Animal Models of Human Cognitive Aging (Aging Medicine)

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Animal Models of Human Cognitive Aging

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Jennifer L. Bizon • Alisa G. Woods Editors

Animal Models of Human Cognitive Aging

Editors Jennifer L. Bizon Behavioral and Cellular Neuroscience Department of Psychology Texas A&M University College Station, TX 77843-4235 USA [email protected]

Alisa G. Woods, PhD Padure Biomedical Consulting Brooklyn, NY 11218 [email protected]

ISBN: 978-1-58829-996-3 e-ISBN: 978-1-59745-422-3 DOI: 10.1007/978-1-59745-422-3 Library of Congress Control Number: 2008940670 © Humana Press, a part of Springer Science+Business Media, LLC 2009 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Humana Press, c/o Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper springer.com

This book is dedicated to Jeanne Ryan, Ph.D., and Michela Gallagher, Ph.D.: true mentors and friends. It is also dedicated to our families, Costel Darie, Ph.D., Constantine Darie, Barry Setlow Ph.D., and Alexander and Anna Bizon-Setlow.

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Preface

Because of significant improvements in health style and medical science, an increasingly large number of individuals are living to advanced ages in the United States and other developed nations. According to 2004 U.S. Census Bureau estimates, the number of people over 65 is expected to rise from 35 to 72 million by 2030, resulting in the elderly comprising one fifth of the population within the next 20 years. Many elderly people will develop cognitive decline ranging from severe dementia to mild impairment, in part due to diseases such as Alzheimer’s disease and myocardial infarction, and in part as a consequence of the “normal” aging process. Importantly, however, cognitive loss associated with advanced age is not inevitable and, as such, modern society has placed new emphasis on “successful” cognitive aging. In addition to increasing the quality of life for elderly individuals, understanding the factors that impact cognitive aging and developing new treatments to combat age-related mnemonic decline also is advantageous from a societal standpoint. Health-care costs are substantial for those elderly who lose independence as a result of impaired cognition and can only be expected to rapidly escalate with the projected increase in life expectancy. Animal models that accurately mimic age-related cognitive loss in humans are essential tools for understanding cognitive changes associated with the aging process and are necessary to developing novel and putatively more effective treatments to combat loss of function. While animal models for understanding human normal biological processes and disease states have long been used in scientific and medical research, models of cognition and aging are relatively new in accordance with the recent increase in human life expectancy. With the completion of the human genome project and other technical advances, significant work in the field of aging has focused on understanding changes of biological phenomena at the molecular and cellular levels across the life span. Solid animal models of cognitive aging remain essential to the interpretation of consequences of such findings. Human research, though clearly most directly relevant, presents barriers with regard to manipulation and also with understanding the temporal sequence of events that may have led to cognitive deficits and abilities. As such, translational research related to improving human health at advanced ages depends upon modeling age-related cognitive decline in rodents and nonhuman primates. vii

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This book is designed to provide substantive background on some of the most widely used animal models in studies of cognition and aging. The goal is to present sufficient detail to aid neurobiological researchers in choosing and implementing appropriate animal models of cognitive aging, understanding the benefits and drawbacks of each. The authors also have related each of these cognitive models to human systems and circumstances. Berchtold and Cotman start the book by discussing normal and pathological processes of brain aging in humans, relating these processes to animal models. The authors emphasize the role that lifestyle choices, such as exercise, may play in successful aging. Since primates are phylogenetically most similar to humans, use of nonhuman primate models is essential to many aging studies and can be critical when investigating complex neocortical-based cognitive functions that are difficult to model in rodents. Lecreuse and Herndon provide a comprehensive overview of the many such models currently used to study cognitive aging, and Baxter provides a comprehensive review of frontal cortical deficits and executive function in primates as related to not only humans but also rodents. Indeed, in many instances rodents provide an excellent model system for human cognitive aging, in part due to the wealth of background data available regarding the neuroanatomy, physiology, and behavior of this species. LaSarge and Nicole detail similarities and differences among different rat models most often used to model medial temporal lobe dysfunction related to nonpathological aging. A separate chapter by Calhoun describes important and often overlooked differences between using rat versus mouse models, while LaFerla and colleagues review the use of transgenic modulation in mice to model Alzheimer’s and other age-related diseases. Sohrabji and Lewis continue an important discussion originally introduced by Berchtold and Cotman relating to sex differences in cognitive aging and the consequence of variations in hormones across the life span on cognition. Finally, Balci, Moore, and Brunner present a comprehensive review on the topic of “timing,” which is well documented as altered in aging and may be related to impaired decision-making and other deleterious cognitive outcomes at advanced ages. With the aging population steadily on the rise, studies focusing on cognitive decline both with normal aging and with age-related disease are a crucial focus of current research. New technologies, such as neuroimaging and molecular techniques, are helping to shed new light on how the brain changes across the life span, but animal models retain, and in many ways demand, an increasingly important role with respect to providing a necessary context by which to evaluate age-related neurobiological changes. It is in this spirit that we have put this book forth, as a collection of expert experience in animal models of cognitive aging. We thank the authors for their valuable contributions and hope that this volume will be of substantial value to neurobiological researchers in their understanding, selection, and implementation of appropriate animal models to aid in the translation of research from the bench to the betterment of human cognition well into advanced ages. College Station, Texas, USA Boston, Massachusetts, USA

Jennifer L. Bizon Alisa G. Woods

Contents

Normal and Pathological Aging: From Animals to Humans .................... Nicole C. Berchtold and Carl W. Cotman

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Nonhuman Primate Models of Cognitive Aging ......................................... Agnès Lacreuse and James G. Herndon

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Age-Related Effects on Prefrontal Cortical Systems: Translating Between Rodents, Nonhuman Primates, and Humans ......... Mark G. Baxter

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Comparison of Different Cognitive Rat Models of Human Aging ............................................................................................. Candi LaSarge and Michelle Nicolle

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Mouse Models of Cognitive Aging: Behavioral Tasks and Neural Substrates ..................................................... Michael E. Calhoun

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Impact of Ab and Tau on Cognition in Mouse Models of Alzheimer’s Disease ..................................................................... Maya A. Koike, Kristoffer Myczek, Kim N. Green, and Frank M. LaFerla Hormonal Influences on Brain Aging and Age-Related Cognitive Decline ..................................................................... Danielle K. Lewis and Farida Sohrabji

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Timing Deficits in Aging and Neuropathology ........................................... Fuat Balci, Warren H. Meck, Holly Moore, and Dani Brunner

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Index ................................................................................................................

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Contributors

Fuat Balci, Ph.D. PsychoGenics, Tarrytown, NY, USA Mark G. Baxter, Ph.D. Department of Experimental Psychology, Oxford University, Oxford, UK Jennifer L. Bizon, Ph.D. Behavioral and Cellular Neuroscience, Department of Psychology, Texas A&M University, College Station, TX, USA Nicole C. Berchtold, Ph.D. Institute for Brain Aging and Dementia, University of California, Irvine, CA, USA Dani Brunner, Ph.D. Biopsychology Department, Columbia University, New York and PsychoGenics, Tarrytown, NY, USA Michael E. Calhoun, Ph.D. Department of Cellular Neurology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany Carl W. Cotman, Ph.D. Institute for Brain Aging and Dementia and Department of Neurology, University of California, Irvine, CA, USA Kim N. Green, Ph.D. Department of Neurobiology and Behavior and Institute for Brain Aging and Dementia, University of California, Irvine, CA, USA James G. Herndon, Ph.D. Division of Neuroscience, Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA Maya A. Koike Department of Neurobiology and Behavior, and Institute for Brain Aging and Dementia, University of California, Irvine, CA, USA xi

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Agnès Lacreuse, Ph.D. Department of Psychology, University of Massachusetts, Amherst, MA, USA Frank M. LaFerla, Ph.D. Department of Neurobiology and Behavior, and Institute for Brain Aging and Dementia, University of California, Irvine, CA, USA Candi LaSarge Behavioral and Cellular Neuroscience, Department of Psychology, Texas A&M University, College Station, TX, USA Danielle K. Lewis Department of Neuroscience and Experimental Therapeutics, Texas A&M Health Science Center, College Station, TX 77843-1114 Warren H. Meck, Ph.D. Department of Psychology and Neuroscience and Center for Behavioral Neuroscience and Genomics, Duke University, Durham, NC, USA Holly Moore, Ph.D. Center for Neurobiology and Behavior in Psychiatry, Columbia University, New York, NY, USA Kristoffer Myczek Department of Neurobiology and Behavior and Institute for Brain Aging and Dementia, University of California, Irvine, CA, USA Michelle Nicolle, Ph.D. Internal Medicine/Section on Gerontology and Geriatric Medicine and Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC, USA Farida Sohrabji, Ph.D. Department of Neuroscience and Experimental Therapeutics, TAMU Health Science Center, College Station, TX, USA Alisa G. Woods, Ph.D. Padure Biomedical Consulting, Brooklyn, NY 11218

Normal and Pathological Aging: From Animals to Humans Nicole C. Berchtold* and Carl W. Cotman

Abstract While aging is associated with modest declines in certain aspects of cognitive function (memory, executive function, processing speed), many cognitive domains can remain relatively stable until late in life. In contrast to the mild decline observed in normal aging, pathological aging such as Alzheimer’s disease (AD) affects global cognitive function – impairing memory, language, thinking, and reasoning, and interferes substantially with daily living capacity. Changes in the structural integrity of the brain underlie the cognitive declines that occur in both aging and AD, however different brain structures are affected. In healthy aging, mild functional changes are predominantly detected in the prefrontal cortex and basal ganglia, while in AD, pathology initially accumulates and disrupts function in the medial temporal lobe (disrupting memory), progresses to cortical structures, and eventually globally impacts the brain. Cognitive decline with normal and pathological aging is mediated by a complex interaction of multiple factors that include genetic and nongenetic risk factors that determine the age of onset as well as the rate of decline. Importantly, the progression and decline can be prevented or slowed by certain lifestyle factors (exercise participation, stress management) and pharmaceutical interventions (statins, hormone replacement therapy for postmenopausal women). While most individuals will experience some degree of cognitive decline with aging, conversion to MCI or AD is not an inevitable consequence of aging. It is likely that additional strategies to promote healthy brain aging will be uncovered in the next years that will further contribute to successful brain aging and will help to maintain a high quality of living through the last decades of life. Keywords Alzheimer’s disease • memory • executive function • risk factors • exercise *N.C. Berchtold Institute for Brain Aging and Dementia, University of California, Irvine, CA, USA C.W. Cotman Institute for Brain Aging and Dementia and Department of Neurology, University of California, Irvine, CA, USA J.L. Bizon, A. Woods (eds.) Animal Models of Human Cognitive Aging, DOI: 10.1007/978-1-59745-422-3_1, © Humana Press, a part of Springer Science + Business Media, LLC 2009

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Introduction The structural integrity of the brain changes with age, and aging is associated with decline of some cognitive function, particularly executive function and mild memory decline. In contrast to what was once believed, dementia is not an inevitable consequence of aging. However aging is the main risk factor for Alzheimer’s disease (AD), the most prevalent cause of dementia in the elderly. AD is a progressive neurodegenerative disorder that results in increasing loss of cognitive function, starting typically with memory loss, and proceeding to affect thinking, language, and global cognition to a severity that interferes with the individual’s ability to function in daily life. The declines in cognitive function that occur in aging and AD are due to changes in the structural integrity of the brain; however the changes that occur in aging versus AD are vastly different. The most notable change in healthy aging is due to declines in the prefrontal cortex (PFC) and basal ganglia, which correspond to executive function deficits and may contribute to the mild memory difficulties characteristic in aging. Through different mechanisms of decline, AD is characterized early in the disease by prominent change in the medial temporal lobe (MTL) which disrupts memory function, as well as by changes in cortical networks (including posterior cingulate and retrosplenial cortex) that occur even before clinical symptoms are recognized. While there is brain volume loss in both aging and AD, human and animal studies indicate that the atrophy in aging is primarily due to synaptic loss rather than cell loss, while both neuronal and synaptic loss are prominent in AD. At the same time, compensatory strategies occur in the brain which counteract loss of function due to atrophy. For example, one compensation strategy relies on recruitment of more brain regions when challenged with a task. The degree of compensatory capacity has been called “cognitive reserve” and is emerging as an important factor determining who ages gracefully versus who undergoes significant cognitive decline. The capacity for cognitive reserve is determined by a complex interplay of aging with genetic risk factors and lifestyle factors that impact brain health and function, and that can initiate and propagate AD. In turn, there are lifestyle strategies that support brain health and help maintain cognitive reserve, that can prevent or delay age-related cognitive decline and even reduce the risk of AD. One lifestyle factor that is emerging as particularly significant for maintaining overall health and cognitive function with aging is exercise participation, based on both human epidemiological studies and basic science research using animal models.

“Healthy” Aging Human aging, particularly after 60 years, is associated with decline of certain aspects of cognitive function, even in healthy “normal” aging of the brain. The cognitive abilities that are particularly sensitive to age-related decline include the ability to encode new memories of events or facts, working memory capacity, executive function, and processing speed, described in Table 1 (for reviews see (1, 2)). Working memory is a form of short-term memory, and requires the simulta-

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Table 1 Definitions Working memory Also called short-term memory, working memory involves the simultaneous short-term maintenance and manipulation of information. A clinical test to assess working memory is the digit-span task. Executive function General cognitive processes involved in attention, planning, multitasking, and capacity for switching among several tasks and sources of information. Executive function is needed to perform complex, goal-oriented tasks. A task that uses executive function is the Stroop test (defined below). Stroop test In this test of executive function, the individual is presented with a series of names of colors that are written using different colors of ink. The color of ink used for the words does not match the name of the color itself. The individual must name the color of the ink rather than read the word, and the number of correct answers and the number of errors performed in 60 s is recorded. The active suppression of the urge to read the word itself requires executive function. Declarative memory A form of long-term memory for information and facts. This contrasts to non-declarative memory (procedural memory) like skill sets, that can operate outside of awareness. Function of the hippocampus and related medial-temporal lobes is critical for declarative memory. Medial temporal lobe (MTL) This neural system is important for encoding and consolidation of information, and is necessary for learning and memory function. In this chapter, the hippocampus is included in the definition of the MTL system. Frontal-striatal neural system The neural system central for executive function. It consists of the prefrontal cortex (PFC) and PFC connections to the striatum. The striatum is important for the motor response, while the PFC is important in the executive processing of the decisionmaking for a motor output.

neous short-term maintenance and manipulation of information. For example, working memory is usually tested with the digit-span task, in which an ordered series of digits is heard and then repeated, with increasing numbers of digits presented in subsequent rounds of testing. Executive function is a high-order cognitive capacity that requires the domains of attention, planning, multitasking, and ability to switch among several tasks and sources of information. Older adults free from dementia often show difficulties on tasks that stress attention and executive function, such as the Stroop test, which is described in Table 1 (for review see (1)). These cognitive domains (encoding, working memory, executive function, and processing speed) constitute the basic mechanisms of the cognitive information processing architecture, and are the functions that are most sensitive to decline with aging. However, the decline in these cognitive capacities is not linear across the life span, in that they remain essentially stable until approximately age 60. For example, longitudinal studies demonstrate that processing speed, episodic memory, spatial ability, and reasoning show small or nonexistent age-related changes from ages 20–60, but then tend to show an approximate linear decline after 60 years (3–5). Similarly, short-term memory such as for the digit-span task, show only slight decline across the adult life span with sharper decline appearing after age 70 (6). This suggests that cognitive function remains largely intact until about the sixth decade of life, at which point declines in function can be detected. While aging is associated with some decline in cognitive function, certain cognitive domains and memory forms are affected more than others. For example, memory

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capacity is particularly sensitive to age-related decline, however not all aspects of memory are equally vulnerable. Both short-term (working memory) and long-term memory show relative decline in aging, while in contrast, measures of vocabulary and semantic knowledge are stable until late in life (3, 7). Aspects of memory that remain relatively stable over aging include short-term memory, autobiographical memory, semantic knowledge, and emotional processing (2). Clearly, different aspects of cognitive function are differentially vulnerable to decline with aging, indicating that aging does not affect the brain in an indiscriminate way (for good overviews see (2, 8)).

The Brain’s Structural and Functional Integrity Changes with Aging Why do certain aspects of cognitive function decline with age? It is known that aging affects the structural and functional integrity of the brain, and these changes are thought to underlie the patterns of cognitive decline that occur with aging or AD. Recent research has provided insight into how particular neural systems are affected in aging, through both postmortem studies and in vivo imaging. Brain imaging studies have been key, allowing us to access the brain while individuals are still alive. Imaging studies have revealed volumetric changes with aging (atrophy) as well as the more subtle functional changes that occur, such as aging-related differences in brain activity when individuals are tasked with problem-solving. These studies have revealed structural changes at the gross anatomical and macroscopic levels, neurochemical changes, and functional changes in patterns of brain activation that occur in aging (9).

General Grey and White Matter Changes: Volume and Connections On a gross anatomical view of the brain, postmortem and in vivo studies reveal that aging-related changes occur in both grey matter (neurons) and white matter (axons) of the brain. Brains of older adults tend to have lower volumes of grey matter than do the brains of younger adults (10, 11). Interestingly, the decreased brain volume is not a result of cell loss, but rather from cell shrinkage and from reduced synaptic densities (10–13). In fact, neocortical synapse density appears to decline steadily across the life span (ages 20–100) (12). While all cortical and subcortical regions show some level of atrophy with age, the atrophy is not uniform across the brain. Specifically, some brain regions like the prefrontal cortex and striatal regions are particularly affected in normal aging while other regions such as the occipital cortex are largely unaffected (2, 11, 14). While grey matter has been the main focus of research on anatomical change, white matter fiber tracts are being increasingly studied to understand connectivity changes that occur between brain regions during the course of aging. MRI studies can assess the integrity of white matter fiber tracts in vivo, and have revealed that

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significant abnormalities in white matter tracts become apparent with aging. One study of nondemented older adults conservatively estimated that 65% of individuals over 75 years show white matter abnormalities (15). Diffuse change in white matter is most often reported, but small infarcts are also present and become more prevalent with increasing age (16, 17). White matter damage can arise from vascular compromise (e.g., small vessel disease), and hypertension is one of the strongest predictors of white matter damage (16). The largest alterations in white matter integrity during healthy aging tend to be in anterior regions of brain, in particular in the prefrontal cortex and the anterior corpus callosum (1) (which allows communication between frontal brain regions located in the left and right hemispheres). MRI studies have linked severity of white matter damage to cognition, indicating that damage to white matter is a likely candidate pathophysiological change that contributes to the declines in executive function and memory that occur in aging.

Executive Function in Humans Depends on the Frontal-striatal Neural System One neural system that is important in executive function is the frontal-striatal system. The executive function deficits that emerge with age are due to changes in the PFC and basal ganglia of the striatum, which are the brain regions most notably affected in healthy aging. Functional magnetic resonance imaging (MRI) and positron-emission tomography (PET) studies have repeatedly demonstrated that neural circuits involving subregions of the PFC are involved in executive control (8). Damage to the frontal region of the brain is associated with impaired executive function such as an inability to suppress interfering information, committing perseverative errors, and an inability to organize the contents of working memory (18). These deficits are similar to the executive function and working memory declines that occur with aging. Indeed, volumetric studies of brain structures reveal that the PFC region shows larger age-related changes than any other cortical region, most notably in the lateral and orbito-frontal PFC (19). In addition, connections between the frontal neural system and the basal ganglia, or striatum, are particularly important in executive function. The striatum is a brain region critical for voluntary movement output and provides a heavy dopaminergic innervation of the PFC. While volumetric declines of the striatum are relatively modest in healthy aging, large age-related changes are observed in the principal neurotransmitter system of the striatum, the dopaminergic system (20). Specifically, striatal dopaminergic function declines with aging, showing decreased dopamine receptor density and decreased availability of the dopamine transporter (20). By age 60, there is >50% decline in striatal uptake and clearance of a levodopa analogue; by comparison, in Parkinson’s disease, a movement disorder caused by loss of dopaminergic function, there are declines of >85% in striatal uptake and clearance of the levodopa analogue (20). Dopaminergic depletion reduces speed of processing, and thus would also affect working memory. In addition to dopamine, other neurotransmitter declines occur in the frontal-striatal region with age, particularly declines in noradrenaline and serotonin (21). Clearly, a number of changes occur in

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the anterior region of the brain with aging that correlate with executive dysfunction. However, it is not yet well understood how the multiple forms of deterioration (white matter lesions, neurotransmitter depletion, atrophy) relate to one another and what their roles are in declining executive function.

Memory Function in Humans Relies on the MTL Neural System Memory function is thought to rely critically on the hippocampus and the related MTL region (these together will be referred to as the MTL system). Interestingly, while aging is associated with significant declines in memory, the brain structures associated with encoding and memory show minimal age-related volumetric declines (for review (8)). In addition, the relatively small changes in hippocampal volume that are observed do not appear to be substantially related to memory function in healthy populations (8, 22). On the other hand, while only small changes in hippocampal volume are observed, functional imaging studies that measure regional cerebral blood flow reveal marked changes in functional activation of the MTL system during memory tasks. Notably, aged individuals show substantially decreased MTL system activation relative to younger controls (23, 24). This indicates that while the MTL system is not showing pathology in the form of structural change, this neural system is showing changed function with aging, a finding that is strongly supported in the human and animal literature (for animal reviews see (25, 26)).

Compensatory Strategies to Maintain Cognitive Performance Interestingly, functional imaging studies are revealing that the aged brain compensates for the declining efficacy of particular brain regions by recruitment of other brain regions to the task. For example, studies looking at the effects of aging on memory and executive function reveal that while older individuals often achieve the same level of performance on a task as the young cohort, the older individuals recruit more brain power to do the task (27). Recent studies indicate that increased activation in the PFC may partially compensate for functional declines in MTL memory systems. For example, some older adults could successfully encode new information through preserved activation in the PFC even as para-hippocampal activation declined (24). Other studies comparing brain activation patterns in old versus young individuals performing a memory task reveal less hippocampal activation and greater PFC activation in the old individuals compared to the young (2, 8, 28). Increased activation in PFC has been interpreted as plasticity that may partially compensate for functional declines in MTL memory systems (8, 29). While the PFC is not generally involved in MTL system-dependent memory in young individuals, the connectivity between the MTL and PFC clearly becomes very important for maintained memory performance in healthy aging, and the PFC appears to be able to compensate for declining MTL function.

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Why Are These Regions Susceptible to Decline with Age? Intensive research has focused on understanding what factors impact brain health and function, and why certain brain regions such as the frontal-striatal and MTL neural system are selectively vulnerable to decline with age. In recent years, it has become clear that many aspects of general health that tend to decline with age impact brain aging and cognitive function as well. Specifically, ailments that become more common with age, such as reduced cardiovascular capacity, hypertension, hyperglycemia, insulin insensitivity, and dyslipidemia, all can compromise brain function, and thus constitute peripheral risk factors for cognitive decline (30). For example, vascular insufficiencies (e.g., hypertension) cause infarct damage to white matter and the axon tracts that carry information between neurons and between brain regions. The frontal region of the brain, including the PFC, is particularly susceptible to infarct damage (1), potentially due to the high density of small capillaries in this brain region, which leads to declines in executive and memory function. In addition, because neurons are metabolically very active and rely on a steady supply of glucose and oxygen, stressors such as hypoxia, ischemia, and hypoglycemia, which interrupt this nutrient and oxygen supply, can rapidly compromise neuron health and function. Further, not all neurons are equally equipped to cope with such stressors or with other stressors that accumulate with age, such as toxic metabolic byproducts or environmental toxins. For example, hippocampal neurons and dopaminergic systems important in striatal function are particularly sensitive to such stressors, while cerebellar neurons are relatively immune (9). The idea that certain neural systems are more vulnerable to decline than others is an area of intense research focus. In addition, the concept that poor general health constitutes risk factors for cognitive decline is an emerging area of research, and will be particularly important for identifying intervention strategies to maintain brain health and function with aging. In particular, the progression of many of these age-related cognitive and anatomical changes can be slowed by certain lifestyle factors, such as exercise participation, stress management, and pharmaceutical interventions such as statins to lower cholesterol, or hormone replacement therapy for postmenopausal women. The roles of these peripheral risk factors for cognitive decline and how they can be counteracted by lifestyle choices to promote healthy aging of the brain is described in the last section of this chapter.

Animal Models of Aging Reveal that Similar Changes Occur in the Brain Across Species The trend of declining cognitive function with aging in humans is paralleled in animal models of aging, where age-associated memory impairment is observed in rodents, canines, and nonhuman primates, accompanying functional changes

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primarily in the hippocampus (13, 31). As in humans, the memory decline in animals is not accompanied by significant neuron loss, and appears to be primarily related to synaptic change, including loss of synapses and changes in synaptic efficacy (13, 31). For example, hippocampal long-term potentiation (LTP), a synaptic analogue of learning, is harder to induce in aged animals and decays faster after induction, paralleled by faster forgetting rates on memory tasks (for review on synaptic efficacy with aging see (32)). A number of excellent overviews of aging in animal models have been written in recent years, and the reader is directed to the following references for in-depth discussion of the literature (13, 25, 31, 33).

Summary Thus, a number of changes occur in the brains of both humans and animals during normal aging that ultimately impact cognitive performance. In humans, these effects do not generally emerge until after the sixth decade of life. The frontalstriatal neural system undergoes the most significant atrophy and change in function, corresponding to declines in executive function, namely attention, multitasking, decision-making, and goal-oriented behavior. These deficits are primarily due to degeneration of the PFC, while losses in the striatum largely impact speeds of processing and responding. The MTL on the other hand shows minimal structural change with age, but undergoes functional decline that impacts encoding and memory for new information. Concurrent with these changes, the brain undergoes functional reorganization that appears to compensate in part for the loss of functional power in each neural system. More of the brain is recruited in response to a task, including bilateral activation of brain structures where previously only activation of one hemisphere was required, as well as recruitment of additional brain structures to the task. In particular, connectivity between the MTL and PFC becomes increasingly important for memory function with age, allowing memory function to be maintained even though MTL memory systems decline. It is likely that without the compensatory reorganization, more cognitive deficits would be apparent in aging. Importantly, the cognitive deficits that occur in aging are mild and restricted to specific capacities, leaving much of global cognition unaffected. While frustrating, the degree of cognitive decline in normal aging does not significantly decrease quality of life or impair the ability to meet the demands of daily life. This contrasts sharply with the severe impairment in multiple cognitive domains that occurs in pathological aging such as in AD, as will be described in the next section. Finally, some individuals retain high cognitive performance even into the late decades of life, with minimal loss of cognitive ability (34). This capacity is of particular interest because it demonstrates that the neural systems of the brain have the biological capacity to retain high performance even late in life, and sets a precedent for the level of cognitive performance that can be targeted as a goal in healthy aging of the brain (Fig. 1).

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Healthy brain aging Age - associated memory impairment MCI AD

Age

Fig. 1 The cognitive continuum. With increasing age, some individuals show no loss in cognitive capacity, while most develop some mild age-related memory impairment that remains stable over time and does not convert to more severe impairment. In contrast, a tier of individuals (3–5% per year) will develop mild cognitive impairment (MCI) which is associated with memory dysfunction outside the normal range for a given age, but which is not associated with impairments in daily living or other features of AD. While some MCI conditions remain stable over time or may even revert to more normal function (~14–40%), approximately 10–15% of MCI cases will convert to AD and dementia every year (148)

Abnormal Aging: AD and MCI While some individuals age successfully with minimal complaint of cognitive decline even into the late decades of life, others develop neurodegenerative disorders that cause progressive loss of cognitive function that develops into global dementia. One such disorder is AD, a progressive degenerative disorder with an extended preclinical phase, which typically starts with memory loss, which then progresses to impair thinking, language, and global cognition. AD is the most common form of dementia, and becomes more prevalent with age. AD prevalence doubles every 5 years in people over age 60 years, increasing from 1% among people aged 60–64, to 40% in those 85 and older (35) (Fig. 2). A succinct description of the progression and devastating consequences of AD was recently provided by Walsh and Selkoe (36): This most common of late life dementias slowly robs individuals of their most human qualities – memory, insight, judgment, abstraction, and language. … The precise onset of clinical AD is difficult to discern by both patient and family. The earliest symptoms are often manifested as subtle intermittent deficits in remembering minor events of everyday life. … Early warning signs are often dismissed as normal aspects of aging. Usually new patients present to the physician in excellent neurological condition. … After many months of gradually progressive impairment of first declarative then also non-declarative memory, other cognitive symptoms appear and slowly advance. Over a further period of years or even a decade or more, a profound dementia develops that affects multiple cognitive and behavioral spheres and is often accompanied by extrapyramidal motor signs, slowed gait, and incontinence. Death usually comes by way of minor respiratory complications, such as aspiration or pneumonia, often in the middle of the night. ((36), pp. 181–182)

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Prevalence (%)

50 40 30 20 10 0 65–74

75–84

85+

Age

Fig. 2 Increasing prevalence of Alzheimer’s disease (AD) with age. In 2000, it was estimated that 13 million individuals were afflicted with this disease worldwide, with ~4.5 million in the U.S. alone. These numbers are estimated to triple by 2050 if no therapy is developed to slow or prevent the disease. The graph is based on data from the Alzheimer’s Association Web site in 2007 (http:// www.alz.org/alzheimers_disease_alzheimer_statistics.asp)

In recent years, a separate tier of individuals has been identified who present with memory complaints and poor performance on memory tasks, but lack other diagnostic criteria for AD (37). This disorder has been defined as mild cognitive impairment (MCI) and may represent a transitional stage between healthy aging and dementia. In a sample of normal adults, ~3–5% of normal adults will develop MCI each year (37). While some individuals with MCI remain cognitively stable for many years and do not show further decline, MCI is a substantial risk factor for future conversion to AD, with an annual progression rate of 10–15% (37).

Imaging Technology and Postmortem Studies Provide Clues to Brain Changes with AD and MCI In vivo imaging studies and postmortem studies have been particularly useful for revealing the global changes that occur during pathological aging of the brain and how this differs from normal aging. Imaging studies provide an important advantage over postmortem studies in which only the endpoint of the disease or aging process can be observed. In vivo imaging studies provide a window to observe the temporal changes that occur in the brain during the course of disease or aging while the individual is still alive. This is important, considering that the progression from

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healthy aging to frank AD occurs in a subtle and graded fashion often for a decade or longer, making it difficult to discern initial brain changes from late-stage changes based on observations in postmortem tissue. Further, imaging studies have been particularly useful for visualizing the changes that occur in MCI. Because most individuals with MCI go on to develop AD, postmortem tissue from MCI cases is rare, making it difficult to study the MCI disease state. On the other hand, postmortem studies provide superior resolution over imaging techniques with respect to studying the anatomical and cellular pathological changes in brain tissue. Such studies have been particularly important for advancing our understanding of the pathological changes that occur in AD. Importantly, they have paved the way to understanding the significance of genetic factors that cause AD, which in turn has led to the development of animal models of AD, which are critical for testing hypotheses on AD pathogenesis as well as for development of interventions to slow or prevent the disease. In the next section, the global changes in cognition and brain structure that occur in MCI and AD will be overviewed and compared with the changes that occur in normal aging. Subsequent sections will then overview the hallmark pathological features that occur in the AD brain, and briefly present the main current hypothesis on AD pathogenesis.

