicious for a cardiogenic source of embolism.

FIG. 4. A noncontrast CT brain scan that demonstrates increased density (arrows) in the right subcortical location of a previously documented acute infarction. This represents spontaneous hemorrhagic transformation.

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with prolonged duration of the TIA and disturbance of higher brain function (Inatomi et al., 2004). This supports the concept that DWI provides a more sensitive indicator of the ongoing pathogenesis, and such changes are seen in approximately 50% of patients with clinically defined TIA (Kidwell et al., 1999). In selected cases, it may be worthwhile to assess hemodynamic status with a technique such as ictal and postacetazolamide SPECT scan (Han et al., 2004). The vascular territory involvement, with cerebral ischemia, can provide additional useful information in terms of potential mechanism. For example, a so-called watershed or borderzone infarction pattern (e.g., overlap of the ACA and MCA territorial supply or the MCA and posterior cerebral artery (PCA) territorial supply [Fig. 5]) suggests a possible hemodynamic mechanism. This might be seen with cardiovascular collapse in which the watershed regions are the most susceptible to the resultant cerebral hypoperfusion. However, the mechanism might also include proximal vascular supply sudden occlusion, a hypercoagulable state, or embolism (Wong et al., 2002). This is just one illustration of why it is so important to correlate the neuroimaging findings with the clinical picture. For example, watershed-type infarction after successful resuscitation of cardiac arrest would be most likely hemodynamic in mechanism. Additional factors could well come into play in terms of the location, size, and clinical sequelae of the cerebrovascular insult. In other words, the extent of associated diVuse cerebral hypoxia or coexistent carotid stenosis, the integrity of the circle of Willis, the capacity of the leptomeningeal collaterals, etc. could all have a potential contribution to the stroke pattern for a particular patient.

FIG. 5. Fluid attenuation inversion recovery (FLAIR) MRI of an infarction involving the borderzone (watershed) between the right middle cerebral artery and right posterior cerebral artery (arrow).

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There is also the potential for routine CT brain scan to provide information about thrombotic or thromboembolic occlusion. For example, acute involvement of the ipsilateral ACA and MCA territories implies severe carotid occlusive disease on the same side. The increased density of the MCA associated with an acute MCA distribution infarction pattern, commonly referred to as the hyperdense MCA sign (Fig. 1), can reflect acute thrombosis of the vessel. Such information might be particularly useful for making a decision about the advisability of thrombolytic therapy. Not unexpectedly, thrombolytic therapy would be expected to be most appropriate when there is an actual clot to lyse (Manelfe et al., 1999). The more the infarct has evolved on admission CT brain scan, the greater the likelihood of seeing hemorrhagic transformation as a potential complication of thrombolytic therapy on follow-up CT scan. Early CT changes that can impact on the decision-making process for thrombolytic therapy include evolution of low density representing actual tissue infarction, sulcal eVacement with loss of sulcal markings as an indicator of an evolving infarction pattern, and loss of gray-white matter demarcation. Of particular concern, in respect to hemorrhagic transformation, is early evidence of large infarction, either clinically and/or radiographically, and one particularly worrisome finding is early ischemic change of greater than two thirds of a particular MCA distribution. Monitoring of disease progression can be an important aspect of neuroimaging, especially in progressive occlusive cerebrovascular disease. In moyamoya disease, characterized by distal ICA and basal collateral circulation stenosis and occlusion, MR perfusion imaging can identify susceptible major arterial border zone regions (Calamante et al., 2001). Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is a genetically transmitted disorder that is characterized by progressive white matter disease on MRI brain scan (Chabriat et al., 1998). It is associated with mutations of Notch 3 gene on chromosome 19 and is characterized by recurrent strokelike symptoms with cumulative neurological deficit over time, including cognitive impairment. DWI has been reported to be a sensitive marker of disease progression in this disorder (Molko et al., 2002). Chu et al. (1998) reported on the sensitivity and specificity of MRI in cerebral vasculitis and reported a positive predictive value of between 43 and 72%, but the findings were often nonspecific. One study suggested that serial MRI could be useful for monitoring response to therapy in this disorder (Ehsan et al., 1995). 2. Hemorrhagic Patterns The location of ICH on the CT brain scan can be helpful from a mechanistic standpoint. For example, a basal ganglionic, thalamic, pontine, or cerebellar hematoma in a patient with well-documented longstanding hypertension, especially that which is poorly controlled, is by far most likely related to cumulative hypertensive vascular damage with secondary rupture of the involved vessel

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FIG. 6. (A) Right thalamic hematoma (arrow) that has extended into the ventricular system with secondary hydrocephalus. (B) The location of the hematoma supports a hypertensive mechanism, and the size and ventricular extension generally translate into a poorer prognosis.

(Fig. 6). On the other hand, lobar hematoma is only directly attributable to hypertension in approximately 50% of patients, whereas other not uncommon etiologies can include vascular anomaly, hemorrhagic tumor, bleeding diatheses, and cerebral amyloid (congophilic) angiopathy. The CT brain scan also provides important prognostic information in hypertensive ICH, because the larger the hematoma, the worse the prognosis, and this can be the most important predictor of prognosis along with the degree of neurological deficit at the time of presentation. SAH related to aneurysmal rupture is often detectable by noncontrast CT brain scan (Fig. 7), but the sensitivity is not 100%. It is also possible, on occasion, to actually image the cerebral aneurysm on CT brain scan. However, a negative CT brain scan does not fully rule out an aneurysmal rupture and, when there is clinical suspicion of this possibility, a lumbar puncture becomes mandatory.

C. REASONS

FOR A

FOLLOW-UP BRAIN SCAN

Several potential reasons exist for a possible follow-up CT or MRI brain scan, outside of clinical interest (Table II). If there is some question about the true etiology of the neurological deficit, a CT brain scan performed 12 hours or later after the onset of symptoms is more likely to reveal an infarction pattern. A follow-up scan is most pertinent if there are concerns about hemorrhagic

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FIG. 7. Prominent subarachnoid increased density on a CT brain scan reflecting an aneurysmal rupture involving multiple cisterns.

TABLE II POTENTIAL USEFULNESS OF FOLLOW-UP CT BRAIN SCAN 1. Clinical worsening in acute ischemic stroke to assess for extension of an infarct, increasing mass eVect, or hemorrhagic transformation of an infarct. 2. Clinical worsening in intracerebral hemorrhage to assess for intraventricular extension, with secondary obstructive hydrocephalus, or expansion of the intraparenchymal hematoma. 3. Clinical worsening in aneurysmal subarachnoid hemorrhage to assess for rebleeding, vasospasm-induced cerebral ischemia, obstructive or communicating hydrocephalus, associated intraparenchymal or subdural bleeding, or diVuse cerebral edema. 4. Documentation of an infarction pattern when there is an initially negative CT brain scan and the clinical presentation is atypical. 5. Routine performance 24 hours after the administration of thrombolytic therapy to assess for possible intracerebral hemorrhage.

transformation of a cerebral infarct, especially in the setting of thrombolytic or anticoagulant therapy. However, once the clinical decision has been made about the use of antithrombotic therapy, one has to question the advisability of a followup CT brain scan if the patient is neurologically stable. The reason for this is that it is not uncommon to see some degree of hemorrhagic transformation on followup CT brain scan without a clinical correlate. Thus, the performance of a CT brain scan may potentially provide a clinical dilemma that does not necessarily

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have to be addressed in the first place. Furthermore, anticoagulant therapy, when clearly clinically indicated, may need to be given even in the setting of cerebral hemorrhage on CT brain scan. Clinical worsening after stroke routinely calls for the performance of a followup brain scan to assess for the explanation. The choice between CT brain scan and MRI brain scan depends on the clinical situation. However, in most instances, the major concern is possible hemorrhagic transformation of a cerebral infarction, rebleeding related to a cerebral aneurysm or AVM, or expansion of an ICH. In such circumstances, CT scan is more readily identified as the neuroimaging method of choice at most medical centers, except, perhaps, for those with access to newer MR techniques that can detect early hemorrhage (Hermier and Nighoghossian, 2004). Furthermore, edematous changes, related to either cytotoxic versus vasogenic edema, are better demonstrated and better delineated with MRI (Schaefer et al., 1997). In addition, infarction patterns in the vertebrobasilar distribution are much better demonstrated by MRI, and this would be the preferred modality for clinical deterioration in such a setting. However, CT brain scan in the acute setting is reported not only to be cost-eVective but also to improve the quality of life (Wardlaw et al., 2004). Prognostic information can be improved when unenhanced CT brain scan is combined with CTA source images (Coutts et al., 2004).

III. Clinical Utility of MRI Brain Scan in Acute Stroke

A. OVERVIEW The standard initial MR image requested for acute stroke is the T2-weighted image with proton density imaging as the second choice. Only later are intensity changes detected by T1-weighted imaging. However, the increased signal intensity, reflective of cerebral ischemia, may take hours, or longer, to develop on T2-weighted and proton density MR imaging. Other than enhanced sensitivity, the MRI had no clear advantage over CT brain scan in acute stroke management, especially when one factors in the inability of standard MRI to detect acute brain hemorrhage. In the subacute stage of brain hemorrhage, one can see evolution of increased signal intensity on both the T1- and T2-weighted MRI scans. Hemorrhagic infarcts may be demonstrated by a mixed pattern of signal intensity changes on the T1- and T2-weighted images (Fig. 8), depending on the evolution of the vascular insult. DiVusion-weighted (MR) imaging (DWI) allows assessment of the transitional mobility of water in tissue. The degree of transitional mobility can be quantified by the apparent diVusion coeYcient (ADC) (Neumann-Haeflin et al., 2000).

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FIG. 8. T1-weighted (A) and T2-weighted (B) MRI brain scan that demonstrates a left posterior parietal hemorrhagic infarction. There is subtle low density on the T1 image (arrow) and mixed density on the T2-weighted image with hyperintensity on the periphery and hypodensity in the core.

FIG. 9. DiVusion-weighted MRI brain scan demonstrating an acute large posterior cerebral artery infarction pattern (arrow).

Assessment of diVusion can be provided by DWI or by calculated maps of the ADC. Rapid diVusing medium, such as CSF, appears dark on DWI, whereas ischemic tissue, associated with reduced diVusion, appears bright (Fig. 9). Perfusion-weighted (MR) imaging (PWI) is based on bolus tracking of dynamic

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susceptibility contrast-enhanced MRI (Beauchamp et al., 1999). Time-to-peak (TTP) delay maps are calculated as time delay from the peak of the arterial input to the maximum of Si (t), which is the signal intensity at the time point (t) after injection of the contrast agent. The evolution of cerebral infarction by early DWI can be detected within minutes (Yoneda et al., 1999). However, the correlation with final infarction is variable, and this limits the value of this study in acute stroke (Thijs et al., 2001). A mismatch between the baseline perfusion deficit by PWI and the lesion volume by baseline DWI (PWI/DWI mismatch) can allow detection of potentially reversible ischemic damage (Beaulieu et al., 1999). One can derive cerebral blood flow (CBF) from the tissue-concentration curve as a function of the arterial input. Cerebral blood volume (CBV) can be derived from integration of the tissue concentration-time curve between the time of bolus arrival and the time when the tissue concentration of the tissue of interest returns to baseline (Parsons et al., 2001). The relationship of CBV to CBF allows calculation of the mean transit time (MTT). MTT-derived images of initial perfusion deficit can be correlated with the DWI to predict final infarction volume (Parsons et al., 2001). In a study of the relationship between PWI/DWI mismatch in acute ischemic stroke (Thijs et al., 2001) in patients who did not have rapid clinical improvement, the enlargement of the initial DWI lesion correlated with the severity of initial perfusion deficit measured by the MTT and the CBV. Staroselskaya et al. (2001) reported that a perfusion–diVusion mismatch was observed in 22 of 24 patients (92%) during the first 24 hours of presentation of acute ischemic stroke, and this correlated with arterial occlusive disease seen on MRA. Despite limitations in predicting final infarct volume, early DWI enhances the sensitivity of stroke detection, by MRI, within the first 24 hours (Keir and Wardlaw, 2000). The detection of multiple acute cerebral infarcts by DWI can identify patients at significant risk of recurrent stroke (Wen et al., 2004). When combined with MRA, it is reported to be a powerful predictor of early neurological deterioration (Arenillas et al., 2002). In a study of DWI in the detection of Trial of ORG 10172 in the Acute Stroke Treatment (TOAST) classification of ischemic stroke (Kang et al., 2003), patterns of infarction were applied to distinguish cardioembolic, large-artery atherosclerosis and small-vessel occlusion (lacunar-type) stroke. Cortico-subcortical single infarcts ( p ¼ 0.01), multiple infarcts in anterior and posterior circulations ( p ¼ 0.03), and multiple infarcts in multiple cerebral circulations ( p ¼ 0.008) were attributed to cardioembolic disease. Multiple infarcts in the unilateral anterior circulation ( p ¼ 0.04) and small, scattered infarcts in one vascular territory ( p ¼ 0.06) were attributed to large-artery atherosclerosis. However, application of the conventional 15-mm criteria for lacunar-type infarction led to apparent underdiagnosis of this stroke subtype. In a study of DWI and PWI in 19 patients with lacunar-type stroke (Gerraty et al., 2002), large artery embolism, as opposed to clinically suspected

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small perforating artery occlusion, was arrived at in 13 of the patients after correlating the results on MRI.

B. NEWER MR TECHNIQUES

IN

ACUTE STROKE

Newer MR imaging techniques are summarized in Table III. Susceptibilityweighted imaging (SWI) allows assessment of magnetic susceptibility eVects of local inhomogeneities of the magnetic field (Hermier and Nighoghossian, 2004). This technique can take advantage of the paramagnetic properties of deoxyhemoglobin that promote signal changes in acute brain hemorrhage and intravascular spontaneous blood-oxygen level–dependent (BOLD) eVect (Ogawa et al., 1990). SWI is reported to allow detection of ICH within 2 hours of symptom onset (Linfante et al., 1999), as well as detection of acute intravascular clot formation (Flacke et al., 2000). Echo-planar T*-weighted MRI is reported to detect cerebral venous thrombosis (Selim et al., 2002). MRI measurement of oxyhemoglobin to deoxyhemoglobin, in the capillary and venous compartments, allows measurement of the oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO2). CBF measurement, derived from MRI bolus tracking, compares favorably with information derived from [15O]H2O PET (Ostergaard et al., 1999). MRI-based derivation of CBF, CMRO2, and OEF allows noninvasive assessment of tissue viability (Sorensen et al., 1999). Functional MRI (fMRI) that uses the BOLD technique allows measurement of signal intensity changes associated with focal increased demands for tissue perfusion with brain activation. This may allow assessment of functional reserve after cerebral ischemic insult. In a study of BOLD fMRI in subcortical stroke (Pineiro et al., 2002), reduction in the activation response with hand movement, compared with controls, was seen in both the aVected and unaVected cerebral

TABLE III POTENTIAL USEFULNESS OF NEWER MR TECHNIQUES IN ACUTE STROKE Technique

Potential utility

Susceptibility-weighted imaging (SWI) Functional MRI with BOLD technique DiVusion tensor-MRI Magnetic resonance spectroscopy (MRS) Magnetization transfer MRI (MT-MRI)

Detection of acute hemorrhage, detection of intravascular clot, evaluation of tissue viability Assessment of functional reserve after cerebral ischemic insult Assessment of main fiber bundles and tissue integrity Assessment of metabolic status and viability of cerebral tissue at risk Determination of the age of an infarct and functional status

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hemisphere, suggesting that such assessment does not have localizing value. The authors concluded that the bihemispheric findings could be reflective of preexisting microvasculature compromise. However, this study does raise questions about whether such functional assessment could provide information about potential for recovery from the stroke. Kato et al. (2002) compared fMRI with near-infrared spectroscopy (NIRS). The latter technique has the potential to noninvasively monitor oxygen availability in the brain, and this correlates with neural activity. In this study of motor recovery from hemispheric stroke, fMRI and NIRS both allowed detection of activation of the ipsilateral motor cortex, and it was thought that such information might, theoretically, allow monitoring of motor reorganization as part of the recovery process. TCD may also provide a noninvasive means of assessing cerebral blood flow velocity changes as a reflection of functional recovery from stroke with simultaneous assessment of both the involved and uninvolved hemisphere (Silvestrini et al., 1995). A diVusion tensor model has been introduced to diVusion imaging, and this has been termed diVusion tensor MRI (DT-MRI) (Davis and Tuch, 2002). On the basis of mathematical-derived modeling of diVusion anisotropy, this newer form of imaging allows visualization of main fiber bundles, such as the corticospinal tracts, with information about tissue integrity (Horsfield and Jones, 2002). In a study by Lie et al. (2004), color-coded DT-MRI identified five diVerent patterns of corticospinal tract stroke with a spectrum of associated neurological deficits. Magnetic resonance spectroscopy (MRS) allows in vivo assessment of the chemical status of tissue (Fig. 10). To date, MRS has provided limited information in acute cerebral infarction such as that provided by 31P MRS in the evaluation of energy metabolism (Levine et al., 1992). 1H MRS has also been of

FIG. 10. Magnetic resonance spectroscopy that allows biochemical evaluation in acute stroke. Lac, lactate; NAA, N-acetyl aspartate; Cr, creatine; Cho, choline; Glx, glutamate; mI, myoinositol. An increase in lactate is seen in early cerebral ischemia stages. A reduction in the choline peak reflects impaired metabolism, whereas one tends to see a high choline peak in lesions associated with increased metabolism, such as tumors.

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limited use in the assessment of metabolic status and clinical outcome in acute infarction (Wardlaw et al., 1998). However, in a study of proton MRS in acute ischemic stroke (Graham et al., 1995), the lactate signal within the lesion correlated with lesion volume, clinical deficit, and SPECT score. A lesser correlation was seen with the N-acetyl level and the Barthel Index and lesion volume. In a study of MRS in patients with TIA in association with carotid artery occlusion (Bakker et al., 2003), lactate detection correlated with cognitive impairment, whereas mean CO2, as measured by TCD, and the ratio of N-acetyl aspartate (NAA) to creatine did not. In a study correlating MRI/MRA findings with MRS in TIA (Bisschops et al., 2002), the NAA/choline ratio in noninfarcted regions was significantly decreased in the symptomatic hemisphere compared with the asymptomatic hemisphere and control subjects. However, the lactate/NAA ratio was significantly increased in the symptomatic hemisphere compared with controls. Patients with a prior history of TIA had significant reduction in the NAA/choline ratio in both hemispheres compared with patients without a prior history of TIA. Metabolic changes associated with acute TIA persisted for up to 3 days from the time of the event supportive of the concept of ‘‘tissue at risk’’ (i.e., patients initially seen with transient symptoms who are at relatively high risk of major infarction within a finite period of time) (Johnston et al., 2000; Warach and Kidwell, 2004). Magnetization transfer magnetic resonance imaging (MT-MRI) is achieved by applying radiofrequency power only to the proton magnetization of the macromolecules. Magnetization saturation and relaxation within the macromolecules of brain tissue aVects the free water observable signal (WolV et al., 1989). Although not as sensitive as DWI (Filippi and Rocca, 2004), MT-MRI has the potential to provide information about the age of a cerebral infarction (Hanyu et al., 1998). Measurements from MTR have been reported to strongly correlate with CMRO2 values in cerebral infarction (Kado et al., 2001) and to correlate with the degree of neurological deficit associated with stroke (Pendlebury et al., 2000).

IV. MRI Versus CT Brain Scan in Acute Stroke

An ongoing evolution of MR techniques has the capability of enhancing management approach and providing prognostic information. In a 6-hour study of recombinant tissue plasminogen activator (rt-PA) (Schramm et al., 2002), the sensitivity of CT brain scan was 64% with an accuracy of 66%. However, one does not necessarily need definition of an ischemic stroke by neuroimaging to initiate appropriate therapy once a hemorrhagic stroke has been eVectively excluded. In a randomized study of DWI versus CT brain scan in ischemic stroke

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imaged within 6 hours of presentation, the sensitivity (91% vs. 61%), specificity (65% vs. 95%), and accuracy (61% vs. 91%) was clearly superior with DWI. However, in a study of DWI and PWI performed within 6 hours of acute cerebral ischemia (Schellinger et al., 2001), acute DWI and PWI lesion volumes correlated poorly with both acute clinical scores and outcome scores. Presumably, this discrepancy is related to failure of the early MRI to distinguish between reversibly and irreversibly aVected cerebral tissue. However, even CT brain scan as a subacute determinant of infarct volume, with studies performed within 6–10 days of the event, correlates relatively poorly in terms of reproducibility (Kucinski et al., 2002; van der Worp et al., 2001). Some correlation exists between the mean decrease in ADC, as measured by DWI, and the CT brain scan hypodensity in acute ischemic stroke (Kucinski et al., 2002). FLAIR MRI is a relatively sensitive indicator of cerebral ischemic insult (Hajnal et al., 1992). In this setting, an inversion pulse is used in conjunction with long repetition and echo times to produce ‘‘heavy’’ T2-weighted sequences in which cerebrospinal fluid (CSF) nulling yields excellent contrast between the parenchyma and CSF (Fig. 5). Despite its increasing use in imaging ischemic stroke, DWI was reported to nearly double the detection rate of acute ischemic stroke lesions, compared with FLAIR, and, in the 0- to 6-hour (hyperacute) period, the detection rate was nearly threefold (Perkins et al., 2001). The ability of MR techniques to detect reversible versus irreversible ischemic damage in the acute setting may hold promise in terms of outcome and response to intervention. Wu et al. (2001) looked at combining information from DWI and PWI performed within 12 hours of acute cerebral ischemia in developing algorithms to predict tissue loss on follow-up MRI brain scan. They concluded that combining the information resulted in a 66% sensitivity and an 84% specificity. Thijs et al. (2001) reported that the severity of the initial perfusion deficit, as measured by mean transit time and cerebral blood volume, correlated with the degree of expansion of the initial DWI lesion in patients who did not display dramatic early clinical improvement. This was supported in a study by Parsons et al. (2001) in which the ischemic lesion, detected by acute regional CBF reduction, correlated best with the PWI > DWI mismatch region at greatest risk for infarction. Newer CT scan techniques may obscure the advantages versus disadvantages of CT versus MRI in acute stroke even further. In a study comparing admission perfusion CT (PCT) brain scan with DWI and PWI in acute ischemic stroke (Wintermark et al., 2002), there was a good correlation for the detection of total ischemia (infarct plus penumbra) with both techniques. Combining routine CT, PCT, CTA, and multidetector row technique, Nabavi et al. (2002) reported that PCT correlated best with infarct size and clinical outcome, whereas combined information (i.e., a MOSAIC score) was superior. Conversely, T2-weighted imaging with gradient echo technique is becoming increasingly recognized as a reliable means of detecting acute cerebral

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TABLE IV STRENGTHS AND WEAKNESSES OF CT VERSUS MRI BRAIN SCAN IN ACUTE STROKE Strengths of CT brain scan 1. Lower Cost 2. Greater availability in ER 3. Ready diVerentiation of primary ischemia from primary hemorrhage 4. Less confining environment 5. Lack of contraindication for certain metallic objects (e.g., pacemakers) 6. Ability to combine vascular imaging with tissue assessment with CTA

Strengths of MRI brain scan 1. Higher detection rate 2. Safer administration of contrast 3. Better visualization of the posterior fossa 4. Potential for greater sensitivity and specificity 5. Ability to combine vascular imaging with tissue assessment with MRA 6. Greater potential for blood flow and metabolic assessment

hemorrhage by MR imaging (Gomori et al., 1988; Imaizumi et al., 2003; Kinosita et al., 2000; Kwa et al., 1998) and may supplant one of the main advantages of the CT brain scan in the acute setting (i.e., distinguishing primary ischemia from primary hemorrhage). One must address the various potential strengths versus weaknesses of the acute CT versus the acute MRI brain scan in stroke (Tatlisumak, 2002). These are summarized in Table IV.

V. Prognostic and Outcome Information Provided by Routine and Functional Brain Scan

A. ACUTE ISCHEMIC STROKE The CT brain scan is valuable in acute ischemic stroke assessment, and CT imaging criteria are particularly useful in decision making regarding rt-PA. The more normal the CT brain scan, the more likely the patient will be responsive to thrombolytic therapy. Conversely, the greater the evolution of the infarct in the hyperacute period, the less likely that there is reversible tissue damage, and the greater the likelihood of hemorrhagic transformation. Early CT findings indicative of a reduced likelihood of response to therapy include loss of gray-white matter diVerentiation, early loss of sulcal markings, and early evolution of a lowdensity (ischemic) lesion. Early evidence of involvement that is greater than two thirds of the middle cerebral artery distribution suggests evolution of a large infarct with limited potential for response to therapy. In a study of DWI and PWI performed within 6 hours of onset of ischemic stroke, Schellinger et al. (2001) found that acute DWI and PWI lesion volumes correlated poorly with acute outcome scores, and there was limited correlation

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with outcome scores. However, coupling this information with MRA of intracranial vessels, they found that early recanalization was associated with smaller infarcts and improved clinical outcome. Conversely, proximal vessel occlusions correlated with larger infarctions and worse outcome. It is becoming increasingly important to assess the ischemic penumbra in assessing the potential for both thrombolytic and neuroprotective therapies (Heiss, 2003; Warach, 2003). In patients who undergo successful intraarterial thrombolytic therapy recanalization, Shih et al. (2003) found that PWI was superior to DWI in distinguishing salvageable tissue from infarcted tissue in the penumbra. The best threshold for such determination was the adjusted Tmax of  6–8 seconds, where Tmax is the time to the peak of the residue function for the perfusion maps. Jovin et al. (2003) assessed core (infarcted) versus tissue at risk (penumbra) with quantitative CBF assessed by xenon-enhanced CT brain scan performed within 6 hours of onset of ischemic stroke in 36 patients with MCA stem occlusion. They found a fairly consistent penumbra, which constituted approximately one third of the cortical MCA territory. However, not unexpectedly, the core correlated best with outcome despite a greater variability in the extent of the core relative to the penumbra. In a recent study (Kang et al., 2004), it was observed that there is a spectrum of lesion recurrence on follow-up DWI and FLAIR compared with the perfusion results seen within 48 hours of ischemic stroke presentation. This study found that there was a 26% risk for recurrent cerebral ischemia, which was more frequently found on the 30-day follow-up MRI than the 90-day follow-up MRI. It was theorized, by the authors, that such a recurrent ischemic pattern may predict clinical recurrent stroke and might serve as a surrogate endpoint in secondary stroke prevention trials. A major clinical concern is the worsening stroke or so-called stroke in evolution. Neuroimaging can be very useful in providing prognostic information, and this will help guide the clinician in terms of the potential for eVective therapy and what is discussed with friends and family in terms of prognosis. Not only is it important to assess for the potential for functional recovery, it is also important to assess for the appropriate aggressiveness of life support. For example, CT parameters of anteroseptal shift >5 mm, pineal shift 2 mm, involvement beyond the MCA territory, complete temporal lobe infarction, and moderate or severe hydrocephalus correlating with an NIH Stroke Scale of >20 within 48 hours all were predictive of fatal outcome in large MCA infarction (Barber et al., 2003). However, DWI is not only more sensitive than the CT brain scan, it also is better at identification of evolution of major ischemia (Barber et al., 1999). Such information might be enhanced by newer techniques such as MRS, which can provide complementary assessment of reversible versus irreversible metabolic status, for example, the lactate/choline ratio in acute ischemic stroke

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(Parsons et al., 2000). In a study of combined DWI/PWI with MRS (Parsons et al., 2002), acute hyperglycemia was associated with increased lactate production and subsequent infarction of hypoperfused tissue at risk. Functional MRI with BOLD response may hold promise in assessing the capacity to improve after sensorimotor stroke (Binkofski and Seitz, 2004). In a study of single photon emission computed tomography (SPECT) with technetium-99m hexamethylpropylene amine oxime (Hirano et al., 2001), if no significant early reperfusion or clinical recovery occurs, a threshold CBF value of 63.7% or less observed on SPECT performed within 6 hours of stroke onset can reliably predict the final infarct size. In a study of combined 99mTc-SPECT with CT brain scan, Barthel et al. (2001) reported that perfusion information was associated with a 94% positive predictive value and 90% negative predictive value for evolving cerebral infarction. The potential value of perfusion imaging was also supported by Parsons et al. (2001) with perfusion MRI. They looked at PWI>DWI mismatch in the patients with acute ischemic stroke studied <6 hours after onset and found that the acute rCBF lesion most accurately predicted tissue destined to infarct. In childhood moyamoya disease, the xenon-enhanced CT brain scan may allow assessment of stroke risk and predict response to surgical intervention (McAuley et al., 2004). In hyperacute stroke, Fiehler et al. (2002) reported that MRI-derived CBF values were useful for predicting lesion growth. Specifically, a tissue volume of 50 ml with a CBF value
12 ml/100 g per minute had a positive predictive value of 0.80 when looking at lesion growth at day 7 with T2-weighted MRI in a study of 32 patients treated with rt-PA within 6 hours of onset. Bolus-delay– corrected perfusion maps allow more accurate prediction of final infarct size (Rose et al., 2004) in a similar fashion to that reported with CT combined with CTA (Coutts et al., 2004). 11C-flumazenil (FMZ) PET is a tracer study that can assess both neuronal integrity and cerebral perfusion. It has been found to be a reliable predictor of final infarct size (Heiss et al., 2001). In a study of FMZ PET by Dohmen et al. (2003), a large, >50% MCA infarct pattern by acute CT brain scan, detection of a cerebral perfusion pressure drop to <50–60 mmHg within 2 to 4 days, correlated with a malignant clinical course. This was defined as edema formation with midline shift. A recent study by Heiss et al. (2004) looked at FMZ-PET compared with DWI in 12 patients with acute ischemic stroke. DWI was performed within a median time of 6.5 hours of symptom onset, whereas PET was obtained within 85 minutes of the DWI. With an endpoint of infarct extension 24–48 hours after stroke onset by T2-weighted MRI, FMZ-PET and DWI had similar predictive value. Overall, 83.5% of the final infarct volume was predicted by PET, 84.7% was predicted by increased signal intensity on DWI, and 70.9% was predicted by a decreased ADC value. The false-positive prediction of final infarct volume was better with FMZ-PET.

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B. HEMORRHAGIC STROKE The immediate determination of hematoma size and location goes a long way toward determining outcome in primary ICH (Broderick et al., 1993). As mentioned previously, the CT brain scan allows ready identification of ICH and remains the neuroimaging procedure of first choice. However, some have questioned the true sensitivity of CT brain scan for detection of acute ICH (Packard et al., 2003). Furthermore, primary ICH may be ‘‘overdiagnosed,’’ because early cerebral infarction, with secondary hemorrhage transformation, was not detected on the initial CT brain scan (Bogousslavsky et al., 1991). Factors impacting on outcome include ventricular extension (Nieuwkamp et al., 2000), early hemorrhage expansion (Brott et al., 1997), and possibly progression of mass eVect (Zazulia et al., 1999). Intraventricular extension correlates with the size of the hematoma and the location. Hematoma expansion has been associated with brain infarction, liver disease, elevated plasma glucose, and admission systolic blood pressure of >200 mmHg (Kazure et al., 1997). In another study (Fujii et al., 1998), hematoma enlargement was associated with early onset of admission, presumably reflecting the degree of neurological deficit at the time of presentation, as well as heavy alcohol consumption, altered consciousness on presentation, irregular shape of the hematoma on CT brain scan, and low fibrinogen level. Inflammatory reaction may also play a role in early neurological deterioration in ICH (Leira et al., 2004). Gebel et al. (2002a) reported that relative edema volume was a strong predictor of functional outcome in patients with supratentorial ICH who did not have intraventricular extension. They also reported that perihematomal edema increases by approximately 75% within the first 24 hours of spontaneous ICH (Gelder et al., 2002b). It has been theorized that the perihematomal region is susceptible to secondary infarction and that this raises concern about aggressive blood pressure control in hypertensive ICH, because this could, theoretically, promote further tissue loss through hypoperfusion (Kidwell et al., 2001). A SPECT study of acute ICH (Mayer et al., 1998) suggested that the potential for susceptible hypoperfusion was greatest within the first hours of onset and that reperfusion injury may be a factor in edema formation around the hematoma. However, there has been no evidence of an ischemic penumbra in ICH either in an experimental model (Qureshi et al., 1999) or in humans studied with 15O-PET (Powers et al., 2001). Using CT brain scan results, including intraventricular and subarachnoid extension, along with clinical scores, Cheung and Zou (2003) reported a reliable ICH score to predict mortality and mortality. Use of MRI in acute hemorrhage is still evolving. Schellinger et al. (1999) reported that ‘‘multimodal stroke MRI,’’ consisting of DWI, PWI [T2*-WI], FLAIR, T2-WI, and MRA, was as reliable as CT brain scan in the assessment of acute ICH. DiVusion-perfusion MRI may be particularly useful for evaluation

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of perihematomal injury (Kidwell et al., 2001). However, CT brain scan remains the primary determinant of acute SAH (Hoggard et al., 2002), with performance of a lumbar puncture if there is any doubt about the clinical presentation and no contraindication to the performance a lumbar puncture (Sidman et al., 1996). The neurological status and amount of subarachnoid (and intracerebral) blood on brain scan are important clues to outcome in aneurysmal SAH, as well as bleeding related to cerebral arteriovenous malformations. In a study of MRI in acute SAH, Wilkinson et al. (2001) reported that gradient echo T2* was the most sensitive sequence, with a sensitivity of 94% in the acute phase and 100% in the subacute phase. The other sequence of potential value was FLAIR, with a sensitivity of 81% for the acute bleeding and 87% for the subacute bleeding. In a study of detection of the deposition of hemosiderin by T2*-weighted MRI in 58 patients scanned >3 months after ictus, Imaizumi et al. (2003) reported a sensitivity of 72.4%.

VI. Vascular Imaging

A. OCCLUSIVE CEREBROVASCULAR DISEASE MRA is now widely used as a noninvasive means of assessing the cerebrovascular anatomy (Fig. 11). Its usefulness has been improved with contrast enhancement, which improves the quality of the imaging (Parker et al., 1998). The time-of-flight MRA technique typically overestimates the degree of vascular stenosis (Stock et al., 1996). Contrast enhancement can potentially correct this overestimation (Yano et al., 1997), and this can be particularly useful for the diagnosis of intracranial vascular occlusive disease (Jung et al., 1995; Pedraza et al., 2004). MRA can be useful for monitoring intracranial occlusive disorders such as sickle cell cerebrovascular disease (Moritani et al., 2004), moyamoya disease (Lee et al., 2004), and vascular dissection (Fullerton et al., 2001). In a study of MRA in childhood ischemic stroke, Husson et al. (2002) reported a close correlation between MRA and digital subtraction angiography (DSA) in the detection of intracranial vascular lesions. Vascular dissection is usually idiopathic, but it can be seen with trauma or can be associated with conditions such as fibromuscular dysplasia, Ehlers–Danlos syndrome type IV, and Marfan’s syndrome. Duplex carotid ultrasound has a sensitivity of between 68% and 95% for extracranial carotid dissection (Sturzenegger, 1995; Treiman et al., 1996). MRA without contrast enhancement is reported to have a sensitivity of 90% (Kasner et al., 1997), and it would be expected to have a greater sensitivity with the use of contrast (Phan et al., 2001). DSA remains the definitive study with the ability to demonstrate irregular

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FIG. 11. (A) Demonstration of a left middle cerebral artery infarction (arrow) on T2-weighted MRI brain scan. (B) Extracranial MRA was unremarkable. (C) The intracranial MRA reveals acute occlusion (arrow) of the left middle cerebral artery as an explanation for the infarction pattern.

constriction and dilation of the involved vessel, as well as flame-shaped tapering, a double lumen, or, on occasion, fusiform aneurysmal dilation. However, MRA, with or without duplex ultrasonography, can serve as a noninvasive monitor of vessel healing (Kasner et al., 1997). Carotid stenosis also tends to be overestimated by routine MRA, and this has led to the information to be combined with carotid noninvasive imaging in an eVort to provide complementary information. However, contrast-enhanced MRA seems to improve the accuracy. Johnston et al. (2002) reported that such imaging was associated with a sensitivity of 92%, a specificity of 62%, a positive predictive value of 78%, and a negative predictive value of 89%. However, combining contrast-enhanced MRA with carotid duplex scan augments the yield. Duplex carotid scanning is generally reliable for the detection of 70% stenosis when applying criteria appropriate for the North American Symptomatic

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Carotid Endarterectomy Trial (NASCET) (Carpenter et al., 1996). On the other hand, the NASCET investigators termed the accuracy of carotid ultrasonography as ‘‘moderate’’ when flow parameters were applied to assess the degree of stenosis (Eliasziw et al., 1995). It is generally recognized that the peak systolic velocity is the best flow parameter for such assessment (Schwartz et al., 1997), and a cutoV value of 140 cm/sec has been identified (Alexandrov et al., 1997). In a study of the preoperative assessment of carotid artery stenosis (Nederkoorn et al., 2002), MRA was reported to display sightly better accuracy than duplex ultrasonography for the identification of 70–99% stenosis. The combination of the results gave a sensitivity of 96.3% and a specificity of 75.7%. This has led some to conclude that this combination obviates the need for DSA in most patients (Patel et al., 1995). CTA is now recognized as a useful noninvasive test of the carotid bifurcation (Koelemay et al., 2004). In a study of ICA stenosis comparing CTA to DSA, Hyde et al. (2004) reported equivalent accuracy. Chen et al. (2004) reported that CTA had excellent correlation with DSA in the assessment of total versus near occlusion of the ICA. This was also supported by Herzig et al. (2004), who combined CTA with ultrasonography for the detection of severe carotid stenosis and compared the results with DSA. However, in a study that compared duplex ultrasonography, contrast-enhanced MRA, and CTA in various combinations, and compared with DSA, the concordance rate for combined duplex ultrasonography and contrast-enhanced MRA (92.53%) was significantly higher than that for duplex ultrasonography-CTA (79.10%) in asymptomatic patients (Nonent et al., 2004). In a study of symptomatic carotid stenosis, Josephson et al. (2004) reported a 100% sensitivity and a 63% specificity when comparing CTA with DSA. The authors concluded that CTA was an excellent screening study for patients initially seen with suspected TIA or stroke. Imaging techniques can also provide information about the carotid plaque composition that may have prognostic value. B-mode ultrasonography allows for eVective detection of carotid plaque along with morphological characteristics (Joakimsen et al., 1997). Both macroscopic and microscopic characteristics can be assessed, including heterogeneous versus homogeneous composition (Kagawa et al., 1996). Densitometric analysis of carotid B-mode plaque composition enhances the potential for distinguishing thrombus, intraplaque hemorrhage/lipid deposition, fibrosis, and calcification (Beletsky et al., 1996). In a recent study of MRI in patients with carotid artery moderate stenosis, Chu et al. (2004) reported a reliable determination of five lesion types in patients with hypercholesterolemia. TCD is of use in the assessment of various occlusive cerebrovascular processes (Sloan et al., 2004). There is a reported sensitivity of 86% and specificity of 91% in the screening of children for stroke risk in sickle cell disease using the criteria of >200 cm/sec mean maximum velocity either within the MCA or distal ICA

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(Adams et al., 1997). Such detection can lead to stroke prevention with blood transfusions (Adams et al., 1998). There is also the potential to detect hemodynamically significant intracranial stenosis when an adequate Doppler signal can be obtained. This technique is most reliable for the detection of stenosis of the carotid siphon and MCA (Ley-Pozo and Ringelstein, 1990). TCD has the potential to detect proximal ipsilateral carotid artery stenosis based on the eVect of hemodynamically significant stenosis on the intracranial circulation (Kelley et al., 1993). It also has potential in the detection of right-to-left cardiac shunts (Droste et al., 2002) and in cerebral embolus detection (Babikian et al., 2000).

B. CEREBRAL ANEURYSMS

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ARTERIOVENOUS MALFORMATIONS

DSA remains the ‘‘gold standard’’ for the detection of cerebral aneurysms. However, MRA can serve as a useful noninvasive screening procedure for patients with suspected aneurysm. Typically, the aneurysm has to be at least 3–4 mm to be detected by MRA (Okahara et al., 2002), and the detection is very much subject to the experience of the reader and the location of the aneurysm. Aneurysms located at the ICA or the anterior cerebral artery were less likely to be detected on 3D-time-of-flight MRA. AVMs tend to be readily identified on MRA, and newer techniques have the capability to enhance the detection and characterization of cerebral vascular malformations (Warren et al., 2001). CTA is capable of providing excellent imaging of cerebral aneurysm. It is believed to be the diagnostic procedure of choice to assess isolated third nerve palsy not associated with SAH (Wong et al., 2004). In a study correlating CTA and MRA with DSA and operative findings with intracranial aneurysms, Kouskouras et al. (2004) reported that helical CT was a fast, relatively inexpensive, noninvasive, and reliable alternative to DSA. This was further supported by a prospective study of CTA as a replacement for DSA in the diagnostic and pretreatment assessment of patients with cerebral aneurysms (Hoh et al., 2004). The detection rate was 100% for symptomatic aneurysms and 97% overall, including incidental multiple aneurysms. TCD is now commonly used to monitor for potential vasospasm in aneurysmal SAH. Serial TCD can detect patients at risk for vasospasm-associated cerebral ischemia, because the mean flow velocity correlates inversely with vessel diameter. A mean flow velocity of >120 cm/sec, for the MCA, suggests a vasospastic tendency. Mean values rising to >200 cm/sec, a rapid rise in the mean flow velocities over time, or a VMCA/VICA ratio of 6 ± 0.3 are reported to provide a reliable indicator of clinically significant vasospasm (Sloan et al., 2004).

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Shih, L. C., Saver, J. L., Alger, J. R., Starkman, S., Leary, M. C., Vinuela, F., Duckwiler, G., Gobin, P., Jahan, R., Villablanca, J. P., Vespa, P. M., and Kidwell, C. S. (2003). Perfusion-weighted magnetic resonance imaging thresholds identifying core, irreversibly infracted tissue. Stroke 34, 1425–1430. Silvestrini, M., Troisi, E., Matteis, M., Cupini, L. M., and Caltagirone, C. (1995). Involvement of the healthy hemisphere in recovery from aphasia and motor deficit in patients with cortical ischemic infarction: A transcranial Doppler study. Neurology 45, 1815–1820. Singer, M. B., Chung, J., Lu, D., Schonreillr, W. J., Tuhrim, S., and Atlas, S. W. (1998). DiVusionweighted MRI in acute subcortical infarction. Stroke 29, 133–136. Sloan, M. A., Alexandrov, A. V., Tegeler, C. H., Spencer, M. P., Caplan, L. R., Feldman, E., Wechsler, L. R., Newell, D. W., Gomez, C. R., Babikian, V. L., Lefkowitz, D., Goldman, R. S., Armon, C., Hsu, C. Y., and Goodin, D. S. (2004). Assessment: Transcranial Doppler ultrasonography. Report of the Therapeutics and Technology Assessment Committee of the American Academy of Neurology. Neurology 62, 1468–1481. Sorensen, A. G., Copen, W. A., Ostergaard, L., Buonanno, F. S., Gonzalez, R. G., Rordorf, G. M., Rosen, B. R., Schwamm, L. H., WeisskoV, R. M., and Koroshetz, W. J. (1999). Hyperacute stroke: Simultaneous measurement of relative cerebral blood volume, relative cerebral blood flow, and mean tissue transit time. Radiology 210, 519–527. Staroselskaya, I. A., Chaves, C., Silver, B., Linfante, I., Edelman, R. R., Caplan, L., Warach, S., and Baird, A. E. (2001). Relationship between magnetic resonance arterial patency and perfusiondiVusion mismatch in acute ischemic stroke and its potential clinical use. Arch. Neurol. 58, 1069–1074. Stock, K. W., Wetzel, S., Kirsch, E., Bongartz, G., Steinbach, W., and Radue, E. W. (1996). Anatomic evaluation of the circle of Willis: MRA versus intraarterial digital subtraction angiography. A. J. N. R. Am. J. Neuroradiol. 17, 1495–1499. Sturzenegger, M. (1995). Spontaneous internal carotid artery dissection: Early diagnosis and management in 44 patients. J. Neurol. 242, 231–238. Tatlisumak, T. (2002). Is CT or MRI the method of choice for imaging patients with acute stroke? Why should men be divided if fate has united? Stroke 33, 2144–2145. Thijs, V. N., Adami, A., Neumann-Haefelin, T., Mosely, M. E., Marks, M. P., and Albers, G. W. (2001). Relationship between severity of MR perfusion deficit and DWI lesion evolution. Neurology 57, 1205–1211. Treiman, G. S., Treiman, R. L., and Foran, R. F. (1996). Spontaneous dissection of the internal carotid artery: A nineteen year clinical experience. J. Vasc. Surg. 24, 597–605. van der Worp, H. B., Claus, S. P., Bar, P. R., Ramos, L. M. P., Algra, A., van Gijn, J., and Kappelle, I. J. (2001). Reproducibility of measurements of cerebral infarct volume on CT scans. Stroke 32, 424–430. Vermeulen, M., and van Gijn, J. (1990). The diagnosis of subarachnoid hemorrhage. J. Neurol. Neurosurg. Psychiatry 53, 365–372. von Kummer, R., Bourquain, H., Bastianello, S., Manelfe, C., Meier, D., and Hack, W. (2000). Early prediction of irreversible brain damage after ischemic stroke at CT. Radiology 219, 95–100. Warach, S. (2003). Measurement of the ischemic penumbra with MRI: It’s about time. Stroke 34, 2533–2534. Warach, S., Dashe, J. F., and Edelman, R. R. (1996). Clinical outcome in ischemic stroke predicted by early diVusion-weighted and perfusion magnetic resonance imaging: A preliminary analysis. J. Cerebr. Blood Flow Metab. 16, 53–59. Wardlaw, J. M., Marshall, I., Wild, J., Dennis, M. S., Cannon, J., and Lewis, S. C. (1998). Studies of acute ischemic stroke with proton magnetic resonance spectroscopy: Relation between time of onset, neurological deficit, metabolites abnormalities in the infarct, and clinical outcome. Stroke 29, 1618–1624.

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Wardlaw, J. M., Sandercock, P. A., Dennis, M. S., and Starr, J. (2003). Is breakdown of the bloodbrain barrier responsible for lacunar stroke, leukoaraiosis, and dementia? Stroke 34, 806–812. Wardlaw, J. M., Seymour, J., Cairns, J., Keir, S., Lewis, S., and Sandercock, P. (2004). Immediate computed tomography scanning of acute stroke is cost-eVective and improves quality of life. Stroke 35, 2477–2483. Warren, D. J., Hoggard, N., Radatz, W. L., Kemeny, A. A., Forster, D. M., Wilkinson, I. D., and GriYths, P. D. (2001). Cerebral arteriovenous malformations: Comparison of novel magnetic resonance angiographic techniques and conventional catheter angiography. Neurosurgery 48, 973–982. Wen, H. M., Lam, W. W. M., Rainer, T., Leung, T. W. H., Chan, Y. I., and Wong, K. S. (2004). Multiple acute cerebral infarcts on diVusion-weighted imaging and risk of recurrent stroke. Neurology 63, 1317–1319. Wintermark, M., Reichart, M., Cuisenaire, O., Maeder, P., Thiran, J.-P., Schnyder, P., Bogousslavsky, J., and Meuli, R. (2002). Comparison of admission perfusion computed tomography and qualitative diVusion- and perfusion-weighted magnetic resonance imaging in acute stroke patients. Stroke 33, 2025–2031. Wong, G. K., Boet, R., Poon, W. S., Yu, S., and Lam, J. M. (2004). A review of isolated third nerve palsy without subarachnoid hemorrhage using computed tomographic angiography as the first line of investigation. Clin. Neurol. Neurosurg. 107, 27–31. Wong, K. S., Gao, S., Chan, Y. L., Hansberg, T., Lam, W. W. M., Droste, D. W., Kay, R., and Ringelstein, E. B. (2002). Mechanism of acute cerebral infarctions in patients with middle cerebral artery stenosis: A diVusion-weighted imaging and microemboli monitoring study. Ann. Neurol. 52, 74–81. Wu, O., Koroshetz, W. J., Østergaard, L., Buonanno, F. S., Copen, W. A., Gonzalez, G., Rordorf, G., Rosen, B. R., Schwamm, L. H., WeisskoV, R. M., and Sorenson, G. (2001). Predicting tissue outcome in acute human cerebral ischemia using combined diVusion-weighted and perfusionweighted MR imaging. Stroke 32, 933–942. Yano, T., Kodoma, T., Suzuki, Y., and Watanabe, K. (1997). Gadolinium-enhanced 3D time-of-flight MR angiography. Acta Radiol. 38, 47–54. Yoneda, Y., Tokui, K., Hanihara, T., Kitagki, H., Taqbuchi, M., and Mori, E. (1999). DiVusionweighted magnetic resonance imaging: Detection of ischemic injury 39 minutes after onset in a stroke patient. Ann. Neurol. 45, 794–797. Zazulia, A. R., Diringer, M. N., Derdeyn, C. P., and Powers, W. J. (1999). Progression of mass eVect after intracerebral hemorrhage. Stroke 30, 1167–1173. Zivin, J. (1997). DiVusion-weighted MRI for diagnosis and treatment of ischemic stroke. Ann. Neurol. 41, 567–568.

Further Readings

Bogoussslavsky, J., Regli, F., Uske, A., and Maeder, P. (1991). Early spontaneous hematoma in cerebral infarct: Is primary cerebral hemorrhage overdiagnosed? Neurology 41, 837–840. Fiehler, J., Kucinski, T., Knudsen, K., Rosenkranz, M., Thomalla, G., Weiller, C., Rother, J., and Zeumer, H. (2004). Are there time-dependent diVerences in diVusion and perfusion within the first 6 hours after stroke onset? Stroke 35, 2099–2104. Falcke, S., Uhrbach, H., Keller, E., Traber, F., Hartmann, A., Textor, J., Gieseke, J., Block, W., Folkers, P. J., and Schild, H. H. (2000). Middle cerebral artery (MCA) susceptibility sign at

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susceptibility-based perfusion MR imaging: Clinical importance and comparison with hyperdense MCA sign at CT. Radiology 215, 476–482. Gebel, J. M., Jr., Jauch, E. C., Brott, T. G., Khoury, J., Sauerbeck, L., Salisbury, S., Spilker, J., Tomsick, T. A., Duldner, J., and Broderick, J. P. (2002b). Natural history of perihematomal edema in patients with hyperacute spontaneous intracerebral hemorrhage. Stroke 33, 2631–2635. Joakimsen, O., Bonaa, K. H., and Stensland-Bugge, E. (1997). Reproducibility of ultrasound assessment of carotid plaque occurrence, thickness, and morphology. The Tromso Study. Stroke 28, 2201–2207. Kazui, S., Minematsu, K., Yamamoto, H., Sawada, T., and Yamaguchi, T. (1997). Predisposing factors to enlargement of spontaneous intracerebral hematoma. Stroke 28, 2370–2375. McAuley, D. J., Poskitt, K., and Steinbok, P. (2004). Predicting stroke risk in pediatric moyomoya disease with xenon-enhanced computed tomography. Neurosurgery 55, 327–332. Neumann-Haefelin, T., Moseley, M. E., and Albers, G. W. (2000). New magnetic imaging methods for cerebrovascular disease: Emerging clinical applications. Ann. Neurol. 47, 559–570. Ostergaard, L., Johannsen, P., Host-Poulsen, P., Vestergaard-Poulsen, P., Asboe, H., Gee, A. D., Hansen, S. B., Cold, G. E., Gjedde, A., and Hyldensted, C. (1998). Cerebral blood flow measurements by magnetic resonance imaging bolus tracking: Comparison with [15O]H2O positron emission tomography. J. Cereb. Blood Flow Metab. 18, 935–940. Wardlaw, J. M., Dennis, M. S., Warlow, C. P., and Sandercock, P. A. (2001). Imaging appearance of symptomatic perforating artery in patients with lacunar infarction: Occlusion or other vascular pathology. Ann. Neurol. 50, 208–215. Yang, J. J., Hill, M., Morrish, W. F., Hudson, M. E., Barber, P. A., Demchuk, A. M., Sevick, R. J., and Frayne, R. (2002). Comparison of pre- and post-perfusion 3D time-of-flight MR angiography for the evaluation of distal branch occlusions in acute ischemic stroke. A. J. N. R. Am. J. Neuroradiol. 23, 557–567.

FUNCTIONAL MRI IN PEDIATRIC NEUROBEHAVIORAL DISORDERS

Michael Seyffert and F. Xavier Castellanos Institute for Pediatric Neuroscience, New York University Child Study Center, New York New York 10016

I. Introduction A. Functional Magnetic Resonance Imaging B. Pediatric f MRI C. Scope of This Review II. Normative Pediatric Functional Studies A. Studies of Executive Function in Typically Developing Children B. Language and Reading in Typically Developing Children C. Studies of Emotional Processing in Typically Developing Children III. Pediatric fMR Studies of Psychopathology A. Autism and Autistic Spectrum Disorders B. f MRI of Mirror System in Typically Developing Children C. Theory of Mind and Aberrant Facial Processing in Autistic Spectrum Disorders D. Aberrant Facial Processing in Children with Autistic Spectrum Disorders E. Aberrant Motor Preparation in Children with Autistic Spectrum Disorders F. Dyslexia G. Studies of ADHD H. Conduct Disorder I. Studies of Tourette’s Disorder (TD) J. Childhood‐Onset Anxiety and Mood Disorders K. Adolescent Eating Disorder L. Adolescent Bipolar Disorder IV. Summary References

Pediatric functional neuroimaging has finally come into its own in this new century. In this brief review, we focus on functional magnetic resonance imaging studies of typically developing children and adolescents that have examined executive function, language, and mood along with studies of autism and autism spectrum disorders, dyslexia, attention deficit hyperactivity disorder, conduct disorder, Tourette’s disorder, anxiety disorders, anorexia, and juvenile bipolar disorder. Studies in autism, anxiety disorders, and dyslexia are beginning to provide replicated observations regarding the role of specific brain structures such as the amygdala, or posterior versus anterior language centers in respective models of pathophysiology. However, as is appropriate, the field is still in its infancy, and most studies cited are still exploratory. The increasing number of investigators and active pediatric imaging centers predicts that functional INTERNATIONAL REVIEW OF NEUROBIOLOGY, VOL. 67 DOI: 10.1016/S0074-7742(05)67008-0

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Copyright 2005, Elsevier Inc. All rights reserved. 0074-7742/05 $35.00

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neuroimaging techniques will open an increasingly wider ‘‘window’’ into brain function of children and adolescents burdened with neuropsychiatric disorders. This may warrant the creation of large pediatric neuroimaging databases that will permit sharing of functional magnetic resonance imaging (f MRI) studies of normal and pathological human behavior. I. Introduction

Advances in brain imaging technologies are increasingly facilitating investigations of the neural mechanisms underlying neurobehavioral disorders. Although neuroimaging techniques such as positron‐emission tomography (PET) and single‐photon emission computed tomography (SPECT) provide unique perspectives on in vivo neuropharmacology in adults (Castellanos, 2002), methodological and ethical considerations make it practically impossible to perform adequately controlled studies in children that expose them to ionizing radiation (Hinton, 2002). The availability of noninvasive techniques is particularly important for the neuropsychiatric disorders of childhood and adolescence, for which postmortem brain specimens are generally unavailable. Structural and functional magnetic resonance imaging have thus come to dominate pediatric brain imaging as noninvasive alternatives with excellent spatial resolution and the added benefit of permitting longitudinal studies (Byars et al., 2002). Recent structural magnetic resonance imaging (MRI) data sets have confirmed that the pruning and reorganization of cortical areas is a protracted process, lasting into the second decade of life (Gogtay et al., 2004). Much work still remains before quantitative developmental trajectories for regional brain anatomy will be suYciently well delineated, but the process is underway, and initial results from the NIH Study of Normal Brain Development are eagerly expected. Less advanced, but no less interesting, are the numerous studies seeking to isolate the anatomical correlates of specific brain functions in children and adolescents, the topic of this chapter. A. FUNCTIONAL MAGNETIC RESONANCE IMAGING Functional MRI (f MRI) technically refers to any use of MRI technology to detect regional changes in signals that are correlated with changing neuronal activity. For our purposes, we will limit ourselves to the currently dominant f MRI technique, called blood oxygenation level–dependent (BOLD) imaging. The BOLD technique is based on the relatively strong paramagnetic properties of deoxygenated hemoglobin, which makes it a natural contrast agent. The

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paramagnetic deoxyhemoglobin disrupts the magnetic fields engendered by the powerful permanent magnetic field within the scanner. Those disruptions are greatest in those regions that have higher local concentrations of deoxygenated hemoglobin. Regional increases in neuronal activity result in substantial overdelivery of oxygenated hemoglobin. The net eVect is a local decrease in deoxygenated hemoglobin and a local increase in the magnetic resonance signal (Wilke et al., 2003). These signal changes must then be detected using an MR sequence that is suitably sensitive. Echo‐planar imaging (EPI) is the most widely used functional imaging technique, because it permits each image slice to be obtained within durations on the order of 100 ms. The rapidity of the EPI technique, and its relatively good signal‐to‐noise ratio made it practical to detect rapidly changing physiological parameters that reflect blood flow (Cohen, 2000).

B. PEDIATRIC f MRI The obverse of the excellent spatial resolution of MRI is the need to minimize motion within the scanner. Clinically indicated anatomical MRI scans are frequently obtained with sedation, but the risks of sedation are unacceptable for research scans, and remaining awake is essential for functional imaging. Thus, the ‘‘rate‐limiting step’’ in pediatric f MRI has been the need to train children to remain suYciently still while in a scanner. Many research imaging centers have invested in MRI simulators in which pediatric or adult subjects can practice undergoing scans ‘‘at 0 Tesla.’’ Although a controlled study of the benefits of simulators has not been conducted, it can certainly be asserted that simulators may be helpful but are not essential. At any rate, as the number of pediatric imaging investigators and centers increase, motion artifacts are less of a problem, although the possibility of diVerential attrition between patients and control subjects must always be kept in mind. Although the importance of obtaining technically adequate f MRI data cannot be ignored, the most critical issue in any type of functional brain imaging always revolves around the scientific question being addressed and which paradigm or task should be used to activate which hypothesized brain regions. This is the dimension of functional imaging that is developing most rapidly, with investigators exploring a range of tasks and paradigms. For example, current language tasks range from silent object naming to passive reading or stem word completion (Gaillard et al., 2004; Palmer et al., 2001). In general, verbal responses are not possible, because the changes in the three‐dimensional geometry of the airway produce rapid fluctuations in susceptibility artifacts that arise at the interfaces of air pockets and solid tissues.

242 C. SCOPE

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THIS REVIEW

This review will focus on f MRI studies of typically developing children and adolescents that have examined executive function, language, and mood along with studies of autism and autism spectrum disorders, dyslexia, attention deficit hyperactivity disorder, conduct disorder, Tourette’s disorder, anxiety and depressive disorders, anorexia, and juvenile bipolar disorder.

II. Normative Pediatric Functional Studies

In 2000, Casey and colleagues wrote, ‘‘Despite significant gains in the fields of pediatric neuroimaging and developmental neurobiology, surprisingly little is known about the developing human brain or neural bases of cognitive development.’’ Halfway through the decade, many questions remain, but progress is notable, particularly in the three domains of executive function, language, and emotion/mood, each of which is pertinent to developmental psychopathology.

A. STUDIES

OF

EXECUTIVE FUNCTION

IN

TYPICALLY DEVELOPING CHILDREN

1. Inhibitory Tasks Executive function (EF) describes higher order cognitive abilities that coordinate specific subsystems (visuospatial working memory, behavioral inhibition, and self‐regulation) in the service of attaining future goals (Eslinger, 1996). Recent authors have highlighted the notion that EF does not represent a unitary construct and that the functions involved arise from a multiplicity of brain regions (Zelazo and Mueller, 2002). Similarly, EF is increasingly seen as involving developmentally based changes in widely distributed neural circuits involving both subcortical and prefrontal areas (Denckla and Reiss, 1997; Diamond, 2000). Despite the multiplicity of ‘‘EF functions,’’ many investigators have focused on behavioral inhibition as a prototypical EF task, in part because of an influential hypothesis that attention deficit‐hyperactivity disorder (ADHD) reflects a primary deficit in the ability to inhibit motor responses and that the capacity for behavioral inhibition is a prerequisite for the normal development of all other executive functions (Barkley, 1997). Functional studies of EF tasks are briefly summarized in the following and in Table I. Rubia and colleagues (2000a) examined age eVects on EF tasks in a small sample of adolescents and young adults. They used a visual version of the Stop Task with fixed 250‐ms intervals between the Go signal (airplane) and the

f MRI

Ref

Mean (SD) age (range) y

OF

EF

TABLE I LANGUAGE IN TDC

AND

Domain

Dx/N

Task

Rubia, 2000

EF (inhibitory control)

9 TD adolescents; 8 adults

(12–40)

Stop and delay

Tamm, 2002

EF (inhibitory control)

19 TDC

(8–20)

Go/NoGo

Rubia, 2003

EF (inhibitory control)

20 TD young adults

(19–43)

Modified Stop

DiVerences in EF/Language "Activations in right PFC, PL, and putamen in adults vs. adolescents during delay "Activations in left PFC region in adults and the R opercular (frontal) and caudate in the adolescents during stop. "Activations in the left IFG/ insula/OFG correlated positively with age, whereas increased activations in the L middle gyrus and SFG negatively correlated with age. "Activations in right inferior PFC during successful response inhibition, whereas "activations in mesial prefrontal cortex, anterior cingulate, and bilateral inferior parietal cortices correlate with failed inhibition.

Comments Hypofunctionality of related prefrontal areas may be responsible for impulsive behaviors as operationalized by the delay task.

Dissociable areas of the prefrontal cortex activate in an age‐dependent fashion during a Go/NoGo task. Dissociable regions of PFC during various phases of inhibitory control. Rapid mixed trial event‐related f MRI design.

(Continued )

TABLE I (Continued)

Ref

Domain

Dx/N

Mean (SD) age (range) y

Task

DiVerences in EF/Language

Comments Interference suppression activated opposite hemisphere (R PFC) in children compared with adults. Adults more consistent than children with ventral PFC activated during inhibitory control. Event‐related f MRI design. A similar pattern of predominant left‐sided language is clearly present in children by the age of 8.

Bunge, 2002

EF (inhibitory control and interference suppression)

16 TDC (10 m); 16 adults

(8–12)

Go/NoGo; Eriksen Flanker interference

" Activations in the left IFG and L insula in children compared with more R insula in adults during successful interference suppression "Activations in bilateral posterior regions (inferior parietal region) in better performing children compared with adults who activated ventral PFC during inhibition.

Balsamo, 2002

Verbal language

11 TDC (4 m)

8.5 (0.9)

"Activations in the left superior and MTG. ROI approach identified significant frontal lobe activations to include IFG and MFG.

Gaillard, 1999

Verbal language

10 TDC (5 m); 10 adults

10.7 28.7

Passive listening and silent naming Word generation

"Activations in left IFG and MFG in children compared with adults.

Similar pattern of predominately left language in adults and children.

Gaillard, 2003

Verbal language

16 TDC (7 m); 29 adults (5 m)

10.2 29.2

Expanded word generation

"Activations in left IFG and MFG in addition to mesial frontal areas to include SMA.

Ahmad, 2003

Verbal language

15 TDC (6 m)

6.8

Short story listening vs. gibberish

"Activations in left MTG and left STG extending back to AG during auditory comprehension.

Gaillard, 2001

Reading

9 TDC (4 m)

10.2

Fable reading and silent naming

"Activations in left MTG and left MFG, # activations in left IFG for both tasks. Reading activated twice as many pixels in the temporal cortex than naming.

Gaillard, 2002

Reading

16 TDC (6 m)

7.2

Fable reading

"Activations in left occipital temporal junction

Similar pattern of predominately left language in adults and children. Neural networks for auditory comprehension are predominantly left hemispheric and in place by 5 y of age. Neural networks for reading are in place by middle to late childhood. Reading a text passage may be useful for determining language lateralization in children. Neural networks for reading are in place by early childhood.

DLPFC, dorsolateral prefrontal cortex; PL, parietal lobe; ROI, region of interest; MFG, midfrontal gyrus; AG, angular gyrus; TD, typically developing; SFG, superior frontal gyrus; ITG, inferior temporal gyrus; TDC, typically developing child; OFG, orbitofrontal gyrus; MTG, middle temporal gyrus; EF, executive function; IFG, inferior frontal gyrus; STG, superior temporal gyrus; SMA, supplementary motor area.

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infrequent Stop signal (bomb) to test motor response inhibition. They also used a motoric‐paced response task at a relatively rapid rate (interstimulus interval (ISI) of 600 ms) or at a slower rate (ISI 5 s), which they termed the ‘‘delay’’ condition and which they anticipated would be more diYcult. Adults and adolescents responded comparably on the Stop Task, but adults performed significantly more accurately and more synchronously than adolescents. In general, power of functional brain response increased in both tasks with increasing age. However, relationships with age were not consistent across tasks. In the Stop Task, adults and adolescents showed distinct patterns of activation, with adults exhibiting more power over left prefrontal cortex (PFC), and adolescents showing more power over right inferior PFC and right caudate. In the ‘‘delay’’ condition, adults showed increased power over a frontal‐striatal‐parietal network, presumably paralleling the improved behavioral performance. Thus, the authors concluded that maturation of PFC could proceed by either continuous improvement in function as reflected by activation diVerences in the ‘‘delay’’ condition or by discontinuous transitions, as seemed to be present in the Stop Task. Such divergence of results was qualified as preliminary, particularly given the modest sample sizes. A follow‐up study with the Stop Task (Rubia et al., 2003) in 20 young right‐ handed male adults (ages19–43) did not report any age‐related analyses. Rather than the fixed‐interval Stop Task used in the prior study, the authors used a tracking Stop Task in which the intervals between the Go and Stop signals varied by 50 ms as a function of performance, so that subjects would inhibit and fail to inhibit on about 50% of all trials on average. Successful response inhibition correlated with activations in right inferior prefrontal cortex. This finding is consistent with the robust relationship between the volume of inferior frontal gyrus damage in 18 adult patients and their performance on the same type of task (r ¼ 0.83, p < 0.0001; Aron et al., 2004). Tamm and colleagues (2002) investigated the maturation of response inhibition in 19 typically developing subjects ages 8–20 using a Go/NoGo task. Although age did not predict errors of omission or commission, age was positively and significantly related to extent of activation in the left inferior frontal gyrus/ insula/orbitofrontal gyrus in regions that roughly overlap with the 2003 Rubia results. By contrast, age was negatively and significantly related to activations in the left middle and superior frontal gyri and anterior cingulate. The authors highlighted the apparent dissociation of developmental processes in PFC, with increasingly focal activation present with increased maturation in inferior frontal gyrus and narrowing of broad areas of presumably nonspecific activation in broad prefrontal regions. EF has been recently conceptualized as playing an essential and dynamic role in ‘‘cognitive control,’’ an ability that requires the flexible constraint of thought and shaping of action to meet internal goal states. Cognitive control requires two

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247

fundamental components, a filtering of unnecessary environmental information (interference suppression) and an inhibition of unwanted action responses (response inhibition). A recent event‐related f MRI study by Bunge and colleagues (2002) used both Go/NoGo (inhibition) and Eriksen Flanker (interference suppression) to examined age and performance patterns in 16 children and 16 adults. The flanker paradigm requires participants to respond to a central stimulus while simultaneously ignoring flanking stimuli. Prior behavioral and brain imaging studies have shown that despite the irrelevance of the flankers, subjects occasionally respond to these distractors. What is more, subjects tend to respond slower to the central target when the flankers indicate an incongruent (diVerent) response rather than a congruent (similar) response (Eriksen and Eriksen, 1974). Regression analyses revealed diVering areas of brain activations in children and adults during the interference tasks but not the inhibitory tasks (surviving statistical threshold of p < 0.001). Specific activations in the left insula and left inferior frontal gyrus during interference trials were found in children compared with areas of right insula in adults. For inhibitory control as operationalized by the Go/NoGo task, adults had greater and more consistent activations in regions of the ventral PFC compared with children. Neither better performing nor worse performing children activated the ventral PFC, although better performing children did activate bilateral regions of the inferior parietal lobule. Of these findings, the lack of PFC activations is notable given the robust lateral PFC activations found in prior Go/NoGo studies (Casey et al., 1997; Vaidya et al., 1998). This discrepancy may be related to diVerences in f MRI design (block design in the earlier studies versus event‐related), as well as diVerences in the relative weighting of the number of Go and NoGo events during the Go/NoGo tasks. 2. Working Memory Tasks Working memory (WM), defined as the ability to keep information ‘‘on‐line’’ for a short period of time, is considered an integral component of EF. EF develops gradually over childhood and adolescence as measured by tests of problem solving and reasoning ability (Dempster and Corkill, 1999) and may be related to maturational changes in regions of the frontal lobe (Prabhakaran et al., 2000), more specifically the prefrontal cortex (Bunge et al., 2001). WM is hypothesized to depend on a cerebellar‐frontostriatal brain network (Bunge et al., 2001; Gottwald et al., 2004). A recent f MRI study of working memory in children (Klingberg et al., 2002) revealed age‐dependent increased activations in the superior frontal and intraparietal cortex in older compared with younger children. This study used a visuospatial WM task consisting of a 4  4 grid with three (low memory load) or five (high memory load) sequentially presented filled circles. After a brief 1500 ms delay, participants indicated whether a probe circle was in the same location as any of the previously presented stimuli. A common fronto‐parieto‐occipital

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network was activated for both WM load conditions. Activations in the bilateral superior frontal sulcus and intraparietal cortex were significantly increased in older compared with younger children, despite the lack of significant interaction between WM load and age on task performance. Given the centrality of WM for most higher order cognitive processes, continued focus on paradigms that isolate spatial and verbal WM is to be expected in pediatric functional imaging.

B. LANGUAGE

AND

READING

IN

TYPICALLY DEVELOPING CHILDREN

The robust evidence for left hemispheric language dominance in adults naturally raises questions regarding the development of hemispheric specialization in childhood. These have been approached from both auditory and orthographic perspectives by Gaillard and colleagues in a series of studies conducted at the National Institute for Neurological Disorders and Stroke. The tasks used have included silently generating words beginning with the letters C, L, and F (Gaillard et al., 2000) generating words within categories such as food, animal, clothes (Gaillard et al., 2003b) silently naming auditorily described objects (Balsamo et al., 2002), reading Aesop’s fables (Gaillard et al., 2001) reading short stories (Ahmad et al., 2003), or reading standardized passages (Gaillard et al., 2003b). This comprehensive body of work (see Table I) has established that verbal or reading tasks produce robustly left lateralized activations in both children and adults, down to 5–7 years of age. An early finding of greater extent of activation in children than adults (Gaillard et al., 2000) was not confirmed in a subsequent study (Gaillard et al., 2003a). The authors pointed out that the earlier study had used surface coils to improve the signal‐to‐noise ratio, which may have resulted in greater activation in children because of their thinner skulls and scalp, thus highlighting the challenge of delineating true developmental diVerences.

C. STUDIES

OF

EMOTIONAL PROCESSING

IN

TYPICALLY DEVELOPING CHILDREN

The increasing awareness of the centrality of the amygdala in aVective processing formed the backdrop for the first of several f MRI studies of emotional face processing in typically developing children (see Table II). Passive viewing of fearful faces versus scrambled objects was used to test the hypothesis that the amygdala would activate during the recognition of facial aVect in 12 adolescents (Baird, 1999). As expected, amygdala activations were found without diVerences between left and right amygdala. This pioneering study established the usefulness of facial aVect recognition in adolescents, despite being limited to fearful faces. The same paradigm was used in a somewhat larger group of typically developing children and adolescents (Killgore and Yurgelun‐Todd, 2001) to

fMRI

Ref

Domain

Dx/N

OF

TABLE II MOOD/EMOTION IN TYPICALLY DEVELOPING CHILDREN Mean (SD) age (range) y

Task (s)

Baird, 1999

Emotional face processing

12 TD adolescents

13.9 (12–17)

Passive viewing of fearful faces vs. fixation

Kilgore, 2001

Emotional face processing

19 TDC and adolescents (9 m)

13.5 (2.1) (9–17)

Passive viewing of fearful faces vs. fixation

Thomas, 2001

Emotional face processing

12 TDC and 6 TD adults

11(2.4), (8–16) 24 (6.6)

Passive viewing of fearful vs. neutral faces

DiVerences in Mood/ Emotions "Activations in bilateral amygdala among adolescents while watching fearful faces vs. scrambled (fixation). "Activations in L PFC relative to L AMYG activations in female adolescents but not in males. "Activations in the left AMYG among adults while watching non‐masked fearful faces vs. neutral faces. "Activations in the AMYG were noted while watching neutral vs. fearful faces among children. #Activations with repeated exposure to fearful stimuli.

Comments Only fearful faces examined.

Preliminary evidence of age‐ and gender‐ related maturational changes in PFC‐ AMYG circuit. Developmental diVerence in the AMYG response to fearful vs. neutral faces.

(Continued )

TABLE II (Continued)

Ref

Domain

Dx/N

Mean (SD) age (range) y

Task (s)

Pine, 2001

Emotional face processing

10 TD adolescents and 10 TD adults

13.9 (1.4), 28.5 (4.3)

Passive viewing of masked happy, fearful and neutral faces.

Yang, 2003

Emotional face processing

12 TD adolescents (6 m)

15.7 (1.57)

Passive viewing of happy, sad, or neutral faces during gender discrimination.

Monk, 2003

Emotional face processing and attention

17 TD adolescents and 17 TD adults

13.1 (2.6), 30.8 (3.1)

(1) Attention to emotional (fearful) vs. nonemotional (nose width) faces 2) Passive viewing of fearful vs. neutral faces.

DiVerences in Mood/ Emotions "Activations in right posterior regions in adolescents vs. adults during masked happy faces vs. neutral faces. No diVerence in activations in adolescents vs. adults during masked fearful vs. neutral faces. "Activations in bilateral AMYG among adolescents while watching happy vs. neutral faces. "Activations in right OFC during attention to fearful features in adults relative to adolescents. "Activations in the ACC, OFC, and AMYG during attention to nonemotional features in adolescents relative to adults.

Comments Adolescents diVer from adults in degree of posterior brain involvement while watching masked emotional faces.

Sad faces relative to neutral faces did not produce any significant AMYG signal.

Nelson, 2003

Emotional face processing

Same as Monk, 2003

Encoding of emotional vs. nonemotional (nose width) features of face.

McClure, 2004

Emotional face processing

Same as Monk, 2003

Viewing emotional faces with judgment of degree of threat

"Activations in ACC among adolescents when viewing subsequently remembered angry faces, and more activity in the right temporal pole when viewing subsequently remembered fear faces. "Activations in the subgenual ACC when viewing subsequently remembered happy faces. "Activations in right OFC and right AMYG during angry vs. neutral faces in adult females vs. adult men. "Activations in right OFC and left AMYG viewing angry vs. fearful faces in adult women vs. adult men. "Activation in right AMYG to angry vs. fearful faces in adult women compared with adult men.

Possible role of ACC in emotional recall

Adolescents adjust response based on emotional content, whereas adults adjust responses based on attentional demands.

AMYG, amygdala; TD, typically developing; ACC, anterior cingulate cortex; TDC, typically developing children; OFC, orbitofrontal cortex; PFC, prefrontal cortex.

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explore potential gender diVerences. The authors reported a significant decrease in left amygdala activation with increasing age in girls but not in boys. They also examined diVerences between activation in dorsolateral PFC and amygdala and found that the diVerence in activation decreased with increasing age in girls, again on the left, but not the right. These preliminary findings would not have withstood statistical correction for multiple comparisons, but they suggested that developmental eVects in amygdala to aVective faces might be gender specific. Age eVects in amygdala response were also examined by Thomas and colleagues (2001b) by presenting fearful and neutral faces versus fixation. The six male adults showed amygdalar activation as expected when viewing fearful faces relative to neutral faces. By contrast, the six boys showed greater amygdala activation while watching neutral versus fearful faces. When viewing fearful faces was contrasted to fixation, activation decreased with repeated exposure in boys but not in girls. These intriguing preliminary results suggested that children might have diYculty diVerentiating fearful from neutral faces. Pine and colleagues adopted a ‘‘masked‐face’’ f MRI paradigm to examine developmental eVects on unconscious emotional processing as reflected in the amygdala (Pine et al., 2001). Compared with 10 adults, the 10 adolescents showed greater right posterior activations (ventro‐medial parieto‐occipital cortex) when viewing masked happy faces relative to masked fearful faces. Contrary to their initial hypothesis, adolescents did not show greater amygdala activation than adults while watching masked fearful faces. However, the authors pointed out that signal dropout from susceptibility artifacts in the medial temporal lobe weakened their ability to definitively test their hypothesis. Although prior studies of aVect recognition focused on the role of the amygdalar response to fear, Yang and colleagues hypothesized a broader role for the amygdala in processing multiple types of aVective information. Participants engaged in passive viewing of happy, sad, or neutral faces during a gender judgment task (Yang et al., 2003). The six male and six female adolescents showed bilateral amygdalar activation when viewing happy faces compared with neutral faces. By contrast, sad compared with neutral faces did not produce significant amygdala activation. Unlike earlier studies, these results, which highlighted the importance of examining role of valence to include less frequently studied positively valenced emotions such as happiness, derived from a random eVects analysis, which characterizes participants as a random factor. This more stringent statistical standard implies that the results can be generalized to a given population, rather than being limited to the enrolled participants (as is implicit in fixed‐eVects analyses). They also suggested that the ability to obtain significant amygdalar activation with happy faces provides a readily obtainable baseline to use as a comparison for functional probes of aVective processing in a range of psychiatric disorders.

f MRI IN PEDIATRIC NEUROBEHAVIOR DISORDERS

253

A series of related articles emerged from studies conducted on 17 adolescents and 17 adults by Pine and colleagues. The three sets of articles derived from a single scanning session during which participants observed faces of eight diVerent individuals, each representing one of four emotions (angry, fearful, happy, and neutral), with each face‐emotion combination displayed four times. In distinct blocks, participants were required to rate how afraid they were while viewing each face; in a second block, they rated nose width; in a third block participants viewed the faces passively (i.e., without a response requirement); and in the fourth block, subjects rated hostility of each face. Fixation trials (2 per 16 conditions) combined with the 128 face presentations to total 160 trials collected over a single 14‐minute run. On the basis of this artfully collected data set, the investigators have been able to address questions pertaining to attention, encoding, and emotional judgment. Monk and colleagues examined eVects of attention in three comparisons: directing attention to fearful subjective responses (ratings of how afraid) versus attending to nose width; viewing fearful versus neutral faces while attending to nose width; and viewing fearful versus neutral faces passively (Monk et al., 2003). SPM99 analyses with an event‐related model using a second‐level random eVects analysis of age showed that across attention tasks, adults displayed greater activation in orbitofrontal cortex than adolescents. By contrast, while attending to nose width (a presumably nonemotional feature), adolescents activated the anterior cingulate more than adults. The same pattern of greater activation in adolescents than adults was found on passive viewing of fearful versus neutral faces in the anterior cingulate, right amygdala, and bilateral orbitofrontal cortex. The authors concluded that adults displayed greater ability to modulate relevant brain activity in response to attentional demands, whereas adolescents showed greater responsivity to emotional content. To examine the neuronal correlates of aVective encoding, Nelson et al. used a posttest memory probe (Nelson et al., 2003) to bin those trials that were associated with correct identification of whether the participant had previously observed the target face or not. This was accomplished by presenting subjects with a surprise recognition memory task that contained 32 previously viewed individuals and 32 foils about 40 minutes after the scan. In the absence of age‐related diVerences in recognition memory, the authors found that adolescents showed greater activations in the anterior cingulate relative to adults when viewing subsequently remembered angry faces and more right‐sided temporal activation when viewing subsequently remembered fearful faces. The authors acknowledged that they could not discern the underlying reasons for their findings of greater activation in adolescents than adults for certain types of emotional faces, although they considered the possibility that age‐related ineYciencies require greater activation in emotional circuitry to maintain comparable recognition. After attention and encoding comes evaluation of the aVective content of pertinent stimuli, in particular judgments about the degree of threat posed by

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SEYFFERT AND CASTELLANOS

emotional faces. Age and gender influences were examined in the same data set to test the hypothesis that females would respond more intensely to unambiguous threat cues (McClure et al., 2004). In rating the hostility of each face, adult women activated areas of the orbitofrontal cortex and amygdala selectively to angry faces, whereas adult men did not show diVerential responses to angry versus neutral or versus fearful faces. Female and male adolescents showed the same undiscriminating patterns as adult men. The authors concluded that gender‐based diVerences in brain activation are likely to emerge beyond mid‐ adolescence, although sample size considerations necessarily require caution in interpreting negative results.

III. Pediatric fMR Studies of Psychopathology

A. AUTISM

AND

AUTISTIC SPECTRUM DISORDERS

Autism, the prototypical pervasive developmental disorder, is acknowledged to occur much more frequently than had been previously documented (Fombonne, 2003). Characterized by persistent deficits in social cognition, language, and circumscribed interests, it is often accompanied by repetitive motor behaviors (Eigsti and Shapiro, 2003). The autistic spectrum disorders (ASD) include higher functioning children with Asperger’s syndrome and children with Rett syndrome and pervasive developmental disorder not otherwise specified (PDD‐NOS). Unlike fragile X syndrome, autism is considered a complex genetic disorder (McCarthy, 2004), with a remarkable degree of clinical heterogeneity. Volumetric MRI studies have failed to identify a discrete underlying cerebral lesion in autism, although diVuse regions of abnormal brain development (including regions of the cerebellum, temporal, and frontal lobes) in addition to nonspecific increases in white matter during development have been noted (Herbert et al., 2004). The profound social impairments of autism have suggested a core deficit in the ability to understand the mental states of others (an ability called the Theory of Mind). The neural network for this mentalizing ability that helps us explain and predict the behavior of others is in the process of being worked out (Castelli et al., 2000). Abnormal activations in a distributed neural network involving the medial PFC, superior temporal sulcus, and the temporal pole have been associated with deficits in social reciprocity in adults with autism (for an excellent overview of the neuroanatomical substrates of social cognition in autism see Adolphs [2001] and Pelphrey et al. [2004]). Contemporaneously, translational investigators discovered that great apes and humans possess a novel type of specialized ‘‘mirror neurons,’’ which form part of goal‐oriented action networks

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f MRI IN PEDIATRIC NEUROBEHAVIOR DISORDERS

or mirror systems that involve areas of inferior frontal gyrus, motor and premotor cortex, temporal and posterior parietal regions. Mirror neurons were first identified in nonhuman primates being studied with deep electrodes. These neurons responded equally during the performance of specific actions as when the same action was being observed (Rizzolatti and Craighero, 2004). Mirror neurons are believed to provide the substrate for imitation, which is also profoundly impaired in autism. The full extent of the functions and roles of mirror neurons are still being worked out. The possibility that Theory of Mind deficits may reflect a deficiency in ‘‘cognitive’’ mirror neurons is purely speculative, but we believe may be worth pursuing. At any rate, dysfunction of one or several of the regions implicated in Theory of Mind and mirror systems may well contribute to the observed social and communication deficits in autism (Adolphs, 2001).

B. f MRI

OF

MIRROR SYSTEM

IN

TYPICALLY DEVELOPING CHILDREN

A recent f MRI study of the mirror system in typically developing children (summarized in the following and in Table III) provides the first evidence that children share the same neural network for the mirror system as adults (Ohnishi et al., 2004). By contrasting videotaped object‐related hand actions (e.g., grasping a cup, picking up a telephone) to common objects (e.g., fork, pencil) in 11 right‐ handed children, the authors showed that observation of the object‐related hand movements activated the left dorsal premotor cortex, right parietal operculum, bilateral intraparietal sulci, bilateral superior temporal sulci, bilateral fusiform gyri, and bilateral visual association areas. In a second experiment, triangles were animated randomly or in sequences that suggested an ‘‘intentional stance’’ (e.g., a bigger triangle nudging a smaller triangle out of a box). During the animations that suggested intentional behaviors compared with random movements, activations were noted in the right medial PFC, right dorsolateral PFC, bilateral superior temporal sulcus, right inferior parietal cortex, bilateral fusiform areas, and left cerebellum. These studies suggest that typically developing children have a common mirror neuron/mentalizing neural network that contributes to forming intentional states and thus forming the basis of Theory of Mind. Imaging groups are doubtlessly exploring these paradigms in individuals aVected with autism.

C. THEORY OF MIND AND ABERRANT FACIAL PROCESSING SPECTRUM DISORDERS

IN

AUTISTIC

The concept of mentalizing in social cognition is related to social reciprocity and trust. Developing trust in a conspecific is a complex cognitive process that requires the appropriate interpretation of intentional cues (such as

f MRI

Ref

Domain

Dx/N

Ohnishi, 2004

Object processing

Muller, 2001

Motor

Aut 8

Muller, 2003

Motor

Aut 8

Mean (SD) age (Range) y

OF

TABLE III MENTALIZATION AND AUTISM SPECTRUM DISORDER

Control N

Cont. mean age/y (SD)

Task (s)

11 (6 m)

10

Object vs. static hand movements; intentional vs. random movements

28.4 (8.9) (15–39)

8

28.5 (21–43)

Paced finger press vs. passive viewing

28.4 (8.9) (15–41)

8

28.1 (8.3)

Pseudorandom vs. sequential six‐digit finger tap

DiVerences in TDC/ PDD Ss "Left PMdr, right parietal operculum, bilateral IPS, bilateral STS, bilateral FG "MPFC, right DLPFC, right > than left STS, right IPL, bilateral FG, left cerebellum #Activations in contralateral primary motor, premotor, SMA "Parietooccipital and prefrontal deactivations "Activations in SPL, bilateral premotor, right medial frontal, and left mid and superior frontal regions

Comments TDC activate common neural network as adults.

Overall reduced pattern of motor activations in autism vs. controls. Overall pattern of abnormal frontoparietal activity related to visuomotor learning among autistic compared with typically developing individuals.

Muller, 2004

Motor

Aut 8

26.89 (8.59) (15–41)

8

26.77 (8.22) (21–43)

Pseudorandom vs. sequential eight‐digit finger tap

#Activations in prefrontal region during late visuomotor learning "Activations in R pericentral and R premotor cortex during late states of visuomotor learning

Allen, 2003

Motor, sensory, and attention

Aut 8 (1 f)

26.89 (8.6) (14–38)

8

26.77 (8.22) (13–39)

Attention: match color and shape; Motor: button press and inhibition; Sensory: alternating visual fixation and passive viewing

#Cerebellar activations during attention task in autistic vs. comparison group

Allen, 2004

Motor

Aut 8 (1 f)

26.89 (8.6) (14–38)

8

26.77 (8.22) (13–39)

Self‐paced button press with dominant thumb

Wang, 2004

Face processing

12 AS

12.2

12

11.8

Emotional face matching

"DiVuse cerebellar activations during motor task in autistic vs. comparison group ##Activations for sensory task in both groups "Activations in ipsilateral anterior cerebellum, vermis, and posterior cerebellum in autistic vs. comparison group "Activations in the precuneus, decreased activations in the fusiform gyrus in between group analyses.

Pattern of increased central‐motor activations and decreased frontal activations related to later stages of visuomotor learning among autistic individuals Cerebellar functioning is reduced during selective attention (even when matched for performance).

Increased activations in autistic group correlated with structural cerebellar abnormalities Children and adolescents with ASD may recruit diVerent neural networks and rely on diVerent strategies when processing facial emotions. (Continued )

TABLE III (Continued)

Ref Piggot, 2004

Domain Face processing

Dx/N

Mean (SD) age (Range) y

14 AS

13.1 (2.5)

Control N 10

Cont. mean age/y (SD) 14.4 (3.3)

Task (s) Emotional face matching emotional label and geometric label

DiVerences in TDC/ PDD Ss #Activations in the fusiform gyrus during emotional matching but not emotional labeling. No diVerence in control task.

Comments Children with high‐functioning ASD have decreased ability to label emotional faces as demonstrated by reaction times; however, no diVerence in AMY or PFC activations suggests AMY is not the source of emotionally related face matching.

ASD, autism spectrum disorder; FG, fusiform gyrus; IFG, inferior frontal gyrus; SPL, superior parietal lobe; AS, Asperger syndrome; AMY, amygdala; ITG, inferior temporal gyrus; IPS, inferior parietal sulcus; Aut, Autism; DLPFC, dorsolateral prefrontal cortex; MPFC, medial prefrontal cortex; IPL, inferior parietal lobe; TDC, typically developing children; PMdr, dorsal premotor cortex; STS, superior temporal sulcus.

f MRI IN PEDIATRIC NEUROBEHAVIOR DISORDERS

259

approach‐avoid signals including facial emotions). Evidence that autistic individuals prefer objects to people led to confirmation of the specific hypotheses that individuals with ASD spend less time looking at emotionally salient features such as eyes when looking at faces (Baron‐Cohen et al., 1999). Face recognition paradigms (both explicit and implicit) have been further investigated in the adult literature (Critchley et al., 2000; Pierce et al., 2001, 2004) and engendered an ongoing debate as to the neuroanatomical substrates for abnormal face processing in ASD. Some investigators suggest that evidence of specific dysfunction in regions of the amygdala may represent a core deficit in ASD (Davidson and Slagter, 2000; Howard et al., 2000).

D. ABERRANT FACIAL PROCESSING IN CHILDREN AUTISTIC SPECTRUM DISORDERS

WITH

Variations of a face recognition task have recently been adopted in pediatric samples (Piggot et al., 2004; Wang et al., 2004b) to examine the neural basis of impairments in interpreting facial emotions in children and adolescents with ASD. In the former study, 12 children and adolescents with ASD and 12 typically developing controls matched faces by emotion and assigned a label to facial expressions while undergoing f MRI scans. Both groups engaged similar neural networks during facial emotion processing, including activity in the fusiform gyrus and PFC. However, between‐group analyses in regions of interest revealed that when matching facial expressions, the ASD group showed significantly less activity than controls in the fusiform gyrus, but reliably greater activity in the precuneus. Furthermore, activity in the amygdala was moderated by task demands in controls but not in the ASD group. These findings suggested that children and adolescents with ASD recruit diVerent neural networks and may rely on diVerent strategies when processing facial emotions. In the study by Wang and colleagues (2004a), a modified face recognition task was used in four men with ASD and 10 matched adolescent controls while performing a perceptual emotion match (EM), a linguistic emotion label (EL), and control tasks. Groups did not diVer significantly in accuracy, response time, or region of interest (ROI) activation during the EL task. The ASD group was as accurate as the control group performing the EM task but had a significantly longer response time and lower average fusiform activation. The hypothesis that the high‐functioning ASD group would be less expert and would have reduced fusiform activation was supported in the perceptual but not the linguistic task. The small sample size limits the conclusions that can be made from these preliminary results.

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SEYFFERT AND CASTELLANOS

E. ABERRANT MOTOR PREPARATION AUTISTIC SPECTRUM DISORDERS

IN

CHILDREN

WITH

Aberrant spatial attention, spatial working memory, and oculomotor processing deficits have been reported among adult individuals with autism (Luna et al., 2002; Minshew et al., 1999; Takarae et al., 2004a,b), and similar studies are now being undertaken in samples that include some younger subjects. A series of five motor‐based f MRI studies were undertaken (Allen and Courchesne, 2003; Allen et al., 2004; Muller et al., 2001, 2004) to determine the relative contributions of aberrant visuomotor and cerebellar neural pathways in ASD (see Table III). These studies contrasted motor learning by presenting repetitive versus pseudorandom sequences as stimuli for finger tapping. Overall, results indicate that subjects with autism showed more prefrontal‐parietal–related activity during visuomotor learning than controls (Muller et al., 2001). In a follow‐up study, early and late motor learning were contrasted (Muller et al., 2004). During late motor learning, the autism group had greater activations in right pericentral and right premotor cortex, whereas comparison subjects had greater activation in more anterior frontal regions, including the middle and superior frontal gyri. These articles complement reports of cerebellar involvement in motor and attentional tasks (Allen and Courchesne, 2003; Allen et al., 2004).

F. DYSLEXIA Dyslexia or reading disorder aVects 5–17 % of the school‐age population (Shaywitz, 1998). Defined as a discrepancy between reading achievement scores despite possessing normal intelligence and motivation (Shaywitz, 1998), many children with dyslexia can with eVort achieve reading and spelling skills that permit academic success (Ingvar et al., 2002). The husband and wife team of Sally and Bennett Shaywitz and their colleagues at Yale University have systematically investigated the phonological hypothesis of dyslexia (Shaywitz et al., 1998, 2002, 2003). Their elegant tasks probed orthographic and phonological dimensions of letter case judgment (e.g., Are [t] and [V] both the same case?); single letter rhyme (Do the letters [B] and [V] rhyme?); sounding out pseudo‐words (Do [FETE] and [ JETE] rhyme?); semantic categorization (Are [CORN] and [RICE] in the same category?) and line judgment (Do [\\V] and [//V] match?). (See Table IV for details of individual studies.) The studies conducted by Shaywitz and colleagues have been marked by substantially larger sample sizes than are typical in pediatric functional imaging. For example, their initial study (Shaywitz et al., 1998) involving 61 right‐handed gender‐matched subjects showed significant diVerences between readers with dyslexia and unimpaired readers in the posterior superior temporal gyrus,

TABLE IV f MRI OF DYSLEXIA

Ref

Domain

Dx/N

Mean (SD) age (range) y

Control N

Control mean (SD) age

Task(s)

Corina, 2001

Lexical, phonological processing

8 DYS m

10.8 (3.9)

8 NI

13.9 (2.8)

Auditory and lexical judgments

Shaywitz, 1998

Lexical, phonological processing

29 DYS (14 m)

(16–54)

32 NI (16 m)

(18–63)

Line orientation judgment, letter case judgment, single‐letter rhyme, non‐ word rhyme, and category judgment

DiVerences in Lang. Delay/Dyslexia "Activations in R > L ITG and in L PCG. During lexical judgment, #activations in bilateral MFG and "activations in L OFC. "Activations in anterior IFG and # in STG, AG, and striate regions during phonological processing compared with the unimpaired readers.

Comments Dyslexic and control children diVer in brain activation during auditory language processing skills that do not require reading.

In the dyslexic group, there exists an anomalous pattern of brain activations that suggests that disrupted phonological processing may be the key brain signature that diVerentiates dyslexics from unimpaired readers. (Continued )

TABLE IV (Continued)

Ref

Domain

Dx/N

Mean (SD) age (range) y

Control N

Control mean (SD) age

Shaywitz, 2002

Phonological processing

70 DYS (49 m)

13.3 (2.7) (7–18)

74 NI (43 m)

10.9 (2.4) (7–17)

Shaywitz, 2003

Phonological processing

43 DYS; 19 AIR; 24 PPR

(18.5–22.5)

27 NI

NA

Shaywitz, 2004

Phonological – processing before and after intervention

49 DYS (29 m) 7‐CI 22‐EI

8.1 (.6)

28 NI (15 m)

8 (.5)

Task(s)

DiVerences in Lang. Delay/Dyslexia

Letter case judgment, single‐letter rhyme, non‐word rhyme, and category judgment Monosyllabic word repetition (low and high frequency)

#Activations in L STG and posterior MTG in DYS compared with NI. "Activations in L IFG in NI as compared with DYS during #L parietotemporal and occipitotemporal activations among compensated readers compared with persistent poor performers.

Forced choice letter identification

"Activations in L IFG and L MTG among DYS compared with NI after EI

Comments In dyslexic children, there is a scarcity of left posterior activations Unlike adult dyslexics, children have decreased L IFG activations. Functional connectivity to subtype dyslexics into PPF and CR demonstrates PPF rely more on memory than analytic strategies. Normalization of activations in left hemisphere correlate with improved reading performance after 100 hours of daily individualized phonemic awareness and alphabetical skills.

Temple, 2003

Phonological processing before and after intervention

20 DYS

8–12

20

8–12

Phonological processing (Fast‐ ForWord) task

Aylward, 2003

Phonological processing before and after intervention

10 DYS (6 m)

11.3 (9.8)

11 (6 m)

11.5 (7.9)

Phoneme and morpheme mapping. (Dyslexics completed 28 hours of reading instruction.)

"Activations in left temporoparietal cortex and L IFG, "activations in right‐hemisphere frontal and temporal regions and in the AC gyrus. #Activations before training in dyslexics in L MFG, IFG, right SFG, left MTG and ITG, and bilateral superior parietal regions for phoneme mapping. #Activations for dyslexics in left MFG, right superior PL, and fusiform/occipital regions.

Partial remediation of language‐ processing deficits results in improved reading and normalization of brain function. Further evidence of gains from comprehensive reading instruction is associated with changes in brain function during performance of language tasks.

IFG, inferior frontal gyrus; PoCG, post central gyrus; MFG, middle frontal gyrus; IPL, inferior parietal lobe; SFG, superior frontal gyrus; MPH, methylphenidate; SMA, supplementary motor area; DYS, dyslexia; ITG, inferior temporal gyrus; NI, nonimpaired readers; MTG, middle temporal gyrus; STG , superior temporal gyrus; AIR, accuracy improved readers; PPR, persistently poor readers; CI, control intervention; EI, experimental intervention.

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SEYFFERT AND CASTELLANOS

Wernicke’s area, angular gyrus, and inferior frontal gyrus (Broca’s area). The overall brain activation patterns indicated over‐activation in anterior regions and under activation in posterior brain regions in readers with dyslexia during phonological processing compared with unimpaired readers. In an even larger follow‐up study (Shaywitz et al., 2002), they again noted decreased activations in the left superior temporal gyrus and medial temporal gyrus in readers with dyslexia. By contrast, unimpaired readers had increased activations in the anterior left inferior frontal gyrus compared with children with dyslexia. They concluded that unlike adults with dyslexia, children seemed to have an impaired left posterior hemisphere rather than an overactive left frontal region. A third study sought to gain more detailed information about aberrant phonological processing in this population by use of a monosyllabic word repetition paradigm. This task was adopted to probe an older population of compensated childhood dyslexics (Shaywitz et al., 2003), who demonstrated decreased activations in a left posterior neural network, in contrast to a group of persistently poor readers, who while demonstrating increased activations in this region, were hypothesized to be using diVerent cognitive strategies emphasizing memory more than analytical approaches. Pursuing the hypothesis that phonological deficits represent the core type of information deficit associated with dyslexia, other groups (Corina et al., 2001; Temple et al., 2001) have also reported findings of aberrant auditory processing, although the latter also found decreased activations in orthographic letter matching and decreased activations in temporoparietal regions during rhyme matching in subjects with dyslexia. One of the goals of functional neuroimaging has been to provide an objective brain‐based index of successful interventions that has been documented in three recent studies (Aylward et al., 2003b; Shaywitz et al., 2004; Temple et al., 2003). A computer‐based phonological intervention (Fast‐ForWord, Scientific Learning Corporation, Oakland, CA) was reported to successfully normalize aberrant brain activations in children with dyslexia (Temple et al., 2003) after training on a series of seven phonologic discrimination exercises for 100 minutes a day, 5 days a week for approximately 30 days. Training was associated with increased activations in the left hemisphere in the temporoparietal cortex and inferior frontal gyrus that correlated positively with improved oral language ability. In a study funded by NIH, Aylward and colleagues scanned 10 school‐aged children and 11 typically developing comparison subjects twice. The children with dyslexia completed 28 hours of comprehensive reading instruction, emphasizing phoneme and morpheme awareness between the two scans (Aylward et al., 2003a). Before training, the children with dyslexia showed less activation than controls in the left middle and inferior frontal gyri, right superior frontal gyrus, left middle and inferior temporal gyri, and bilateral superior parietal regions for phoneme mapping. Treatment was associated with improved reading scores and increased

f MRI IN PEDIATRIC NEUROBEHAVIOR DISORDERS

265

brain activation during both tasks, such that quantity and pattern of activation for children with dyslexia after treatment closely resembled that of controls. In an even more impressive study, reading fluency was examined in readers with dyslexia and controls, with the readers with dyslexia undergoing a year‐long intervention in the community or through an experimental program (Shaywitz et al., 2004). Children with dyslexia receiving the experimental intervention made the greatest gains in reading performance, which was correlated with increased left hemispheric activations in the left inferior frontal gyrus and left medial temporal gyrus. These studies provide further evidence that gains from comprehensive reading instruction can be associated with observable changes in brain function during performance of language tasks, even a year after completion of training (Shaywitz et al., 2004).

G. STUDIES

OF

ADHD

ADHD is characterized by excessive hyperactivity, impulsivity, and inattention for a given developmental age. It has a prevalence of approximately 5–7.5% among school‐aged children (Barbaresi et al., 2004). ADHD is arguably the most extensively studied behavioral disorder and has also been extensively studied by pediatric neuroimaging. Numerous reports have found structural deficits associated with ADHD. These volumetric studies show decreased volumes in the cerebellum, basal ganglia, frontal lobes, and corpus callosum (Durston, 2003). In one of the early f MRI studies in ADHD, Vaidya and colleagues (1998) used two Go/NoGo tasks in 16 boys (ADHD n ¼ 10) both on and oV the stimulant methylphenidate. Their complex design (two task versions, oV‐ and on‐medication) has made it diYcult to interpret the interesting but unreplicated findings of a diVerential eVect of stimulant medication during one of the tasks. Methylphenidate increased striatal activation in children with ADHD but reduced it in healthy comparison children. In a series of studies conducted at King’s College, London, Rubia and colleagues (1999, 2000b) examined specific prefrontal regions as substrates for inhibitory deficits operationalized by the Stop Task and a motor timing task (see Tables V and VI for details of individual studies). Overall, less brain activation was found in the adolescents with ADHD than the controls (Rubia et al., 1999). Moreover, specific regions of hypoactivation were noted in right mesial prefrontal cortex during both tasks and in the right inferior prefrontal cortex and left caudate during the Stop Task. Neuropsychological data were also integrated with a battery of motor timing and inhibitory tasks to compare ADHD with a neuropsychiatric control group (Rubia, 2002). Once again, a pattern of decreased right prefrontal activations was observed in the group with ADHD. These aberrant activations were related to increasing task diYculty during inhibitory tasks and not specifically related to motor timing

f MRI

Ref

Domain

Sunshine, 1997

Attention

Vaidya, 1998

Inhibitory control

Rubia, 1999

Inhibitory control

Dx/N 10

Mean (SD) age (range) y

OF

TABLE V ATTENTION DEFICIT HYPERACTIVITY DISORDER

Control n

Control mean (SD) age

Task(s)

14–51

N/A

N/A

Sustained visual attention

10 m

10.5 (1.4)

6m

9.3 (1.5)

Go/NoGo on and oV MPH

7m

15.7

9m

15.0

Stop and Delay with intermittent vs. continuous finger tap

(7 m, 3 f )

DiVerences in ADHD "R‐sided activations in MFG and SFG with bilateral activations in superior and inferior PL. #Striatal activations during response inhibition oV MPH. "Striatal activations on MPH.

"Activations in right pre and PoCG, right IPL and right caudate; #activations in the right mesial frontal lobe during stop task. "Activations bilateral putamen, R SMA, and right extrastriate; #activations in ant., post. cingulate gyrus during delay task.

Comments Poor study design. Sustained visual attention correlates most highly with R MFG. First fMRI study of medicated vs. unmedicated children. ADHD children have poor inhibitory performance that is improved on MPH. Predominant hypoactivation found in right mesial frontal cortex suggests developmental lag in ADHD.

Rubia, 2000

Inhibitory control

7m

15.7

17 m

21.6 (8.45)

Stop and Delay with intermittent vs. continuous finger tap

Rubia, 2001

Inhibition, temporal processing

16 (13 m, 3 f)

11 (2.4)

16 (12 m, 4f)

11.2 (2.3)

MARS battery: Go/NoGo, Stop, Delay, reversal, synchronized finger tap and inhibition of finger tap.

#Activations in right frontal areas. Activated diVerent region than adolescents or young adults with R pre‐and PoCG, right IPL and right caudate during stop task. # Frontal activations (except for right SMA) during delay task and bilateral caudate during Stop task. #R prefrontal activations noted in adolescent ADHD group during higher diYculty inhibitory tasks, not on sensorimotor timing tasks.

F/U study. Significant age eVect on motor timing with older TD group performing better than ADHD group. Suggests frontal functionalization part of normal adolescent development.

ADHD shows deficits in motor inhibition but not motor timing.

(Continued )

TABLE V (Continued)

Ref Durston, 2003

Domain Inhibitory control

Dx/N 7

Mean (SD) age (range) y 8.7 (1.5)

(6 m, 1 f )

Control n 7

Control mean (SD) age 8.6 (1.6)

(6 m, 1 f)

Task(s) Variation of Go/NoGo

DiVerences in ADHD "Activation in MFG, SFG vs. comparison, which had "activations in caudate. "Activation for the Go vs. NoGo group in the left PMC. "Activations for the NoGo vs Go condition in R IPL.

Comments TDC have more ventral striatal activations compared with ADHD children.

IFG, inferior frontal gyrus; PoCG, post central gyrus; MPH, methylphenidate; MFG, medial frontal gyrus; IPL, inferior parietal lobe; PFC, prefrontal cortex; SFG, superior frontal gyrus; SMA, supplementary motor area; TD, typically developing.

f MRI

Ref

Domain

Dx/N

OF

TABLE VI ATTENTION DEFICIT HYPERACTIVITY DISORDER—CONTINUED

Mean (SD) age (range) y

Tamm, 2004

Inhibitory control

14 adolescent m

16 (1.4)

Schulz, 2004

Inhibitory control

10 adolescent m

17.9 (1.6)

Control n 12

Control mean (SD) age

Task (s)

15.6 (0.8)

Go/NoGo

"Activations in L temporal gyrus; #activations of R anterior cingulate cortex, SMA

17.5 (1.2)

Go/NoGo

"Activations in R MFG, IFG, and IPL.

adolescent m

9 adolescent m

DiVerences in ADHD

Comments Hypoactivation of R frontal structures suggests problem with stimulus response selection and task switching in ADHD. Results consistent with previous fMRI studies. Increased vPFC activity may correspond to response inhibition in ADHD. (Continued )

TABLE VI (Continued)

Ref

Domain

Dx/N

Mean (SD) age (range) y

Schulz, 2005

Inhibitory control

10 adolescent m

8.8 (1.1)

Shafritz et al., 2004

Attention processing

15 adolescents 11 m, 4 f (8 RD and 4 ADHD þ RD)

15.1 (0.3); 15.0 (0.5)

Control n 5

Control mean (SD) age

Task (s)

10

Go/NoGo

""Activations in vPFC among persisters during NoGo vs. Go with "activations in remitters and fewest activations in controls.

16.6 (0.8)

Selective attention

#Activations of L vPFC in MPH, ADHD, and RD groups. #Activation of L MTG in ADHD alone. "Activations in L vPFC during MPH.

adolescent m

14 adolescents

DiVerences in ADHD

Comments Small sample size for each group. Gradients of ventral PFC brain activation related to persistent vs. remitting nature of ADHD. RCT with f MRI study. Subcategorization according to comorbid RD.

Booth, 2005

Inhibitory control and attention

12 children 8 m, 4f

12 children (7 m, 5 f)

10.9

Selective attention, Go/NoGo

#Activations in SPL during selective visual attention. #Activations frontostriatal (interior, middle, and superior as well as medial frontal gyri and caudate) during Go/ NoGo.

Hypoactivity in response inhibition task vs. selective visual attention task supports hypothesis that response inhibition is a separable deficit in ADHD.

IFG, inferior frontal gyrus; SMA, supplementary motor area; MFG, middle frontal gyrus; RCT, randomized controlled trial; PFC, prefrontal cortex; SPL, superior parietal lobe; MTG, middle temporal gyrus; IPL, inferior parietal lobe; MPH, methylphenidate; vPFC, ventral prefrontal cortex.

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problems. These studies have supported the conclusion that a developmental lag in related frontal‐striatal circuits underlies ADHD. Investigators continue to explore versions of the Go/NoGo task to tease apart the relationship of inhibitory control to other cognitive control deficits in children with ADHD (Durston et al., 2003; Schulz et al., 2004, 2005; Tamm et al., 2004). Durston and colleagues used an oddball version of a Go/NoGo task in an event‐ related design and found that children with ADHD have diYculty even with a single Go trial preceding a NoGo trial. Moreover, children with ADHD demonstrated fewer frontostriatal activations and more in the posterior and dorsolateral prefrontal regions. Tamm and colleagues also used an event‐related Go/NoGo task modified to control for novelty. Individuals with ADHD showed marked abnormalities in brain activation during response inhibition, including hypoactivation of the anterior/mid‐cingulate cortex extending to the supplementary motor area and hyperactivation of the left temporal gyrus. These results suggest that decreased activation in frontal regions may reflect a core deficit in response/ task‐switching abilities in ADHD. The clinical heterogeneity of ADHD has been recently approached in a pair of small but instructive studies (Schulz et al., 2004, 2005). Using a Go/NoGo task to scan 10 male adolescents who had been diagnosed with DSM‐III‐R ADHD when they were 7–11 years old and 9 age‐, gender‐, and IQ‐matched comparison subjects, they found greater activation in the left anterior cingulate gyrus, bilateral frontopolar regions, bilateral ventrolateral prefrontal cortex, and left medial frontal gyrus in the adolescents with childhood ADHD than in the typically developing adolescents (Schulz et al., 2004). In a subsequent study, five adolescents who continued to meet full diagnostic criteria for DSM‐IV ADHD (persisters) were contrasted with five whose symptoms had remitted at least partially. Persisters, remitters, and five controls were scanned. Persisters made the most commission errors (33%) and showed the greatest activations on the Go/NoGo task in the ventrolateral prefrontal cortex. Remitters made fewer commission errors (24%) and demonstrated decreased activations. The lowest activations and fewest errors (13%) were noted in the comparison group. These preliminary results were interpreted as evidence that developmental changes in ADHD symptoms are associated with functional changes in ventrolateral prefrontal cortex activity.

H. CONDUCT DISORDER Conduct disorder (CD) represents the malignant extreme of the disruptive behavior disorders. In the first f MRI study of children with CD, negative aVective pictures were used to demonstrate a pronounced deactivation in the right dorsal anterior cingulate cortex of 13 male adolescents with severe CD aged

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9–15 years (Sterzer et al., 2005). After correcting for anxiety and depressive symptoms, additional regions of deactivation were found in response to negative pictures in the left amygdala compared with control subjects. This initial study suggests that deficits in emotional processing and in cognitive control play a role in severe CD among children and adolescents.

I. STUDIES

OF

TOURETTE’s DISORDER (TD)

Tourette’s disorder is another disorder that was once considered rare and is now acknowledged to exist in a spectrum of severity that includes a broad range of comorbid disorders, notably ADHD and obsessive‐compulsive disorder. Dysfunction of corticostriatal circuitry has been implicated in Tourette’s disorder. Tic suppression in adult patients with TD highlighted the involvement of a dysfunctional corticostriatothalamocortical circuit (Peterson et al., 1998). A recent case study in a 15‐year‐old girl with coprolalia could be conducted because phonic tics consisting of the ‘‘f. . .’’ word occurred every 2 seconds. A comparison subject was asked to mimic the same pattern of coprolalia. Activations in the right precentral gyrus, central gyrus, cuneus, occipital, and prefrontal gyri during phonic tics is consistent with the Peterson results (Gates et al., 2004). J. CHILDHOOD‐ONSET ANXIETY AND MOOD DISORDERS Approximately 10% of children and adolescents are aVected by some kind of anxiety disorder, such as general anxiety, social anxiety, separation anxiety, specific phobias, panic disorder, and posttraumatic stress disorders (Asbahr, 2004). Converging neuroimaging and neuroscience evidence suggest that a common emotional circuit may link these disparate entities (Charney, 2003; Kent and Rauch, 2003). The role of the amygdala in mood disorders in children (Thomas et al., 2001a) was examined in a block‐design study that used passive viewing of angry and neutral faces to test the hypothesis that activation of the amygdala would correlate directly among children with anxiety and inversely among children with depression (see Table VII for details of individual studies). Children with anxiety showed greater activations in the right amygdala. In a second experiment, five girls with major depressive disorder were compared with five anxious and five healthy girls during the same passive viewing task. Increased activations were detected in the right amygdala to fearful versus neutral faces among anxious children. No change was observed in the right amygdala responses to fearful versus neutral faces among depressed children. Decreased activations during fearful versus neutral faces were noted in the left amygdala in depressed children. The authors concluded that although the amygdala was

f MRI

Ref Thomas, 2001

Domain Emotional face processing

Dx/N

Mean (SD) age (range) y.

12 children with anxiety or panic disorder. In second experiment, 5 anxious and 5 depressed children

12.8 (2.1) and 12.3 (2.7)

OF

TABLE VII ANXIETY AND BIPOLAR DISORDERS

Control n

Control mean (SD) age

12 TDC (ages 8–16). In second experiment, 5 TDC contrasted.

12.1 (2.6)

Task (s) Angry and neutral faces

DiVerences in ADs "Activations in AMY among anxious children responding to fearful faces compared with healthy children

#Activations in AMY among depressed children responding to angry faces.

Paulus, 2004

Decision making

13 (12f, 1 m) adolescents/ young adults with HTA

18.3 (.8)

13 (12 f, 1m) adolescents/ young adults with NTA

18.5 (.9)

Two choice prediction task at three error rates

"Activations in the rostral cingulate and lateral prefrontal cortex during low error‐rate condition.

Blumberg, 2004

Inhibitory control

10 adolescents

13.6 (2.8)

10 TD adolescents

16,7 (2.2)

Color naming Stroop

"Activations in subcortical and paralimbic regions to include putamen and thalamus.

Comments AMY function is aVected in both anxiety and depression during childhood and adolescence. This disruption is specific to the child’s own rating of everyday anxiety. HTA individuals expend more processing time than NTA group when there is low chance of an incorrect response. Bipolar symptoms associated with significant rostroventral striatal and ventral prefrontal activity in contrast to depressive symptoms that correlated with activations in the ventral striatum.

Chang, 2004

VSWM & emotional processing

12 children

14.5 (3.0)

10 TDC

14.4 (3.2)

Emotional decision and VSWM

"Activations in the bilateral ACC, L putamen, L thalamus, L DLPFC, and R IFG during VSWM.

"Activations in bilateral DLPFC, inferior prefrontal gyrus, and right insula for negative pictures, and " activation in the caudate and thalamus (bilaterally) as well as the L MFG and left SFG and left ACC for positive pictures.

Children with bipolar disorder may have aberrant functioning of prefrontal and subcortical circuits related to mood and arousal.

ACC, anterior cingulate cortex; VSWM, visuospatial working memory; MFG, medial frontal gyrus; AMY, amygdala; TDC, typically developing children; HTA, high trait anxious; DLPFC, dorsolateral prefrontal cortex; NTA, normal trait anxious; IFG, inferior frontal gyrus; PFC, prefrontal cortex; SFG, superior frontal gyrus.

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implicated in both anxiety and depression in children, disruption of amygdala function seems to be specific to the child’s own rating of anxiety. State anxiety was examined in a two‐choice prediction task designed for adolescent girls (Paulus et al., 2004). Individuals with normal and high trait anxiety were asked to predict on which side of a diagram of a house a car would appear. Preassigned error rates were 20%, 50%, or 80%. Increased activations were found in the rostral cingulate and lateral prefrontal cortex of individuals with high trait anxiety during the low error‐rate condition. The authors suggest that subjects with high trait anxiety may expend more processing time than participants with normal trait anxiety during times when there is low chance of an incorrect response.

K. ADOLESCENT EATING DISORDER Eating disorders include anorexia nervosa and bulimia. Anorexia particularly is associated with significant morbidity and mortality, and volumetric studies of adolescent girls recovering from anorexia have revealed persistent decreases in cortical gray matter (Katzman et al., 1997). Functional MRI has been used to delineate the relationship between aberrant neural circuitry and distorted body image among adolescent girls. Seeger and colleagues performed a pilot study of three adolescent female patients with anorexia and three healthy controls (Seeger et al., 2002). The investigators used digitally distorted body images using an innovative computer‐based video technique. Adolescents with anorexia showed significant activations in the right amygdala, right fusiform gyrus, and brainstem. The authors attributed these activations to an underlying fear network associated with seeing the distorted body images. In a larger study, patients showed greater activation in the prefrontal cortex and the inferior parietal lobule, including the anterior intraparietal sulcus, than comparison subjects, only in response to their own pictures, whereas controls did not diVerentiate between images of themselves and others (Wagner et al., 2003). These findings point to an aberrant neural network that consists of structures involved in attention, visuospatial processing, and self‐reflection.

L. ADOLESCENT BIPOLAR DISORDER Bipolar disorder among children and young adolescents has been the focus of substantial and vigorous debate. The first study of adolescent bipolar disorder used the color‐naming Stroop paradigm and found increased significantly greater activations in the left putamen and thalamus in the bipolar than in the comparison group (Blumberg et al., 2004). These findings suggest the presence of

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subcortical deficits among adolescents with bipolar disorder (see Table VII for details of individual studies). A block design study explored visuospatial WM and aVective processing in bipolar disorder in children and adolescents (Chang et al., 2004). A nonemotional n‐back task, as well as emotionally provocative and neutral pictures from the International AVective Picture System, was used to probe possible mood‐based dysfunction of interacting prefrontal WM and anterior cingulate cortex (ACC) attentional circuits. During the WM task, subjects with bipolar disorder had greater activations in the bilateral ACC, left putamen, left thalamus, left dorsolateral PFC, and right inferior frontal gyrus. For the aVective probe, subjects with bipolar disorder had greater activations in the bilateral dorsolateral PFC, inferior prefrontal gyrus, and right insula when viewing negative pictures, whereas comparison subjects had greater activations in the right posterior cingulate gyrus. When viewing positive pictures, youth with bipolar disorder had greater activation in the caudate and thalamus (bilaterally), as well as the left middle and left superior frontal gyri and left ACC. The authors concluded that their results suggest aberrant functioning of prefrontal and subcortical circuits related to mood and arousal in juvenile bipolar disorder.