Global Brain Changes in MCI and AD Postmortem and in vivo studies have provided tremendous insight to the pattern of changes that occur in the brain with MCI or AD, and have demonstrated that these are very different from the changes that transpire during cognitively intact aging. There is tremendous atrophy that occurs in the AD brain, sufficient to result in gross reduction in brain size by the time that AD is identified (38). This atrophy is due to synaptic loss in combination with neuronal loss, particularly among subcortical neurons that project to the forebrain. In particular, the MTL is severely affected in MCI and AD, in contrast to the small volumetric changes associated with this region in healthy aging. In addition, in AD, there is a far more substantial atrophy in the MTL region than in the lateral PFC, which is the region that shows the most significant decline during healthy aging. The atrophy and functional decline of the MTL is directly responsible for the marked memory impairment characteristic of MCI and AD. Atrophy appears to initiate in the entorhinal cortex of the MTL, a critical relay for information coming into the hippocampus and for information subsequently going out of the hippocampus to the association cortices (39, 40). Atrophy of the entorhinal cortex is the main feature of MCI, in which declines in the entorhinal cortex are of a much greater magnitude than declines in hippocampal volume (2). Indeed, changes in the entorhinal cortex have been proposed as a potential diagnostic target for classifying those individuals who are most likely to convert to AD, as the largest declines in entorhinal cortex are observed in those who do progress to develop AD

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(1, 41). Pathological changes in the entorhinal cortex thus occur early in the disease, before clinical diagnosis of AD. As the disease progresses to AD, entorhinal atrophy spreads to affect the hippocampus, which eventually shows equal magnitude of decline to the entorhinal cortex (8). In addition to overt atrophy, functional imaging studies of regional cerebral blood flow reveal decreased MTL activation relative to healthy elderly controls, beyond the decreases that occur in healthy aging (8, 9). Clearly, the MTL neural system undergoes heavy deterioration early on in MCI and AD, accounting for memory impairments being the first complaint of functional decline. In addition to the overt degeneration of the MTL system, some atrophy also occurs in the PFC system in AD. However, the atrophy that occurs in the PFC is far less substantial than that that which occurs in the MTL (8, 42, 43). In contrast to aging, where predominant changes occur in the lateral PFC, the area of the PFC most affected in AD is the inferior PFC, and the deterioration of PFC does not occur early in the disease (44). White matter changes are consistent with the observation that the PFC alterations are more age-related than disease-related, at least early in the disease progression. Specifically, the agerelated alterations that occur in frontal white matter appear to be specific to healthy aging, because AD does not display further white matter atrophy beyond that observed in healthy controls. However, MCI and AD additionally show decreases in white matter integrity in more posterior regions of the brain as well as in temporal regions.

What Causes AD? The global changes in cognitive decline that occur in AD are accompanied by the presence of a number of cellular abnormalities in brain tissue. In particular, the hallmark pathologies of this disease that were first identified by Alois Alzheimer in 1906 are the abundant presence of “amyloid plaques” and “neuronal tangles” in distinct regions of the brain, particularly the hippocampus, MTL, and neocortex (45). Accompanying these pathologies is a profound loss of neurons and synapses, which results in increasing synaptic disconnection and impaired communication in the AD brain (36). Amyloid plaques form due to the accumulation of a small peptide, beta amyloid (Ab), into toxic oligomeric forms and into progressively insoluble plaques in the extracellular brain parenchyma (46). While plaques are extracellular deposits, tangles form inside neurons, as a result of abnormal phosphorylation of a microtubule-associated protein, called tau. The presence of these pathological features is diagnostic of AD, and much research has focused on elucidating their roles in the disease pathophysiology. Of long-standing debate has been the question of whether these hallmark plaques and tangles are the cause of the neuronal and cognitive loss in AD, or whether one or both of these pathological features are merely side effects of other processes that are the actual culprits in the disease.

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Amyloid Hypothesis for AD Accumulating evidence suggests that plaques and tangles are not bystander effects in AD and do contribute to disease progression. Most research has focused on the role of Ab as a key component linked to synaptic change and brain dysfunction (47). According to the amyloid hypothesis, the central mechanism underlying pathological processes in AD is the abnormal processing and accelerated deposition of particularly toxic forms of Ab, e.g., oligomeric Ab (48–50). In AD, Ab plaques are associated with dead/dying neurons, neurofibrillar tangles, and other pathology such as inflammation and oxidative damage. It is now clear that Ab accumulation triggers molecular events that compromise neuronal health, brain plasticity, and cognitive function, suggesting a causal role for Ab in AD pathogenesis. In addition, a role for Ab in AD pathogenesis is supported by genetic studies based on inheritable forms of AD (47). Specifically, autosomal dominant mutations in three genes have been identified that lead to familial AD: the gene for amyloid precursor protein (APP) from which Ab is derived, and the presenilin 1 and 2 genes, which encode proteins involved in the processing of APP. More than 160 mutations in APP and the presenilins have been described so far. Remarkably, all these mutations share a common biochemical pathway that converges on altered production of Ab, leading to a relative overproduction of neurotoxic Ab species that eventually result in neuronal cell death and dementia. These genetic mutations drive excessive and faster AB deposition, accelerating disease onset in these families such that the disease begins as early as the third and fourth decades of life (47). Interestingly, some Ab also accumulates normally with age, though at a much slower rate than in AD, and generally less toxic forms are produced in the normal brain. However, the accumulation of Ab in the healthy brain may be one factor that contributes to compromise functional integrity and health of neurons and makes them more susceptible to additional insults that the brain encounters during aging (51, 52). Much insight has been gained to the harmful effects of Ab accumulation on neuronal health and brain function by studying transgenic mouse models of AD that contain the genetic mutations associated with familial AD in humans. These animals accumulate Ab with age, develop plaques, show deficits in synaptic plasticity, and have impairments in various forms of learning (for review see (53)). The accumulation of soluble Ab (in particular oligomeric Ab), associated with synaptic change similar to that observed in human AD brains, correlates with compromised synaptic plasticity and loss of synapses, and impairs learning and memory in transgenic mouse models of AD (50, 54–57). In addition, studies in transgenic animals have revealed that Ab accumulation precedes the development of neurofibrillar tangles, the other pathological hallmark of AD, and that Ab oligomers may play a role in the induction of tau pathology (58). Encouragingly, pharmacological interventions that reduce Ab have been tested in these mice, and have resulted in improvements in cognitive function and synaptic plasticity (59). These data suggest that Ab is causally linked to the neuronal atrophy, synaptic loss, and cognitive impairments present in transgenic mouse models of AD, and further suggest that developing interventions to reduce Ab is an important therapeutic goal for AD (for reviews see (52, 60)).

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Tau Hypothesis In addition to the accumulation of Ab, another hallmark pathological feature of the AD brain is the development of intracellular neurofibrillar tangles (NFTs) composed of hyperphosphorylated tau protein. Tau is a microtubule-binding protein that normally acts to stabilize microtubules, cellular structures that are essential for axonal transport. Because tau phosphorylation negatively regulates the binding of tau to microtubules, the abnormal hyperphosphorylated state of tau in AD destabilizes microtubules, leading to disrupted axonal transport and compromised viability of the affected neurons (61, 62). The tau hypothesis of AD neurodegeneration thus predicts that tau hyperphosphorylation and development of NFTs cripples neuronal function, health, and communication, making NFTs a critical variable in the onset and/or progression of AD. While there are no tau mutations yet identified that are associated with AD, tau mutations are associated with other hereditary neurodegenerative disorders that cause dementia (60), indicating that improper tau function likely contributes to impaired brain health and function. Recent studies in transgenic mouse models of AD suggest that accumulation of Ab can drive tau pathology (58), and interestingly, individuals with MCI appear to have NFTs in the temporal lobes (63). Tau accumulation correlates with poorer memory in MCI, suggesting that tau pathology is a component of the disease pathogenesis. Thus, tau hyperphosphorylation and microtubule destabilization appear to play a role in dementia, and disease-modifying therapies that target stabilization of microtubules are intervention strategies that are currently being pursued for AD and other neurodegenerative disorders (60).

Overview The amyloid cascade hypothesis and tau hypothesis of AD pathogenesis are the two most widely pursued explanations for AD pathogenesis. However, additional pathogenic processes that could not be covered in this chapter have also been proposed to be instrumental to AD, and are discussed in other reviews (the reader is referred to (36, 60, 64)). While knowledge of AD pathophysiology remains far from complete and there is no universally accepted hypothesis for AD initiation or pathogenesis, it is agreed that the hallmark pathological features of AD are accumulation of Ab, NFTs, neuron loss, and synaptic disconnection. One well-accepted hypothesis for AD pathogenesis proposes that accumulation of toxic forms of Ab is an initiating pathological feature that drives later pathology such as tau hyperphosphorylation and formation of NFTS. NFTs, in turn, interfere with intracellular transport in neurons, impairing neuronal function and health, eventually leading to synaptic disconnection of neurons and neuron death. In parallel, there is growing evidence that a variety of factors can accelerate Ab deposition, including insufficient vascular perfusion, inflammation, and oxidative damage, among others, which act as amplifiers of Ab toxicity and of AD pathogenesis.

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Factors that Place the Brain at Risk for AD Familial and early-onset forms of AD have been instrumental in identifying APP, presenilins, and accelerated AB accumulation as principal etiologic agents in AD. However, hereditary early-onset forms of AD (familial AD) only account for ~ 5% of AD cases (65, 66), while the vast majority of AD cases are late onset by nature, occurring after age 65, and are apparently “sporadic,” e.g., without overt familial link. However, there is a growing body of evidence that these “sporadic” nonfamilial forms are also significantly influenced by genetic risk factors that have complex interactions with each other as well as with nongenetic factors. The main risk factors known to date for “sporadic” AD and impaired cognitive health are outlined below, followed by what is known about lifestyle strategies that can promote healthy aging of the brain (Fig. 3).

Cognitive health and function Growth factor cascades (BDNF, IGF…)

Estrogen

CNS pathology

Peripheral risk factors • Hypertension • High cholesterol • Diabetes • Vascular insufficiency

Chronic stress

EXERCISE

• AB accumulation • Tau, NFTs • Inflammation • Oxidative damage

• AD genetics • ApoE4 • Age

Fig. 3 Cognitive health and function are impacted by lifestyle and genetic factors. Many general health conditions constitute peripheral risk factors for cognitive decline even in normal aging, including hypertension, high cholesterol, diabetes, and vascular insufficiency. These conditions are exacerbated by chronic stress and exposure to stress-related hormones. Other factors that impact cognitive decline are genes that drive familial Alzheimer’s disease (AD), and age and ApoE4 genotype that constitute risk factors for developing sporadic AD. These factors drive accumulation of pathology in the central nervous system (CNS), including accumulation of betaamyloid, neuronal fibrillary tangles, inflammation and oxidative damage, all of which impair brain health and function. In contrast, exercise participation is a central factor that can foster cognitive health and counteract age-related cognitive decline. Exercise can indirectly improve brain function by counteracting many of the risk factors for cognitive decline, and can additionally directly modulate cellular and molecular pathways in the brain such as growth factor signaling cascades that support brain health and function. Finally, for women, estrogen levels play an important role in brain health with aging, and estrogen replacement after menopause can have beneficial effects during a certain time frame. Some benefits of estrogen replacement may be mediated by the stimulatory effect of estrogen on physical activity.

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Age Is the Main Risk Factor for AD Age is the main known risk factor for sporadic AD, and the prevalence of AD steadily rises with age (Fig. 2). Accompanying the increased average life span that has occurred in the last century, there has been a large increase in the number of individuals with AD. In 2000, it was estimated that 13 million individuals were afflicted with this disease worldwide, with ~4.5 million in the U.S. alone (67). Alarmingly, these numbers are estimated to triple by 2050 if no therapy is developed to slow or prevent the disease (67). The heavy emotional costs and financial burden of this disease have intensified research efforts to identify risk factors for AD, particularly ones that may be amenable to treatment intervention, as well as to identify factors that can prevent or slow cognitive decline in pathological as well as normal aging of the brain. ApoE4 Genotype and Cholesterol Increase Risk of AD In addition to age, another important risk factor for late-onset AD is related to the apolipoprotein E (ApoE) gene, which is currently the only genetic risk factor identified for sporadic AD. The ApoE gene has three naturally occurring allelic variants, named E2, E3, and E4. Possession of the E4 allele increases the risk of AD, and lowers the age of disease onset in a gene–dose-dependent manner by as much as 7–9 years per allele (68–70). The risks of developing AD are threefold increased with possession of one E4 allele, and eightfold greater in individuals possessing two E4 alleles. While 40% of all AD patients have at least one E4 allele, possession of the E4 genotype is neither necessary nor sufficient for developing AD (71). How ApoE4 is involved in AD pathogenesis is currently under intense debate. Some clues are emerging from the role of ApoE in cholesterol transport. While ApoE and cholesterol previously have been viewed largely as a topic for cardiovascular research, recent results demonstrate an important role for cholesterol in AD (71–73). A decisive role for lipoprotein and cholesterol metabolism in AD was recently established by the finding that statins, pharmaceuticals which lower cholesterol levels, delay the onset of AD (72). While it is not yet clear how lowering cholesterol is involved in AD onset, there is evidence that one mechanism may be via decreasing Ab accumulation in the brain (74). Another possible mechanism is that decreasing hypercholesterolemia provides benefits to vascular function and overall health, which are emerging as important factors in brain health and function, as described in the next section. General Health Impacts the Brain – Peripheral Risk Factors for Cognitive Decline In recent years, it has become clear that many aspects of general health that tend to decline with age also impact brain aging and cognitive function. Specifically, conditions that become more common with age such as reduced cardiovascular capacity, hypertension, hyperglycemia, insulin insensitivity, and dyslipidemia can all compromise brain function and constitute peripheral risk factors for cognitive decline (30, 75–77). In addition, a common feature coexisting in many of these

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conditions is inflammation, which feeds back to exacerbate these peripheral risk factors, and can increase susceptibility of the brain to functional decline (78, 79). For example, serum inflammation markers such as interleukin 6 and C-reactive protein, which are often increased with aging, increase the risk for cognitive impairment (80). Supporting the idea that inflammation is a contributing feature to cognitive decline, evidence from mouse models suggests that inflammation may trigger or promote AD (81), while multiple epidemiological studies have reported an association between the use of nonsteroidal anti-inflammatory drugs (NSAIDs) and a reduced risk of AD. For example, a recent meta-analysis of the epidemiological literature revealed that use of non-aspirin NSAIDS was associated with a 26% risk reduction of AD, with further reduction if NSAID use was sustained for at least 2 years (82). Inflammation is thus emerging as a potentially key component of the various peripheral risk factors that contribute to cognitive decline.

Lifestyle Strategies to Slow Cognitive Decline While peripheral risk factors that negatively impact brain health and function tend to accumulate with age, the progression of age-related cognitive and physiological declines can be prevented or slowed by certain lifestyle factors. These include exercise participation and stress management as well as some pharmaceutical interventions, such as statins to lower cholesterol, anti-inflammatory drugs (NSAIDS), and short-term hormone-replacement therapy (HRT) for postmenopausal women. Many of these factors converge on vascular health, and it has been suggested that maintaining vascular health and treating vascular risk factors are potentially the most important variables to consider for successful cognitive ageing and prevention of cognitive decline (77). One of the most effective ways to maintain vascular health is exercise participation, which is also emerging as an important modulator of brain health. Interestingly, exercise is uniquely positioned to improve brain health and function by both indirect and direct mechanisms. For example, exercise can indirectly improve brain health and neuronal resilience by reducing the peripheral risk factors for cognitive decline, and can in parallel directly modulate cellular and molecular pathways in the brain that support brain health and cognitive function (83, 84). In addition, exercise can modulate the efficacy of other lifestyle factors such as hormone replacement therapy for women, and can provide effective stress management (Fig. 3).

Hormone Replacement Therapy (HRT) Interacts with Exercise to Modulate Brain Health For women, an inevitable component of aging is menopause, which is associated with a drastic decline in estrogen accompanied by changes in certain cognitive functions (85). Estrogen affects multiple aspects of general health and directly impacts the brain (86–90), all of which can contribute to changes in cognitive function with menopause (for review on estrogen, menopause, and the aging brain, see (85)). For example, animal

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studies in rodents demonstrate that estrogen replacement is neuroprotective, stimulates neurogenesis in the hippocampus, and increases synaptic plasticity (86, 91, 92). Additionally, studies in nonhuman primates further reveal that estrogen replacement promotes spine growth in the frontal cortex and hippocampus, has positive effects on cognitive behavior, and plays a key role in the neurobiology of aging (85). Reports of these beneficial effects of estrogen on the brain, in combination with pursuing relief from menopausal symptoms such as hot flashes, increased incidence of depression, and difficulties in attention and other cognitive abilities have encouraged many women to undertake hormone replacement therapy (HRT) at menopause. However, general enthusiasm for HRT has been tempered in recent years by the Women’s Health Initiative Memory study (WHIMS) study (93) released in 2003 indicating that HRT did not improve cognitive function and “increased the risk for probable dementia in postmenopausal women aged 65 or older” (94). However, more recent studies suggest that the conclusions from the WHI study on HRT effects on cognitive function are contentious, because a number of factors were not taken into consideration including duration of HRT, age at HRT initiation, and type of HRT (for discussion, see (95)). For example, meta-analysis of the literature suggests that HRT replacement is beneficial for cognitive health and function but only with short-term use (<10 years), while long-term HRT use (>16 years) appears to negatively affect both cognition and age-related declines in brain volume (95). Importantly, estrogen status interacts with other interventions such as exercise to modulate brain health and function. When exercise participation and estrogen status are both taken into account, it appears that the combination of exercise and HRT can offset negative effects of long-term HRT use, and augment the benefits gained from short-term HRT use (95, 96). Interestingly, the presence of estrogen itself is well known to increase physical activity in animal studies (97, 98), suggesting one way that estrogen may influence brain health. Animal studies additionally support the idea that exercise benefits in females may depend on estrogen status, and are providing insight to molecular mechanisms that may be important for these benefits (99). Thus, for women, HRT appears to be a strategy that can be effective in combating age- and menopause-related cognitive decline. However, the relative effectiveness of HRT will depend on a number of variables, including the duration of HRT use, age at HRT initiation, and type of HRT. Further, long-term HRT efficacy may depend on the interaction with a number of other lifestyle factors, one of which being exercise participation. The interaction of HRT with other lifestyle factors represents a fertile area for future research, and will be important to pursue in order to define the extent of the benefits to cognitive function that can be derived from HRT.

Exercise Improves Brain Function by Improving General Health One of the best studied interventions for improving overall health is exercise. It is well known that exercise has broad-reaching health benefits, including improved cardiovascular health, lipid/cholesterol balance, energy metabolism, glucose utilization,

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insulin sensitivity, immune function, and reduced weight (100–104). All these conditions tend to worsen with age, a trend that has negative implications not only for overall health, but also for brain health and function. Reduction of these conditions by exercise is one mechanism by which exercise can benefit brain function. Indeed, beneficial effects of exercise on brain function and health are well documented in both human and animal studies. In humans, robust effects of exercise have been most clearly demonstrated in aging populations, where exercise enhances learning and memory, improves executive function, decreases susceptibility to depression, counteracts age- and disease-related mental decline, prevents age-related declines in cerebral perfusion, and protects against age-related atrophy in brain areas critical for higher cognitive processes (for review, see (96)). Functional imaging studies demonstrate that aerobically trained older adults showed superior performance on tasks involving attentional control, in conjunction with increased activity in the frontal and parietal regions of the brain (areas important in efficient attentional control processes) and reduced activity in the dorsal anterior cingulate cortex (105). Interestingly, meta-analysis of the exercise intervention literature reveals that the largest fitness training benefits on cognition were observed when the intervention studies included women, suggesting that exercise interacts with estrogen hormone status in women (95). Further, clinical studies (retrospective, cross-sectional, and intervention studies) suggest that physical activity participation delays onset and reduces the risk for AD, as well as other neurodegenerative diseases such as Parkinson’s disease (PD), and can even slow functional decline after neurodegeneration has begun (106–113).

Exercise Protects from Negative Effects of Stress on the Body and the Brain As one general mechanism that may mediate benefits of exercise, exercise can counteract negative effects of stress which are well established to impact both the body and the brain. For example, chronic stress exacerbates the various peripheral risk factors for cognitive decline, including hypertension, hyperglycemia, vascular insufficiency, dyslipidemia, and insulin insensitivity (diabetes) (114, 115). In addition, chronic stress takes a toll on brain function by directly compromising neuronal health and function, particularly in the hippocampus and the PFC, leading to impaired hippocampal-PFC function and synaptic plasticity (116, 117) (for reviews, see (118–120)). These effects lead to declines in memory and executive function, and can lead to depression, which itself also impairs cognitive function. One of the most effective stress-management strategies is exercise participation, which helps increase resistance to stress-system dysregulation, and reduces stressassociated comorbidity (for review, see (115)). In addition, exercise protects against stress-related cognitive dysfunction and damage to certain brain regions. For example, exercise prevents behavioral deficits resulting from chronic stress, including increased resistance to depression, demonstrated in both humans and animals (121–126).

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The protection that exercise can afford the body and the brain protection from stressrelated injury and decline adds to the idea that exercise is a key intervention strategy to promote brain health and function. Mechanisms in the brain that are thought to underlie some of these protective effects are discussed in the next section.

Direct Effects of Exercise on the Brain – Molecular and Cellular Biology Increasing evidence demonstrates that exercise directly modulates cellular and molecular pathways in the brain that affect brain health and cognitive function. Animal studies are providing insight to the mechanisms of how exercise affects brain health and function. In animals, as in humans, exercise facilitates learning and memory, an effect that is measurable in both young and aged animals (33, 127, 128) and decreases susceptibility to developing learned helplessness, an animal model of depression (121, 122, 126). In addition, exercise provides a number of other measurable benefits to the brain, including increased resistance to brain injury due to stroke or neurotoxins, enhanced synaptic plasticity, increased neurogenesis in the hippocampus, and stimulation of angiogenesis (vascular growth and complexity) in the brain (33, 129–133). Importantly, many of these benefits are documented to occur in the hippocampus (for review, see (83, 96)), the brain structure that shows functional decline in aging and that undergoes dramatic atrophy and decline in MCI and AD. What mechanisms might drive these varied benefits to brain health and function? As one possibility, it is known that exercise induces several classes of growth factors (134–139) that can impact all the endpoints improved with exercise, such as providing neuroprotection, enhancing plasticity, stimulating neurogenesis, and promoting angiogenesis. In fact, there is growing evidence that growth factors, including brain-derived neurotrophic factor (BDNF), insulin-like growth factor-1 (IGF-1), and vascular endothelial-derived growth factor (VEGF) are key mediators of these exercise-driven brain responses, and may represent a hub through which exercise can drive these beneficial effects in the brain. In particular, induction of BDNF signaling with exercise is emerging as a key central molecule for many of the beneficial effects of exercise, especially the benefits of exercise on learning and resistance to negative effects of stress (140–147). For further reading on mechanisms of exercise effects on the brain, and the growth factor hypothesis, the reader is directed to the recent reviews by (83, 84, 96).

Summary In summary, cognitive decline with aging is mediated by a complex interaction of multiple factors that include genetic and nongenetic risk factors that determine the age of onset of decline as well as the rate of decline. While most

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individuals will experience some degree of cognitive decline with aging, conversion to MCI or AD is not an inevitable consequence of aging. The cognitive capacities that are affected in pathological aging are more global than the mild decline observed in normal aging, which is primarily accompanied by mild short-term memory loss, general slowing, and declines in executive function. MCI is defined by AD-like memory impairment in the absence of other symptoms of AD, such as impairments in language, thinking, reasoning, and global cognitive capacity that interfere substantially with daily living capacity. MCI is considered by some to be a prodromal state of AD, but while a substantial proportion of MCI conditions do eventually develop AD, not all MCI convert to AD. The progression of normal and pathological cognitive aging is not immutable, but rather is impacted by general health. Importantly, many of the health factors that can accelerate cognitive decline (e.g., hypertension, hypercholesterolemia, insulin-insensitivity, chronic stress) can be directly improved by exercise participation, which is becoming well established as an effective strategy to slow cognitive decline with aging. It is likely that additional strategies to promote healthy brain aging will be uncovered in the next years, that will further contribute to successful brain aging and will help to maintain a high quality of living through the last decades of life.

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Nonhuman Primate Models of Cognitive Aging Agnès Lacreuse* and James G. Herndon

Abstract Nonhuman primates are indispensable for the study of aging processes. Like other animals, they permit us to observe the effects of age in the absence of the confounds inherent in studies of human beings. Additionally, because they are phylogenetically close to humans and possess certain uniquely primate morphological, endocrine, behavioral, and cognitive traits, they can provide data uniquely relevant to human aging. Among nonhuman primates, the rhesus monkey is by far the most widely studied in the context of aging, as verified in the large number of reviews that have summarized the studies on this species. To date, however, there is no published overview of the many other species of nonhuman primates in which age-related changes have been studied. This chapter is intended to fill that gap. Thus, we discuss results from a wide variety of prosimian, monkey, and ape species, ranging from the mouse lemur to the great apes. We include species about which a great deal is known as well as those, such as the gorilla and chimpanzee, on which only one or two studies have been conducted. For each species or group of species, we describe what is known about age-related changes in cognition, in the brain, and in patterns of reproductive senescence. We conclude that, although studies on the rhesus monkeys have provided the greatest depth of knowledge about cognitive aging processes, the many other primate species, with their wide variety of reproductive, morphological, and behavioral adaptations, can shed new light on the factors underlying age-related cognitive changes in our own species. Keywords Brain aging • cognition • memory • estrogen • menopause

*A. Lacreuse Department of Psychology, University of Massachusetts, Amherst, MA, USA J.G. Herndon Division of Neuroscience, Yerkes National Primate Research Center, Emory University, Atlanta GA 30322

J.L. Bizon, A. Woods (eds.) Animal Models of Human Cognitive Aging, DOI: 10.1007/978-1-59745-422-3_2, © Humana Press, a part of Springer Science + Business Media, LLC 2009

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Introduction Preventing or slowing age-related cognitive decline remains one of the greatest challenges of our times. As this chapter is being written, people over 65 years make up about 13% of the US population. In this group, approximately 13% are estimated to suffer from mild to severe memory impairment, a percentage that increases to 32% in the 85+ population. The proportion of aged persons is expected to increase in the years ahead, with old people making up as much as 20% of the population in the 2050 projections and cognitive impairment reaching 16% of the 65+ group and 67% of the 85+ group (Federal Interagency Forum on AgingRelated Statistics. Older Americans Update 2006: Key Indicators of Well-Being). As our population ages, the undesirable effects, both societal and personal, of agerelated decline in cognitive function can be expected to increase as well. Thus, there is a pressing need to develop strategies aimed at promoting cognitive health in the later years of life. Animal models in which cognitive aging can be studied without confounds inherent in human studies are indispensable to accomplish this goal. As can be gleaned from several chapters of this book, a great deal of our knowledge concerning the mechanisms of cognitive and brain aging has been provided by rodent studies. Because of their phylogenetic proximity to humans, however, nonhuman primates offer some advantages over other species as models for human age-related cognitive decline. In principle, species phylogenetically closest to humans, in particular the genus Pan, with which we shared a common ancestor about 6 million years ago, constitute the most relevant models for human cognitive aging. Yet, practical considerations have led to the selection of more distantly related primate species as primary models. Thus, species of the genus Macaca, with which we shared a common ancestor about 25 million years ago, have provided the bulk of the data concerning cognitive and brain aging in nonhuman primates. An overview of these findings will be presented in the first section of this chapter. The use of macaque species as models, however, presents a number of drawbacks. Alternative nonhuman primate models are being explored in an effort to circumvent some of these problems and/ or to focus on one aspect of cognitive aging for which a particular species might be well suited. Among these alternative models, a few medium- and small-sized (<500 g) primate species, which exhibit some features of human cognitive and brain senescence, have great potential. The advantages and disadvantages of using such primate models will be discussed in subsequent sections. Finally, the last part of this chapter presents findings related to cognitive aging in the great apes. We will argue that the comparison of cognitive and brain aging in apes and humans should elucidate unique characteristics of the aging phenotype of the human, and provide insight into the origins of human-specific neurodegenerative diseases such as Alzheimer’s disease (AD). Throughout this chapter, we will emphasize what can be gained from species with distinct life histories and adaptations. In addition, we will highlight one aspect of cognitive aging that has received intense scrutiny in recent decades concerning

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possible interactions between age-related changes in the endocrine milieu and changes in cognitive functioning. In particular, the animal and human literature strongly suggest that sex and adrenal steroids are key modulators of cognitive and brain aging. Research on nonhuman primate models should help to elucidate the mechanisms by which these hormones influence age-related cognitive change. Thus, when possible, we will present data that reveal the effects of gonadal and adrenal steroids on patterns of cognitive and brain aging in each species of interest.

The Aged Macaque as a Model for Human Cognitive Aging The macaque model of human cognitive aging has been described in many review articles (1–10). In this section, we provide a brief overview of the findings relevant to human cognitive aging, before focusing on some of the endocrine factors that modulate cognitive and brain aging in macaque species.

Cognitive Aging Studies investigating cognitive aging in the genus Macaca have primarily used the species Macaca mulatta (rhesus monkey), to a lesser extent M. fascicularis (cynomolgus monkey), and more rarely M. fuscata (Japanese macaque; e.g. (11, 12)), M. nemestrina (pig-tailed macaque; e.g. (13)), and M. radiata (bonnet macaque; (14, 15)). Since the rhesus monkey is by far the most prominently used model for studying human cognitive aging, most of the data reported here concern this species. Rhesus monkeys weigh from 5 to 10 kg in adulthood and have a life span that is about one third of that of humans. Their adult life extends from 4 to 40 years, with very few individuals surviving past 30 years (16). On this basis, monkeys over the age of 20 are generally considered aged, monkeys between 15 and 19 as middle-aged, and monkeys younger than 15 as young adults. One advantage of nonhuman primates like macaques is that they can be tested in settings and tasks that are very similar to those given to humans. One apparatus that has been used for several decades as a main tool to probe a variety of cognitive domains in monkeys is the Wisconsin General Testing Apparatus (WGTA; (17), which allows the presentation of three-dimensional objects and their delayed recognition. In more recent years, computerized systems with either a joystick or touch screen allowing enhanced flexibility of stimuli presentation have also been developed. Using either the WGTA or computerized systems, several research groups have found striking similarities between the aging phenotype of macaques and humans, with most aged monkeys showing impairments in visual recognition memory, spatial memory, executive function, and attention (1–3, 6–8, 18–23). As in humans, some of these deficits develop during middle age, with impairments in spatial memory (21) and executive function (24) being the earliest cognitive domains affected. These cognitive deficits are not uniform, however, as some individuals demonstrate little cognitive impairment, while others are severely affected (21). One of

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the challenges of cognitive aging research is to elucidate the neurobiological and physiological bases for these differences.