IV. Summary

Pediatric functional neuroimaging has finally come into its own in the past few years, as this brief review has shown. Although f MRI holds great promise for explaining the neural bases of neuropsychiatric and behavioral disorders, the field is still in its infancy, and most of the studies cited are still exploratory. Longitudinal f MRI studies would be optimal given the developmental aspects, but the continuing changes in hardware, software, and paradigms make it diYcult to establish suYciently reliable and impossible to obtain absolute quantitative baseline measures. Thus, the field is progressing, but within fairly restrictive constraints. The relationship between physiological response and hemodynamic activation as reflected in BOLD f MRI is sluggish at best, and susceptibility artifacts make signal interpretation problematic in key regions such as the amygdala or orbitofrontal cortex. For these reasons, investigations of brain development in health and in psychopathology will still depend on a combination of techniques that include longitudinal structural MRI studies and diVusion tensor imaging to provide necessary spatial information concerning the developmental trajectories that will inform future functional imaging studies (Durston et al., 2001). In addition to the necessary synthesis of structural and functional imaging data, it may be necessary to combine diVering functional modalities to improve the quality of information that is available. Simultaneous recording of

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f MRI and electroencephalographic waveforms can improve spatial and temporal resolution of neuronal events. Likewise, proton‐magnetic resonance spectroscopy (1‐H‐MRS) provides neurochemical information that will help shed light on regional metabolic diVerences (Lyoo and Renshaw, 2002). Another strategy that holds promise for brain imagers is the growing movement to create human electronic databases that permit sharing of f MRI studies of normal and pathological human behavior (Toga and Thompson, 2003; Van Horn, 2003, 2004; also, see Chapters 1 and 2, Part A of this volume). The advantage of these databases is twofold: (1) to systematically organize the voluminous information that is contained in neuroimaging observations of the human brain, (2) to disseminate primary imaging data while respecting ethical, legal, and social considerations. Although a separate pediatric neurobehavioral f MRI database has yet to be established, there would be theoretical advantages to this kind of undertaking. Creating systematic atlases that would serve to categorize normal and atypical language development, for example, would be a significant advantage to those neuroscientists interested in dyslexia or other types of language disorders. Given the dynamic relationship between development and genetic factors, the addition of genetic information to such databases may ultimately be a direction worth pursuing. In the meantime, f MRI studies continue to explore neurobehavioral disorders. As numerous clinical and basic science research studies have demonstrated a remarkable degree of neuroplasticity in the pediatric brain, it remains to be seen what additional cognitive or behavioral interventions may ultimately be incorporated into the clinical arsenal of the pediatric behavioral specialist over the next several years.

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STRUCTURAL MRI AND BRAIN DEVELOPMENT

Paul M. Thompson,* Elizabeth R. Sowell,* Nitin Gogtay,y Jay N. Giedd,y Christine N. Vidal,* Kiralee M. Hayashi,* Alex Leow,* Rob Nicolson,z Judith L. Rapoport,y and Arthur W. Toga* *Laboratory of Neuro Imaging, Brain Mapping Division, Department of Neurology, University of California, Los Angeles School of Medicine, Los Angeles, California 90095-1769 y Child Psychiatry Branch, National Institute of Mental Health, Bethesda, Maryland 20892 z Department of Psychiatry and Biomedical Physics, The University of Western Ontario London N6A 5B8, Ontario, Canada

I. Introduction II. MRI Scanning and Image Analysis A. 3D Anatomical Scanning B. Types of MRI Analysis III. Growth Curves for DiVerent Brain Regions A. Gray Matter in Each Lobe of the Brain B. Modeling Developmental Trajectories with Mixed Models C. Trajectories in Large Samples of Subjects D. Mechanisms IV. Cortical Mapping A. Mapping the Cortex B. Statistical Analysis V. Time‐Lapse Maps of Brain Change A. Gray Matter Changes over the Lifespan B. Time‐Lapse Maps of Development C. Brain–Behavior Relationships D. Childhood‐Onset Schizophrenia VI. Mapping Brain Growth A. Tensor Maps B. Corpus Callosum Growth C. Tensor‐Based Morphometry in Autistic Children VII. Conclusion References

Magnetic resonance imaging (MRI) scans provide exceptionally detailed information on how the human brain changes throughout childhood, adolescence, and into old age. We describe several approaches for understanding developmental changes in brain structures based on MRI. Atlas‐based ‘‘parcellation’’ methods, for example, measure volumes of brain substructures, revealing how they change with age. Growth curves for diVerent brain structures can be

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compiled, describing the expected trajectories of normal development. Additional computational anatomy techniques can be used to map spatial patterns of brain growth and tissue loss in individual children. Changes in cortical features, such as gray matter thickness, asymmetry, and complexity, can also be mapped. Individual maps can then be combined across subjects to make statistical maps or dynamic ‘‘time‐lapse movies’’ that reveal systematic features of brain development in population subgroups while retaining information on their variance. We review several large‐scale studies of brain development, including longitudinal studies in which children were scanned repeatedly with structural MRI at 2‐year intervals for periods of up to 10 years. Image processing algorithms were then applied to recover detailed information from the resulting image databases. We describe the approaches necessary to compare brain MRI data across groups diVering in age, covaried with gender, developmental disorders, or genotype. These methods reveal unsuspected links between development and cognition and can help discover genetic and environmental factors that aVect development. These brain maps also chart the anatomical sequence of healthy brain maturation and visualize how it is derailed in neuropsychiatric disorders such as schizophrenia, autism, fetal alcohol syndrome, and Williams syndrome.

I. Introduction

The quest to understand how the human brain develops is one of the most fascinating challenges in modern science. Brain cells proliferate in early embryonic life in a carefully orchestrated sequence of neural cell migration and maturation. This leads to a human brain with approximately 100 billion neurons at birth. A newborn child’s brain is only a fifth of its adult volume, and it continues to grow and specialize according to a precise genetic program, with modifications driven by environmental influences, both positive and negative. Negative environmental influences, such as malnutrition, maternal drug abuse, or viral infection, can impair or delay brain development. With stimulation and experience, the dendritic branching of neurons greatly increases, as do the numbers of synaptic connections. As layers of insulating lipids are laid down on axons through the process of myelination, the conduction speed of fibers that interconnect diVerent brain regions also increases 100‐fold. This exuberant increase in brain connections is followed by an enigmatic process of dendritic ‘‘pruning’’ and synapse elimination, which is thought to lead to a more eYcient set of connections that are continuously remodeled throughout life.

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MRI scanning of the brain can document these large‐scale processes of brain development in detail. It enables insight into the sequence and timing of these developmental processes, documenting how they occur in living subjects. Individual brain scans provide exquisitely detailed maps of the brain’s anatomy, whereas functional and metabolic scans (e.g., functional MRI and positron emission tomography [PET] scans) provide complementary information on brain activation and physiology as these change and mature. In the 1990s, databases began to be assembled containing brain scans from hundreds of children scanned repeatedly over time (Giedd et al., 2005; Gogtay et al., 2004; Jernigan et al., 1991; Reiss et al., 1996). Repeated scanning of the same individual at diVerent times during brain development makes it possible to capture ‘‘snapshots’’ of these growth processes and reconstruct dynamic maps that describe how they play out in time. This wealth of anatomical data has, in turn, fueled the development of sophisticated image processing techniques that measure growth rates for diVerent brain structures (Giedd et al., 1999; Lange et al., 1997; Thompson et al., 2000). These developmental patterns vary during adolescence and over the adult lifespan (Sowell et al., 1999, 2003, 2004), and they diVer in clinical populations with developmental disorders (Gogtay et al., 2005; Rapoport et al., 1999). More recently, time‐lapse movies have been reconstructed to describe the dynamic sequence of cortical development (Gogtay et al., 2004, 2005). They show shifting patterns of tissue growth and loss, even in healthy children. In those with early‐onset schizophrenia and bipolar illness, these processes are believed to be exaggerated or derailed (Gogtay et al., 2004; Thompson et al., 2001, 2003). Finally, statistics have been developed to capture how growth rates diVer among brain substructures. With this normative data in hand, brain deficits in a variety of developmental disorders can be more readily distinguished from changes within the normal range. At the cutting edge of these neuroimaging projects are worldwide eVorts to identify factors that aVect brain development positively or negatively. Quantitative genetic maps of the brain can clarify how genes and environmental factors (such as family upbringing, skill acquisition, and learning) can impact development, as well as cognition and intelligence (Cannon et al., 2005; Gray and Thompson, 2004; Thompson et al., 2001). Other brain‐mapping eVorts are discovering how neurological or psychiatric disorders aVect the brain and how and where in the brain medications prevent or retard these changes. As such, MRI data are a witness to the sequence of brain development. Some of the observed changes correlate strongly with clinical, behavioral, and cognitive diVerences, but in other cases, the cellular basis for the changes is not yet completely understood (Bartzokis et al., 2004). In this chapter, we review imaging advances that have revealed new information on brain development. Although the imaging technologies themselves are maturing (e.g., functional MRI and diVusion imaging of fiber pathways), a quiet

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revolution is occurring in the analysis techniques to obtain information from the resulting brain images. In some respects, progress in the image analysis arena has vastly improved the power of MRI. Here we focus on the new information that these image analysis techniques oVer, what they have revealed about normal brain development, and what they have found in a variety of childhood brain disorders (e.g., early‐onset schizophrenia, autism, fetal alcohol syndrome, and genetic disorders such as Williams syndrome). We first summarize some basic information on brain MRI and the structural development of the brain. We then analyze these changes in more detail with brain maps and time‐lapse movies.

II. MRI Scanning and Image Analysis

A. 3D ANATOMICAL SCANNING By the early 1990s, MRI was gradually replacing CT (computed tomography, or CAT scanning) as the technique of choice to image brain structure. On the basis of nuclear magnetic resonance (Bloch, 1946; Lauterbur, 1973), MRI scanning requires no ionizing radiation (i.e., no radioactive tracers or exposure to X‐ rays), so it is safe for use in developmental studies of children and for repeated image acquisitions over time. A detailed description of MRI physics is provided elsewhere (Elster, 1994). The subject is placed in a strong magnetic field created by a superconducting magnet surrounding the bore of a scanner. The subject, lying in the scanner, is exposed to brief pulses of radiofrequency radiation from a transmission coil around the subject’s head. The energy of a radiofrequency signal transmitted into the brain tissue can be absorbed by the nuclei of its constituent hydrogen atoms. This energy is then released, and the rate at which it is released (magnetic ‘‘relaxation’’) depends on the local molecular environment (diVering, for example, in gray and white matter). Gray and white matter, as well as other tissues, can therefore be distinguished with MRI. MRI scanners are also programmed to create spatial gradients in the underlying magnetic field. The spatial location of tissues with diVerent molecular content is retained using Fourier encoding, creating an MR image. A typical MR image is shown in Fig. 1A. Note the clear intensity contrast between gray matter structures (such as the cortex and basal ganglia) and the intervening white matter (consisting of myelinated fiber pathways that interconnect brain structures). The quantity of gray and white matter in the brain, as well as cerebrospinal fluid (CSF) in the ventricles and cortical sulci, can, therefore, be computed and compared across subjects and over time. To create maps of tissue types (Fig. 1B), tissue classification (or ‘‘segmentation’’) algorithms can be used. These computer programs typically model the intensities in the image as arising

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FIG. 1. Typical processing steps in an analysis of MRI brain scans. (A) A typical coronal section from a T1‐weighted MRI scan of the brain. (B) The result of applying a tissue classification approach to classify image voxels as gray matter (green colors), white matter (blue colors), or cerebrospinal fluid (CSF; red colors). Non‐brain tissues such as scalp and meninges surrounding the brain have also been digitally edited from the image. (C) A parcellation of the brain into the frontal lobe (shown in blue), parietal lobe ( green), occipital lobe (red ), and temporal lobe ( yellow). This subdivision of anatomy is performed with the aid of a cortical surface model on which sulcal landmarks separating the lobes can be reliably identified. Once partitioned in this way, the volumes of each tissue type in the major lobes can be computed and growth curves established for each major lobe.

from a set of Gaussian functions. By estimating the parameters of these functions, the programs infer how likely it is that each pixel in the image is primarily made up of gray matter, white matter, CSF, or a background class (e.g., consisting of dura and meningeal tissue or air surrounding the head). Once the 3D maps of these tissue classes are determined, their volumes are measured. They may also be subdivided into smaller regions to determine the amount of each tissue type in each lobe (see e.g., Fig. 1C).

B. TYPES

OF

MRI ANALYSIS

Regional volumes of brain structures can be analyzed statistically. Most commonly, total brain volumes and regional volumes are computed for gray and white matter in specific lobes of the brain, as well as volumes for deep gray matter nuclei, such as the basal ganglia and hippocampus ( Jernigan et al., 1991; Kennedy et al., 1998). MRI analysis methods have advanced significantly in recent years. We discuss three such methods (with increasing complexity) that have each yielded considerable information about brain development. These are the following. 1. Parcellation Methods In these, an image analyst uses a formalized anatomical protocol (usually based on a brain atlas or rules agreed on by anatomists) to trace the boundaries of individual structures in cross‐sections from each MRI scan. In a more automated

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version of this approach, a standard anatomical brain data set, such as a digital brain atlas, is labeled by hand and is elastically deformed or ‘‘warped’’ onto the MRI data sets, adapting to variations in individual anatomy (Collins et al., 1995; see Thompson and Toga, 2003 for a review of these ‘‘deformable atlas’’ approaches). By transferring the labels from the digital atlas to the MRI scan of the individual aligned with it, 3D models of the structure in each individual are then reconstructed. Depending on how well the atlas anatomy fits that of the individual, this process may be automated, or the structure models may be treated as approximate and adjusted later by hand. The measured volumes can then be analyzed statistically (e.g., with multiple regression or analysis of variance) to assess the magnitude and significance of any changes with age (Fig. 2) or gender diVerences. Group diVerences in brain structure can also be assessed, and factors can be identified that correlate with regional brain volumes, such as gender (Fig. 3), genotype, or disease, or clinical measures obtained from the subjects in the study. See Chapter 1 (this volume) for a more detailed description. 2. Anatomical Mapping Methods Rather than splitting the brain up into components, these methods provide 3D statistical maps of anatomy for diVerent groups of subjects or subjects of diVerent ages. Group average maps of anatomy can be computed and compared to identify where diVerences in brain structure can be detected. Maps of statistics are compiled showing brain regions with statistical diVerences in structure across groups. Regions can also be identified that show significant changes in specific tissue parameters (e.g., gray matter thickness, hemispheric asymmetry) over time. Before brain maps can be created, multiple brain MRI data sets are first aligned into a common 3D coordinate space, so that anatomy can be indexed using 3D coordinates. Geometrical models of structures are then reconstructed for each individual, often by hand or with the aid of automated software. These models are sometimes represented as geometrical surfaces in 3D, and their shape can be averaged across subjects to provide a composite or ‘‘average’’ representation of anatomy for a group. Quantitative maps and models can then be made to show shape changes, as well as growth and atrophy in specific structures such as the hippocampus (Csernansky et al., 1999; Narr et al., 2000; Thompson et al., 2004), corpus callosum (Sowell et al., 2001a), or basal ganglia (Thompson et al., 2000). The cortical surface provides unique challenges when trying to find consistent and systematic patterns of change during development. Because gyral patterns of the cortex diVer markedly across individuals, more complex methods are required to average and compare cortical data across subjects and groups. One such approach is called cortical pattern matching, which we describe in the following (Fig. 4; Thompson et al., 2004).

FIG. 2. Developmental trajectories for the volumes of tissue types in diVerent lobes of the brain (data from Giedd et al. [2005]). (A) The total cerebral volume for 224 females (375 scans) and 287 males (532 scans) scanned longitudinally between the ages of 4 and 26. Blue dots indicate males, red dots females, and scans of the same subject over time are indicated by connected dots. Note the wide individual variation. Boxes indicate the subject with largest and smallest brain volumes, and these diVer by a factor of two. Mixed models are used to fit quadratic functions of age to the developmental trajectories of white matter (B) and gray matter in the frontal (C), parietal (D), and temporal (E) lobes; 95% confidence limits on the fitted curves are also plotted. Males are shown in blue, females in red. Note the gender diVerences in shape and peak ages for diVerent tissue types.

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3. Time‐Lapse Movies Anatomical modeling methods can be used to create static statistical maps of anatomy, and the same idea can be extended to create dynamic representations, illustrated as time‐lapse movies. These animations reveal how specific features of the brain (e.g., gray matter thickness or cortical shape) change with age, over the entire human lifespan, or in specific diseases. Thus far, these movies have been used to track shifting processes of cortical development in childhood and adolescence (Gogtay et al., 2004), subtle changes with normal aging (Sowell et al., 2003), and progressive brain deficits in schizophrenia (Thompson et al., 2001) and Alzheimer’s disease (Thompson et al., 2003). Time‐lapse movies can be made for any brain structure (see Thompson et al. [2004] for an example mapping the hippocampus and ventricles). Cortical time‐lapse movies can be made by combining cortical pattern‐matching methods with statistical models that describe how diVerent attributes of the cortex change over time.

III. Growth Curves for Different Brain Regions

To illustrate the application of these methods, we describe data from an ongoing longitudinal pediatric brain MRI study at the Child Psychiatry Branch of the National Institute of Mental Health. To date, more than 1000 scans have been analyzed, and Fig. 2 shows data on total brain volume from 224 girls and 287 boys. These children are evaluated with MRI and neurocognitive testing at approximately 2‐year intervals, and the images are analyzed with a combination of manual and automated tracing techniques in collaboration with imaging centers throughout the world. Data on regional brain volumes were obtained in collaboration with the Montreal Neurological Institute, and brain maps and movies were created in collaboration with the UCLA School of Medicine. Fig. 2 shows that brain volume is approximately 90% of its final adult volume by age 6, and girls’ brains are on average approximately 12% smaller than boys’. This gender diVerence is explained largely by diVerences in height and is not thought, in itself, to account for any gender diVerences in cognitive domains (see

FIG. 3. Sex diVerences in developmental trajectories for the volumes of tissue types in diVerent lobes of the brain (data from Giedd et al. [2005]). (A) Cubic polynomial models fitted to total cerebral volumes for 224 females (375 scans) and 287 males (559 scans) scanned longitudinally between the ages of 4 and 26. Blue curves indicate the models fitted for males, red curves for females. Sex diVerences are found for the shape of the developmental trajectory of white matter (B), as well as total (C) and frontal (D) gray matter. Some structures, such as the cerebellum, exhibit a more protracted developmental time‐course; peak volumes are achieved only in the late teens (E).

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Kimura [1999] for a review of these diVerences). Despite this overall gender diVerence in brain volume, it is worth noting that it would be diYcult to distinguish the brain MRI scan of a boy or a girl on the basis of brain volume or any other MRI parameter given the large intersubject variability as illustrated in Fig. 2.

A. GRAY MATTER

IN

EACH LOBE

OF THE

BRAIN

The earliest pediatric brain MRI studies suggested that gray matter (GM) volumes generally declined after age 5, perhaps because the advancement of white matter myelination throughout childhood began to overtake the overall rate of brain volume expansion, causing a net decrement in the amount of tissue appearing gray (or unmyelinated) on MRI. The distinction of gray and white matter in the neonate brain is somewhat artificial, because axonal fiber tracts are not suYciently myelinated at birth to appear brighter (i.e., hyperintense) on T1‐ weighted MRI. Later longitudinal studies found that cortical gray matter volume increased throughout childhood, peaking in early or late adolescence and falling thereafter. Giedd et al. (1999) compiled normative growth curves for each of the lobes of the brain, demonstrating the heterochronous nature of brain development, in which the diVerent lobes develop at diVerent rates. Parietal, frontal, and temporal GM volumes peaked at ages 11.8, 12.1, 16.2 in boys and at ages 10.2, 11.0, 16.7 years in girls.

FIG. 4. Image analysis steps for detecting developmental changes and group diVerences in cortical anatomy. An image analysis pipeline is shown here. It can be used to create maps that reveal group diVerences and age‐related changes in cortical thickness, gray matter density, gyral patterning, and asymmetries in these features. In general, 3D MRI scans from patients and controls are aligned (1) with an average brain template based on a population (here the ICBM template is used, developed by the International Consortium for Brain Mapping; Mazziotta et al. [2001]). Tissue classification algorithms then generate maps of gray matter, white matter, and CSF (2). In a simple analysis, these tissue maps can be parcellated into lobes (2a) and their volumes assessed with analysis of variance or other simple statistics (2b). Or, to compare cortical features from subjects whose anatomy diVers, sulcal patterns can be traced onto individual cortical models and used to guide the alignment of data from one subject to another. Individual sulcal curves and the surrounding cortical surfaces can then be flattened (3b, 3c) and aligned with a group average gyral pattern (4). If a color code indexing 3D cortical location is flowed along with the same deformation field (5), a crisp group average model of the cortex can be made (6). Relative to this average, individual gyral pattern diVerences (7), measures of cortical complexity (7a), or cortical pattern asymmetry (8) can be computed. Once individual gyral patterns are aligned to the mean template, diVerences in gray matter density or thickness (9) can be mapped after pooling data across subjects from homologous regions of cortex. Correlations can be identified between diVerences in gray matter density or cortical thickness and genetic risk factors (10). Maps may also be generated visualizing regions in which linkages are detected between structural deficits and clinical symptoms, cognitive scores, and medication eVects, as well as changes in these parameters with age.

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B. MODELING DEVELOPMENTAL TRAJECTORIES

WITH

MIXED MODELS

The trajectories plotted in Fig. 2 are computed using mixed models, or random eVects models, a statistical technique that allows both longitudinal and cross‐ sectional data to be combined to compute a single average trajectory of development for a specific brain measure. It is worth describing how these curves are actually computed. For the ith individual’s jth measure we have: Yij ¼ f ðAgeij ; bÞ þ Eij

ð1Þ

Here Yij is the outcome measure derived from the brain scan, such as a tissue volume for a particular lobe, f ( ) is a constant, linear, quadratic, cubic, or other function of the individual’s age for that scan, and the regression/ANOVA coeYcients to be estimated are included in a vector,
. In studies with multiple scans over time, it is usual to fit a random eVects model with correlated errors: Yij ¼ ai þ f ðAgeij ; bÞ þ Eij

ð2Þ

Here the model is the same as the General Linear Model except for the i term, which is called a random eVect (Davidian and Giltinan, 1995; Verbeke and Molenberghs, 1997). "ij and "ik (k not equal to j) are assumed correlated, and the correlation is a function of the time that has elapsed between the two measurements. In our own work, we typically use the SAS nonlinear ‘‘PROC MIXED’’ function to develop equations that model each brain variable as a quadratic function of age, incorporating random person eVects for the intercept terms, and using SAS MIXED’s capacity to model spatial covariance between the repeated measures as a function of the time span between the two measurements for each participant (Thompson et al., 2004). We usually implement the simpler models directly in C and validate them against SAS. Our general strategy is to model growth curves using mixed‐model analyses, including covariates and interaction terms to test hypothesized diVerences among various strata (e.g., gender diVerences or disease eVects; Gogtay et al., 2005).

C. TRAJECTORIES

IN

LARGE SAMPLES

OF

SUBJECTS

Fig. 3 shows the plots obtained by fitting a cubic model to age‐related changes in total cerebral volume, total gray and white matter, frontal gray matter, and the volume of a particular substructure, the cerebellum (data from Giedd et al., 2005). In this largest MRI study of brain development to date (224 girls, 287 boys), the total volume of the brain rose gently until puberty in boys and girls (with a higher and later peak in boys), and then followed a very gradual decline. When these changes are split into white matter and gray matter components, the white matter

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volume continued to increase rapidly throughout the teenage years, increasing both in absolute terms and as a proportion of brain volume well after puberty and into adulthood. In Sowell et al. (2003), we were able to fit a quadratic model to entire brain white matter volumes over the lifespan. This cross‐sectional study evaluated 176 healthy subjects between the ages of 7 and 87. In agreement with earlier reports by Bartzokis et al. (2003) and others, the white matter volume seemed to peak in the mid‐forties, and its overall trajectory was well‐described by a quadratic (inverted ‘‘U’’) profile, despite considerable individual variation.

D. MECHANISMS It is tempting to try to break down these changes into several hypothetical processes that might lead to gray and white matter gain and reduction and relate them to those that are known to occur on a cellular level. The white matter volume expansion is not attributed to the emergence of additional axonal fibers. Instead, myelination increases the volume of the insulating sheaths surrounding axonal fibers and bulks up the volume of the white matter compartment. This process can also be seen as increased white matter diVusion anisotropy with diVusion tensor imaging (DTI), because greater myelin deposition increasingly constrains the diVusion of water within axons. By mid‐life (40s), white matter breakdown overtakes the process of increased myelination, and the net eVect is a white matter volume decline. Age‐related myelin breakdown can be readily visualized with electron microscopy. It consists primarily of splits in the lamellae of the myelin sheaths or ballooned sheaths in the absence of neuronal or synaptic loss (Nielsen and Peters, 2000; Peters et al., 2000). This myelin degeneration creates microscopic fluid‐filled spaces and increases in MR‐detectable water, and thus decreases in the relaxometric parameter, R2 (Bartzokis et al., 2002; Englund et al., 1987; Kamman et al., 1988). Complementary to assessing volumes of tissues on MRI, MR relaxometry describes quantitative changes in the MRI signal itself, using parameters that assess tissue integrity such as transverse relaxation rates (R2; R2 is defined as 1/T2, where T2 is the transverse relaxation time). Work by Bartzokis and others has shown a decline in frontal lobe white matter (FLWM) R2 that begins in the late 30s (peak at age 38) and markedly accelerates after approximately age 60. These data are consistent with the view that myelin breakdown may be a major factor in higher cognitive functions declining with age and may be accompanied by breakdown in white matter connectivity (Davatzikos et al., 2003). Histological studies demonstrate that neuronal loss is minimal in normal aging (Terry et al., 1987), making Wallerian (axonal) degeneration an unlikely explanation for the white matter loss. A precipitous decline in gray matter volume also occurs in both genders after puberty (Fig. 3). The cellular basis of this change is more contentious, but it likely

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reflects reductions in dendritic arborization, dendritic length, synapse loss, and other neuronal parameters. Earlier cross‐sectional studies of normative brain maturation during childhood and adolescence have led researchers to conclude that gray matter loss does occur as part of the ultimate sculpting of the brain into the fully functioning adult nervous system (Jernigan et al., 1991; PfeVerbaum et al., 1994; Reiss et al., 1996; Sowell et al., 1999, 2003). Gray matter loss has been observed consistently with longitudinal MRI in late childhood and adolescence (Gogtay et al., 2004; Jernigan et al., 2001; Sowell et al., 2004). This loss is usually attributed to neuropil pruning seen histologically. The neuronal origin of these changes is supported, to some degree, by a general developmental decline in metabolites such as N‐acetylaspartate, which are localized primarily in neurons and can be observed in vivo using MR spectroscopy. It is also important to note here that the ‘‘loss’’ of gray matter observed with MRI during childhood and adolescence may also be associated with increases in myelination. Unmyelinated axonal and dendritic fibers presumably have an MRI signal value that is indistinguishable from that of gray matter. Thus, tissue in the peripheral neuropil that has a signal value like gray matter in the young child may continue to myelinate, resulting in a white matter signal value in the same individual as an adolescent. This hypothesis is further supported by recent evidence showing brain growth that spatially and temporally coincides with regional patterns of gray matter density reduction between childhood and young adulthood (Sowell et al., 2001b). If, in fact, gray matter ‘‘loss’’ was associated solely with regressive changes such as reductions in synaptic density, it is not likely that we would see continued brain growth in the same regions. Further studies using DTI may help disambiguate the potential cellular processes that contribute to the further sculpting of cortical structures during childhood. To understand the anatomical sequence of these developmental brain changes, we now turn to a second method, cortical mapping. This method creates maps of gray matter changes in the cortex. By contrast with methods that measure regional volumes of brain tissues, cortical maps oVer the ability to localize brain changes relative to the underlying gyral anatomy and visualize them in the form of statistical maps that reveal the topography of group diVerences.

IV. Cortical Mapping

A. MAPPING

THE

CORTEX

Figure 4 shows a general image analysis process, known as cortical pattern matching, that we developed for understanding how development and disease aVect the cortex (Thompson et al., 2004). These methods have been used to reveal the

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profile of structural brain deficits in childhood and adult‐onset schizophrenia (Cannon et al., 2002; Narr et al., 2004; Thompson et al., 2001), attention‐ deficit/hyperactivity disorder (Sowell et al., 2003), fetal alcohol syndrome (Sowell et al., 2002), Tourette syndrome (Sowell et al., 2004), bipolar disorder (Gogtay et al., 2005), and Williams syndrome (Thompson et al., 2005). In the following we describe some examples selected to illustrate the concepts. The goal of cortical mapping is to create group average maps of cortical features of interest such as gray matter thickness, gray matter density, cortical shape, average sulcal patterning, and hemispheric asymmetries in these measures, all of which change during development. Next, statistics are defined that help localize group diVerences in these measures, such as brain structure diVerences between healthy children and those with neuropsychiatric disorders like schizophrenia or bipolar illness, or genetic disorders of brain development such as Williams syndrome. The resulting maps can reveal where in the brain diVerences are detected, how significant they are, and whether they are stable or progressive. Many variations on the basic mapping approach are possible. Any observed structural diVerences could also be correlated with measures such as medication or genotype to investigate their origin and the potential eVects of intervention. The same mapping techniques maps have also been used to assess functional diVerences in the cerebral and cerebellar cortex with functional MRI (see Rasser et al. [2005] and Zeineh et al. [2003] for examples). The cortical mapping process is described in detail in Thompson et al. (2004), so we review it only briefly here. As shown in Fig. 4, brain MRI volumes pass through a number of preprocessing steps using several manual and automated procedures. First, we create an intracranial mask of the brain using a brain surface extraction algorithm tool (BSE) that is based on a combination of nonlinear smoothing, edge detection, and morphological processing (Shattuck and Leahy, 2002). Any small errors in the masks are corrected manually to separate intracranial regions from surrounding extracranial tissue. Using these modified brain masks, all extracerebral tissues are removed from the image volumes. Brain masks and anatomical images are corrected for head alignment and individual diVerences in brain size by using an automatic nine‐parameter linear registration (Woods et al., 1998) to transform each brain volume into the target space of the ICBM‐305 average brain created by the International Consortium for Brain Mapping (Mazziotta et al., 2001; Fig. 4, step 1). After applying radiofrequency (RF) bias field corrections to eliminate intensity drifts because of magnetic field inhomogeneities in the scanner, each image volume is segmented into diVerent tissue types by classifying voxels based on their signal intensity values (Shattuck et al., 2001; Fig. 4, step 2), followed by manually separating the left hemisphere from the right. Next, cortical pattern matching methods (Thompson et al., 2004; Fig. 4, steps 3–7) are used to spatially relate homologous regions of cortex between subjects

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to permit the interindividual comparison of cortical features, such as local cortical thickness, in equivalent surface locations. For that purpose, we create 3D cortical surface models for each hemisphere based on automatically generated mesh surfaces that are continuously deformed to fit a threshold intensity value that best diVerentiates extracortical cerebrospinal fluid from underlying cortical gray matter (MacDonald et al., 1998). The idea of this deformation process is to use a starting mesh that is deformed until its borders fit to a given intensity threshold of the respective image. As a result of the linear transformation procedure, the generated 3D cortical surface models correspond globally in size, orientation, and parameter space coordinates. Nevertheless, the same parameter space coordinates within each cortical surface model do not yet index the same anatomy across all subjects. Therefore, the cortical surface models from each individual are used to identify and manually outline the major cortical sulci on the brain surface. Detailed anatomic protocols for delineating cortical anatomy are available at http://www.loni.ucla.edu/esowell/edevel/proto.html and have been previously validated, and their interrater and intrarater reliability have been reported (Blanton et al., 2000; Narr et al., 2001; Sowell et al., 2002). The manually derived sulcal landmarks are then used as anchors to drive the surrounding cortical surface anatomy of each individual into correspondence. During the surface‐warping procedures, the algorithm computes a 3D vector deformation field that records the amount of x, y, and z coordinate shift (or deformation), associating the same cortical surface locations in each subject with reference to the average anatomical pattern of the entire study group (Thompson et al., 2004). Tissue classified brain volumes are resampled to 0.33‐mm cubic voxels to improve the precision of subsequent thickness measurements. Cortical thickness—defined as the 3D distance (in mm) between inner gray matter/white matter border and the closest point on the outer surface (CSF/gray matter border)—is calculated using the Eikonal fire equation (Thompson et al., 2004) applied to voxels classified as gray matter. Cortical thickness is estimated voxel by voxel and projected as a local value (mm) onto the cortical surface. To increase detection sensitivity for group diVerences (i.e., signal to noise), a smoothing kernel is used to average thickness measures within a 15‐mm sphere at each cortical surface point (Fig. 4, step 9).

B. STATISTICAL ANALYSIS The mean values for cortical thickness (or any other cortical measure obtained at each cortical surface point) can be computed to provide maps of average cortical thickness across the entire cortical surface. Analysis of variance or multiple regression is then performed at each 3D cortical surface location to

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assess covariates of interest, such as possible eVects of disease, gender, and hemisphere, and their potential interactions for cortical thickness. Uncorrected two‐tailed probability values ( p < 0.05) from these tests are mapped directly onto the average cortical surface model of the entire sample providing detailed and spatially accurate maps of local thickness diVerences between groups. Regions where correlations exist between cortical thickness and symptoms or cognitive test scores can also be mapped (Fig. 4, step 10), as can links with genotype or even medication. Finally, permutation testing is used to assign an overall significance value to the observed pattern of diVerences in the map (Thompson et al., 2004). For permutation testing, subjects are randomly assigned to groups, and a new statistical test is performed at each cortical surface point for each random assignment. The number of significant results from these randomizations is then compared with the number of significant results in the true assignment to produce a corrected overall significance value for the uncorrected statistical maps. The statistical validity of the findings can then be verified even in the presence of multiple comparisons and spatial correlations in the neuroimaging data. Figure 5 shows some examples of cortical pattern matching applied to several developmental populations. We highlight examples of work on developmental disorders that are environmental in origin (e.g., fetal alcohol syndrome, which results from high maternal alcohol intake during embryonic development), as well as those that are primarily genetic in origin (e.g., Williams syndrome, which results from a chromosomal anomaly). The brain is most fragile during development, and anomalies of maturation can result from perturbations in genetic or environmental factors. 1. Developing Brain Asymmetries The lateralization of brain function is of great interest in neuroscience, because it provides vital information on the specialization of brain systems and the communication of information between brain hemispheres (see Toga and Thompson [2003] for a review). Asymmetry is also of interest developmentally, because a number of disorders have been hypothesized to be associated with failures of the lateralization process, from dyslexia to schizophrenia (see Narr et al. [2004] for a study of schizophrenia using these methods). Cortical pattern matching provides a particularly attractive method to evaluate the magnitude and development of structural brain asymmetries. The pattern of asymmetries in sulcal patterning and gray matter thickness can be plotted spatially and related to chronological age or diagnosis. Figure 5 (I–P) shows some interesting maps of brain asymmetry in populations of diVerent ages. In Thompson et al. [2001], we developed a general approach to map the mean profile of asymmetry in the gyral pattern and assess its statistical significance, essentially by creating digital models of 3D curves representing sulci on the brain surface and averaging their geometrical shapes across subjects. Regions can be distinguished that exhibit structural

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asymmetries that exceed the normal within‐hemisphere variation in gyral patterning, and the anatomy of the right hemisphere can be seen to be shifted forwards by approximately 5–10 mm relative to the left. This asymmetry can be plotted on the mean sulcal pattern using a color code to emphasize regions where brain asymmetry is greatest. Sylvian fissures and superior temporal gyri exhibit the greatest asymmetries in the brain, with greatest values at their most posterior limits. Fig. 5 (I–K) shows the mean pattern of asymmetry in groups of children, teenagers, and adults (data from Sowell et al. [2002]). By computing mean geometric models of the cortical sulci, the models from one hemisphere can be ‘‘mirrored’’ or reflected so that they can be compared with corresponding features in the opposite hemisphere. Then a 3D deformation map, or vector field, can be computed to express how much deformation would be required to force the left hemisphere mean sulcal pattern to match the right hemisphere sulcal pattern (see arrows in Fig. 5 L, M). In elderly subjects, the gyral pattern asymmetry approaches 20 mm and is highly significant (Thompson et al. [2001]; Fig. 5 N, P). The dynamic emergence of asymmetry in the developing human brain is yet one more feature for which normative statistical data have been

FIG. 5. Statistical maps of cortical structure. A variety of maps can be made that describe diVerent aspects of cortical anatomy. These include maps of cortical thickness (A–H), gyral pattern asymmetry (L–P), and heritability of brain structure (Q–T). Explanations of these features are in the main text. Panels (A–D) show steps involved in measuring cortical thickness (for related work, see Jones et al. [2000]; Annese et al. [2000]; Fischl and Dale [2000]; Miller et al. [2000]; Kruggel et al. [2001]; and Yezzi and Prince [2001]). In our approach, the MRI scan (A) is classified into gray matter, white matter, CSF, and a background class (respectively represented by green, red, black, and white colors in [B]). To quantify cortical gray matter thickness, we use the 3D distance measured from the cortical white–gray matter boundary in the tissue‐classified brain volumes to the cortical surface (gray–CSF boundary) in each subject (C). (D) Mean cortical thickness in a group of 40 healthy young adults, ranging from low values in primary sensorimotor and visual cortices (2–3 mm, yellow colors) to highest values on the medial wall in cingulate areas (up to 6 mm, purple colors). The regional variations in these maps agree with those found in the classical cortical thickness maps derived post mortem by von Economo (Sowell et al., 2004). (E, F) Profile of mean cortical thickness in Williams syndrome and healthy controls. (G) Group diVerence expressed as a percentage of the control mean and its significance (H). (I–K) Increasing gyral pattern asymmetry in groups of children, adolescents, and adults (data from Sowell et al. [2002]), computed from the 3D deformation fields (L, M) required to align the mean left hemisphere sulcal pattern to match a reflected version of the mean right hemisphere sulcal pattern. Asymmetry measures can also be extended to the rest of the cortical surface (N,P) and expressed in millimeters. (Q,R) Intraclass correlations in gray matter density gi,r(x) for groups of identical and fraternal twins, after cortical pattern matching (giving maps rMZ(, ) and rDZ(, )). An estimate of gray matter heritability h2 (S) can be defined as 2(rMZ – rDZ), with standard error: 2 ðh2 Þ ¼ 4½ðð1 rMZ 2 Þ 2 =nMZ Þ þ ðð1 rDZ 2 Þ 2 =nDZ ފ. Regions in which significant genetic influences on brain structure are detected are shown in the significance map (T), p[h2(, )]. Genetic influences on brain structure are pronounced in some frontal and temporal lobe regions, including the dorsolateral prefrontal cortex and temporal poles (denoted by DLPFC and T ).

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established. This helps to delimit the range of normal variations in brain structure and distinguish them from variants outside of the normal range. 2. Fetal Alcohol Syndrome Intriguingly, very similar gray matter excesses in perisylvian zones were also observed in a recent study of fetal alcohol syndrome (FAS; Sowell et al., 2002). Subjects with FAS exhibited a 15% increase in gray matter density in the perisylvian and inferior parietal cortices bilaterally ( p < 0.001, L and R hems.). Language systems may be especially vulnerable to alcohol neurotoxicity during cortical maturation, and excess cortical gray matter is most likely due to a failure of cortical formation during gyrogenesis or a concomitant failure or delay in myelination, perhaps specifically in subcortical U fibers (these two possibilities cannot be distinguished with conventional MRI). 3. Heritability and Brain Structure In the quest to understand what factors contribute to the trajectory of brain development, genetic and imaging methods can be combined to answer questions about the influence of genes and environment on brain structure. These methods are of two major types. The first of these methods uses twin or family designs to assess the proportion of genetic and environmental contribution to the observed variance in specific brain measures. By comparing the resemblance of relatives with diVerent degrees of genetic relatedness, variance attributable to additive genetic, shared, and unique environment can be established. The second approach also uses quantitative genetic modeling but is based on modifying the concept of transmission disequilibrium to images. As a result, it is possible to assess the eVects of individual polymorphic markers on brain structure (Cannon et al., 2005). This provides enormous potential for relating developmental trajectories to information on normal and abnormal genetic variations. The heritability of brain structure (i.e., proportion of observed variance explained by genetic variation in a population) can also be visualized in the form of a map. Fig. 5 (Q–T) shows several examples of maps identifying the degree to which genetic variations influence brain structure (Thompson et al., 2001, 2002). Essentially, MRI scans of identical and fraternal twins are compared, and the degree of structural similarity is computed for a range of structural features—in this case cortical gray matter density—a measure related to cortical gray matter thickness (Thompson et al., 2004). Identical twins, who share all their genes, resembled each other more closely in brain structure than fraternal twins, who share only half their genes on average. This genetic control can be estimated quantitatively by examining intraclass correlations in brain measures between pairs of twins of each zygosity (i.e., identical or fraternal). The covariance of genetic aYnity and structural aYnity was highest for the volume of frontal gray matter in the brain, which, perhaps surprisingly, is almost entirely determined by

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genetic factors. In other words, the fact that each twin was exposed to diVerent experiences throughout life made almost no diVerence to the quantity of gray matter. The quantity of frontal gray matter is moderately correlated with general cognitive ability as measured with standardized tests of intellectual function (or IQ), and IQ is also highly heritable (Gray and Thompson, 2004; Thompson et al., 2001; this link was replicated by Posthuma et al. [2002]). It is important to be aware of misinterpretations and misuses of heritability data, which have led to erroneous conclusions on the sources of individual and group diVerences in brain structure and cognition and to a vitriolic debate (reviewed in Gray and Thompson, 2004). Because there can be strong gene by environment correlations, the proportion of genetic variance is not necessarily independent of, or at the expense of, environmental contributions to variance. The fact that a feature is heritable does not mean that it cannot be aVected or manipulated by changes in the environment (see Gray and Thompson [2004] for a discussion). The almost complete genetic determination of gray matter volumes contrasts with the observed variation in sulcal and gyral patterns, which are less genetically constrained (Lohmann et al., 1999; see Thompson et al. [2001, 2003] for a review of neuroimaging studies of genetic influences on brain structure). Cerebellar volume exhibits almost zero heritability, despite the high heritability of cerebral volumes (Giedd et al., 2001). The search for heritable or genetically mediated variations in brain structure is important because it can help identify features in images that are associated with imminent disease onset or liability for disorders such as schizophrenia (Cannon et al., 2002). Brain maps that link genes and structure can also better define the mechanisms and molecular pathways that lead to the organization of brain structure and deficits that imaging can identify. They also can be used to identify commonalities and unique signatures for specific developmental disorders, including systems in which functional deficits may overlap in disorders with diVering etiologies (Thompson et al., 2005, for an example on fetal alcohol and Williams syndrome). 4. Williams Syndrome Williams syndrome is an enigmatic developmental disorder associated with a deletion in the 7q11.23 chromosomal region (Korenberg et al., 2000). Subjects with WS have disrupted cortical development and mild to moderate mental retardation but have relative proficiencies in language skills, social drive, and musical ability (Bellugi et al., 2000). Progress in understanding how this genetic anomaly impacts brain structure and behavior is impaired by the lack of detailed maps establishing the scope and anatomical extent of brain anomalies in WS. Fig. 5 shows a series of maps that reveal that the cortical thickness in WS is systematically increased in the perisylvian language area (data from Thompson et al., 2005). On th basis of MRI scans of 42 subjects with WS and 40 matched controls, maps of individual cortical thickness were made (Fig. 5A–D), and mean

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thickness maps compared between patients with WS (Fig. 5E) and controls (Fig. 5F). In WS, cortical thickness was significantly increased (by 5–10%) in a discrete sector (Fig. 5G), encompassing perisylvian regions important for language function, specifically language comprehension. Against a backdrop of widespread brain tissue deficits, the perisylvian cortex was thicker than in controls, and this may partly account for the verbal strengths and unusually expressive language in subjects with WS. Nonetheless, the structural alterations in this study were not correlated with behavioral or cognitive measures, so any links between structural and functional disturbances require further study to explain.

V. Time‐Lapse Maps of Brain Change

An exciting extension of the maps described so far is the computation of time‐ lapse maps of brain structure. These show, using animation, spreading waves of cortical maturation in the developing brain in spatial and temporal detail. Before introducing these dynamic maps, we review some of the major brain changes observable with MRI as healthy subjects age. Brain changes with aging can be viewed as a continuation of development, and the assessment of large imaging databases across the human lifespan can shed light on the waxing and waning of brain tissue volumes, with diVerent trajectories for diVerent regions of the brain. Several studies have measured or mapped brain changes in children and adolescents scanned repeatedly during childhood and adolescence (Chung et al., 2001; Giedd et al., 1999; Gogtay et al., 2004; Sowell et al., 1999, 2001, 2002, 2004; Thompson et al., 2000, 2001). The dynamics of brain change across the adult human life span are highly nonlinear ( Jernigan et al., 1991; Sowell et al., 2003). To help understand these changes, we recently developed a set of statistical mapping approaches to estimate nonlinear (quadratic) eVects of aging on brain structure.

A. GRAY MATTER CHANGES

OVER THE

LIFESPAN

In a recent study (Sowell et al., 2003; Fig. 6B,E), we used MRI and cortical matching algorithms to map gray matter density (GMD) in 176 normal individuals aged 7–87 years. GMD declined nonlinearly with age, most rapidly between ages 7 and 60, over dorsal frontal and parietal association cortices, on both the lateral and interhemispheric surfaces. Age eVects were inverted in the left posterior temporal region, where GMD gain continued up to age 30 and then rapidly declined. This was the first study to diVerentiate the trajectory of maturational and aging eVects as they vary over the cortex. Visual, auditory, and limbic cortices, which myelinate early, showed a more linear pattern of aging

FIG. 6. Mapping brain change over time. The ability to resolve brain changes over time relies on fitting appropriate time‐dependent statistical models to data collected from subjects cross sectionally, longitudinally, or both. Nonlinear or and/or mixed statistical models are fitted to brain maps collected at diVerent ages to estimate the eVects of brain aging or development on the cortex. (A) Measures (Yij) are defined that can be obtained longitudinally (green dots) or once only (red dots) in a group of subjects at diVerent ages. These measures might be gray matter density or cortical thickness, for example. Fitting of statistical models to these data (statistical model, lower right) produces estimates of significance values or statistical parameters such as rates of change or eVects of drug treatment or risk genes. These parameters are then plotted onto the cortex using a color code. (B) and (E) Trajectory of gray matter loss over the human lifespan based on a cohort of 176 subjects aged between 7 and 87 (Sowell et al., 2003). (C) and (D) Trajectory of cortical gray matter density in 13 children scanned longitudinally every 2 years for 8 years (Gogtay et al., 2004). (Data reproduced, with permission, from Gogtay et al., Proceedings of the National Academy of Sciences, 2004 [C] and [D], and from Sowell et al., Nature Neuroscience, 2003 [B and E]).

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than the frontal and parietal neocortices, which continue myelination into adulthood. Posterior temporal cortices, primarily in the left hemisphere, which typically support language functions, have a more protracted course of maturation than any other cortical region, and myelination is the putative cause driving these changes. B. TIME‐LAPSE MAPS

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Another developmental study (Gogtay et al., 2004) created a quantitative time‐lapse map of human cortical development reconstructed from serial brain MRI scans of 13 children aged 4–21. Dynamic video maps localizing brain changes were derived using high‐dimensional elastic deformation mappings to match gyral anatomy across subjects and time. A quadratic statistical model, with random eVects, was fitted to the profile of gray matter density against time at each of the 65,536 cortical points (Fig. 6C). The resulting trajectory was animated to create a time‐lapse movie (specific frames are shown in Fig. 6D). This revealed a shifting pattern of gray matter loss, appearing first in dorsal parietal and primary sensorimotor regions near the interhemispheric margin and spreading laterally and caudally into temporal cortices and anteriorly into dorsolateral prefrontal areas. This also supports findings of earlier studies (Giedd et al., 1999; Sowell et al., 1999), with a long‐term longitudinal sample. The shifting profile of these changes is observed in a set of video sequences (see URL, http://www.loni. ucla.edu/thompson/DEVEL/dynamic.html for several of these time‐lapse movies).

C. BRAIN–BEHAVIOR RELATIONSHIPS Changes in cortical thickness also relate to cognitive changes as children and adolescents mature. Sowell et al. (2004) found that cortical thinning in the left dorsal frontal and parietal lobes was correlated with improved performance on a test of general verbal intellectual functioning. To establish this, cortical thickness in millimeters was computed from structural MR images of 45 children scanned twice (2 years apart) between the ages of 5 and 11. Local brain growth progressed at a rate of approximately 0.4–1.5 mm per year, most prominently in frontal and occipital regions. Gray matter thinning coupled with cortical expansion was highly significant in right frontal and bilateral parietooccipital regions. Forty‐ two of the studied children completed the Vocabulary subtest of the Wechsler Intelligence Scale for Children–Revised (Wechsler, 1991) at both scanning sessions. Negative correlations were observed primarily in the left hemisphere between the change scores and cortical thinning. Specifically, greater gray matter

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thinning was associated with improved performance on the Vocabulary subtest. Permutation analyses confirmed the significance of relationships between gray matter thinning and improved Vocabulary scores in the left lateral dorsal frontal ( p ¼ 0.045) and the left lateral parietal ( p ¼ 0.030) regions. D. CHILDHOOD‐ONSET SCHIZOPHRENIA In developing time‐lapse maps for clinical studies, there is a particular interest in modeling atrophic or developmental processes that speed up or slow down. Diseases may accelerate, or they may be slowed down by therapy. Among the most intriguing findings has been the observation of a progressively spreading wave of gray matter (GM) loss in subjects with childhood‐onset schizophrenia (COS; Thompson et al., 2001; Vidal et al., 2005; Fig. 7). This medication‐ controlled study followed 12 subjects with COS every 2.5 years for 5 years, 12 matched healthy controls, and 10 medication‐matched psychiatric controls with serial MRI. As seen in Fig. 7 (left panels), healthy subjects lost gray matter at a subtle rate of 1–2% per year in parietal cortices. By contrast, the patients with COS showed a more rapid progressive loss of GM in superior frontal and temporal cortices, reaching 3–4% per year in some local regions. The same pattern was replicated in both boys and girls and agreed with early findings of progressive GM loss when the volumes of brain tissue in each of the lobes were assessed over time (Rapoport et al., 1999). Subtraction of the mean GM map in COS from the corresponding map for controls, at each time point, revealed a

FIG. 7. Rates of brain change in childhood‐onset schizophrenia and in Alzheimer’s disease. (left column) Average maps of gray matter loss rates are shown for healthy boys (top row), girls (middle row), and both genders pooled (bottom row), scanned longitudinally over 5 years. Also shown are maps of the considerably faster loss rates in age‐ and gender‐matched subjects with childhood‐onset schizophrenia (COS) also scanned at the same ages and intervals. The frontal cortex underwent a selective rapid loss of gray matter (up to 3–4% per year faster in patients than controls). Subtraction maps contrasting patients with controls revealed early deficits in parietal regions (red colors; right column, top row) that spread forward into the rest of the cortex at follow‐up ([right column, second row]; superior temporal gyrus [STG], and the dorsolateral prefrontal cortex [DLPFC] are indicated with arrows). These changes may be, in some respects, an exaggeration of changes that normally occur in adolescence (Rapoport et al., 1999; Thompson et al., 2001). By contrast, deficits occurring as Alzheimer’s disease (AD) progress are show (right column, bottom two rows) by comparing average profiles of gray matter density between patients with 12 AD (age, 68.4 ± 1.9 years) and 14 elderly matched controls (age, 71.4 ± 0.9 years; data from Thompson et al., 2003). Patients and controls are subtracted at their first scan (when mean Mini‐Mental State Exam [MMSE] score ¼ 18 for the patients; [right column, third row]) and at their follow‐up scan 1.5 years later (mean MMSE, 13; [right column, fourth row]). In AD, gray matter loss sweeps forward in the brain from limbic to frontal cortices in concert with cognitive decline, but in the patients with schizophrenia, the frontal cortices lose gray matter the fastest.

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dynamic wave of gray matter loss in patients with COS (Fig. 7; top right panels). Early deficits in parietal brain regions that support language and associative thinking progressed anteriorly to temporal lobes, supplementary motor cortices, and frontal eye fields. The deficits spread anatomically over a period of 5 years, consistent with the characteristic neuromotor, sensory, and visual search impairments in the disease. In temporal cortices, including primary auditory regions, severe gray matter loss was absent at disease onset but became pervasive later in the disease. The spreading deficits are shown in a set of animation sequences (see URL, http://www.loni.ucla.edu/thompson/MOVIES/SZ/sz.html). Several aspects of this study are noteworthy. First, as time‐lapse maps of normal development are expanded to larger samples (Gogtay et al., 2004), it will be possible to assess whether the changes represent an acceleration of normally occurring brain changes (Gogtay et al., 2004) or a separate process entirely that begins in the adolescent years. These time‐lapse maps of COS may oVer support or counterevidence when evaluating neurobiological models of the onset and progression of schizophrenia. The spreading patterns of deficits corroborate the idea that the normal process of dendritic and synaptic pruning may be abnormally accelerated or derailed in schizophrenia (Feinberg and Guazzelli, 1999). Nonetheless, the spreading deficits are quite diVerent than those seen in neurodegenerative diseases, for example. Schizophrenia is not thought to be a neurodegenerative disorder because of the lack of gliosis in autopsy tissue from patients. Empirical studies also show that the degenerative sequence of dementia is anatomically selective and quite diVerent from that seen in schizophrenia. Fig. 7 (bottom right column) shows the pattern of GM loss in a group of 12 patients with mild to moderate Alzheimer’s disease compared with 14 matched healthy elderly subjects (data from Thompson et al., 2003). Both cohorts were scanned longitudinally, and a time‐lapse movie was created. Over a 2‐year period, the patients with Alzheimer’s disease exhibited a spreading wave of GM loss beginning in limbic and entorhinal cortices and spreading to frontal cortices, sparing sensorimotor cortices. This is, in some respects, the opposite of the sequence of cortical maturation during development, in the sense that the primary sensory cortices mature first and degenerate last. Some have suggested that limbic regions have high neuronal plasticity throughout life, and the resulting turnover of cellular metabolites makes them especially vulnerable to neurodegenerative disease (Mesulam et al., 2000). The cellular basis of the progressive changes in COS is not clear. Progressive loss of frontal and temporal GM has been seen in first‐episode adult‐onset patients, and there is some evidence that it may be resisted, at least in adults, by atypical antipsychotics such as olanzapine (Keefe et al., 2004; Lieberman J., 2005, personal communication). Some histological studies support the notion of decreased neuropil in schizophrenia, but autopsy material is relatively scarce in adolescent‐onset populations, and findings are inconsistent. This has led some to

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speculate that the cortical changes may be vascular or glial in origin rather than purely neuronal (Weinberger and McClure, 2002). Intracortical contrast may also be aVected by changes in myelination and by changes in lipid metabolism during the use of atypical antipsychotics (Bartzokis et al., 2003). Second, the rate of GM loss may be unusually high in these severe, early‐onset cases and may not be typical of schizophrenia in general. The loss rate would be expected to decelerate or plateau over a period of several years after the onset of psychosis. With these caveats, time‐lapse maps may still oVer a biological marker of disease progression for medication trials. The gray matter diVerences have been found to correlate with fMRI measures in tasks that recruit the frontal lobes (Rasser et al., 2005). Frontal GM deficits have been observed in genetically at‐risk relatives of patients (Cannon et al., 2002). Their magnitude may also provide a measure of imminent risk for disease. Progressive GM deficits have been seen in subjects in the prodromal phase of the illness (i.e., even before the first psychotic episode) (Pantelis et al., 2003).

VI. Mapping Brain Growth

A final question is whether these brain‐mapping techniques are applicable to mapping brain growth in individual children. In the examples presented so far, data from multiple subjects has been averaged together to create group average maps or time‐lapse movies, and statistical maps have been computed to identify diVerences between groups of subjects. An interesting variation of these approaches is to compute maps of brain growth in an individual subject using serial images to identify the region and rate of maximal growth in the brain.

A. TENSOR MAPS One such approach for mapping brain growth in an individual is known as tensor mapping (Thompson et al., 2000) or tensor‐based morphometry (Ashburner et al., 1998; Chung et al., 2001; Leow et al., 2005). This method essentially compares two scans of the same individual over time by elastically warping the earlier scan to match the later one. A high‐dimensional elastic deformation, or warping field, is calculated, typically with millions of degrees of freedom, which drives the baseline image to match its shape in a later scan (see Fig. 8). (These tensors have nothing to do with those that are mapped using diVusion imaging, which measure fiber orientation based on the directional properties and orientation of water diVusion in the brain). The tensor that is mapped in this context is the gradient of the deformation field that warps the baseline to the later anatomy;

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FIG. 8. Tensor‐based morphometry. (A) Corpus callosum of a healthy 3‐year‐old girl (indicated by a green box) in a sagittal section from a 3D MRI scan. (B) Using a follow‐up scan acquired 3 years later, an elastic deformation field is computed that digitally aligns, or warps, the anatomy of the earlier time point to match its shape at the later time point. The amount of local stretching of the anatomy is coded in color in (B), indicating the fastest growth rates (red colors) in the anterior corpus callosum. Mathematically, the growth rates are derived by evaluating the determinant (dilation factor) of the spatial gradient of the deformation vector field, which is a symmetric positive definite tensor field (C). In a related approach, maps can be compiled to represent the average expansion factor required to elastically deform an average corpus callosum shape onto each subject in a set of healthy children (D) and matched autistic children (E). When the logarithm of these expansion factors is taken (color coded in [D] and [E]), values below 0 denote contraction relative to the average. This logarithm transform is applied to reduce the skew in the Jacobian null distribution. Student’s t tests (or other statistical models, such as the general linear model) may then be computed at each voxel in the expansion maps to localize group diVerences in structure. (F) Blue color regions of the splenium (S) and rostrum (R) of the corpus callosum in which the cross‐sectional area is significantly reduced in the autistic children (data in [A–C] are adapted from Thompson et al. [2000], and data in [D–F] are adapted from Leow et al. [2005]).

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mathematically it is equivalent to a 3  3 matrix attached to each point in the anatomy, which describes the principal directions of deformation at that point. The determinant of this matrix, called the Jacobian, is often used to summarize the transformation; this single number represents the local expansion factor (plotted in color in Fig. 8) and can be converted into a growth rate based on the time interval between scans. A notable feature of tensors, by contrast with displacement vectors, is that they distinguish intrinsic volumetric changes from bulk shifts in anatomy; unlike displacement vectors, tensors are invariant to translational shifts of a structure in stereotaxic space, but they are still sensitive to intrinsic volumetric changes. This distinction can help in studying developmental changes, because intrinsic changes in some structures may cause other structures to shift translationally. These two types of change usually will not be distinguished by voxel‐based methods, unless structures are perfectly aligned using high‐dimensional registration (Bookstein, 2001).

B. CORPUS CALLOSUM GROWTH Fig. 8 shows typical results of a tensor‐mapping approach we developed to map growth of the corpus callosum (shown in Fig. 8A) and basal ganglia in young children. We used it to detect an anterior‐to‐posterior wave of growth in the brains of children scanned repeatedly between the ages of 3 and 15 (Thompson et al., 2000). Parametric surface meshes were built to represent anatomical structures in a series of scans over time, and these were matched using a fully volumetric deformation. Dilation and contraction rates, and even the principal directions of growth, can be derived by examining the eigenvectors of the deformation gradient tensor or the local Jacobian matrix of the transform that maps the earlier anatomy onto the later one (Fig. 8). By analyzing the deformation fields, tensor maps can be created to reflect the magnitude and principal directions of tissue dilation or contraction. This mapping process is illustrated in Fig. 8. The validity of the approach can also be assessed by visualizing ‘‘null maps’’ of brain change over short intervals. The increased spatial detail aVorded by these mapping approaches makes them of particular interest for assessing fine‐scale changes in anatomy during development.

C. TENSOR‐BASED MORPHOMETRY

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AUTISTIC CHILDREN

Tensor maps may also be used to identify structural brain diVerences between two groups of subjects. Rather than warp the subject’s anatomy at baseline onto a follow‐up image from the same subject, all subjects’ images are warped to match

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a common template. Highly flexible elastic or fluid transformations are used to deform the anatomy of each subject so it matches a template exactly. A measure of the dilation or contraction applied during this transformation (the Jacobian determinant) is an index of the local shape diVerences in anatomy across groups. Once registered across subjects, the resulting Jacobian determinant images may be analyzed using voxel‐based methods, an approach known as tensor‐based morphometry (TBM; Ashburner et al., 1998; Chung et al., 2001; Davatzikos et al., 1996). Recently, we used this method to identify white matter reductions in the corpus callosum of autistic children (Leow et al., 2005). Fig. 8 D, E, respectively, show the average compression factor required to map normal children and autistic children to an average corpus callosum shape, and Fig. 8 F, shows regions where these are diVerent. White matter regions of the splenium were significantly reduced in autistic children. One benefit of this approach, as with the other mapping approaches, is that it does not require a priori specification of the regions in which diVerences are expected to occur. Regions can be searched for diVerences and multiple comparison corrections can be applied to make sure that any eVects that are found are genuine.

VII. Conclusion

In this chapter, we reviewed several of the major morphometric methods for mapping developmental changes in brain structure. As image analyses become increasingly automated and as the scope and power of brain imaging studies expands to larger and more complex studies, substantial benefits will accrue. For developmental research in particular, key information is likely to come from the large‐scale analysis of neuroimaging data, especially from children scanned longitudinally with MRI. Image analysis tools can now make time‐lapse maps of developmental trajectories, as well as growth curves for individual structures. Future work will likely reveal factors that aVect these developmental trajectories, both genetic and environmental. There is rapid progress in fusing genetic and neuroimaging methods for this purpose (Cannon et al., 2005). This chapter has focused on mapping changes in brain structure with conventional MRI, but advances in other MRI techniques are now beginning to oVer additional perspectives on the functional development of the brain. Functional MRI studies are now becoming more routine in young children, as are fiber‐mapping studies using diVusion tensor imaging, which previously required lengthy scanning protocols. Some of the more enigmatic changes in the cortex, described in this chapter, are likely to be ultimately understood by combining conventional imaging with spectroscopy and DTI, because each is sensitive to diVerent aspects of cellular maturation.

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As has been noted numerous times, the real power of these methods lies in identifying subtle developmental changes that would go unnoticed in individual images. No matter how large existing MRI databases become, specialized mathematics are still required to derive the most information from them. For example, mapping gene and medication eVects on development is now within reach. Scan databases are only just now becoming large enough to stratify these cohorts by symptom profiles, therapeutic response, and currently identified risk factors. These studies are likely to help us better understand the links between neuroimaging markers and normal brain development, as well as the clinical course of developmental illnesses.

Acknowledgments

This work was funded by grants from the National Institute for Biomedical Imaging and Bioengineering, the National Center for Research Resources, and the National Institute on Aging (to P. T.: R21 EB01651, R21 RR019771, P50 AG016570), by the National Institute of Mental Health, the National Institute of Drug Abuse, and the March of Dimes, (to E. R. S.: K01 MH01733, R21 DA15878, R01 DA017831, MOD 5FY03–12), and by the following grants from NCRR, NIBIB, NINDS, and NIMH: PO1 EB001955, U54 RR021813, MO1 RR000865, and P41 RR13642 (to A. W. T.). Additional support was provided by NIMH intramural funding (N. G., J. N. G., J. L. R.).

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PfeVerbaum, A., Mathalon, D. H., Sullivan, E. V., Rawles, J. M., Zipursky, R. B., and Lim, K. O. (1994). A quantitative magnetic resonance imaging study of changes in brain morphology from infancy to late adulthood. Arch. Neurol. 51, 874–887. Rapoport, J. L., Giedd, J. N., Blumenthal, J., Hamburger, S., JeVries, N., Fernandez, T., Nicolson, R., Bedwell, J., Lenane, M., Zijdenbos, A., Paus, T., and Evans, A. (1999). Progressive cortical change during adolescence in childhood‐onset schizophrenia. A longitudinal magnetic resonance imaging study. Arch. Gen. Psychiatry 56, 649–654. Reiss, A. L., Abrams, M. T., Singer, H. S., Ross, J. L., and Denckla, M. B. (1996). Brain development, gender and IQ in children. A volumetric imaging study. Brain 119, 1763–1774. Shattuck, D. W., Sandor‐Leahy, S. R., Schaper, K. A., Rottenberg, D. A., and Leahy, R. M. (2001). Magnetic resonance image tissue classification using a partial volume model. Neuroimage 13, 856–876. Sowell, E. R., Levitt, J., Thompson, P. M., Holmes, C. J., Blanton, R. E., Kornsand, D. S., Caplan, R., McCracken, J., Asarnow, R., and Toga, A. W. (1999). Brain abnormalities in early‐onset schizophrenia spectrum disorder observed with statistical parametric mapping of structural magnetic resonance images. Am. J. Psychiatry 157, 1475–1484. Sowell, E. R., Peterson, B. S., Thompson, P. M., Welcome, S. E., Henkenius, A. L., and Toga, A. W. (2003). Mapping cortical change across the human lifespan. Nat. Neurosci. 6, 309–315. Sowell, E. R., Thompson, P. M., Holmes, C. J., Jernigan, T. L., and Toga, A. W. (1999). In vivo evidence for post‐adolescent brain maturation in frontal and striatal regions. Nat. Neurosci. 2, 859–861. Sowell, E. R., Thompson, P. M., Leonard, C. M., Welcome, S. E., Kan, E., and Toga, A. W. (2004). Longitudinal mapping of cortical thickness and brain growth in normal children. J. Neurosci. 24, 8223–8231. Sowell, E. R., Thompson, P. M., Peterson, B. S., Mattson, S. N., Welcome, S. E., Henkenius, A. L., Riley, E. P., Jernigan, T. L., and Toga, A. W. (2002). Mapping cortical gray matter asymmetry patterns in adolescents with heavy prenatal alcohol exposure. Neuroimage 17, 1807–1819. Sowell, E. R., Thompson, P. M., Rex, D. E., Kornsand, D. S., Jernigan, T. L., and Toga, A. W. (2002). Mapping sulcal pattern asymmetry and local cortical surface gray matter distribution in vivo: Maturation in perisylvian cortices. Cereb. Cortex 12, 17–26. Sowell, E. R., Mattson, S. N., Thompson, P. M., Jernigan, T. L., Riley, E. P., and Toga, A. W. (2001a). Mapping callosal morphology and cognitive correlates: EVects of heavy prenatal alcohol exposure. Neurology 57, 235–244. Sowell, E. R., Thompson, P. M., Tessner, K. D., and Toga, A. W. (2001b). Mapping continued brain growth and gray matter density reduction in dorsal frontal cortex: Inverse relationships during postadolescent brain maturation. J. Neurosci. 21, 8819–8829. Sowell, E. R., Thompson, P. M., and Toga, A. W. (2004). Mapping changes in the human cortex throughout the span of life. Neuroscientist 10, 372–392. Sowell, E. R., Thompson, P. M., Welcome, S. E., Henkenius, A. L., Toga, A. W., and Peterson, B. S. (2003). Cortical abnormalities in children and adolescents with attention‐deficit hyperactivity disorder. Lancet 362, 1699–1707. Sowell, E. R., Thompson, P. M., Yoshii, J., Kan, E., Toga, A. W., and Peterson, B. S. (2004). Gray matter thickness abnormalities mapped in children with Tourette syndrome. Paper presented at the Proceedings of the 34th Annual Conference of the Society for Neuroscience, San Diego, CA, Oct. 23–27. Terry, R. D., De Teresa, R., and Hansen, L. A. (1987). Neocortical cell counts in normal human adult aging. Ann. Neurol. 21, 530–539. Thompson, P. M., Cannon, T. D., Narr, K.L, van Erp, T., Khaledy, M., Poutanen, V.‐P., Huttunen, M., Lo¨ nnqvist, J., Standertskjo¨ ld‐Nordenstam, C.‐G., Kaprio, J., Dail, R.,

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Zoumalan, C. I., and Toga, A. W. (2001). Genetic influences on brain structure. Nat. Neurosci. 4, 1253–1258. Thompson, P. M., Cannon, T. D., and Toga, A. W. (2002). Mapping genetic influences on human brain structure. Ann. Med. 34, 523–536. Thompson, P. M., Giedd, J. N., Woods, R. P., MacDonald, D., Evans, A. C., and Toga, A. W. (2000). Growth patterns in the developing brain detected by using continuum‐mechanical tensor maps. Nature 404, 190–193. Thompson, P. M., Hayashi, K. M., de Zubicaray, G., Janke, A. L., Rose, S. E., Semple, J., Herman, D., Hong, M. S., Dittmer, S., Doddrell, D. M., and Toga, A. W. (2003). Dynamics of gray matter loss in Alzheimer’s disease. J. Neurosci. 23, 994–1005. Thompson, P. M., Hayashi, K. M., de Zubicaray, G., Janke, A. L., Rose, S. E., Semple, J., Hong, M. S., Herman, D., Gravano, D., Doddrell, D. M., and Toga, A. W. (2004). Mapping hippocampal and ventricular change in Alzheimer’s disease. NeuroImage 22, 1754–1766. Thompson, P. M., Hayashi, K. M., Simon, S., Geaga, J., Hong, M. S., Sui, Y., Lee, J. Y., Toga, A. W., Ling, W. L., and London, E. D. (2004). Structural abnormalities in the brains of human subjects who use methamphetamine. J. Neurosci. 24, 6028–6036. Thompson, P. M., Hayashi, K. M., Sowell, E. R., Gogtay, N., Giedd, J. N., Rapoport, J. L., de Zubicaray, G. I., Janke, A. L., Rose, S. E., Semple, J., Doddrell, D. M., Wang, Y. L., van Erp, T. G. M., Cannon, T. D., and Toga, A. W. (2004). Mapping cortical change in Alzheimer’s disease, brain development, and schizophrenia. In ‘‘Mathematics in brain imaging’’ (P. M. Thompson, M. I. Miller, J. T. Ratnanather, R. Poldrack, and T. E. Nichols, Eds.) [special issue]. NeuroImage 23(Suppl. 1), S2–S18. Thompson, P. M., Lee, A. D., Dutton, R. A., Geaga, J. A., Hayashi, K. M., Eckert, M. A., Bellugi, U., Galaburda, A. M., Korenberg, J. R., Mills, D. L., Toga, A. W., and Reiss, A. L. (2005). Abnormal cortical complexity and thickness profiles mapped in Williams syndrome. J. Neurosci. 25, 4146–4158. Thompson, P. M., Mega, M. S., Vidal, C., Rapoport, J. L., and Toga, A. W. (2001). ‘‘Detecting disease‐specific patterns of brain structure using cortical pattern matching and a population‐ based probabilistic brain atlas,’’ IEEE Conference on Information Processing in Medical Imaging (IPMI), UC Davis, 2001. In ‘‘Lecture Notes in Computer Science (LNCS) 2082: 488– 501’’ (M. Insana and R. Leahy, Eds.). Springer‐Verlag, New York. Thompson, P. M., Vidal, C. N., Giedd, J. N., Gochman, P., Blumenthal, J., Nicolson, R., Toga, A. W., and Rapoport, J. L. (2001). Mapping adolescent brain change reveals dynamic wave of accelerated gray matter loss in very early‐onset schizophrenia. Proc. Natl. Acad. Sci. USA 98, 11650–11655. Thompson, P. M., Woods, R. P., Mega, M. S., and Toga, A. W. (2000). Mathematical/computational challenges in creating population‐based brain atlases. Hum. Brain Mapp. 9, 81–92. Thompson, P. M., Narr, K. L., Blanton, R. E., and Toga, A. W. (2003). Mapping structural alterations of the corpus callosum during brain development and degeneration. In ‘‘The parallel brain’’ (M. Iacoboni and E. Zaidel, Eds.). MA. MIT Press, Cambridge. Toga, A. W., and Thompson, P. M. (2003). Mapping brain asymmetry. Nat. Rev. Neurosci. 4, 37–48. Wechsler, D. (1991). ‘‘Manual for the Wechsler Intelligence Scale for Children, Third Edition.’’ The Psychological Corporation, San Antonio, TX. Weinberger, D. R., and McClure, R. K. (2002). Neurotoxicity, neuroplasticity, and magnetic resonance imaging morphometry: What is happening in the schizophrenic brain? Arch. Gen. Psychiatry 59, 553–558. Yezzi, A., and Prince, J. (2001). A PDE approach for measuring tissue thickness. In ‘‘IEEE Conference on Computer Vision and Pattern Recognition (CVPR), December 2001.’’ Kauai, HI, USA. Zeineh, M. M., Engel, S. A., Thompson, P. M., and Bookheimer, S. (2003). Dynamics of the hippocampus during encoding and retrieval of face‐name pairs. Science 299, 577–580.

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Further Readings

Annese, J., Pitiot, A., and Toga, A. W. (2002). 3D cortical thickness maps from histological volumes. NeuroImage 13((6)Part 2), S858. Ballmeier, M., Sowell, E. R., Thompson, P. M., Kumar, A., Lavretsky, H., Wellcome, S. E., DeLuca, H., and Toga, A. W. (2004). Mapping brain size and cortical surface gray matter changes in elderly depression. Biol. Psychiatry 55, 382–389. Bartzokis, G., Beckson, M., Lu, P. H., Nuechterlein, K. H., Edwards, N., and Mintz, J. (2001). Age‐ related changes in frontal and temporal lobe volumes in men: A magnetic resonance imaging study. Arch. Gen. Psychiatry 58, 461–465. Courchesne, E., Chisum, H. J., Townsend, J., Cowles, A., Covington, J., Egaas, B., Harwood, M., Hinds, S., and Press, G. A. (2000). Normal brain development and aging: Quantitative analysis at in vivo MR imaging in healthy volunteers. Radiology 216, 672–682. Csernansky, J. G., Joshi, S., Wang, L., et al. (1998). Hippocampal morphometry in schizophrenia by high dimensional brain mapping. Proc. Natl. Acad. Sci. USA 95, 11406–11411. Davatzikos, C., and Resnick, S. M. (2002). Degenerative age changes in white matter connectivity visualized in vivo using magnetic resonance imaging. Cereb. Cortex 12, 767–771. Giedd, J. N., Blumenthal, J., JeVries, N. O., Castellanos, F. X., Liu, H., Zijdenbos, A., Paus, T., Evans, A. C., and Rapoport, J. L. (1999b). Brain development during childhood and adolescence: A longitudinal MRI study. Nat. Neurosci. 2, 861–863. Giedd, J. N., JeVries, N. O., Blumenthal, J., Castellanos, F. X., Vaituzis, A. C., Fernandez, T., Hamburger, S. D., Liu, H., Nelson, J., Bedwell, J., Tran, L., Lenane, M., Nicolson, R., and Rapoport, J. L. (1999a). Childhood‐onset schizophrenia: Progressive brain changes during adolescence. Biol. Psychiatry 46, 892–898. Lin, J. J., Salamon, N., Lee, A. D., Dutton, R. A., Geaga, J. A., Hayashi, K. M., London, E. D., Toga, A. W., Engel, J., and Thompson, P. M. (2004). Mapping of neocortical gray matter loss in patients with mesial temporal lobe epilepsy with hippocampal sclerosis. Paper presented at Proceedings of the 34th Annual Conference of the Society for Neuroscience, San Diego, CA, Oct. 23–27. Luders, E., Narr, K. L., Thompson, P. M., Rex, D. E., Jancke, L., and Toga, A. W. (2004). Gender diVerences in cortical complexity. Nature Neurosci. 7, 799–800. Luders, E., Narr, K. L., Thompson, P. M., Rex, D. E., Woods, R. P., De Luca, H., Jancke, L., and Toga, A. W. (2005). Gender eVects on cortical thickness and the influence of scaling [submitted]. Luders, E., Narr, K. L., Woods, R. P., Rex, D. E., Thompson, P. M., Jancke, L., Steinmetz, H., and Toga, A. W. (2005). Mapping cortical gray matter in the young adult brain: EVects of gender. NeuroImage 26(2), 493–501. Luders, E., Thompson, P. M., Narr, K. L., Toga, A. W., Jancke, L., and Gaser, C. (2005). A Surface Shape‐based Approach to Local Cortical Complexity [submitted]. Narr, K. L., Bilder, R. M., Toga, A. W., Woods, R. P., Rex, D. E., Szeszko, P. R., Robinson, D., Sevy, S., Gunduz‐Bruce, H., Wang, Y. P., DeLuca, H., and Thompson, P. M. (2005). Mapping cortical thickness and gray matter concentration in first episode schizophrenia. Cereb. Cortex 15, 708–719. Narr, K. L., Cannon, T. D., Woods, R. P., Thompson, P. M., Kim, S., Asunction, D., van Erp, T. G., Poutanen, V. P., Huttunen, M., Lonnqvist, J., Standerksjold‐Nordenstam, C. G., Kaprio, J., Mazziotta, J. C., and Toga, A. W. (2002). Genetic contributions to altered callosal morphology in schizophrenia. J. Neurosci. 22, 3720–3729. Narr, K. L., Thompson, P. M., Sharma, T., Moussai, J., Zoumalan, C. I., Rayman, J., and Toga, A. W. (2001). 3D mapping of gyral shape and cortical surface asymmetries in schizophrenia: Gender eVects. Am. J. Psychiatry 158, 244–255.

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Narr, K. L., Toga, A. W., Szeszko, P., Thompson, P. M., Woods, R. P., Robinson, D., Sevy, S., Wang, Y. P., Schrock, K., and Bilder, R. M. (2005). Cortical thinning in cingulate and occipital cortices in first episode schizophrenia. Biol. Psychiatry 58(1), 32–40. Peters, A., Morrison, J. H., Rosene, D. L., and Hyman, B. T. (1998). Feature article: Are neurons lost from the primate cerebral cortex during normal aging? Cereb. Cortex 8, 295–300. Posthuma, D., De Geus, E. J., Baare, W. F., HulshoV Pol, H. E., Kahn, R. S., and Boomsma, D. I. (2002). The association between brain volume and intelligence is of genetic origin. Nat. Neurosci. 5, 83–84. Rapoport, J. L., Giedd, J., Kumra, S., Jacobsen, L., Smith, A., Lee, P., Nelson, J., and Hamburger, S. (1997). Childhood‐onset schizophrenia. Progressive ventricular change during adolescence [see comments]. Arch. Gen. Psychiatry 54, 897–903. Rasser, P. E., Johnston, P. J., Lagopoulos, J., Ward, P. B., Schall, U., Thienel, R., Bender, S., Toga, A. W., and Thompson, P. M. (2005). Analysis of f MRI BOLD activation during the Tower of London Task using gyral pattern and intensity averaging models of cerebral cortex. NeuroImage 26(3), 941–951. Thompson, P. M., Schwartz, C., Lin, R. T., Khan, A. A., and Toga, A. W. (1996). 3D statistical analysis of sulcal variability in the human brain. J. Neurosci. 16, 4261–4274. Thompson, P. M., and Toga, A. W. (2002). A framework for computational anatomy. Comput. Vis. Sci. 5, 1–12. Verbeke, G., and Molenberghs, G. (2000). ‘‘Linear mixed models for longitudinal data.’’ Springer, New York. Vidal, C. N., Thompson, P. M., Hayashi, K. M., Geaga, J. A., Sui, Y, McLemore, L. E., Alaghband, Y., Giedd, J. N., Gochman, P., Blumenthal, J., Gogtay, N., Nicolson, R., Toga, A. W., and Rapoport, J. L. (2005). Dynamically spreading frontal and cingulate deficits mapped in adolescents with schizophrenia [in press]. Von Economo, C. V. (1929). ‘‘The cytoarchitectonics of the human cerebral cortex.’’ Oxford Medical Publications, London. Woods, R. P., Mazziotta, J. C., and Cherry, S. R. (1993). MRI‐PET registration with automated algorithm. J. Comput. Assist. Tomogr. 17, 536–546.

NEUROIMAGING AND HUMAN GENETICS

Georg Winterer,*y Ahmad R. Hariri,z David Goldman,y and Daniel R. Weinberger* *Genes, Cognition and Psychosis Program, National Institute of Mental Health National Institutes of Health, Bethesda, Maryland 20892 y Laboratory of Neurogenetics, National Institute of Alcohol Abuse and Alcoholism National Institutes of Mental Health, Bethesda, Maryland 20892 z Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15213

I. Introduction II. Historical Perspective III. General Issues A. Why Study Genes? B. Why Neuroimaging? C. Neuroimaging and Genetics—Basic Principles IV. Heritability A. Heritability of Brain Structure B. Heritability of Brain Function V. Application of the Principles A. Dementia B. Mental Disability C. Schizophrenia D. Mood and Anxiety Disorders VI. Conclusions References

The past few years have seen a rapid expansion of the application of neuroimaging tools to the investigation of the genetics of brain structure and function. In this chapter, we will (1) highlight the most important steps during the historical development of this research field, (2) explain the major purposes of using neuroimaging in genetic research, (3) address methodological issues that are relevant with regard to the application of neuroimaging in genetic research, (4) give an overview of the present state‐of‐research, and (5) provide several examples of how neuroimaging was successfully applied. I. Introduction

Identifying the biological underpinnings that contribute to brain structure and complex cognitive and emotional behaviors is paramount to our understanding of INTERNATIONAL REVIEW OF NEUROBIOLOGY, VOL. 67 DOI: 10.1016/S0074-7742(05)67010-9

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how individual diVerences in these behaviors emerge and how such diVerences may confer vulnerability to neuropsychiatric disorders. Advances in both molecular genetics and noninvasive neuroimaging have provided us with the tools necessary to address these questions on an increasingly sophisticated level (Hariri and Weinberger, 2003). With completion of a rough draft of the reference human genome sequence (Lander et al., 2001; Venter et al., 2001), a major eVort is underway to identify common variations in this sequence that impact gene function and subsequently to understand how such functional variations alter human biology. Because approximately 70% of all genes are expressed in the brain, many of these functional gene variations will account for interindividual variability of brain structure and function. A variety of neuroimaging methods have the capacity to assay gene function in the brain. These methods are complementary with regard to their ability to characterize diVerent aspects of brain structure and function and currently include structural magnetic resonance imaging (MRI), functional magnetic resonance imaging (f MRI), magnetic resonance spectroscopy (MRS), positron emission tomography (PET), single photon emission tomography (SPECT), as well as the two related techniques electroencephalography (EEG) and magnetoencephalography (MEG). In the near future, this list of tools will probably be extended by additional imaging methods such as diVusion tensor imaging (DTI). In this chapter, we will describe (1) the conceptual basis for, and potential of, using neuroimaging in human genetic research; (2) propose guiding principles for the implementation and advancement of this research strategy; and (3) highlight recent studies that exemplify these principles.

II. Historical Perspective

The idea that human brain function might be influenced by genetic factors has a long tradition and can be traced back to Davis and Davis (1936) who studied brain function (i.e., the electroencephalogram [EEG]) of twins. They visually examined the frequency characteristics of resting EEG in eight monozygotic (MZ) twins and compared them with repeatedly conducted EEG recordings of 31 controls. In essence, they found a striking similarity of EEG between MZ twins at any point in time that was comparable to the similarity of recordings from the control individuals at multiple time points. Over the next few years, this finding was substantiated by Loomis et al. (1936), Raney (1937), and, in particular, Lennox et al. (1945), who conducted the first large‐scale EEG study in twins. A meticulous methodological basis to the investigation of the heritability of human EEG was then developed by Vogel (1958), who was the first to adopt both a geneticist’s and an electroencephalographer’s point of view in his work. Subsequently, the knowledge base on the genetic foundation of particular EEG

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patterns was rapidly expanded with contributions from numerous investigators who not only conducted twin studies but also described brain function abnormalities in subjects with chromosomal aberrations (for a review see Vogel [2000]) or in patients with mostly rare neuropsychiatric disorders with monogenic Mendelian inheritance (for a review see Naidu and Niedermeyer [1993]). A remarkable observation of some of these studies was that even clinically unaVected subjects with familial risk for a certain illness showed abnormal EEG patterns. For instance, Patterson et al. (1948) found bilateral groups of slow EEG waves in subjects at risk for Huntington’s disease, a rare autosomal dominant progressive disorder of motor, cognitive, and psychiatric disturbances. These findings had far‐reaching consequences, because they provided strong evidence of a higher sensitivity of measures of brain function compared with clinical symptom assessment and because this kind of investigation helped to understand the pathophysiology of this particular genetic illness. With the availability of modern computer algorithms, the entire field entered a new era. Now, the estimation of the heritability of particular EEG patterns or the description of the pathophysiology of genetic disorders can rely on exactly quantified EEG signals, and it is also possible to determine the genetic impact on event‐related potentials (ERPs) (Fig. 1) (for a comprehensive reviews of the literature, see van Beijsterveldt and Boomsma [1994]; Vogel [2000]; and van Beijsterveldt and van Baal [2002]). In addition, EEG/ERP scalp surface maps were increasingly used for genetic investigations to take advantage of the spatial information in the brain signal (Trubinikov et al., 1993; van Beijsterveldt et al., 1998b; Winterer et al., 2003), and this methodological approach was recently advanced by applying electromagnetic source analyses in realistic head models (Fig. 2) (Winterer et al., 2000a). Thus, electrophysiological phenotypes that were used for genetic analyses gradually adopted a ‘‘neuroimaging approach’’ in the narrow sense as more sophisticated data analysis techniques became available. These new tools turned out to be useful not only for the estimation of the heritability of certain brain operations (outlined later) but also improved the potential ability to detect subjects at risk for genetic disorders. In particular, the quantification of certain electrophysiological features now allowed phenotyping of subjects who are more or less at risk for polygenic disorders. As opposed to monogenic disorders, polygenic disorders are common in the general population and usually interact more strongly with environmental factors. In these complex polygenic disorders, phenotype expression is governed by the action of many genes, and, as a result, phenotype expression is likely characterized by a Gaussian distribution. In other words, subjects at risk for a polygenic disorder usually cannot simply be described by the presence or absence of a ‘‘qualitative’’ phenotype—as in subjects with monogenic disorders—but by the presence of a ‘‘quantitative’’ phenotype. Examples in which this approach of quantitative electrophysiological phenotyping has been most successfully adopted are the

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FIG. 1. Examples of the P300 targets measured on P3, Pz, and P4. In both figures is the P300 target depicted of the youngest (line) and oldest (line with dots) of two MZ twin pairs (A) and two DZ twin pairs (B). With permission, from Van Beijsterveldt et al. [1998a]).

two polygenic disorders alcoholism (for a review, see Porjesz and Begleiter [2003]) and schizophrenia (Freedman et al., 1999; Winterer et al., 2003). During the past 30 years, neuroimaging modalities such as computed tomography (CT) and particularly MRI were increasingly used for the purpose of genetic studies. The first genetic study using CT was conducted by Weinberger

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FIG. 2. First genetic investigation based on a tomographic event‐related potential analysis with LORETA (low‐resolution electromagnetic tomography analysis). Association of GABAA‐
2 polymorphism and prefrontal/temporal activation in 95 healthy subjects. The analysis was preceded by a more ‘‘robust’’ principal component analysis of second order (test–retest stability: Cronbach’s  > 0.9), which was based on ERP‐measures taken from the entire electrode grid across the scalp and diVerent task conditions (F ¼ 3.8, p ¼ 0.02). dbSNP/HGVbase: SNP001493976. Accession No.: AF165124 (OMIM). Adapted, with permission, from Winterer et al. [2000a]).

et al. (1981), who investigated the possibility that lateral cerebral ventricular size may be under genetic control. They compared CT scans of 17 healthy siblings from 7 normal sibships, as well as 10 patients with chronic schizophrenia and 12 of their siblings without schizophrenia. In essence, they found a trend for a correlation of ventricular size between siblings in the healthy sibships but not in schizophrenic sibships. As expected from earlier CT investigations of patients with schizophrenia (Johnstone et al., 1976; Weinberger et al., 1979), the patients with schizophrenia had the largest ventricles, exceeding the normal range in seven cases. In contrast, their discordant were all within the normal range;

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however, their ventricles were larger than those of the controls. Accordingly, the authors suggested that a genetic component may determine ventricular size and that some genetic predisposition to larger ventricles exists in families of patients with schizophrenia but that illness‐related state processes may also contribute to ventricular enlargement. In the subsequent years, several twin studies reported abnormal brain structures in other illnesses such as birth defects involving agenesis of corpus callosum (Atlas et al., 1988; Pascual‐Castroviejo and Izquierdo, 1982). In healthy twins, a strong similarity of corpus callosum size using MR technology was first described by Oppenheim et al. (1989). In 1997, Barteley et al. found that cortical gyral patterns are more similar in MZ than in DZ twins (Fig. 3). Since then, numerous (in part large‐scale) twin studies have been conducted that established heritability for a variety of brain structures, which is now

FIG. 3. Schematic illustration of cross‐correlation analysis. The left‐hand columns show the cortical renderings involved in each comparison; the right‐hand column contains perspective views of the 2‐D cross‐correlation matrices resulting from each comparison. The top row shows the analysis of lateral cortical renderings from 3D MRI of a rhesus monkey. Before cross‐correlation, the right hemisphere rendering is ‘‘flipped’’ to face in the same direction as the left. In the rightmost column, a perspective view shows the full cross‐correlation matrix whose peak value indicates a high degree of similarity (R ¼ 0.70). The lower portion is an example of applying this process to a pair of twins (middle) and a pair of unrelated subjects (lower). Visual scrutiny reveals more similarities between the gyral patterns of the twins than between the unrelated brains; this diVerence in the degree of similarity is reflected in the peak values (R ¼ 0.38 twins versus R ¼ 0.21 unrelated) of the cross‐correlation maps. With permission, from Bartley et al. [1997]).

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considered to be particularly high for entire brain and cortical and gray matter volume (see later). Other investigators focussed on the description of structural brain abnormalities in subjects with chromosomal aberrations (for a review, see Kumar et al. [1992]) and with rare monogenic disorders of known mode of inheritance. For instance, CT and structural MRI scans have demonstrated decreased basal ganglia volumes in patients with Huntington’s disease ( Jernigan et al., 1991; Sax et al., 1989) and also in clinically unaVected subjects at risk for the disease (Aylward et al., 2004). In the clinically aVected patients, volume reductions can be substantial. Harris et al. (1992) described the greatest volume reduction in the putamen (>50%), enabling 100% separation of patient and control groups when corrections were made for overall brain size. A considerable number of structural imaging studies also accumulated over the years that addressed the question of abnormal brain volumes in complex polygenic illnesses like Alzheimer’s disease (AD), schizophrenia, or alcoholism. It became rapidly apparent from these studies that structural abnormalities are found in patients with these illnesses and also to some extent in their unaVected relatives (e.g., Fox et al., 1996; McDonald et al., 2004; Rosenbloom et al., 2003; Shenton et al., 2001; Suddath et al., 1990). In general, however, a broad overlap of measurements between groups of patients and control groups, which is consistent with the notion of a Gaussian distribution of phenotype expression in polygenic‐complex disorders, has been observed. Functional neuroimaging using SPECT, PET, and, more recently, f MRI increasingly plays an important role in genetic studies. Early PET studies reported diVerences of cerebral metabolism in twins discordant for pathological conditions such as AD (Luxenberg et al., 1987) or schizophrenia (Weinberger et al., 1992). With the increasing use of radioligands for assaying receptor availability in the brain, researchers also started to investigate the genetic impact on receptor binding. In 1996, WolV et al. described for monozygotic twins discordant for Tourette syndrome severity diVerences in D2 dopamine receptor binding in the head of the caudate nucleus, which predicted diVerences in phenotypic severity (r ¼ 0.99). More recently, an increasing number of f MRI studies have been published reporting diVerences of brain activation in twins who were discordant for certain neuropsychiatric illnesses or handedness (Lipton et al., 2003; Sommer et al., 2002; 2004; Spaniel et al., 2003). Over many years, the most substantial contribution of functional neuroimaging, however, was the description of abnormalities in functional neuroanatomy of neuropsychiatric disorders including those with a genetic background. For example, SPECT and PET studies were able to demonstrate decrements in striatal functioning of patients with Huntington’s disease (Hasselbalch et al., 1992; Kuhl et al., 1982), which was well in accordance with the findings obtained by structural MRI investigations. A number of functional neuroimaging studies also investigated presymptomatic at‐ risk subjects for Huntington’s disease. In general, these studies found that the age

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of at‐risk subjects was an important factor (Ichise et al., 1993; Maziotta et al., 1987). Remarkably, one study (Harris et al., 1999) found that during the years before the onset of clinical symptoms, SPECT perfusion in the putamen starts to be abnormal at an earlier time than volumetric measures of this brain structure, which indicates that functional measures might be more sensitive than structural measures. In the meantime, innumerable functional neuroimaging studies have also been conducted on complex polygenic neuropsychiatric conditions such as AD, schizophrenia, or alcoholism. As with the electrophysiological and structural MRI findings, there is generally a considerable overlap of the obtained measurements with those in healthy control subjects—even more so when clinically unaVected subjects at risk for one of these disorders are investigated. On the other hand, with increasingly advanced data analysis techniques and the use of multivariate instead of univariate designs, it has turned out that it is possible to overcome this problem of overlap at least to some extent. For instance, a recent multicenter study with [18F]‐fluorodeoxy‐D‐glucose (FDG) PET was able to diVerentiate patients with AD from healthy controls relatively well (Herholz et al., 2002a). The study was composed of 110 normal controls and 395 patients, and FDG uptake was measured in the posterior cingulate, temporoparietal, and prefrontal association cortex. With these three variables for diVerentiation, 93% sensitivity and specificity was provided for distinction of mild to moderate AD from normal subjects and 84% sensitivity at 93% specificity for detection of very mild AD. Within the past 15 years, the molecular revolution has brought a profound change to the entire field of human genetics and is continuing to do so. This also has had a considerable impact on genetic studies using structural or functional phenotypes of the brain (‘‘endophenotyping’’ or ‘‘intermediate phenotyping’’). Now, it is feasible to assay endophenotypic changes that are associated with variations within specific chromosomal locations (markers) or within specific genes. In 1989, Delgado‐Escueta et al. conducted the first endophenotype study of this kind and reported linkage of the Bf‐HLA locus marker in chromosome 6p21.3 to the clinical manifestations of juvenile myoclonic epilepsy ( JME) and its associated EEG traits (e.g., epileptic spikes). Evidence of linkage, however, was stronger (lod score > 3) when clinically asymptomatic family members with similarly abnormal EEG traits were counted as ‘‘aVected.’’ Since this landmark study has been published, a number of marker‐based linkage studies have taken advantage of the high sensitivity of EEG measures to detect otherwise asymptomatic at‐risk subjects within families of symptomatic patients (probands). In particular, numerous studies have been undertaken to explain the genetics of a variety of epileptic disorders. However, the same approach has also been successfully adopted in genetic studies on alcoholism (Porjesz et al., 2002) using phenotypes such as EEG beta‐frequency enhancement or in schizophrenia

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studies (Blackwood et al., 2001; Freedman et al., 1997) with the event‐related potentials P50 or P300 as endophenotypes, which are both known as being generated in specific structures within temporal lobe area. For many years, such family‐based linkage analyses using genetic markers at specific loci of the chromosomes, which are thought to be in the vicinity of certain candidate genes, have been the preferred strategy to study simple and complex genetic diseases. However, as the base sequence of an increasing number of genes became known and particularly because a complete draft of the human genome sequence became available (Venter et al., 2001), researchers started to directly investigate the impact of sequence variations within specific candidate genes (genomics). Once again, research on Huntington’s disease played a prominent role in neuropsychiatric research. In 1993, a six‐team international research group discovered that Huntington’s disease is caused by excessive and unstable repeating of the DNA bases CAG (trinucleotide repeats) in the Huntington gene on chromosome 4 (The Huntington’s Disease Collaborative Research Group, 1993). The number of repeats is critical for the clinical penetrance of the illness, and some subjects with less than 40 repeats may remain clinically asymptomatic until old age. The discovery of this mutation made it possible to directly detect presymptomatic subjects, to estimate their likely age of onset of illness, and to investigate these at‐ risk subjects with neuroimaging (Harris et al., 1999). For instance, the number of CAG repeats was found to correlate with the degree of neurodegeneration (i.e., with an individual’s striatal N‐acetyl‐aspartate [NAA] loss and lactate increase) with correlation coeYcients of 0.8 and 0.7, respectively ( Jenkins et al., 1998). In contrast, earlier studies without knowledge of the genotype had to take an indirect approach and to investigate a number of family members of a clinically aVected patient under the assumption that at least some of them are becoming ill at a later point in time. During the past few years, this way of testing for association between specific gene variants on one hand and phenotypic changes on the other hand has become the predominant research strategy in human genetics as exemplified in Fig. 4.

III. General Issues

A. WHY STUDY GENES? Genes represent the ‘‘go’’ square on the Monopoly board of life. They are the biological toolbox with which one negotiates the environment (Hariri and Weinberger, 2003a). Although most human behaviors cannot be explained by genes alone, and certainly much variance in aspects of brain structure and function will not be genetically determined, variations in genetic sequence that

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FIG. 4. EVect of BDNF val66met genotype on in vivo hippocampal f MRI response. (A) Brain map showing locales where BDNF genotype groups diVered in blood oxygenation, an indirect measure of neuronal activity, measured with f MRI during a working memory task. Regions marked in red are groups of voxels (‘‘activation clusters’’) where subjects with the val/met genotype showed abnormal hippocampal activation and were significantly diVerent when compared with val/val subjects. The statistical results are rendered on a canonical averaged T1 brain image and localized according to the standard 3D stereotactic space of Talairach and Tournoux. The maximally activated voxels are: right hippocampus (t ¼ 3.77, p < 0.01, cluster size (k) ¼ 25, 3D coordinates: 26–22–12); left hippocampus (t ¼ 2.39, p ¼ 0.02, k ¼ 10, –38–5–12). Inset, these activation clusters are rendered on a canonical averaged smoothed 3D rendered brain. Color BAR ¼ t values. R ¼ right hemisphere. (B) BDNF genotype eVect on hippocampal f MRI response in a second independent cohort. The activation clusters are those areas where BDNF val/met subjects again showed abnormal activation of bilateral hippocampi locales during the 2‐back working memory task and were significantly diVerent compared with val/val subjects. f MRI data rendered in the same manner as in (A) ( p < 0.05, cluster size > 8). Two clusters were identified in the left hippocampus (–30–35–16, t ¼ 2.49, p < 0.01, k ¼ 40 and –30–14–13, t ¼ 2.45, p ¼ 0.01, k ¼ 13) and one in the right hippocampus (28–31–2, t ¼ 2.55, p ¼ 0.01, k ¼ 37). Adapted, with permission, from Egan et al. [2003]).

impact gene function will contribute some variance to these more complex biological phenomena. This conclusion is derived implicitly from the results of twin studies that have revealed heritabilities of 40–70% for various aspects of cognition, temperament, and personality and the recognition that these human characteristics are genetically correlated with brain structure and function, which themselves show heritabilities in about the same range (Winterer and Goldman, 2003). Genes are thought to have a considerable impact on all levels of biology. In the context of disease states, particularly neuropsychiatric disorders, genes not only transcend phenomenological diagnosis they also represent mechanisms of disease. Moreover, genes oVer the potential to identify at‐risk individuals and biological pathways for the development of new treatments. In many major

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psychiatric illnesses such as schizophrenia or bipolar disorder, genes seem to be the most relevant risk factors that have been identified across populations, and the lion’s share of susceptibility to these disorders is accounted for by inheritance (Moldin and Gottesman, 1997). Although the strategy for finding susceptibility genes for complex disorders by traditional linkage and association studies may seem relatively straightforward (albeit not easily achieved), developing a comprehensive understanding of the mechanisms by which such genes act and increase biological risk is a much more daunting challenge. How does a gene aVect brain structure and information processing with regard to certain personality traits or cognitive abilities, and how does it increase risk for a neuropsychiatric disorder? How many genes contribute to a particular complex behavior, clinical symptom, or disease? What genetic overlap exists across behaviors, symptoms, and diseases? How large are the eVects of candidate genes on particular brain functions? Most recently, the ‘‘candidate gene association’’ approach has been a particularly popular strategy for attempting to answer these questions. Genetic association is a test of a relationship between a particular phenotype and a specific allele of a gene. This approach usually begins with selecting a biological aspect of a particular condition or disease, then identifying variants in genes thought to impact on the candidate biological process, and next searching for evidence that the frequency of a particular variant (‘‘allele’’) is increased in populations having the disease or condition. A significant increase in allele frequency in the selected population is evidence of association. When a particular allele is significantly associated with a particular phenotype, it is potentially a causative factor in determining that phenotype. There are, however, caveats to the design and interpretation of genetic association studies. Among them are linkage disequilibrium and ancestral stratification, issues that have been discussed in detail elsewhere (Emahazion et al., 2001). Another caveat is related to the question of whether a particular genetic variation observed in association studies is actually of major relevance to a distinct human condition. That is, how, if at all, do associations with laboratory measures translate to daily functioning and well‐being?

B. WHY NEUROIMAGING? Traditionally, the impact of genetic variations on human behavior and disease has been examined using indirect assays such as personality questionnaires, neuropsychological batteries, or symptom‐based diagnostic categories. Although several of these studies reported significant associations between specific genetic variations and a particular behavior or diagnosis, their collective results have been weak or inconsistent in many cases (Malhotra and Goldman, 1999). This is not surprising given the interindividual variability and because the

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used assays and diagnostic categories are frequently imprecise, vague, and prone to subjectivity error. As a result, it has been necessary to use very large samples, often exceeding several hundred subjects, to identify even small gene eVects (Glatt and Freimer, 2002). In addition, behavioral probes and neuropsychological tests allow for the use of alternative task strategies by diVerent individuals that may obscure potential gene eVects on the underlying neural substrates meant to be engaged by the tests. Because the structure and response of brain regions subserving specific cognitive and emotional processes may be more objectively measurable, functional genetic variations may have a more robust impact at the level of brain than at the level of behavior and clinical symptoms. Thus, functional genetic variations weakly related to behaviors and, in an extended fashion, neuropsychiatric syndromes may be more strongly related to the structure and function of neural systems involved in processing sensorimotor, cognitive, or emotional information in brain. The potential for marked diVerences at the neurobiological level in the absence of significant diVerences in behavioral and clinical measures underscores the need for the application of direct assays of brain structure and function that have higher sensitivity. On a very practical level, another advantage of neuroimaging has recently emerged and is related to the problem that arose from the increasing number of positive genetic associations with certain behavioral or disease states that have been recently reported. Even when functionality has been established by in vitro experiments and/or transgenic animal studies for these gene variations that have been found to show significant association, it frequently remains unclear which of these genetic variations is actually relevant in humans. Given the enormous expenses that are required to develop a new drug, a preselection among the potential molecular targets is needed that should be based on positive answers to three questions: (1) Is functionality for any particular genetic variation likely in humans? (2) If yes, is the direction of the biological eVect plausible and consistent with existing data? (3) If yes, is the biological eVect relevant in humans with regard to eVect size and pathological conditions? These questions can hardly be answered suYciently by traditional research strategies alone. The ‘‘validation of relevance in humans’’ requires additional tools, such as neuroimaging, that enable us to measure a genetic eVect on the biological level in humans. C. NEUROIMAGING

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GENETICS—BASIC PRINCIPLES

1. Selection of Candidate Genes The direct implication of heritability of brain structure and function is that functional alleles inherited from parent to child influence brain structure and function. However, the complexity of molecular genetic mechanisms that could

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potentially be involved in human brain structure and function is overwhelming and, at first glance, might suggest that gene identification would fail. Fortunately, this is an empirical question. There are two main levels of complexity. The first is the sheer number of genes expressed in any region of the brain being involved in neurodevelopment and synaptic organization as well as presynaptic and postsynaptic neurotransmission plus secondary neuronal downstream eVects and neuronal/glial exchange. The second level of complexity is the linear and nonlinear gene–gene and gene–environment interactions within the context of cellular compartmentalization and neuronal networks. The complexity leads to genocopies in which entirely diVerent genotypes (Fig. 5) lead to the same

FIG. 5. Schematic illustration of the complexity of the molecular genetics of cognitive function. Major genes interact with modifying genes, random noise, and environmental influences giving rise to biological phenotypes and ultimately cognitive phenotypes. With permission, from Winterer and Goldman [2003]).

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functional pattern, and phenocopies in which diVerent environments (environtypes) do the same. In addition, it would not be surprising if epigenetic phenomena, such as genomic imprinting, were found to be operative in the control of expression or degree of expression of genes aVecting brain function. For instance, prefrontal function is critical in social interactions and social behaviors that diVer in their degree of selective advantage/disadvantage to the transmitting parent, maternal versus paternal. It is well known, for example, that oVspring behaviors may enhance the reproductive potential of the mother, but the same behavior may not contribute a commensurate advantage to the reproductive potential of the father. It is this complexity that led Venter et al. (2001) to the suggestion that mathematical models, borrowed from complex system theory, may be beneficial in decoding genetic information derived from the first draft of the human genome. In practice, the application of neuroimaging techniques toward the study of genetic eVects should start when studying gene eVects on behavior or clinical symptoms would also start (i.e., from well‐defined functional polymorphisms). The genetic variation in such genes should have already been associated with specific physiological eVects at the cellular level, and their impact should have been described in distinct brain regions and brain circuits in animal experiments. Preferentially, data from post mortem studies (e.g., gene expression) also should be available. Imaging paradigms can then be developed to explore their eVects on brain structure and information processing in both normal and impaired human populations (translational neuroscience). Short of well‐defined functional polymorphisms, candidate genes with identified single nucleotide polymorphisms (SNPs) or other allele variants in coding or promoter regions with likely functional implications (e.g., nonconservative amino‐acid substitutions or missense mutation in a promoter consensus sequence) involving circumscribed neuroanatomical systems would also be attractive substrates. The investigation of genes and variations without well‐established structural or functional implications in brain, however, necessarily requires greater caution not only in the design of imaging tasks but also in the interpretation of diVerential brain responses or variations in brain structure. However, the relative rapidity with which functional eVects at the systems level can be identified and replicated will lead to an increasing shift in the value imaging genetics research will have in informing and directing molecular and cellular dissection of specific polymorphisms. 2. Control for Nongenetic Factors The contribution of single genes to the characteristics of brain systems, although putatively more substantial than to the emergent behavioral or clinical phenomena, is still presumably small. Furthermore, typically large eVects of age,

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gender, and IQ, as well as environmental factors such as illness, injury, substance abuse, daytime measurements (e.g., drowsiness) on phenotypic variance can easily obscure these small potential gene eVects. Because association studies with neuroimaging are susceptible to population stratification artefacts, as in any case– control association study, ethnic matching is also potentially critical. Thus, the identification and contribution of genetic variation to specific phenotypes should be limited to studies in which other potential and contributing factors are carefully matched across genotype groups. In the best of all worlds, heritability and its interaction with environment should also be known before any imaging parameter is used for genetic studies, because this allows an educated guess on whether a particular imaging parameter is suitable for genetic studies at all. If the imaging protocol involves performance of a task, the groups should be carefully matched for level of performance, or, at least, any variability in performance should be considered in the analysis and interpretation of the imaging data. This is because task performance and imaging responses are linked pari passu, and systematic diVerences in performance between genotype groups could either obscure a true gene eVect or masquerade for one. 3. Task Selection The past 5 years have been witness to a tremendous proliferation of functional neuroimaging studies and, with them, behavioral tasks designed specifically for this experimental setting. Many of these are modified versions of classic neuropsychological (e.g., Wisconsin Card Sorting Task; Axelrod [2002]) or neurophysiological tests (e.g., Oddball Task; Linden et al. [1999]) designed to tap neural systems critical to particular behaviors. More recent paradigms have emerged that focus on interactions of specific behaviors and disease states as these questions have become newly accessible with noninvasive imaging (e.g., the emotional Stroop and OCD [Whalen et al., 1998]). Because of the relatively small eVects of single genes in complex polygenic brain responses that are associated with certain behavioral traits, even after having controlled for non‐genetic and other confounder variables, imaging tasks must maximize sensitivity and interferential value. Because the interpretation of potential gene eVects depends on the validity of the information‐processing paradigm, it is best to select well‐characterized paradigms that are eVective at engaging specific brain regions and systems, that have suYcient test–retest stability and that produce robust signals in every individual while showing variance across individuals and for which heritability has been established by twin studies (see later). In short, imaging genomics studies are probably not the appropriate venue to design and test entirely new functional tasks, although it will be critical in the future to continue to develop new phenotypes that eventually permit a deeper insight into brain processes that are under genetic control.

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IV. Heritability

The most common way for deciding whether, and to what extent, interindividual variation in a certain phenotype is caused by genetic variation is the study of MZ and DZ twins, their similarities and diVerences. Because DZ twins are thought to be aVected largely by the same environmental diVerences as MZ twins but to have only one half of their genes in common by descent, they are used as suitable controls (Vogel, 2000).

A. HERITABILITY

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BRAIN STRUCTURE

The question regarding the degree or extent to which the size of the entire brain or of gray and white matter compartments is under genetic control has been well investigated. In contrast, little is known about the heritability of deeper brain structures, including the hippocampus, brainstem, cerebellum, and midbrain. Heritability of the entire brain, as well as gray and white matter volume, is substantial. Derived in vivo by MRI, Pearson’s R and intraclass correlations between MZ twins range from 0.6–0.9 for these brain quantities and heritabilities have been estimated as 0.90–0.95 (Baare´ et al., 2001; Bartley et al., 1997; Bonan et al., 1998; Geschwind et al., 2002; Lohmann et al., 1999; Pennington et al., 2000; PfeVerbaum et al., 2001, 2004; Posthuma et al., 2002; Scamvougeras et al., 2003; Thompson et al., 2001; Todd et al., 1999; White et al., 2002; Wright et al., 2002). There is some evidence that heritability is higher for left hemispheric volumes, and this eVect was found most pronounced in the frontal and temporal lobe (Geschwind et al., 2002; Tramo et al., 1995). In addition, shared environmental influence was also observed to be twice as high for the left hemisphere. These findings, if replicated, could indicate that the development of the left hemisphere is more aVected by early acting (and thus shared) environmental perturbations and by early acting genetic variation. Interestingly, the authors also described that in non‐right‐handed individuals, brain volume measures, particularly frontal lobe measures, are less heritable than in right‐handed persons, indicating an increased role for environmental exposures in this population subgroup. Heritability of gyral/sulcal structures seems to be lower than volume measures, but potentially in part for methodological reasons (i.e., the diYculty to quantify these brain structures reliably). For instance, Wright et al. (2002) reported no evidence of heritability for gyral and sulcal pattern. Others have found that cortical gyral and sulcal patterns vary considerably between MZ twins, particularly in the more frontally located regions, and particularly the shallow, superficial sulci (Bartley et al., 1997; Bonan et al., 1998; Lohmann et al., 1999; White et al., 2002).

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Heritability of gyral and sulcal patterns was estimated to be 0.1–0.6, suggesting that these patterns are under substantial influence of nongenetic factors. So far, only one study estimated genetic and environmental contributions to individual diVerences in hippocampal volumes. Sullivan et al. (2001) investigated 44 MZ and 22 DZ male twins in their seventh and eighth decade of life (i.e., after a lifetime of environmental exposure). None of the subjects exhibited obvious signs of dementia. It was found that only 40% of hippocampal size variance was attributable to genetic influences, whereas 80% of the entire brain volume variance was estimated to be under genetic control in the same study. The authors concluded that environment, whether by itself or in interaction with genes, exerts greater and possibly longer control in modifying hippocampal size than in other brain regions. Most studies agree that general intelligence, which is about equally under genetic and environmental control ( Winterer and Goldman, 2003), correlates with entire brain and gray matter volume and that this correlation is in part under genetic control (i.e., ‘‘genetic correlation’’) (Pennington et al., 2000; Posthuma et al., 2002; Thompson et al., 2001; Tramo et al., 1995), although a few studies also reported negative findings (Anderson and Harvey, 1996; Eliez et al., 2001; Schoenemann et al., 2000). With regard to white matter volume, comparable correlations with cognitive ability have been less well established. Both positive and negative findings have been published (Andreasen et al., 1993; Eliez et al., 2001; Reiss et al., 1996; Yurgelun‐Todd et al., 2002). Relatively little is known about the genetic relationship between more circumscribed brain regions (e.g., prefrontal cortex) or cortical surface profile measures (e.g., variance of gyri) and cognitive ability. Of interest in the context is a recent study by Schoenemann et al. (2000). This study directly addressed the question of the genetic influence on the correlation between cognitive abilities and prefrontal cortex volume by investigating sibling pairs. The authors described a genetic correlation between frontal lobe volume and performance on the Stroop test, which is known to involve the prefrontal cortex—a correlation they did not find with respect to other brain volume measures. B. HERITABILITY

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BRAIN FUNCTION

1. Positron Emission Tomography/Single Photon Emission Tomography Functional imaging has been widely and successfully applied to the investigation of genetic neuropsychiatric disorders. So far, however, little is known from twin or family studies about the heritability of metabolic or hemodynamic activation patterns (i.e., brain function). The limited heritability data derive from older studies conducted before the time when functional images were coregistered with MRI scans informative for individual brain anatomy. Using FDG‐PET, Buchsbaum et al. (1984) found a low correlation of resting prefrontal

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glucose metabolism among MZ quadruplets with schizophrenia. However, Clark et al. (1988), using essentially the same method in a set of seven healthy MZ twin pairs, found intraclass correlations between 0.4–0.7 for frontal lobe and putamen/caudate activation and somewhat lower correlations for other cortical and subcortical regions of the brain. Despite these limited data, PET (and receptor‐ binding SPECT) investigations are principally useful for genetic studies, because the basic prerequisite (i.e., a relatively high test–retest stability) is generally obtained with these measures (e.g., Kegeles et al., 1999; Martinez et al., 2001; Seibyl et al., 1996; White et al., 1999; Yasumo et al., 2002). 2. Functional Magnetic Resonance Imaging The more recently introduced functional magnetic resonance imaging (f MRI) does not involve exposure to ionizing radiation and, therefore, oVers a critical advantage for genetic studies, in which no potential benefit to the individual participant is expected by allowing for much larger samples. However, at this point, data on f MRI heritability or intrafamilial correlations are entirely absent. Also, the currently available test–retest stability data—in particular for hemodynamic response patterns during cognitive tasks—frequently do not allow definitive conclusions on the stability of the f MRI phenotypes. In general, a signal change of 1% needs to be detected against a backdrop of noise reaching a signal‐ to‐noise ratio value of 3–5% in the averaged data of a single subject (Bandettini and Wong, 1997; Manoach et al., 2001; Rutten et al., 2002; Schaefer et al., 2000). Relevant signal changes after cognitive or emotional challenge may be even smaller in brain regions, contributing to cognitive information processing such as the prefrontal cortex. However, improvements can be achieved by comparing regions of interest or activation clusters or by using the more recently introduced independent component analysis ( ICA) (Calhoun et al., 2002; Kimura et al., 1999; Ojemann et al., 1998). In addition, there is preliminary evidence that high‐field scanners may yield better data under certain circumstances. Whenever possible, functional imaging measures for genetic investigations, therefore, should be accompanied by task‐ and scanner‐specific test–retest reliability data. When there are insuYcient data on measurement stability and heritability, functional imaging data may be best used in conjunction with neuropsychological and/or electrophysiological measures whose stability and heritability are known (Egan et al., 2001; Kwon et al., 2001) and when there is convergent molecular, cellular, and non‐human primate evidence for functionality (e.g., 5‐hydroxytryptamine transformer [HTT]). 3. Electrophysiology The heritability of electrophysiological parameters has been extensively described in literature (for a review, see Vogel [2000]). Historically, most studies on the heritability of electrophysiological parameters investigated ERPs and EEG

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oscillations that are generated in the posterior part of the brain. Only recently, a growing number of electrophysiological studies have provided insights for the genetic determination of frontal lobe–related electrophysiology. The largest body of studies exists on the resting EEG condition. According to Van Beijsterveldt et al. (1996), heritabilities of EEG power spectra range between 0.7–0.9 across frequency bands (0.5–30 Hz) and cortical areas, and the largest part of variance of the EEG is explained by additive genetic factors with some limited additional influence from nonshared environment. A structural equation model across electrode positions for the alpha frequency band (8.0–12.5 Hz) found high genetic correlations (0.8–1.0) between electrode positions, suggesting that the same genes contribute to the observed variance of the EEG (for a specific frequency band) at diVerent scalp positions—even if there are topographic maxima for certain EEG frequencies. On the other hand, there is also evidence that the heritability of EEG is somewhat lower in the frontal region, where slow activity (0.5–7.5 Hz) is predominant, than in posterior brain regions, where alpha activity is most abundant (8.0–12.5 Hz) (Trubnikov et al., 1993; Van Beijsterveldt et al., 1996). The view of a higher EEG heritability in the posterior compared with the anterior region is to some extent supported by a longitudinal genetic analysis of EEG coherence (i.e., functional coupling of EEG oscillations between or within cortical regions) (Van Baal et al., 1998, 2001). Between the ages of 5 and 7 years, there seems to be a gradual increase in heritability of coupling within the occipital cortical region, potentially indicating an age‐related decrease in environmental variance or the emergence of new genetic factors. Interestingly, it was also found that heritability decreased at the same time for prefrontal cortical connections, indicating a decrease of genetic variance. Overall, heritabilities were moderate to high for all intrahemispheric EEG coherences (average, 0.6). At the age of 7, heritabilities were in the range between 0.6–0.8 for posterior coherences, whereas heritabilities were substantially lower for frontal coherences (i.e., approximately 0.3–0.5). Heritabilities were highest for long‐range EEG coherences between the frontal and parietooccipital cortex ( 0.6–0.8) (i.e., cortical regions that have strong anatomical connections). Of note, heritabilities of frontal coherences seem to increase again at puberty, then reaching values in the range between 0.3–0.8, with the lowest heritability for the delta‐frequency and highest heritability for the alpha‐frequency band (Van Beijsterveldt et al., 1998c). Also, there is no longer any obvious diVerence of heritability between posterior and anterior brain regions. These findings suggest that there are diVerent dynamic interactions between genetic and environmental factors in diVerent brain regions at diVerent stages during development. Task‐ and event‐related electrophysiological pattern also seems to be under substantial genetic control and, in many cases, to the same extent as that described for resting EEG. However, some diVerences can be found in dependency of task condition and electrophysiological parameter (Van Bejisterveldt et al., 1994;

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Vogel, 2000). For instance, Hansell et al. (2001) have recently investigated 391 adolescent twin pairs (291 MZ and 100 DZ twins)—a study that has been part of a large international twin study funded by the Human Frontier Science Program. The authors addressed the question whether the increase of task‐related frontal slow‐wave activity during a working memory task (delayed‐response task) is genetically controlled. For comparison, they also investigated the same group with a sensory choice reaction task without a delay component but otherwise identical task conditions. As expected, they found a significantly stronger increase of task‐related frontal and parietal slow‐wave activity during the memory task compared with the choice reaction task. Structural equation modeling, however, revealed that this relative slow‐wave increase during the memory condition is under little, if any, genetic influence. Only the task‐independent frontal slow‐wave activity was influenced by a genetic factor (approximately 35% of genetic variance). Moreover, approximately 50% of the genetic variance was explained by the parietal slow‐wave increase in both task conditions. This suggests (from a geneticist’s perspective) that cortical activation related to working memory task performance is under stronger parietal than frontal control, which would be to some extent in agreement with functional neuroimaging literature showing not only frontal but also parietal activation during working memory tasks (Cornette et al., 2001). Also, the findings could indicate that one core component of working memory (i.e., the frontal lobe activation associated with the delay [‘‘hold on line’’] component) is not under substantial genetic influence. At least one study, which looked at the eVect of variation in a specific gene related to frontal lobe function, found that the delay component of working memory was not the primary component of the task predicted by genotype (Goldberg et al., 2003). It is important to note, however, that we are only beginning to understand the phenotypical relationship among behavior, hemodynamic, and neuronal response.