Changes in Brain Anatomy and Function Rhesus monkeys do not develop AD, and, therefore, provide a model in which aging can be studied in the absence of concomitant age-related dementing illnesses. The recently completed sequence of the M. mulatta genome may help identify the molecular bases for the differential vulnerability to neuropathology in humans and other primates. Indeed, a unique characteristic of aged human brains is that they accumulate extracellular, aggregated b-amyloid peptide (Ab) in the form of plaques (senile plaques), as well as neurofibrillary tangles, which consist of intracellular fibrils made of aberrantly polymerized, hyperphosphorylated tau, a microtubule protein. Ab and tau accumulation are greatly increased in AD and are a hallmark of this disease. Interestingly, in contrast to humans, rhesus monkeys do not develop neurofibrillary tangles (25); nor do they show significant declines in brain weight (26) or hippocampal atrophy with aging (27). Such findings suggest that age-related cognitive dysfunction is not dependent upon a shrinking brain and that agerelated hippocampal atrophy might reflect pathology rather than normal aging. Indeed, normal aged humans show only small declines in hippocampal volume, while hippocampal regions are severely affected in pathological processes (28). On the other hand, many of the senescent changes that are typical of human aging populations are present in the rhesus monkey (for reviews see (25, 29)), including overall reduction of gray matter volume (30), decline in specific regions such as the striatum (31, 32), amyloid deposition in several brain structures (33, 34), myoinositol elevations suggestive of myelin alterations (35), increased activation of microglia (36), severe axonal pathology (37, 38), loss of cholinergic fibers in the hippocampus (39), alterations of all neurotransmitter systems (40–44), and minimal changes in total neuron numbers in the cerebral cortex (25). Functional differences have also been noted between young and aged monkeys, such as reduction of presynaptic cholinergic function in the striatum (44), increased dopamine turnover and clearance in the striatum (45), and reduction of resting state cerebral metabolic rate for glucose (46–48) and cerebral blood flow (48) in several brain regions. These marked structural and functional differences between young and old monkeys suggest possible bases for cognitive impairment, yet relatively few studies have been able to assess the relevance of these age-related brain changes for the development of cognitive impairment. Age-related brain changes that have been reported to correlate with specific cognitive deficits include thinning of layer I in area 46 of the prefrontal cortex and widespread disruption of myelinated axons (49), alterations of white matter integrity connecting frontal regions to other forebrain regions (50), loss of neurons in specific subcortical regions (51, 52), and reduction of monoamine receptor binding in the prefrontal cortex (43). In contrast,

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reduction in hippocampal cholinergic innervation, which has been proposed to play a major role in cognitive decline, was not related to performance on a task dependent on medial temporal structures (39). Similarly, no relationship could be found between amyloid plaque content and cognitive dysfunction in aged macaques (34), supporting the view that concomitant tau pathology is necessary for the development of the cognitive deficits characteristic of AD. Importantly, most studies with aged monkeys reported large individual variations both at the neurobiological and cognitive levels. The source of this variability is not currently understood, but may be related to factors that have been largely ignored in nonhuman primate aging research, such as biological sex or endocrine influences. New evidence indicates that age-related changes in the endocrine milieu have important consequences for the aged brain. In the following sections, we examine how age-related alteration of sex steroids and adrenal steroids may modulate age-related cognitive decline in primates.

Endocrine Influences on Cognitive and Brain Aging Gonadal Hormones The brain is an important target organ for the action of ovarian hormones (53, 54). Estrogen receptors are present in areas crucial for cognition, such as the hippocampus, cortex, and amygdala (in humans (55, 56); in nonhuman primates (57, 58); and in rodents (59)). Estrogens exert a multitude of effects in these regions: they enhance neuronal connectivity in the hippocampus (60), provide neuroprotection against a variety of toxic stimuli (61), modulate all neurotransmitter systems (62– 64), and affect brain activity patterns in women (65, 66). Less attention has been given to progestins, but it is clear that they also affect brain regions involved in cognition: progesterone decreases hippocampal dendritic spine density (67), interacts with several neurotransmitter systems (68, 69), and is neuroprotective (70). Recent studies have reported similar effects of androgens. In both primates (71, 72) and rodents (73), androgen receptor mRNA is found in several brain regions typically not involved in reproduction, including the hippocampus. Testosterone increases spine density in CA1 of the hippocampus in both male and female rats (74, 75), and circulating levels of androgens are necessary for the maintenance of spine density in male nonhuman primates (76, 77). In humans, testosterone treatment has been shown to increase cerebral blood perfusion in hypogonadal aged men (78), and endogenous testosterone levels predict regional cerebral blood flow in regions critical for cognition (79). Given the dramatic effects of sex steroids on neuronal morphology and brain activity in regions involved in cognition, one might expect age-related changes in the endocrine milieu to have important consequences for cognitive function. Indeed, sex steroids appear to be important modulators of cognitive aging. In women, estrogen deficiency associated with menopause may exacerbate age-related

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cognitive decline (80), especially in the domain of verbal memory and verbal fluency; estrogen replacement therapy (ERT) may protect against these verbal deficits (81). ERT may also reduce a woman’s risk of AD or delay the onset of the disease (82), perhaps through an inhibition of Ab accumulation (83). The rationale for using estrogen as an agent against cognitive decline has been seriously challenged by the findings of the Women Health Initiative and Memory Study (WHIMS), which reported decreased cognitive performance (84) and a higher risk of dementia (85) in women over 65 years using a combination of Conjugated Equine Estrogen (CEE) and Medroxy Progesterone Acetate (MPA) for 5 years. However, caution must be taken when interpreting these results: The particular hormonal regimen used, the limited range of cognitive function assessed, and the characteristics of the population tested may explain this negative outcome (86–90). Given these limitations in the human studies conducted thus far, a final verdict on possible beneficial effects of estrogen treatment on cognition in aging women will have to await more exhaustive studies of combinations of hormone treatments and schedules. Such studies should first be carried out on nonhuman primates. In men the steady decline of testosterone levels after 50 years of age (91) is associated with impairments in spatial cognition, spatial memory, and working memory that can be reversed by testosterone supplementation (for reviews see (92–96)). In addition, both cross-sectional (97) and longitudinal studies (98) have revealed positive correlations between bioavailable testosterone levels and cognitive function in older men. Interestingly, testosterone may also reduce the risk of AD in men (99), likely via protection against Ab toxicity (100, 101). As in women studies, however, questions concerning the risks and benefits of testosterone treatment and its efficacy on different systems have been raised, and the Institute of Medicine recommended that future studies on testosterone treatment be limited to hypogonadal men (Liverman & Blazer, 2004). Studies on nonhuman primates will be needed to overcome some of these limitations. Because of their similarity to humans in terms of reproductive endocrinology and cognitive ability, macaques are excellent models to study the effects of sex steroids on cognitive aging. Young female rhesus monkeys have a 28-day menstrual cycle that is almost identical to that of women (102), and aged females undergo menopause, although relatively late in life (~25 years old) compared to women (103, 104). Ovarian senescence (105) and reproductive hormone profiles of female rhesus monkeys during pre-, peri-, and postmenopause (106) are equivalent to those of women at the same stages. In males, patterns of androgen secretion resemble those of men (107), but it remains unclear whether aged rhesus monkeys experience a decline in serum testosterone levels with age, as occurs in humans (108). Several studies found differences in patterns of pulsatile testosterone secretion between young and old animals, but failed to find age differences in serum testosterone levels (109–111). In contrast, a recent study reported reduced testosterone levels in old versus young male rhesus monkeys (112). If confirmed, such a decrease in testosterone levels could account for the sex difference in age-related decline in rhesus monkeys performing a spatial working memory task, the spatial Delayed Recognition Span Test (DRST) (113). The study revealed that while young males

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outperformed young females, the sex difference was no longer present among old individuals, apparently due to a greater decline in performance in aged males than in aged females. The low performance of aged males could be directly related to declining testosterone levels; studies are underway to test the validity of this hypothesis. To our knowledge, the effects of androgen deficiency and replacement on cognition have not been investigated in nonhuman primates. Indeed, only a few studies have examined the activational effects of gonadal hormones on cognition in adult nonhuman primates and all have focused on the effects of estrogens in females. We and others have found that some aspects of learning and memory in aged female macaques are sensitive to estrogen deficiency after natural (114) or surgical menopause (115) and to estrogen treatment ((116–118) see for reviews (6, 119)). The effects appear to be task-specific and sensitive to the duration of estrogen deprivation prior to estrogen replacement. For example, in the delayed response (DR) task, a classic test of prefrontal function, performance was impaired in postmenopausal compared to age-matched premenopausal rhesus monkeys (114), suggesting that estrogen depletion associated with menopause was detrimental to prefrontal function. Congruent with this hypothesis, estradiol-replaced aged monkeys that had been ovariectomized for 7 months performed better than age-matched controls (118). In contrast, DR performance was not influenced by estradiol treatment in another study with aged monkeys that had been deprived of estrogens for a much longer period (about 12 years; (116)). Notwithstanding experimental differences among studies, this pattern of results supports the idea that the timing of estrogen replacement is crucial for estrogens to benefit cognitive function (120–122). However, our data suggest that “the window of opportunity hypothesis” may not apply to all cognitive domains studied. Indeed, while estradiol treatment had no influence on the DR in these long-term ovariectomized monkeys, it enhanced, in the same individuals, performance on the spatial-DRST, a task dependent on medial temporal lobe function (116). A crucial question concerns the mechanisms by which estrogens may exert their influence on the aged primate brain. Recent neuromorphological studies indicate that, unlike the aged rat brain (123), the aged primate brain retains the plasticity of the young brain in its response to estradiol in regions important for cognitive processing, including the prefrontal cortex (124), the hippocampus (125), and the basal forebrain (126). Hormone-induced changes in dendritic spines have been largely assumed to underlie associated changes in cognition, yet evidence for such a relationship remains merely correlational (124, 127). Moreover, the increase in dendritic spine numbers in the young primate brain following estradiol treatment is not necessarily accompanied by changes in cognition (119). This suggests that the young brain may undergo a range of neuromorphological changes without measurable changes in cognitive processes, while similar changes in the aged brain may systematically affect cognition. Another important question is whether brain plasticity is maintained after a long period of estrogen deprivation. Based on our results in aged long-term ovariectomized monkeys, we predict that medial temporal regions retain their plasticity even

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years after estrogen deprivation, but that plasticity in the frontal cortex may be timed to a temporal window that remains to be identified.

Adrenal Hormones In addition to gonadal hormones, adrenal hormones such as glucocorticoids and dehydroepiandrosterone and its sulfate DHEA(S) have been implicated in the maintenance of cognitive function during aging. Indeed, cortisol levels increase linearly with aging in men and women (128) and there is a substantial decrease of DHEA(S) plasma levels with aging in both sexes (129, 130). In addition, glucocorticoids have been shown to play a crucial role in age-related hippocampal atrophy (131–135). For example, in elderly people, prolonged exposure to elevated glucocorticoid levels resulted in hippocampally dependent memory deficits and a 14% reduction in hippocampal volume (131, 132). The role of DHEA(S) is less clear, with most studies failing to find a relationship between DHEA(S) and cognitive impairment (for reviews see (136, 137)). Nevertheless, DHEA(S) may have beneficial effects on well-being, mood (128, 138–140), and major depression (139, 141). Although basal levels of cortisol do not appear to be significantly different between young and aged macaques, the hypothalamic-pituitary-adrenal (HPA) system of aged animals shows a relative resistance to the suppressing effects of glucocorticoids via negative feedback (142–144). However, the hypothesis that chronic exposure to glucocorticoid levels results in hippocampal damage was not supported when tested in aged macaques: Leverenz et al. (1999) (145) failed to find a change in hippocampal volume or neuron numbers in pigtail macaques treated daily for 12 months with high levels of oral cortisol, in the absence of stress. These findings suggest that other factors related to stress could account for changes in hippocampal neuronal integrity. Elevated plasma cortisol has been associated with anxious temperament in macaques (146), but there is no published report on the effects of glucorticoids on cognition in macaques. Studies in the rhesus monkey have found marked reduction in DHEA(S) in old compared to young monkeys, similar to findings in humans (142, 146–149). As in human studies, no relationship was found between endogenous DHEAS levels and cognitive impairment in the rhesus monkey (146).

Advantages and Drawbacks Although macaques have proven to be very useful to further our understanding of age-related decline in brain function, the use of these species as models for human aging presents a number of drawbacks (10, 150, 151). One of these is the great expense associated with acquisition and maintenance of macaques in the laboratory. Health issues pose another problem, as special precautions must be followed

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when working with a species that is a Herpes B virus carrier (152). Also, the limited availability of old macaques makes future aging research with macaques particularly challenging. For example, access to naturally menopausal monkeys is seriously impeded by the short post-reproductive life of the female macaque, which in turn makes the study of the effect of menopause on cognition very difficult. As a result, premenopausal ovariectomized monkeys serve as the primary models for human menopause (6, 119, 153). As it is becoming increasingly clear that the effects of estrogen on cognition differ in young and aged animals (119), the value of young ovariectomized monkeys as models for menopause is questionable. Finally, seasonal cessation of reproductive function in certain species of macaques, such as rhesus monkeys (154), may potentially interfere with endocrine studies. Although efforts are underway to counteract some of these shortcomings (151), researchers might consider alternative primate models of human cognitive aging. In the next sections, we first examine the potential of other large monkey species as models, before we discuss the usefulness of small-sized primate species (less than 500 g). Great ape species, which have been largely ignored in cognitive aging research, will be reviewed in the final section of this chapter.

Other Medium-Sized Monkey Species Old World Monkeys Baboon Baboon species (genus Papio) are well characterized genetically and show over 90% similarities with the human genome (155, 156). Their life span is about 25–30 years in the wild (157). Baboons are of particular interest for cognitive aging research for several reasons. First, they are capable of performing a variety of complex cognitive tasks and can be tested in settings almost identical to those used with humans (158–163). Second, baboons are unique among nonhuman anthropoids in that they exhibit a characteristic age-related increase in amyloid deposition and in neuronal and glial tau pathology (25, 164, 165), which makes them excellent models for age-related human tauopathies. Finally, the reproductive endocrinology of female baboons is very similar to that of women (166), with aged baboons undergoing menopause around 26 years of age (153, 167, 168). If aged female baboons prove to be more readily available than aged female macaques, studies in naturally postmenopausal baboons could be carried out. Less is known about the reproductive endocrinology of male baboons, but nonsignificant increases in luteinizing hormone and decreases in testosterone were noted in male P. hamadryas (169). Like humans, baboons undergo age-related changes in adrenal hormones such as cortisol and DHEA (144, 169–171), but the relationship between these changes and age-related cognitive decline has not yet been examined.

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The similarities in cognitive processes, brain aging, and physiological parameters between baboons and humans suggest that baboons may be excellent models for human cognitive aging. Thus, it is quite puzzling that aged baboons have not been cognitively characterized. One disadvantage of using baboons is their relatively large size, with adult males of certain species reaching more than 25 kg. Nevertheless, it would be of particular interest to determine whether the unique feature of combined tau pathology and amyloid deposition in the aged baboon brain – reminiscent of human AD pathology – is systematically accompanied by cognitive impairments not observed in other nonhuman primates. In addition, the impact of the age-related endocrine changes on brain function should be examined, in particular in naturally postmenopausal baboons.

Vervet Monkey The vervet monkey (Chlorocebus aethiops) weighs between 3 and 7 kg and has a life span of about 20 years. This species has seldom been used in aging research. However, a recent study (172) has shown that Caribbean vervet monkeys may be useful as model of Ab deposition in the brain. It was found that vervets develop cerebral Ab plaques with aging that can be significantly reduced by prior immunization with Ab peptides. Cognitive assessments are required to determine whether these amyloid plaques deposits are associated with cognitive impairments.

New World Monkeys Squirrel Monkey Squirrel monkeys (Saimiri sciureus) are relatively small primates weighing about 1 kg in adulthood, with a life span of about 20–30 years in captivity (173). This species serves as a model for human aging in caloric restriction studies (174, 175), but cognitive assessments have yet to be carried out. Neuromorphological studies have investigated amyloid deposition in Saimiri, revealing that aged individuals exhibit pronounced Ab accumulations (176). However, these accumulations are almost entirely associated with blood vessels, and are reminiscent of cerebral amyloid angiopathy rather than AD. Other studies have investigated motor aging, as there is a significant decline of the nigrostriatal system in this species (177). Only two studies have investigated aspects of cognitive aging in Saimiri, as assessed by inhibitory motor control (178). Old females made more errors than young females when reaching into a box the opening of which had been placed in a different orientation from that used during training. Moreover, old adults had greater HPA responses following stress (restraint) and a modest reduction in glucocorticoid feedback sensitivity. Finally, white matter volume in the anterior brain region, as measured by MRI, was larger in old than in young adults and correlated with impaired inhibitory

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control. Such findings should encourage further investigations of cognitive aging in Saimiri species.

Capuchins Capuchins (Cebus species) are medium-sized monkeys ranging in weight from 2.5 to 5 kg. They are more encephalized than any other monkey species (179–181), and are known for their highly developed manipulative skills, tool usage, and high cognitive abilities (182), which could make them interesting models for human cognitive aging. Indeed, early work of Bartus and collaborators (183–185) employed aged capuchins as models for age-related memory impairment. However, probably due to their long life span of up to 46 years in captivity, capuchins are not currently used as models for human cognitive aging.

What Can Be Gained from Small-Sized Primates? The usefulness of small primate species (<500 g) as models for human aging has been discussed in depth by Austad (186). These species, although phylogenetically more distant from humans, present important advantages compared to larger primates, as they are generally less costly to maintain and easier to handle. They are usually short-lived (no more than a decade or two), and some species exhibit cognitive behavioral traits that are particularly relevant for human aging. In this section we focus on small primate species relevant to cognitive aging research.

Strepsirrhini Strepsirrhini are the primate group phylogenetically most distant from humans, having diverged from the rest of the primates about 60 to 70 millions years ago (187). One member of this group, the gray mouse lemur (Microcebus murinus), has received particular attention as a potential model for human cognitive aging. M. murinus is a small nocturnal and solitary primate whose life span reaches about 12–14 years in captivity. Aged mouse lemurs have been shown to exhibit a decline in working memory (188), spatial memory (189), and executive function (190) that resemble the cognitive deficits seen in other primates. Gray mouse lemurs also show many age-related changes in the brain, including cerebral atrophy, amyloid deposits in several regions of the brain, gliosis, and changes in all neurotransmitter systems (for review see (191)). An additional important feature of this primate is that aging is associated with hyperphosphorylated tau pathologies that have only been observed in humans. Of particular interest, a subgroup of about 20% of aged individuals demonstrates a particularly severe pattern of neuropathology that is

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accompanied by aggressiveness, social withdrawal, and inability to perform cognitive tests (191). Such traits are reminiscent of AD and suggest that the gray mouse lemur may be especially useful as a model for the disease (191). The reproductive endocrinology of gray mouse lemurs is quite different from that of humans, however, possibly limiting their usefulness for understanding the impact of human reproductive senescence on brain function. Gray mouse lemurs are seasonal breeders, and while males show a significant age-related decline in testosterone levels (192), females do not show menopause or fertility loss with advancing age (193). As far as adrenal steroids are concerned, aged males show a decline in DHEA-S after the age of 3 years that accelerates after the age of 6 (194), but it is not known whether females experience a similar decline. Unfortunately, the potential link between these endocrine decreases and age-related cognitive impairment has not been investigated. To our knowledge, age-related changes in cortisol have not been investigated in this primate. Finally, even though the gray mouse lemur has successfully been used in several cognitive tasks, as described above, the range of cognitive abilities that can be explored in a lemur species may be limited. For example, M. murinus was among the species performing the poorest in reversal learning tests, a measure of cognitive flexibility and obtained the lowest scores on an overall measure of cognitive ability in the meta-analysis of (195).

Callitrichidae Common Marmoset The common marmoset (Callithrix jacchus) is a social and diurnal species that can live up to 12 years in captivity. Marmosets are extensively used in biomedical research because of attractive advantages conferred by their small size, high fertility, and ease of handling. Several studies have demonstrated that the marmoset can perform at a high level on a range of cognitive tasks administered either in the WGTA (196) or in computerized settings (197–199). However, they appear unable to master tasks of visual recognition memory such as the delayed nonmatching to sample (DNMS) (197) and perform poorly on a global measure of cognitive ability (195). Of particular interest for cognitive aging research is the fact that marmosets can be successfully trained for conscious functional neuroimaging studies (200– 202). They also may provide a very exciting model for studying the effects of different drugs on the activity of the aged primate brain. The use of the marmoset as a model for cognitive and brain aging is in its infancy and we know relatively little about age-related changes in brain anatomy in this primate. Several studies have reported amyloid deposits in the brain of aged marmosets (203, 204) and a recent article identified an age-related loss in basal forebrain cholinergic neurons calcium-binding protein (205). It is worth noting that the sequencing of the marmoset genome is underway. The genetic information

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provided should help determine the molecular bases for neuropathological processes associated with aging in this primate. The reproductive endocrinology of the marmoset has been well characterized and differs in many respects from that of other primates (206–209). Callitrichids are the only primates that may ovulate more than one follicle per cycle. An important advantage of marmosets for aging research is their high fertility, with breeding females typically producing dizygotic twins every 6 months. Although there is a decline in ovarian follicles with age, there is no evidence of reproductive senescence or in ovulation rate. Concerning adrenal steroids, there is a diurnal variation in cortisol levels in the marmoset, with higher levels in the morning than in the evening (210), but it is not known whether aged individuals show increases characteristics of human aging. As far as we know, changes in DHEA(S) levels with age have not been investigated in this species. Cotton-Top Tamarins Another potentially interesting small-size primate for cognitive aging research is the cotton-top tamarin (Saguinus oedipus). It is a diurnal and social primate that lives up to 20 years in captivity. Although cognitive aging data are not available for this species, the cognitive abilities of young cotton-top tamarins have been thoroughly investigated. Despite their small brain, these primates are able to master a range of cognitive tasks from numerosity discrimination (211) to tool competence (212). Like marmosets, they have great potential for future use in functional neuroimaging studies. In one study (213), female tamarins (S. oedipus and S. fuscicollis) were found to ovulate up to at least 17 years of age, but females over 17 showed signs of cycling cessation or aberrant cycles, suggesting that reproductive senescence might occur till close to the time of death in these species. However, unlike Old World monkeys and women, old anovulatory females had measurable concentrations of progesterone and estrone. Although several studies have investigated testosterone and cortisol levels in this species (e.g.,), they were not studied in the context of aging.

Great Apes Many of the studies on aging of monkeys and prosimians described above have been motivated by the relative evolutionary proximity of these primates to our species. In contrast, humans’ closest relatives, the apes, have been largely neglected in studies on aging and cognition. If evolutionary pressures actively shaped our species’ aging phenotype (see for alternative views (214, 215)), it is imperative that age-related processes in our closest relatives be investigated. The need for information on aging in the apes has been widely recognized, particularly with regard to the chimpanzee (188, 216), which shares 98% of its genetic material with humans. In this section, we will examine the literature on aging, cognition, and the brain in apes and outline some of our ideas for future research.

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Cognitive Aging The only species of apes in which cognitive aging has been studied are the chimpanzee and the gorilla, and these studies are few; for bonobo, orangutan, and gibbon there are none. In contrast to the sparse literature on cognition in aged apes, that on cognitive function in general is a rich one, especially for the chimpanzee. Many studies within this field of psychological research, beginning with those of Wolfgang Köhler, have demonstrated that chimpanzees have remarkable cognitive capacities. Among them are the ability to recognize themselves in a mirror (217, 218), performance on tasks involving concept formation (219, 220), tool use and understanding of causal relations (221), and some understanding of the psychological states of others (but see for an alternative view). Apes are also able to perform complex linguistic tasks. Despite the rich cognitive repertoire of the chimpanzee, only relatively mundane tasks have been examined in the context of aging, and only three studies have actually been conducted. The first of these (222) compared the performance of eight young (11–19 years of age) and eight old (28–40 years of age) chimpanzees of unspecified sex in rate of learning, memory, and response variability. The tasks employed included object discriminations in which trials with novel objects were interspersed, a wheel-rotating task that became more difficult if the chimpanzee did not change direction of rotation in successive trials, and a series of object discrimination tasks with a contingency cue present. No differences between the age groups were found in any of the tasks. Riopelle and Rogers (223) studied 19 chimpanzees of unspecified sex, ranging in age from 7 to 41 years. Confirming the Bernstein study, they found no effect of age on two different object discrimination tasks designed to stimulate novel responses. However, they did observe poorer performance with age on two tasks. In a version of the DR, a decline in performance was observed with age, similar to that reported in monkeys. However, age differences were observed only at the short delays of 0 or 5 s. In contrast to the monkey studies, in which performance was most impaired at the longest delays, this chimpanzee study found that at the longest delay, 10 s, the performance of the young subject declined to the same level as that of the older subjects. The second task in which a significant decline with age was noted was a four-choice oddity task, in which chimpanzees were presented with one odd and three identical stimuli and were required to select the odd one. The third study was an attempt to replicate the findings of Riopelle and Rogers, but this study revealed no differences in performance as a function of age. Two studies have been published on aging and cognition in the gorilla. In the first (224), 16 gorillas (seven females and nine males) were tested on the DR, with delay intervals up to 90 s and with two, three, or four food wells to choose from in the test. Although performance worsened with lengthening delays and increasing number of food wells, no effect of age on these measures was detected. There was, however, a nonsignificant tendency toward increased side bias with age. Anderson and colleagues (225) examined the ability to detect “numerousness” in 11 gorillas ranging in age from 6 to 43 years. They tested whether gorillas would select the larger of two food quantities of food from food wells. While neither young nor old gorillas relia-

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bly selected the larger quantity without training, they all could be trained to do so. Following this training, they also reliably selected the larger of two pairs of quantities, suggesting the ability to summate the two quantities. While there was no relationship between age and the correctness in choosing the larger quantity, the old gorillas did respond significantly more slowly than the young. The authors attribute this slower responding to cognitive slowness rather than motor speed differences because the two age groups responded at the same speed prior to training.

Changes in Brain Anatomy and Function The brain of the chimpanzee has been far more thoroughly investigated than that of any other apes. This is in part because the chimpanzee is thought to be the most similar of the great apes to humans, as noted above, but is also a consequence of the development of several research stations with a particular focus on chimpanzees (226). With regard to aging of the brain, the preponderance of research on chimpanzees is extreme; there is almost no information on the other species. For this reason, we will not present separate subsections for the four ape genera: Pan, Gorilla, Pongo (orangutan), and Hylobates (gibbon and siamang). The most obvious distinguishing characteristic of the great ape brain is its size. The chimpanzee brain, with an average adult weight of about 370 g for females and 405 g for males (227), is about four to five times the size of the rhesus brain (27). While the bonobo, gorilla, and orangutan have brains of comparable size to that of the chimpanzee, the brains of gibbons and siamangs are about 100 g (228), near the 86 g reported for the rhesus monkey (27). With respect to age-related differences in the brain, we previously reported a nonsignificant trend toward a decrease in brain weight at death among chimpanzees (227). Additional data accumulated since that publication, as well as volumetric MRI data collected by Dr. William Hopkins (personal communication) extended our original observation by showing the modest cross-sectional decline in brain size with age to be statistically significant. There may be no neuron loss however, as a stereological study of a series of six chimpanzee brains, aged 11 to 45 years (229), suggested that neuron number may be conserved with age, a finding similar to that in rhesus monkeys (49, 230). Like rhesus monkeys, the aged great apes also have amyloid plaques without the tau-associated neurofibrillary tangles found in humans. Gearing (231) examined three chimpanzees aged 45, 56, and 59 years. Vascular amyloid was observed in the two oldest specimens. In addition, the 59-year-old exhibited sparse amorphous plaques in the cortex and hippocampus. Neurofibrillary tangles were not observed in any of the chimpanzees. An examination of three orangutans, aged 28, 31, and 36 years revealed sparse plaques in restricted locations in the 28- and 36-year-old individuals, and the 36-year-old animal also exhibited vascular amyloid (232). As with the chimpanzees, no neurofibrillary tangles were observed. Kimura (233) reported that the brain of a 44-year-old gorilla contained amyloid plaques, but this great ape also lacked neurofibrillary tangles.

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One remarkable similarity of the human brain to those of the great apes is the presence of a large spindle-shaped neuron in the anterior cingulate gyrus. These spindle cells are abundant in humans, bonobo, and chimpanzee, less frequent in gorilla, but altogether absent in gibbon and in all monkey species studied (234, 235). While agerelated loss of these cells has not yet been demonstrated in apes, their frequency is substantially decreased in humans afflicted with AD (236, 237). The existence of this cell type and its vulnerability in AD clearly underscore the importance of understanding the aging process and its effect on the brain in the great apes.

Endocrine Influences on Cognitive and Brain Aging Endocrine studies in apes are logistically difficult and quite expensive. For this reason there have been very few of them. Although some studies have reported agerelated declines in testosterone levels (238, 239), cortisol (240), and DHEAS (241) in apes, none of them has related these hormones to cognition or to aging. Aging of ovarian function, i.e., the question of menopause, has received a reasonable amount of attention, although any effects of reproductive aging in the female remain to be elucidated. The chimpanzee was reported not to undergo menopause (241, 242), but this observation was based on only 12 individuals. In contrast, cessation of menstrual cycles was observed in a study of wild chimpanzees (Mahale, Tanzania), where 7 out of 25 females above 30 years had ceased cycling for 3 years before their death (243). Moreover, Videan and colleagues recently concluded that menopause occurs between 35 and 40 years of age in the chimpanzee, based upon FSH and LH levels taken twice per year from 14 individuals studied longitudinally (244). Although only very little endocrine data were available to support the prior belief that chimpanzees did not normally experience menopause, we caution that this new finding suggesting a relatively early menopause be considered tentative since it includes only FSH and LH measurements and does not actually identify ovulatory cycles and determine their disappearance. A thorough characterization of the menstrual cycle is critical if future studies are to reveal any influence of female reproductive hormones on cognitive and brain function in the chimpanzee. Our analysis of archival menstrual records from the Yerkes chimpanzee colony indicates indeed that even much older female chimpanzees (>50 years) appear to cycle until their death. Thus, additional studies need to be undertaken before firm conclusions can be reached. Little is known about menopause in the other great apes. A recent study in captive western lowland gorillas indicated that menopause occurs around 35 years of age in this species based upon analysis of fecal estrogens and progestogen metabolites (245).