V. Application of the Principles

A. DEMENTIA Alzheimer’s disease (AD) is a complex polygenic disorder in most cases (Emahazion et al., 2001; Farrer et al., 1991) and is the most common form of dementia in adults, aVecting approximately 7% of people older than 65 and perhaps 40% of people older than 80 (Price, 2000). The disease is typically characterized by a severe decline in memory performance (American Psychiatric Association, 1995), and from an imaging perspective, slowing of resting EEG in the temporoparietal region (Dierks et al., 1991; DuVy et al., 1984), prolonged latency (and amplitude reduction) of the temporoparietal P300 event‐related

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potential component (Brown et al., 1983; Syndulko et al., 1982), volume reduction of the medial temporal lobe in MRI or CT scans ( Jack et al., 1992; Jobst et al., 1992; Scheltens et al., 1992), a decline of white and gray matter tissue anisotropy that is most prominent in the temporal lobe (Bozalli et al., 2001), and decreased parietotemporal regional blood flow and glucose‐uptake in PET and SPECT scans (Benson et al., 1981; Herholz et al., 2002a,b; Mazziotta et al., 1992). To some extent, comparable, but more subtle, abnormalities are also observed in subjects at familial risk for the disease (Boutros et al., 1995; Burggren et al., 2002; Green and Levey, 1999; Ponomareva et al., 1998) or in early stages of the illness (De Santi et al., 2001; Fellgiebel et al., 2004; Grundmann et al., 2002; Huang et al., 2000; Killiany et al., 2002). In the latter subject group, compensatory increases of activity in the prefrontal cortex also have been described (Grady et al., 1994). Hallmarks in post mortem investigations of patients with AD are senile plaques in the temporoparietal cortex with the principal components: neurofibrillary tangles in the neuronal cell bodies, neuropil threads, and neurites, as well as extracellular A amyloid (Price, 2000) that can now be detected in vivo using the most recently developed PET tracers (Nordberg, 2004). A amyloid is cleaved from a larger precursor protein of unknown function, amyloid precursor protein (APP), and it is encoded by the APP gene in the midportion of the long arm of human chromosome 21 (Selkoe, 1996). Mutations of this gene lead to an accumulation of A amyloid in patients with early‐onset AD (<60 years) and also in patients with Down’s syndrome. Cerebral ischemia as assessed by conventional CT or MRI is thought to chronically upregulate expression of the amyloid precursor protein (APP) and to damage the blood–brain barrier, aVecting A peptide clearance from the brain (Sadowski et al., 2004). Recognition of the importance of vascular risk factors for AD‐related dementia and their treatment, therefore, could be beneficial not only for preventing cardiac, cerebral, and peripheral complications of vascular disease but also will likely have a direct impact on the occurrence of AD in at‐risk subjects. A genetic variant that has been consistently associated with the more common form (90%) of AD (i.e., late‐onset AD [>60 years]), is the epsilon (") 4 allele of the apolipoprotein E (APOE) gene on the long arm of chromosome 19, whereas the epsilon (") 2 allele seems to be less frequently associated with AD and may be even protective (Corder et al., 1993; Farrer et al., 1997). The "4 allele has been shown to be more common among individuals from Northern countries and African‐Americans than in subjects with a Southern European origin (Gerdes et al., 1992; Pablos‐Mendez et al., 1997; Srinivasan et al., 1993) (i.e., population stratification can be a serious confounder when investigating this gene). The primary role of its plasma protein APOE is thought to remove lipoprotein particles from the circulation through binding to specific receptors belonging to the low‐density lipoprotein (LDL) family (Piedrahita et al., 1992; Puglielli et al., 2003; Zhang et al., 1992). The mechanism by which the "4 allele elevates risk for late‐onset AD is largely unknown; however, it has been

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suggested that increased cholesterol levels and its distribution within neurons may play a critical role (Puglielli et al., 2003). APOE is present in senile plaques, neurofibrillary tangles, and cerebrovascular amyloid, which is correlated with the gene dose for the "4 allele (Ohm et al., 1995). This gene‐dose eVect is also observed with respect to age of onset and risk for the disease (Corder et al., 1993). Episodic memory decline (Bondy et al., 1995) or cognitive decline (Harwood et al., 2002; Helkala et al., 2001; Reed et al., 1994; YaVe et al., 1997) has been demonstrated in older adults carrying the "4 allele, whereas the "2 allele may play a protective role in normal aging (Farrer et al., 1997). Also, APOE "4 allele status predicts slowing of resting EEG, regional cerebral blood flow, and glucose uptake in the temporoparietal region as measured with EEG, SPECT, and PET in patients with AD and in cognitively normal, even middle‐aged, subjects (Burggren et al., 2002; Higuchui et al., 1997; Lehtovirta et al., 1996, 2000; Reiman et al., 1996; Small et al., 1995, 2000). These findings also suggest that neuroimaging measures might be more sensitive than cognitive measures with regard to the genetic eVects on brain function. Taking an even more sophisticated approach with direct comparison of cognitive performance and brain activity, evidence of high sensitivity of functional imaging measures was also obtained by Bookheimer et al. (2000), who used f MRI during a challenging memory task to explore the genetic eVects of the APOE "4 allele on memory‐ related brain activity. In their landmark study, 16 subjects carrying the APOE "4 allele and 14 subjects homozygous for the APOE "3 allele, which is not associated with increased risk for AD, were asked to memorize and recall unrelated word pairs, a demanding memory task previously used to identify damage to the medial temporal lobe memory system (Rausch and Babb, 1993), while undergoing f MRI. Although all subjects were cognitively intact and performed the task equally well, the pattern of brain activation between the two groups was strikingly diVerent. Compared with subjects with the APOE "3 allele, those with the high‐ risk APOE "4 allele exhibited significantly greater activation (both magnitude and extent) in memory‐related brain regions such as the prefrontal cortex and left hippocampus. Such relatively increased neural activation in those with the at‐risk allele was interpreted by the authors as reflecting possible compensatory phenomena through the recruitment of additional cognitive resources in the face of greater task diYculty and demand. Interestingly, the magnitude of task‐related brain activity was significantly correlated with subsequent memory decline. These data suggest that changes in cortical information processing during declarative memory are associated with the biological eVects of APO "4 even if compensation is made at the level of observable behavior (i.e., task performance). Thus, the authors concluded that observed diVerences in memory‐related brain activity associated with the APOE gene in the absence of behavioral impairments may provide a useful tool for predicting the course of cognitive decline.

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B. MENTAL DISABILITY Trisomy 21 (Down’s syndrome) occurs at a frequency of 1.5 in 1000 live births and results in moderate to severe mental disability. The syndrome illustrates the complexity of the clinical, behavioral, functional, molecular, and genetic dimensions of cognitive ability. Trisomy 21 usually occurs as a new mutation resulting from nondisjunction during first meiotic prophase in the process of maternal gametogenesis and more rarely because of the transmission of an extra translocated segment of chromosome 21. The relevant chromosomal area has been localized on 21q21.3‐q22.2 (called Down’s Syndrome Critical Region [ DSCR]), although a few other chromosomal regions also may be involved (Epstein et al., 1991; Korenberg et al., 1990; McCormick et al., 1989). Human 21q21.3‐q22.2 is homologous to portions of mouse chromosome 16 (Kola and Herzog, 1998). Reciprocal translocation involving this area in mice results in learning defects (Reeves et al., 1995). In another mouse model of Down’s syndrome, transgenic mice with a 180‐kb YAC containing a 100‐kb segment of the human 21q22.2 locus develop learning deficits (Smith et al., 1997). Several genes in this region have been implicated, notably the amyloid precursor protein gene (Ohira et al., 1997). The localization of the amyloid gene to this region is congruent with the identification of point mutations on the amyloid precursor protein of several early‐onset Alzheimer families, although most familial AD is not caused by such mutations (Kamino et al., 1992; St. George‐Hyslop et al., 1987; Tanzi et al., 1992). Amyloid protein accumulates in plaques in older patients with Down’s syndrome (>30 years) and in patients with AD. The DSCR1 (Down Syndrome Candidate Region 1) gene is another gene in this chromosomal region whose transcripts have been detected in high concentrations in the brain. It is also expressed in muscle, placenta, and kidney coding for at least four amino‐ acid isoforms resulting from alternative splicing and an alternative promoter (fourth isoform) (Casas et al., 2001; Fuentes et al., 1995, 1997; Price, 2000). DSCR1 is a gene that has a high sequence identity with ZAKI‐4 (Myazaki et al., 1996), and both belong to a family of proteins called myocyte‐enriched calcineurin interacting protein, because they bind and inhibit calcineurin signaling (Miyazaki et al., 1996). Chronic overexpression of DSCR1 is found in AD (Ermak et al., 2001). Calcineurin is activated by calcium‐calmodulin signaling and regulates the nuclear import of NF‐AT (nuclear factor–activated T cells), which ultimately stimulates transcription of a variety of genes, including interleukin‐2 (IL2) (Porter et al., 2000). Release of cytokines such as IL‐2 may be critical to inflammatory aspects of CNS pathology in neurodegeneration (Borrell et al., 2002; Raber et al., 1998). The complexity of these processes is still far from being understood. Also, it is unclear how the chromosomal aberration exactly relates to the phenotypic changes seen in these patients (e.g., post mortem findings), which show that patients

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with Down’s syndrome have degeneration of cholinergic basal forebrain (CBF) neurons like patients with AD ( McGeer et al., 1985; Sendera et al., 2000). Patients with Down’s syndrome are usually severely impaired in a wide range of cognitive domains, including episodic learning deficits and capabilities that involve the prefrontal cortex such as working memory ( Jarrold and Baddeley, 2001; Numminen et al., 2001). Electrophysiological abnormalities in trisomy 21 encompass a variety of ERP components (N100/P200; P300) (Karrer et al., 1995; Vieregge et al., 1992), as well as EEG coherence (Schmid et al., 1992). These electrophysiological studies suggest that a wide variety of, or all, cortical areas are aVected in trisomy 21. The EEG diVerences are consistent with structural MRI studies showing smaller overall brain volume, although the cerebellum, brainstem, frontal lobe, and hippocampus seem to be disproportionately smaller (Aylward et al., 1999; Pinter et al., 2001; Weis et al., 1991; White et al., 2003). This generalized pattern of abnormal brain structure and function contrasts with that found in AD, where abnormal function and structural changes are more limited to the hippocampus and temporoparietal area and only during later illness stages may involve prefrontal regions (e.g., Burggren et al., 2002; Grady et al., 1988). In patients with Down’s syndrome, premature aging and dementia of the Alzheimer‐syndrome type is typically observed, and the onset of this decline is accompanied by an increase in slow‐wave resting EEG‐power (Murata et al., 1994). A recent voxel‐based morphometry study suggested that these clinical and functional changes have a structural equivalent with relatively selective cortical gray matter volume loss in the temporoparietal and frontal lobe area, whereas other brain areas are spared ( Teipel et al., 2004). Thus, it seems that the most devastating aspect of Down’s syndrome (i.e., premature aging and dementia) shares similarities with AD on various levels, including clinical symptoms, cognitive deficits, functional and structural changes, post mortem brain changes, and the molecular genetic level. It can be expected that in the near future imaging genomics—because of its high sensitivity with regard to pathological brain processes—will contribute significantly to the explanation of the responsible genetic mechanisms and by extension may even help to better understand the dysfunctional molecular cascades in AD.

C. SCHIZOPHRENIA Schizophrenia illness perhaps provides one of the best examples of how neuroimaging can contribute to the dissection of the genetic basis of neuropsychiatric disorders. Schizophrenia is a common mental disorder aVecting approximately 1% of the general population with frequent onset of illness during early adulthood (American Psychiatric Association, 1995). In a recent meta‐analysis of twin studies (Sullivan et al., 2003), heritability to schizophrenia was estimated to

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be high (80%) with evidence for substantial additive genetic eVects and for common or shared environmental influences of approximately 10%, which is consistent with the view of schizophrenia being a complex polygenic disorder. Clinically, schizophrenia is typically associated with delusions and hallucinations during acute psychotic episodes, whereas negative symptoms may predominate between episodes. Cognitive deficits in working memory or attention are also found and are thought to be more closely related to the neurobiology of the illness (Goldberg and Weinberger, 2004; Winterer and Weinberger, 2004). There is presently general agreement that abnormal brain function in schizophrenia involves an extended network of cortical and subcortical brain structures, including temporal and parietal cortices, the basal ganglia, cerebellum, hippocampus and thalamus, and particularly the prefrontal cortex (Weinberger et al., 2001). In fact, evidence for subtle gray matter pathology in schizophrenia is legion, as shown by innumerable structural neuroimaging studies and post mortem analyses and is further supported by PET, f MRI, and electrophysiological studies. Thus, structural deficits and functional impairments that are compatible with cortical pathology most notably of the prefrontal and temporal cortex have been found in patients with first‐episode (Andreasen et al., 1997; Braus et al., 2002; Salisbury et al., 1998; Zipursky et al., 1998) and chronic schizophrenia (Callicott et al., 2000a; Ingvar and Franzen, 1974; Morstyn et al., 1983; Weinberger et al., 1986; Wright et al., 1999; Zipursky et al., 1992), as well as before the outbreak of psychosis (Lim et al., 1996; Pantelis et al., 2003) and in nonpsychotic family members (Callicott et al., 2003a; Gogtay et al., 2003; Staal et al., 2000; Winterer et al., 2003a); the latter findings suggest a relationship to primary genetic susceptibility. However, there is also some evidence that gray matter volume changes occur during the early course of the illness (Rapoport et al., 1999; Wiegand et al., 2004), implicating dynamic processes related to gray matter volume. The molecular basis for cortical microcircuit dysfunction has been the subject of an increasing body of research. Although most of this work has been done in post mortem tissue, several clinical investigations using proton magnetic resonance spectroscopy (MRS) have found reduced concentrations of N‐acetyl aspartate (NAA) in hippocampal and prefrontal cortices of patients with schizophrenia and also in their healthy siblings, again suggesting a genetic basis of cortical pathology (Weinberger et al., 2001). NAA is an intraneuronal measure primarily of mature pyramidal neurons and their processes; NAA levels vary with changes in mitochondrial oxidative phosphorylation and with glutamate levels (PetroV et al., 2002), and reduced NAA levels are found in numerous brain disorders. As such, NAA is interpreted as a nonspecific but sensitive measure of synaptic activity and abundance, and low NAA in schizophrenia implicates abnormal synaptic activity in these cortical regions. A reduction of prefrontal NAA in patients with schizophrenia also has been found to predict abnormal prefrontal cortical activation patterns in patients as measured with PET and with f MRI (Bertolino et al., 2000a;

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Callicott et al., 2000b). In addition, cortical NAA concentrations seem to be inversely correlated with negative symptoms, which have been linked to prefrontal function (Callicott et al., 2000). Consistent with the notion that abnormal local circuit processing could have distributed ramifications in the brain ( Winterer and Weinberger, 2004), NAA concentrations in the prefrontal cortex also have been shown to predict cortical activity in a distributed cortical network engaging parietal and temporal cortices (Bertolino et al., 2000a) and to predict the exaggerated ‘‘downstream’’ response of dopamine neurons in the striatum to amphetamine in patients with schizophrenia (Bertolino et al., 2000b) which is thought to be related to positive symptoms in schizophrenia (Laruelle et al., 1996). Accumulating evidence of a disturbed cellular architecture of cortical gray matter neurons also comes from post mortem investigations of synaptic proteins. Reductions of presynaptic vesicle proteins such as the synapsins and, less consistently, synaptophysin have been described (Browning et al., 1993; Glantz and Lewis, 1997). Three proteins of the SNARE receptor complex, which are involved in neurotransmitter vesicle docking to the inner plasma membrane, have been found to be downregulated: synaptosomal‐associated protein‐25 (SNAP‐25) (Young et al., 1998) and complexin 1 and 2 (Eastwood and Harrison, 2001). Decreased densities of dendritic spines have also been reported (Glantz and Lewis, 2000) as has reduced expression of reelin (Impagnatiello et al., 1998), which is secreted by GABAergic neurons in association with dendritic postsynaptic specializations. The molecular genetic basis of schizophrenia illness is currently a field of intensive research. Family‐based association studies have lately provided strong evidence for several schizophrenia susceptibility genes such as NRG1, DTNBP1, G72, RGS4, CHRNA7, and GRM3, which are all thought to interfere with synaptic transmission (Harrison and Owen, 2003). Another potential risk gene for schizophrenia that has been recently a matter of extensive investigation including the application of neuroimaging for the purpose of ‘‘endophenotyping’’ is the COMT (catechol‐O‐methyltransferase) gene. There are diVerent reasons why this particular gene is studied. A microdeletion (22q11), containing the COMT gene, has been observed in conjunction with velo‐cardio‐facial syndrome, which carries with it distinct clinical phenotypes, including schizophrenia‐like psychotic features (Murphy et al., 1999; Pulver et al., 1994). In addition, a recent meta‐ analysis suggested on the basis of the available family‐based association studies that a functional polymorphism (val108/158met) in this gene on exon 4 (see later) might be a small but reliable risk factor for schizophrenia illness—at least for people of European ancestry (Glatt et al., 2003). Consistent with this interpretation of the data is another recent large‐scale case‐control study of an ethnically highly homogenous sample of Ashkenazi Jews (Shifman et al., 2002) showing a significant association of Val/Met with schizophrenia illness. The idea to study this particular gene was primarily encouraged by the notion that prefrontal

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synaptic dopamine (DA) signaling is altered in schizophrenia (Abi‐Dargham et al., 2002; Akil et al., 1999, 2003; Weinberger et al., 1988). COMT, a methylation enzyme that converts released dopamine to inactivate 3‐methoxytyramine, is believed to play an important role in DA neurotransmission (Weinshilboum et al., 1999). In rats, COMT accounts for >60% of DA degradation by methylation in the prefrontal cortex (Karoum et al., 1994). Microdialysis studies have shown that pharmacological inhibition of COMT aVects dopamine flux in the prefrontal cortex but has no eVect on norepinephrine and that COMT does not impact on DA flux in the striatum (Li et al., 1998; Tunbridge et al., 2004). Moreover, COMT knockout mice show increases in prefrontal DA levels in an allele dosage fashion and also show no changes in norepinephrine metabolism (Gogos et al., 1998; Huotari et al., 2002). Expression of COMT is especially abundant in the prefrontal cortex relative to the striatum in both human and rodent brains (Matsumoto et al., 2003). In humans, the COMT gene contains a highly functional and common variation in its coding sequence (i.e., a substitution of valine by methionine [val158/108met] in the peptide sequence), which is caused by a transition of guanine to adenine at codon 158 of the COMT gene (Lachman et al., 1996; Lotta et al., 1995). This single amino acid substitution aVects the activity and temperature lability of the enzyme; at body temperature the Met allele has significantly less enzyme activity than the Val allele and is a less stable protein (Chen et al., 2004; Lachman et al., 1996; Lotta et al., 1995; Weinshilboum et al., 1999). In addition, a recent post mortem analysis found that the Val/Met polymorphism aVects protein abundance and enzyme activity in human brain (Chen et al., 2004). Using site‐directed mutagenesis of mouse COMT cDNA followed by in vitro translation, Chen et al. (2004) demonstrated that conversion of leu at the homologous position into Val or Met progressively diminished enzyme activity. These data would suggest that individuals with Val alleles would have relatively greater inactivation of prefrontal DA, therefore less eVective prefrontal DA signaling, and, by extension, diminished prefrontal function as frequently seen in patients with schizophrenia illness (see preceding). To assay directly the impact of the COMT Val/Met polymorphism on prefrontal physiology, Egan et al. (2001) used fMRI during the performance of a well‐ characterized working memory test (the n‐back task) that has been eVective at engaging the dorsolateral prefrontal cortex in prior imaging studies (Callicott et al., 1999; Cohen et al., 1997). The authors found that in two separate cohorts of healthy volunteers (n ¼ 11–16), all matched for age, gender, education, and task performance, the load of the high‐activity Val allele consistently predicted a relatively exaggerated prefrontal response during the working memory task (Fig. 6). Notably, this study also highlights the statistical power of the endophenotype approach compared with more traditional behavioral measures. In addition to the f MRI investigation, Egan et al. (2001) also used a measure of executive cognition in terms of working memory test performance (Wisconsin Card Sorting

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FIG. 6. Abnormal cortical signal‐to‐noise pattern in schizophrenia. Patients with schizophrenia and their healthy siblings show ineYcient prefrontal engagement (measured using functional magnetic resonance imaging, f MRI) and increased prefrontal response variability (measured as an electroencephalogram, EEG). (A) Statistic maps from f MRI during an n‐back working memory task, showing areas where a group of patients (i) and a group of healthy siblings of patients with schizophrenia (ii) are ineYcient compared with normal controls when performance on the task does not diVer between comparisons groups (Callicott et al., 1999; 2003b). In f MRI data, ineYciency (an empirical term indicating excessive activity for a given level of performance) is assumed to reflect unfocused or unstable response circuits. (B) Topographic maps of event‐related EEG during an auditory oddball task, showing increased ‘‘noise’’ in patients with schizophrenia, their healthy siblings, and normal controls in delta (i) and theta (ii) frequency bands (Winterer et al., 2004). ‘‘R’’ and ‘‘L’’ indicate right and left sides of the brain, respectively. Based on the extensively investigated oddball‐ evoked potential paradigm, Winterer et al. (2000b; 2003; 2004) derived a measure of variability (‘‘noise’’) of the response to a P300 electromagnetic source in a large sample of patients with schizophrenia, their healthy siblings, and a normal comparison group. Response variability (‘‘noise’’; i.e., activity not time‐locked to the stimuli) was approximated by subtracting the mean magnitude of the single trials from the magnitude of the average potential. Winterer et al. showed that, in addition to the classic pattern of reduced P300 amplitude in parietal cortex of the patient sample, patients and their healthy siblings had a relatively unstable prefrontal response. In healthy siblings, this measure of cortical processing instability was intermediate between the patients and the controls. Moreover, intraclass correlation between siblings was 0.5–0.6, and nonpsychotic siblings were three to four times more likely to show increased variability than healthy control subjects with no family history of schizophrenia. These results suggest that schizophrenia and the genetic risk for schizophrenia involve unstable processing in prefrontal cortical microcircuits. In addition, this EEG measure was inversely correlated with working memory performance even in normal individuals, suggesting that it reflects a functional state of microcircuits subserving the cognitive behavior of prefrontal cortex (Winterer et al., 2004). Adapted, with permission, from Winterer and Weinberger [2004]).

Test, WCST). The small eVect size of genotype on WCST perseverative errors, in which COMT genotype predicted approximately 3–4% of the variance, required several hundred subjects to achieve statistical significance. In contrast, powerful statistical diVerences were observed in imaging samples of fewer than 15 subjects.

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Since this initial study, a number of studies in various clinical and healthy populations using neuropsychological, neurophysiological, and f MRI measures of prefrontal cortical function have consistently shown that COMT Val/Met genotype indeed aVects prefrontal function (Bearden et al., 2004; Bertolino et al., 2004; Bilder et al., 2002; Diamond et al., 2004; Egan et al., 2001; Foltynie et al., 2004; Gallinat et al., 2003a; Goldberg et al., 2003; Malhotra et al., 2002; Mattay et al., 2003; Rosa et al., 2004; Weickert et al., 2004). More recently, it also could be shown that the underlying functional deficit of the association between COMT genotype and abnormal prefrontal activation is characterized by a decreased signal‐to‐noise ratio (i.e., an increased variability of task‐related prefrontal response in val‐carriers) (Winterer et al., 2005). Prior studies of patients with schizophrenia and of their unaVected siblings have demonstrated that increased prefrontal response variability is highly heritable and related to genetic risk for schizophrenia (Winterer et al., 2004) (Fig. 6). The rationale to investigate the eVect of COMT genotype on prefrontal response variability came from computational models on the basis of electrophysiological primate data that suggested that an increased prefrontal response variability reflects a lack of stimulus‐induced cortical synchronization (i.e., phase‐resetting or ‘‘noise’’) and that the degree of the response variability depends at least in part on cortical DA signaling (Winterer and Weinberger 2004) (Fig. 7). Overall, the functional investigations of COMT genotype provide direct evidence that the eVects of the COMT Val/Met polymorphism may reflect alterations in prefrontal dopamine catabolism related to COMT enzymatic activity. The COMT endophenotype results also illuminate the aforementioned evidence from traditional genetic studies that the Val allele is a susceptibility allele for schizophrenia and possibly other psychoses (Eberhard et al., 1989; Nurnberger and Foroud, 2000).

D. MOOD

AND

ANXIETY DISORDERS

Mood and anxiety disorders are a heterogeneous group of clinically overlapping syndromes, which show high comorbidity among each other and symptomatic fluidity with frequent changes of diagnostic subtypes over time (Angst and Merikangas, 2001; Merikangas et al., 2003). Most of these disorders are thought to have a complex polygenic background in common with a substantial environmental component. Whereas heritability estimates are high for bipolar disorder (0.8–0.9), considerably lower heritability rates (0.35–0.45) are found for the genetically correlated syndromes major depression, anxiety disorder, phobia, and panic disorder, whereby anxiety disorder also seems to show a strong genetic correlation (0.8–0.9) with the personality trait neuroticism (Hettema et al., 2003, 2004; Kendler et al., 1995, 2001; Kieseppa¨ et al., 2004). Lifetime prevalences of

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FIG. 7. Cortical dopamine signaling and disruption of connectivity and cortical signal‐to‐noise ratio in schizophrenia. Reduced prefrontal dopamine D1/D2‐receptor activation ratio, together with decreased NMDA and GABA signaling and altered activity of synaptic proteins and signaling genes, at the synaptic level leads to unfocused cortical excitation and reduced recurrent inhibition (i.e., patients with schizophrenia are ‘‘D2‐receptor‐dominated’’). This results in lower cortical signal‐to‐ noise ratio (SNR). Patch‐clamp studies show that dopamine signaling by D1 receptors increases NMDA conductance and tends to increase GABA excitability, whereas D2 stimulation tends to have opposing eVects. In computational network models (Durstewitz et al., 2000; 2002), the bidirectional dopamine eVects on GABAA and NMDA conductances were simulated by changing the equations for ionic currents according to in vitro patch‐clamp data. At the microcircuit level, early D2‐mediated decrease of inhibition might allow multiple cortical representations of an event to be activated closely in time, and even weak representations could pop easily into the delay‐active state (state 1). Conversely, weakly active representations would be subsequently suppressed by D1‐mediated activation, and a single or limited number of strongly active representations would become stable and resistant to additional inputs and noise (state 2). In other words, D1 stimulation can be conceived as widening and deepening the basins of attraction of low‐activity (e.g., spontaneous) and high‐activity (e.g., working memory) states of the network, whereas the opposite eVect is found with D2 activation. The lower SNR subsequently leads to impaired macrocortical connectivity (downstream eVects). The clinical syndrome is assumed to represent behavioral phenomena related to these changes in microcircuit and macrocircuit dynamics. Abbreviation: PFC, prefrontal cortex. Adapted, with permission, from Seamans et al. [2001] and Winterer and Weinberger [2004]).

aVective disorders may range up to 20%, with the notable exception of bipolar disorder with a morbidity risk of 1% in the general population (Faraone et al., 1999; Nurnberger and Berretini, 1998). A number of mostly relatively recent structural and functional neuroimaging studies have pointed out that the amygdala, among other brain areas, might be a

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particularly promising region to study in aVective and anxiety disorders. The amygdala is a limbic brain structure important for the generation of both normal and pathological emotional behavior, especially fear (LeDoux and Muller, 1997). Across diVerent mood and anxiety disorders, including bipolar disorder, evidence has been obtained from f MRI and FDG‐PET studies for elevated amygdala activity under fearful or stressful conditions and, less consistently, amygdala volume reduction also has been described (Blumberg et al., 2002; Drevets et al., 1992, 2002; Hasler et al., 2004; Hastings et al., 2004; Massana et al., 2003; Pissiota et al., 2003; Stein et al., 2002; Tillfors et al., 2001; Wik et al., 1997). As of today, however, it is not suYciently resolved whether the observed structural and functional changes reflect state or trait characteristics and whether equivalent changes are also seen in family members with genetically increased risk for these illnesses (Blumberg et al., 2002; Drevets, 2003). In addition, the amygdala is a small, complex, and heterogenous brain structure diYcult to quantify, characteristics that may, in part, account for the reported inconsistencies of volumetric studies (Hasler et al., 2004). The amygdala is densely innervated by serotonergic neurons, and 5‐HT receptors are abundant throughout amygdala subnuclei (Azmitia and Gannon, 1986). Thus, the activity of this subcortical region may be uniquely sensitive to alterations in serotonergic neurotransmission, and any resulting variability in amygdala excitability is likely to contribute to individual diVerences in emergent phenomena such as mood and temperament (Hariri and Weinberger, 2003b). For several reasons, this notion rests on solid ground: (1) the amygdala is thought to be critically involved in processing fearful and stressful conditions (Davis and Whalen, 2001; Zald, 2003), (2) the therapeutic eVect of serotonin‐reuptake inhibitors in mood and anxiety disorders is well established (Schatzberg and NemeroV, 2001), and (3) reduced serotonin (5‐hydroxytryptamin, HT) transporter (5‐HTT) availability has been associated with mood disturbances, including major depression (Malison et al., 1998) and the severity of depression and anxiety in a variety of psychiatric disorders (Eggers et al., 2003; Heinz et al., 2002; Willeit et al., 2000). Therefore, it has been logical to ask whether functional variations in the 5‐HTT gene are associated with mood and anxiety disorders and, by extension, whether genetic eVects are observable at the level of amygdala biology. In 1996, a relatively common polymorphism was identified in the human 5‐HTT gene (SLC6A4) located on chromosome 17q11.1‐q12 (Heils et al., 1996). The polymorphism is a variable repeat sequence in the promoter region (5‐HTTLPR), resulting in two common alleles: the short (s) variant composed of 14 copies of a 20–23 base pair repeat unit, and the long (l) variant composed of 16 copies. In populations of European ancestry, the frequency of the s allele is approximately 0.40, and the genotype frequencies are in Hardy–Weinberg equilibrium (l/l ¼ 0.36, l/s ¼ 0.48, s/s ¼ 0.16). These relative allele frequencies, however, can vary substantially across populations (Gelernter et al., 1997).

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After the identification of this polymorphism, Lesch and colleagues demonstrated in vitro that the 5‐HTTLPR alters both SLC6A4 transcription and the level of 5‐ HTT function (Lesch et al., 1996). Cultured human lymphoblast cell lines homozygous for the l allele have higher concentrations of 5‐HTT mRNA and express nearly twofold greater 5‐HT reuptake compared with cells possessing either one or two copies of the s allele. Subsequently, both in vivo imaging measures of radioligand binding to 5‐HTT (Heinz et al., 2000) and post mortem calculation of 5‐ HTT density (Little et al., 1998) in humans reported nearly identical reductions in 5‐HTT binding levels associated with the s allele as observed in vitro. These data are consistent with ‐CIT SPECT studies in humans and nonhuman primates reporting an inverse relationship between 5‐HTT availability and CSF concentrations of 5‐hydroxyindoleacetic acid (5‐HIAA), a 5‐HT metabolite (Heinz et al., 1998, 2002) and indicate that the 5‐HTTLPR is functional and impacts on serotonergic neurotransmission. In their initial study, Lesch and colleagues also demonstrated that individuals carrying the s allele are slightly more likely to display abnormal levels of anxiety in comparison to l/l homozygotes (Lesch et al., 1996). Since their original report, others have confirmed the association between the 5‐HTTLPR s allele and heightened anxiety (Du et al., 2000; Katsuragi et al., 1999; Mazzanti et al., 1998; Melke et al., 2001) and have also demonstrated that individuals possessing the s allele more readily acquire conditioned fear responses (Garpenstrand et al., 2001) and develop aVective illness (Lesch and Mossner, 1998) compared with those homozygous for the l allele. Recent studies that used pharmacological challenge paradigms of the 5‐HT system suggest that these diVerences in aVect, mood, and temperament may reflect 5‐HTTLPR–driven variation in 5‐HTT expression and subsequent changes in synaptic concentrations of 5‐HT (Moreno et al., 2002; Neumeister et al., 2002; Whale et al., 2000). Not surprisingly, however, several additional studies have failed to identify a relationship between the 5‐HTTLPR genotype and subjective measures of emotion and personality (Ball et al., 1997; Deary et al., 1999; Flory et al., 1999; Glatt and Freimer et al., 2002; Katsuragi et al., 1999), likely reflecting the vagueness and subjectivity of the behavioral measurements but also raising some concern that the relationship may be spurious (Ohara et al., 1998). In addition, such replication failures may reflect inadequate control for nongenotype factors such as gender and ethnicity (Williams et al., 2003), as well as chronic alcohol use (Heinz et al., 1998; Little et al., 1998) and exposure to environmental stress (Caspi et al., 2003), all of which have been shown to influence the eVect of the 5‐HTTLPR on both brain and behavior. Although the potential influence of genetic variation in 5‐ HTT function on human mood and temperament was bolstered by subsequent studies demonstrating increased anxiety‐like behavior and abnormal fear conditioning in 5‐HTT knockout mice (Holmes et al., 2003), the underlying neurobiological correlates of this functional relationship remained unknown. Because the physiologic response of the amygdala during the processing of fearful stimuli may

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be more objectively measurable than the subjective experience of emotionality, the 5‐HTTLPR may have a more obvious impact at the level of amygdala biology than at the level of individual responses to questionnaires or ratings of emotional symptoms. In 2002, Hariri et al. (2002) used f MRI to directly explore the neural basis of the apparent relationship between the 5‐HTTLPR and emotional behavior (Fig. 8). Specifically, it was hypothesized that 5‐HTTLPR s allele carriers, who presumably have relatively lower 5‐HTT function and higher synaptic concentrations of 5‐HT (analogous to the 5‐HTT knockout mice) and have been reported to be more anxious and fearful, would exhibit greater amygdala activity in response to fearful or threatening stimuli than those homozygous for the l allele, who presumably have lower levels of synaptic 5‐HT and have been reported to be less anxious and fearful (analogous to the contrasting wild‐type mice). Critical to this study was that this hypothesis was tested in normal subjects with no history of depression or anxiety disorders. It was found that subjects carrying the less eYcient 5‐HTTLPR s allele exhibited significantly increased amygdala activity compared with subjects homozygous for the l allele. In contrast, there were no significant group diVerences in subjective behavioral measures of anxiety‐like or fear‐related traits. In fact, the diVerence in amygdala

FIG. 8. Genotype‐based parametric comparisons illustrating significantly greater activity in the right amygdala of the s group versus the l group in both the first and second cohort. BOLD f MRI responses in the right amygdala (white circle) are shown overlaid onto an averaged structural MRI in the coronal plane through the center of the amygdala. Talairach coordinates and voxel level statistics (p < 0.05, corrected) for the maximal voxel in the right amygdala for the first and second cohort are as follows: x ¼ 24 mm, y ¼ –8 mm, z ¼ –16 mm; cluster size ¼ 4 voxels; voxel level corrected p value ¼ 0.021; t ¼ 2.89, and x ¼ 28 mm, y ¼ –4 mm, z ¼ –16 mm; cluster size ¼ 2 voxels; voxel level corrected p ¼ 0.047; t ¼ 2.03, respectively. With permission, from Hariri et al. [2002]).

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activity between 5‐HTTLPR genotype groups in this study was nearly fivefold, accounting for 20% of the total variance in the amygdala response during this experience, an eVect size 10‐fold greater than any previously reported behavioral associations. This finding suggests that the increased anxiety and fearfulness associated with individuals possessing the 5‐HTTLPR s allele may reflect the hyperresponsiveness of their amygdala to relevant environmental stimuli. Recently, three independent functional imaging studies have reported identical 5‐HTTLPR s allele–driven amygdala hyperreactivity in cohorts of healthy German (Heinz et al., 2005) and Italian (Bertolino et al., 2005) volunteers, as well as Dutch patients with social phobia (Furmark et al., 2004). Moreover, Hariri et al. (2005) have also replicated their initial finding of 5‐HTTLPR s eVects on amygdala reactivity in a large, independent cohort of volunteers (n ¼ 92). This large sample also allowed for the exploration of both gender‐specific and s allele load eVects on amygdala function and, in turn, dimensions of temperament associated with depression and anxiety. Specifically, Hariri et al. again observed that 5‐HTTLPR s allele carriers exhibit significantly increased right amygdala activation in response to their f MRI challenge paradigm (Hariri et al., 2004). In addition, their latest data reveal that 5‐HTTLPR s allele–driven amygdala hyperresponsivity is equally pronounced in both genders and independent of s allele load. The equivalent eVect of one or two s alleles on amygdala function is consistent with the original observations of Lesch et al. (1996) on the influence of the 5‐HTTLPR on in vitro gene transcription eYciency and subsequent 5‐HTT availability. The absence of gender diVerences suggests that the increased prevalence of mood disorders in females may be related to factors other than the direct risk eVect of the 5‐HTTLPR s allele. The collective results of these imaging genomics studies reveal that the 5‐HTTLPR s allele has a robust eVect on human amygdala function. Importantly, the absence of group diVerences in age, gender, IQ, and ethnicity in each of these studies indicates that the observed eVects are not likely a reflection of systematic variation in such nongenotype factors. Rather, the data suggest that heritable variation in 5‐HT signaling associated with the 5‐HTTLPR results in relatively heightened amygdala responsivity to salient environmental cues. That these results primarily emerged in samples of ethnically matched normal volunteers carefully screened to exclude any lifetime history of psychiatric illness or treatment argues that they represent genetically determined biological traits not related to manifest psychiatric illness. In contrast to these striking imaging genomics findings of 5‐HTTLPR short allele–driven amygdala hyperreactivity, attempts to link these eVects on brain function with measures of emergent behavioral phenomena, namely the personality trait of harm avoidance, have failed to detect any significant relationships. Specifically, in both the initial and replication studies of Hariri et al., there were

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no significant 5‐HTTLPR genotype eVects on subjective behavioral measures of anxiety‐like or fear‐related traits as indexed by the Harm Avoidance (HA) component of the Tridimensional Personality Questionnaire, a putative personality measure related to trait anxiety and 5‐HT function (Cloninger, 1986; Cloninger et al., 1993). Although the sample sizes and thus power in both imaging cohorts were small relative to traditional behavioral association studies, the absence of an eVect of 5‐HTTLPR on HA is consistent with several published reports in larger samples (Schinka et al., 2004). Thus, these results and those of previous studies suggest that the 5‐HTTLPR does not have a robust and consistent eVect on the dimensions of anxious and fearful personality measured by the HA subset of the TPQ. Moreover, in their replication study, Hariri et al. failed to find any relationship, independent of 5‐HTTLPR genotype or other factors (e.g., age, gender, IQ), between amygdala reactivity and HA in a subsample of 83 subjects with overlapping f MRI and behavioral data sets) (Hariri et al., 2005). These findings provide compelling evidence that genetically driven diVerences in the response of brain regions underlying emotional behavior may be readily investigated in relatively small sample populations in the absence of significant diVerences in behavioral measures. They also raise the intriguing possibility that 5‐HTTLPR s allele–driven variation in phasic amygdala function biases toward a heightened brain response to environmental threat, but that this relative hyperresponsivity alone does not predict individual diVerences in harm avoidance. Although it is likely that constitutive variation in 5‐HT signaling impacts on the biology of distributed brain systems beyond the amygdala, these investigations have focused on the eVects of the 5‐HTTLPR on amygdala function, because this region plays a central role in the generation of behavioral arousal and orientation, as well as specific emotional states such as fear. It is important to emphasize that the 5‐HTTLPR s allele eVect on amygdala reactivity in the Hariri et al. studies, as well as those by Heinz et al. and Bertolino et al., exist in samples of healthy volunteers with no history of aVective or other psychiatric disorders. This is consistent with a recent f MRI study reporting that whereas amygdala hyperexcitability reflects a stable, heritable trait associated with inhibited behavior, it does not by itself predict the development of aVective disorders (Schwartz et al., 2003). The study of Caspi et al. (2003) suggests that the existence of significant stressors in the environment of individuals carrying the 5‐HTTLPR s allele is necessary to further tip the balance toward the development of pathology and illness. Similarly, abnormal social behavior (Champoux et al., 2002) and 5‐HT metabolism (Bennett et al., 2002) have been reported in rhesus macaques with the 5‐HTTLPR s allele homologue, but only in peer‐reared, and thus environmentally stressed, individuals. This shift toward pathology may reflect the eVects of environmental stress on brain regions, most notably the prefrontal cortex, critical in the regulation of amygdala activity (Hariri et al., 2003; Keightley et al., 2003; Rosenkranz et al.,

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2003). For example, the experience of environmental insult before the maturation of relatively late developing prefrontal regulatory circuits (Lewis, 1997) may result in further biased amygdala drive in s allele carriers. Such relative hyperamygdala and hypoprefrontal activity has been documented in aVective disorders (Phillips et al., 2003; Siegle et al., 2002) and thus, may reflect a critical predictive biological marker. The importance, and perhaps even necessity, of such environmental stressors acting on an extended neural circuitry in facilitating 5‐HTTLPR s allele influences on behavior is underscored by the absence of significant genotype or genotype‐by‐gender eVects on HA, as well as correlations between amygdala reactivity and HA in the replication study in healthy subjects by Hariri et al. (2005). This suggests that 5‐HTTLPR–driven variation in the responsivity of the amygdala, although robust and consistent, does not necessarily result in altered mood and temperament per se. Rather, these results suggest that individual diVerences in complex, emergent phenomena, such as harm avoidance, will likely reflect the eVects of genetic variation on a distributed brain system involved in not only mediating physiological and behavioral arousal (e.g., amygdala) but also regulating and integrating this arousal in the service of adaptive responses to environmental challenges (e.g., prefrontal cortex). Along these lines, recent imaging genomics studies have reported changes of prefrontal and auditory cortex activation (Fallgatter et al., 1999, 2004; Gallinat et al., 2003), as well as of functional coupling of the amygdala and prefrontal cortex during aVect processing (Heinz et al., 2005; Pezawas et al., 2005) in healthy s allele carriers. Pezawas et al. have demonstrated that s allele carriers exhibit altered functional coupling of the amygdala and subgenual PFC and that this coupling contributes to individual diVerences in HA. Thus, intact dynamic interactions of the amygdala and prefrontal cortex may be critical for normal behavioral responses in individuals possessing the 5‐HTTLPR s allele. Because the impact of genetically driven variation in dopamine availability (e.g., COMT) on prefrontal function has been well documented (Egan et al., 2001; Mattay and Goldberg, 2004; Winterer et al., 2005), it will be of increasing importance to model heritable variation in both amygdala and prefrontal activity in exploring the influence of genes on behavior. Furthermore, it will be of critical importance to explore the impact of acute and/or chronic environmental stress on such genetically driven variation in brain function contributing to the etiology of mood and other aVectively laden disorders. Some clarification on the system level eVects of 5‐HTTLPR beyond the amygdala may be provided by electrophysiological studies (Fallgatter et al., 1999, 2004; Gallinat et al., 2003b). These three studies investigated the impact of 5‐HTTLPR on three diVerent electrophysiological measures of inhibition (i.e., [1] on the nogo P3, which is generated in the anterior cingulate cortex [ACC] when inhibiting an anticipated motor response [Fallgatter et al., 2002], [2] on the error‐related

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negativity that is seen during conflict monitoring and contains an inhibitory subcomponent that is generated in the ventral prefrontal cortex; Lavric et al., 2004], and [3] on loudness dependence, i.e., the dependence of the [tangential] N1/P2 component amplitude increase in response to diVerent stimulus intensities—a component that is thought to be generated in layer IV of the primary auditory cortex [Hegerl and Juckel, 1993] and that has been proposed to reflect a central protection mechanism from sensory overload [Buchsbaum et al., 1976]). Notably, one of these three electrophysiological components (i.e., the loudness‐dependent N1/P2 component) has been well investigated as an indicator of serotonin neurotransmission in humans and animal models (Hegerl and Juckel, 1993; Juckel et al., 1997, 1999; Pogarell et al., 2004; von Knorring and Perris, 1981). A strong loudness dependence N1/P2‐component is thought to indicate low serotonergic activity and vice versa. As far as 5‐HTTLPR is concerned, the finding of Gallinat et al. (2003b) indicates that homozygous carriers of the 5‐HTTLPR l allele have a weaker loudness dependence, indicating higher serotonergic neurotransmission while at the same time a higher 5‐HT uptake must be assumed (Heinz et al., 2000; Lesch et al., 1996). In line with this, a higher transport capacity of the l/l genotype was suggested to exert a somatodendritic 5‐HT1a‐receptor–mediated negative feedback with an overall increase of 5‐HT neurotransmission (Lesch and Mossner, 1998). The observed 5‐HTTLPR mechanism could thus be summarized so that l‐carriers are characterized by increased serotonergic neurotransmission, which provides them with an inhibitory protective mechanism against sensory overload. Accordingly, the result is to some extent analogous to what has been described by Hariri et al. (2002) for the amygdala, whereas s‐ allele carriers are hyperresponsive. By extension, the argument could be made that the physiological 5‐HTTLPR gene eVect is observable in diVerent brain circuits in a comparable way. However, this physiological gene eVect may not be easily generalized to the entire brain. Thus, the findings of Fallgatter (1999, 2004) actually suggest for the ACC and ventral PFC an opposite mechanism of 5‐HTTLPR. Here, inhibitory eVects seem to be mediated the other way around by the s allele. Currently, it can only be speculated that the eVect of the s allele on the ACC and ventral prefrontal cortex may mediate behavioral inhibition in patients with depression and anxiety disorders. Taken together, the f MRI and electrophysiological results on the eVect of 5‐HTTLPR are striking for several reasons: they provide evidence for genetically driven diVerences in the response of brain regions that underlie emotional behavior and sensory and motor inhibition. In addition, these genetic diVerences at the neurobiological level were marked in relatively small sample populations in the absence of significant diVerences in behavioral measures. Moreover, the imaging results provide an explanation of a potential biological mechanism for the genetic association of the 5‐HTTLPR with vague psychiatric disturbances, including various dimensions of anxiety and neuroticism. Although the finding of amygdala hyperexcitability in 5‐HTTLPR s allele carriers using f MRI provides a

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potential breakthrough in our understanding of the neurobiological underpinnings of abnormal mood and aVect associated with variation in 5‐HT signaling, the intrinsic mechanisms by which this brain response bias emerges remain poorly understood. The application of additional functional neuroimaging modalities, most notably radioligand‐specific positron emission tomography (PET), may provide a powerful tool for explaining these pathways. For example, PET‐radioligands of increasing specificity and flexibility have been developed to probe both 5‐HT synthesis (Hagberg et al., 2002) and 5‐HTT availability (Meyer et al., 2001; Sandell et al., 2002; Szabo et al., 1999; Wilson et al., 2000) and could be used to determine and substantiate presumably diVerential transporter and 5‐HT levels based on the 5‐HTTLPR genotype. In this field of research, the major challenge will be to provide radioligands of suYcient sensitivity as the inherent dosing limitations when injecting radioactive substances. For instance, a lack of sensitivity, among other reasons, might have been one important issue why several independent studies using the SPECT‐ligand [123I ]beta‐CIT failed to demonstrate an unambiguous eVect of the 5‐HTTLPR on SERT availability in the area of the ncl raphe, where SERT accumulation is highest in brain (Heinz et al., 2000; Jacobsen et al., 2000; Van Dyck et al., 2004). If a lack of sensitivity, and by extension a lack of statistical power, were indeed a major reason for these contradictory results, suYciently large sample sizes would be required, which in the case of applying [123I ]beta‐CIT means more than 100 subjects, because the most recent study by Dyck et al. investigated 96 subjects without obtaining the expected results. Thus, although it seems at first glance to be particularly attractive to use pharmaco‐PET and SPECT for endophenotyping because it is the most direct (i.e. molecular) approach and therefore allows us to build hypotheses in a straightforward way, methodological and logistic limitations can be a major challenge.

VI. Conclusions

The application of neuroimaging in genetic research has experienced an exponential growth during the past decade. In the beginning of this development, electrophysiologists worked out the basic scientific principles, which were then adopted by researchers using other imaging modalities. Today, a multitude of imaging tools are available that complement each other and allow probing the genetic foundation of brain structure and function from diVerent perspectives. The entire field of genetics was revolutionized when modern molecular genetic methods became available, and it is now possible to assay directly the eVects of variations within specific genes on brain structure and function. The value and relevance of using imaging for genetic investigations became

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particularly apparent when it turned out that specific genetic eVects are not easily discernible on the behavioral or clinical level, because the latter phenotypes are in many cases under the control of multiple genes in complex interaction with the environment. The ‘‘black box’’ between genotype on one side and clinical/ behavioral phenotype on the other side is the true realm of neuroimaging in genetic research, because neuroimaging provides ‘‘endophenotypes’’ that are more directly related to the genetic level, allowing at the same time a deeper ‘‘neuroscientific’’ insight into the genetics of the brain. In the years ahead, we will likely see numerous studies conducted in this rapidly expanding field that will help us to better understand the molecular genetic mechanisms of brain function and particularly how these mechanisms interact with environmental influences. However, because the number of discovered mechanisms is growing, it will be increasingly necessary to obtain estimates on the relevance of any single and combined gene eVect in quantitative terms. Beyond the necessity to study the quantitative genetic eVect on local brain circuits, it will also be required to assess the genetic downstream eVect on distributed brain systems and how these eVects relate to clinical symptoms and behavior. Here, a critical issue will be to take into account arguments from complex system theory suggesting that large‐scale interactions between distant and indirectly connected brain regions are generally more susceptible to intervening influences than interactions between closely neighboring and directly linked brain areas (Winterer et al., 2003b). To deal with this complexity, it can be anticipated that after the ‘‘molecular genetic age’’ the next revolution will be the implementation of tools from the emerging field of computational neuroscience. Increasingly sophisticated models of the brain, which incorporate multiple layers from the network system level down to the levels of synaptic transmission and genetic transcription, will be fed with experimental data, which will be provided to some considerable extent by neuroimaging experiments. These models will allow the simulation and analysis of specific genetic eVects and how they interact with other brain mechanisms, which eventually will make it possible to make increasingly precise predictions on the properties and relevance of any genetic eVect. Ultimately, it can be expected that computational models derived from neuroimaging data will considerably contribute to the dissection of those molecular mechanisms that will give rise to new treatment options with respect to neuropsychiatric disorders.

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Further Readings

Bondi, M. W., Salmon, D. P., Monsch, A. U., Galasko, D., Butters, N., Klauber, M. R., Thal, L. J., and Saitoh, T. (1995). Episodic memory changes are associated with the APO"4 allele in nondemented older adults. Neurology 45, 2203–2206. Bozzali, M., Franceschi, M., Falini, A., Pontesilli, S., Cercignani, M., Magnani, G., Scotti, G., Comi, G., and Filippi, M. (2001). Quantification of tissue damage in AD using diVusion tensor and magnetization transfer MRI. Neurology 57, 1135–1137. Buchsbaum, M. (1976). Self‐regulation of stimulus intensity: Augmenting reducing and the average evoked response. In ‘‘Consciousness and self‐regulation’’ (G. E. Schwartz, and D. Shapiro, Eds.). Plenum Press, New York. Durstewitz, D., and Seamans, J. K. (2002). The computational role of dopamine D1 receptors in working memory. Neural Netw. 15, 561–572. Grundman, M., Sencakova, D., Jack, C. R., Petersen, R. C., Kim, H. T., Schultz, A., Weiner, M. F., De Carli, C., De Kosky, S. T., van Dyck, C., Thomas, R. G., and Thai, L. J. (2002). Brain MRI

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NEURORECEPTOR IMAGING IN PSYCHIATRY: THEORY AND APPLICATIONS

W. Gordon Frankle,* Mark Slifstein,* Peter S. Talbot,* and Marc Laruelle*y *Departments of Psychiatry and Radiology, Columbia University College of Physicians and Surgeons and New York State Psychiatric Institute, New York, New York 10032

y

I. Introduction II. Conceptual Framework A. Compartmental System B. Distribution Volumes C. Relationship between Distribution Volumes and Rate Constants III. Deriving Outcome Measures from Imaging Data A. Equilibrium Analysis B. Kinetic Analysis C. Graphical Method IV. Psychiatric Disorders A. Schizophrenia B. AVective Disorders C. Anxiety Disorders D. Substance Abuse References

The ability of single photon emisssion computed tomography (SPECT) and positron emission tomography (PET) to image specific biomolecules in the living brain provides a unique tool for clinical researchers. Given this, it is not surprising that the use of neuroreceptor imaging techniques has become more widespread over the past decade. These tools are currently being used to study neurological and psychiatric disorders and to inform early‐stage drug development. The accurate derivation of receptor parameters from PET or SPECT brain activity data involves model‐based methodology. These models take into account the various sources comprising the total activity observed in the brain and fluctuations in the relative contributions from these sources over the time course of the scan. The goal of this chapter is to first present the theory underlying these models providing the reader with a conceptual framework for understanding this technique. Subsequently, we will discuss the specific methods used to generate the outcome measures from neuroreceptor studies in detail. Finally,

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the application of neuroreceptor imaging to the study of specific psychiatric illnesses will be reviewed.

I. Introduction

Single photon emission computed tomography (SPECT) or positron emission tomography (PET) neuroreceptor imaging has grown into an increasingly used investigational technique for the study of neurological and psychiatric disorders as well as for early‐stage drug development. The ability of PET and SPECT to image specific biomolecules is unmatched by any other method currently available to clinical investigators. This chapter will review the use of neuroreceptor imaging in psychiatry. We will start by reviewing the theory and basic methodology underlying the technique of neuroreceptor imaging, and, subsequently, we will review the application of this technique to the study of psychiatric illness. Neuroreceptor imaging allows investigators to obtain quantitative information regarding the distribution of the target receptors in the living human brain. To accurately derive this quantitative information from PET or SPECT scans, a variety of factors must be taken into account. These include the fact that the total activity observed by the scanner represents a combination of the activity coming from radiotracer specifically bound to receptor targets, nonspecifically bound radiotracer, and free radiotracer. The relative contribution of these activity sources to the total activity fluctuates over the course of the scan in an interdependent manner. Additional factors that play a role in the interpretation of data from PET/SPECT studies are the distribution of the receptors in the brain, the peripheral clearance of the radiotracer, the regional cerebral blood flow, and transport of the radiotracer across the blood–brain barrier. The potential for these factors to vary from subject to subject necessitates that they be accounted for to provide interpretable results.

II. Conceptual Framework

The conceptual basis for quantification of receptor parameters by means of neuroreceptor imaging stems directly from in vitro work with membrane preparations. In vitro, it is possible to derive the aYnity (KD) and number (BMAX) of binding sites using a radioactive tracer by manipulating the concentration of the unlabeled ligand; however, in vivo studies of this type cannot be performed in humans given the high concentration of unlabeled drug required. Therefore, it is not

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possible to independently measure KD or BMAX in humans using PET or SPECT. Instead, the outcome measure derived in neuroreceptor imaging studies is called the binding potential (BP ) and is equal to BMAX/KD (Mintun et al., 1984). A variety of model‐based methods have emerged to allow for the determination of BP by relating the activity observed in the region of interest (ROI) to the activity in the arterial plasma over the time course of the scan. Integral to the understanding and derivation of most of the model‐based methods are the concepts of physiological compartments, fractional rate constants, and distribution volumes.

A. COMPARTMENTAL SYSTEM A compartment is a physiological or biochemical ‘‘space’’ in which the concentration of the radioactive tracer is assumed to be homogenous. Figure 1 shows the generalized compartment model used in neuroreceptor imaging. Four compartments are described in regions of the brain containing specific binding sites. These include the plasma compartment (C1), and intracerebral compartments in which the radiotracer is free (C2) and nonspecifically bound (C20 ), as well as the compartment with specific binding (C3). Transfer between these compartments is governed by the fractional rate constants k1, k2, k3, k4, k5, and k6, as shown. These rate constants are the fraction of the concentration that moves from one compartment to the other per unit time. In practice, this model is diYcult to implement because of the high number of parameters, therefore, C2 and C20 are combined into one compartment on the basis of the assumption that equilibrium between the free and nonspecifically bound tracer in the brain is rapid compared with the kinetics of the specific binding. This introduces

FIG. 1. General compartment configuration used in neuroreceptor imaging. The compartments are the plasma (C1), the free (C2) and nonspecifically (C20 ) bound in the brain, and the specifically bound (C3).

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FIG. 2. Three–compartment model. Note the free (C2) and nonspecifically (C20 ) bound in Fig. 1 have been pooled into one compartment, C2. In addition, C1 has been labeled as Ca to indicate that this compartment contains the arterial and capillary concentrations of the tracer.

the concept of the ‘‘free fraction’’ ( f2), which is the fraction of the total tracer in C2 (i.e., both the free and nonspecifically bound) in the free state. Similarly, in the plasma, equilibrium between the free tracer and the tracer bound to plasma proteins is assumed to be rapid, with the term f1 denoting the fraction of total tracer free in the plasma. The resulting three‐compartment model is shown in Fig. 2. The plasma compartment has been labeled Ca to indicate that this compartment contains the arterial and capillary concentration of the radiotracer and as such the rate constants governing tracer transport across the blood–brain barrier (BBB), K1 and k2, depend on blood flow and permeability (Frey et al., 1987; Gjedde and Wong, 1990). B. DISTRIBUTION VOLUMES The distribution volume of a radioactive tracer is the factor by which the volume of the tissue of interest would need to change to maintain the same mass of tracer in the tissue but at a concentration equal to that in the arterial plasma. In other words, the distribution volume of a radiotracer in a specific tissue region is the ratio of the tissue concentration to the free concentration in plasma at equilibrium. Returning to the three‐compartment model described previously, the distribution volume (V ) of C2 or C3 is equal to: V2 ¼

C2 f1 Ca

or

V3 ¼

C3 ; respectively f1 Ca

ð1Þ

It is important to point out that the distribution volumes defined are the equilibrium distribution volumes (i.e., when no net transfer of the radiotracer is present between the plasma and the compartment). In this case, the total distribution volume (VT) of any brain region is equal to the sum of V2 and V3. As stated earlier, the basis for neuroreceptor quantification using PET or SPECT stems from in vitro work; therefore, to gain further insight into the VT, V2, and V3, we consider a hypothetical in vitro experiment in which a ligand, L, and receptor, R, are freely mixed. Assuming the ligand and receptor behave according to biomolecular association and unimolecular dissociation, the concentration of the receptor– ligand complex [LR] will increase in proportion to the free concentration of

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the ligand [L] and the free concentration of receptor [R] and decrease in proportion to [LR]: d½LRŠ ¼ kon ½LŠ½RŠ koff ½LRŠ ð2Þ dt d½LRŠ where dt is the change in the concentration of the receptor–ligand complex with time, and kon and koV are the proportionality constants. When the system is at equilibrium and d½LRŠ dt ¼ 0 ½LŠ½RŠ koff ¼ ¼ KD ð3Þ ½LRŠ kon where KD is the equilibrium dissociation constant. This relationship, derived from in vitro studies, is applicable to equilibrium conditions in the brain. Using pharmacological notation, the concentration of free radiotracer, F ¼ [L], the concentration of radiotracer bound to the receptor, B ¼ [LR], and the total concentration of receptors, BMAX, the preceding equation can be written as: ½LŠ½RŠ F ðBMAX ¼ ½LRŠ B

BÞ

¼ KD

ð4Þ

Solving this equation for B yields the Michaelis–Menten relationship: B ¼ F  BMAX KD þ F

ð5Þ

In the setting of in vivo PET or SPECT experiments, the radioactive ligand is typically given at tracer dose such that F  KD and the Michaelis–Menten equation simplifies to: B BMAX ¼ ¼ BP F KD

ð6Þ

By simple substitution, it can be demonstrated that the BP is equivalent to the distribution volume, V3. Assuming that the radiotracer crosses the BBB by passive diVusion, then the free concentration in the plasma equals the free concentration in the brain at equilibrium (i.e., f1Ca ¼ f2C2. Therefore, because F ¼ f2C2 ¼ f1Ca and B ¼ C3: B BMAX C3 ¼ ¼ ¼ V3 ¼ BP F KD f1 C a

ð7Þ

It can be seen that determination of BP, as described by the preceding equation, requires the accurate measurement of the unmetabolized fraction of radiotracer in the arterial plasma (Ca) as well as the fraction of unmetabolized radiotracer that

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is not bound to plasma protein (f1). Frequently, one or both of these measurements are unable to be obtained, in which case two outcome measures, related to BP, can be derived (Laruelle et al., 1994e). In the case in which f1 is unable to be determined, the quantity C3/Ca may be derived. This quotient has been termed V30 to diVerentiate it from V3 and is equivalent to f1BP; implicit in the use of V30 as an outcome measure is the assumption that f1 does not vary across experiments. In the case in which neither f1 nor Ca is available, the quantity V3/V2 (termed V300 ), equivalent to f2BP, can be determined as VT/V2 1. Generally, V2 is inferred from the VT in a region devoid of target receptors (reference region, RREF), with the assumption that, under equilibrium conditions, the concentrations of free and nonspecifically bound radiotracer do not vary across brain regions. It is important to note that in the current PET and SPECT literature, a tendency has arisen to use the term BP to denote either V30 (f1BMAX/KD) or V300 (f2BMAX/KD) rather than BMAX/KD. C. RELATIONSHIP BETWEEN DISTRIBUTION VOLUMES RATE CONSTANTS

AND

The compartment model in Fig. 2 describes the movement of quantities between distinct states with well‐defined rules for passing from state to state. The arrows between Ca and C2 represent transport, and the arrows between C2 and C3 represent the simplified mass action law (Eq. 2). The multiple routes of influx and eZux from C2 show the coupling that occurs between transport and binding. This is a linear model, in the sense that the rate of flux from any compartment is proportional to the concentration in that compartment. The proportionalities are the rate constants, defined as: K1 ¼ FL  EðmLg 1 min 1 Þ f2 K 1 ðmin 1 Þ k2 ¼ f1 k3 ¼ kon f2 Bmax ðmin 1 Þ k4 ¼ koff ðmin 1 Þ

ð8Þ

K1 is flow (FL) times the unidirectional extraction fraction E. The components of all other parameters have been previously described. Fig. 2 can also be expressed as a system of diVerential equations: dC 2 ðtÞ dt

¼ K1 Ca ðtÞ

ðk2 þ k3 ÞC2 ðtÞ þ k4 C3 ðtÞ

dt

¼ k3 C2 ðtÞ

k4 C3 ðtÞ

dC 3 ðtÞ

ð9Þ

In PET and SPECT, it is a convention that the injection is given at t ¼ 0 and, therefore, the initial conditions are constrained such that at t ¼ 0, C2 ¼ C3 ¼ 0,

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because no radiotracer will be present before the injection. We also define the total concentration, the quantity that can be measured, as CT ¼ C2 þ C3. Another important point is that in a reference region, because no receptors are present, both Bmax and k3 ¼ 0 and the system reduces to: dCðtÞ ¼ K1 Ca ðtÞ dt

k2 C

ð10Þ

The distribution volumes can be derived from Eqs. 1, 9, and 10 with the assumption that the system is at equilibrium and, therefore, no net change in concentration is occurring with time (i.e., the time derivatives on the left sides of Eqs. 9 and 10 ¼ 0). K1 f1 k 2 K1 k3 V3 ¼ f1 k 2 k 4
 K1 k3 VT ¼ 1þ f1 k 2 k4

V2 ¼

ð11Þ

III. Deriving Outcome Measures from Imaging Data

The derivation of the outcome measures used in neuroreceptor imaging using the conceptual framework described previously can be divided into equilibrium, kinetic, and graphical methods. Equilibrium methods derive information about the receptors by analyzing the activity distribution at equilibrium (Farde et al., 1986; Laruelle et al., 1994a). Kinetic methods determine quantitative information regarding the receptors by estimating the rate constants that govern the transfer between the arterial, brain, and receptor compartments (Mintun et al., 1984). Graphical analysis transforms the data into variables that are linearly related, and the parameters can then be determined by means of linear regression (Logan et al., 1990; Patlak et al., 1983). In the following section, we will briefly review these methods and the limitations associated with each. A. EQUILIBRIUM ANALYSIS All the information inferred from neuroreceptor studies pertains to the relationship between the brain and radiotracer under equilibrium conditions. As will be discussed later, it is frequently not possible to obtain a state of equilibrium, and, hence, methods relying on mathematical models to infer the equilibrium parameters are necessary. However, with some tracers it is possible,

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by administering a bolus followed by a constant infusion of radioligand, to attain conditions under which plasma, reference region, and brain ROIs are all simultaneously in steady state (i.e., at constant concentrations) (Abi‐Dargham et al., 1994; Carson et al., 1993, 1997; Koeppe et al., 1997; Laruelle et al., 1994a,b, 1995; van Dyck et al., 2000). As described previously, in the steady state, time derivatives all equal 0, and concentration ratios can be used to infer distribution volumes. For this method to give accurate quantification, it is important that all concentrations are actually constant and not just in constant ratio to each other (the latter has been referred to as pseudoequilibrium, and will lead to biased distribution volume estimates [Carson et al., 1993]). When available, this method is particularly robust, because multiple samples of the relevant ratios can be obtained during the steady state. To be successful, the ligand must attain steady state when there are still adequate radioactive counts available for the counting statistics to be reliable. This is not always possible, especially with short‐lived isotopes such as [11C]‐labeled tracers. In the case in which it is not possible to use a bolus/constant infusion paradigm, several diVerent approaches, based on the equilibrium method, have been developed. After the injection of a radiotracer as a single bolus, there will be a point of instantaneous equilibrium at which the specific binding compartment changes from net influx to net eZux of activity; this point has been termed the time of peak equilibrium (Farde et al., 1989) or transient equilibrium (Ito et al., 1998). At this time, the net change in concentration in the specific binding compartment (C3) is 0, and as such, V300 ¼ C3/C2. The diYculty with using the peak equilibrium method to derive V300 comes in determining the exact point in time at which the net flux is zero. Some have proposed that this time can be estimated by forming the diVerence between the time–activity curve in the ROI and the RREF, fitting this to a smooth curve and finding the time and value at the peak (Farde et al., 1986; 1989). V300 could then be computed as this peak value divided by the value of RREF at that same time (Ito et al., 1998). This method makes the assumption that the peak can be accurately determined from the data, but because the data are made up of discrete data points, it is possible that the peak actually occurs between points. A second assumption is that at the peak diVerence between the ROI and RREF data curves, RREF provides an accurate approximation of C2 in the ROI that is not necessarily the case (Slifstein and Laruelle, 2001).

B. KINETIC ANALYSIS The equilibrium method described previously represents the least computationally intensive method for PET/SPECT analysis; however, given the diYculty in achieving equilibrium during the course of a scan with many radiotracers,

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most scan data are analyzed using either kinetic or graphical methods. Kinetic analysis derives information about the distribution volumes by estimating the rate constants (K1, k2, k3, and k4) governing transfer between the brain compartments. Providing that the rate constants are in fact constant, the transfer between compartments shown in Eqs. 9 and 10 become a system of constant coeYcient linear ordinary diVerential equations. It is important to keep several factors in mind; first, it is assumed that the activity measured by the camera has been corrected for the contribution from the brain vasculature. Second, because both K1 and k2 are flow dependent, it is necessary that cerebral blood flow be constant over the time of the scan for K1 and k2 to be constant. Finally, k3 is proportional to the change in the number of available receptors with time (BMAX B, Eq. 4). This means that the term k3C2 will be nonlinear in general; however, when the experiment is performed at tracer dose BMAX  B and k3 is approximately constant. With these assumptions, this system can then be characterized by an impulse response function, h(t), treating the injection of the radiotracer as an input into the system, with the response being the change in concentration over time in the compartments. Then the system in Eq. 9 has an explicit solution, with the total curve CT equal to a ‘‘convolution integral’’ of the impulse response function, h(t), with the input, Ca, denoted Ca(t) h(t): Ðt CT ðtÞ ¼ Ca ðtÞ hðtÞ ¼ 0 hðt t 0 ÞCa ðt 0 Þdt 0 hðt; K1 ; k2 ; k3 ; k4 Þ ¼ Aþ e aþ t þ A e a t qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi   ð12Þ a ¼ 0:5 k2 þ k3 þ k4  ðk2 þ k3 þ k4 Þ2 4k2 k4 k 3 þ k 4 aþ A ¼ K1 a aþ The expression h(t, K1, k2, k3, k4) emphasizes that for every possible set of rate constants, a diVerent h exists, leading to a diVerent convolution curve. The name ‘‘impulse response function’’ is derived from systems theory and signal processing terminology. From this perspective, the brain is treated as an input/output system, with input given by the arterial plasma concentration of radioligand over time and output given by the brain concentration over time. Under the given assumptions and experimental conditions, this is a ‘‘linear time invariant system.’’ In this setting, the term linear is with respect to the input functions, so that the response to a sum of inputs will equal the sum of the responses to the individual inputs. Time invariant means that there is no preferred time origin, or formally that if the response to a given input, Ca(t ), is CT(t ), then the response to the identical input shifted in time, Ca(t t0 ), will be the response to Ca(t ) with the same time shift, CT (t t0 ). An impulse input is a vanishingly brief input of large magnitude with the property that its total area under the curve

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equals 1 (note that in actuality such an input could never be given in an imaging experiment). It can be shown that the mathematical response to the impulse is the previously described function h(t). From Eq. 11 it is apparent that given the appropriate set of rate constants, all distribution volumes can be derived. Most strategies for finding the appropriate rate constants use some variant of least squares minimization between the data and the model to obtain the best set of rate constants. The method generally will be an iterative procedure that searches over the set of possible rate constants until the sum of squares of the residuals is less than a prespecified tolerance, and the solution to this will not be unique; many diVerent sets of rate constants can be used to construct curves that fall within the tolerances. All such solutions tend to share certain desirable qualities. Modeled CT curves with small residuals will all tend to be ‘‘close’’ to each other, regardless of the set of rate constants used to generate them. In particular, the area under the fitted portion of the curves will be similar, and if they have similar slopes in the tail portion of the curve (or if they are very near zero in the tail), the projected area under the curve for infinite time will be similar as well. The total distribution volume VT can be shown to be proportional to this area, so that ‘‘good fits’’ to the data will tend to have similar distribution volumes. By several criteria, VT turns out to be the most robust parameter that can be estimated from the data (Carson et al., 1993; Gunn et al., 1998; Laruelle et al., 1994; Parsey et al., 2000e). Furthermore, if a reference region exists, its distribution volume can be used as an estimate of V2, so that V30 and V300 can be derived as VT V2 and VT/ V2 1, respectively (Lammertsma et al., 1996; Parsey et al., 1999, 2000e). The classical approach to parameter estimation has been to fit the data to the model CT (t) ¼ Ca(t) h(t) by minimizing the residual sum of squares: X N PETðt j ÞÞ2 ð13Þ j¼1 ðCa ðt j Þ hðt j Þ over parameter space (i.e., all possible combinations of non‐negatively valued rate constants), where tj represents the time of the jth sample point of the data. Because Eq. 13 is a nonlinear function of the parameters, the minimization must be done with an iterative optimization routine such as the Levenberg Marquardt algorithm (Levenberg, 1944; Marquardt, 1963). The approach gives estimates of all four individual rate constants, but, as described previously, usually only VT is reliably measured. A related strategy is to constrain the ratio K1/k2 in all brain regions to the VT value of the reference region (Abi‐Dargham et al., 2000b). On theoretical grounds, one would expect that receptor‐rich regions would always be fitted according to Eq. 9 and reference regions according to Eq. 10, but that is not always the case. It is sometimes found that Eq. 10 produces more stable and reproducible distribution volume estimates in receptor‐rich regions (Koeppe et al., 1991; Parsey et al., 2000a). In that case, the eZux parameter is sometimes referred to as k2a rather than k2, to indicate that it encompasses

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receptor parameters and brain eZux. Conversely, it is sometimes found that Equation 10 does not provide adequate fit in the reference region, possibly because of model assumption violations such as a slow, nonspecific binding component. In this case, Eq. 9 may be used to obtain a better fit (Parsey et al., 2000e), and the parameters k3 and k4 are relabeled k5 and k6 to indicate that they are not meant to imply the presence of receptors. The decision to use a model with more or fewer compartments than that which the physiology seems to dictate falls into the category of model order determination. (See Gunn et al. [2002], Landlaw and DiStefano [1984], and Slifstein and Laruelle [2001] for discussions of this subject). 1. Reference Region Method The previously described approach requires arterial blood samples to be drawn and analyzed over the course of the scan. This may be a prohibitive requirement either because of diYculty in obtaining subject compliance or diYculty in measuring metabolite‐corrected radioligand concentration in blood plasma because of technical challenges. Reference region methods were devised to allow receptor parameter estimates when blood sampling is not possible. The two common variants are the full (FRTM) (Lammertsma et al., 1996) and simplified (SRTM) (Lammertsma and Hume, 1996) reference region approaches. Both are derived from the same principle, which is that by the use of a transform method Equation 10 can be inverted to solve for Ca in terms of the reference region curve CREF, and this expression can in turn be substituted back into the equation for the brain region of interest by use of the same device. When the brain region of interest is modeled as in Eq. 9, the method is referred to as FRTM. The solution is given as: CT ðtÞ ¼
R1 CREF þ ½B1 expð 
a1 tÞ þ  B2 expð a2 tފ CREF ðtÞ k 3 þ k 4 a1 k2 a1 B1 ¼
a2 a1 
R1  a2 k 4 k 3 k2 B2 ¼ a2 a2 a1 R1

ð14Þ

where R1 is the ratio of K1 in the region of interest to K1 in the region of reference. Because the delivery constant K1 is not measured directly, but only in relation to K1 in the reference region, VT cannot be obtained with this approach. The estimated parameter set is [R1 k2 k3 k4] so that only V300 can be obtained with FRTM. The method has been found to be numerically fragile (diYcult to obtain convergence to a physiologically plausible solution) on a number of data sets, and this inspired the formulation of SRTM (Lammertsma and Hume, 1996). This approach uses the same derivation of Ca in terms of the reference region curve but fits the ROI to the model in Eq. 10 in analogy with the use of the lower order

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model in standard kinetic modeling discussed previously. The estimated parameter set is [R1 k2 V300 ]. With standard kinetic modeling using the lower order model, the ratio K1/k2a can be used as an estimator for VT. With SRTM, K1 is not measured, so VT cannot be estimated. Therefore, an analogy is made between 1/k2a and the coeYcient of K1 given in the expression for VT in Eq. 11. The solution is: CT ðtÞ ¼ R1 CREF ðtÞ þ k2 ð1

R1 k2 t ÞCREF ðtÞ expð Þ 1 þ k3 =k4 1 þ k3 =k4

ð15Þ

Parameters obtained with either the FRTM or SRTM algorithm can be obtained by iterative least squares minimization. In addition, SRTM is conducive to a spectral analysis approach (Cunningham and Jones, 1993) that is computationally faster and more stable than the conventional iterative approach (Gunn et al., 1997). Even though the underlying theory of FRTM and SRTM is the same as that of conventional kinetic modeling, the methods can be particularly sensitive to small deviations from the modeling assumptions (Slifstein et al., 2000). Thus, it is found that they give similar results to conventional methods with some but not all ligands (Bonab et al., 2000; Gunn et al., 1998; Parsey et al., 1998).

C. GRAPHICAL METHOD A method devised by Logan et al. (1990) consists of formally integrating the system in Eq. 9 and summing the result to obtain an integral equation for CT. When divided through by CT, the result is an asymptotically linear relationship between two new transformed variables with slope VT: Ðt

Ðt

CT ðtÞdt

0

CT ðtÞ

¼ VT

Ca ðtÞdt

0

CT ðtÞ

þ bðtÞ

ð16Þ

where b(t) rapidly approaches a constant. The slope VT can be computed by linear regression onto the portion of the data after b nears its asymptote. Because the variables in the regression are highly correlated with each other as a result of the transformation, the method will be biased (VT underestimated on average) when data are noisy (Slifstein and Laruelle, 2000). On the other hand, the derivation of Eq. 16 is independent of compartment configuration, so that when the noise level in the data is low, the model order considerations referred to earlier are not necessary. A number of correction strategies have been proposed to correct the bias (Logan et al., 2000a; Ogden et al., 2002; Varga and Szabo, 2002). The graphical method can also be adapted to a reference region approach by applying Equation 16 in the reference region and solving for the integral of Ca

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(Logan et al., 1996). Substitution of this expression in the equation of the region of reference results in: Ðt

Ðt

CT ðtÞdt

0

CT ðtÞ

¼ DVR

CREF ðtÞdt

0

CT ðtÞ

þ b0 ðtÞ

ð17Þ

where the outcome measure is the slope DVR, equal to the ratio of the distribution volume in the region of interest to the distribution volume in the region of reference, and b0 (t) is a new intercept term that also asymptotically becomes constant. An alternative version is: Ðt

Ðt

CT ðtÞdt

0

CT ðtÞ

¼ DVR

0

ðtÞ CREF ðtÞdt þ CREF k 2

CT ðtÞ

þ b00 ðtÞ

ð18Þ

where the k2 with the overbar is a population average. This is designed to cause the new intercept b00 to approach its asymptote more rapidly. IV. Psychiatric Disorders

The techniques outlined in the previous section have been applied to the study of neurological and psychiatric illnesses. The scope of the neuroreceptor imaging literature necessitates limiting our discussion to the application of neuroreceptor imaging in the study of specific psychiatric illnesses. Determining the pathophysiology of psychiatric illnesses has been hampered by the fact that no major abnormalities in brain integrity have been detected for most of these disorders. At best, subtle brain abnormalities have been found in postmortem studies of individuals with psychiatric illnesses, and these findings have been inconsistently replicated. The introduction of neuroreceptor imaging brought enormous promise to the field of psychiatry in that this technique allows for the in vivo study of specific molecular brain functions in psychiatric illnesses. In this section, we will review the major findings across diVerent domains within the field of psychiatry, including schizophrenia, aVective disorder, anxiety disorder, and substance abuse. A. SCHIZOPHRENIA Most neuroreceptor studies in psychiatry have focused on the illness of schizophrenia, and, although there are exceptions, most studies focus on one of two neurotransmitter systems, either the serotonin system or the dopamine

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system. This is due, in large part, to the radiotracers available for use and to current theories on the etiology of schizophrenia. The classical dopamine (DA) hypothesis, formulated more than 30 years ago, proposed that schizophrenia is associated with hyperactivity of dopaminergic neurotransmission (Carlsson and Lindqvist, 1963; Rossum, 1966). This hypothesis was essentially based on the observation that all eVective antipsychotic drugs provided at least some degree of D2 receptor blockade, an observation that is still true today (Creese et al., 1976; Seeman and Lee, 1975). On the other hand, negative and cognitive symptoms are generally resistant to treatment by antipsychotic drugs. Functional brain imaging studies have suggested that these symptoms are associated with prefrontal cortex (PFC) dysfunction (Weinberger and Berman, 1996). A contemporary view of the role of DA in schizophrenia is that subcortical mesolimbic DA projections may be hyperactive (resulting in positive symptoms) and that the mesocortical DA projections to the PFC may be hypoactive (resulting in negative symptoms and cognitive impairment). The advent of techniques based on PET and SPECT to measure indices of DA activity in the living human brain opened the possibility of direct investigation of these hypotheses. 1. Dopamine a. Striatal DA. Studies of striatal DA transmission in schizophrenia examined both postsynaptic (D2 receptors and D1 receptors) and presynaptic (DOPA decarboxylase activity, stimulant‐induced DA release, baseline DA release, and dopamine transporter [DAT]) functions. i. D2 receptors. Striatal D2 receptor density in schizophrenia has been extensively studied with PET and SPECT imaging. In a recent meta‐analysis (Weinberger and Laruelle, 2001), we identified 17 imaging studies comparing D2 receptor parameters in patients with schizophrenia (including a total of 245 patients, 112 neuroleptic naive, and 133 neuroleptic free) and controls (n ¼ 231), matched for age and gender (Abi‐Dargham et al., 1998a, 2000c; Blin et al., 1989; Breier et al., 1997; Crawley et al., 1986; Farde et al., 1990; Hietala et al., 1994a; Knable et al., 1997a; Laruelle et al., 1996b; Martinot et al., 1990, 1991, 1994; Nordstrom et al., 1995a; Okubo et al., 1997; Pilowsky et al., 1994; Tune et al., 1993; Wong et al., 1986). Radiotracers included butyrophenones ([11C]N‐methyl‐spiperone, [11C] NMSP, n ¼ 4, and [76Br]bromospiperone, n ¼ 3), benzamides ([11C]raclopride, n ¼ 3, and [123I]IBZM, n ¼ 5), or the ergot derivative [76Br]lisuride, n ¼ 2). Only 2 of 17 studies detected a significant elevation of D2 receptor density parameters. However, meta‐analysis revealed a small (12%) but significant elevation of striatal D2 receptors in patients with schizophrenia. No clinical correlates of increased D2 receptor–binding parameters have been reliably identified. Studies performed with butyrophenones (n ¼ 7) show an eVect size of 0.96  1.05, significantly larger than the eVect size observed with other ligands (benzamides and lisuride, n ¼ 10; 0.20  0.26, p ¼ 0.04). This diVerence might be due to diVerences in vulnerability

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of the binding of these tracers to competition by endogenous DA and elevation of endogenous DA in schizophrenia (Seeman, 1988; Seeman et al., 1989). Regarding striatal D1 receptors, three imaging studies (Abi‐Dargham et al., 2001; Karlsson et al., 1997; Okubo et al., 1997) have confirmed the results of postmortem studies of unaltered levels of these receptors in the striatum of patients with schizophrenia. ii. DOPA Decarboxylase activity. Five studies reported rates of DOPA decarboxylase in patients with schizophrenia, using [18F]DOPA (Dao‐Castellana et al., 1997; Hietala et al., 1995, 1999; Reith et al., 1994) or [11C]DOPA (Lindstrom et al., 1999). Four of five studies reported increased accumulation of DOPA in the striatum of patients with schizophrenia, and the combined analysis yielded a significant eVect size of 0.92  0.45 ( p ¼ 0.01). Several of these studies reported the observation of high DOPA accumulation in psychotic paranoid patients and low accumulation in patients with negative or depressive symptoms and catatonia. Although the relationship between DOPA decarboxylase and the rate of DA synthesis is unclear (DOPA decarboxylase is not the rate‐limiting step of DA synthesis), these observations are compatible with higher DA synthesis activity of DA neurons in schizophrenia, at least in subjects experiencing psychotic symptoms. iii. Amphetamine‐induced DA release. D2 receptor imaging, combined with pharmacological manipulation of DA release, enables more direct evaluation of DA presynaptic activity. Numerous groups demonstrated that acute increase in synaptic DA concentration is associated with decreased in vivo binding of [11C] raclopride and [123I]IBZM. These interactions have been demonstrated in rodents, nonhuman primates, and humans, using a variety of methods to increase synaptic DA (for review of this abundant literature, see Laruelle, 2000). It has also been consistently observed that the in vivo binding of spiperone and other butyrophenones is not as aVected by acute fluctuations in endogenous DA levels as the binding of benzamides (Laruelle, 2000). The decrease in [11C]raclopride and [123I]IBZM in vivo binding after acute amphetamine challenge has been well validated as a measure of the change in D2 receptor stimulation by DA because of amphetamine‐induced DA release. Manipulations that are known to inhibit amphetamine‐induced DA release, such as pretreatment with the DA synthesis inhibitor alpha‐methyl‐para‐tyrosine (
MPT) or with the DA transporter (DAT) blocker GR12909 also inhibit the amphetamine‐induced decrease in [123I]IBZM or [11C]raclopride binding (Laruelle et al., 1997b; Villemagne et al., 1999). Combined microdialysis and imaging experiments in primates demonstrated that the magnitude of the decrease in ligand binding was correlated with the magnitude of the increase in extracellular DA induced by the challenge (Breier et al., 1997; Laruelle et al., 1997b), suggesting that this noninvasive technique provides an appropriate measure of the changes in synaptic DA levels.

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Three of three studies demonstrated that amphetamine‐induced decrease in [11C]raclopride or [123I]IBZM binding was elevated in untreated patients with schizophrenia compared with well‐matched controls (Abi‐Dargham et al., 1998a; Breier et al., 1997; Laruelle et al., 1996b). A significant relationship was observed between magnitude of DA release and transient induction or deterioration of positive symptoms. The increased amphetamine‐induced DA release was observed in both first‐episode/drug‐naive patients and patients previously treated with antipsychotic drugs (Laruelle et al., 1999). Patients who were experiencing an episode of illness exacerbation (or a first episode of illness) at the time of the scan showed elevated amphetamine‐induced DA release, whereas patients in remission showed DA release values not diVerent from controls (Laruelle et al., 1999). These findings were generally interpreted as reflecting a larger DA release after amphetamine in the schizophrenic group. Another interpretation of these observations would be that schizophrenia is associated with increased aYnity of D2 receptors for DA. Development of D2 receptors imaging with radiolabeled agonists is needed to settle this issue (Hwang et al., 2000). iv. Baseline DA release. A limitation of the amphetamine challenge imaging studies is that they measure changes in synaptic DA transmission after a nonphysiological challenge (i.e., amphetamine) and do not provide any information about synaptic DA levels at baseline (i.e., in the unchallenged state). Several laboratories have reported that, in rodents, acute depletion of synaptic DA is associated with an acute increase in the in vivo binding of [11C]raclopride or [123I]IBZM to D2 receptors (for review, see Laruelle, 2000). The increased binding was observed in vivo but not in vitro, indicating that it was not due to receptor upregulation (Laruelle et al., 1997a) but to removal of endogenous DA and unmasking of D2 receptors previously occupied by DA. The acute DA depletion technique was developed in humans using
MPT to assess the degree of occupancy of D2 receptors by DA (Fujita et al., 2000b; Laruelle et al., 1997a). Using this technique, higher occupancy of D2 receptors by DA was reported in patients with schizophrenia experiencing an episode of illness exacerbation compared with healthy controls (Abi‐Dargham et al., 2000c). Again, assuming normal aYnity of D2 receptors for DA, the data are consistent with higher DA synaptic levels in patients with schizophrenia. Interestingly, increased D2 receptor stimulation by DA at intake as measured with the
MPT paradigm was predictive of rapid clinical response to antipsychotic drugs (Abi‐Dargham et al., 2000c). This finding illustrates the potential of PET or SPECT molecular imaging to predict treatment response. v. DA Transporters (DAT). The data reviewed in the preceding are consistent with higher DA output in the striatum of patients with schizophrenia, which could be explained by increased density of DA terminals. Because striatal DAT are exclusively localized on DA terminals, this question was investigated by measuring binding of [123I]‐CIT (Laruelle et al., 2000) or [18F]CFT (Laakso

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et al., 2000) in patients with schizophrenia. Both studies reported no diVerences in DAT binding between patients and controls. In addition, Laruelle et al. (2000) reported no association between amphetamine‐induced DA release and DAT density. Thus, the increased presynaptic output suggested by the studies reviewed in the preceding does not seem to be due to higher terminal density, an observation consistent with postmortem studies that failed to identify alteration in striatal DAT binding in schizophrenia (for references, see Laruelle et al. [2000]). Taken together, studies of striatal DA transmission in schizophrenia have provided support for the time‐honored DA hypothesis of schizophrenia. Because animal data suggest that the antipsychotic eVect of D2 receptor antagonism is mediated by blockade of D2 receptors in the mesolimbic as opposed to the nigrostriatal DA system (Deutch et al., 1991; Robertson and Fibiger, 1992; Robertson et al., 1994), future studies will focus on studying striatal subsystems. Recent progress in PET instrumentation has provided the resolution necessary to diVerentiate the signal from ventral (i.e., limbic) and dorsal (i.e., motor) regions of the anterior striatum (Drevets et al., 2001a; Mawlawi et al., 2001). Moreover, the development of radiotracers suitable for imaging extrastriatal D2 receptors such as [11C]FLB 497 (Halldin et al., 1995) and [18F]fallypride (Mukherjee et al., 1995) will enable the study of D2 receptor transmission in other critical limbic regions such as the amygdala, the hippocampus, and the cingulate cortex. b. Prefrontal DA. Most DA receptors in the PFC are of the D1 subtype (De Keyser et al., 1989; Hall et al., 1994). A PET study with [11C]SCH 23390 reported decreased density of D1 receptors in younger patients with schizophrenia (Okubo et al., 1997). In addition, low PFC D1 density was associated with the severity of negative symptoms and poor performance on the Wisconsin Card Sort Test (WCST). In contrast, a more recent study using the superior radiotracer [11C]NNC 112 reported increased D1 receptor availability in the dorsolateral PFC (DLPFC) of patients with schizophrenia (Abi‐Dargham et al., 2001). Furthermore, increased [11C]NNC 112 binding was associated with poor performance on the ‘‘n‐back’’ test of working memory (Abi‐Dargham et al., 2001). The reason for the discrepancy in the results obtained with [11C]SCH 23390 and [11C]NNC 112 remains to be explained, but it is interesting to note that the binding of both radiotracers is diVerentially aVected by endogenous DA competition and receptor traYcking (Laruelle, 2000). For example, chronic DA depletion in rodents is associated with decreased and increased in vivo binding of [11C]SCH 23390 and [11C]NNC 112, respectively (Abi‐Dargham et al., 2001). Thus, the contradictory observations of decreased [11C]SCH 23390 binding (Okubo et al., 1997) and increased [11C]NNC 112 binding (Abi‐Dargham et al., 2001) observed in the PFC in patients with schizophrenia might in fact both represent consequences of sustained deficit in prefrontal DA function. Much work remains to be done to validate this hypothesis. However, this point illustrates that the in vivo binding of radiotracers is aVected by several factors that are

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not present in the typical in vitro situation, such as the impact of receptor traYcking on ligand aYnity (Laruelle, 2000). This situation represents both a challenge, because the interpretation of the results is less straightforward, and an opportunity, because more information can be gained about the functions of the living neurons. 2. Serotonin The idea that serotonin (5‐HT) may be involved in the pathogenesis of schizophrenia is linked to the observation that the hallucinogen LSD acts as both a serotonin antagonist and agonist (Aghajanian and Marek, 2000). Subsequently, dysfunction in the serotonin system in schizophrenia has been supported by evidence from CSF and postmortem studies, as well as studies with pharmacological challenges (Abi‐Dargham et al., 1997a). Abnormalities of 5‐HT transporters (SERT), 5‐HT2A receptors and, more consistently, 5‐HT1A receptors have been described in postmortem studies in schizophrenia, and these alterations might play a role in the pathophysiology of negative symptoms (see references in Abi‐Dargham and Krystal [2000]). Given the relatively recent development of radiotracers to study 5‐HT receptors, only a limited number of imaging studies have been published. The concentration of SERT in the midbrain measured by [123I]‐CIT is unaltered in patients with schizophrenia ( Laruelle et al., 2000). Results of postmortem studies of SERT have been mixed. Two studies report a decrease in SERT density in the frontal cortex of individuals with schizophrenia ( Joyce et al., 1993; Laruelle et al., 1993a), whereas two studies report no change in this same region (Dean et al., 1995; Gurevich and Joyce, 1997). Of the other brain regions examined, a decreased density has also been reported in the anterior and posterior cingulate (Dean et al., 1995), whereas SERT density has been reported as unchanged in the caudate (Dean et al., 1995), the hippocampus (Dean et al., 1995; Joyce et al., 1993), and as the parietal (Gurevich and Joyce, 1997; Joyce et al., 1993), temporal ( Joyce et al., 1993), and occipital cortices (Laruelle et al., 1993a). One study reported a decrease in aYnity of [3H]paroxetine binding to the SERT in the hippocampus but not in the other regions examined (Dean et al., 1995). Recently, with PET, we reported no diVerence between controls and subjects with schizophrenia in SERT density in subcortical or limbic regions ( Frankle et al., 2004). However, studies with more specific SERT ligands are warranted to assess the distribution of SERT in the neocortex. Decreased 5HT2A receptors have been reported in the PFC in four of eight postmortem studies (see references in Abi‐Dargham and Krystal [2000]). Three PET studies in drug‐naive or drug‐free patients with schizophrenia reported normal cortical 5HT2A receptor binding (Lewis et al., 1999; Okubo et al., 2000; Trichard et al., 1998; VerhoeV et al., 2000), whereas one study reported a significant decrease in PFC 5HT2A binding in a small group (n ¼ 6) of drug‐naive patients with schizophrenia (Ngan et al., 2000). The most consistent

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abnormality of 5‐HT parameters reported in postmortem studies in schizophrenia is an increase in the density of 5‐HT1A receptors in the PFC, reported in seven of eight studies (Abi‐Dargham and Krystal, 2000). Two PET studies have reported in vivo 5‐HT1A density using the radiotracer [11C]WAY100907. One group reports increased [11C]WAY100907 binding in the left medial temporal cortex (Tauscher et al., 2002), whereas the other group reports decreased binding in the amygdala (Yasuno et al., 2004). 3. GABAergic Transmission A robust body of findings suggests deficiency of GABAergic function in the PFC in schizophrenia (for reviews, see Benes [2000]; for reviews, see Lewis [2000]). In vivo evaluation of GABAergic systems in schizophrenia has so far been limited to evaluation of benzodiazepine receptor densities with SPECT and [123I]iomazenil, and three of three studies comparing patients with schizophrenia and controls reported no significant regional diVerences (Abi‐Dargham et al., 1999, 1997a; VerhoeV et al., 1999). Although significant correlations between symptom clusters and regional benzodiazepine densities have been observed in some studies, these relationships have not been replicated. Thus, taken together, these studies are consistent with an absence of marked abnormalities of benzodiazepine receptor concentration in the cortex and patients with schizophrenia. Alterations of GABAergic systems in schizophrenia might not involve benzodiazepine receptors (Benes et al., 1997) or might be restricted to certain cortical layers or classes of GABAergic cells that are beyond the resolution of current radionuclide‐based imaging techniques. 4. Antipsychotic Drug Occupancy Studies Perhaps the most widespread use of neuroreceptor imaging in schizophrenia over the past decade has been the assessment of receptor occupancy achieved by typical and atypical antipsychotic drugs, a topic that has been the subject of recent reviews (Kapur et al., 1999; Nyberg et al., 1998). The main focus has been on D2 receptor occupancy, but 5HT2A and D1 receptors have also been studied. Studies have repeatedly confirmed the existence of a threshold of occupancy of striatal D2 receptors (approximately 80%) above which extrapyramidal side eVects (EPS) are likely to occur (Farde et al., 1992). In general, studies have failed to observe a relationship between the degree of D2 receptor occupancy and clinical response (Pilowsky et al., 1992; Wolkin et al., 1989). However, most studies were performed at doses achieving more than 50% occupancy, and the minimum occupancy required for therapeutic response remains undefined. Two studies performed with low doses of relatively selective D2 receptor antagonists (haloperidol and raclopride) suggested that 50–60% occupancy was required to observe a rapid clinical response (Kapur et al., 2000a; Nordstrom et al., 1993). Clozapine, at clinically therapeutic doses, has been found to achieve only 40–60% D2 receptor occupancy

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(Farde et al., 1992; Nordstrom et al., 1995b; Pilowsky et al., 1992), which, in conjunction with its anticholinergic properties, may account for its low liability for EPS. Occupancy of 5‐HT2A receptors by ‘‘5‐HT2A/D2 balanced antagonists’’ such as risperidone does not confer protection against EPS, because the threshold of D2 receptor occupancy associated with EPS is not markedly diVerent between these drugs and drugs devoid of 5HT2A antagonism (Kapur et al., 1995, 1998; Knable et al., 1997b; Nyberg et al., 1993). Studies with quetiapine suggest that, at least with this agent, transient high occupancy of D2 receptors might be suYcient to elicit clinical response (Gefvert et al., 1998; Kapur et al., 2000b). An interesting question relates to putative diVerences in the degree of occupancy achieved by atypical antipsychotic drugs in striatal and extrastriatal areas. Pilowsky et al. (1997) reported lower occupancy of striatal D2 receptors compared with temporal cortex D2 receptors in seven patients treated with the atypical antipsychotic drug clozapine, using the high‐aYnity SPECT ligand [123I]epidipride. In contrast, typical antipsychotics were reported to achieve similar occupancy in striatal and extrastriatal areas, as measured with [11C]FLB 457 (Farde et al., 1997) or [123I]epidipride (Bigliani et al., 1999). It should be noted, however, that these very high‐aYnity ligands do not allow accurate determination of D2 receptor availability in the striatum (Olsson and Farde, 2001). Conversely, [18F] fallypride enables accurate determination of D2 receptor availability in both striatal and extrastriatal areas (Abi‐Dargham et al., 2000a), and preliminary PET experiments in primates with [18F]fallypride indicate that clozapine and risperidone achieve similar D2 receptor occupancy in striatal and extrastriatal regions (Mukherjee et al., 2000). A recent study combining [11C]FLB 457 imaging for extrastriatal D2 receptor receptors and [11C]raclopride imaging for striatal D2 receptors suggested similar occupancy of D2 receptors in both regions for both typical and atypical antipsychotic drugs (Talvik et al., 2001). Finally, it is important to point out that the most robust evidence relative to the site of therapeutic eVect of antipsychotic drugs in rodents points toward the nucleus accumbens (Deutch et al., 1992; Robertson et al., 1994), whereas the imaging studies reviewed in the preceding contrasted striatal versus mesiotemporal D2 receptor binding. Improved resolution of PET cameras now allows the dissociation of signals from ventral and dorsal striatum (Drevets et al., 2001; Mawlawi et al., 2001), and it is now feasible to specifically study the clinical correlates of D2 receptor occupancy in ventral striatum in humans.

B. AFFECTIVE DISORDERS Numerous abnormalities of regional cerebral blood flow (rCBF) and metabolism (rCMRglu) have been demonstrated in aVective disorders using SPECT and PET. These studies have implicated anatomical circuits involving subregions of

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prefrontal cortex, striatum, amygdala, and hippocampus in the pathophysiology of these disorders (Drevets, 2000; Mayberg et al., 1999). From a neurochemical perspective, abnormalities in several neurotransmitter systems may be relevant to the pathophysiology of depression. The 5‐HT system has been the most extensively implicated, in part because of the antidepressant eVect of medications that inhibit the synaptic reuptake of serotonin, as well as a wealth of postmortem, preclinical, and clinical data suggesting that reduced serotonergic function may be associated with depression (Blier et al., 1990). The recent availability of suitable PET radioligands for 5HT2A receptors, 5‐HT1A receptors and the 5‐HT transporter (SERT) has allowed the in vivo investigation of their putative abnormalities in depression. In addition, a number of studies have evaluated potential alterations in DA systems in major depression. 1. Major Depressive Disorder a. Serotonin Transmission. i. 5‐HT2 receptors. The earliest PET study of 5HT2 receptors and depressive symptoms used [11C]‐N‐methyl‐spiperone to investigate binding in patients with post‐stroke depression and reported increased binding (Mayberg et al., 1988). Yet, it is not clear how this finding can be generalized to more common clinical presentations of depression. Another early study using 2‐[123I]‐ketanserin and SPECT reported increased and asymmetrical cortical uptake of the tracer in depressed patients compared with controls (D’haenen et al., 1992). However, 2‐[123I]‐ketanserin has significant limitations because of high nonspecific binding. Since then, six PET studies have used newer 5HT2 PET radiotracers, [18F]‐ setoperone (Blin et al., 1988) and [18F]‐altanserin (Lemaire et al., 1991), to investigate cortical 5HT2A receptor binding in drug‐free depressed patients. Biver et al. (1997), using [18F]‐altanserin, reported reduced tracer uptake in a region of the right hemisphere including the orbitofrontal cortex and the anterior insular cortex. A more recent study with [18F]‐altanserin reported decreased hippocampal binding in depressed individuals compared with controls (Mintun et al., 2004). However, one limitation of [18F]‐altanserin is that it produces radioactive lipophilic metabolites that probably cross the blood–brain barrier and contribute activity in the nondisplaceable compartment (Tan et al., 1999). Two studies investigated mid‐life depression using [18F]‐setoperone and concluded that there is no major change or asymmetry in 5HT2A receptors (Attar‐Levy et al., 1999; Meyer et al., 1999). In both studies, most patients had been free of antidepressant medication for more than 6 months. A fifth study supported these negative findings and reported no significant alteration in 5HT2A receptor binding in an untreated group of patients with late‐life depression without cognitive impairment (Meltzer et al., 1999). Finally, the sixth study (Yatham et al., 2000) found a widespread reduction in 5HT2A receptor BP and concluded that brain 5HT2A receptors are decreased in patients with major

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depression. However, 40% of the patients in this study had been drug free for only 2 weeks before scanning. This factor may be significant, because most antidepressants downregulate 5HT2A receptors (Attar‐Levy et al., 1999; Meyer et al., 2001a; Yatham et al., 1999). In summary, three studies reported no significant alteration in 5HT2A receptor binding in major depression, and three studies found reduced 5HT2A receptors. DiVerences between studies might stem from methodological issues, illness, heterogeneity, and medication eVects. None of the recent studies confirmed the earlier findings of increased binding (D’haenen et al., 1992; Mayberg et al., 1988). Similarly, the increase in 5HT2A receptors found in some, but not all, postmortem studies of suicide‐depressed victims (for review see Mann [1999]) has not been confirmed by in vivo investigations. Therefore, there is currently no strong evidence supporting the hypothesis that depression per se is associated with marked alterations of 5HT2A receptor density. ii. 5‐HT1A receptors. Two lines of evidence have implicated the 5‐HT1A receptors in depression. The first is the finding that depressed patients have blunted neuroendocrine responses to 5‐HT1A receptor agonists in vivo, and the second is the dense distribution of these receptors in the hippocampus. Recent theories have implicated interactions among stress, corticosteroids, growth factors, and hippocampal 5‐HT1A receptors in depression (Drevets et al., 1999; Duman et al., 1997; Fujita et al., 2000a). Postmortem studies of 5‐HT1A receptors in suicide and depression have been inconsistent, showing increased, decreased, and unchanged 5‐HT1A receptors levels in various regions (Arango et al., 1995; Arranz et al., 1994; Dillon et al., 1991; Lowther et al., 1997; Matsubara et al., 1991). These discrepancies may reflect the possible confounding eVects of suicidality, antemortem medications, diVerences between radioligands, and diVerences in the regulation of 5‐HT1A receptors by corticosteroids and local levels of 5‐HT in diVerent brain regions. The results of in vivo PET imaging of 5‐HT1A receptors in depressed patients are, therefore, of interest. Two PET studies have investigated 5‐HT1A receptors in unmedicated depressed subjects using [carbonyl‐11C]WAY‐100635, and both have reported reductions in receptor binding. The first study (Sargent et al., 2000) found modest (approximately 10%) but significant widespread reductions in BP in cortical regions including medial temporal cortex (hippocampus and amygdala) in a group of 15 men with major depression. Subsequently, Drevets et al. (1999) reported reductions in the medial temporal cortex (27%) and raphe (41%) in a study that limited its primary hypothesis to these two regions. This group of subjects included both unipolar and bipolar depressed patients. All subjects had first‐degree relatives with mood disorders. Interestingly, the diVerences found were largely accounted for by the subjects with bipolar disorder and those with unipolar depression who had relatives with bipolar disorder.

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Additional studies are warranted to confirm these findings of generalized decrease in 5HT1A receptors in depression. Because a major depressive episode is associated with hyperactivity of the HPA axis and increased cortisol levels might be associated with 5‐HT1A receptor downregulation (ChaouloV, 1995; Lopez et al., 1997, 1999; Porter et al., 1999), these findings might be secondary to the neuroendocrine dysregulation associated with depression. iii. 5‐HT transporter. Reductions in SERT levels in depressed patients have been reported in numerous postmortem studies (for review, see Mann [1999]). The first ligand used to image SERT in vivo was the SPECT radiotracer [123I]‐ CIT. ‐CIT binds to both DAT and SERT with comparable aYnity (Ki 1.4 and 2.4 nM for DAT and SERT, respectively) (Laruelle et al., 1994d; Wang et al., 1993). The lack of DAT versus SERT selectivity is not a problem for measuring DAT in the striatum, because the density of SERT in striatum is much lower than that of DAT (Laruelle et al., 1994d). However, in the midbrain, this proportion is reversed, and the ‐CIT midbrain uptake mostly corresponds to SERT binding (Bru¨cke et al., 1993; Laruelle et al., 1993b). Studies in nonhuman primates and humans have shown that, in the midbrain, [123I]‐CIT is selectively displaced by administration of SSRIs (but not by DAT‐selective drugs) (Laruelle et al., 1993c; Pirker et al., 1995). [123I]‐CIT has been extensively used in clinical studies both for striatal DAT (Eising et al., 1997; Laruelle et al., 2000; Malison et al., 1998a; Marek et al., 1996; Muller et al., 2000; Seibyl et al., 1998; Seibyl et al., 1995) and midbrain SERT evaluation (Heinz et al., 1998a,b; Laruelle et al., 2000; Malison et al., 1998c). In depression, findings from two SPECT studies using [123I]‐CIT were in agreement with postmortem results. A reduction in SERT binding was found in the midbrain in patients with unipolar depression (Malison et al., 1998d) and in thalamus–hypothalamus in depressed patients with seasonal aVective disorder (Willeit et al., 2000). A selective SERT radiotracer is required to investigate SERT density in other regions of the brain. The first PET radiotracer available to measure SERT in humans was [11C]McN 5652 (Suehiro et al., 1993). The usefulness of [11C]McN 5652 as a PET tracer for SERT was validated in primates (Szabo et al., 1995) and humans (Buck et al., 2000; Parsey et al., 2000c; Szabo et al., 1999). One PET study used [11C]McN 5652 to examine SERT availability in four depressed subjects compared with controls (Reivich et al., 2004). The authors report an increase in [11C]McN 5652 distribution volume ratio in the frontal and right cingulate cortices in the depressed subjects. However, [11C]McN 5652 has many limitations, which include high nonspecific binding, poor signal‐to‐noise ratio, nonmeasurable free fraction in the plasma, and slow clearance from the brain (Parsey et al., 2000b). Therefore, studies using [11C]McN 5652 requires long scanning time (up to 120 minutes), and this ligand can provide reliable quantification

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of SERT only in regions of relatively high SERT density (midbrain, thalamus, and striatum). More recently, compounds from the phenylamine class have emerged as promising targets for both SPECT and PET tracer development. [123I]ADAM (Oya et al., 2000) is a highly selective SPECT imaging agent for SERT. Its C‐11–labeled counterpart, [11C]ADAM, was recently reported (Vercouillie et al., 2001). Another compound in this series, [11C]DASB, was recently introduced and has been evaluated in rats (Wilson et al., 2000) and humans (Houle et al., 2000). Thus, it is anticipated that, in the near future, several studies will be performed to evaluate SERT density with PET in patients with major depression. If the results obtained with [123I]‐CIT are confirmed, the reduction in SERT density might provide a useful biomarker for this disorder. b. Dopamine Transmission. The critical role of DA in brain reward systems, the reports of low cerebrospinal fluid homovanillic acid levels in depressed patients, the association of major depression with Parkinson’s disease, and the enhancement of dopaminergic activity by several antidepressant treatments suggest that a deficiency of dopaminergic function might be associated with major depression (for a review, see Brown and Gershon [1993], Diehl and Gershon [1992], Kapur and Mann [1992] and Willner et al. [1992]). Five studies compared striatal D2 receptor availability with [123I]IBZM in patients with major depression and control subjects. Two of the five studies reported higher [123I]IBZM specific binding in the striatum of depressed subjects than in controls (D’Haenen and Bossuyt, 1994; Shah et al., 1997), whereas three studies reported no change (Ebert et al., 1996; Klimke et al., 1999; Parsey et al., 2001). Amphetamine‐induced DA release was also assessed in patients with major depression and found to be unchanged (Parsey et al., 2001). Two studies examined [123I]‐CIT striatal binding in patients with major depression and yielded conflicting results: one study reported normal levels of striatal DAT in patients with major depression (Malison et al., 1998c), whereas the other one reported increased DAT levels (Laasonen‐Balk et al., 1999). Finally, [18F]DOPA uptake in the left caudate was observed to be significantly lower in depressed patients with psychomotor retardation than in depressed patients with high impulsivity and comparison subjects (Martinot et al., 2001). Thus, major depression per se does not seem to be consistently associated with alteration of the dopaminergic parameters at the level of the entire striatum. However, DA might play a role in the neurobiology underlying some clinical features of depression, such as psychomotor retardation. c. Antidepressant Drug Occupancy Studies. Given that ligands suitable to label SERT were only developed recently, a limited number of studies assessing the occupancy of SERT achieved by antidepressant drugs have been published so far. Pirker et al. (1995) compared [123I]‐CIT midbrain specific binding with SERT in a group of 12 depressed patients treated with 20–60 mg/day of citalopram for a minimum of 1 week and a group of control subjects. The reduction in [123I]‐ CIT binding in the midbrain of the citalopram‐treated patients was reported to

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be approximately 50% of controls. In contrast, Meyer et al. (2001b), using [11C] DASB, reported 77% SERT occupancy during treatment with 20 mg/day of citalopram. Kent et al. (2000), using [11C]McN 5652 and Meyer et al. (2001b), using [11C]DASB, reported near complete (>80%) occupancy of SERT by paroxetine at therapeutic doses (20–40 mg/kg). Thus, the minimal occupancy of SERT associated with therapeutic response to selective serotonin reuptake inhibitors (SSRIs) is still not defined, but it seems that, at least with citalopram and paroxetine, high SERT occupancies are achieved after administration of these drugs at therapeutic doses. The time lag (typically 1–2 weeks) in the onset of therapeutic eVect of several classes of antidepressant medication may be related to the need for downregulation of 5‐HT1A somatodendritic autoreceptors in the raphe before a net increase in forebrain 5‐HT neurotransmission can occur (Blier and de Montigny, 1998). Because of this phenomenon, several groups have investigated whether the concomitant use of pindolol (antagonist at 5‐HT1A receptor and ‐adrenoceptor) with an SSRI antidepressant might accelerate the onset of an improvement in mood. The results of clinical trials were inconsistent (for reviews, see Artigas et al. [2001] and Martinez et al. [2000]). Most clinical studies have used a dose of 7.5 mg daily of pindolol. Several PET centers have recently conducted human occupancy studies of pindolol at the postsynaptic and somatodendritic 5‐HT1A receptor (Andree et al., 1999; Martinez et al., 2001; Rabiner et al., 2000). The consensus from these studies is that the dose used in clinical studies was too low to provide appropriate and reliable blockade of 5‐HT1A receptors and that this factor might explain the limited success of this strategy in previous clinical trials. These studies provide another illustration of the potential of PET neuroreceptor imaging to facilitate drug development. 2. Bipolar Disorder Compared with major depressive disorder, only limited radioligand PET studies have been reported in patients with bipolar disorders. As discussed previously, it may be significant that the findings of reduced 5‐HT1A receptor binding in the medial temporal cortex and raphe of depressed patients (Drevets et al., 1999) were largely accounted for by the subjects with bipolar disorder and those with unipolar depression who had relatives with bipolar disorder. Because of the relationship between mania and psychosis, a number of PET studies have investigated the DA system in bipolar disorders. D1 receptor binding in the frontal cortex was reported to be decreased in a study of 10 symptomatically heterogeneous, drug‐free bipolar patients (Suhara et al., 1992). Increases in D2‐like (i.e., D2, D3, and D4) receptor density in the striatum were found in seven psychotic patients with bipolar disorder compared with seven nonpsychotic patients with bipolar disorder and 24 control subjects. The authors concluded that an increase in D2‐like receptors is associated with the state of psychosis

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rather than with a diagnosis of bipolar disorder (Wong et al., 1997). As part of the same studies, Gjedde and Wong also reported findings consistent with an elevated concentration of synaptic DA in bipolar patients with psychosis but not in nonpsychotic bipolar patients (Gjedde and Wong, 2001). On the other hand, amphetamine‐induced DA release was reported to be normal in euthymic patients with bipolar disorders (Anand et al., 2000). In conclusion, few investigations have been reported using PET molecular imaging techniques in patients with bipolar disorders, and the findings reported so far might be related to clinical states (depression, mania with psychosis) rather than to the bipolar condition per se.

C. ANXIETY DISORDERS A number of PET studies have investigated rCBF changes associated with induced anxiety in healthy volunteers (Benkelfat et al., 1995; Chua et al., 1999; Javanmard et al., 1999; Kimbrell et al., 1999; Liotti et al., 2000; Simpson et al., 2001). Although there is considerable variability in the findings, a number of paralimbic–cortical regions have been consistently implicated, including medial prefrontal cortex, anterior cingulate cortex, orbital prefrontal cortex, anterior temporal cortex, parahippocampal gyrus, and the claustrum‐insular‐amygdala region. In contrast to this abundant functional literature, anxiety disorders have been less studied with PET molecular imaging techniques. 1. Generalized Anxiety Disorder Because benzodiazepines are the prototypical anxiolytic drugs, evaluation of potential abnormalities in the BDZ receptor distribution is of interest in anxiety disorders. An initial study in generalized anxiety disorder (GAD) with [123I] NNC13–8241 reported reduced binding in the left temporal pole in 10 drug‐ naive female patients with GAD compared with age‐ and gender‐matched healthy controls (Tiihonen et al., 1997b). However, this has not been confirmed in a PET study using [11C]flumazenil that found no diVerences in drug‐free patients (Abadie et al., 1999). 2. Panic Disorder Two studies using [123I]iomazenil SPECT reported decreased uptake in the lateral temporal region (Kaschka et al., 1995) and increased binding in the right orbitofrontal cortex in benzodiazepine‐naı¨ve patients (Brandt et al., 1998). A third [123I]iomazenil SPECT study, using a more quantitative measurement of regional binding potential, reported decreased binding in left hippocampus and precuneus in patients with panic disorder relative to controls. Interestingly, patients who had a panic attack at the time of the scan had a relative decrease in binding

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in prefrontal cortex, suggesting that benzodiazepine function in prefrontal cortex may be involved in changes in state‐related panic (Bremner et al., 2000b). In a fully quantitative PET study using [11C]flumazenil in medication‐free patients, Malizia et al. found a global reduction in BDZ binding throughout the brain in patients with panic disorder compared with controls. The largest regional decreases were in the right orbitofrontal cortex and right insula (Malizia et al., 1998). Thus, there is relatively consistent evidence that panic disorder might be associated with alterations in the GABAergic system, the primary ‘‘endogenous’’ anxiolytic system. Nevertheless, the anatomical localization of these changes and their relationship with illness states remain to be clarified. 3. Social Phobia (Social Anxiety Disorder) Neurobiological mechanisms underlying social phobia, including neuroimaging findings, have been reviewed recently (Bell et al., 1999; Dewar and Stravynski, 2001; Nutt et al., 1998). One SPECT study using [123I]‐CIT to label DAT in the striatum reported that densities were markedly lower in patients with social phobia than in age‐ and gender‐matched controls (Tiihonen et al., 1997a). Another study using [123I]IBZM reported significant decrease in D2 receptor binding potential in patient with social phobia compared with controls (Schneier et al., 2000). Together, these studies suggest that the DA system might play a role in the pathophysiology of this illness. A recent study using [11C]McN 5652 failed to find marked alterations in SERT in patients with social phobia, despite an excellent response to SSRI treatment (Kent et al., 2000). 4. Obsessive‐Compulsive Disorder Obsessive‐compulsive disorder (OCD) has been extensively studied by SPECT and PET metabolism studies. These studies have generated remarkably consistent results (i.e., increased metabolism in orbitofrontal cortex and striatum in symptomatic patients that normalizes with successful treatment) (Baxter, 1994; Biver et al., 1995; Brody et al., 1998; Mallet et al., 1998; Perani et al., 1995; Rauch et al., 1997; Saxena et al., 1999). These findings have implicated abnormalities in the prefrontal cortex‐basal ganglia‐thalamic circuits that originate from the orbitofrontal cortex in the pathophysiology of OCD and the related neuropsychiatric disorder Gilles de la Tourette’s syndrome. Lower pretreatment metabolism in the orbitofrontal cortex has been found to predict greater improvement on SSRI medication (Saxena et al., 1999); however, diVerent treatment modalities may have diVerent predictive levels of pretreatment metabolism (Brody et al., 1998). Abnormalities of serotonergic neurotransmission in OCD have been hypothesized on the basis of the therapeutic eYcacy of medications that selectively increase synaptic 5‐HT levels (including SSRIs and clomipramine) in nondepressed patients with OCD and the high level of comorbid depression in OCD.