Advantages and Drawbacks The most important advantage of the great apes as a model of the influence of aging on cognition, brain, and endocrine functions and their interactions is their especially close evolutionary relationship to humans. Thus, they may not only

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show age-related changes that are particularly enlightening with respect to the similarities with humans but also with respect to differences that may be observed between this group of nonhuman primates and our own species. These advantages of study of the apes are offset by a large number of challenges that make observations on these species particularly difficult. Among the problems encountered are the rarity of the apes, the expense and difficulty of maintaining them, and the special adaptations of cognitive tests and equipment that are required to apply standard cognitive tests to the great apes. However, perhaps the most difficult challenge is that any differences between old and young apes cannot automatically be attributed merely to differences in their age, but could instead result from other differences between the young and old cohorts tested in any particular study. This type of interpretational difficulty, a special case of the cohort effect, which commonly plagues cross-sectional studies of aging, may be particularly problematic in long-lived species such as chimpanzees and gorillas, in which the younger members of the species may have experienced completely different lives, and even different birth circumstances. This problem can best be addressed by longitudinal studies in which contemporaneous comparisons between age groups are viewed in the light of observations of changes that occur with aging within the same individual. With the development of non-interventionist methods, such as Magnetic Resonance Imaging (MRI), through which changes in brain morphology can be observed over time, the possibility now exists for future studies in which brain and cognitive changes are observed simultaneously. As pointed out by Erwin and Hof, researchers now have an important opportunity to study the effects of age on the chimpanzee; although the overall number of chimpanzees available is quite small, a large proportion of these are reaching relatively advanced ages (246). We and other researchers are soon embarking upon studies in which we plan to undertake a focused investigation of cognitive aging of chimpanzees. The work will involve investigators with backgrounds in chimpanzee research, human cognitive aging, human and nonhuman primate anatomy, and other disciplines who will observe the cognitive behavior of young and aged chimpanzees. Our group will investigate longitudinal cognitive changes by administering cognitive tasks that have been the mainstay of studies of the rhesus monkey, the species that served as the “workhorse” among primate models of aging. Chimpanzees will not only be tested on these tasks, but will also undergo periodic MRI examinations so that changes in behavior can be examined in the light of specific anatomical characteristics. Given the dwindling population of chimpanzees, now may be the only time in history that such a study can be undertaken.

Conclusions This overview of nonhuman primate models of cognitive aging points to a few primate species that have great potential to further our understanding of human age-related cognitive decline. The list is by no means exhaustive, but highlights species for which the most information is available.

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It is likely that macaque species will remain primary models of human cognitive aging, as our knowledge of aging in the macaque, from cognitive, behavioral, physiological, and endocrine perspectives, is more detailed than the limited knowledge that we have gained so far from other species. If cognitive aging is indeed a consequence of a multitude of factors, it is crucial to seek an understanding of aging at different levels of organization, from behavioral to molecular processes. As such, the macaque remains to date the model of choice. Nevertheless, we have shown that other primate species may complement studies on the macaque. Medium-sized nonhuman primates that present particularly interesting features in brain and endocrine senescence, without having some of the drawbacks associated with macaques, are baboon species. The investigation of cognitive aging in the baboon should provide some new insights into the neural bases of cognitive impairment, in particular in relation to neuropathology associated with tau and Ab accumulation. Another possible avenue of research for this species concerns the cognitive deficits associated with natural menopause. From a practical point of view, however, small-sized species are probably most attractive as potential models for human cognitive aging. Gray mouse lemurs and marmosets exhibit some aspects of age-related cognitive and brain declines that appear similar to those of humans and present obvious advantages over larger primates in terms of purchase and maintenance costs. However, important differences in reproductive systems and brain function between humans and these phylogenetically distant species suggest caution in extrapolating findings to humans. In spite of these limitations, they present interesting alternative models in which speciesspecific aging mechanisms can illuminate our understanding of human cognitive aging. One exciting opportunity for future research is the further development of these primate models for functional neuroimaging studies. At the opposite end of the size spectrum, the great apes are the most expensive and also most difficult to study. In spite of these hurdles, studying great apes’ cognitive aging is critical to understanding potentially unique features of human senescence. Studies are underway to gather important data concerning brain and cognitive aging in our closest relative, the chimpanzee. Acknowledgements We acknowledge funding from the National Institutes of Health, through grants AG00001, AG12610, AG18998, AG026423, and MH59243. Base grant support to the Yerkes National Primate Research Center of Emory University was provided by RR00165. We thank Johannes Tigges for his comments on the manuscript. The Yerkes Center is fully accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International.

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Age-Related Effects on Prefrontal Cortical Systems: Translating Between Rodents, Nonhuman Primates, and Humans Mark G. Baxter

Abstract Impairments in cognition related to dysfunction of the prefrontal cortex occur as a consequence of aging in rodents, nonhuman primates, and humans. This chapter describes evidence from several different cognitive tasks that can be administered to these several species, and that provide convergent evidence about the behavioral and neurobiological consequences of advanced chronological age on prefrontal cortex function. Aging results in impairments in spatial working memory, flexibility of stimulus–reward associations, and shifting of attentional sets and behavioral strategies. These cognitive tasks provide a setting in which translational studies of prefrontal cortex function in aging can be conducted, facilitating the search for therapeutic strategies that can improve cognitive functions of prefrontal cortex in aged humans. Keywords Aging • prefrontal reversal • set-shifting

cortex • spatial

memory • discrimination

The prefrontal cortex is generally associated with integrative and supervisory roles in behavior and cognition. Its many functions include aspects of memory, decision-making, attention, rule implementation, cognitive control, and, in humans, language. Dysfunction of prefrontal cortex is a hallmark of aging in rodents, nonhuman primates, and humans, resulting in functional deficits in abilities dependent on it. Of course, these deficits are much less severe than those seen after gross damage to the frontal lobes, perhaps because neural alterations in aging are much more subtle than a frank loss of cortical neurons. Nevertheless, impairments in cognitive flexibility, working memory, source memory, and other aspects of cognition can prove troubling for humans as they grow older. These impairments, apparent as a consequence of advanced chronological age in the absence of any disease state, can be dramatically more severe in age-related neurodegenerative conditions such as frontotemporal dementia and Alzheimer’s disease, among others. M.G. Baxter Department of Experimental Psychology, Oxford University, Oxford, UK J.L. Bizon, A. Woods (eds.) Animal Models of Human Cognitive Aging, DOI: 10.1007/978-1-59745-422-3_3, © Humana Press, a part of Springer Science + Business Media, LLC 2009

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Thus, researchers have attempted to devise animal models in which alterations in prefrontal function with aging can be studied, as well as to determine which neurobiological alterations that occur within the aged prefrontal cortex are responsible for cognitive impairments. Animal models also have the advantages of controlled or known life histories, and provide for the exclusion or control of lifestyle variables such as nutrition, education, and socioeconomic status that can confound attempts to understand the neurobiology of cognitive aging. Also it appears that neurodegenerative diseases such as Alzheimer’s disease are uniquely human conditions, so animals provide an opportunity to examine the impact of “normal aging” on brain function independently of pathological conditions, which may go undetected in humans especially in their early stages. Some of these advantages of studying aging of prefrontal cortex in animal models are offset by questions about the homology of prefrontal cortex between nonhuman species and humans, as well as problems in translating tests of human prefrontal cortex function to nonhumans, particularly tests based on general knowledge or linguistic information. The present chapter aims to provide some insight into animal models of prefrontal function, particularly how studies in aged rodents and aged nonhuman primates (specifically, macaque monkeys) can be related to one another. Rodents are particularly well suited for neurobiological studies of aging; an “aged” rat is roughly 24–30 months old, depending on the strain. This makes even longitudinal studies across the life span of rodents practical in a laboratory setting. In contrast, “aged” rhesus monkeys are older than 20 years, based on an approximate 1:3 ratio of monkey years to human years (1). Thus, the availability of aged macaque monkeys for neurobehavioral research in aging is extremely limited and these animals represent a very rare and precious resource. On the other hand, the cognitive abilities of macaque monkeys are much more sophisticated than those of rodents. In particular, the prefrontal cortex is much larger and more differentiated in macaque monkeys compared to rodents. Neuroanatomists debate the extent to which rodents can be said to possess a prefrontal cortex at all (2). With these considerations in mind, several points related to the study of prefrontal cortical function in aged rodents and nonhuman primates will be considered. First, the evidence for homology between rodent and primate prefrontal cortex will be briefly reviewed. Then, the effects of aging on some behaviors dependent on the integrity of the prefrontal cortex will be described, focusing on tasks where comparisons can be made across species. Finally, some issues in translating findings between animal models and humans will be discussed.

Homology Between Rodent and Nonhuman Primate Prefrontal Cortex The presence of homology between species can be argued on several levels, including cytoarchitecture, connectivity, physiological properties, and behavioral correlates. It is important to point out that the prefrontal cortex comprises a number of different subdivisions, usually described by their location in the primate frontal

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lobe, including dorsolateral prefrontal cortex and orbital prefrontal cortex (or orbitofrontal cortex). Cytoarchitectonic arguments originally provided the basis for defining cortical areas and led to the belief that the prefrontal cortex was unique to primates (3). However, Rose and Woolsey (4) defined the prefrontal cortex as cortex receiving projections from the mediodorsal thalamus (MD), and the overall arrangement of MD connections in rats parallels the arrangement observed in primates (5). Later work has shown that extensive connections with parts of the brain in addition to the thalamus, including subcortical and cortico-cortical connections, are defining features of the prefrontal cortex shared across rodents and primates (2, 6). However, the hypothesis that the rat MD-projection cortex includes cortex homologous to the primate dorsolateral prefrontal cortex has come under criticism. One difficulty is that the rodent MD-projection cortex lacks a granular layer (7, 8) and some details of connectivity differ between rodents and primates (reviewed in (9)). As a result, some have concluded that the rat does possess a frontal cortex, but one with no homologue of the primate dorsolateral prefrontal cortex (2). Recent studies suggest that there may be some degree of functional homology between regions of rat prefrontal cortex and the lateral prefrontal cortex of primates, even though cytoarchitectonic features of frontal cortex in rodent and primate differ (10). This will be elaborated further as particular domains of function for the prefrontal cortex are considered. With regard to orbital prefrontal cortex, homology between rodent and primate appears to be better-established and accepted (11). To the extent that certain information processing and neurophysiological mechanisms may be common to multiple subdivisions of prefrontal cortex, this may suggest that information gained from studying effects of aging within orbitofrontal cortex could generalize to other regions that are less well elaborated in the rodent brain.

Cognitive Functions of the Prefrontal Cortex It is beyond the scope of this chapter to provide a comprehensive review of the neuropsychology of prefrontal cortex in addition to the effects of aging on behaviors dependent on this region. Instead, we will focus on three behavioral domains that are commonly used to probe prefrontal function in rodents and nonhuman primates in the laboratory: spatial working memory, discrimination reversal learning, and attentional set-shifting. Where possible we will consider issues related to the comparative neuropsychology of these tasks in different species.

Spatial Working Memory The spatial delayed response task is a classic test of dorsolateral prefrontal function in macaque monkeys (e.g., (12)). In this task the monkey sits in front of a test tray with two recessed wells in it. He watches as the experimenter baits one of the wells by placing a small food reward within it. Then both wells are covered with neutralcolored plaques and, after a short delay (during which a screen may be interposed

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between the monkey and the test tray to occlude his view of it) the monkey is allowed to displace one of the plaques in order to obtain the food reward. The monkey’s task is simply to remember the location of the hidden food reward so he can retrieve it. Monkeys with dorsolateral lesions perform poorly on this task when it is taught before surgery (12, 13). Both dorsolateral and ventromedial prefrontal lesions impair learning the delayed response task (14), although lesions limited to orbital prefrontal cortex do not (15). The ability to remember a single location across a short delay is not dependent on the hippocampus in monkeys (16–18). It has been known for some time that spatial working memory is impaired in aged monkeys (19). It is not clear, however, that this impairment is specific to the spatial domain; instead it may represent an impairment in dealing with interference that accumulates in the course of testing in the delayed response task, where only two spatial locations are repeatedly reinforced across multiple test sessions. Rapp and Amaral (20) demonstrated that aged monkeys that were impaired on delayed response were also impaired in a delayed nonmatching to sample (DNMS) task with objects, provided that only two objects were used in testing, so that monkeys had to discriminate the relative recency of objects rather than simply whether or not they had seen the object before. Because dorsolateral prefrontal lesions in young monkeys do not produce this kind of generalized impairment – for example, these lesions do not impair performance on the related task of object alternation (21), the finding of impaired DNMS with repeated objects in aged monkeys may suggest a wider dysfunction of the prefrontal cortex in aged monkeys. Spatial memory impairments also emerge in other settings in aged monkeys. In the exploration of a large octagonal platform in which there are eight locations and the monkey must apply a similar nonmatching rule (a reward can be obtained in each location only once), aged monkeys display memory impairments and a greater reliance on a systematic search strategy (22). However, because this task involves locomotion through space, such impairments may relate more to hippocampal dysfunction with aging rather than prefrontal function, a point taken up in succeeding paragraphs. Spatial working memory is commonly tested in rats on a T-maze. Often the rats are taught a spatial delayed alternation task where they must remember where they went on the last trial and choose the opposite arm on the current trial, in a spatial nonmatching to sample procedure. This task is exquisitely sensitive to lesions of the hippocampus or fornix (23, 24). Notably however it is not impaired by neurotoxic lesions of medial (25, 26) or orbital prefrontal cortex (T. Y. Mariano, M. G. Baxter, and D. M. Bannerman, unpublished observations), including when the delay between sample and choice is extended. The lesion methodology in these studies is critical because of the role of the white matter of the cingulum bundle in these kinds of tasks, and this structure may be damaged by conventional lesions (26). In rats, prefrontal cortex may be more related to the ability to switch between different performance rules for the T-maze, for instance matching versus nonmatching to position (27). Tests of spatial memory that impose a greater memory load – for example by requiring memory of multiple locations – may require medial prefrontal cortex (28).

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In the analysis of impairments in spatial working memory in aging, and how they relate to disruptions in prefrontal cortex function, it is important to distinguish the contributions of spatial cognition components of the memory task from other components, such as memory decay across delays and interference caused by repeated tests of memory for a limited number of spatial locations – two in the standard T-maze testing paradigm or monkey delayed response task. Thus, tasks such as T-maze alternation in rats may place a greater burden on spatial cognition, because the rat must navigate through space in order to reach the goal locations it is alternating between. The analysis of effects of aging on memory for spatial information is discussed in other chapters in this volume. However, this issue complicates the use of spatial working memory kinds of tasks to compare the neurobiology of cognitive aging within prefrontal cortex between rats and humans. Notwithstanding this concern, some pharmacological manipulations within medial prefrontal cortex show convergent effects in rats and nonhuman primates, and these effects are distinct between the prefrontal cortex and hippocampus (e.g. (29)). Aged rats are impaired relative to young rats in the T-maze (30, 31), although it has been noted that once the task is mastered at short delays, performance does not deteriorate across delays and that impairments are exacerbated by inclusion of multiple locations to be remembered (31). Impaired spatial working memory is also seen in tasks of this kind where escape performance is motivated by escape from water rather than food (e.g. (32)). Based on the lesion data discussed above, some of the components of these impairments may derive from disrupted neural function outside the prefrontal cortex. With regard to comparisons with human cognitive aging, aged humans are impaired on delayed-response types of spatial working memory tasks (33). Such a task is also impaired by damage to right dorsolateral/dorsomedial prefrontal cortex in humans (34). Thus, there is good evidence for comparability and translation of these tasks between humans and nonhuman primates. The extensive contribution of hippocampal neural circuits to performance of spatial working memory tasks in rats complicates comparisons between rodents and primates, however.

Discrimination Reversal Learning The learning of simple discrimination problems is often unaffected by aging, suggesting that the ability to form stimulus–reward associations is intact (35–40). The ability to modify these associations, once learned, is a common test of cognitive flexibility and perseveration. This is done by simply reversing the associations: so for two discriminative stimuli A and B, if initially the subject learns responding to A is rewarded and B is not, then in the reversal B is rewarded and A is not. Impairment in discrimination reversal learning is a well-known consequence of damage to orbitofrontal cortex in rats, monkeys, and humans (15, 41–45). The effects of aging on discrimination reversal learning are variable, with different experiments in monkeys reporting impairments in spatial reversal learning but

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not visual reversal learning, visual but not spatial reversal learning, or both (37–40, 46–49). This variability may be due, in part, to effects of the order in which reversal problems are encountered ((50), see discussion in (51)). Aged rats have been reported to be impaired in reversal of olfactory discriminations (36), although reversal impairments in the context of a set-shifting discrimination design (see the next section) did not reach statistical significance in two other studies in rats (35, 52). These latter findings may suggest that orbitofrontal function, specifically, is less sensitive to aging in rats relative to other aspects of prefrontal function. Alternatively, the relative ease with which discrimination problems are acquired in the set-shifting design may reduce the sensitivity of the reversal component of the task to age-related impairments. It is interesting to note that orbitofrontal function may be more susceptible to pathological conditions associated with aging, for example amyloid-beta deposition as part of the development of Alzheimer’s disease. Impaired reversal learning is seen at an early age in a mouse model of Alzheimer’s disease, and this impairment correlates with levels of Aß42 in the prefrontal cortex (53). Aged humans are also impaired in a version of the reversal learning task that uses probabilistic reward contingencies, so that choices of the “correct” stimulus is not rewarded 100% of the time (54), which increases task difficulty, decreases the utility of verbal strategies to mediate performance, and presumably avoids floor effects on performance. Orbital prefrontal damage is associated with impairment on this kind of task (42). Although these kinds of probabilistic reversal tasks are less frequently used in animals, reversal learning appears to translate well across rodents, nonhuman primates, and humans as a test of prefrontal function in cognitive aging. An important cautionary note in this context is that damage outside the prefrontal cortex can impair reversal learning. For example, lesions of entorhinal and perirhinal cortex in monkeys impair reversal learning (55). Also, recent data suggest that the involvement of orbital prefrontal cortex in reversal learning may be complex and mediated via interactions with neural processing in other brain structures, as lesions of the amygdala eliminate impairments in reversal learning following orbitofrontal lesions in rats (56).

Attentional Set-Shifting The Wisconsin card-sorting task (WCST) is a classical test of prefrontal function (57). In it human subjects are given a deck of cards, each card labeled with different numbers of differently colored shapes. The subject is asked to sort the deck of cards into four piles, with feedback from the tester about whether the cards are being sorted correctly or not. The cards can be sorted based on shape, the color of the shapes, or the number of the shapes. Once a fixed number of consecutive correct sorts are accomplished, the tester imposes a new sorting rule and the subject must discover by trial and error what the new rule is. Patients with prefrontal damage achieve fewer successful shifts in sorting. There is some evidence for regional spe-

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cificity within prefrontal cortex, with dorsolateral lesions being associated with more perseverative errors (continuing to sort according to the previously correct rule) and orbitofrontal lesions being associated with more failures to maintain the currently correct sorting rule (58, 59). Aged humans are impaired in the WCST, although these impairments may derive from impaired working memory or use of feedback about errors (60, 61) rather than an impairment in the use of previously correct rules. Of course, there are a number of reasons why subjects might perform poorly on this task, including a deficit in remembering the sorting rule they are currently meant to apply, difficulty in shifting from a response strategy that was correct, or difficulty in inhibiting responses that were correct before the rule switch. An attempt to design a related test procedure that could dissociate some of these deficits used a discrimination learning paradigm and “extradimensional/intradimensional” (ED/ID) shifting (62, 63). A schematic of this task is presented in Fig. 1. In this particular example, stimuli are composed of colored shapes. Subjects learn that one of the two stimulus dimensions – shape or color – is relevant to the discrimina-

Fig. 1 Example of ED/ID (extradimensional/intradimensional) shifting task design. Each phase (1–4) contains two-choice discrimination problems; the left/right position of the correct stimulus varies from trial to trial and each stimulus attribute within a dimension can be paired with either attribute of the other. For example, in the first phase, a choice can be offered between a red circle and a blue square, or between a red square and a blue circle. In this example, the initially correct dimension is color, so in the first phase the red stimulus is always correct regardless of its shape. In phase 2, the intradimensional shift, two new shapes and colors are used, but it is a color (in this case orange) that always denotes reward. In phase 3, the reversal, the same stimuli as before are encountered but now the reward contingencies are reversed, so the green shapes are correct and the orange ones are not. Again, the shape of the stimulus (triangle or hexagon) is not relevant. In phase 4, the extradimensional shift, two new shapes and colors are encountered, but now it is the diamond that is correct, regardless of its color. The relevant dimension for solution of the discrimination problem has shifted from color to shape (see Color Plate 1)

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tion and the other is irrelevant. This task is more commonly presented to humans and nonhuman primates with compound visual stimuli composed of line segments superimposed on complex polygonal shapes. Subjects learn that either lines or shapes are relevant to the solution of the discrimination problem (62, 63). This task has also been adapted to rodents by presenting compound stimuli composed of scented digging media in which the rats can dig to attempt to retrieve a reward (64). As before, either the odor applied to the digging media, or the kind of digging medium itself, is relevant to the discrimination problem. Surprisingly, perhaps, rodents have also shown some ability to solve compound visual discrimination problems composed of superimposed lines and shapes presented on a computer screen (65). Reversal problems are also included as elements of the discrimination problems, allowing for assessment of multiple aspects of prefrontal cortex function in the context of a single testing paradigm. Patients with damage to prefrontal cortex, but not temporal cortex (including the hippocampus and amygdala) perform poorly on the ED/ID task (63). On the view that deficits in WCST performance following prefrontal damage represent a “perseveration of central sets,” that is, subjects persist in using a sorting rule after receiving feedback that it is no longer correct, poor performance on the ED/ID task may also be interpreted as continuing to solve discrimination problems based on the previously relevant dimension, in the face of feedback that this dimension no longer provides information relevant to the solution of the current problem. Importantly, different patterns of impairment in the ED/ID task can be resolved depending on the location of the lesion within prefrontal cortex. Marmosets with damage to lateral prefrontal cortex are selectively impaired on the ED shift phase of the task, whereas marmosets with orbitofrontal lesions succeed at ED shifts but are impaired on reversals of previously learned discrimination problems (44, 50). Parallel results are found in rats with lesions of medial or orbital prefrontal cortex (64, 66). Aged rats display impairment in ED shifts in this paradigm (35, 52), as do aged humans (63, 67). The psychological interpretation of the ED/ID task is often presented in terms of attention to the relevant stimulus dimension, and the difficulty in ED shift problems (which is exacerbated by prefrontal damage) is attributed to an impairment in redirecting attentional resources away from the previously relevant dimension (or to the previously irrelevant dimension). An alternative explanation is that the ED shift problems require a shift in the strategy employed to discover the solution to the discrimination problem. This may be particularly apparent in the version that is commonly presented to rats, that will use different sampling strategies to discriminate odors (sniffing) versus digging media (whisking with vibrissae). Other investigators have noted impairment in strategy switching after inactivation of medial frontal cortex in rats (68, 69) which would be congruent with this interpretation, in tasks in which response rules that govern correct performance are switched between task phases. An important aspect of the ED/ID design is that perseverative responding to particular rewarded stimuli cannot explain poor performance in the ED phase. Because new stimuli are encountered in each shifting phase of the task, impaired

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performance must derive from some other source. In contrast, tests in which rules are repeatedly switched with the same set of stimuli – for example, between using brightness and spatial cues to select the correct arm in a maze – could be impaired because subjects persist in making specific choices that were previously reinforced. For example, if a rat learns to turn left at a choice point to receive a reward, but that whether the choice arm is black or white is irrelevant, and is then taught to choose the white arm, and whether it requires a left or right turn, the requirement to switch response rules is confounded with learning that the previously correct left turn response is no longer rewarded 100% of the time, and the white arm, which was wrong (on average) half of the time in the previous training phase, is now consistently rewarded. These are elements of reversal learning rather than shifting of attentional set or strategy. Similar complications are encountered in a “conceptual set shifting task” on which aged monkeys are impaired (70); monkeys are given a repeated set of colored shapes and learn three-choice discrimination problems in which one color (red, blue, or green) or shape (circle, triangle, or star) is consistently rewarded. Because colors and shapes are repeated across phases of the task, performance on rule switches (e.g., from red to triangle) are confounded with learning to avoid the particular previously rewarded stimulus. Of course, this is a problem with the WCST task itself (indeed, one which motivated the development of the ED/ID design for neuropsychological testing). To summarize, there is evidence that aging also impairs performance in behavioral tasks related to shifting of attentional sets or strategies for solving discrimination problems. Neuropsychological investigations in rats and monkeys reveal that distinct prefrontal subregions may be involved in attentional/strategy shifts, as measured in the ED/ID paradigm, as opposed to flexibility of stimulus–reward associations, measured by reversal learning.

Translating Between Animals and Humans The primary goal of most studies of cognitive aging in animals is to provide a model for understanding neurobiological changes that underlie human cognitive aging. The presence of cognitive aging in animals should not be underestimated on its own. Some analyses of human cognitive aging make appeals to social or cultural factors (for example, expectation that the elderly are slower, senile, and so forth), to account for cognitive impairments in aging (71). Alternatively, aging may diminish cultural influences on cognition as prefrontal-dependent mechanisms for supporting these influences are impaired with advancing chronological age (72). Notwithstanding experimental demonstrations of influences of social and cultural factors on performance in cognitive tasks, the presence of cognitive aging in nonhuman animals that do not have these factors is powerful evidence in favor of a primary basis in biology. The extent to which the profile of cognitive impairments in aging resembles deficits in prefrontal cortex function is also open to discussion. Many experimental

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studies, particularly in rodents, have focused on hippocampal dysfunction in aging. These studies are discussed in other chapters in this volume. Evidence for impaired performance in hippocampal-dependent learning tasks in aging is undeniable. For example, elderly humans are also impaired on tests of spatial learning (e.g., (73)). Impairments in spatial information processing mediated by the hippocampus may complicate the use of spatial tasks for assessing working memory functions of prefrontal cortex, as in the case of T-maze performance in aged rats. Generally, this illustrates a problem with translation of behavioral tasks between species, as it may be difficult to test nonhuman animals and humans with equivalent stimulus material. Rats would find it challenging to perform a spatial working memory task that involved directing their gaze to a location on a computer screen, and it would be challenging to ask elderly humans to repeatedly walk through a corridor and alternatively turn left or right at the end in order to collect rewards. Nevertheless, in some circumstances remarkable congruence across species can be seen despite differences in stimuli and testing procedures, as appears to be the case with the ED/ ID paradigm. Impairments in prefrontal cortex function may be more disruptive to activities of daily life than a mild medial temporal lobe syndrome, the primary effect of which is impaired memory. The effective use of strategies such as consistent daily routines, the checking of lists, retrieval cues, and so forth, can compensate for impairments in memory, but the efficacy of such aids is reduced in the presence of cognitive deficits beyond the domain of memory (74). The ability of these strategies to compensate for these impairments may be reduced in the presence of impaired prefrontal function, which interferes with the application of strategic information. Impairments in working memory and cognitive flexibility disrupt the ability to follow a chain of instructions and to switch between tasks. Thus, we may consider that impairments in prefrontal function may be more problematic in normal aging, to the extent that they may be more difficult to compensate for via cognitive strategies. This motivates attempts to understand the neurobiology of cognitive aging within prefrontal cortex, to aid in the developments of interventions that may improve agerelated impairments in the function of this structure. Acknowledgment Trust.