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Two neuroreceptor studies have been reported in OCD, both examining the SERT. One study reported elevated midbrain SERT in OCD using [123I]‐CIT SPECT (Pogarell et al., 2003) whereas a PET study using [11C]McN 5652 failed to find any regional diVerences between control and OCD subjects (Simpson et al., 2003). Additional studies of the regional binding of 5‐HT1A, 5‐HT2A receptors, and the SERT are currently under progress in several PET centers. 5. Posttraumatic Stress Disorder The results of functional neuroimaging and other studies in posttraumatic stress disorder (PTSD) have recently been reviewed in depth (Bremner, 1999; Villarreal and King, 2001). It has been hypothesized that symptoms of PTSD are mediated by a dysfunction of the anterior cingulate, with a failure to inhibit amygdalar activation and/or an intrinsic lower threshold of amygdalar response to fearful stimuli. The model further proposes that hippocampal atrophy is a result of the chronic hyperarousal symptoms mediated by amygdalar activation (Villarreal and King, 2001). Only one neuroreceptor imaging study has been reported in this population showing lower [123I]iomazenil binding in the prefrontal cortex of patients with PTSD compared with comparison subjects, suggesting that this condition is associated, like panic disorder and perhaps general anxiety disorder, with low BDZ receptor levels (Bremner et al., 2000a).

D. SUBSTANCE ABUSE 1. Cocaine Cocaine abuse has been extensively studied using PET‐based molecular neuroimaging techniques. Most of the work has focussed on changes in striatal DA that occur with chronic cocaine use. a. D2 Receptors. A reduction in striatal DA D2 receptors has been demonstrated by Volkow et al. using both [18F] N‐methylspiroperidol and [11C] raclopride (Volkow et al., 1990, 1993a, 1996c, 1997b). The decreases reported in these studies are 35%, 14%, 20%, and 11%, respectively. In addition, this group has shown that these decreases correlate with years of use and seem to be long lasting in a group of subjects rescanned after 3 months of inpatient rehabilitation (Volkow et al., 1993a). Of note, studies of D2 receptor availability using PET have also shown a reduction in D2 receptors in heroin abuse (Wang et al., 1997b) and alcoholism (Hietala et al., 1994b; Volkow et al., 1996d). The results of these studies raise the question of whether a decrease in D2 receptors is the result of years of drug abuse or represents a neurochemical risk factor for developing substance abuse. Volkow et al. (1999a) investigated this question in a study of healthy controls who were administered methylphenidate

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and asked to describe their subjective eVects as pleasant, unpleasant, or neutral. Subjects who rated their experience as pleasant were found to have lower measures of D2 receptor availability (14%) than subjects who reported an unpleasant experience. Nader et al. (2000, 2001) more definitively addressed this question in a study of rhesus monkeys who were scanned before and after becoming exposed to a social stress and before cocaine self‐administration studies. The monkeys, who had been reared in individual cages, were placed in social groups such that a social hierarchy was established. After this change, the authors reported that the monkeys who were lowest in the social hierarchy had 20% lower D2 receptor availability than monkeys who were highest in social order. Furthermore, the monkeys who were lower were found to self‐administer cocaine more readily than the high‐ranking monkeys. This is a study of critical importance, because it demonstrates that low D2 receptors may be a risk factor for developing substance dependence. Furthermore, this risk factor is not static, in that it is aVected by environment, which has strong implications in the development of treatment and prevention strategies. b. Stimulant‐Induced DA Release. As described previously, PET and SPECT studies can been used to measure changes in subcortical DA transmission in the human brain after psychostimulant administration. In control subjects, stimulant‐induced decrease in D2 receptor availability has been well characterized and is in the order of 10–20% (Abi‐Dargham et al., 1998a; Breier et al., 1997; Drevets et al., 2001b; Kegeles et al., 1999; Laruelle et al., 1995, 1996a; Volkow et al., 1994, 1997b, 1999b). Studies of healthy controls have shown that the percentage decrease in radioligand binding (i.e., the increase in DA release) is positively correlated with pleasurable subjective eVects (Drevets et al., 2001b; Laruelle et al., 1995; Volkow et al., 1999b). DA transmission in the ventral striatum mediates the reinforcing eVects of drugs of abuse (Di Chiara and Imperato, 1988; Le Moal and Simon, 1991; Wise and Rompre`, 1989). PET studies showed a greater decrease in [11C]raclopride binding in the ventral versus dorsal striatum in healthy controls in response to an amphetamine challenge (Drevets et al., 2001b) and in response to a monetary reward (Koepp et al., 1998). Collectively, these studies suggest that an excess in subcortical DA would correlate with increased reward value and might mediate the reinforcing eVects of drugs of abuse. However, cocaine abusers have been shown to have a blunted DA response to psychostimulants. A study of Volkow et al. used [11C]raclopride to measure the change in D2 receptor availability before and after an intravenous dose of 0.5 mg/kg methylphenidate in healthy controls and cocaine abusers who had been abstinent for 3–6 weeks (Volkow et al., 1997b). The authors reported a 9% decrease in [11C]raclopride binding in the cocaine abusers compared with a 21% decrease in healthy controls. Malison et al. (1999) performed a similar study in abstinent cocaine abusers and controls using [123I]IBZM and an amphetamine

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challenge (0.3 mg/kg IV) and reported a 1% change in binding in the cocaine abusers compared with a 10% decrease in controls. c. DOPA Decarboxylase. The preceding findings of blunted presynaptic DA function in cocaine abusers are supported by the study of Wu et al. (1997) showing a reduction in the rate of uptake of [18F]6‐FDOPA in abstinent cocaine abusers. d. DAT. Three in vivo studies of DAT in cocaine abusers have been published, and this body of work has failed to provide a clear picture of the status of DAT in cocaine abusers. Using [11C]cocaine, no changes in DAT were observed in detoxified (>1 month) cocaine abusers (Volkow et al., 1996b). We should, however, mention that, in this study, a decrease in [11C] cocaine total distribution volume (VT) was seen in both striatum and cerebellum (Volkow et al., 1996b). When normalizing striatal VT by cerebellum VT (i.e., using the distribution volume ratio or V300 ), no diVerences were apparent between cocaine abusers and controls, and the authors concluded that DAT availability was unchanged. Yet, another interpretation of these data could be that cocaine abusers show a decrease in DAT density and a decrease in [11C] cocaine nonspecific binding. The same group also studied [11C] cocaine uptake in currently abusing subjects (Wang et al., 1997a) and found no diVerences between these subjects and controls in DAT availability. However, 15 of the 20 currently abusing subjects had a urine test positive for cocaine at the time of scan, which is expected to impact on the results of the imaging study. Finally, Malison et al. (1998a) showed a significant upregulation of DA transporters, measured with SPECT and [123I]‐CIT, in the striatum of recently detoxified (<96 hours) cocaine abusers. Such upregulation was not observed after prolonged abstinence. On the other hand, studies of DAT occupancy by cocaine have generated critical information. Volkow et al. (1997a) reported that a DAT occupancy of at least 47% is needed to produce the subjective eVects of cocaine. These data suggest that any treatment approach to cocaine abuse in which the transporter is blocked would need to produce somewhere between 60% and 90% occupancy of the transporters. This issue was addressed in an occupancy study of mazindol, a nonselective catecholamine reuptake inhibitor (Malison et al., 1998b). This study showed that the clinical dosage generally used produced only a modest occupancy of 16–23%, and would therefore not be expected to have suYcient eYcacy. e. SERT. Jacobsen et al. (2000) reported increased SERT availability in the diencephalon and midbrain (17% and 32%, respectively) using [123I]‐CIT, suggesting that chronic cocaine abuse aVects the serotonin system as well. f. Mu Opiate Receptors. Zubieta et al. (1996) reported an increase in mu receptor availability using [11C]carfentanil in the caudate, thalamus, cingulate, frontal, and temporal cortices. This finding is of particular interest given the interaction between the dopaminergic and opioid systems in the direct and indirect pathways of the striatum (Hurd and Herkenham, 1993; Steiner and Gerfen, 1998).

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Overall, the studies in cocaine abuse demonstrate a dysregulation of the DA system in this disorder. The findings of decreased [18F]DOPA accumulation, decreased amphetamine‐ and methylphenidate‐induced DA release, and decreased D2 receptor density suggest a functional deficit in D2 receptor transmission at the level of the entire striatum in this population. The reduction in D2 receptors seems to be a risk factor for cocaine abuse (or addiction in general) rather than a consequence of the disorder. 2. Methamphetamine Two imaging studies in methamphetamine abusers have demonstrated a significant decrease in the DAT using PET. McCann et al. (1998b) reported on six methamphetamine abusers and four methcathinone users using [11C] WIN‐ 35,428. The methamphetamine abusers had a decrease in DAT availability of approximately 25% in the putamen and caudate compared with controls, and a similar reduction was seen in the methcathinone abusers (McCann et al., 1998b). Volkow et al. (2001) studied 15 methamphetamine abusers who had 2 weeks of monitored abstinence before scanning with [11C] d‐threo‐methylphenidate. Compared with controls, the methamphetamine abusers had a 28% decrease in DAT availability in the caudate and a 21% decrease in the putamen (Volkow et al., 2001). The authors also found that the decrease in DAT availability correlated with years of abuse and with impairment in motor and memory tasks. Both studies are in agreement with a postmortem report of reduced DA transporter density in the striata of chronic methamphetamine abusers, as well as decreases in DA and tyrosine hydroxylase (Wilson et al., 1996a). Evidence from studies in Parkinson’s disease supports the hypothesis that the reduction in DAT availability reflects a loss of DA neurons, which is detectable with functional imaging (Guttman et al., 1997; Seibyl et al., 1997; Wilson et al., 1996b). On the basis of this interpretation, these studies raise the issue of whether this decrease is reversible, or whether methamphetamine abuse results in neurotoxicity to the dopaminergic neurons. PET and postmortem studies in nonhuman primates have shown that methamphetamine exposure results in decreased DAT and other markers of dopaminergic transmission, suggesting a frank loss of dopaminergic neurons (Melega et al., 1998; Villemagne et al., 1998). However, one study suggested that this reduction might be reversible after prolonged abstinence (Harvey et al., 2000). Overall, the PET data demonstrate that methamphetamine abuse in humans results in a reduction in the DAT and raises concerns about the neurotoxicity associated with this addiction. 3. Ecstasy PET and SPECT were also used to evaluate the potential neurotoxic eVects of methylenedioxymethamphetamine (MDMA, ecstasy). Two studies have imaged MDMA abusers with the SPECT radioligand [123I]‐CIT (Reneman et al., 2001;

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Semple et al., 1999). Both studies have serious methodological problems. The authors report results in cortical regions using [123I]‐CIT, yet the suitability of [123I]‐CIT to quantify SERT in cortical regions has not been established (Heinz and Jones, 2000; Laruelle et al., 1993c, 1994f ). Neither study measured binding in the midbrain, which is the only region in which the SERT can be reliably measured with this radioligand. One study presented [123I]‐CIT binding in the striatum as a measure of SERT density, despite that fact that [123I]‐CIT– specific binding in the striatum corresponds to DAT binding (Laruelle et al., 1993c, 1994f; Pirker et al., 1995). McCann et al. (1998a), using the radioligand [11C]McN 5652, measured SERT availability in 14 subjects who had abused MDMA on at least 25 occasions. In this study, the authors used the inactive enantiomer ( )[11C]McN 5652 as a measure of nonspecific binding. They reported a significant global decrease in specific binding, including in the cerebellum, in abstinent MDMA abusers. However, Parsey et al. (2000d) reported that the use of ( )[11C]McN 5652 to measure nonspecific binding resulted in an overestimation of specific binding, specifically in cortical regions. A more recent report, with a larger sample size, also used [11C]McN 5652 PET to study this issue. Buchert et al. (2003, 2004) reported reduced SERT in the midbrain and thalamus of current MDMA users compared with healthy controls. In the same study, they found that [11C]McN 5652 in former MDMA abusers was similar to that of drug‐naı¨ve controls in all brain regions. Two studies used SPECT and the radioligand [123I]R93274 to measure 5‐ HT2A receptors in MDMA abusers (Reneman et al., 2000a,b) but reported conflicting results. Although the specific binding of [123I]R93274 is selective for the 5‐HT2A receptor, the ratio of specific binding to nonspecific binding is very low in the cortex (Abi‐Dargham et al., 1997b; Busatto et al., 1997b), making a reliable measurement of this receptor problematic (Abi‐Dargham et al., 1997b). Overall, the data regarding NMDA neurotoxicity for serotonergic neurons in human abusers indicates a reduction in SERT availability, possibly representing reduced serotonergic innervation, particularly in the thalamus and midbrain. 4. Heroin Surprisingly few radioligand imaging studies have been conducted in opiate‐ dependent subjects, despite the clear indication from decades of treatment studies of a significant change in brain chemistry in this disorder. Two groups have measured opioid receptor occupancy in heroin‐dependent subjects undergoing treatment. Kling et al. (2000) reported on heroin‐dependent subjects maintained on methadone using [18F]cyclofoxy, a mu and kappa opioid antagonist (Carson et al., 1993). A decrease in receptor availability of 19–32% was seen in methadone‐treated subjects compared with healthy volunteers. Zubieta et al. (2000) reported on the occupancy of buprenorphine, a mu partial agonist

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soon to be approved as an alternative to methadone. Subjects were given 2 mg and 16 mg sublingual doses and scanned using the radioligand [11C]carfentanil, which is selective for the mu opioid receptor (Frost et al., 1989). The 2‐mg dose resulted in 36–50% occupancy, and the 16‐mg dose resulted in 79–95% occupancy of the mu receptors across brain regions. Behavioral pharmacology studies show that doses of 8–16 mg of buprenorphine are needed to reduce heroin self‐ administration (Comer et al., 2001; Mello et al., 1982). Therefore, the study of Zubieta et al. suggests that a higher occupancy of the mu receptor than that reported by Kling et al. may be necessary for a therapeutic eVect. Zubieta et al. (2000) reported marked increases in mu receptor availability in detoxified heroin‐dependent subjects compared with healthy controls. However, this study included only three heroin‐dependent subjects. Last, reductions in D2 receptor availability of 18% in the putamen and 13% in the caudate were reported in the opiate‐dependent subjects compared with healthy controls (Wang et al., 1997b). This study is of particular interest, given the reduction in D2 receptor availability associated with other addictions, including cocaine dependence (Volkow et al., 1990, 1993a, 1997b), alcoholism (Hietala et al., 1994b; Volkow et al., 1996d), and even obesity (Wang et al., 2001). 5. Nicotine PET studies have investigated alterations in the dopaminergic system in the basal ganglia in cigarette smoking and demonstrated some interesting findings. In a series of studies, Fowler et al. (1996a,b, 1998, 2000) investigated levels of monoamine oxidase (MAO) A and B in smokers and showed marked and global decreases in both enzymes. MAO A and B exist in neurons and glial cells, and both enzymes degrade DA. MAO B activity was measured using [11C]L‐ deprenyl (Logan et al., 2000b). Smokers were found to have a 42% decrease in global MAO B activity compared with controls (Fowler et al., 1996a, 2000). Interestingly, a study in former smokers showed that levels of MAO B activity returned to baseline after smoking cessation (Fowler et al., 1998). In a later study, this same group demonstrated a decrease in MAO A activity in the brains of cigarette smokers using [11C]clorgyline (Fowler et al., 1996b). In this study, smokers had an average reduction of 28% in MAO A activity across brain regions, with a 22% decrease in the basal ganglia (Fowler et al., 1996b). Decreased activities of MAO A and B are expected to be associated with increased DA availability. Salokongas et al. (2000) used [18F]fluorodopa to measure presynaptic DA and reported higher uptake in the striatum in smokers, a finding that could be explained by an increase in DOPA–decarboxylase activity or a decrease in MAO activity. A study by Dagher et al. (2001) reported a reduction in D1 receptor availability using [11C]SCH23390 in the striatum. The authors reported a greater decrease in the ventral striatum (15.6%) compared with the

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caudate and putamen (9.5% and 10.1%, respectively). Last, Staley et al. (2001) investigated DAT and SERT density in the striatum and midbrain, respectively, in smokers and healthy controls using [123I]‐CIT. No diVerence was seen in DAT availability between these groups, but there was a trend level increase in [123I]‐CIT binding in the midbrain. Overall, these findings are consistent with the hypothesis of alterations of the DA system in nicotine smokers, but much work remains to be done to better understand the potential role of this dysregulation in the maintenance of nicotine addiction. 6. Alcohol The dopaminergic system has been the most investigated neurochemical system using SPECT and PET in alcohol research because of the wealth of preclinical data suggesting a role for DA in the reward system and clinical data suggesting alterations in DA function in alcoholic patients. Genetic studies have suggested, although not conclusively, an association between a nonencoding polymorphism of the D2 receptor and alcoholism. An initial postmortem study by Noble et al. (1991) described a lower D2 receptor density in A1 carriers, but not in the alcoholic group per se. This was followed by two PET studies in small samples of alcoholic patients that found low striatal D2 receptor BP in alcoholics. A study performed with [11C]raclopride reported a 20% decrease in D2 receptor BP in nine abstinent (1–60 weeks) alcoholics and eight age‐matched controls (Hietala et al., 1994c). Similar findings have been reported by Volkow et al. (1996a) in a group of 10 alcoholic subjects. An interesting study attempting to relate relapse and D2 density found an increased density of D2 receptors as measured with [123I]IBZM and SPECT. This was interpreted by the authors as suggesting that low levels of DA could be related to early relapse in alcohol‐dependent patients (Guardia et al., 2000). However, information about DA levels cannot be derived from measurements of D2 receptor availability, because endogenous DA levels do not significantly account for the variance in baseline D2 receptor availability in humans (Laruelle et al., 1997a). Most preclinical data do not indicate that chronic alcohol exposure aVects D2 receptor density (Fuchs et al., 1987; Hietala et al., 1990; Muller et al., 1980; Rabin et al., 1983; TabakoV and HoVman, 1979), but conflicting results have been published suggesting that the eVects of chronic alcohol on DA receptors might vary according to the dose and duration of exposure (Hamdi and Prasad, 1993; Hruska, 1988; Lai et al., 1980; Lucchi et al., 1988). Such diVerences in duration of exposure, as well as interspecies diVerences in the response of D2 receptors to alcohol, may undermine the relevance of rodent studies in answering the question of whether decreased D2 receptor BP measured with PET in chronic alcoholics is a risk factor for, or an eVect of, chronic alcohol intake. Studies

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targeting a nonalcoholic population at increased risk of developing alcoholism (e.g., children of alcoholics) are needed to resolve this issue. Another important question is whether the alterations in D2 receptor density in recently detoxified alcoholics are transient or permanent (i.e., whether this abnormality persists with a prolonged period of abstinence). Few studies have reported DAT measurements in chronic alcoholics. Tijhonen et al. (1995) compared the binding of [123I]‐CIT to DAT in habitually violent alcoholics, nonviolent alcoholics, and controls. These authors reported a significant reduction in [123I]‐CIT binding in nonviolent alcoholics versus controls (n ¼ 9 per group) and secondly, no change in [123I]‐CIT binding in habitually violent alcoholics versus controls (n ¼ 19 per group). More recently, the same group reported that reduced DAT binding was correlated with severity of depression in 24 recently detoxified alcoholic subjects (Laine et al., 1999a). In contrast, Volkow et al. (1996a) measured DAT density in a group of five alcoholics abstinent for variable intervals (5–180 days) and found no diVerences compared with controls. Similarly, Heinz et al. (1998a) found no diVerences in DAT density in 20 alcoholics compared with controls. One study showed reduced DAT levels that normalized to the levels of the healthy controls after 4 weeks of abstinence (Laine et al., 1999b). The most substantial recovery in DAT binding was reported to occur during the first 4 days of abstinence. This study suggests the time course of withdrawal and duration of abstinence has eVects on the measurements of the DAT. Despite the availability of PET tracers for diVerent 5‐HT receptors, to date no PET studies of serotonergic transmission have been reported in this population. Heinz et al., (1998b) using [123I]‐CIT SPECT in male alcoholics, found decreased binding in the midbrain, an area where specific binding of this tracer is associated with the SERT. However, no information could be obtained about the levels of the transporter in other regions with [123I]‐CIT because of its lack of specificity and the low sensitivity of SPECT. Finally, PET imaging has contributed to the study of alterations in brain GABAergic function related to alcoholism. A blunted metabolic response to lorazepam in the thalamus, basal ganglia, and orbitofrontal cortex has been described in alcoholic subjects (Volkow et al., 1993b) and in the cerebellum of subjects at risk for alcoholism (Volkow et al., 1995). Initial in vivo studies of BDZ receptor density failed to demonstrate abnormalities in [11C]flumazenil binding in limited samples of patients (Farde et al., 1994; Litton et al., 1992; Pauli et al., 1992). However, a larger study reported a significant decrease in [11C]flumazenil VT in the medial frontal lobes and cingulate gyrus in nine alcoholic subjects and a decrease in the same regions as well as the cerebellum in eight alcoholic subjects with alcoholic cerebellar degeneration (Gilman et al., 1996). Another study, using SPECT and [123I]iomazenil, found lower receptor levels in patients compared with controls in the frontal, anterior cingulate, and cerebellar cortices

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(Abi‐Dargham et al., 1998b). These alterations have also been reported by a third group (Lingford‐Hughes et al., 1998). Another study examined the issue of gender diVerences and found a trend toward reduced GABA–benzodiazepine receptor levels in alcohol‐dependent women. However, this did not reach significance (Lingford‐Hughes et al., 2000). Lower levels were seen primarily in the cerebellum, occipital lobes, and parietal cortex but not in the frontal cortex. Gray matter atrophy, a well‐documented finding in alcoholic brains, has not been found to play a role in these measured reductions of receptor levels in general (Lingford‐ Hughes et al., 1998). Taken together, these studies suggest that alcoholism might be associated with decreased BDZ/GABAA receptor complex in some brain regions such as the frontal cortex, the cingulate cortex, the hippocampus, and the cerebellum. However, the studies do not all agree on which regions are implicated. The heterogeneity of the alcoholic patients, including the presence of neurological impairment in some, might have contributed to the discrepancies between studies in the regions involved.

References

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Further Readings

Busatto, G. F., Pilowsky, L. S., Costa, D. C., Ell, P. J., David, A. S., Lucey, J. V., and Kerwin, R. W. (1997a). Correlation between reduced in vivo benzodiazepine receptor binding and severity of psychotic symptoms in schizophrenia. Am. J. Psychiatry 154, 56–63. Salokangas, R. K., Vilkman, H., Ilonen, T., Taiminen, T., Bergman, J., Haaparanta, M., Solin, O., Alanen, A., Syvalahti, E., and Hietala, J. (2000). High levels of dopamine activity in the basal ganglia of cigarette smokers. Am. J. Psychiatry 157, 632–634. Tiihonen, J., Kuikka, J., Bergstro¨ m, K., Hakola, P., Karhu, J., Ryyna¨ nen, O., and Fo¨ hr, J. (1995). Altered striatal dopamine re–uptake site densities in habitually violent and non‐violent alcoholics. Nat. Med. 1, 654–657.

International REVIEW OF

Neurobiology Volume 67 SERIES EDITORS RONALD J. BRADLEY Department of Psychiatry, College of Medicine The University of Tennessee Health Science Center Memphis, Tennessee, USA

R. ADRON HARRIS Waggoner Center for Alcohol and Drug Addiction Research The University of Texas at Austin Austin, Texas, USA

PETER JENNER Division of Pharmacology and Therapeutics GKT School of Biomedical Sciences King’s College, London, UK EDITORIAL BOARD ERIC AAMODT PHILIPPE ASCHER DAVID FINK MICHAEL F. GLABUS BARRY HALLIWELL JON KAAS LEAH KRUBITZER KEVIN MCNAUGHT JOSE´ A. OBESO CATHY J. PRICE SOLOMON H. SNYDER STEPHEN G. WAXMAN

HUDA AKIL MATTHEW J. DURING MARTIN GIURFA PAUL GREENGARD NOBU HATTORI DARCY KELLEY BEAU LOTTO MICAELA MORELLI JUDITH PRATT EVAN SNYDER JOHN WADDINGTON

CONTRIBUTORS

Numbers in parentheses indicate the pages on which the authors’ contributions begin.

F. Xavier Castellanos (239), Institute for Pediatric Neuroscience, New York University Child Study Center, New York, New York 10016 C. Donaghey (43), Division of Psychiatry, University of Edinburgh, Kennedy Tower, Morningside Park, Edinburgh EH10 5HF, United Kingdom N. J. Dougall (43), Division of Psychiatry, University of Edinburgh, Kennedy Tower, Morningside Park, Edinburgh EH10 5HF, United Kingdom K. P. Ebmeier (43), Division of Psychiatry, University of Edinburgh, Kennedy Tower, Morningside Park, Edinburgh EH10 5HF, United Kingdom G. Ende (95), Central Institute of Mental Health, NMR-Research in Psychiatry, Faculty of Clinical Medicine Mannheim, University of Heidelberg, 68072 Mannheim, Germany W. Gordon Frankle (385), Department of Psychiatry, New York State Psychiatric Institute, New York, New York 10032 Jay N. Giedd (285), Child Psychiatry Branch, National Institute of Mental Health, Bethesda, Maryland 20892 Nitin Gogtay (285), Child Psychiatry Branch, National Institute of Mental Health, Bethesda, Maryland 20892 David Goldman (325), Laboratory of Neurogenetics, National Institute of Alcohol Abuse and Alcoholism, National Institutes of Mental Health, Bethesda, Maryland 20892 Eduardo Gonzalez-Toledo (165, 203), Department of Radiology, Louisiana State University School of Medicine, Shreveport, Louisiana 71103 Ahmad R. Hariri (325), Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15213 Kiralee M. Hayashi (285), Laboratory of Neuro Imaging, Brain Mapping Division, Department of Neurology, University of California, Los Angeles School of Medicine, Los Angeles, California 90095-1769 Wendy Heller (1), Psychology Department and the Beckman Institute for Advanced Science and Technology, University of Illinois, Champaign-Urbana, Illinois 61820 F. A. Henn (95), Central Institute of Mental Health, NMR-Research in Psychiatry, Faculty of Clinical Medicine Mannheim, University of Heidelberg, 68072 Mannheim, Germany ix

x

CONTRIBUTORS

Stephen L. JaVe (165), Department of Neurology, Louisiana State University School of Medicine, Shreveport, Louisiana 71103 Roger E. Kelley (203), Department of Neurology, Louisiana State University Health Sciences Center, Shreveport, Louisiana 71103 Marc Laruelle (385), Departments of Psychiatry and Radiology, Columbia University College of Physicians and Surgeons and New York State Psychiatric Institute, New York, New York 10032 Alex Leow (285), Laboratory of Neuro Imaging, Brain Mapping Division, Department of Neurology, University of California, Los Angeles School of Medicine, Los Angeles, California 90095-1769 Kristen L. Mackiewicz (73), Waisman Laboratory for Brain Imaging and Behavior, Departments of Psychiatry and Psychology, University of Wisconsin, Madison, Wisconsin 53705 A. Meyer-Lindenberg (95), Neuroimaging Core Facility and Unit on Integrative Neuroimaging, Genes, Cognition and Psychosis Program, National Institute of Mental Health, Bethesda, Maryland 20892 Alireza Minagar (165), Department of Neurology, Louisiana State University School of Medicine, Shreveport, Louisiana 71103 Rob Nicolson (285), Department of Psychiatry and Biomedical Physics, The University of Western Ontario, London N6A 5B8, Ontario, Canada Jack B. Nitschke (1, 73), Waisman Laboratory for Brain Imaging and Behavior, Departments of Psychiatry and Psychology, University of Wisconsin, Madison, Wisconsin 53705 James Pinkston (165), Department of Neurology, Louisiana State University School of Medicine, Shreveport, Louisiana 71103 Judith L. Rapoport (285), Child Psychiatry Branch, National Institute of Mental Health, Bethesda, Maryland 20892 M. Ruf (95), Central Institute of Mental Health, NMR-Research in Psychiatry, Faculty of Clinical Medicine Mannheim, University of Heidelberg, 68072 Mannheim, Germany Michael SeyVert (239), Institute for Pediatric Neuroscience, New York University Child Study Center, New York, New York 10016 Mark Slifstein (385), Department of Psychiatry, New York State Psychiatric Institute, New York, New York 10032 Elizabeth R. Sowell (285), Laboratory of Neuro Imaging, Brain Mapping Division, Department of Neurology, University of California, Los Angeles School of Medicine, Los Angeles, California 90095-1769 Peter S. Talbot (385), Department of Psychiatry, New York State Psychiatric Institute, New York, New York 10032 Paul M. Thompson (285), Laboratory of Neuro Imaging, Brain Mapping Division, Department of Neurology, University of California, Los Angeles School of Medicine, Los Angeles, California 90095-1769

CONTRIBUTORS

xi

Arthur W. Toga (285), Laboratory of Neuro Imaging, Brain Mapping Division, Department of Neurology, University of California, Los Angeles School of Medicine, Los Angeles, California 90095-1769 H. Tost (95), Central Institute of Mental Health, NMR-Research in Psychiatry, Faculty of Clinical Medicine Mannheim, University of Heidelberg, 68072 Mannheim, Germany Christine N. Vidal (285), Laboratory of Neuro Imaging, Brain Mapping Division, Department of Neurology, University of California, Los Angeles School of Medicine, Los Angeles, California 90095-1769 Daniel R. Weinberger (325), Genes, Cognition and Psychosis Program, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892 Georg Winterer* (325), Genes, Cognition and Psychosis Program, National Institute of Mental Health, and Laboratory of Neurogenetics, National Institute of Alcohol Abuse and Alcoholism, National Institutes of Mental Health, Bethesda, Maryland 20892 Patrick B. Wood (119), Department of Family Medicine, Louisiana State University Health Sciences Center, Shreveport, Louisiana 71103

*Current affiliation: Department of Psychiatry, Heinrich-Heine University, Du ¨ sseldorf, Germany.

INDEX

A A
amyloid AD and Down’s syndrome and accumulation of, 345 APP cleavage of, 345 ACC. See Anterior cingulate cortex Acetylcholinesterase inhibitors, in AD, 60 ACG. See Anterior cingulate gyrus AD. See Alzheimer’s disease ADC map. See Apparent diffusion-weighted map ADHD. See Attention deficit hyperactivity disorder Adolescent bipolar disorder of, 274, 276–277 eating disorders of, 276 Agoraphobia, panic disorder and, 14 AIDS dementia complex, CFS v., 123–124 Alcoholism DA role in, 418–419 DAT measurements and, 419 functional imaging of, 332–333 GABAergic systems alterations in, 419–420 genetic studies of, 332–333 SPECT and PET imaging of, 418–420 structural imaging of, 331 Alosetron, IBS treatment with, 141 Alzheimer’s disease (AD). See also Dementia A
amyloid accumulation in, 345 acetylcholinesterase inhibitors in, 60 advancement symptoms of, 57 cholinergic transmission reduction in, 63 CT brain scan study of, 344–345 description of, 44, 46 diagnostic accuracy of, 49–50 DLB v., diagnostic patterns differentiating, 55, 63 Down’s syndrome v., 345, 347–348 DSCR1 overexpression in, 347 EEG study of, 344–346

" 2 allele association with, 345–346 " 4 allele association with, 345–346 executive functioning decline in, 58 f MRI study of, 59, 346 FTD v., diagnostic patterns differentiating, 54–55 functional imaging of, 56–57, 332 diagnostic patterns for, 51–52 hippocampus role in, 57 human genetics and, 344–346 hypometabolism and hypoperfusion in, 51–52 memory deficits of, 57–58 MRI study of, 51, 53, 344–345 nicotinic receptors reduction in, 62, 63 PET study of, 59, 331–332, 344–345 PET v. SPECT diagnosis of, 49–53 SPECT study of, 344–345 structural imaging of, 331 symptoms of, 46 VD and MID v., 54 VD v., diagnostic patterns differentiating, 53–54 Amitriptyline, FMS treatment with, 132 Amygdala 5-HTTLPR activation of, 358, 360 ACC projections to, 83 anxiety disorders involvement of, 354–355 ASD core deficits from dysfunction of, 259 CD and, 273 emotional face processing in, 248–252, 249 f MRI studies of, 355 in GAD, 25 OCD documentation of, 7–8 PET studies of, 355 PTSD activation by, 12–13 in social phobia, 21–23 specific phobia activation of, 18 Amyloid, compounds for imaging of, 61

441

442

INDEX

Amyloid precursor protein (APP) A
amyloid cleavage from, 345 Down’s syndrome implication of, 347 Anterior cingulate cortex (ACC) 5-HTTLPR mechanism in, 361–362 amygdala projections from, 83 concluding remarks on, 85–86 decreased activity in regions of, 84–85 depression and, 83–85 DLPFC interplay between, 81–82 monitoring and detection of response conflict, 81–83 OCD hyperactivity of, 12–13 panic disorder activation of, 16–17 PTSD activation of, 12–13 section functions of, 82–83 in social phobia, 21–22 structural differences in, 85 volition and, 81–83 volition’s neural substrates DLPFC and, 74, 75 Anterior cingulate gyrus (ACG) description of, 103 schizophrenia activation of, 103–104 Anxiety disorders 5-HTT variations and, 355–362 5-HTTLPR and, 355–362 amygdala involvement in, 354–355 child studies of, 273–276 concluding remarks of, 27–28 description of, 1–3, 353–355 human genetics and, 353–362 introduction to, 1–2 neuroreceptor imaging of, 410–412 PET studies of, 410–412 research on, 2–4 serotonin and, 355 Apolipoprotein E (APOE) " 2 allele of, 345–346 " 4 allele of, 345–346 f MRI exploration of " 4 allele of, 346 primary role of, 345–346 Apparent diffusion-weighted map (ADC map) MS analysis with, 169, 177, 178 stroke analysis with, 214–215, 223 ASD. See Autistic spectrum disorders Attention deficit hyperactivity disorder (ADHD) children study of, 265–272 description of, 265

functional v. developmental changes, 272 medication study for, 265 Autism. See also Autistic spectrum disorders (ASD) description of, 254 mirror system contribution to deficits of, 254–255, 256 Autistic spectrum disorders (ASD) amygdala dysfunction representing core deficits in, 259 children with aberrant motor preparation, 256, 260 facial processing in, 259 description of, 254 mirror system contribution to deficits of, 254–255, 256 B Basal ganglia CFS metabolite concentrations in, 129 FMS role of, 131 Huntington’s disease volumes of, 331 RLS role of, 131 BBB. See Blood-brain barrier BCR. See Bicaudate ratio Benzodiazepine, GAD therapy with, 25 Bicaudate ratio (BCR) description of, 192 MS measure by, 192–193 Bipolar disorder of adolescents and children, 274, 276–277 DA system in, 409–410 genetic studies of, 334–335 neuroreceptor imaging of, 409–410 PET studies of, 409–410 WM in children and adolescents with, 277 Blood oxygen-level dependent (BOLD) description of, 183 as f MRI dominant technique, 240–241 MS study with, images, 183–184 stroke analysis with, 217, 223 Blood-brain barrier (BBB), MS and breakdown of, 167–168, 174, 175–176 BOLD. See Blood oxygen-level dependent Brain development asymmetries of, 301–305 brain-behavior relationships, 310–311 cortical mapping of, 294, 299–307

INDEX

COS and, 310, 311–313 DTI study of, 297–298 fetal alcohol syndrome, 305 growth curves of, 291, 292, 293–298 gray matter, 295 large samples trajectories of, 292, 296–297 mechanisms of, 293, 297–298 modeling developmental trajectories of, 291, 296 heritability and brain structure in, 302, 305–306 mapping of, 313–316 corpus callosum growth, 314, 315 tensor maps, 313–315 tensor-based morphometry, 314, 315–316 MRI analysis of concluding remarks on, 316–317 introduction to, 286–288 time-lapse maps of, 308, 309 Williams syndrome in, 302, 306–307 Brain function heritability of, 341–344 EEG study of, 342–343 electrophysiology study of, 342–344 f MRI study of, 342 PET and SPECT study of, 341–342 Brain structure brain development heritability in, 302, 305–306 heritability of, 340–341 general intelligence and, 341 gray and white matter v. deeper brain structures, 340–341 human genetics and complexity of, and function, 336–338 C CAT. See Computerized assisted tomographic imaging Catechol-O-methyltransferase (COMT) gene, schizophrenia and, 350–353, 354 Caudate in GAD, 25 OCD hyperactivity of, 8 OFC and, in, 6 volume reduction of, 6

443

CD. See Conduct disorder Central nervous system (CNS), as potential seat of pathology, 120 Cerebral aneurysms CTA detection of, 228 DSA detection of, 228 MRA detection of, 228 CFS. See Chronic fatigue syndrome Child-onset schizophrenia (COS) dementia v., 312 gray matter loss in, 310, 311–313 MRI study of, 310, 311–313 origin of, 312–313 time-lapse maps of, 312 Children ADHD study in, 265–272 anxiety disorders study in, 273–276 with ASD aberrant motor preparation, 256, 260 facial processing in, 259 bipolar disorder of, 274, 276–277 CD study in, 272–273 with dyslexia, 260–265 adults v., 261, 264 improvement of, 264–265 study of, 260–265 emotional processing studies in, 248–254 age differences in, 249, 252–254 amygdala in, 248–252 executive function studies in, 242–248 age-related, 242–247 inhibitory tasks, 242–247 interference tasks, 243, 247 working memory tasks, 247–248 f MRI in, 241, 277–278 language and reading in, 243, 248 PET in, 240 SPECT in, 240 TD studies in, 273 Cholinergic, AD reduction of transmission of, 63 Chronic fatigue syndrome (CFS) AIDS dementia complex and HNC v., 123–124 basal ganglia metabolite concentrations in, 129 cerebral metabolites in, 128–129 concluding remarks on, 153 demyelination in, 127–128 description of, 121

444 Chronic fatigue syndrome (CFS) (Cont.) EPA improvement of, 128 epilepsy v., 124 FDG study of, 123, 126 f MRI scanning of, 126 HNC v. patients with, 122–129 brain region metabolism in, 124 cerebral metabolites in, 128–129 erroneous performance in, 126 f MRI analysis of, 126 MRI analysis of, 126–128 MRS analysis of, 128–129 PET analysis of, 125–126 with presumed viral illness, 127 rCBF in, 124 SPECT analysis of, 122–125 TGF-
in, 123 hypoperfusion in brain regions in, 124 hypopermetabolism in brain regions in, 125 MDD v., 123–125 MRI scanning of, 126–128 MRS scanning of, 128–129 PET scanning of, 125–126 rCBF ratios in, 122–125, 128 SPECT scanning of, 122–125 study in twins, 122–123 summary of, 129 T2 signal in, 126–128 TGF-
magnification in, 123 Chronic low back pain (CLBP) description of, 145 f MRI analysis of, 145–146 FMS v., 145–146 HNC v. patients with, 145–147 PET analysis of, 145 MRI analysis of, 146–147 PET analysis of, 145 rCBF abnormalities in, 145 Chronic whiplash description of, 147 FDG study of, 147 f MRI analysis of, 148 MRI analysis of, 147–148 PET analysis of, 147 SPECT analysis of, 147–148 Citalopram, social phobia treatment with, 22 CLBP. See Chronic low back pain Cocaine DA system dysregulation in, abusers, 415 DAT studies of, abusers, 414

INDEX

neuroreceptor imaging and, 412–415 PET study of, 412–413 Cognitive control CD and, 273 description of, 246–247 in DLPFC, 76, 78, 82 EF role in, 246–247 as volition’s vital constituent, 76–77 Computed tomography angiography (CTA) cerebral aneurysms detection with, 228 DSA v., 227 stroke analysis with, 227 Computed tomography (CT) brain scan AD study with, 344–345 genetic study with, 328–331 of Huntington’s disease, 331 limitations of, 205 stroke MRI v., 205, 208, 214, 219–221 stroke neuroimaging with, 204–205, 204, 206 evaluation of, 207–208, 207, 209, 211–212 follow-up of, 212–214 outcome information of, 221–225 Computerized assisted tomographic imaging (CAT), MS imaging with, 167, 171, 172 COMT gene. See Catechol-O-methyltransferase gene Conduct disorder (CD) amygdala and, 273 children study of, 272–273 cognitive control and, 273 Continuous performance test (CPT) f MRI investigation of, 103 as schizophrenia research method, 103 Cortical mapping description of, 294, 299–300 goal of, 299 statistical analysis with, 300–307 COS. See Child-onset schizophrenia CPT. See Continuous performance test CT brain scan. See Computed tomography brain scan CTA. See Computed tomography angiography D DA. See Dopamine DAT. See Dopamine transporters Dawson’s fingers, as lesions of MS, 178–179

INDEX

Dementia concluding remarks on, 63–64 description of, 43–44 diagnosis of, 45–46 diagnostic accuracy of clinical criteria v. neuropathology, 49 functional imaging, 49–50 functional imaging of, 48–50, 56–60 correlations of function with baseline scan, 57–58 functional activation studies, 59–60 summary, 60 human genetics and, 344–346 memory defining, 44–45 MRI studies of, 47–48 neuroimaging of, 43–63 new image analysis techniques, 55–56 normal intelligence v. learning disability with, 45 pharmacological imaging of, 60–63 acetylcholine receptors in, 63 amyloid imaging of, 61–63 dopamine transporter in, 63 introduction to, 60–61 research of, 44 types of, 43 Depression ACC and, 83–85 description of, 79, 83–84 DLPFC and, 79–81 memory deficits in, 75–76, 79–80 structural differences and, 85 symptoms of, 73–74 volition and, 73–76 volition as symptom of, 86–87 Diffusion tensor imaging (DTI) brain development study with, 297–298 description of, 184 MS study with, 184–185 stroke study with, 217, 218 Diffusion-weighted imaging (DWI) MS imaging with, 169, 177, 178 stroke FLAIR v., 220 stroke imaging with, 208–210, 214–217, 222–223, 224 Digital subtraction angiography (DSA) cerebral aneurysms detection with, 228 CTA v., 227 MRA v., 225, 228 stroke analysis with, 225–228

445

Dizygotic (DZ) twins MZ twins v., 340, 344 WM study in, 344 DLB AD v., diagnostic patterns differentiating, 55, 63 PET and SPECT diagnosis of, 55 DLPFC. See Dorsolateral prefrontal cortex Dopamine (DA) alcoholism role of, 418–419 bipolar disorder and, system, 409–410 cocaine abusers dysregulation of, system, 415 MDD, transmission of, 408 PET and SPECT measurements of, activity, 398–402 PET and SPECT measurements of, transmission, 413 schizophrenia and, 398–402 amphetamine induced release of DA, 399–400 DAT in, 400–401 prefrontal DA, 401–402 striatal DA, 398–401 schizophrenia and, hypothesis, 398 Dopamine transporters (DAT) alcoholism and measurements of, 419 cocaine abusers studies of, 414 in nicotine users, 418 PET measurement of decrease in, 415 schizophrenia and DA with, 400–401 Dopaminergic cells, LBD reduction of, 60–61 Dorsolateral prefrontal cortex (DLPFC) aberrant activity patterns in, 79 ACC interplay between, 81–82 cognitive control in, 76, 78, 82 concluding remarks on, 85–86 depression and, 79–81 left hemisphere activity, 80–81 memory performance association with, activity, 80 as PFC sector, 77, 78 right hemisphere activity, 80 role in acquiring novel behavioral responses, 77 schizophrenia working memory dysfunction of, 105–106 top-down processing in, 76, 78 volition and, 76–78 volition’s neural substrates ACC and, 74, 75

446

INDEX

Down Syndrome Candidate Region 1 (DSCR1) AD overexpression of, 347 Down’s syndrome and transcripts of, 347 Down’s syndrome A
amyloid accumulation in, 345 AD v., 345, 347–348 APP implication in, 347 DSCR1 transcripts in, 347 EEG study of, 348 human genetics and, 347–348 occurrence of, 347 WM impairment in, 348 DSA. See Digital subtraction angiography DTI. See Diffusion tensor imaging DWI. See Diffusion-weighted imaging Dyslexia adults v. children with, 261, 264 description of, 260 improvement of, 264–265 study of, 260–265 DZ twins. See Dizygotic twins E Eating disorders, of adolescents, 276 Echo-planar imaging (EPI), as f MRI technique, 241 EEG. See Electroencephalogram EF. See Executive function Eicosapentaenoic acid (EPA), CFS improvement with, 128 Electroencephalogram (EEG) AD study with, 344–346 Down’s syndrome study with, 348 heritability of brain function study with, 342–343 human genetics and, 326–328, 332 JME and, 332 MZ twins study with, 326–327, 328 EPI. See Echo-planar imaging Epilepsy, CFS v., 124 " 2 allele, of apolipoprotein E (APOE), 345–346 " 4 allele AD association of, 345–346 of apolipoprotein E (APOE), 345–346 f MRI exploration of APOE, 346 Executive function (EF) AD decline of, 58 children studies of, 242–248 age-related, 242–247

inhibitory tasks, 242–247 interference tasks, 243, 247 working memory tasks, 247–248 cognitive control role of, 246–247 description of, 242 WM as component of, 247 F FA maps. See Fractional anisotropy maps FDG. See 18F-fluorodeoxyglucose Fetal alcohol syndrome, 305 Fibromyalgia syndrome (FMS) amitriptyline treatment of, 132 basal ganglia role in, 131 CLBP v., 145–146 concluding remarks on, 151–152, 154 description of, 129–130 FDG study of, 136 f MRI analysis of, 132–135 HNC v. patients with, 131–137 brain region metabolism in, 136 f MRI analysis of, 132–135 heat stimuli in, 134–135 hypnotic suggestion in, 135 MRI analysis of, 136–137 PET analysis of, 135–136 SPECT analysis of, 131 hypnotic suggestion in, 135–136 IBS comorbidity with, 140 MRI analysis of, 136–137 MRS analysis of, 138 pain catastrophizing in, 134 PET analysis of, 135–136 rCBF abnormalities in, 130–131, 135–136 RLS v., 131 SPECT analysis of, 130–132 summary of, 138 FLAIR. See Fluid-attenuated inversion recovery Fluid-attenuated inversion recovery (FLAIR) MS imaging with, 169, 179, 180 stroke DWI v., 220 stroke imaging with, 210, 220, 222 18 F-fluorodeoxyglucose (FDG) CFS study with, 123, 125 chronic whiplash study with, 147 FMS study with, 136 MCS study with, 150

INDEX

f MRI. See Functional magnetic resonance imaging FMS. See Fibromyalgia syndrome Fractional anisotropy (FA) maps, MS study with, 185 FTD AD v., diagnostic patterns differentiating, 54–55 SPECT diagnosis of, 54 Functional magnetic resonance imaging (f MRI) 5-HTTLPR study with, 357–358, 361 AD analysis with, 59, 346 amygdala studies with, 355 APOE " 4 allele exploration with, 346 CFS analysis with, 126 in children, 241, 277–278 chronic whiplash analysis with, 148 CLBP analysis with, 145–146 CPT investigation by, 103 description of, 240 EPI as technique of, 241 FMS analysis with, 132–135 heritability of brain function study with, 342 human genetics and, 331 IBS analysis with, 141–144 introduction to, 96–97 MS imaging with, 169, 183–184 in schizophrenia auditory system, 101–102 schizophrenia imaging with, 349, 351–353 stroke analysis with, 217–218, 223 Functional somatic syndromes concluding remarks on, 151–154 description of, 119–120 G GABA, PMDD and changes in levels of, 148 GABAergic systems alcoholism and alterations in, 419–420 panic disorder and, 411 schizophrenia and, 403 SPECT evaluation of, 403 GAD. See Generalized anxiety disorder Generalized anxiety disorder (GAD) amygdala in, 25 benzodiazepine therapy in, 25 caudate in, 25 description of, 23 left hemisphere regions in, 23–24, 26 left v. right hemisphere activity in, 24

447

memory bias in, 24 neuroreceptor imaging of, 410 OFC in, 25 PFC in, 25–26 PTSD v., 24 right hemisphere regions in, 24, 26 Stroop test in, 23 temporal lobes in, 25 GMD. See Gray matter density Gray matter density (GMD) MRI analysis of changes in, 307–309 schizophrenia changes of, 349 Gulf War syndrome (GWS) description of, 148–149 MRS analysis of, 149 GWS. See Gulf War syndrome H Hamilton Rating Scale for Depression (HRSD), OCD scores from, 9 Healthy normal controls (HNC) CFS patients v., 122–129 brain region metabolism in, 124 cerebral metabolites in, 128–129 erroneous performance in, 126 f MRI analysis of, 126 MRI analysis of, 126–128 MRS analysis of, 128–129 PET analysis of, 125–126 with presumed viral illness, 127 rCBF in, 124 SPECT analysis of, 122–125 TGF-
in, 123 CLBP patients v., 145–147 PET analysis of, 145 FMS v., 131–137 brain region metabolism in, 136 f MRI analysis of, 132–135 heat stimuli in, 134–135 hypnotic suggestion in, 135 MRI analysis of, 136–137 PET analysis of, 135–136 SPECT analysis of, 131 IBS patients v., 139–144 f MRI of, 141–144, 143 IBD in, 142 PET of, 139–141 sexual or physical abuse history in, 139–140 MCS patients v., 150

448

INDEX

Heroin, neuroreceptor imaging and, 416–417 Hippocampus AD role of, 57 in panic disorder, 15–16 PTSD involvement of, 11, 13 PTSD memory deficit and, 11 in social phobia, 21–22 HNC. See Healthy normal controls HRSD. See Hamilton Rating Scale for Depression 5-HT. See Serotonin 5-HTT. See Serotonin transporter 5-HTTLPR. See Serotonin transporter promoter region Human genetics AD and dementia in, 344–346 alcoholism and, 332–333 biological impact of, 334–335 bipolar disorder and, 334–335 brain structure and function complexity and, 336–338 Down’s syndrome and, 347–348 EEG and, 326–328, 332 f MRI and, 331 Huntington’s disease and, 333 LORETA investigation of, 327, 329 of mental disability, 347–348 method of study of, 335 mood and anxiety disorders and, 353–362 neuroimaging and, 325–363 concluding remarks on, 362–363 control for nongenetic factors, 338–339 CT brain scan, 328–331 historical perspective of, 326–333 introduction to, 325–326 monogenic v. polygenic disorders, 327–328 reasons for, 335–336 selection of candidate genes, 336–338 task selection, 339 PET and, 331–332 reasons for study, 333–335 schizophrenia and, 332–335, 348–353 SPECT and, 331–332 WM and, 344

Huntington’s disease basal ganglia volumes in, 331 human genetics and, 333 PET and SPECT studies of, 331–332 Hypnotic suggestion, in FMS, 135–136 Hypometabolism, in AD, 51–52 Hypoperfusion, in AD, 51–52 I IBS. See Irritable bowel syndrome Inflammatory bowel disorder (IBD), IBS v., 142 Irritable bowel syndrome (IBS) alosetron treatment of, 141 concluding remarks on, 152–154 description of, 138–139 f MRI analysis of, 141–144 FMS comorbidity with, 140 HNC v. patients with, 139–144 f MRI of, 141–144 IBD in, 142 PET of, 139–141 sexual or physical abuse history in, 139–140 IBD v., 142 men v. women with, 140–141 PET analysis of, 139–141 rCBF abnormalities in, 139–141 sexual or physical abuse history in, 139–140, 143–144 summary of, 144–145 J JME. See Juvenile myoclonic epilepsy Juvenile myoclonic epilepsy ( JME), EEG and, 332 L LBD. See Lewy body dementia Left hemisphere regions, in GAD, 23–24, 26 Lewy body dementia (LBD) description of, 46 dopaminergic cells reduction in, 60–61 symptoms of, 46

INDEX

LORETA. See Low-resolution electromagnetic tomography analysis Low-resolution electromagnetic tomography analysis (LORETA), genetic investigation with, 327, 329 M Magnetic resonance angiography (MRA) cerebral aneurysms detection with, 228 DSA v., 225, 228 stroke assessment with, 225–228 Magnetic resonance imaging (MRI) AD study with, 51, 53, 344–345 analysis types of, 289–293 anatomical mapping methods, 290, 294 parcellation methods, 289–290, 291, 292 time-lapse movie, 293 brain development analysis with, 286–317 brain-behavior relationships, 310–311 cortical mapping of, 294, 299–307, 302 COS and, 310, 311–313 growth curves of, 291, 292, 293–298 heritability and brain structure in, 302, 305–306 introduction to, 286–288 mapping of, 313–316 time-lapse maps of, 308, 309 Williams syndrome, 302, 306–307 brain scan description of, 288–289 CFS analysis with, 126–128 chronic whiplash analysis with, 147–148 CLBP analysis with, 146–147 cortical gyral pattern analysis with, 330 dementia studies with, 47–48 FMS analysis with, 136–137 GMD changes analysis with, 307–309 of Huntington’s disease, 331 image analysis techniques of, 287–288 MS imaging with, 166–169, 170, 175–185 T1-weighted, 175–177 neuroimaging techniques of, in MS, 188–193 PET and SPECT with, 41–48 PMS analysis with, 148 processing of, 288–289 sroke and newer techniques of, 217–219 stroke CT brain scan v., 205, 208, 214, 219–221 stroke neuroimaging with, 205, 207, 226 evaluation of, 207–208, 209, 210, 211–212

449

follow-up of, 212–214 outcome information of, 221–225 stroke utility of, 214–219 Magnetic resonance spectroscopy (MRS) CFS analysis with, 128–129 FMS analysis with, 138 GWS analysis with, 149 MS analysis with, 166, 169, 179–180, 181 NAA concentrations studied with, 349–350 schizophrenia investigations with, 349 stroke analysis with, 217, 218–219 Magnetic transfer imaging (MTI) MS imaging with, 169, 180–182 methods, 180–181 white matter changes with, 182 MTR enabling of, 180 stroke imaging with, 217, 219 Magnetization transfer ratio (MTR) MS studies with, 181, 182 MTI enabling with, 180 Major depressive disorder (MDD) antidepressant drug occupancy studies, 408–409 CFS v., 123–125 DA transmission in, 408 hypometabolism in brain regions in, 125 neuroreceptor imaging of, 404–409 serotonin transmission in, 405–408 SERT reductions and, 407–408 MCS. See Multiple chemical sensitivies MDD. See Major depressive disorder ME. See Myalgic encephalomyelitis Memory AD deficits of, 57–58 dementia defined by, 44–45 depression deficits of, 75–76, 79–80 DLPFC activity association with, performance, 80 GAD bias of, 24 OCD deficits of, 4–6 panic disorder influences of, 14 PTSD bias and deficits of, 10–11 hippocampus influences on, 11 schizophrenia, dysfunction, 104–106 social phobia, bias, 20–21 specific phobia bias of, 17 Mental disability, human genetics of, 347–348

450 Methamphetamine neuroreceptor imaging and, 415 PET imaging in abusers of, 415 Methylenedioxymethamphetamine (MDMA) (ecstasy) neuroreceptor imaging and, 415–416 PET and SPECT imaging of abusers of, 415–416 SERT and, 416 MID. See Multiinfarct dementia Mirror system autism and ASD deficits contribution of, 254–255, 256 description of, 254–255 Monozygotic (MZ) twins DZ twins v., 340, 344 EEG study of, 326–327, 328 WM study in, 344 Mood disorders 5-HTT variations and, 355–362 5-HTTLPR and, 355–362 description of, 353–355 human genetics and, 353–362 serotonin and, 355 MRA. See Magnetic resonance angiography MRI. See Magnetic resonance imaging MRS. See Magnetic resonance spectroscopy MS. See Multiple sclerosis MTI. See Magnetic transfer imaging Multiinfarct dementia (MID). See Vascular dementia Multiple chemical sensitivies (MCS) description of, 149 FDG study of, 150 HNC v. patients with, 150 PET analysis of, 150 rCBF abnormalities in, 150 SPECT analysis of, 149–151 Multiple sclerosis (MS) BBB breakdown and, 167–168, 174 BCR measure of, 192–193 BOLD images study of, 183–184 clinical manifestations of, 173–174 cognitive dysfunction in, 188–192 advanced MRI techniques, 193–195 central atrophy measures, 192–193 MRI neuroimaging techniques, 188–192 psychosocial manifestations, 195–196 Dawson’s fingers as lesions of, 178–179 description of, 166–167

INDEX

diagnosis of, 167, 170 DTI study of, 184–185 FA maps study of, 185 memory functioning with, 188 NAA indicator of axon losses in, 180, 181 neuroimaging of, 165–196 ADC map analysis of, 169, 177, 178 CAT scan of, 167, 171, 172 DWI imaging of, 169, 177, 178 FLAIR imaging of, 169, 179, 179, 180 f MRI imaging of, 169, 183–184 history of, 167–172 MRI imaging of, 166–169, 170, 175–185 MRS imaging of, 166, 169, 179–180, 181 MTI imaging of, 169, 180–182 PET scanning of, 169–172, 186–187, 189 SPECT analysis of, 169–172, 185–186 pathology of, relating to neuroimaging, 174–175 acute plaque, 174–175 chronic plaque, 175 T1-weighted MRI of, 175–177 T2-weighted images of, 177–179 variants of, 167, 168, 169 Myalgic encephalomyelitis (ME). See Chronic fatigue syndrome MZ twins. See Monozygotic twins N NAA. See N-acetylaspartate N-acetylaspartate (NAA) MRS study of concentrations of, 349–350 MS axon losses indicated by, 180, 181, 194–195 schizophrenia levels of, 349–350 Neuroimaging human genetics and concluding remarks on, 362–363 control for nongenetic factors, 338–339 historical perspective of, 326–333 human genetics and, 325–363 introduction to, 325–326 monogenic v. polygenic disorders, 327–328 reasons for, 335–336 selection of candidate genes, 336–338 task selection, 339 Neuroreceptor imaging of anxiety disorders, 410–412 of bipolar disorder, 409–410

INDEX

cocaine and, 412–415 conceptual framework, 386–391 compartmental system, 387–388 distribution volumes, 388–390 distribution volumes and rate constants relationship in, 388, 390–391 deriving outcome measures from imaging data, 391–397 equilibrium analysis, 391–392 graphical method, 396–397 kinetic analysis, 392–396 of generalized anxiety disorder, 410 heroin and, 416–417 introduction to, 386 of MDD, 404–409 MDMA and, 415–416 methamphetamine and, 415 nicotine and, 417–418 of OCD, 411–412 of panic disorder, 410–411 of PTSD, 412 of schizophrenia, 397–404 of social phobia, 411 Nicotine DAT and SERT in users of, 418 neuroreceptor imaging and, 417–418 PET imaging of, users, 417 Nicotinic receptors, AD reduction of, 62, 63 O Obsessive-compulsive disorder (OCD), 4–9 ACC hyperactivity in, 6–8 amygdala documentation in, 7–8 caudate and OFC in, 6 caudate involvement in, 6, 8 cognitive studies of, 4–6 description of, 4–5 executive functions impaired in, 5 HRSD scores in, 9 memory deficits in, 4–6 neuroimaging studies of, 6–9 neuroreceptor imaging of, 411–412 OFC hyperactivity in, 6–8 PFC influences on, 5 PTSD v., 13 right hemisphere regions in, 8–9 serotonin levels and, 411–412 social phobia v., 21 specific phobia v., 18