°Support for preparation of this chapter was provided by the Wellcome

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48. Voytko, M. L. (1999) Impairments in acquisition and reversals of two-choice discriminations by aged rhesus monkeys. Neurobiol Aging 20, 617–627. 49. Peters, A., Rosene, D. L., Moss, M. B., Kemper, T. L., Abraham, C. R., Tigges, J., and Albert, M. S. (1996) Neurobiological bases of age-related cognitive decline in the rhesus monkey. J Neuropathol Exp Neurol 55, 861–874. 50. Dias, R., Robbins, T. W., and Roberts, A. C. (1997) Dissociable forms of inhibitory control within prefrontal cortex with an analog of the Wisconsin Card Sort Test: restriction to novel situations and independence from “on-line” processing. J Neurosci 17, 9285–9297. 51. Baxter, M. G. (2001) Cognitive aging in nonhuman primates. In P.R. Hof and C.V. Mobbs (eds.), Functional Neurobiology of Aging. Academic Press, San Diego, pp. 407–419. 52. Rodefer, J. S., and Nguyen, T. N. (2008) Naltrexone reverses age-induced cognitive deficits in rats. Neurobiol Aging 29, 309–313. 53. Zhuo, J. M., Prakasam, A. Murray, M. E., Zhang, H. Y., Baxter, M. G., Sambamurti, K., and Nicolle, M. M. (2008) An increase in Abeta42 in the prefrontal cortex is associated with a reversal-learning impairment in Alzheimer’s disease model Tg2576 APPsw mice. Curr Alzheimer Res 5, 385–391 54. Mell, T., Heekeren, H. R., Marschner, A., Wartenburger, I., Villringer, A., and Reischies, F. M. (2005) Effect of aging on stimulus-reward association learning. Neuropsychologia 43, 554–563. 55. Murray, E. A., Baxter, M. G., and Gaffan, D. (1998) Monkeys with rhinal cortex damage or neurotoxic hippocampal lesions are impaired on spatial scene learning and object reversals. Behav Neurosci 112, 1291–1303. 56. Stalnaker, T. A., Franz, T. M., Singh, T., and Schoenbaum, G. (2007) Basolateral amygdala lesions abolish orbitofrontal-dependent reversal impairments. Neuron 54, 51–58. 57. Milner, B. (1963) Effects of different brain lesions on card sorting: the role of the frontal lobes. Arch Neurol 9, 100–110. 58. Stuss, D. T., Levine, B., Alexander, M. P., Hong, J., Palumbo, C., Hamer, L., Murphy, K. J., and Izukawa, D. (2000) Wisconsin Card Sorting Test performance in patients with focal frontal and posterior brain damage: effects of lesion location and test structure on separable cognitive processes. Neuropsychologia 38, 388–402. 59. Stuss, D. T., Benson, D. F., Kaplan, E. F., Weir, W. S., Naeser, M. A., Lieberman, I., and Ferrill, D. (1983) The involvement of orbitofrontal cerebrum in cognitive tasks. Neuropsychologia 21, 235–248. 60. Hartman, M., Bolton, E., and Fehnel, S. E. (2001) Accounting for age differences on the Wisconsin Card Sorting Test: decreased working memory, not inflexibility. Psychol Aging 16, 385–399. 61. Fristoe, N. M., Salthouse, T. A., and Woodard, J. L. (1997) Examination of age-related deficits on the Wisconsin Card Sorting Test. Neuropsychology 11, 428–436. 62. Roberts, A. C., Robbins, T. W., and Everitt, B. J. (1988) The effects of intradimensional and extradimensional shifts on visual discrimination learning in humans and non-human primates. Q J Exp Psychol 40B, 321–341. 63. Owen, A. M., Roberts, A. C., Polkey, C. E., Sahakian, B. J., and Robbins, T. W. (1991) Extradimensional versus intra-dimensional set shifting performance following frontal lobe excisions, temporal lobe excisions or amygdalo-hippocampectomy in man. Neuropsychologia 29, 993–1006. 64. Birrell, J. M., and Brown, V. J. (2000) Medial frontal cortex mediates perceptual attentional set shifting in the rat. J Neurosci 20, 4320–4324. 65. Brigman, J. L., Bussey, T. J., Saksida, L. M., and Rothblat, L. A. (2005) Discrimination of multidimensional visual stimuli by mice: intra- and extradimensional shifts. Behav Neurosci 119, 839–842. 66. McAlonan, K., and Brown, V. J. (2003) Orbital prefrontal cortex mediates reversal learning and not attentional set shifting in the rat. Behav Brain Res 146, 97–103. 67. Robbins, T. W., James, M., Owen, A. M., Sahakian, B. J., Lawrence, A. D., McInnes, L., and Rabbitt, P. M. (1998) A study of performance on tests from the CANTAB battery sensitive to

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Comparison of Different Cognitive Rat Models of Human Aging Candi LaSarge* and Michelle Nicolle

Abstract Multiple rat strains have been used for behavioral tests of cognitive function. This chapter will discuss the advantages and disadvantages of commonly used rat strains in aging studies and offer insight concerning the ability to generalize behavioral data. Differences of the outbred and inbred strains in the behavioral tasks, middle-aged data, and life expectancy will be discussed comprehensively to facilitate the design of future experiments to maximize comparability across rat strains. Aged Long Evans (LE) rats have been thoroughly characterized using the Gallagher spatial learning protocol in the water maze, performance of which depends on the integrity of the medial temporal lobe. However, due to availability from the National Institute of Aging, much of the reported aging studies in rats use the Fischer 344 (F344), Brown Norway (BN), or the F344 x BN hybrid strains. Recently, the performance of aged F344 and F344xBN hybrid strains has been examined using the Gallagher protocol. This chapter will compare the performance of each of these strains with the Long Evans rats using the same water maze protocol. Additionally, middle-aged cohorts have been introduced into experiments with the hope of increasing the sensitivity of the task on honing in on the earliest age where cognitive decline can be detected. Although the water maze is a valid and reliable task for measuring spatial learning, limitations include a problematic test–retest effect making pharmacological, interventional, or longitudinal studies difficult to conduct. However, an odor discrimination task has recently been developed that is appropriate for a wide variety of experiments requiring a retest situation. An explanation of this task will include discussion of other experimental designs involving odor cues used in aging studies and the validity in various rat strains. Keywords Normal aging • rats • cognitive • decline • water maze • olfactory discrimination • executive function *C. LaSarge Behavioral and Cellular Neuroscience, Department of Psychology, Texas A&M University, College Station, TX, USA M. Nicolle Internal Medicine/Section on Gerontology and Geriatric Medicine and Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC, USA J.L. Bizon, A. Woods (eds.) Animal Models of Human Cognitive Aging, DOI: 10.1007/978-1-59745-422-3_4, © Humana Press, a part of Springer Science + Business Media, LLC 2009

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Introduction Multiple behavioral tasks have been characterized to model cognitive aging in rodents. Researchers interested in extending mechanistic work into cognitive/functional domains must consider which task is most appropriate to assess changes in specific brain systems, including the selection of rat strain that best complements the needs of the model. Although some tasks will have common behavioral implications across rat strains, some behaviors are more easily modeled and/or are not detectable to a comparable degree across strains. Indeed, outbred and inbred strains have different genetic characteristics, including life span, which can affect the experimental design optimal for testing and comparison across strains. Moreover, behavioral tasks can be selected that target different neurological systems. The most commonly utilized task to assess hippocampal/medial temporal lobe-dependent spatial memory is the Morris water maze which has been used with a variety of protocols in many strains of rats (1, 2). Additionally, other tasks have been used to assess the function of other neural systems that are vulnerable in aging such as the prefrontal cortex. These tasks include spatial working memory, olfactory discrimination, and attentional set-shifting. This chapter will summarize important differences in rat strains available for age-related research and the cognitive tasks that have been effectively used to model human cognitive aging, highlighting the appropriate similarities and differences that have been reported across rat strains.

Choice of Rat Strain Outbred Versus Inbred The human population shows substantial variability in mnemonic abilities and the age in which a decline in cognition is detected; thus, chronological age is not an accurate indicator of memory abilities (3–5). Therefore, in order to model the pattern of human age-related loss in cognition it is helpful if the rat strain possesses a similar variability in performance. Outbred rat strains, such as Long Evans (LE) or Wistar rats, have more genetic variation than inbred strains and may thus be particularly well suited to model the individual variability observed in aged humans (6, 7). However, it should be noted that there is also more variability in the pathological lesions and other health conditions seen throughout the aging process in outbred strains (7). Due to genetic variability continually introduced in the outbred rat populations, larger sample sizes are needed for studies to show significance because the statistical variance is usually higher than when using inbred populations (for review see (8)). Also of note, investigators choosing outbred strains should be cognizant of the size of the breeding population, particularly because if it is too small the characteristics of the model are likely to change over time (for review see (8)).

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Inbred strains and F1 hybrids also have advantages and disadvantages for use in aging research. Genetic uniformity in a population allows experiments to be more easily replicated across laboratories, and thus increases the usability of a particular model throughout the scientific community (for review see (7)). The biological commonalities between rats in an inbred population also allow experiments in which tissue or other biological materials can be transplanted; for example, a transplantation technique was recently used to graft fetal hippocampal cells into the hippocampus of middle-aged and aged F344 rats (7, 9–11). In general, inbred strains require smaller sample sizes to detect significant differences between age groups since there is less variability between rats when compared to outbred populations. However, when a colony is continually inbred, strain differences in behavioral characteristics and pathologies become more pronounced, as do differences between other colonies of the same strain that are maintained in isolation (7). Recent characterization of the NIA F344 colony has revealed robust individual differences can currently be detected in this strain (12). Hybrid strains can be a good alternative to balance the competing advantages of the inbred and outbred strains. Hybrids usually have a lower incidence of the pathologies than either of the parent strains, longer life spans, and reduced genetic variability that allow for small sample sizes similar to inbred strains (for review see (8)). However, hybrid strains do not have as long a history in the scientific community compared to other strains, and therefore they have not been characterized as well as the more traditional strains, like F344s (13). The popularity of certain hybrid strains in aging research is increasing, however, due to their vigor and lower incidences of age-related pathology.

Rat Strains Multiple rat strains have been used to examine the age-dependent decline in mnemonic function; however, there are biological differences between strains that confound interstrain generalizability. First, rat strains differ with respect to life span. Life expectancy directly affects the age at which animals should be selected for use in an aging study. Life expectancy and the associated age-related decline in physical performance and mortality rate will influence design of the experiment. Furthermore, understanding the point of the life span when measures are taken is critical in comparing data between studies, as commonly used terms like “middleaged” and “aged” refer to different points in the life span in different strains. A second consideration is the general health of the rats; some strains are more susceptible to pathological health conditions that may confound assessment of cognitive status (for review see (7)). Careful assessment of physical health including such factors as monitoring food/water intake, jaundice, pituitary tumors, and viral infection is critical for obtaining solid cognitive data independent of disease in aged rats. A third consideration in choosing a rat strain is of a more practical nature and relates to the availability of rats. Three strains of rats (see below) are currently

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available at advanced ages from the National Institute of Aging (NIA). Strains not available from the NIA must be obtained from commercial vendors as retired breeders or young animals which are housed by investigators to advanced ages appropriate for testing.

NIA Available Strains The NIA offers three strains of rats for aging research, allowing researchers to easily obtain animals at advanced ages. The inbred F344 strain, which has been available through NIA since the mid-1970s, is the most common strain used in aging research (13). Typical studies utilizing F344 rats label 3–6 months as young, 11–18 months as middle-aged, and 22–24 months as aged animals (12, 14–17). The availability of 22-month-old animals from NIA makes it convenient for researchers to conduct studies at very advanced ages, considering the mean age of mortality in this strain is 24–26 months (8, 13, 18, 19). Likely due to the inbred nature of the F344 rats, great care needs be taken to ensure health of the subjects. Some of the more common health problems noted in aged F344 rats are adenomas of Leydig cells, leukemia, pituitary adenoma, bile duct hyperplasia, and hepatic microabscesses in the F344 rats (7, 20). As these health concerns and others described above almost assuredly result in exclusion and/or attrition in a study, researchers utilizing this strain for cognitive aging research need to consider such factors in experimental design. The Brown Norway (BN) and the F344 x BN hybrid (F344BNF1) rats are also available from NIA. The BN rats, initially utilized in Europe, became available from the NIA after the 1978 program determined the incidence of lesions in aged BN and F344BNF1 rats was significantly less than Buffalo, Wistar-Lewis, and the F344 rats (13). Health concerns specifically related to BN include testicular atrophy, chronic dacryoadenitis of the Harderian gland, and nodular vacuolation of the adrenal cortical cells (7). The BN rats typically live longer than the F344 rats, with an average age of mortality in males at 32 months; the hybrid animals live slightly longer with an average age of 34 months at death (8, 18). The F344BNF1 hybrid rats express the expected hybrid vigor and have lower levels of the strain-specific pathologies seen in the parent populations, making them a good model to examine age-related cognitive decline in the presence of maintained physical function (8). However, limited cognitive data exists on either BN or F344BNF1 although the prevalence of research with these strains is growing (e.g. (21, 22)).

Other Non-NIA Strains in Aging Research There are many other rat strains available as young adults from commercial vendors that are commonly used in aging research. Long Evans (LE) rats have been the subject of a vast number of learning and memory studies and have been extensively

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characterized ((6), for review (23, 24)). However, aged LE rats are not commercially available, so they are acquired as either retired breeders (around 9 months of age) or at young ages in which they are housed until the desired age. Assessment of ten recent aging studies utilizing LE rats showed the typical age of young rats used was 3–8 months and aged rats were typically 23–28 months (25–34). Past studies on longevity of the LE strain reported an average age of mortality at 32.6 months (24, 35). Thus, housing and feeding can become costly over time for those investigators without established colonies. One advantage of the LE rats is their robust health, and thus attrition is not as problematic compared to some inbred strains. One health consideration that should be monitored however is adult-onset diabetes, as it has been noted among these subjects (24). Other outbred strains, including the albino Sprague-Dawley (SD) and Wistar rats have been regularly used for many behavioral studies, particularly in Europe (e.g. (36–42)). A descendant from the Wistar strain, the SD strain was historically available from NIA, although it was discontinued when the BN and F344BNF1 were introduced (13, 43). Typical testing is at 20–27 months for aged rats with a life expectancy of 30 months (36, 41, 44). In the aged SD rats, the common pathologies that require screening include pituitary adenoma (the most common cause of death in SD rats) and chronic renal disease (45–49). The outbred albino Wistar rat was the first domesticated rat used for scientific purposes; the original colony started at the Wistar Institute in 1906 (for review see (43)). The Wistar rats have a life span of 30 months and aging studies have used aged rats that are, on average, 24 months of age (38, 40, 44). One important issue to consider when designing experiments with aged subjects or comparing the aging literature is the ratio between chronological age and life span. For example, most of the studies using LE rats are not examining rats at the same age relative to life span as most studies using NIA strains, as seen in Table 1. F344 rats (average testing age of 23 months), BN (30 months), and F344BNF1 (31 months) are all more advanced in age in comparison to their life expectancy when compared to the 26-month-old LE. This difference in life span can influence the extent of impairment detected and also make comparisons between strains more difficult, especially since some strains are tested closer to the life expectancy than other strains.

Table 1 Ratio of mean testing age to life expectancy by strain. Some strains, namely the F344, BN, and F344BNF1 which are available from the NIA, are used in studies at more advanced ages when compared with the outbred strains of the LE, SD, and Wistar rats Mean testing age: life Strain Mean testing age Life expectancy expectancy F344 BN F344BNF1 LE SD Wistar

23 30 31 25.5 23.5 24

25 32 34 32.6 30 30

0.92 0.94 0.91 0.78 0.78 0.80

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Behavioral Tasks to Assess Age-Related Cognitive Dysfunction Declarative/Explicit Memory Age-related cognitive decline is a substantial problem in the United States and other developed nations. Dementia is commonly reported in the increasingly aged population; 7–8% of people over the age of 65 have pathological conditions causing this memory loss, whereas another 20% show cognitive impairment prior to, or independent of, pathological illness (50–52). The most notable deficit in the dementia affected population is the loss of declarative/explicit memory abilities, the ability to encode and recall information about people, places, and things (52–59). Declarative/explicit memory is dependent upon the medial temporal lobe (MTL), particularly the hippocampal formation. Information flows multi-directionally through this system such that the hippocampus communicates with the entorhinal cortex through bidirectional projections, which both receive direct projects from other regions, including the basal forebrain. The entorhinal cortex communicates with the parahippocampal gyrus (including both perirhinal and postrhinal cortices) which, in turn, projects to the neocortex. This system is particularly sensitive to age-related deficits, and this can be demonstrated in rodents through the use of the water maze (2, 6, 12, 60–65). Typically, water maze protocols that test MTL function target spatial reference memory, or memory for information held constant over time (14, 66). The water maze task was introduced in 1982 when Morris published a seminal paper describing his development of this task (1, 2). In this protocol rats must use distal spatial cues to find a hidden escape platform just under the surface of the water. Training consisted of 8 days, one trial on the first day and four trials a day during the other 7. Each training trial was started from a different quadrant of the tank so that the rat had to use environmental cues to navigate to the escape platform. Importantly, Morris tested three different groups in this water maze: a group with hippocampal lesions, another group with cortical lesions, and a control group. The group with hippocampal lesions had significantly longer path lengths to reach the platform when compared to the other groups. Furthermore, when the platform was removed from the tank the hippocampal-lesioned group performed significantly worse than the other groups, indicated by a decrease in their search in the location the platform was previously available for escape. These probe trials provide a more sensitive measure in determining where the rat searches for the platform; it prevents both accidental bumps into the platform and other nonhippocampal-dependent strategies (i.e., circling the tank) from being utilized as a means to locate the platform. The hippocampal-lesioned group was not impaired in a task using the same sensorimotor and motivational components as the spatial/hidden platform training but requiring a different type of navigation that relies on a prominent local cue, a visible escape platform. Thus, these data indicated that the spatial/hidden platform water maze Morris developed is indeed hippocampal-dependent.

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The spatial/hidden platform water maze task has been used in many different laboratories, with many different protocols, to assess hippocampal/MTL function in aged rats (e.g. (1, 15, 67, 68)). However, Gallagher and colleagues published a protocol in 1993 that appears to be very sensitive to age-related decline in the hippocampal/MTL system (6). Gallagher’s protocol for the water maze is similar to that originally described by Morris, but in this case curtains surround the tank, to which patterns are affixed to serve as distal visual cues and probe trials are interpolated throughout the behavioral test. This protocol uses three trials a day for 12 days with a 60 s intertrial interval. For each trial the rat is started in pseudorandom positions around the tank and allowed to swim for up to 90 s to escape onto the hidden platform. Every third trial is a probe trial in which the platform is retracted to the bottom of the tank for the first 30 s of the trials, after which it is raised to the normal position and the rats are allowed to escape normally. This important procedural element prevents extinction which could occur during the probe trial. Similar to the Morris protocol, a visibly prominent, moveable platform that protrudes from the water by 2 cm is used for cue/visual platform training. Cue/visual training consists of six trials, on one day, in which the visible platform is placed in a novel position for each trial. The assessment for performance, developed by Gallagher et al. (6), focuses on measures of proximity to the platform as opposed to traditional measures of latency or path length. Cumulative search error is used for training trials, calculated using the rat’s distance to the platform, sampled ten times per second and averaged into 1 s bins. The cumulative search error is the sum of these bins minus the optimal path from the start location to the platform. This measure is considered more sensitive than other measures because it takes into account the location of the rat’s search relative to the target platform throughout the entire trial. Thus, the cumulative search error measure can distinguish a rat that spent the majority of its time searching around the platform location from one conducting a more random search, even if the latencies and path lengths are identical. The interpolated probe trials are analyzed using mean search error; this is derived by dividing the cumulative search error by 30 s (i.e., the probe trial duration). Gallagher’s Spatial Learning Index (SLI) is based on the interpolated probe trials, where the rat is unable to escape onto the platform during the first 30 s of the trial (6). The SLI is calculated by weighting and summing the mean search error from probe trials 2 through 4 to provide an overall measure of spatial learning ability of each individual rat. The weights are typically derived by dividing the mean search error in the young group on probe trial 1 (on which no age differences are observed) by the mean search errors on the following probe trials. Lower SLI scores indicate better performance Using such proximity measures, Gallagher et al. (6) showed an age-related decline in spatial reference memory performance during training and probe trials in the spatial/hidden platform water maze. In this seminal paper, traditional measures (path length and latency) revealed a significant age difference; however, the search error measure had a greater magnitude of change over the course of training.

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Indeed, search error showed enhanced sensitivity for detecting age-related differences on training trials. On probe trials, there was a significant difference between age groups in the average proximity to the platform that was asymptotic by the fourth probe trial. Also, a significant interaction of age and probe trial was detected such that the aged animals had an attenuated learning curve compared to young. The cue/visible platform training showed no group differences, and thus the decline in spatial memory performance was indeed related to aging and not motivation, sensorimotor skills, or visual acuity problems. The natural aging progression in humans shows substantial variability in the decline of mnemonic abilities such that some humans begin to show impairments in their fourth decade while others perform on par with young adults past their seventieth year of life (3, 4, 60). Additionally, the variance in memory performance in humans significantly increases with age (3, 4, 60). The LE model of natural aging has many important traits, including variability in the aged population and the decline in spatial reference memory performance with age (6). Using the SLI to calculate a performance score for each individual rat, Gallagher et al. (6) demonstrated a significant age difference such that the young rats had better performance in the task. As shown in Fig. 1 using representative data from LE rats in the water maze where SLI scores were normalized to young rat, individual variability is present in both age groups but is greater among aged rats. In the aged group some animals performed as well as the young cohort where the other aged animals performed outside the range of young, demonstrating impairment. Thus, not all aged animals show a decline in task performance, and using this protocol in LE rats reliably separated the aged rats into two groups: aged-unimpaired and aged-impaired rats.

Gallagher Protocol in Other Strains F344 As previously mentioned, a popular rat strain provided by the NIA and available at advanced ages is the inbred F344 strain. A concern about the effects of inbreeding on cognitive tasks, possibly predisposing them to perform more poorly in the water maze compared to outbred rats, and questioning of the ability to detect individual differences led to studies of direct comparison between different strains on the same water maze task. In general, inbred strains, including the F344 strain, were shown to have impaired performance compared to the outbred strains (19). The age-related impairment in the aged F344 rats in relation to the young F344 rat is valid and comparable to LE in that the adult F344 rats are able to acquire the task and become more proficient at finding the hidden escape platform and the aged rats are less proficient in comparison (12, 14, 16, 19, 42, 69–72). Albinism is a trait of the F344 rat strain and contributes to a deterioration in visual ability that becomes more prevalent with increasing age (19, 73). The F344

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5

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Fig. 1 Individual learning index scores normalized to young for BNF344F1 (calculated by summing the proximity to the platform for probes 1–4) and the F344 and LE rats (both calculated from Gallagher et al. (6) protocol). Note that each circle represents an individual rat. These data demonstrate the individual variability seen in each of these rat populations, with some aged animals of each strain performing on par with young, namely unimpaired, and others performing outside the range of young, demonstrating impairment in the water maze

rats are prone to age-related cue/visual platform training differences due to impaired vision, as well as gross motor difficulties that confound the interpretation of spatial/ hidden platform data (16, 19, 73). These results point to the importance of the stringent screening of aged rats for sensorimotor deficits to assure that the data are not confounded by poor physical ability. Age-related decline in water maze performance has been reported in multiple studies utilizing the F344 rats. Frick et al. (14) showed an age-related decline in spatial reference memory abilities as early as 11 months when compared to 4-monthold rats. In this experiment, F344 rats at 24 months of age took significantly longer to reach a hidden platform, spent significantly less amount of time in the target quadrant during probe trials, and were significantly impaired in their swim distance (path length) over the training sessions compared to 4-, 11-, and 18-month-old rats. Aged rats also had significantly worse performance across probe trials with less accurate platform searches when compared to young controls. Other studies have yielded similar results using path length and/or latency measures, showing that there is indeed a detectable impairment in aged F344 rats when compared to young (12, 13, 69, 74, 75). Even though an age-related decline in spatial reference memory has been reported, there is been debate over the measures analyzed to make this

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conclusion. Multiple studies report a significant decrease in swim speed in aged F344 rats compared to young, suggesting that latency to platform is not a valid measure to use when analyzing data from the F344 strain (12, 15, 75). As such, path length and proximity measures originally described by Gallagher et al. ((6); see above) are better performance measure for this strain (12, 70). Bizon et al. (12) recently tested young (6 months), middle-aged (12 months), and aged (22 months) F344 rats using the age-sensitive Gallagher protocol, confirming previous age related differences reported between young and aged F344 rats using the same protocol (70). For both studies, cumulative search error (Fig. 2) and path length were analyzed for training trials, and both measures showed that all rats improved over the course of training. Additionally, using both measures the aged were significantly impaired relative to young. In Bizon et al. (12) the middle aged were significantly impaired compared to young when cumulative search error was used. Analyzing probe trials using mean search error showed similar results such that all rats improved over the course of training, but there were group differences with the aged rats significantly impaired compared to young and middleaged, and that middle-aged rats performed significantly worse than young. Importantly, as with the outbred LE rats, individual differences in the F344 population can be detected when probe trials are analyzed using the SLI, demonstrated in Fig. 1. The existence of individual variability within this population was previously unknown, but was doubted due to its inbred nature. Using the SLI scores, significant age-group differences were seen such that the aged rats were significantly impaired compared to the young and middle-aged, and the young performed significantly better

125 1 4 2 3 Block of 5 training trials

b

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Chronological age

Fig. 2 Group spatial learning performance in young and aged F344 rats during training trials. (a) Cumulative Search Error is averaged into four blocks of five training trials. Although all rats did indeed learn over the course of training, the aged rats were significantly impaired relative to young rats. (b) Individual rat performance on probe trials in the spatial learning task. Lower learning index scores reflect better performance. A higher degree of variability was present in aged compared to young subjects. Note that some aged rats (open circles, n = 6) performed within the range of young rats (closed circles, n = 8); whereas others performed outside this range, demonstrating spatial impairment (open circles, n = 6). (Reprinted from (70); Copyright (2007), with permission from Elsevier)

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than middle-aged (12, 70). Importantly, modeling the data seen in the LE strain, a large degree of variability was seen in the aged F344 population such that about half of the population performed as well as the young cohort whereas the other half performed outside the range of young, demonstrating impairment. The data show that aged F344 rats, like LE, can be distinguished as impaired and unimpaired groups, as seen in Fig. 1 (12, 70). The ability to model the individual variability seen in the human and make a distinction of impairment group in aged animals, and even at middle-age, makes F344 rats an effective model of the natural aging progress that can be used to delineate the neurobiological mechanisms responsible for age-related decline in cognition when stringent controls are used and careful attention is given to the selection of performance measures.

Emergence of Spatial Learning Deficits at Middle Age in F344 Rats Although there is a large literature on deficits in spatial reference memory in advanced age, data reporting cognitive changes at middle age is minimal. There is an important line of research emerging that suggests in humans the decline in declarative memory likely begins during middle age (3, 60). Middle-age-related differences have been detected as early as 11 months in F344 rats (12, 14). Frick et al. (14) reported a significant decline in performance in the 11- and 17-month-old rats compared to the 4-month-old rats when tested in a water maze protocol. In agreement, Lindner et al. (75) reported a significant increase in the distance that the 16-month-old F344 rats swam in search of the platform compared to 2.5-month-old rats. As previously mentioned, Bizon et al. (12) also reported middle-age (12 months) impairments in both training and probe trials, measured by cumulative search error and mean search error respectively. Furthermore, as a group middleage SLI scores were significantly worse than young (6 months). Despite shifts towards worse learning, a scatter plot of the young and middle-age individual SLI scores indicate that almost all the middle-age perform within the range of young. This demonstrated that slight, but detectable, changes in spatial reference memory ability are starting earlier than at 12 months. This ability to detect early changes in cognitive functioning makes the F344 model of natural aging more attractive, as it is able to accurately model the middle-age memory decline seen in the human population (3). Future work directed at distinguishing individual differences at earlier ages will be of substantial interest.

Brown Norway and Hybrid (F344XBN) Research on BN and the hybrid F344BNF1 rat strains utilizing the water maze is limited but increasing due to the availability of aged rats from the NIA colony. A direct comparison between BN, F344BNF1, and F344 adult rats revealed that the

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BN and the F344BNF1 rats have a steeper learning curve, indicating faster learning, when compared to the F344 rats (11). The hybrid F344BNF1 rats also had a faster swim speed than the BN and F344, which did not differ. BN have shown a progressive decline in water maze performance over 3, 12, 24, and 30–32 months of age (76). The F344BNF1 have been studied in a 6-day protocol using young (4–6 months), old (23–25 months), and very old (31 months) rats (77). In this study all animals learned, but when directional heading error was measured only the very old animals learned significantly slower than the other ages. Swim speed also decreased over age, as seen with the F344 rats (described above). A newer study showed that 33–34-month-old F344BNF1 rats perform significantly better in the water maze when compared to aged 35–36-month-old rats, indicating a significant decline around the median life span of this strain (78). Recently the Gallagher water maze protocol was used to evaluate spatial memory in young (6 months) and aged (28 months) male F344BNF1 rats (79). As in the previously described LE rats, there was a significant effect of age in F344BNF1 rats when the interpolated probe trials were used to measure the development of a spatial bias for the training quadrant (Fig. 1). The scatter plot of the individual proximity scores demonstrate that there is the predictable performance overlap of a subgroup of aged rats that perform similarly to the young rats (“learning unimpaired”) while the other subset is impaired relative to the young performance (“learning impaired”) (Fig. 1). Sensory and motor ability in aged F344xBN rats was assessed by latency to reach a visible platform and swim speed during probe trails. Like the F344 parent strain, the aged rats take significantly longer to escape to a visible platform and have a slower swim speed compared to young rats (not shown). These results indicate that latency is a confounded measure in aging studies of F344BNF1 rats. Confidence in the neurobiological significance of this division of “learning unimpaired” versus “learning impaired” aged rats is supported by the parallel findings of a decline in muscarinic receptor function in the aged impaired rats, a finding that was observed in both LE and F344BNF1 rats using the same spatial learning protocol and muscarinic receptor function assay (Fig. 3) (80).

Spatial Working Memory Spatial working memory is another type of declarative memory affected by age, defined as the storage, and manipulation of consistently changing information over a brief period of time ((14, 81), for review see 82)). The neural substrates of spatial working memory are somewhat different from those used during spatial reference memory and include a greater role of the prefrontal cortex (PFC). Lesion studies indicate that the PFC and the ventral portion of the hippocampus, from which projections connect the two in the system, are critical in spatial working memory (83–85). Human research has used positron emission tomography (PET) to delineate the neural substrates activated during spatial working memory between adults and elderly subjects. Differential activation in the prefrontal cortex (PFC) has been

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F344xBN F1 160

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Fig. 3 Oxotremorine-M-mediated GTP-Eu binding in Long-Evans rats (A,C) and F344xBN (B,D) rats in the hippocampus (A,B) and the PFC (C,D). B and D are from (80) and demonstrate that the aged impaired rats had significantly less oxotremorine-M stimulated GTP-Eu binding than either the young or the aged-unimpaired groups. Similar results were in F344BNF1 rats (unpublished). *p < 0.01

observed between age groups in a spatial matching task (for review see (82, 86)). In the task there is a presentation of a target object on a video screen, a 3 s delay, and a probe that tests for memory of the target object and its location on the screen. During this task young adults (18–30 years old) had neural activation concentrated in the left hemisphere of the dorsolateral PFC whereas the elderly subjects (62–75 years old) used both the left and right hemispheres of the dorsolateral PFC (86). Thus, there may be compensation mechanisms utilized by individuals with advancing age that are involved in spatial working memory. Impairments in spatial working memory have been reported in aged rats of various strains (12, 14, 16, 40, 75, 87–91). The most commonly used task, termed repeated acquisition or delayed match-to-place, is modeled after spatial/hidden platform training in the Morris water maze (12, 14, 16, 75, 87). Again, a large

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circular tank is filled with opaque water which hides an escape platform. The rat is started from different positions around the tank so that the animal must learn the location of the platform in relation to extramaze cues. There are only two trials in each session: an information and retention trial. Each day starts with an information trial where the platform is placed in a novel position. Upon entry to the maze, the rat must locate the platform and escape onto it. After an intertrial interval, the rat is started at a different position in the tank, and is required to locate the platform in the same location a second time using the extramaze cues; this is called the retention trial. The intertrial interval varies between different protocols, and some experiments use multiple intertrial intervals to determine the time period in which different age groups can retain the information of the platform location (12, 14, 16, 75). Age-related working memory deficits in the water maze can be detected in F344 rats throughout their life span. A recent study by Bizon et al. (12), using young (6 months), middle-aged (12 months), and aged (22 months) F344 rats, showed an age-related decline in working memory that interacted with an increase in the intertrial interval. Although there were no significant differences between age groups when retention trials occurred after a 30 min delay, at 2 h the aged group performed worse than middle-aged rats and significantly worse than the young ones. Furthermore, with an increase to a 6 h delay, both middle-aged and aged rats were significantly impaired when compared to the young ones (12). Other studies with small variations to the protocol in this task design also support this age-related impairment. Frick et al. (14) showed that 24-month-old rats were impaired compared to 4-, 11-, and 17-month-old rats when a 3–4 min intertrial interval was used. In another study using a 10 min intertrial interval, 18- and 22-month-old rats were significantly impaired when compared to 6-, 12-, and 15-month-old F344 rats; unlike the 6-month-old rats, the 12- and 15-month-old rats did not improve between the information and retention trials (16). A 1 h intertrial interval in yet another study showed a significant impairment between 24- and 2.5-month-old F344 rats, but no difference was detected between 16- and 2.5-month-old rats (75). Common to all the aforementioned studies with varying intertrial intervals, there was an agerelated impairment in working memory regardless of differences in intertrial intervals or protocol variations. Age-related sex differences in working memory have also been reported, although the data are minimal. Wistar rats show an age-related impairment in spatial working memory that is mainly attributed to an impairment in the aged males but not females (38). Specifically, when males and females are grouped together, 24-month-old rats were impaired in working memory abilities compared to both 7- and 16-month-old rats. However, when the performance was analyzed with sex as a factor, only the 24-month-old males were impaired and there was no impairment in the females (38). These data suggest a sex-dependent preservation of spatial working memory in female rats. Human data has shown greater age-related neuronal atrophy in men, particularly in the frontal and temporal regions, that may support such a sex difference, but research in this area in extremely limited (for review see (82)). Nonhuman primate data support the general pattern observed in rats and humans: Aged female rhesus monkeys perform better than males in a spatial

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delayed recognition span test, where they must discriminate between the location a stimulus is previously seen and subsequent locations it is viewed in (see Lacreuse chapter; (92)). Working memory task protocols are more variable than reference memory tasks conducted in the water maze and some may be more sensitive than others. As previously mentioned, there is inconsistency in the intertrial interval needed to detect impairment in different age groups, and there are sex differences in this age-related decline in performance (12, 14, 16, 38, 75). Although there is more variability in the protocols utilized for the working memory tasks, the variability may be produced by prior exposure to the water maze, since some subjects are previously tested in the reference memory version (12, 14). There is indeed evidence from human experiments that exposure to a stimulus can affect subsequent neural activation when that stimulus is again encountered. Using fMRI, Stern and colleagues (84) showed using a two-back working memory task, requiring the subject to identify any visual scenes that were repeated after one intervening stimulus, that exposure to novel stimuli results in an increased signal in medial temporal lobe structures and familiar stimuli induces greater activation in the PFC. These data suggest that prefrontal cortical regions may be recruited to a greater degree during working memory tasks performed under circumstances in which there is a possibility of interference from previously learned stimuli. Thus, protocol variations, including changing the positioning of the platform to novel positions with respect to distance to the water maze wall as well as quadrant for each trial, the intertrial interval, and the type of testing preceding the working memory task, may affect the sensitivity of the task in detecting age-related impairments.

Attentional Set-Shifting The age-related decline in PFC-dependent tasks affects more than just those utilizing spatial working memory abilities. Executive function, defined as the ability to change behavior when contingencies are altered, is also dependent on the integrity of the PFC (for review see (4)). In fact, executive function is one of the first cognitive domains to be affected by the aging process (93–95). Set-shifting tasks have been used in animal and human studies to determine the effects of aging on executive function; these tests often use discrimination learning to determine a set of rules about multiple dimensions of a stimulus (e.g., shape and color) before changing the importance of each dimension, such that a previously disregarded feature will become important. Impairments in the Wisconsin Card Sorting Test (WCST), used in human research to test shifting abilities, have been correlated with dysfunction of the PFC, as well as the basal ganglia (4, 96–99). Tests using nonhuman primates yielded the same results as humans; middle-aged and aged monkeys are impaired in their ability to set shift, with a tendency toward perseverative responding (100, 101). Additionally, these deficits are not due to working memory impairment, as a decrease in working memory demands in a human task does not correct for shifting impairments (102).

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A set-shifting task was created for rodents, based on the WCST, to show the functional similarity of the rodent medial frontal cortex to the lateral PFC in humans (103). This task was later used to model age-related changes in PFC function as seen in the human population (104). In the set-shifting task, stimuli in each of the dimensions were manipulated such that a series of five types of discriminations could be tested. The animal would have to learn which dimension was important, and then associate the positive stimulus with a food reward. For the set-shifting task the animal started with only one dimension in a simple discrimination problem (SD); either different odorants were placed on the lip of two terracotta pots filled with home cage bedding, or two pots free of odor were filled with different digging media (104). Only one of the pots was baited with a food reward, and that remained constant throughout the SD problem. The pots were placed at one end of a large Plexiglas container; an opaque Plexiglas divider separated the box into a smaller compartment from the pots. Rats had access to the pots once the divider was raised, which was subsequently lowered after the rats entered the other compartment. The rats were required to choose the correctly baited pot, by digging in it first, on six consecutive trials, in order to reach criterion and move to the next problem. The compound discrimination (CD) used the same positive stimulus as the SD, only now the other dimension was included but was irrelevant. For example, if the SD was an odor discrimination then the same odorant would continue to be positive, but now instead of home cage bedding the two pots were filled with two different media that would vary in combination with the odorants between discriminations. The medium CD problems would add an odor pair. For the intradimensional shift (IDS) the odor pair and media pair used changed, but the relevant dimension was held consistent – so if odor was the relevant dimension before then it would continue to be relevant. The fourth problem was a reversal; the previous pairs and relevant dimension from the IDS problem were held constant, but now the odorant or medium that predicted the reward switched to the previously negative stimulus. Last in the protocol was an extradimensional shift (EDS), in which a new odor and media pair was introduced, but now the dimension that had been irrelevant throughout the entire protocol was now switched – if the odor dimension was always relevant then the rat had to discriminate between media for the EDS and ignore the odorants. Evaluation of attentional set-shifting in outbred Listar rats with lesions to the medial frontal cortex verified the functional similarity between the rodent medial frontal cortex to the lateral PFC in primates (103). Intact rats were able to complete the set-shifting task; however, they took longer complete EDS problems, when compared to IDS problems, indicating that an attentional set was formed (103). Rats with medial frontal cortex lesions were impaired specifically on the EDS phase of the task when compared to intact rats. Interestingly, the performance of aged LE and SD rats on the attentional set-shifting task is comparable to rats with lesioned medial frontal cortex (104, 105). This impairment implicates a decline in set-shifting with the natural aging process. Investigations into the neurobiological mechanisms of this age-related EDS impairment in rats have revealed a correlated age decrease in medial frontal and cingulate cortex kainate binding of the glutamatergic

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system; conversely, age-related decreases in NMDA receptor binding in the dorsomedial striatum and cingulate cortex were associated with preserved performance, possibly indicating a compensatory mechanism (106). In addition to slower EDS performance, young rats took longer to acquire reversals when compared to SD, CD, and IDS problems in the set-shifting task; however, the reversal problems were not affected by lesioning the medial frontal cortex (103, 104). Throughout the literature there is more inconsistency in data describing an age-related decline in discrimination reversal problems (for review see (99)). Barense et al. (104) reported that, although not significant for the group, some aged rats showed impairment in reversal learning; but it was also theorized that reversal deficits seen in this study and others may only be present during the initial encounter of this type of problem. Rodefer and Nguyen (105) showed a trend of age-related reversal impairment. Conflicting data of reversal deficits suggest that this part of the task may be dependent on a different brain system, possibly more resistant or variable to age-related deficits (105). Indeed, orbital frontal cortex lesion studies have previously demonstrated impairments in reversal learning (106, 107–109). More recently shown, nonspecific lesions, and not cholinergic lesions, of the basal forebrain produced impairments on the first reversal problem during a set-shifting task; this implicates the involvement of an alternative system such as GABAergic and/or glutamatergic neurons in reversal deficits (110). Further examination of the neurobiological mechanism of reversal learning is still needed. Although the set-shifting task previously described has great utility in assessing age-related decline in the medial frontal lobe, the task has been shown to have some strain specificity. The set-shifting task has been successfully used with LE, SD, and Listar rats (103–106, 110). However, some aged (22 months) F344 rats impaired in the Morris water maze were shown to have differential abilities in performing odor and medium discriminations ((70)) as described in detail below.

Odor Discrimination Olfactory detection and discrimination deficits are seen in the human population with advancing age (111–115). The olfactory system projects to the medial temporal structures to provide olfactory sensory information to the hippocampus (111). Odor information is initially processed by the olfactory bulb and then in the piriform and orbital frontal cortices, which in turn have reciprocal connections with the perirhinal and entorhinal cortices (reviewed in (111)). There are also direct connections from the olfactory bulb to the entorhinal cortex, and the hippocampus is activated during odor discrimination learning in rodents and humans (116–119). Studies show that young people have greater uniformity in olfactory detection compared to aged individuals (120). Other research in humans indicates that olfactory deficits, particularly the inability to identify a common odor when smelled, are associated with impairments on cognitive assessments and Mild Cognitive Impairment (112, 113, 115).

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Olfactory functioning in an animal model has been addressed, although it has yet to be thoroughly characterized in relation to aging. Previous research showed that aged rats were significantly impaired in a Go–No-Go odor–reward association task, in which only 40% of aged F344 rats could learn at the same rate as young cohorts (121). Recently in our laboratory a naturalistic odor discrimination task was created to test the connection between odor abilities and cognition, and it has shown great utility in the detection of age-related cognitive impairments. In our laboratory we discovered that the aged F344 rats that were impaired in their ability to perform odor discrimination problems were the same rats impaired in spatial reference memory, indicated by a significant correlation between the trials to criterion in olfactory discrimination with probe trial performance in the Gallagher spatial reference memory version of the water maze (70, 6). These data perhaps reflect a common deterioration of the function of the hippocampus, although the exact neural substrates responsible for this relationship remain the subject of future experiments. The odor discrimination task in LaSarge et al. (70) resembles the simple discrimination phase of the set-shifting task described in the previous section (104). Upon completion of the Gallagher protocol in the water maze, young (6 months) and aged (22 months) F344 rats were food-deprived to 85% of their free-feeding weight. The discrimination apparatus consisted of a plastic box separated into start and test compartments by an opaque removable Plexiglas divider, as seen in Fig. 4. On each trial, a rat was placed into the start compartment and the divider was raised, allowing the rat access to the terracotta pots in the test compartment. As in Barense et al. (104)), the rat must learn to discriminate between two odors (e.g., lemon and rosemary) or two digging media (e.g., shredded paper and plastic beads), digging in the correct pot, to obtain a food reward. The rats were tested in four simple discriminations, alternating two digging media and two odor discrimination problems. Acquisition was achieved by six consecutive choices of the correct (baited) pot, after which they immediately began the next problem. Both number of trials and errors to criterion were recorded and used as measures of performance. In LaSarge et al. (70), F344 rats previously characterized on the spatial water maze were split into aged-impaired and aged-unimpaired groups depending on their SLI score. As shown in Fig. 5, rats that performed poorly in the water maze also had significantly worse performance during odor discrimination problems when compared to young and aged-unimpaired, measured by trials- and errors-tocriterion. Additionally, this deficit in the aged-impaired rats did not improve during subsequent odor problems, even though they were able to reach the six-trial criterion to finish each previous odor discrimination problem. When medium discriminations (Fig. 5) were tested there was no significant difference between groups, indicating that this deficit was not due to a global learning deficit but specific to odor problems. Following the discrimination task, rats were tested for their ability to detect and respond to decreasing concentrations of odorants to determine if aging produces an impairment in this sensory domain (70). The test began with a new odor discrimination problem using one drop of full-strength odorant versus one drop of mineral oil

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Fig. 4 Odor Discrimination Testing Apparatus. For odor discrimination testing, rats were placed in a translucent box with a Plexiglas barrier between the start and test compartments. The rats had a choice of two pots that differed in either odor, such as rose and citrus, or digging medium, such as sequins and Styrofoam. The rat must learn to associate the positive stimulus with a food reward (see Color Plate 2)

placed on the rims of two pots filled with home cage bedding. The food reward was in the odorant pot, and rats were trained until they reached criterion performance. Testing continued with three further discrimination problems, using increasingly dilute concentrations of the same odorant (diluted 1:10, 1:100, or 1:1000 in mineral oil) paired against mineral oil for 16 trials per dilution, to determine if the rat could detect the odorant above chance level; indeed, the aged rats were able to smell indicating that this was an odor discrimination deficit in the aged-impaired rats. One notable attribute of the odor discrimination task that makes it a useful model of cognitive decline is its test–retest ability and adaptability for longitudinal testing. The water maze is not optimal for within-subject tests, or those that require a comparison of a baseline measure with later performance, due to the improved performance resulting from the procedural components of the task that endures for long periods of time; even “aged-impaired” rats will show a practice effect up to 12 months or more following water maze exposure (122). While between subjects

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designs are possible for testing therapeutic options for mnemonic impairment using water maze, a task that has test–re-test reliability has advantages in that it would be more similar to that used in human clinical trials and thus have value in the added translational approach. Although impaired aged rats do eventually acquire each odor discrimination problem, they do not improve in their ability to acquire new odor discrimination problems, as evidenced by a correlation between subsequent odor problems (70). Thus, this task can be useful for identifying underlying neurobiological causes of age-related cognitive impairments and perhaps particularly useful for testing pharmacological agents or other interventions designed to enhance cognitive function in aging. Recently this odor discrimination task was utilized in our laboratory to test the cognitive enhancing effects of donepezil, a cholinesterase inhibitor commonly prescribed to Alzheimer’s disease patients. A group of 15-month-old F344 rats were trained on the odor discrimination task as described above, using two odor and two media discrimination problems. After baseline characterization, donepezil effects

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on odor discrimination performance were evaluated using a within-subjects design. Rats received injections of saline vehicle or donepezil (1.0 or 3.0 mg/kg) 40 min prior to testing in each of four novel odor discrimination problems, with a 48 h washout period between problems. The order of drug doses was randomized across rats. As shown in Fig. 6, rats performed significantly better after the administration of 1.0 mg/kg, but not 3.0 mg/kg, of donepezil when compared to saline administration. This data suggests that the cholinergic system may play a role in odor discrimination performance. Additionally, this experiment demonstrates that the odor discrimination task can indeed be reliably used to test pharmacological agents using a within-subjects design. This odor discrimination task has only been used in the F344 strain; as previously seen in the set-shifting task, odor and medium discrimination abilities do not differ in aged LE, Listar, or SD rats (70, 103–106). One possible reason for such a strainspecific task may be the age in which the rat strains are tested. As addressed in the beginning of this chapter, different strains have varying life expectancies; however, in aging studies many different strains are tested at the same age (Table 1). This also means that the F344 rats are tested at more advanced ages compared to the other strains; thus, it is a possibility that other strains could take advantage of this odor

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discrimination task if the animals are tested at later ages. Additionally, this difference in strains and olfactory functioning may be due to differences in threshold of their olfactory functioning and the sensitivity of the task to detect difference.

Summary of Cognitive Impairments Across Domains It has been established that spatial reference memory declines with advancing age; however, whether this reflects a global age-related decline in mnemonic function or the decline of a specific cognitive system is still largely questioned. Rodent models have been used to compare different behavioral tasks for associations between neural systems. Although some behaviors decline in concert, others seem to depend on different neural systems that do not decline at the same rate in aging. Both the reference and the working memory versions of the water maze show an age-related decline in performance over the life span, but no correlation has been observed between the two tasks (12, 14, 40). Notably, the individual variability observed in the reference memory task was not evident to the same degree in the working memory task (12). Even when rats were grouped by performance in the reference memory water maze (by impairment groups), the aged impaired and unimpaired groups did not perform differently on the working memory version (12, 40). Moreover, there is also evidence that decline in both spatial reference and working memory is also sex-dependent with males having a larger age-related decrease in their performance ((38), for review see (82, 92, 123)). The evidence supports the conclusion that multiple neural systems decline independently in aging, and that these system declines may occur at different rates in the two sexes. Moreover, both spatial reference memory and set-shifting abilities are susceptible to age-related decline, but existing evidence strongly suggests these impairments are also independent. Studies have tested aged rats on both the reference memory water maze and in the set-shifting protocol, but no relationship was seen between performance in the two tasks (104, 106). In each study, aged rats showed the previously reported age-related decline in reference memory. However, the absence of a correlation indicates that the set-shifting deficits, namely in the EDS and reversal phases, are likely unrelated to hippocampal performance. Since setshifting abilities involve the PFC in rats, like spatial working memory, it is speculated that these two types of decline would have a common decline in performance; however, this has yet to be tested. As described, there are some task decline commonalities in aging. Using the odor discrimination task, LaSarge et al. (70) reported an age-related concurrent decline in both odor discrimination abilities and spatial reference memory in F344 rats. In this study, when aged rats were separated into impaired and unimpaired groups by their SLI scores in comparison to young rats, it was the same impaired aged rats from the water maze that performed outside the range of young on odor discrimination problems while the unimpaired aged rats performed on par with the young rats. Additionally, there was a correlation between

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Fig. 7 Scatterplots of individual rat performance, normalized by the mean of each group (young and aged). (a.) Spatial learning index on the water maze versus mean trials-to-criterion across all three odor discrimination problems. (b.) Spatial learning index on the water maze versus mean errors-to-criterion across all three odor discrimination problems (Reprinted from (70); Copyright (2007), with permission from Elsevier)

trials to criterion on the odor discrimination problems and SLI scores from the water maze task (Fig. 7). However, only the F344 rats have been tested using this sensitive odor discrimination task; other rat strains have yet to be tested with this protocol and do not show this decline between odor discrimination and spatial reference memory in other odor tasks (109, 124, 125). The correlated decline in hippocampal-mediated spatial reference memory and odor discrimination observed in F344 rats suggests that there is either a common component to both behavioral tasks that declines in aging or there is concurrent decline in two systems that is also strain-dependent.

Conclusion The primary considerations in the selection of a rodent model of cognitive aging include strain selection, longevity, and the appropriate behavioral task to assess the cognitive domain of interest. Tasks appropriate for longitudinal testing with low test–retest effects, such as olfactory discrimination, have the added advantage of being analogous to human clinical studies that investigate the effects interventions on age-related cognitive decline in reference to baseline performance at a younger age. In addition, standardization of the ages of rats used relative to the strain life span would facilitate comparison of results across laboratories. Acknowledgments We would like to thank Jennifer L. Bizon, William E Sonntag, and Karienn Montgomery for their assistance. Donepezil data presented was supported by AG029421 (from J. L. Bizon) from the National Institute of Aging and a grant from the Office of the Vice President for Research at Texas A&M University (J. L. Bizon).

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Mouse Models of Cognitive Aging: Behavioral Tasks and Neural Substrates Michael E. Calhoun

Abstract In large part because of the comparative ease of genetic manipulation, the last 20 years have seen an exponential increase in the use of laboratory mice for behavioral research, with prominent genetic models available for neurodegenerative disease, and for directly manipulating and visualizing functional parameters in the living organism. Progress in understanding higher cognitive function in mice has however been hampered by several unique characteristics of this species, some of which become more challenging when examining age-related changes. Performance in several commonly used behavioral tasks is often influenced by noncognitive factors or preference for alternate strategies. Strain differences are prominent including in the manifestation of age-related sensorimotor impairments. Although previous research and commentary has sought to address these shortcomings, only recently have alternative testing procedures become more prominent. This chapter thus seeks to first rereview some of the underlying reasons to why the mouse may be ill-suited for rat-testing protocols, and summarize newer protocols which, if widely adopted, may address these long-standing issues and bring more consistency to the field. Finally, the last section will present a limited sample of studies that have already provided compelling data on cognitive aging despite the known challenges. Keywords Mouse • cognition • aging • learning • hippocampus • prefrontal cortex • set-shifting • water maze

Anatomical Species Differences Critical differences in neural organization in regions relevant to higher cognitive function have been reported between mice and rats, although many aspects of general brain organization are similar and care should be taken how such comparisons are performed (1). A comprehensive study of the organization of the dentate gyrus and response to entorhinal cortex lesion (reviewed in (2)) has indicated that the prominent M.E. Calhoun Department of Cellular Neurology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany J.L. Bizon, A. Woods (eds.) Animal Models of Human Cognitive Aging, DOI: 10.1007/978-1-59745-422-3_5, © Humana Press, a part of Springer Science + Business Media, LLC 2009

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commissural entorhinal cortex projection is not present in mice, and although the commissural/associational projection in mice occupies a relatively smaller percentage of the dentate molecular field, its sprouting response is more likely to cross layer boundaries and innervate the denervated zone. Recently, detailed delineation of the CA2 subfield of both mouse (3) and rat (4) have been published which emphasize unique characteristics of this subfield (see also (5)), and although the extent of CA2 in mice appears different in the mouse brain atlas ((6); also see Fig. 3 for reference), further direct interspecies study is necessary. An early study of mediodorsal thalamic projections to prefrontal cortex (PFC) in the mouse found broad similarities to the rat (7), and although a report in preparation on mouse PFC areas applying cytoarchitectonic and neurochemical criteria again suggests broad similarities with rat PFC areas (8–11), both criteria also differentiate particular mouse PFC subregions (Drs. Uylings and van de Werd, VU University Medical Center, Amsterdam, personal communication, 2008). Although the delineation of anatomical and molecular differences between the species is only beginning, these first results provide potential neural substrates for observed interspecies differences in behavior.

Morris Water Maze The prominence of noncognitive factors which affect the performance of mice particularly on the Morris water maze (MWM) have been eloquently covered elsewhere (e.g. (12–15)), but a brief overview is worth repeating. Not surprisingly, given the evolutionary divergence of rats and mice, prominent ecological differences include their behavior in water and when exposed to open space (also covered in (16)). For analysis of MWM performance, David Wolfer and colleagues have developed a decision tree whereby swim-path data are analyzed for stereotypical behaviors such as wall-hugging or floating. The potential influence of these types of parameters and differences in search strategy (discussed below) can be measured with available analysis packages (e.g., WinTrack (17), and HVS Image (Buckingham, UK)), and only then can conclusions be drawn about any difference in primary parameters such as spatial memory. However, even then, no standard approach has been successful because in most studies, mice do not exhibit the spatial bias in a probe trial that has been evident in rats. Tuning this behavioral setup, by, for instance, altering tank size, water temperature, and the location of visual cues (discussed in the above citations, those below on genotype interactions, and (18, 19)), can increase the likelihood of a demonstrable spatial preference, but no reliable criteria have emerged across laboratories that would allow consistent application. Use of the MWM thus continues to present a serious efficiency issue.

Search Strategy Aside from simple stereotypical behaviors, the above studies also highlight basic cognition differences between rats and mice in the strategy used to solve a task, with spatial memory being perhaps the most efficient but certainly not the only

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possibility (12, 13). Indeed, the use of chaining, or sequential search of all locations at a fixed distance from the maze wall, is prominent in mice (20) and requires a minimum of additional swim time/distance. Thus, despite attempts to isolate distinct cognitive functions with selective behavioral tests, in many cases strategy ambiguity remains, and a shift in strategy type may represent altered processing in another related neural system. Particularly with respect to aging where noncognitive phenomena may alter (for instance) the reward–punishment contingencies of a particular strategy, such strategy shifts may not necessarily be considered a deficit. Anxiety and what has been called “impulsivity” in mice (16) also lead to differences in strategy, or potentially, an increase in the likelihood of a lack of strategy. For instance, we have noticed in the tasks described below, a relationship between task latency and performance of a subset of the mice, typically those that appear subjectively to be the most agitated, also perform a choice most quickly (lowest latency), but tend to select the first available stimulus rather than performing a discrimination. Again, viewed in terms of reward contingencies, this “strategy” can also reap rewards – although the percentage of correct choices in a two-choice discrimination would on average be 50%, such mice perform many more trials, have minimal negative feedback (a 30 s Inter-trial Interval (ITI)), and in the end will retrieve more rewards than a mouse performing at 100% accuracy. An inverse relationship between speed and accuracy has also been reported in a radial maze task (21), although the authors in this case chose to interpret this as an independent retention of valence. More attention (by researchers!) is necessary to anxiety as a confounding factor in all mouse behavioral paradigms (22), and perhaps with more direct study, ways can be found to minimize or at least control for stress-induced confounds.

Strain Differences A major challenge in all research on the mouse is that the typically inbred strains have varying life spans, basic physiology, and behaviors (23), including parameters such as seizure-induced behavioral deficits and neurodegeneration (24). For studies of aging and cognition, these differences go beyond simple confounding factors in that some strains simply do not perform certain behavioral tasks, and age-related pathologies may prevent visual-based tasks in other strains. In a study of 13 of the most frequently used strains of mice, visual ability was a strong predictor of MWM performance, with only four of the studied strains performing “very well” in the vision test even at the young-adult age tested (25). Parameters of the MWM testing apparatus have been adapted for mice as mentioned above, but also interact with strain (18), with some strains only able to perform the task in a reduced maze. These, studies summarized above, and others which demonstrate effects of testing environments and strain interactions (26–30), further highlight the need to control for and address strain differences. Further complicating analysis of divergent strains, the generations resulting from crosses between strains themselves differ from the individual strains and may

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even have increased performance on functional measures compared to either parental strain (e.g. (31, 32)). Such crosses have traditionally been necessary for creation of transgenic lines, making this problem difficult to avoid. To prove basic applicability beyond an individual mouse strain, research needs to be done in parallel on multiple strains, and the use of mice backcrossed to a parent strain reduces the possibility of results unique to the hybrid strain (see (33) for nomenclature and breeding details). Although the use of outbred strains would potentially reduce strain-specific anomalies, genetic models would need to be reestablished and a benefit of reduced genetic variability would be lost.

New Behaviors Based on groundbreaking initial work on attentional set-shifting (34) and temporal sequences (35) in rats, we and others have more recently begun using a set of discrimination tasks based on recovery of a buried food reward. The stimuli can be presented either singly or in pairs/groups, and can differ with respect to their intrinsic (e.g., odor or visual/tactile) or extrinsic (e.g., spatial location or position in sequence) characteristics (Fig. 1A). The flexible presentation of these combinations, even in a very simple apparatus (Fig. 1B), can yield a highly complex set of stimulus conditions, and allow testing of multiple higher cognitive functions and neural systems (e.g., set-shifting, Fig. 1C) without the requirement of learning additional procedural aspects. Once procedural learning is mastered, learning of even complex relationships between stimuli can occur in a single trial, satisfying an important requirement of episodic memory research. Since its initial description in rats and first studies in mice, use of the attentional set-shifting paradigm has yielded consistent results in a variety of mouse models and laboratories (36–40). This is not however a silver bullet – as with any task, there may exist multiple strategies to solve individual aspects, anxiety and other confounding factors cannot be eliminated, and sensorimotor deficits will continue to impact performance. However, because of the many stimulus types and arrangements, age-related problems with visual acuity for instance can be overcome by use of highly tactile stimuli. The stepwise nature of procedural training, with simple discriminations preceding more complex tasks, allows for straightforward inclusion criteria and interpretation. To date, the published work in mice has been using the set-shifting paradigm that, although as mentioned above, does test several neural systems, but does not include a component strictly dependent upon the hippocampus. We have thus adapted the temporal sequence paradigm from rats (35), and in unpublished pilot experiments, mice were also able to learn this episodic memory task. As was the case in rats, many sessions were necessary to learn the protocol (26 sessions for three mice to reach average criterion of 80% correct in the ten probe trials; two sessions/day; in each session 5 random odors selected from a pool of 20), but once the procedural aspects were learned and criterion reached, all mice performed at 90% correct in

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Odor (stimulus): various spices or oils vary: type (alfactory) Container (difficulty) vary: height

Digging medium (stimulus): sand, stones, wood-chips, etc. vary: type (tactile/visual) Reward typically cheerios, froot-loops or similar vary: amount (reward titration)

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Fig. 1 (a) An example of a single stimulus element used for burial of a food reward in discrimination problems. The digging medium is flavored with powder of the reward to avoid direct detection, and can then be scented with additional odors. Other parameters are indicated on the diagram. (b) The simple apparatus used for odor/medium-based discriminations. Two such cups are placed within a testing chamber for, in this case, a two-choice compound discrimination (divided here by a white wall to facilitate ITI). (c) Data from set-shifting in the mouse, divided between starting with medium and odor as the relevant dimension. No significant difference is found between sensory paradigm, and mice readily learn all aspects of the task (criterion line indicated at sixth consecutive bar is correct). SD – simple discrimination; CD – compound discrimination; CDrev – compound discrimination reversal; IDS – intra-dimensional shift; EDS – extra-dimensional shift; EDSrep – extra-dimensional trial repetition. See (40) for the first description of this task in mice (see Color Plate 3)

subsequent sessions (Fig. 2). In other unpublished work, Isabel Muzzio has described an alteration of the set-shifting discriminations to include a spatial component, and simultaneously detected hippocampal neurons with place-fields – in the same apparatus when discriminating for odor, these neurons encoded other nonspatial aspects. Although incomplete at this point, these two newer studies indicate room for continued development of mouse cognitive testing using flexible discrimination problems. And research using other paradigms may well also bear fruit – mice have been recently shown to be adept at performing set-shifting and other discriminations on a touch-screen apparatus (41), and another group has used a novel adaptation of radial maze discrimination tasks to test flexible recall (21). The reader is also referred to an excellent overview of mouse behavioral testing (15).

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Fig. 2 Performance of mice on a task whereby five odors are first presented in sequence, and are subsequently paired (two of the ten possible probe trials are shown). The correct choice is the odor that previously appeared first in the sequence (see Color Plate 4)

Neural Substrates As anticipated, the more widespread use of genetic models has indeed fueled progress in elucidating molecular mechanisms of learning and memory, and has in the last decade provided some additional background on cognitive aging – despite the above snafus. In the C57BL/6 strain, we and others have completed stereological studies of the primary morphology of the hippocampus. In this line, at 28–31 months of age, no deficit in water-maze learning was seen compared to adult (14 months) or young (2 months) mice (42). In the same subjects, we then quantified the total numbers of neurons in CA1 and DG and found no differences (Fig. 3). The same was true for the numbers of synaptophysin-positive presynaptic boutons in both regions, although the numbers in DG did correlate with water-maze performance (42). Astrocytes and microglia were quantified in an independent study (43), and also revealed no significant age-related differences. Thus it appears, at least in baseline conditions in this strain, that hippocampal-dependent spatial learning and broad structural measures are unchanged as a consequence of normal aging. Moving beyond structural measures, a number of genetic approaches have yielded interesting data on molecular targets and age-related cognitive decline, including several that have been able to rescue observed deficits. A few of these are briefly summarized here. Through use of a two-stage radial maze discrimination paradigm, whereby a go/no-go problem is followed by a related two-choice discrimination and thus requires cognitive flexibility, a deficit was found in 21–23-month-old C57BL/6 mice that was rescued by administration of retinoic acid (44). Overexpression of superoxide dismutase in a transgenic mouse line is reported to ameliorate an agerelated decline in MWM spatial probe trial quadrant preference ((45) although strain is not indicated in this paper, the background seems to be a mix of C57BL/6 × C3H).

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Fig. 3 No obvious age-related differences in hippocampal structure, neuronal morphology, or presynaptic bouton staining in B6 mice. Cresyl-violet staining of young, adult, and aged animals is shown in panels A, C, and E. Synaptophysin immunohistochemistry is shown in panels B, D, and F (Reprinted with permission from (42))

Protein phosphatase 1 has been shown to be involved in memory decay by use of an inducible mutant and the application of a unique variant of the MWM task which included a long-term retention component and faster decay curves in late adulthood (mostly C57BL/6 mice, 15–18 months) versus inhibited or young mice (46). Although these three studies are unrelated in terms of molecular mechanisms (and are certainly

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not the only ones that fit this category), they all use powerful behavioral techniques to address functional measures in an aging context – in a way that would be difficult in any other model system.