451

SPECT and PET imaging of, 412–413 striatum inactivity in, 7 Stroop test in, 5–6 OCD. See Obsessive-compulsive disorder OFC. See Orbital frontal cortex Orbital frontal cortex (OFC) caudate and, in OCD, 6 in GAD, 25 OCD hyperactivity of, 6–8 panic disorder activation of, 16–17 panic disorder v., 14, 16–17 PTSD activation by, 12–13 in social phobia, 22 P Panic disorder ACC activation in, 16–17 agoraphobia and, 14 caudate involvement in, 17 cognitive studies of, 14–15 description of, 13–14 GABAergic system and, 411 hippocampus in, 15–16 left v. right hemisphere activity in, 14–15 memory influences in, 14 neuroimaging studies of, 15–17 neuroreceptor imaging of, 410–411 OCD v., 14, 16–17 OFC activation in, 16–17 PFC involvement in, 15–17 PTSD v., 14, 16–17 research on, 14 right hemisphere regions in, 14–17 SPECT and PET imaging of, 410–411 Stroop test and, 14 temporal lobes in, 15 PET. See Positron emission tomography PFC. See Prefrontal cortex Phobias. See Social phobia; Specific phobia PMS. See Premenstrual syndrome Positron emission tomography (PET) AD analysis with, 59 AD study with, 49–53, 344–345 alcoholism imaging with, 418–420 amygdala studies with, 355 anxiety disorders studies with, 410–412 bipolar disorder studies with, 409–410 CFS analysis with, 125–126 in children, 240

452 Positron emission tomography (PET) (Cont.) Chronic whiplash analysis with, 147 CLBP analysis with, 145 cocaine study with, 412–413 DA transmission measurements with, 413 DAT decrease measurement with, 415 dementia study with MRI and, 47–48 DLB diagnosis with, 55 dopamine activity measurements with, 398–402 equilibrium analysis of, 391–392 FMS analysis with, 135–136 graphical method analysis of, 396–397 heritability of brain function study with, 341–342 human genetics and, 331–332 Huntington’s disease study with, 331–332 IBS analysis with, 139–141 kinetic analysis of, 392–396 MCS analysis with, 150 MDMA abusers imaging with, 415–416 methamphetamine abusers imaging with, 415 MRI co-registration with, 186–187, 189 MS scanning with, 169–172, 186–187, 189 neuroreceptor imaging with, 386 nicotine users imaging with, 417 OCD imaging with, 412–413 panic disorder imaging with, 410–411 semantic dementia examination with, 59–60 serotonin activity measurements with, 362, 402–403, 405–408 SERT study with, 407–408 Posttraumatic stress disorder (PTSD) ACC activation by, 12–13 amygdala activation by, 12–13 cognitive studies of, 10–11 concluding remarks on, 153–154 GAD v., 24 hippocampus involvement in, 11, 13 introduction to, 9–10 memory bias and deficits in, 10–11 neuroimaging studies of, 11–13 neuroreceptor imaging of, 412 OCD v., 13 OFC activation by, 12–13 right hemisphere regions in, 10, 13 specific phobia v., 18 Stroop test in, 10

INDEX

PPMS. See Primary progressive multiple sclerosis Prefrontal cortex (PFC) 5-HTTLPR mechanism in, 361–362 DLPFC as sector of, 77, 78 in GAD, 25–26 goal-oriented behavior involvement of, 77–78 OCD influences of, 5 panic disorder involvement of, 15–17 panic disorder v., 14, 16–17 in social phobia, 21–22 top-down processing in, 77 volition instantiation by areas of, 78 Premenstrual dysphoric disorder (PMDD), GABA level changes and, 148 Premenstrual syndrome (PMS), MRI analysis of, 148 Primary progressive multiple sclerosis (PPMS), description of, 167 Psychiatry, imaging impact on, 95–96 Psychosomatics, description of, 120 Psychotherapy, somatotherapy v., 96 PTSD. See Posttraumatic stress disorder R rCBF. See Regional cerebral blood flow Regional cerebral blood flow (rCBF) CFS abnormalities of, 122–125, 128 CLBP abnormalities of, 145 FMS abnormalities of, 130–131, 133, 135–136 IBS abnormalities of, 139–141 MCS abnormalities of, 150 Relapsing-remitting multiple sclerosis (RRMS) cognitive functioning in, 194–195 description of, 166–167 lesion detection in, 176 Restless legs syndrome (RLS) basal ganglia role in, 131 FMS v., 131 Right hemisphere regions in GAD, 24, 26 in OCD, 8–9 in panic disorder, 14–17 in PTSD, 10, 13

INDEX

in social phobia, 20–21 in specific phobia, 18 RLS. See Restless legs syndrome RRMS. See Relapsing-remitting multiple sclerosis S Schizophrenia ACG activation in, 103–104 antipsychotic drug effects on, 106–107, 108 concluding remarks on, 107 history of, 106 antipsychotic drug occupancy studies and, 403–404 auditory system in, 100–102, 108 f MRI study of, 101–102 STG in, 100–102 brain structures involved in, 349 COMT gene and, 350–353, 354 DA and, 398–402 amphetamine induced release of, 399–400 DAT with, 400–401 prefrontal, 401–402 striatal, 398–401 description of, 348–349 dopamine hypothesis and, 398 f MRI imaging of, 349, 351–353 functional imaging of, 332–333 GABAergic systems and, 403 genetic studies of, 332–335 GMD changes in, 349 heritability of, 348–349 human genetics and, 348–353 molecular genetic basis of, 350–353 MRS investigations of, 349 NAA levels in, 349–350 neurodevelopmental hypothesis of, 96 neuroimaging genomics of, 107–111 current work on, 111 introduction to, 107 neuroreceptor imaging of, 397–404 PET imaging of, 331, 349 psychomotor disturbances of, 97–98, 108 selective attention in, 102–104, 108 CPT in, 103 description of, 102–103 serotonin and, 402–403 SERT abnormalities and, 402 STG in, 100–102

453

structural imaging of, 331 visual processing deficits of, 98–100, 108 f MRI to study, 98–99 in higher order areas of visual processing stream, 99–100, 102 not in V5, 98–100, 101 WM deficits in, 349 working memory dysfunction in, 104–106, 108 description of, 104 DLPFC in, 105–106 location of, 106 testing of, 104–105 Semantic dementia, PET examination of, 59–60 Serotonin (5-HT) anxiety and mood disorders and, 355 MDD, transmission in, 405–408 OCD and levels of, 411–412 PET study of, 362, 402–403, 405–408 schizophrenia and, 402–403 SPECT study of, 362, 405, 407–408 Serotonin transporter (5-HTT) (SERT) MDD and reductions in, 407–408 MDMA and, 416 mood and anxiety disorders and variations in, 355–362 in nicotine users, 418 PET study of, 362, 407–408 schizophrenia and abnormalities of, 402 social phobia and, 411 SPECT study of, 362, 407–408 Serotonin transporter promoter region (5-HTTLPR) ACC and PFC mechanism of, 361–362 alleles of, 355–356 amygdala activation with, 358, 360 anxiety and mood disorders and, 355–362 environmental stress and, 359–360 f MRI study of, 357–358, 361 PET study of, 362 SPECT study of, 362 SERT. See Serotonin transporter Simple phobia. See Specific phobia Single-photon emission computed tomography (SPECT) AD study with, 49–53, 344–345 alcoholism imaging with, 418–420 CFS analysis with, 122–125 in children, 240 Chronic whiplash analysis with, 147–148

454 Single-photon emission computed tomography (SPECT) (Cont.) DA transmission measurements with, 413 dementia study with MRI and, 47–48 DLB diagnosis with, 55 dopamine activity measurements with, 398–402 equilibrium analysis of, 391–392 FMS analysis with, 130–132 FTD diagnosis with, 54 GABAergic systems evaluation with, 403 graphical method analysis of, 396–397 heritability of brain function study with, 341–342 human genetics and, 331–332 Huntington’s disease study with, 331–332 kinetic analysis of, 392–396 MCS analysis with, 149–151 MDMA abusers imaging with, 415–416 MS analysis with, 169–172, 185–186 neuroreceptor imaging with, 386 OCD imaging with, 412–413 panic disorder imaging with, 410–411 serotonin activity measurements with, 405, 407–408 SERT study with, 407–408 social phobia imaging with, 411 Social anxiety disorder. See Social phobia Social phobia ACC in, 21–22 amygdala in, 21–23 citalopram treatment of, 22 cognitive studies of, 19–21 description of, 19 face-in-the-crowd paradigm in, 20 hippocampus in, 21–22 memory bias in, 20–21 neuroimaging studies of, 21–23 neuroreceptor imaging of, 411 OCD v., 21 OFC in, 22 PFC in, 21–22 right hemisphere regions in, 20–21 SERT and, 411 SPECT imaging of, 411 Stroop test in, 20 temporal lobes in, 21 Somatotherapy, psychotherapy v., 96

INDEX

Specific phobia amygdala activation in, 18 attentional bias in, 17 cognitive studies of, 17 description of, 17 memory bias in, 17 neuroimaging studies of, 18–19 OCD v., 18 PTSD v., 18 research of, 17 right hemisphere regions in, 18 Stroop test and, 17 SPECT. See Single-photon emission computed STG. See Superior temporal gyrus Striatum, OCD inactivity of, 7 Stroke ADC map analysis with, 214–215, 223 BOLD analysis of, 217, 223 brain scan follow-up, 212–214 CT brain scan neuroimaging of, 204–205, 206 evaluation with, 207–208, 209, 211–212 follow-up of, 212–214 outcome information with, 221–225 CT brain scan v. MRI, 205, 208, 214, 219–221 CTA analysis of, 227 DSA analysis of, 225–228 DTI study of, 217, 218 DWI imaging of, 208–210, 214–217, 222–223, 224 evaluation of, 207–212 hemorrhagic patterns, 207, 211–212, 224–225 ischemic patterns, 207–211, 221–223 FLAIR imaging of, 210, 220, 222 f MRI analysis of, 217–218, 223 indications of, 205–207 MRA assessment of, 225–228 MRI neuroimaging of, 205, 207, 226 evaluation of, 207–208, 209, 210, 211–212 newer techniques in, 217–219 utility in, 214–219 MRS analysis of, 217, 218–219 MTI imaging of, 217, 219 T1-weighted imaging, 214, 215 T2-weighted imaging, 214, 215 vascular imaging of, 205–206, 225–228 Stroop test in GAD, 23 OCD results of, 5–6

INDEX

panic disorder and, 14 PTSD results of, 10 in social phobia, 20 in specific phobia, 17 Superior temporal gyrus (STG) description of, 100 in schizophrenia, 100–102 T TD. See Tourette’s disorder Temporal lobes in GAD, 25 in panic disorder, 15 in social phobia, 21 Tourette’s disorder (TD), children studies of, 273 Transforming growth factor beta (TGF-
), CFS magnification of, 123 Trisomy 21. See Down’s syndrome V V5, schizophrenia not deficiency in, 98–100, 101 Vascular dementia (VD). See also Dementia AD v., 54 AD v., diagnostic patterns differentiating, 53–54 AD v. MID and, 54 description of, 46 diagnosis of, 46 symptoms of, 46

455

Vascular imaging, of stroke, 205–206, 225–228 VD. See Vascular dementia Volition ACC and, 81–83 cognitive control as vital constituent of, 76–77 concluding remarks on, 85–87 definition of depression and, 73–76 as depression symptom, 86–87 DLPFC and, 76–78 DLPFC and ACC as neural substrates of, 74, 75 PFC areas instantiation of, 78 requirements for, 81 W WCST. See Wisconsin card sorting test Williams syndrome, 302, 306–307 Wisconsin card sorting test (WCST), as schizophrenia test, 104 Working memory (WM) in bipolar disorder in children and adolescents, 277 children studies of, 247–248 Down’s syndrome impairment of, 348 as EF component, 247 human genetics and, 344 MZ and DZ twins study of, 344 schizophrenia deficits of, 349

CONTENTS OF RECENT VOLUMES

Volume 37

Memory and Forgetting: Long-Term and Gradual Changes in Memory Storage Larry R. Squire

Section I: Selectionist Ideas and Neurobiology in

Implicit Knowledge: New Perspectives on Unconscious Processes Daniel L. Schacter

Population Thinking and Neuronal Selection: Metaphors or Concepts? Ernst Mayr

Section V: Psychophysics, Psychoanalysis, and Neuropsychology

Selectionist and Neuroscience Olaf Sporns

Instructionist

Ideas

Selection and the Origin of Information Manfred Eigen

Phantom Limbs, Neglect Syndromes, Repressed Memories, and Freudian Psychology V. S. Ramachandran

Section II: Populations

Neural Darwinism and a Conceptual Crisis in Psychoanalysis Arnold H. Modell

Development

and

Neuronal

Morphoregulatory Molecules and Selectional Dynamics during Development Kathryn L. Crossin

A New Vision of the Mind Oliver Sacks

Exploration and Selection in the Early Acquisition of Skill Esther Thelen and Daniela Corbetta

index

Population Activity in the Control of Movement Apostolos P. Georgopoulos Section III: Functional Integration in the Brain

Segregation

and

Reentry and the Problem of Cortical Integration Giulio Tononi Coherence as an Organizing Principle of Cortical Functions Wolf Singerl

Volume 38 Regulation of GABAA Receptor Function and Gene Expression in the Central Nervous System A. Leslie Morrow Genetics and the Organization of the Basal Ganglia Robert Hitzemann, Yeang Olan, Stephen Kanes, Katherine Dains, and Barbara Hitzemann

Section IV: Memory and Models

Structure and Pharmacology of Vertebrate GABAA Receptor Subtypes Paul J. Whiting, Ruth M. McKernan, and Keith A. Wafford

Selection versus Instruction: Use of Computer Models to Compare Brain Theories George N. Reeke, Jr.

Neurotransmitter Transporters: Biology, Function, and Regulation Beth Borowsky and Beth J. Hoffman

Temporal Mechanisms in Perception Ernst Po¨ppel

457

Molecular

458

CONTENTS OF RECENT VOLUMES

Presynaptic Excitability Meyer B. Jackson

Volume 40

Monoamine Neurotransmitters in Invertebrates and Vertebrates: An Examination of the Diverse Enzymatic Pathways Utilized to Synthesize and Inactivate Biogenic Amines B. D. Sloley and A. V. Juorio

Mechanisms of Nerve Cell Death: Apoptosis or Necrosis after Cerebral Ischemia R. M. E. Chalmers-Redman, A. D. Fraser, W. Y. H. Ju, J. Wadia, N. A. Tatton, and W. G. Tatton

Neurotransmitter Systems in Schizophrenia Gavin P. Reynolds

Changes in Ionic Fluxes during Cerebral Ischemia Tibor Kristian and Bo K. Siesjo

Physiology of Bergmann Glial Cells Thomas Mu¨ ller and Helmut Kettenmann

Techniques for Examining Neuroprotective Drugs in Vitro A. Richard Green and Alan J. Cross

index

Volume 39

Techniques for Examining Neuroprotective Drugs in Vivo Mark P. Goldberg, Uta Strasser, and Laura L. Dugan

Modulation of Amino Acid-Gated Ion Channels by Protein Phosphorylation Stephen J. Moss and Trevor G. Smart

Calcium Antagonists: Their Role in Neuroprotection A. Jacqueline Hunter

Use-Dependent Regulation Receptors Eugene M. Barnes, Jr.

GABAA

Sodium and Potassium Channel Modulators: Their Role in Neuroprotection Tihomir P. Obrenovich

Synaptic Transmission and Modulation in the Neostriatum David M. Lovinger and Elizabeth Tyler

NMDA Antagonists: Their Role in Neuroprotection Danial L. Small

of

The Cytoskeleton and Neurotransmitter Receptors Valerie J. Whatley and R. Adron Harris

Development of the NMDA Ion-Channel Blocker, Aptiganel Hydrochloride, as a Neuroprotective Agent for Acute CNS Injury Robert N. McBurney

Endogenous Opioid Regulation of Hippocampal Function Michele L. Simmons and Charles Chavkin

The Pharmacology of AMPA Antagonists and Their Role in Neuroprotection Rammy Gill and David Lodge

Molecular Neurobiology of the Cannabinoid Receptor Mary E. Abood and Billy R. Martin

GABA and Neuroprotection Patrick D. Lyden

Genetic Models in the Study of Anesthetic Drug Action Victoria J. Simpson and Thomas E. Johnson Neurochemical Bases of Locomotion and Ethanol Stimulant Effects Tamara J. Phillips and Elaine H. Shen Effects of Ethanol on Ion Channels Fulton T. Crews, A. Leslie Morrow, Hugh Criswell, and George Breese index

Adenosine and Neuroprotection Bertil B. Fredholm Interleukins and Cerebral Ischemia Nancy J. Rothwell, Sarah A. Loddick, and Paul Stroemer Nitrone-Based Free Radical Traps as Neuroprotective Agents in Cerebral Ischemia and Other Pathologies Kenneth Hensley, John M. Carney, Charles A. Stewart, Tahera Tabatabaie, Quentin Pye, and Robert A. Floyd

CONTENTS OF RECENT VOLUMES

Neurotoxic and Neuroprotective Roles of Nitric Oxide in Cerebral Ischemia Turgay Dalkara and Michael A. Moskowitz

Sensory and Cognitive Functions Lawrence M. Parsons and Peter T. Fox

A Review of Earlier Clinical Studies on Neuroprotective Agents and Current Approaches Nils-Gunnar Wahlgren

Skill Learning Julien Doyon

index

Volume 41

Section V: Clinical and Neuropsychological Observations Executive Function and Motor Skill Learning Mark Hallett and Jordon Grafman

Section I: Historical Overview

Verbal Fluency and Agrammatism Marco Molinari, Maria G. Leggio, and Maria C. Silveri

Rediscovery of an Early Concept Jeremy D. Schmahmann

Classical Conditioning Diana S. Woodruff-Pak

Section II: Anatomic Substrates

Early Infantile Autism Margaret L. Bauman, Pauline A. Filipek, and Thomas L. Kemper

The Cerebrocerebellar System Jeremy D. Schmahmann and Deepak N. Pandya Cerebellar Output Channels Frank A. Middleton and Peter L. Strick Cerebellar-Hypothalamic Axis: Basic Circuits and Clinical Observations Duane E. Haines, Espen Dietrichs, Gregory A. Mihailoff, and E. Frank McDonald Section III. Physiological Observations Amelioration of Aggression: Response to Selective Cerebellar Lesions in the Rhesus Monkey Aaron J. Berman Autonomic and Vasomotor Regulation Donald J. Reis and Eugene V. Golanov Associative Learning Richard F. Thompson, Shaowen Bao, Lu Chen, Benjamin D. Cipriano, Jeffrey S. Grethe, Jeansok J. Kim, Judith K. Thompson, Jo Anne Tracy, Martha S. Weninger, and David J. Krupa

Olivopontocerebellar Atrophy and Friedreich’s Ataxia: Neuropsychological Consequences of Bilateral versus Unilateral Cerebellar Lesions The´re`se Botez-Marquard and Mihai I. Botez Posterior Fossa Syndrome Ian F. Pollack Cerebellar Cognitive Affective Syndrome Jeremy D. Schmahmann and Janet C. Sherman Inherited Cerebellar Diseases Claus W. Wallesch and Claudius Bartels Neuropsychological Abnormalities in Cerebellar Syndromes—Fact or Fiction? Irene Daum and Hermann Ackermann Section VI: Theoretical Considerations Cerebellar Microcomplexes Masao Ito

Visuospatial Abilities Robert Lalonde

Control of Sensory Data Acquisition James M. Bower

Spatial Event Processing Marco Molinari, Laura Petrosini, and Liliana G. Grammaldo

Neural Representations of Moving Systems Michael Paulin

Section IV: Functional Neuroimaging Studies Linguistic Processing Julie A. Fiez and Marcus E. Raichle

459

How Fibers Subserve Computing Capabilities: Similarities between Brains and Machines Henrietta C. Leiner and Alan L. Leiner

460

CONTENTS OF RECENT VOLUMES

Cerebellar Timing Systems Richard Ivry

Volume 43

Attention Coordination and Anticipatory Control Natacha A. Akshoomoff, Eric Courchesne, and Jeanne Townsend

Early Development of the Drosophila Neuromuscular Junction: A Model for Studying Neuronal Networks in Development Akira Chiba

Context-Response Linkage W. Thomas Thach

Development of Larval Body Wall Muscles Michael Bate, Matthias Landgraf, and Mar Ruiz Gmez Bate

Duality of Cerebellar Motor and Cognitive Functions James R. Bloedel and Vlastislav Bracha Section VII: Future Directions Therapeutic and Research Implications Jeremy D. Schmahmann

Volume 42 Alzheimer Disease Mark A. Smith Neurobiology of Stroke W. Dalton Dietrich Free Radicals, Calcium, and the Synaptic Plasticity-Cell Death Continuum: Emerging Roles of the Trascription Factor NFB Mark P. Mattson AP-I Transcription Factors: Short- and LongTerm Modulators of Gene Expression in the Brain Keith Pennypacker

Development of Electrical Properties and Synaptic Transmission at the Embryonic Neuromuscular Junction Kendal S. Broadie Ultrastructural Correlates of Neuromuscular Junction Development Mary B. Rheuben, Motojiro Yoshihara, and Yoshiaki Kidokoro Assembly and Maturation of the Drosophila Larval Neuromuscular Junction L. Sian Gramates and Vivian Budnik Second Messenger Systems Underlying Plasticity at the Neuromuscular Junction Frances Hannan and Yi Zhong Mechanisms of Neurotransmitter Release J. Troy Littleton, Leo Pallanck, and Barry Ganetzky Vesicle Recycling at the Drosophila Neuromuscular Junction Daniel T. Stimson and Mani Ramaswami Ionic Currents in Larval Muscles of Drosophila Satpal Singh and Chun-Fang Wu

Ion Channels in Epilepsy Istvan Mody

Development of the Adult Neuromuscular System Joyce J. Fernandes and Haig Keshishian

Posttranslational Regulation of Ionotropic Glutamate Receptors and Synaptic Plasticity Xiaoning Bi, Steve Standley, and Michel Baudry

Controlling the Motor Neuron James R. Trimarchi, Ping Jin, and Rodney K. Murphey

Heritable Mutations in the Glycine, GABAA, and Nicotinic Acetylcholine Receptors Provide New Insights into the Ligand-Gated Ion Channel Receptor Superfamily Behnaz Vafa and Peter R. Schofield

Volume 44

index

Human Ego-Motion Perception A. V. van den Berg Optic Flow and Eye Movements M. Lappe and K.-P. Hoffman

CONTENTS OF RECENT VOLUMES

The Role of MST Neurons during Ocular Tracking in 3D Space K. Kawano, U. Inoue, A. Takemura, Y. Kodaka, and F. A. Miles Visual Navigation in Flying Insects M. V. Srinivasan and S.-W. Zhang Neuronal Matched Filters for Optic Flow Processing in Flying Insects H. G. Krapp A Common Frame of Reference for the Analysis of Optic Flow and Vestibular Information B. J. Frost and D. R. W. Wylie Optic Flow and the Visual Guidance of Locomotion in the Cat H. Sherk and G. A. Fowler Stages of Self-Motion Processing in Primate Posterior Parietal Cortex F. Bremmer, J.-R. Duhamel, S. B. Hamed, and W. Graf Optic Flow Perception C. J. Duffy

Analysis

for

Self-Movement

Neural Mechanisms for Self-Motion Perception in Area MST R. A. Andersen, K. V. Shenoy, J. A. Crowell, and D. C. Bradley Computational Mechanisms for Optic Flow Analysis in Primate Cortex M. Lappe Human Cortical Areas Underlying the Perception of Optic Flow: Brain Imaging Studies M. W. Greenlee

461

Brain Development and Generation of Brain Pathologies Gregory L. Holmes and Bridget McCabe Maturation of Channels and Receptors: Consequences for Excitability David F. Owens and Arnold R. Kriegstein Neuronal Activity and the Establishment of Normal and Epileptic Circuits during Brain Development John W. Swann, Karen L. Smith, and Chong L. Lee The Effects of Seizures of the Hippocampus of the Immature Brain Ellen F. Sperber and Solomon L. Moshe Abnormal Development and Catastrophic Epilepsies: The Clinical Picture and Relation to Neuroimaging Harry T. Chugani and Diane C. Chugani Cortical Reorganization and Seizure Generation in Dysplastic Cortex G. Avanzini, R. Preafico, S. Franceschetti, G. Sancini, G. Battaglia, and V. Scaioli Rasmussen’s Syndrome with Particular Reference to Cerebral Plasticity: A Tribute to Frank Morrell Fredrick Andermann and Yuonne Hart Structural Reorganization of Hippocampal Networks Caused by Seizure Activity Daniel H. Lowenstein Epilepsy-Associated Plasticity in gammaAmniobutyric Acid Receptor Expression, Function and Inhibitory Synaptic Properties Douglas A. Coulter

What Neurological Patients Tell Us about the Use of Optic Flow L. M. Vaina and S. K. Rushton

Synaptic Plasticity and Secondary Epileptogenesis Timothy J. Teyler, Steven L. Morgan, Rebecca N. Russell, and Brian L. Woodside

index

Synaptic Plasticity in Epileptogenesis: Cellular Mechanisms Underlying Long-Lasting Synaptic Modifications that Require New Gene Expression Oswald Steward, Christopher S. Wallace, and Paul F. Worley

Volume 45 Mechanisms of Brain Plasticity: From Normal Brain Function to Pathology Philip. A. Schwartzkroin

Cellular Correlates of Behavior Emma R. Wood, Paul A. Dudchenko, and Howard Eichenbaum

462

CONTENTS OF RECENT VOLUMES

Mechanisms of Neuronal Conditioning David A. T. King, David J. Krupa, Michael R. Foy, and Richard F. Thompson

Biosynthesis of Neurosteroids and Regulation of Their Synthesis Synthia H. Mellon and Hubert Vaudry

Plasticity in the Aging Central Nervous System C. A. Barnes

Neurosteroid 7-Hydroxylation Products in the Brain Robert Morfin and Luboslav Sta´ rka

Secondary Epileptogenesis, Kindling, and Intractable Epilepsy: A Reappraisal from the Perspective of Neuronal Plasticity Thomas P. Sutula Kindling and the Mirror Focus Dan C. McIntyre and Michael O. Poulter Partial Kindling and Behavioral Pathologies Robert E. Adamec The Mirror Focus and Secondary Epileptogenesis B. J. Wilder Hippocampal Lesions in Epilepsy: A Historical Review Robert Naquet Clinical Evidence for Secondary Epileptogensis Hans O. Luders Epilepsy as a Progressive (or Nonprogressive ‘‘Benign’’) Disorder John A. Wada Pathophysiological Aspects of Landau-Kleffner Syndrome: From the Active Epileptic Phase to Recovery Marie-Noelle Metz-Lutz, Pierre Maquet, Annd De Saint Martin, Gabrielle Rudolf, Norma Wioland, Edouard Hirsch, and Chriatian Marescaux

Neurosteroid Analysis Ahmed A. Alomary, Robert L. Fitzgerald, and Robert H. Purdy Role of the Peripheral-Type Benzodiazepine Receptor in Adrenal and Brain Steroidogenesis Rachel C. Brown and Vassilios Papadopoulos Formation and Effects of Neuroactive Steroids in the Central and Peripheral Nervous System Roberto Cosimo Melcangi, Valerio Magnaghi, Mariarita Galbiati, and Luciano Martini Neurosteroid Modulation of Recombinant and Synaptic GABAA Receptors Jeremy J. Lambert, Sarah C. Harney, Delia Belelli, and John A. Peters GABAA-Receptor Plasticity during LongTerm Exposure to and Withdrawal from Progesterone Giovanni Biggio, Paolo Follesa, Enrico Sanna, Robert H. Purdy, and Alessandra Concas Stress and Neuroactive Steroids Maria Luisa Barbaccia, Mariangela Serra, Robert H. Purdy, and Giovanni Biggio

Local Pathways of Seizure Propagation in Neocortex Barry W. Connors, David J. Pinto, and Albert E. Telefeian

Neurosteroids in Learning and Processes Monique Valle´e, Willy Mayo, George F. Koob, and Michel Le Moal

Multiple Subpial Assessment C. E. Polkey

Neurosteroids and Behavior Sharon R. Engel and Kathleen A. Grant

Transection:

A

Clinical

The Legacy of Frank Morrell Jerome Engel, Jr. Volume 46 Neurosteroids: Beginning of the Story Etienne E. Baulieu, P. Robel, and M. Schumacher

Memory

Ethanol and Neurosteroid Interactions in the Brain A. Leslie Morrow, Margaret J. VanDoren, Rebekah Fleming, and Shannon Penland Preclinical Development of Neurosteroids as Neuroprotective Agents for the Treatment of Neurodegenerative Diseases Paul A. Lapchak and Dalia M. Araujo

CONTENTS OF RECENT VOLUMES

Clinical Implications of Circulating Neurosteroids Andrea R. Genazzani, Patrizia Monteleone, Massimo Stomati, Francesca Bernardi, Luigi Cobellis, Elena Casarosa, Michele Luisi, Stefano Luisi, and Felice Petraglia Neuroactive Steroids and Central Nervous System Disorders Mingde Wang, Torbjo¨rn Ba¨ ckstro¨m, Inger Sundstro¨m, Go¨ran Wahlstro¨m, Tommy Olsson, Di Zhu, Inga-Maj Johansson, Inger Bjo¨rn, and Marie Bixo Neuroactive Steroids in Neuropsychopharmacology Rainer Rupprecht and Florian Holsboer Current Perspectives on the Role of Neurosteroids in PMS and Depression Lisa D. Griffin, Susan C. Conrad, and Synthia H. Mellon index

463

Processing Human Brain Tissue for in Situ Hybridization with Radiolabelled Oligonucleotides Louise F. B. Nicholson In Situ Hybridization of Astrocytes and Neurons Cultured in Vitro L. A. Arizza-McNaughton, C. De Felipe, and S. P. Hunt In Situ Hybridization on Organotypic Slice Cultures A. Gerfin-Moser and H. Monyer Quantitative Analysis of in Situ Hybridization Histochemistry Andrew L. Gundlach and Ross D. O’Shea Part II: Nonradioactive in Situ hybridization Nonradioactive in Situ Hybridization Using Alkaline Phosphatase-Labelled Oligonucleotides S. J. Augood, E. M. McGowan, B. R. Finsen, B. Heppelmann, and P. C. Emson

Volume 47

Combining Nonradioactive in Situ Hybridization with Immunohistological and Anatomical Techniques Petra Wahle

Introduction: Studying Gene Expression in Neural Tissues by in Situ Hybridization W. Wisden and B. J. Morris

Nonradioactive in Situ Hybridization: Simplified Procedures for Use in Whole Mounts of Mouse and Chick Embryos Linda Ariza-McNaughton and Robb Krumlauf

Part I: In Situ Hybridization with Radiolabelled Oligonucleotides In Situ Hybridization with Oligonucleotide Probes Wl. Wisden and B. J. Morris

index

Cryostat Sectioning of Brains Victoria Revilla and Alison Jones

Volume 48

Processing Rodent Embryonic and Early Postnatal Tissue for in Situ Hybridization with Radiolabelled Oligonucleotides David J. Laurie, Petra C. U. Schrotz, Hannah Monyer, and Ulla Amtmann

Assembly and Intracellular GABAA Receptors Eugene Barnes

Trafficking

of

Processing of Retinal Tissue for in Situ Hybridization Frank Mu¨ ller

Subcellular Localization and Regulation of GABAA Receptors and Associated Proteins Bernhard Lu¨ scher and Jean-Marc Fritschy D1 Dopamine Receptors Richard Mailman

Processing the Spinal Cord for in Situ Hybridization with Radiolabelled Oligonucleotides A. Berthele and T. R. To¨lle

Molecular Modeling of Ligand-Gated Ion Channels: Progress and Challenges Ed Bertaccini and James R. Trudel

464

CONTENTS OF RECENT VOLUMES

Alzheimer’s Disease: Its Diagnosis and Pathogenesis Jillian J. Kril and Glenda M. Halliday DNA Arrays and Functional Genomics in Neurobiology Christelle Thibault, Long Wang, Li Zhang, and Michael F. Miles

The Treatment of Infantile Spasms: An Evidence-Based Approach Mark Mackay, Shelly Weiss, and O. Carter Snead III

index

ACTH Treatment of Infantile Spasms: Mechanisms of Its Effects in Modulation of Neuronal Excitability K. L. Brunson, S. Avishai-Eliner, and T. Z. Baram

Volume 49

Neurosteroids and Infantile Spasms: The Deoxycorticosterone Hypothesis Michael A. Rogawski and Doodipala S. Reddy

What Is West Syndrome? Olivier Dulac, Christine Soufflet, Catherine Chiron, and Anna Kaminski

Are there Specific Anatomical and/or Transmitter Systems (Cortical or Subcortical) That Should Be Targeted? Phillip C. Jobe

The Relationship between encephalopathy and Abnormal Neuronal Activity in the Developing Brain Frances E. Jensen

Medical versus Surgical Treatment: Which Treatment When W. Donald Shields

Hypotheses from Functional Neuroimaging Studies Csaba Juha´ sz, Harry T. Chugani, Ouo Muzik, and Diane C. Chugani Infantile Spasms: Unique Sydrome or General Age-Dependent Manifestation of a Diffuse Encephalopathy? M. A. Koehn and M. Duchowny

Developmental Outcome with and without Successful Intervention Rochelle Caplan, Prabha Siddarth, Gary Mathern, Harry Vinters, Susan Curtiss, Jennifer Levitt, Robert Asarnow, and W. Donald Shields Infantile Spasms versus Myoclonus: Is There a Connection? Michael R. Pranzatelli

Histopathology of Brain Tissue from Patients with Infantile Spasms Harry V. Vinters

Tuberous Sclerosis as an Underlying Basis for Infantile Spasm Raymond S. Yeung

Generators of Ictal and Interictal Electroencephalograms Associated with Infantile Spasms: Intracellular Studies of Cortical and Thalamic Neurons M. Steriade and I. Timofeev

Brain Malformation, Epilepsy, and Infantile Spasms M. Elizabeth Ross

Cortical and Subcortical Generators of Normal and Abnormal Rhythmicity David A. McCormick Role of Subcortical Structures in the Pathogenesis of Infantile Spasms: What Are Possible Subcortical Mediators? F. A. Lado and S. L. Moshe´ What Must We Know to Develop Better Therapies? Jean Aicardi

Brain Maturational Aspects Relevant to Pathophysiology of Infantile Spasms G. Auanzini, F. Panzica, and S. Franceschetti Gene Expression Analysis as a Strategy to Understand the Molecular Pathogenesis of Infantile Spasms Peter B. Crino Infantile Spasms: Criteria for an Animal Model Carl E. Stafstrom and Gregory L. Holmes index

CONTENTS OF RECENT VOLUMES

Volume 50 Part I: Primary Mechanisms How Does Glucose Generate Oxidative Stress In Peripheral Nerve? Irina G. Obrosova Glycation in Diabetic Neuropathy: Characteristics, Consequences, Causes, and Therapeutic Options Paul J. Thornalley Part II: Secondary Changes Protein Kinase C Changes in Diabetes: Is the Concept Relevant to Neuropathy? Joseph Eichberg Are Mitogen-Activated Protein Kinases Glucose Transducers for Diabetic Neuropathies? Tertia D. Purves and David R. Tomlinson Neurofilaments in Diabetic Neuropathy Paul Fernyhough and Robert E. Schmidt Apoptosis in Diabetic Neuropathy Aviva Tolkovsky Nerve and Ganglion Blood Flow in Diabetes: An Appraisal Douglas W. Zochodne Part III: Manifestations Potential Mechanisms of Neuropathic Pain in Diabetes Nigel A. Calcutt Electrophysiologic Measures of Diabetic Neuropathy: Mechanism and Meaning Joseph C. Arezzo and Elena Zotova Neuropathology and Pathogenesis of Diabetic Autonomic Neuropathy Robert E. Schmidt Role of the Schwann Cell in Diabetic Neuropathy Luke Eckersley

465

Nerve Growth Factor for the Treatment of Diabetic Neuropathy: What Went Wrong, What Went Right, and What Does the Future Hold? Stuart C. Apfel Angiotensin-Converting Enzyme Inhibitors: Are there Credible Mechanisms for Beneficial Effects in Diabetic Neuropathy? Rayaz A. Malik and David R. Tomlinson Clinical Trials for Drugs Against Diabetic Neuropathy: Can We Combine Scientific Needs With Clinical Practicalities? Dan Ziegler and Dieter Luft index

Volume 51 Energy Metabolism in the Brain Leif Hertz and Gerald A. Dienel The Cerebral Glucose-Fatty Acid Cycle: Evolutionary Roots, Regulation, and (Patho) physiological Importance Kurt Heininger Expression, Regulation, and Functional Role of Glucose Transporters (GLUTs) in Brain Donard S. Dwyer, Susan J. Vannucci, and Ian A. Simpson Insulin-Like Growth Factor-1 Promotes Neuronal Glucose Utilization During Brain Development and Repair Processes Carolyn A. Bondy and Clara M. Cheng CNS Sensing and Regulation of Peripheral Glucose Levels Barry E. Levin, Ambrose A. Dunn-Meynell, and Vanessa H. Routh

Part IV: Potential Treatment

Glucose Transporter Protein Syndromes Darryl C. De Vivo, Dong Wang, Juan M. Pascual, and Yuan Yuan Ho

Polyol Pathway and Diabetic Peripheral Neuropathy Peter J. Oates

Glucose, Stress, and Hippocampal Neuronal Vulnerability Lawrence P. Reagan

466

CONTENTS OF RECENT VOLUMES

Glucose/Mitochondria in Neurological Conditions John P. Blass Energy Utilization in the Ischemic/Reperfused Brain John W. Phillis and Michael H. O’Regan

Stress and Secretory Immunity Jos A. Bosch, Christopher Ring, Eco J. C. de Geus, Enno C. I. Veerman, and Arie V. Nieuw Amerongen Cytokines and Depression Angela Clow

Diabetes Mellitus and the Central Nervous System Anthony L. McCall

Immunity and Schizophrenia: Autoimmunity, Cytokines, and Immune Responses Fiona Gaughran

Diabetes, the Brain, and Behavior: Is There a Biological Mechanism Underlying the Association between Diabetes and Depression? A. M. Jacobson, J. A. Samson, K. Weinger, and C. M. Ryan

Cerebral Lateralization and the Immune System Pierre J. Neveu

Schizophrenia and Diabetes David C. Henderson and Elissa R. Ettinger

Behavioral Conditioning of the Immune System Frank Hucklebridge Psychological and Neuroendocrine Correlates of Disease Progression Julie M. Turner-Cobb

Psychoactive Drugs Affect Glucose Transport and the Regulation of Glucose Metabolism Donard S. Dwyer, Timothy D. Ardizzone, and Ronald J. Bradley

The Role of Psychological Intervention in Modulating Aspects of Immune Function in Relation to Health and Well-Being J. H. Gruzelier

index

index

Volume 52 Volume 53 Neuroimmune Relationships in Perspective Frank Hucklebridge and Angela Clow Sympathetic Nervous System Interaction with the Immune System Virginia M. Sanders and Adam P. Kohm Mechanisms by Which Cytokines Signal the Brain Adrian J. Dunn Neuropeptides: Modulators of Responses in Health and Disease David S. Jessop

Immune

Brain–Immune Interactions in Sleep Lisa Marshall and Jan Born Neuroendocrinology of Autoimmunity Michael Harbuz Systemic Stress-Induced Th2 Shift and Its Clinical Implications Ibia J. Elenkov Neural Control of Salivary S-IgA Secretion Gordon B. Proctor and Guy H. Carpenter

Section I: Mitochondrial Structure and Function Mitochondrial DNA Structure and Function Carlos T. Moraes, Sarika Srivastava, Ilias Kirkinezos, Jose Oca-Cossio, Corina van Waveren, Markus Woischnick, and Francisca Diaz Oxidative Phosphorylation: Structure, Function, and Intermediary Metabolism Simon J. R. Heales, Matthew E. Gegg, and John B. Clark Import of Mitochondrial Proteins Matthias F. Bauer, Sabine Hofmann, and Walter Neupert Section II: Primary Respiratory Chain Disorders Mitochondrial Disorders of the Nervous System: Clinical, Biochemical, and Molecular Genetic Features Dominic Thyagarajan and Edward Byrne

CONTENTS OF RECENT VOLUMES

Section III: Secondary Respiratory Chain Disorders Friedreich’s Ataxia J. M. Cooper and J. L. Bradley Wilson Disease C. A. Davie and A. H. V. Schapira

467

The Mitochondrial Theory of Aging: Involvement of Mitochondrial DNA Damage and Repair Nadja C. de Souza-Pinto and Vilhelm A. Bohr index

Hereditary Spastic Paraplegia Christopher J. McDermott and Pamela J. Shaw Cytochrome c Oxidase Deficiency Giacomo P. Comi, Sandra Strazzer, Sara Galbiati, and Nereo Bresolin Section IV: Toxin Induced Mitochondrial Dysfunction Toxin-Induced Mitochondrial Dysfunction Susan E. Browne and M. Flint Beal Section V: Neurodegenerative Disorders Parkinson’s Disease L. V. P. Korlipara and A. H. V. Schapira Huntington’s Disease: The Mystery Unfolds? A˚sa Peterse´n and Patrik Brundin Mitochondria in Alzheimer’s Disease Russell H. Swerdlow and Stephen J. Kish Contributions of Mitochondrial Alterations, Resulting from Bad Genes and a Hostile Environment, to the Pathogenesis of Alzheimer’s Disease Mark P. Mattson Mitochondria and Amyotrophic Lateral Sclerosis Richard W. Orrell and Anthony H. V. Schapira

Volume 54 Unique General Anesthetic Binding Sites Within Distinct Conformational States of the Nicotinic Acetylcholine Receptor Hugo R. Ariaas, William, R. Kem, James R. Truddell, and Michael P. Blanton Signaling Molecules and Receptor Transduction Cascades That Regulate NMDA ReceptorMediated Synaptic Transmission Suhas. A. Kotecha and John F. MacDonald Behavioral Measures of Alcohol Self-Administration and Intake Control: Rodent Models Herman H. Samson and Cristine L. Czachowski Dopaminergic Mouse Mutants: Investigating the Roles of the Different Dopamine Receptor Subtypes and the Dopamine Transporter Shirlee Tan, Bettina Hermann, and Emiliana Borrelli Drosophila melanogaster, A Genetic Model System for Alcohol Research Douglas J. Guarnieri and Ulrike Heberlein index

Section VI: Models of Mitochondrial Disease Models of Mitochondrial Disease Danae Liolitsa and Michael G. Hanna

Volume 55

Section VII: Defects of Oxidation Including Carnitine Deficiency

Section I: Virsu Vectors For Use in the Nervous System

Defects of Oxidation Including Carnitine Deficiency K. Bartlett and M. Pourfarzam

Non-Neurotropic Adenovirus: a Vector for Gene Transfer to the Brain and Gene Therapy of Neurological Disorders P. R. Lowenstein, D. Suwelack, J. Hu, X. Yuan, M. Jimenez-Dalmaroni, S. Goverdhama, and M.G. Castro

Section VIII: Mitochondrial Involvement in Aging

468

CONTENTS OF RECENT VOLUMES

Adeno-Associated Virus Vectors E. Lehtonen and L. Tenenbaum Problems in the Use of Herpes Simplex Virus as a Vector L. T. Feldman Lentiviral Vectors J. Jakobsson, C. Ericson, N. Rosenquist, and C. Lundberg Retroviral Vectors for Gene Delivery to Neural Precursor Cells K. Kageyama, H. Hirata, and J. Hatakeyama

Processing and Representation of SpeciesSpecific Communication Calls in the Auditory System of Bats George D. Pollak, Achim Klug, and Eric E. Bauer Central Nervous System Control of Micturition Gert Holstege and Leonora J. Mouton The Structure and Physiology of the Rat Auditory System: An Overview Manuel Malmierca Neurobiology of Cat and Human Sexual Behavior Gert Holstege and J. R. Georgiadis

Section II: Gene Therapy with Virus Vectors for Specific Disease of the Nervous System

index

The Principles of Molecular Therapies for Glioblastoma G. Karpati and J. Nalbatonglu

Volume 57

Oncolytic Herpes Simplex Virus J. C. C. Hu and R. S. Coffin

Cumulative Subject Index of Volumes 1–25

Recombinant Retrovirus Vectors for Treatment of Brain Tumors N. G. Rainov and C. M. Kramm

Volume 58

Adeno-Associated Viral Vectors for Parkinson’s Disease I. Muramatsu, L. Wang, K. Ikeguchi, K-i Fujimoto, T. Okada, H. Mizukami, Y. Hanazono, A. Kume, I. Nakano, and K. Ozawa HSV Vectors for Parkinson’s Disease D. S. Latchman Gene Therapy for Stroke K. Abe and W. R. Zhang Gene Therapy for Mucopolysaccharidosis A. Bosch and J. M. Heard index

Volume 56 Behavioral Mechanisms and the Neurobiology of Conditioned Sexual Responding Mark Krause NMDA Receptors in Alcoholism Paula L. Hoffman

Cumulative Subject Index of Volumes 26–50

Volume 59 Loss of Spines and Neuropil Liesl B. Jones Schizophrenia as a Disorder of Neuroplasticity Robert E. McCullumsmith, Sarah M. Clinton, and James H. Meador-Woodruff The Synaptic Pathology of Schizophrenia: Is Aberrant Neurodevelopment and Plasticity to Blame? Sharon L. Eastwood Neurochemical Basis for an Epigenetic Vision of Synaptic Organization E. Costa, D. R. Grayson, M. Veldic, and A. Guidotti Muscarinic Receptors in Schizophrenia: Is There a Role for Synaptic Plasticity? Thomas J. Raedler

CONTENTS OF RECENT VOLUMES

469

Serotonin and Brain Development Monsheel S. K. Sodhi and Elaine Sanders-Bush

Volume 60

Presynaptic Proteins and Schizophrenia William G. Honer and Clint E. Young

Microarray Platforms: Introduction and Application to Neurobiology Stanislav L. Karsten, Lili C. Kudo, and Daniel H. Geschwind

Mitogen-Activated Protein Kinase Signaling Svetlana V. Kyosseva Postsynaptic Density Scaffolding Proteins at Excitatory Synapse and Disorders of Synaptic Plasticity: Implications for Human Behavior Pathologies Andrea de Bartolomeis and Germano Fiore Prostaglandin-Mediated Signaling in Schizophrenia S. Smesny Mitochondria, Synaptic Plasticity, and Schizophrenia Dorit Ben-Shachar and Daphna Laifenfeld Membrane Phospholipids and Cytokine Interaction in Schizophrenia Jeffrey K. Yao and Daniel P. van Kammen Neurotensin, Schizophrenia, and Antipsychotic Drug Action Becky Kinkead and Charles B. Nemeroff Schizophrenia, Vitamin D, and Brain Development Alan Mackay-Sim, Franc¸ois Fe´ron, Darryl Eyles, Thomas Burne, and John McGrath Possible Contributions of Myelin and Oligodendrocyte Dysfunction to Schizophrenia Daniel G. Stewart and Kenneth L. Davis Brain-Derived Neurotrophic Factor and the Plasticity of the Mesolimbic Dopamine Pathway Oliver Guillin, Nathalie Griffon, Jorge Diaz, Bernard Le Foll, Erwan Bezard, Christian Gross, Chris Lammers, Holger Stark, Patrick Carroll, Jean-Charles Schwartz, and Pierre Sokoloff S100B in Schizophrenic Psychosis Matthias Rothermundt, Gerald Ponath, and Volker Arolt Oct-6 Transcription Factor Maria Ilia NMDA Receptor Function, Neuroplasticity, and the Pathophysiology of Schizophrenia Joseph T. Coyle and Guochuan Tsai index

Experimental Design and Low-Level Analysis of Microarray Data B. M. Bolstad, F. Collin, K. M. Simpson, R. A. Irizarry, and T. P. Speed Brain Gene Expression: Genomics and Genetics Elissa J. Chesler and Robert W. Williams DNA Microarrays and Animal Models of Learning and Memory Sebastiano Cavallaro Microarray Analysis of Human Nervous System Gene Expression in Neurological Disease Steven A. Greenberg DNA Microarray Analysis of Postmortem Brain Tissue Ka´ roly Mirnics, Pat Levitt, and David A. Lewis index Volume 61 Section I: High-Throughput Technologies Biomarker Discovery Using Molecular Profiling Approaches Stephen J. Walker and Arron Xu Proteomic Analysis of Mitochondrial Proteins Mary F. Lopez, Simon Melov, Felicity Johnson, Nicole Nagulko, Eva Golenko, Scott Kuzdzal, Suzanne Ackloo, and Alvydas Mikulskis Section II: Proteomic Applications NMDA Receptors, Neural Pathways, and Protein Interaction Databases Holger Husi Dopamine Transporter Network and Pathways Rajani Maiya and R. Dayne Mayfield Proteomic Approaches in Drug Discovery and Development Holly D. Soares, Stephen A. Williams,

470

CONTENTS OF RECENT VOLUMES

Peter J. Snyder, Feng Gao, Tom Stiger, Christian Rohlff, Athula Herath, Trey Sunderland, Karen Putnam, and W. Frost White Section III: Informatics Proteomic Informatics Steven Russell, William Old, Katheryn Resing, and Lawrence Hunter Section IV: Changes in the Proteome by Disease Proteomics Analysis in Alzheimer’s Disease: New Insights into Mechanisms of Neurodegeneration D. Allan Butterfield and Debra Boyd-Kimball Proteomics and Alcoholism Frank A. Witzmann and Wendy N. Strother Proteomics Studies of Traumatic Brain Injury Kevin K. W. Wang, Andrew Ottens, William Haskins, Ming Cheng Liu, Firas Kobeissy, Nancy Denslow, SuShing Chen, and Ronald L. Hayes Influence of Huntington’s Disease on the Human and Mouse Proteome Claus Zabel and Joachim Klose Section V: Overview of the Neuroproteome Proteomics—Application to the Brain Katrin Marcus, Oliver Schmidt, Heike Schaefer, Michael Hamacher, AndrA˚ van Hall, and Helmut E. Meyer index

Volume 62 GABAA Receptor Structure–Function Studies: A Reexamination in Light of New Acetylcholine Receptor Structures Myles H. Akabas Dopamine Mechanisms and Cocaine Reward Aiko Ikegami and Christine L. Duvauchelle Proteolytic Dysfunction in Neurodegenerative Disorders Kevin St. P. McNaught Neuroimaging Studies in Bipolar Children and Adolescents

Rene L. Olvera, David C. Glahn, Sheila C. Caetano, Steven R. Pliszka, and Jair C. Soares Chemosensory G-Protein-Coupled Receptor Signaling in the Brain Geoffrey E. Woodard Disturbances of Emotion Regulation after Focal Brain Lesions Antoine Bechara The Use of Caenorhabditis elegans in Molecular Neuropharmacology Jill C. Bettinger, Lucinda Carnell, Andrew G. Davies, and Steven L. McIntire index Volume 63 Mapping Neuroreceptors at work: On the Definition and Interpretation of Binding Potentials after 20 years of Progress Albert Gjedde, Dean F. Wong, Pedro Rosa-Neto, and Paul Cumming Mitochondrial Dysfunction in Bipolar Disorder: From 31P-Magnetic Resonance Spectroscopic Findings to Their Molecular Mechanisms Tadafumi Kato Large-Scale Microarray Studies of Gene Expression in Multiple Regions of the Brain in Schizophrenia and Alzeimer’s Disease Pavel L. Katsel, Kenneth L. Davis, and Vahram Haroutunian Regulation of Serotonin 2C Receptor PREmRNA Editing By Serotonin Claudia Schmauss The Dopamine Hypothesis of Drug Addiction: Hypodopaminergic State Miriam Melis, Saturnino Spiga, and Marco Diana Human and Animal Spongiform Encephalopathies are Autoimmune Diseases: A Novel Theory and Its supporting Evidence Bao Ting Zhu Adenosine and Brain Function Bertil B. Fredholm, Jiang-Fan Chen, Rodrigo A. Cunha, Per Svenningsson, and Jean-Marie Vaugeois index

CONTENTS OF RECENT VOLUMES

Volume 64 Section I. The Cholinergic System John Smythies Section II. The Dopamine System John Symythies Section III. The Norepinephrine System John Smythies

Mechanistic Connections Between Glucose/ Lipid Disturbances and Weight Gain Induced by Antipsychotic Drugs Donard S. Dwyer, Dallas Donohoe, Xiao-Hong Lu, and Eric J. Aamodt Serotonin Firing Activity as a Marker for Mood Disorders: Lessons from Knockout Mice Gabriella Gobbi

Section IV. The Adrenaline System John Smythies

index

Section V. Serotonin System John Smythies

Volume 66

index

Volume 65 Insulin Resistance: Causes and Consequences Zachary T. Bloomgarden Antidepressant-Induced Manic Conversion: A Developmentally Informed Synthesis of the Literature Christine J. Lim, James F. Leckman, Christopher Young, and Andre´s Martin Sites of Alcohol and Volatile Anesthetic Action on Glycine Receptors Ingrid A. Lobo and R. Adron Harris Role of the Orbitofrontal Cortex in Reinforcement Processing and Inhibitory Control: Evidence from Functional Magnetic Resonance Imaging Studies in Healthy Human Subjects Rebecca Elliott and Bill Deakin Common Substrates of Dysphoria in Stimulant Drug Abuse and Primary Depression: Therapeutic Targets Kate Baicy, Carrie E. Bearden, John Monterosso, Arthur L. Brody, Andrew J. Isaacson, and Edythe D. London The Role of cAMP Response Element–Binding Proteins in Mediating Stress-Induced Vulnerability to Drug Abuse Arati Sadalge Kreibich and Julie A. Blendy G-Protein–Coupled Receptor Deorphanizations Yumiko Saito and Olivier Civelli

471

Brain Atlases of Normal and Diseased Populations Arthur W. Toga and Paul M. Thompson Neuroimaging Databases as a Resource for Scientific Discovery John Darrell Van Horn, John Wolfe, Autumn Agnoli, Jeffrey Woodward, Michael Schmitt, James Dobson, Sarene Schumacher, and Bennet Vance Modeling Brain Responses Karl J. Friston, William Penny, and Olivier David Voxel-Based Morphometric Analysis Using Shape Transformations Christos Davatzikos The Cutting Edge of f MRI and High-Field f MRI Dae-Shik Kim Quantification of White Matter Using DiffusionTensor Imaging Hae-Jeong Park Perfusion f MRI for Functional Neuroimaging Geoffrey K. Aguirre, John A. Detre, and Jiongjiong Wang Functional Near-Infrared Spectroscopy: Potential and Limitations in Neuroimaging Studies Yoko Hoshi Neural Modeling and Functional Brain Imaging: The Interplay Between the Data-Fitting and Simulation Approaches Barry Horwitz and Michael F. Glabus Combined EEG and fMRI Studies of HumanBrain Function V. Menon and S. Crottaz-Herbette index

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Contributors............................................................................

ix

Distinguishing Neural Substrates of Heterogeneity Among Anxiety Disorders Jack B. Nitschke and Wendy Heller I. II. III. IV. V. VI. VII.

Obsessive-Compulsive Disorder . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Posttraumatic Stress Disorder . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Panic Disorder. . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Specific Phobia (Simple Phobia) . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Social Phobia (Social Anxiety Disorder) . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Generalized Anxiety Disorder. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Discussion . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. References . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . ..

4 9 13 17 19 23 27 29

Neuroimaging in Dementia K. P. Ebmeier, C. Donaghey, and N. J. Dougall I. II. III. IV. V. VI. VII.

Introduction . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Dementia . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Imaging Modes . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Diagnostic Imaging . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Functional Imaging . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Pharmacological Imaging. .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Conclusion . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. References . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . ..

44 44 47 48 56 60 63 64

Prefrontal and Anterior Cingulate Contributions to Volition in Depression Jack B. Nitschke and Kristen L. Mackiewicz I. II. III. IV. V.

Defining Volition and Outlining its Relationship to Depression. . . . . .. DLPFC and Volition . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. DLPFC and Depression . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. ACC and Volition . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. ACC and Depression . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. v

74 76 79 81 83

vi

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VI. DLPFC and ACC in Volition: Synthesis and Future Directions for Depression Research . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . References . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . .

85 87

Functional Imaging Research in Schizophrenia H. Tost, G. Ende, M. Ruf, F. A. Henn, and A. Meyer-Lindenberg I. II. III. IV. V. VI. VII.

Psychomotor Disturbances . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Early Visual Processing Deficits . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Auditory System. . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Selective Attention . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Working Memory Dysfunction. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Antipsychotic Drug EVects . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Neuroimaging Genomics. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . References . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . .

97 98 100 102 104 106 107 112

Neuroimaging in Functional Somatic Syndromes Patrick B. Wood I. Introduction. . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . II. The Disorders . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . III. Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . References . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . .

119 121 151 155

Neuroimaging in Multiple Sclerosis Alireza Minagar, Eduardo Gonzalez-Toledo, James Pinkston, and Stephen L. Jaffe I. II. III. IV. V. VI.

Introduction. . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Clinical Manifestations. . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . The Pathology of MS as it Relates to Neuroimaging . . . . . . . . . . . . . . . . .. . . . MRI in Multiple Sclerosis . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . SPECT and PET Scanning . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . The Neuroimaging of Neuropsychological Dysfunction in Multiple Sclerosis . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . References . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . .

166 173 174 175 185 189 196

Stroke Roger E. Kelley and Eduardo Gonzalez-Toledo I. Introduction. . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . II. Brain Scan in Acute Stroke . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . .

204 206

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III. Clinical Utility of MRI Brain Scan in Acute Stroke. . . . . . . . . . . . . . . . . . . . . .. IV. MRI Versus CT Brain Scan in Acute Stroke . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. V. Prognostic and Outcome Information Provided by Routine and Functional Brain Scan. . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. VI. Vascular Imaging. . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. References . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . ..

vii 214 219 221 225 229

Functional MRI in Pediatric Neurobehavioral Disorders Michael Seyffert and F. Xavier Castellanos I. II. III. IV.

Introduction . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Normative Pediatric Functional Studies . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Pediatric fMR Studies of Psychopathology . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Summary . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. References . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . ..

240 242 254 277 278

Structural MRI and Brain Development Paul M. Thompson, Elizabeth R. Sowell, Nitin Gogtay, Jay N. Giedd, Christine N. Vidal, Kiralee M. Hayashi, Alex Leow, Rob Nicolson, Judith L. Rapoport, and Arthur W. Toga I. II. III. IV. V. VI. VII.

Introduction . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. MRI Scanning and Image Analysis . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Growth Curves for DiVerent Brain Regions. . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Cortical Mapping . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Time-Lapse Maps of Brain Change . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Mapping Brain Growth.. . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Conclusion . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. References . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . ..

286 288 293 298 307 313 316 317

Neuroimaging and Human Genetics Georg Winterer, Ahmad R. Hariri, David Goldman, and Daniel R. Weinberger I. II. III. IV. V. VI.

Introduction . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Historical Perspective. . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. General Issues . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Heritability . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Application of the Principles. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. Conclusions . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. References . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . ..

325 326 333 340 344 362 363

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Neuroreceptor Imaging in Psychiatry: Theory and Applications W. Gordon Frankle, Mark Slifstein, Peter S. Talbot, and Marc Laruelle I. II. III. IV.

Introduction. . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Conceptual Framework . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . Deriving Outcome Measures from Imaging Data . . . . . . . . . . . . . . . . . . . . .. . . . Psychiatric Disorders . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . References . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . .

386 386 391 397 420

Index ........................................................................................ Contents of Recent Volumes .....................................................

441 457