Summary It has been more than 10 years since Jucker and Ingram stated in their review (47) of mouse brain aging: The recent rapid evolution of mouse genetics has offered the neurobiology of aging a highly promising, ever-expanding tool to understand and model brain aging in mice. … Therefore, we anticipate that over the next decade neuromorphological research using mouse models will accelerate at a pace that will bring this area of research closer to the level of interest demonstrated in rat models.

While we could not agree more, in order for this potential to be realized in the coming years, several avenues need to be pursued in a more systematic way. The field would benefit by standardization of a behavioral battery for higher cognitive function and should include means to evaluate the potentially independent effects of aging on hippocampal and prefrontal function, and in a full battery also include control of sensorimotor tests and evaluation of potentially confounding changes such as in amygdala function. To this end, a set of behavioral tasks based on olfactory discrimination seems to have the most current potential. As described above, numerous laboratories have achieved comparable results with attentional set-shifting, and these tasks offer the flexibility to differentiate distinct aspects of cognitive function. As has happened in the study of rat cognitive aging, if these tasks are tested on multiple strains and models, further refinements and the selection of the most appropriate genetic background for aging research will become possible. A clear recommendation by scientific societies and/or funding organizations toward this end and away from more problematic designs could accelerate this process. There is also some evidence that maintaining mice in an enriched environment throughout the life span can improve behavioral measures (e.g. (48)). It may be worthwhile to consider this approach as standard – thus allowing for an increased overall cognitive reserve and testing quotient, and potentially influencing both the success of the behavioral measures being developed and the search for appropriate mouse strains. Working toward a more unified approach to cognitive testing in the mouse is essential. Many laboratories performing such testing were not originally experts in cognition but have created a mouse model that could inform many other aspects of neuroscience and aging research. These studies and existing models of aging and neurodegenerative disease cannot afford to be hampered by the lack of an appropriate testing protocol. Acknowledgments I would like to thank Thomas Deller, Harry Uylings, Isabelle Muzzio, Donald Ingram, and Mathias Jucker for comments on parts of the chapter and for sharing their observations on mouse behavior and anatomy; and Monica Jimenez-Rodriguez, Ulrich Pfeiffer, Chris Feichtinger, and Bettina Braun for insights on and development of the described behavioral tasks in my laboratory.

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25. Brown, R. E. and Wong, A. A. (2007) The influence of visual ability on learning and memory performance in 13 strains of mice. Learn Mem 14(3), 134–144. 26. Mackay, T. F. and Anholt, R. R. (2007) Ain’t misbehavin’? Genotype-environment interactions and the genetics of behavior. Trends Genet 23(7), 311–314. 27. Lewejohann, L., et al. (2006) Environmental bias? Effects of housing conditions, laboratory environment and experimenter on behavioral tests. Genes Brain Behav 5(1), 64–72. 28. Chesler, E. J., et al. (2002) Identification and ranking of genetic and laboratory environment factors influencing a behavioral trait, thermal nociception, via computational analysis of a large data archive. Neurosci Biobehav Rev 26(8), 907–923. 29. Kafkafi, N., et al. (2005) Genotype-environment interactions in mouse behavior: a way out of the problem. Proc Natl Acad Sci U S A 102(12), 4619–4624. 30. Wahlsten, D., Cooper, S. F., and Crabbe, J. C. (2005) Different rankings of inbred mouse strains on the Morris maze and a refined 4-arm water escape task. Behav Brain Res 165(1), 36–51. 31. Owen, E. H., et al. (1997) Assessment of learning by the Morris water task and fear conditioning in inbred mouse strains and F1 hybrids: implications of genetic background for single gene mutations and quantitative trait loci analyses. Neuroscience 80(4), 1087–1099. 32. Upchurch, M. and Wehner, J. M. (1989) Inheritance of spatial learning ability in inbred mice: a classical genetic analysis. Behav Neurosci 103(6), 1251–1258. 33. Linder, C. C. (2003) Mouse nomenclature and maintenance of genetically engineered mice. Comp Med 53(2), 119–125. 34. Birrell, J. M. and Brown, V. J. (2000) Medial frontal cortex mediates perceptual attentional set shifting in the rat. J Neurosci 20(11), 4320–4324. 35. Fortin, N. J., Agster, K. L., and Eichenbaum, H. B. (2002) Critical role of the hippocampus in memory for sequences of events. Nat Neurosci 5(5), 458–462. 36. Brooks, S. P., et al. (2006) Selective extra-dimensional set shifting deficit in a knock-in mouse model of Huntington’s disease. Brain Res Bull 69(4), 452–457. 37. Glickstein, S. B., et al. (2005) Mice lacking dopamine D2 and D3 receptors exhibit differential activation of prefrontal cortical neurons during tasks requiring attention. Cereb Cortex 15(7), 1016–1024. 38. Laurent, V. and Podhorna, J. (2004) Subchronic phencyclidine treatment impairs performance of C57BL/6 mice in the attentional set-shifting task. Behav Pharmacol 15(2), 141–148. 39. Zhuo, J. M., et al. (2007) Early discrimination reversal learning impairment and preserved spatial learning in a longitudinal study of Tg2576 APPsw mice. Neurobiol Aging 28(8), 1248–1257. 40. Colacicco, G., et al. (2002) Attentional set-shifting in mice: modification of a rat paradigm, and evidence for strain-dependent variation. Behav Brain Res 132(1), 95–102. 41. Brigman, J. L. and Rothblat, L. A. (2008) Stimulus specific deficit on visual reversal learning after lesions of medial prefrontal cortex in the mouse. Behav Brain Res 187(2), 405–410. 42. Calhoun, M. E., et al. (1998) Hippocampal neuron and synaptophysin-positive bouton number in aging C57BL/6 mice. Neurobiol Aging 19(6), 599–606. 43. Long, J. M., et al. (1998) Stereological analysis of astrocyte and microglia in aging mouse hippocampus. Neurobiol Aging 19(5), 497–503. 44. Etchamendy, N., et al. (2001) Alleviation of a selective age-related relational memory deficit in mice by pharmacologically induced normalization of brain retinoid signaling. J Neurosci 21(16), 6423–6429. 45. Hu, D., et al. (2006) Aging-dependent alterations in synaptic plasticity and memory in mice that overexpress extracellular superoxide dismutase. J Neurosci 26(15), 3933–3941. 46. Genoux, D., et al. (2002) Protein phosphatase 1 is a molecular constraint on learning and memory. Nature 418(6901), 970–975. 47. Jucker, M. and Ingram, D. K. (1997) Murine models of brain aging and age-related neurodegenerative diseases. Behav Brain Res 85(1), 1–26. 48. Bennett, J. C., et al. (2006) Long-term continuous, but not daily, environmental enrichment reduces spatial memory decline in aged male mice. Neurobiol Learn Mem 85(2), 139–152.

Impact of Ab and Tau on Cognition in Mouse Models of Alzheimer’s Disease Maya A. Koike, Kristoffer Myczek, Kim N. Green, and Frank M. LaFerla*

Abstract Since the first patient was diagnosed with Alzheimer’s disease, scientists have attempted to study the pathological features associated with the disease in an attempt to find a cure. Although several biological markers have been implicated, including amyloid-beta plaques and neurofibrillary tangles, these correlative discoveries make it difficult to tease out cause-and-effect relationships. In an attempt to understand these relationships, genetic mutations that have been linked to Alzheimer’s disease have been replicated in several mouse models. These transgenic animals provide scientists with a valuable tool to study which pathologies can cause cognitive deficits and to discover what compounds or therapies may decrease these pathologies and behavioral impairments. Keywords Dementia • brain aging • Alzheimer • amyloid • tau • transgenic

Introduction Alzheimer’s disease (AD) is a progressive neurodegenerative disease and the most common cause of dementia among the elderly. Clinical symptoms generally manifest with a loss of episodic memory, which refers to memories that have a spatial M.A. Koike Department of Neurobiology and Behavior, and Institute for Brain Aging and Dementia, University of California, Irvine, CA, USA K. Myczek Department of Neurobiology and Behavior, and Institute for Brain Aging and Dementia, University of California, Irvine, CA, USA K.N. Green Department of Neurobiology and Behavior, and Institute for Brain Aging and Dementia, University of California, Irvine, CA, USA *F.M. LaFerla Department of Neurobiology and Behavior, and Institute for Brain Aging and Dementia, University of California, Irvine, CA, USA J.L. Bizon, A. Woods (eds.) Animal Models of Human Cognitive Aging, DOI: 10.1007/978-1-59745-422-3_6, © Humana Press, a part of Springer Science + Business Media, LLC 2009

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and temporal context, often leading to feelings of disorientation among patients. As the disease progresses, other cognitive domains are impacted and loss of semantic memory becomes apparent, which refers to memories that are generic and contextfree. Gradually, deficits in attention, executive function, language, and spatial orientation also develop (1, 2). Along with these symptoms, there may be behavioral changes that are triggered by the memory loss including paranoia, easy frustration, and social withdrawal (3). Near the terminal stages of the disease, there is global loss of mental function, and the patient is unable to care for themselves.

Pathological Features and Biology of AD In 1907, Alois Alzheimer described two lesions in the brain of one of his female patients that he believed were pathognomonic. The first lesion he described was called amyloid plaques and occurred extracellularly. Plaques are composed of aggregates of the amyloid-β (Ab) peptide, which is a hydrophobic peptide ranging in length from 38 to 43 amino acids. The second lesion Alzheimer reported occurs intracellularly and is referred to as neurofibrillary tangles (NFTs), which are composed of aggregates of the tau protein that have become hyperphosphorylated. Plaques and NFTs are not uniformly distributed throughout the brain, but rather affect specific brain regions, particularly those critical for learning and memory. Most affected by NFTs are the subiculum and the CA1 pyramidal layer of the hippocampus, the amygdala, the posterior parahippocampal cortices, and the entorhinal cortex. Later, NFT pathology develops in the parietal and frontal medial lobes, and finally in the occipital lobes (4–6). In contrast, the hippocampus and entorhinal cortex contain the fewest number of Ab plaques, although these are more uniformly distributed than NFTs (6). Ab plaques initially develop in subcortical nuclei and in white matter tracts (7). Toward the end stages of AD, Ab plaques and NFTs are found throughout the cerebral cortex (4). Besides plaques and tangles, a number of other secondary pathologies occur during the course of AD pathogenesis. These include increased inflammatory events (8–10), oxidative stress and damage (11), as well as synaptic (12) and neuronal loss (13).

Genetics of AD The vast majority of AD cases occurs sporadically, without any obvious cause and typically manifest with a late onset, which is considered onset after 65 years of age. About 5% of the total cases, however, exhibit a Mendelian pattern of inheritance, and are referred to as familial AD (FAD). The age of onset of FAD cases can vary widely, but generally occurs with an earlier onset than sporadic cases. FAD cases have been linked to three genes: β-amyloid precursor protein (APP), presenilin-1

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(PS1), and presenilin-2 (PS2) (14–19). The study of these mutations has proven invaluable in elucidating the biology of Aβ, and has given rise to a numβer of therapeutic targets for the disease. Aβ was found to be a cleavage product from APP (20), while the presenilins were found to be integrally involved in the cleavage and release of the Aβ peptide from the APP holoprotein (21). Given that all three mutations that caused these aggressive, early onset, forms of AD clustered around the production and biology of the Aβ protein, which is highly abundant in the AD brain, it suggested that it was the Aβ peptide which was the cause of the disease – rather than NFTs or other pathologies such as increased inflammation and oxidative damage. This was known as the “amyloid cascade hypothesis” which became refined to suggest that all other pathologies in AD occurred because of the Aβ peptide, including NFT formation and neuronal loss (22). These mutations, and the proteins in which they occur, have been vital in creating transgenic mouse models of AD, which have advanced our understanding of the disease process innumerably, as well as allowed us to test potential therapeutics. The major genetic risk factor for AD is the apolipoprotein E (ApoE) gene (23). ApoE is a polymorphic gene in which three alleles exist in the human population: ApoE2, ApoE3, and ApoE4. Inheritance of an ApoE4 allele is associated with an increased risk for development of AD, and decreases the age of onset compared to non-ApoE4 carriers. Inheritance of an ApoE4 allele exacerbates almost all the neuropathological features of AD including increased Aβ deposition, NFT formation, oxidative stress, and cell loss. ApoE is known to have a role in cholesterol transport (24), which may lead to increased Aβ production, as cholesterol is known to affect APP processing. The mechanism by which the E4 allele increases the risk for AD is still not known, although there is evidence supporting the idea that ApoE can bind to Aβ and affect its clearance.

Production of Ab APP is a large type 1 membrane-spanning protein. Aβ exists as a segment of APP partly within the membrane-spanning domain, 99 amino acids from the C terminal of APP. The APP molecule can be processed in one of two ways that either precludes or leads to the formation of Aβ. Most APP molecules are not fated to be processed to Aβ peptides, but are instead cleaved by another proteolytic enzyme within the Aβ sequence (Fig. 1). This is known as the non-amyloidogenic pathway and the proteolytic enzymes are known as α-secretases; three such enzymes have so far been identified – ADAM 9, 10, or 17 (25). This cleavage precludes the formation of an Aβ peptide from this APP holoprotein, and is thought to be protective against AD as stimulation of this pathway by phorbol esters or muscarinic agonists has been demonstrated to reduce Aβ production in vitro (26) and in vivo (27). Production of Aβ is central to AD, and a thorough understanding of its biology and production is vital for potential therapies, and for the development of AD mouse models. In AD, steady-state levels of Aβ are increased. This could be due to

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α

β

y

ISEVKM DAEFRHDSGYEVHHQKLVFFAEDVGSNKGAIIGLMVGGVV IATVIVIT

Nonamyloidogenic

APP α secretase (ADAM 9, 10, 17)

C83

sAPPα

BACE1 C99

sAPPβ

y

Amyloidogenic

secretase

AlCD

AB42

y AB40

secretase

AlCD

Fig. 1 APP processing. APP can be processed into amyloid-β (Aβ) by two pathways, the nonamyloidogenic pathway and amyloidogenic pathway. Alpha-secretase cuts APP within the Aβ domain along the non-amyloidogenic pathway precluding Aβ generation and release. The result of this cleavage is sAPPα and C83. C83 can then be further cleaved by gamma secretase generating P3. In the amyloidogenic pathway, APP is cleaved by β-secretase (BACE) releasing sAPPβ and leaving C99 in the membrane. C99 is then cleaved by gamma-secretase, made up of presenilin-1, presenilin-2, nicastrin, anterior pharynx defective, and presenilin enhancer-2, generating Aβ 40 and Aβ 42 as well as AICD (see Color Plate 5)

increased production of the peptide, decreased degradation, or a combination of the two. Aβ production from APP occurs along an enzymatic processing pathway known as the amyloidogenic pathway. Here, instead of the α-secretase cleaving APP, a different proteolytic enzyme, known as β-secretase (β-site amyloid precursor protein cleaving enzyme, BACE), cleaves APP 99 amino acids from the C-terminal – this stub, called C99, is retained within the membrane. Amino acid 1 of C99 is the N terminal of Aβ. In addition to forming C99, BACE cleavage also releases a large ectodomain, known as sAPPβ, into the interstitial fluid (28). C99 is then further cleaved to release the Aβ peptide, and this cleavage is rather unique in that proteolysis occurs within the membrane rather than an aqueous environment (29). This is known as regulated intramembranous proteolysis and is carried out by the g-secretase complex. This releases full-length Aβ in one of two forms, either the more common 40 residue peptide (Aβ40) or the more hydrophobic and easily aggregating, and therefore pathogenic, 42 residue peptide (Aβ42). Notably, it was found that the presenilins formed the catalytic core of this enzymatic complex

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(which also includes nicastrin, APH-1, and PEN-2 (30)), and that the mutations in presenilin linked to FAD increased the amount of the Aβ42 produced, highlighting the importance of this longer, more amyloidogenic peptide. While it is produced as a non-neurotoxic monomer, Aβ, especially the highly hydrophobic Aβ42, easily aggregates into complexes ranging from dimers to large molecular weight fibrils. Aβ monomers aggregate into oligomers, such as dimers and trimers, which aggregate in turn into higher molecular weight complexes such as protofibrils and fibrils. Aβ oligomerization appears to occur within cells (31, 32), and while interacting with lipid rafts (33, 34). Aβ oligomeric complexes have been demonstrated to be detrimental (35, 36), for example, dimers have been shown to disrupt synaptic function in transgenic AD mice affecting long-term potentiation (LTP), which is believed to be the molecular correlate of learning and memory (37).

Tau The tau protein is encoded by a single gene (MAPT) located on chromosome 17, although it is alternatively spliced to yield six major protein isoforms in the adult human brain (38). The tau gene contains 15 exons, and exons 2, 3, and 10 can be alternatively spliced. Four imperfect tandem repeats are encoded by exons 9–12, hence, alternative splicing of exon 10 yields isoforms with three or four repeat domains (3R and 4R tau), depending if exon 10 is absent or present, respectively. Alternative splicing of exons 2 and 3 yields variants containing zero (0N), one (1N), or two (2N) inserts at the amino terminus, such that six tau isoforms are formed: 3R0N, 3R1N, 3R2N, 4R0N, 4R1N, and 4R2N. In the adult human brain, the proportion of 3R to 4R tau is ~1:1, whereas in the adult mouse brain, 4R tau is the only tau isoform present (39). Tauopathies can be further classified based on whether tangles are comprised of 3R or 4R tau isoforms. For example, in AD, both 3R and 4R tau accumulate in NFTs; other disorders are marked by only 3R tau (e.g., Pick’s disease) or 4R tau (e.g., cortical basal degeneration (CBD) and progressive supranuclear palsy (PSP)) (40, 41). In AD, tau pathology is restricted to neurons, but in certain other tauopathies, such as 4R tauopathies CBD and PSP, tau inclusions are also observed in glia (42). Although no genetic mutations in the tau gene have been found to cause AD, mutations have been found to cause frontotemporal dementia with parkinsonism (FTD) linked to chromosome 17 (FTDP17) (43). Tau deposits in FTD are similar, but not identical, to those found in AD.

Animal Models of AD Animal models allow us to systematically examine aspects of AD pathology that would be impossible in humans. Natural models of AD develop symptoms and pathology without manipulation and include cats (44), dogs (45), nonhuman

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primates (46), and polar bears (47–49). Although these animals develop pathology and cognitive decline, they are difficult to use because of their large size, gestation time, internal variability, and extensive housing needs. While the APP sequence is largely conserved between species, the traditional laboratory animals (mice and rats) do not develop plaques spontaneously. Rodent Aβ differs from human Aβ at three key sites – at residues 5, 10, and 13 (48). These differences in the Aβ sequence affect the ability of the peptide to form aggregates, preventing the formation of high molecular weight aggregates such as those found in Aβ plaques (48, 50). Due to these differences, development of transgenic models of AD has utilized human genes containing mutations associated with FAD.

APP Models Several mouse models have been developed to mimic Aβ pathology. These models overproduce human APP under different promoters, which can alter the location and strength of transgene expression. These models are summarized in Table 1. All these APP mice exhibit extracellular Aβ plaques. Aβ plaques accumulate in an agedependent manner, increasing in number and distribution as the mice age (51–56). Although none show signs of NFTs, some evidence of tau hyperphosphorylation has been observed in all models. Antibody staining has revealed an accumulation (but not tangle aggregation) of hyperphosphorylated tau in distorted neurites prominently near plaques, possibly suggesting that elevated Aβ and plaques may be driving tau pathology (12, 54, 57). Interestingly, only the APP23, APP(SL)PS1KI, and APPPS1-21 mice had any reported neuronal cell loss, which occurred specifically in the CA1 region (58, 59) or the dentate gyrus (56) of the hippocampus, but also in the neocortex of the APP23 mice (60). Overproduction of APP is sufficient to recapitulate the oxidative damage and increased inflammation seen in the AD brain, but does not cause overt neuronal loss, as seen in human AD. Furthermore, APP overexpression does not lead to the formation of NFTs in rodent neurons, although this could be due to differences between the human and rodent tau proteins, but APP overexpression does lead to an increase in tau phosphorylation. These observations show us that the amyloid cascade hypothesis is at least partway true – Aβ accumulation does lead to inflammation, oxidative damage, and tau phosphorylation. The important question was whether APP overexpression, and these pathologies associated with it, could cause cognitive deficits. Indeed, all these mouse models demonstrated cognitive decline, showing that APP overproduction is sufficient to negatively affect cognition. Though all mice show some sort of cognitive deficit compared to wild-type controls, variations in the timing and correlation with pathology have revealed findings vital to the understanding of AD as cognitive decline is apparent prior to the appearance of Aβ plaques (52, 53, 61–69).

Table 1 APP mouse models of Alzheimer’s disease with Aβ deposition and cognitive decline Pathology Mouse strain name

Genes/promoter

Hyperphosphorylated tau

Behavior

Cell NFTs Aβ plaques death

MWM memory deficits

Motor deficits Other behavioral deficits

PDAPP

APP Indiana mutation/PDGF Yes promoter

No

Yes

No

3 months

No

Tg2576

APP Swedish mutation/prion Yes protein gene promoter APP Indiana and Swedish Yes mutations/PDGF promoter APP Swedish mutation/ Yes Thy1.2 promoter

No

Yes

No

9 months

No

No

Yes

No

6 months

No

No

Yes

Yes

3 months

Yes

Yes

No

Yes

No

3 months

No

Yes

No

Yes

Yes

Inconclusive No

Yes

Yes

Yes

No

4 months

PDAPP-J20 APP23

TgCRND8

APPPS1-21

3xTg-AD

APP Indiana and Swedish mutations/hamster prion promoter APP Swedish and PS1 L166P mutations/Thy1 promoter Tau P301 and APP Swedish mutations/Thy1.2 promoter, PS1 M146V/ endogenous mouse PS1 promoter

No

Primary reference

Radial arm maze at 3 months, (51) object recognition at 6 months, cued fear conditioning at 11 months, operant bar press at 3 months, and circular holeboard maze at 3 months Y-maze at 10 months and circu- (52) lar holeboard maze 7 months (53) Open field testing at 3 months and passive inhibitory avoidance at 25 months Prepulse inhibition at 2 months and auditory startle at 2 months Y-maze at 6 months and open field at 15 months

(54)

(55)

(56)

(79)

MWM: Morris water maze; NFTs: neurofibrillary tangles; Aβ: amyloid-beta; APP: amyloid precursor protein; PDFG: platelet-derived growth factor; PS1: presenilin-1

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On a cellular level, LTP deficits were also discovered in the Tg2576 and PDAPP mice. At 4 months of age, disruptions in LTP function in hippocampal PDAPP slices revealed that LTP decayed more quickly than wild types (70). LTP deficits were apparent in the Tg2576 mice as early as 5 months of age. (71), in which there was a decrease in cell responsiveness to electrical stimulation. Interestingly, these LTP deficits manifest before plaque formation, like the cognitive deficits, suggesting the importance of soluble Aβ. Indeed, the role of soluble Aβ was also explored by manipulating Aβ levels in hippocampal slices and observing the effects on LTP. At time points before Aβ plaque formation, treatments with enzymes that degrade monomeric Aβ were shown not to restore LTP function (36). These treatments left the oligomeric Aβ intact, indicating that this soluble oligomeric species may be responsible for the LTP and cognitive deficits seen in AD. Furthermore, addition of oligomeric Aβ to non-transgenic mouse hippocampal slices also inhibited LTP (72), strongly suggesting that these low molecular weight soluble species have acute detrimental effects on synaptic transmission, which could underlie memory deficits. Recently, a specific soluble Aβ dodecamer oligomer, Aβ*56, has been shown to strongly correlate with cognitive deficits in the Tg2576 mice, and injection of this species into non-transgenic rats was sufficient to cause behavioral deficits (73). Investigations using these transgenic mice have revealed several key insights into AD pathogenesis: (1) Aβ is sufficient to cause memory impairments in mice similar to human AD, (2) increased Aβ levels do not fully recapitulate all the disease markers including NFTs and neuronal loss; however, Aβ causes cognitive impairments in the absence of neuronal loss, and (3) cognitive deficits are apparent before plaque deposition, indicating a specific form of Aβ, possibly oligomeric, may be responsible for these deficits, and may act by affecting synaptic transmission.

Tau Models The amyloid cascade hypothesis stipulates that Aβ is the trigger of all cases of AD, and tau pathology is a consequence of the amyloid pathology (22). It proposes the existence of a link (direct or indirect) between Aβ and tau, where Aβ induces a cascade of events that leads to the accumulation of NFTs and eventually cell death. Based on this hypothesis, therefore, the introduction of mutant APP or PS genes into mice should trigger a wide spectrum of AD neuropathology. However, although mutant APP or APP/PS1 mice develop extensive Aβ deposits, surprisingly, this has proven insufficient to trigger other key aspects of AD neuropathology – most notably, substantial NFT pathology. NFT pathology can be modeled in mice by introducing mutations in the tau gene that are associated with FTDP-17 (74). Alternatively, removing mouse tau leads to the development of NFT pathology in transgenic mice that overexpress

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human tau (75), although in the presence of the mouse tau gene no pathology occurs (76). These studies show that mice are capable of developing robust tau pathology. Hence, it is unclear why APP or APP/PS1 mice fail to develop tau pathology, but perhaps the life span of mice is too short for Aβ to trigger neurofibrillary changes. Consequently, the development of mice with both hallmark lesions requires aggressive experimental strategies. One approach used by Hutton and coworkers involved crossing mutant APP and mutant tau mice (77). They found that the double transgenic mice developed enhanced NFT pathology compared to single mutant tau mice. Gotz et al. showed that intracranial administration of Aβ into mutant tau mice led to the generation of NFTs within the amygdala (78). Both these findings suggest an interaction between Aβ and tau, although the mechanism underlying this effect is unknown. Because of poor breeding, the development of a non-AD-related motor deficit, and short life expectancy, these models have practical constraints that preclude their utility in certain applications, for example, learning and memory studies.

APP and Tau in One Mouse Model: The 3xTg-AD Mouse Generating mice with both plaques and NFTs is crucial for studies of the molecular relationship between Aβ and tau, for testing the effectiveness anti-AD interventions have on both pathologies, and to determine the relative contribution of each pathology to the underlying learning and memory deficits that are the hallmark clinical feature. To more closely model AD neuropathology, we used a novel approach that involved co-microinjecting two transgenes (encoding APPswe and tauP301L both under the control of the Thy1.2 promoter) into single-cell embryos harvested from PS1M146V KI mice (79). The 3xTg-AD show intraneuronal Aβ staining from 4 months of age, followed shortly thereafter by somatodendritic accumulation of human tau, which becomes progressively phosphorylated and conformationally altered as the mice age (79). Extracellular Aβ plaques begin to appear at 12 months progressing with age, primarily affecting the cortex, hippocampus, and amygdala. NFTs also appear from 12 months of age. Notably, despite extensive Aβ and tau pathologies, these mice still fail to show any signs of extensive neuronal cell death as seen in the human AD. Deficits in Morris water maze (MWM) performance, a hippocampal-dependent spatial-learning task, appear early at 4 months of age, which interestingly precedes plaque and tangle formation, but correlates with the appearance of intraneuronal Aβ. Clearance of this intraneuronal Aβ via immunotherapy rescues the cognitive deficits back to non-transgenic levels (80). Notably, cognitive impairments coincided with LTP deficits in the hippocampus (79). The 3xTg-AD mice also display an age-related progressive accumulation of oligomeric Aβ intraneuronally. Injection of antibodies specifically targeting and clearing oligomeric Aβ decreased tau pathology (81). It was further shown that

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clearance of soluble Aβ and the resulting clearance of soluble tau rescued cognitive deficits in these mice, but clearance of soluble Aβ alone was not enough to improve behavior in a hippocampally dependent T-maze task. Since behavior tests were conducted soon after soluble Aβ immunotherapy but before time was allowed for clearance of soluble tau, it was concluded that tau may be the culprit behind cognitive deficits (82). This further illustrates Aβ’s possible role as the cause of cognitive deficits through interaction with tau. Although Aβ may be the initial trigger of cognitive impairments, and may facilitate the development of the tau pathology, once the tau pathology becomes established, it may cause even greater cognitive decline. An unresolved question, however, relates to whether soluble tau species can affect cognition, particularly in the presence of concomitant Aβ pathology. If so, reducing its levels within the brain should produce beneficial effects. To address this question, we evaluated the cognitive phenotype following Aβ immunotherapy in advanced aged mice (82). We specifically investigated the effect of passive and active immunization on cognition in transgenic mice harboring both plaques and NFTs. Because of their advanced age, active immunization did not lead to a significant reduction in the plaque burden, consistent with previous reports (83), and likewise had no effect on the NFT load. In contrast, passive immunization significantly reduced the plaque burden but had no effect on NFTs, consistent with our previous study (81). Although neither immunization strategy reduced the NFT load, both approaches reduced soluble Aβ and tau levels, despite their advanced age, and this led to improved cognitive performance. Notably, we found a greater decrease in the steady-state levels of Aβ42 (as detected by ELISA) in the passively immunized mice compared to actively immunized mice, despite equivalent behavioral performance. These data indicate that total Aβ42 levels (monomeric and oligomeric) do not correlate with behavioral performance, which is consistent with previous results indicating that oligomeric Aβ correlates better with cognitive decline. Toward this end, we found that a 56 kDa band, immuno-positive with antibodies 6E10, 4G8, and 20.1, was equally decreased in all immunized groups and correlates with the amelioration in cognition observed following active and passive immunization. The data obtained from these advanced-age mice strongly suggest that soluble tau plays an important role in cognition during later stages of the disease. Notably, reducing soluble Aβ only without reducing soluble tau did not improve cognitive performance. While we cannot rule out that a selective decrease in soluble Aβ in a specific subregion of the brain may be necessary to rescue the behavioral deficit in the tasks used in this study, it is tempting to speculate that as certain soluble Aβ species appear to be more toxic than Aβ plaques, certain soluble pathological forms of tau may be more detrimental than NFTs. However, the use of other interventions directly aimed at decreasing the NFT burden will be needed to determine their contribution, if any, to the cognitive decline during the late stages of AD. Reducing soluble Aβ in APP transgenic mice is sufficient to rescue cognitive impairments suggesting that the accumulation of this Aβ species is responsible

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for the cognitive impairments in these mice (84, 85). These data are consistent with our previous results showing that Aβ accumulation triggers the learning and memory deficits in the 3xTg-AD mice (80). However, tau does not accumulate in APP transgenic mice and our previous experiments were done at an age where in the 3xTg-AD mice there is no evident accumulation of tau pathology. Thus, although Aβ may be the initial trigger of the cognitive decline in the 3xTg-AD mice, once soluble tau accumulates, it further exacerbates the cognitive decline. At this stage, a reduction of both soluble Aβ and tau seems necessary to ameliorate memory impairments. Even in the Aβ overproducing models that do not contain human tau, Aβ may be interacting with endogenous mouse tau, possibly causing subtle changes in conformation and phosphorylation, which may affect cognition. Recent work has shown that while the J20 mice exhibit cognitive deficits, after knocking out tau, cognition decline was avoided in these mice (86). This experiment helps illustrate that, although Aβ levels (oligomers specifically) may be correlated with cognitive deficits in mouse models of AD, Aβ may be acting through subtle effects on tau.

Conclusions The production and study of transgenic mouse models of AD has been critical to our understanding of the biology and pathogenesis of this neurodegenerative disease. Overexpression of APP has demonstrated that Aβ is sufficient to cause plaques, inflammation, and oxidative damage in line with observations in AD. Importantly, and of high relevance to the human disorder, these mice all exhibit cognitive impairments associated with the production of Aβ. Furthermore, these mice exhibit some subtle modifications of endogenous tau, which some have suggested mediates cognitive decline. These findings give prudence to the amyloid cascade hypothesis, which is founded upon the genetic mutations that lead to familial, early onset, forms of AD, and suggest that it is also the Aβ peptide, which is at the root of sporadic AD as well. However, overproduction of Aβ in mice, despite robust Aβ pathologies and cognitive decline, does not cause neuronal loss, which is a prominent feature of the human disease. The lack of cell death may be due to the life span difference between mice and humans – the neuronal loss seen in human AD progresses over decades, and the 2-year life span of a mouse may make this characteristic of AD difficult to imitate. Overexpression of APP combined with overexpression of mutant tau in the 3xTg-AD mice has shown that the presence of Aβ causes the modification of tau into states that resemble tau pathologies seen in human AD, yet this overexpression is not sufficient to cause neuronal loss. While we have learned much from current FADbased models, perhaps future mouse models of AD will be able incorporate characteristics of sporadic AD, providing additional resources in the fight against this debilitating disease.

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Acknowledgments We thank Dr. Anna Parachikova for critically reading the manuscript. This work was supported in part by grants from Alzheimer’s Association and the National Institutes of Health AG0212982 and AG16573 to FML.

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Hormonal Influences on Brain Aging and Age-Related Cognitive Decline Danielle K. Lewis and Farida Sohrabji*

Abstract Steroid hormones play a critical role in the developing brain and continue to modulate cognition with aging. The main steroid hormones include the adrenal gland hormones, mineralocorticoids, and glucocorticoids; and the gonadal hormones, androgen, estrogen, and progesterone. Steroid hormones transduce their effects via hormone-specific receptors which are localized throughout the brain. More importantly, the androgen, estrogen, progesterone, and the glucocorticoid receptors have been identified in brain regions associated with learning and memory such as the hippocampus. One consequence of aging is a shift in the relative proportion of hormone availability. For example, the sex hormones estrogen, testosterone, and progesterone decline with age, while hormones regulating the HPA axis, such as corticosteroids have been shown to increase with age. However, the change in hormone levels with age is oftentimes gender-specific. Steroid hormones act in concert with each other, thus separating out the contribution of one specific hormone can be difficult. For example, testosterone can be aromatized to estrogen, thus the loss of testosterone may also result in a reduction in estrogen. Further, the progesterone receptor contains an imperfect estrogen-binding site, thus loss of estrogen can potentially impact actions initiated through the progesterone receptor. The role of steroid hormones in cognition has been studied using both animal models and clinical trials. This chapter summarizes both the animal and clinical data on the effects of estrogen, progesterone, androgen, and the glucocorticoids on cognition with age and their role in neurodegenerative processes, specifically targeting Alzheimer’s disease. Keywords Dehydroepiandrosterone sulfate (DHEA) • selective estrogen modulators (SERMS) • hypothalamic–pituitary–adrenal (HPA) axis • estrogen • progesterone • androgen • corticosterone D.K. Lewis Department of Neuroscience and Experimental Therapeutics, Texas A&M Health Science Center, College Station, TX 77843-1114 *F. Sohrabji Department of Neuroscience and Experimental Therapeutics, TAMU Health Science Center, College Station, TX, USA J.L. Bizon, A. Woods (eds.) Animal Models of Human Cognitive Aging, DOI: 10.1007/978-1-59745-422-3_7, © Humana Press, a part of Springer Science + Business Media, LLC 2009

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Introduction Gonadal and adrenal steroids regulate various physiological functions in the body and some play a key role in cognition during development and impact cognitive performance during aging. These include mineralocorticoids and glucocorticoids, produced by the adrenal glands, and the sex hormones, androgen, estrogen, and progesterone which are mainly produced by the gonads and secondarily by the adrenal glands. Small amounts of estrogen can also be produced in the liver and mammary glands while other sources of progesterone include the brain, and during pregnancy, the placenta. These secondary sources of steroid hormone production can be especially critical to women following ovarian aging. This review will focus on the sex steroids and corticosteroids, since they play the largest role in cognition. The upstream precursor, or prohormone of mineralocorticoids, glucocorticoids, testosterone, estrogen, and progesterone is pregnenolone (Fig. 1), which is synthesized from cholesterol (1). Steroid metabolism of pregnenolone leads to production of progesterone which can be further converted to the mineralocorticoid – aldosterone; the glucocorticoid – cortisol; and androstenedione – the precursor to estrogen and testosterone. The parallel metabolic pathway leading to the synthesis of estrogen and testosterone is also through pregnenolone. Pregnenolone can be metabolized to 17-hydroxy-pregnenolone which is a precursor of dehydroepiandrosterone (DHEA). Like progesterone, DHEA and its derivative dehydroepiandrosterone sulfate (DHEAS) is a precursor for androstenedione whose metabolism leads to production of testosterone and estrogen synthesis. Estrogen can also be aromatized from testosterone. The concentration and relative proportions of these precursors and the end products of cholesterol metabolism can have a significant impact on cognitive abilities and can potentially lead to neurodegenerative processes with age. Steroid hormones profoundly impact neural development and differentiation. The sex hormones, in particular, play a critical role in shaping the connections in the brain and continue to affect the brain into adulthood. Several studies using animal models have shown that estrogen is important for the growth and development of the brain using either organotypic explant cultures (2–6) or in situ analysis (7–11). Following reproductive maturity, estrogen plays a key role in regulating spine density in neurons of the CA1 subfield in the hippocampus of female rats (12–14), and this effect is enhanced by progesterone (12). However, with age, estrogen replacement has been shown to attenuate spine density in this same region (15). Learning and memory are also influenced by steroid hormones. In female rats, estrogen treatment has been implicated in improving learning acquisition on the radial-arm maze, performance on a working memory test and the Morris water maze, as well as changing the problem-solving strategy in a two-point discriminative test (for review see (16)). However, high estrogen levels coupled with exposure to glucocorticoids, as would occur during a stressful event, can lead to impairments (17). In female rats, associative learning was impaired when females were subjected to intermittent tail shocks during proestrus, a time when estrogen levels are at their highest, whereas associative learning was unaffected in males following exposure to the same stressful event (18). Moreover, differences in hippocampal

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Cholesterol

Pregnenolone

17-hydroxy-pregnenolone

Progesterone

Dehydroepiandosterone (DHEA)

Aldosterone (Mineralcorticoid)

17-hydroxy-progesterone

Glucocorticoids

Androstenedione

Estrogen

Testosterone

(Cortisol)

Fig. 1 Simplified schematic of cholesterol metabolism ultimately leading to production of select mineralocorticoids, glucocorticoids, and the sex steroids, progesterone, testosterone and estrogen

spine density had opposing effects depending on gender. In females stress resulted in reduced spine density, whereas in males spine density increased (19). Some of the estrogen effects observed on spine density in the hippocampus may be related to estrogen receptor expression. During proestrus, a time of high estrogen levels, mRNA and protein expression of the estrogen receptor-beta (ERb) is low in the hippocampus (20). Further, learning on a hippocampal-dependent, inhibitoryavoidance task was impaired in ovariectomized ERa knockout mice, whereas learning was unaffected in similarly treated ERb knockout mice (21), indicating that ERa may play a stronger role in hippocampal-mediated events.

Localization of Steroid Hormone Receptors Steroid hormones modulate their effects on the brain mainly through their receptors. Estrogen receptor-alpha (ERa) and estrogen receptor-beta (ERb), the androgen receptor, progesterone receptor-A (PR-A) and progesterone receptor-B (PR-B), and the Type I and Type II corticosteroid receptors have all been detected in areas associated with learning and memory. Localization of each of these receptor types will be discussed in detail in the proceeding discussion.

Estrogen Receptor Localization Rodent studies have shown ER mRNA expression in portions of the hypothalamus, amygdala, hippocampus, and cerebral cortex (22); and hormone-primed, ER protein expression was reported in the preoptic-septal area, hypothalamus, and amygdala

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(23). However, these studies failed to distinguish between ERa and ERb. Specifically, in rodents ERb protein expression has been found localized to the hippocampus, lateral septum (24), portions of the hypothalamus and amygdala (24, 25), as well as the olfactory nuclei, cerebral cortex, medial septum, bed nucleus of the stria terminalis, cerebellum, nucleus of the solitary tract, ventral tegmental area, and spinal trigeminal nucleus (25). ERa mRNA expression in postmortem human forebrain tissue and brain tissue from the cynomologous monkey was also found in areas that contain ERb receptors, to include portions of the amygdala, hypothalamus, cerebral cortex, and hippocampus (26). In rats, ERa was observed in these same regions plus the thalamus, mesencephalon, pons, cerebellum, and medulla oblongata (27). Interestingly, in ovariectomized, female cynomologous monkeys, a high ERb/ERa ratio was detected in the hippocampus and hypothalamus indicating that in this species, ERb may be more critical in these brain regions (28). These two receptor types appear to function both in concert and independently of each other based on co-localization studies. In ovariectomized females, both ERa and ERb mRNA were expressed in the hippocampus and cerebral cortex, but ERb expression was significantly greater (29). Differences have also been observed in ovariectomized, estrogen-replaced female rats where our laboratory has found that both ERa and ERb mRNA are expressed in the olfactory bulb, but ERa expression is significantly greater (30), and decreases in both receptor subtypes have been reported in the hippocampus with age (31). However, in hippocampal, postmortem brain tissue collected from Alzheimer’s patients and age-matched controls, ERb but not ERa protein was expressed in the perinuclear region of neurons and astrocytes located in regions of the hippocampus, and the intensity and number of stained cells increased dramatically in the brain tissue from Alzheimer’s patients only (32). Differential localization and expression of the receptors was also observed in the nucleus basalis of Meynert, a region severely compromised in the brains of Alzheimer’s patients (for review see (33)). In the human nucleus basalis of Meynert, ERa was more highly expressed and was localized to the nucleus in both postmortem brain tissue from controls and Alzheimer’s patients while ERb was localized mainly in the cytoplasm (34). In patients with Alzheimer’s disease, nuclear expression of both receptors and cytoplasmic expression of ERb increased (34). It should be pointed out, however, that although nuclear expression of both receptors increased with Alzheimer’s disease, ERa was the predominate receptor (34). Localization of the receptor is important as it is an indicator of function. Estrogen receptors are found both in the cytoplasm and nucleus, but upon estradiol stimulation cytoplasmic expression is dramatically reduced and nuclear expression also decreased with some antibodies but not others, suggesting that the receptor may be undergoing a conformational change (35). In the hippocampal subfields of CA1, protein expression of nuclear ERa and ERb (36) or ERa alone (37) decreased in Alzheimer’s patients. In both cases, conformational changes in the receptors may have influenced the ability of the antibodies to detect the receptors. The cell type may also be important as nuclear protein expression of ERa was shown to be increased in the brains of Alzheimer’s patients in glial fibrillary acidic proteinimmunoreactive astrocytes (38). The neurodegeneration observed in the brain of

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Alzheimer’s patients might be due to increased activation of ERa or ERb; conversely increased expression of either ERa or ERb may be a compensatory mechanism to the ongoing neurodegeneration observed in Alzheimer’s patients. Epidemiological studies suggest that these receptors are associated with neurodegenerative processes. In a mixed-gender cohort, interactions between ERa and ERb polymorphisms (39) or polymorphisms in ERa alone (40) were associated with an increased risk for Alzheimer’s disease. Further, in women but not men, interactions between ERa polymorphisms and ApoE epsilon 4 allele correlated with increased risk for Alzheimer’s disease (41). The effect of estrogen replacement may also differentially regulate the actions of ERa and ERb on Apolipoprotein E (ApoE), a lipid-binding protein important for transporting triglycerides and cholesterol throughout the body. Mutations in the gene for ApoE have been associated with increased neurodegeneration. When the ERa-specific agonist propylpyrazole or 17b-estradiol, a compound that acts on both receptors, was injected into 6-month-old, ovariectomized, female rats, Apolipoprotein E mRNA and protein expression increased (42). However, application of diarylpropionitrile, an ERb-specific agonist, resulted in decreased expression of ApoE mRNA and protein (42).

Androgen Receptor Localization Similar to the estrogen receptors, the androgen receptor mRNA (22) and protein (43, 44) have been found in the hypothalamus, amygdala, hippocampus, and cerebral cortex of rodents (22, 44) and nonhuman primates (43). This receptor is both age- and gender-regulated. In rodents, expression of this receptor was higher in males than females, and testosterone down-regulated the receptor in both genders of adult and aged mice. However, estradiol only up-regulated the androgen receptor in adult, and not aged, mice (45).

Progesterone Receptor Localization Earlier studies that examined the localization of the progesterone receptor (PR) focused on progesterone binding in the presence and absence of exogenous estrogen. In animals studies, protein expression of estrogen-primed, progesterone receptors were localized mainly to neurons of the basal hypothalamus and select regions of the preoptic area (46, 47) and progestin binding studies suggested that progesterone receptors were also localized to the cerebral cortex (48, 49). Further, progesterone- and glucocorticoid-binding sites overlapped in portions of the hippocampus of postmortem, human brain tissue (50). In the early 1980s the presence of two receptor subtypes PR-A and PR-B was reported (51), of which an imperfect estrogen-binding domain was identified in PR

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cDNA. Distribution of these subtypes changes with age and brain region. In prepubescent male rats, mRNA for both PR subtypes was similarly expressed in the hypothalamus, but in adults, PR-A expression was greater than PR-B (52). Further, in adult male rats, PR-B mRNA expression predominated in the cerebellum and preoptic area, PR-A predominated in the hippocampus, and PR-A and PR-B were equally expressed in the frontal cortex and olfactory bulb (52). Given the interplay between the progesterone receptor and estrogen it is not too surprising that studies have shown that PR expression fluctuates over the estrous cycle. In female rats, PR-B mRNA expression was more highly expressed as compared to PR-A in the hypothalamus, preoptic area, and frontal cerebral cortex while both isoforms were similarly expressed in the hippocampus (53). Moreover, PR-B mRNA expression fluctuated with the estrous cycle in these various regions with the exception of the hippocampus, a region in which PR expression remained constant (53). In these same brain regions, estradiol and progesterone differentially regulated PR gene expression in ovariectomized female rats. Estradiol induced both PR-A and PR-B in the hypothalamus, only PR-B in the preoptic area, and only PR-A in the hippocampus (54). Likewise, progesterone down-regulated both PR-A and PR-B in the hypothalamus, only PR-B in the preoptic area, and had no affect on either isoform in the hippocampus (54).

Corticosteroid Receptor Localization The corticosteroid receptors were first identified in rodents and consist of two corticosteroid-binding receptor systems, the Type I and Type II receptors (for review see (55)). The Type I receptors can further be subdivided into receptors that preferentially bind corticosterone (CR) and those that are specific to mineralocorticoids (MR), while the Type II receptor is thought to be the true glucocorticoid receptor (GR) (55). These receptor types appear to be developmentally regulated as Type I receptors were not observed until 8 days after birth and the Type II receptor appears to gradually increase over time (56). Further, Type I (GR) protein was transiently present in the CA3 and CA4 subfields of the hippocampus during the first postnatal week, and as the rats aged this expression became more restricted to the CA1 and CA2 hippocampal subfields (57). Studies specifically examining glucocorticoid binding found sites in the hippocampus, amygdaloid complex, and the fimbria of postmortem human brain tissue (50), and extensive corticosterone-binding sites were also found in the hippocampus of rhesus monkeys (58). In rodents, corticosteroid-binding assays have shown that both receptor types (Types I and II) are present, however, Type I receptors have only been observed in the hippocampal (59) and septal-hippocampal region (60). Protein expression studies on the Type II receptors (GR) have identified GR protein expression in the cerebral cortex, thalamus, hypothalamus (61), hippocampus, amygdala, and septum (62). Further, GR mRNA has been detected in similar regions to include the cerebral cortex, hippocampal formation, thalamus, hypothalamus,

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locus coeruleus and dorsal nucleus raphne (63, 64), olfactory cortex, and amygdala (63). In total these studies show that the machinery for steroid hormone effects extends throughout the brain. However, having receptors present in areas critical for learning and memory may not be enough. It is also important for these distinct brain regions to exchange information via the cholinergic projection system which is also influenced by steroid hormones.

Connecting the Dots, the Effects of Steroid Hormones on the Cholinergic Projections Cholinergic projections in the basal forebrain extend into the cortex and hippocampus, and act as the conduits or highways by which information for learning and memory are stored and accessed, and thus are critical for cognitive processing. This has been shown in several lesion models using animals. In rats, lesions in the medial septum, an area with extensive projections to the hippocampus, led to impaired acquisition on a radial-arm maze task (65), while combined lesions in the medial septum plus the diagonal Band of Broca projections impaired acquisition on an eyeblink classical conditioning task (66), impaired acquisition of a conditional visual discrimination task, decreased acquisition and retention of a spatial navigation task (67), and reduced spatial reference memory (68). In rats, lesions directed towards the nucleus basalis magnocellularis, a region that sends projections to the neocortex, resulted in recent memory impairments (69), working memory impairments (70), reduced spatial memory, an inability to inhibit a previously learned response (71), deficits in reference memory (72), and reduced performance on an active avoidance task (73). In marmosets, lesions to the vertical limb of the diagonal band of Broca which sends projections to both the hippocampus and entorhinal cortex led to deficits in retention but not acquisition on a repeated visuospatial task (74). Lesions to the basal forebrain, a region thought to be associated with age-related cognitive deficits, resulted in impairments in aged rats when they were required to shift attention between two visual stimuli only when the task was most difficult to solve as compared to young rats (75). Differences in cognitive impairments may be due in part to the method used to administer the lesion. When multiple small injections of a commonly used immunotoxin, 192 IgG-saporin, were applied to the medial septal area in rats, no impairments in spatial working memory were reported (76). In contrast, rats given a high dose of 192 IgG-saporin, but not ibotenate, a nonselective excitatory neurotoxin, showed impairments in acquisition of a delayed matching-to-position task (77). With age, impairments in cognitive processes such as spatial memory occur and primarily result from degeneration in the cholinergic projections in the basal forebrain (78). Further, these circuits have steroid hormone receptors and as such are sensitive to hormone levels (25, 29, 79), such as estrogen (80–82) and combined estrogen plus progesterone (82), indicating that steroid hormones are a biological substrate for learning and memory. Thus, these lesion studies have shown that the

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cholinergic projections are task-specific and dose-dependent, and suggest that these superhighways differentially process information and, as we will cover in the proceeding section, are affected by age and steroid hormones.

Steroid Hormone Status with Age An important age-related event in women is the menopause which results in lowered levels of estrogen, progesterone, and testosterone. In aging rodents this has functional consequences as there is typically a decline in mnemonic ability (83, 84) that is oftentimes coincident with diminished ovarian function and subsequent loss of gonadal hormones (84). Males may also experience a reduction in gonadal hormones with age, but unlike women, not all men become hypogonadal (for review see (85)). Thus, correlating testosterone deficiency or the impact of testosterone supplementation in elderly men has been difficult to assess. Not only is testosterone diminished with age (86), but dramatic declines in the precursor androgenic hormones such as DHEA or its derivative DHEAS have also been reported with age (87). Degenerative changes in the brain and gender can also influence hormone concentrations. In postmortem brain tissue collected from healthy controls, testosterone concentrations were similar between males and females, while estradiol levels were 3.5-fold higher in females (88). Interestingly, in Alzheimer’s patients the concentration of estradiol and testosterone was not significantly different when compared to samples collected from the healthy controls (88). Glucocorticoid secretion, and in particular cortisol, is due to the interactions of glands, hormones, and portions of the midbrain including the hypothalamus, pituitary, and the adrenal glands, and its effectiveness is regulated in part by a complex negative feedback system. The impact of age on cortisol concentrations has not been fully elucidated, and longitudinal studies have been contradictory to the cross-sectional studies which have shown no correlation between increasing age and higher cortisol levels (89). Further, some studies point toward an interaction between cortisol and DHEA/DHEAS and suggest that with age the ratio of cortisol to DHEA(S) increases (for review see (90)). This review will highlight some of these findings and will examine the effects of estrogen, testosterone, progesterone, and cortisol on cognition and neurodegenerative diseases such as Alzheimer’s disease.

Sex Steroids, Age, and Cognition Studies Using Animal Models Effects of Estrogen and Progesterone on Learning and Memory with Age Animal models have suggested that aging is associated with reduced production of estrogen, progesterone, and testosterone. In rodent studies, estrogen replacement seems to be beneficial to various aspects of learning and memory, but these affects

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are oftentimes gender- and task-dependent. In both aged and young ovariectomized mice, estrogen replacement increased object recognition as compared to ovariectomized, non-estrogen-replaced females (91), and also enhanced working memory (81). Further, a short-term (5 day), high dose (5 µg) of beta-estradiol-3 benzoate improved spatial reference memory in intact, aged (27–28 months) female mice (92). In female, senescence-accelerated (SAM)P-8 transgenic mice, loss of endogenous sex hormones resulted in an impairment in learning but not retention in a foot shock-avoidance T-maze task (93) and long-term estrogen replacement in ovariectomized, aged mice increased performance on a reference memory task but not a working memory task (94). Similarly, in a transgenic mouse model of Alzheimer’s disease that includes a mutation in the amyloid precursor protein, estrogen replacement significantly improved position discrimination in the T-maze task and reduced the number of errors on the radial-arm maze but did not significantly improve the amyloid beta-dependent cognitive deficits such as the spatial or cued versions of the water maze (95). Gender is also an important consideration. Males rats, but not female gonadectomized rats, benefited from estradiol with increased performance on a spatial memory task (96) while in a transgenic mouse model of Down’s syndrome, a syndrome that has symptoms similar to Alzheimer’s, estrogen ameliorated the cognitive deficits in 11–15-month-old Ts65Dn female mice (97) but had no effect on younger (6–8-month-old) male mice (98). Some of the discrepancies in estrogen’s affects may be due to dose, timing, and estrogen replacement strategy. Studies in which estrogen was given soon after hormone loss or administered cyclically have shown more positive results. When 17b-estradiol was administered to intact, aged mice (24 months) immediately following training, both male and female mice showed improvements on an inhibitory avoidance task after a 24 h exposure to estrogen and a spatial water task following a 30 min estrogen exposure (99). Short-term estrogen treatment (5 µg/day for 5 days prior to testing) followed by continuous treatment during testing led to increased expression of the synaptic protein, synaptophysin, in the hippocampus of aged mice (92). In ovariectomized, middle-aged rats, estrogen replacement enhanced working memory only when estrogen was administered at the time of ovariectomy (100) or when females were primed with repeated injections of estrogen as would occur during the normal estrous cycle (101). Acute (injected on 2 consecutive days) and chronic treatment (injected for 10 days) of a 10 µg dose of estradiol benzoate improved spatial working memory retention in young, ovariectomized female rats (102). Mimicking the natural estrous cycle also reversed impairments in spatial memory in ovariectomized, aged (22 years) rhesus monkeys (103). Similarly, ovariectomized rats that received estrogen replacement within 3 months, but not 10 months, performed significantly better on a delayed matchingto-position spatial memory task (104). Hormone replacement therapies (HRT) that have included progesterone have held some promising results but suggest that the benefits of progesterone, in some cases, may be task-specific or work better when combined with estrogen. In ovariectomized rats, estrogen-plus-progesterone replacement (104, 105), but not progesterone replacement alone, improved performance on a spatial memory task (105). Further, progesterone replacement or estrogen-plus-progesterone treatment

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was effective in improving both working memory and reference memory following administration of a cholinergic antagonist, scopolamine, in ovariectomized female rats (105). In 3- and 8-month-old, ovariectomized female rats, memory retention was enhanced when injected with estradiol (10 µg) at 48 and 72 h prior to testing (106). Moreover, progesterone augmented this enhancement in memory retention if testing occurred within 8 h following the progesterone injection (106). Although combined estrogen–progesterone treatments as discussed above have shown some cognitive benefits, a study by Bimonte-Nelson et al. (107) suggested that decreased progesterone was beneficial to several aspects of memory. In aged female rats, ovariectomy enhanced spatial memory while progesterone replacement resulted in impaired learning on the working and reference memory aspect of the radial-arm maze task (107). Further, in middle-aged rats (12 months), progesterone reversed the improvements in spatial memory observed when either a tonic, low-dose of estradiol (0.25 mg time-release pellet for 60 days) or cyclic estradiol treatment (10 µg every other week) was administered (108). The dose of estrogen may also influence the effectiveness of estrogen replacement therapy. In ovariectomized young, middle-aged, and aged rats, estradiol benzoate had different effects in these age groups depending on the dose. Impaired memory performance was observed in aged rats at the high dose (>200 pg/ml) while low doses (40 pg/ml) negatively impacted performance in middle-aged rats (109). Low physiological doses of 17b-estradiol were also ineffective in improving age-related memory impairments in aged female rats (110) and both continuous estrogen treatment and intermittent estrogen treatment (0.2 mg/kg 17b-estradiol) failed to improve spatial or object memory in aged, ovariectomized mice (111). Differences in outcomes due to dose and timing of estrogen therapy underscore the importance of using an appropriate model to mimic the menopause. Markham et al (112) reported that acute estrogen replacement, chronic estrogen replacement, and chronic estrogen/progesterone replacement improved task acquisition in female ovariectomized rats. What is important to note in this study is that the animals were “middle aged” as they were only 14 months old at the time of ovariectomy but had been retired from a breeding program and thus may have more closely mimicked aging associated with the loss of hormones versus chronological aging (112). Studies that more closely mimic the human menopause may be able to better gauge the impact of estrogen replacement on cognition rather than chronological age alone.

Effects of Androgens on Learning and Memory with Age The impact of androgens on cognition using animal models is not clear and suggests that the effects of androgen replacement or androgen loss may be species-specific. In two studies, decreases in androgen concentrations were observed in 12-month-old SAMP8 mice as compared to younger (4 months) mice (113) and in aged, 56-monthold deer mice as compared to 3–3.5-month-old male mice (114). In the latter study,

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no differences were observed in middle-aged mice (38 months) relative to the young males suggesting that the mouse strain may influence testosterone concentrations. Further, in the aged mice (56 months) the testosterone concentration was not a critical determinant in reduced performance on a spatial learning paradigm (114), whereas another study suggested that reduced testosterone was correlated with poor memory retention in SAMP8 mice (113). However, in the SAMP8 mice, reducing the endogenous level of testosterone in young mice did not cause a reduction in learning and memory, indicating that the combined effect of age and testosterone led to the cognitive impairments. Some studies have also examined the effect of the androgenic neurosteroid, DHEAS. Oral DHEAS supplementation improved learning and memory on the T-maze foot-shock avoidance test in intact, middle-aged (18 months), and old (24 months) mice (115) and in intact male SAMP8 mice (116). Females also benefited from DHEAS treatment as aged male and female mice both showed improvements in working memory in a win-shift task using a Y-maze (117). It should be pointed out however that studies using nonhuman primates such as the rhesus monkey have not found a correlation between DHEAS and age-related cognitive decline as is discussed in the chapter by Lacreuse and Herndon.

Sex Steroids, Age, and Cognition-Clinical Studies The Impact of Estrogen and Progesterone on Cognition The clinical data suggests both beneficial and detrimental affects of estrogen on human cognition. The effects of estrogen in younger women who used estrogen following an oophorectomy, which results in a surgical menopause, was one of the early reports suggesting that conjugated equine estrogen (CEE) replacement therapy following this procedure was beneficial in preserving long- and short-term memory (118). Moreover, preservation of working memory was observed in women who received CEE replacement therapy following treatment with a gonadotropin-releasing hormone (GnRH) agonist, a treatment that lowers estradiol levels, as compared to the placebo controls (119). In studies that compared postmenopausal women who were using conjugated equine estrogen replacement therapy versus those that had not, performance on recall of proper names and words (120) and verbal and figural memory tasks (121) increased. In these two studies, the number of women also taking progesterone was approximately half. When serum estradiol levels have been examined in aged women, increased serum estradiol correlated with improved function (122) and reduced mild cognitive impairment (123). Transdermal estradiol therapy in women who had not received estrogen for at least a year following menopause correlated with a greater positive change in executive functioning following estrogen replacement therapy as compared to the placebo controls (124), and in women who did not suffer any of the postmenopausal symptoms associated with menopause, short-term, transdermal, 17b-estradiol therapy

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resulted in improvements in memory function and visuospatial abilities as compared to baseline scores (125). In contrast, some studies suggest that hormone replacement therapy may not be beneficial to cognition. Declines in cognitive functioning were observed in postmenopausal women (126–128) or women who had used hormone replacement therapy for 10 years following a surgical menopause (129). It is important to point out that in these studies progesterone therapy (progestin or medroxyprogesterone acetate) was included in the analysis (126–128), and in one study, the subjects were significantly postmenopausal (17–18 years after) and had been diagnosed with coronary heart disease (128). In a prospective, observational cohort of Japanese-American postmenopausal women, current medroxyprogesterone acetate therapy but not current unopposed estrogen was associated with lower scores on the Cognitive Abilities Screening Instrument examination as compared to women who had never used hormone therapy (126). Moreover, when aged women who had never used hormone therapy (never-users) were compared with those that were current-users and past-users, a negative correlation between current hormone therapy use and brain atrophy as well as cognitive function was observed (127). A confound also observed in this study was that the current-users included both combined hormone (estrogen/progestin) and estrogen alone (127). Finally, some studies suggest that hormone therapy may have no effect on cognition. In two randomized, placebo-controlled, double-blinded trials, cognition was not improved in postmenopausal women given estrogen plus a trimonthly dose of medroxyprogesterone acetate (130) or a transdermal application of estradiol (131). In a large, population-based study (132) and a large, cross-sectional study of postmenopausal women, no significant correlations were observed in women that had been current-users, never-users, or past-users of hormone replacement therapy with cognitive performance (133). Nor were any associations observed with estradiol or estradiol/progesterone therapy over a 4- or 24-week period in hysterectomized, aged women and cognitive function when compared to the placebo controls (134). Further, no significant correlations were observed in total gray matter, white matter, hippocampal, or amygdalar volumes (135) or different measures of brain atrophy (121, 135) when examined by magnetic resonance imaging. In studies that compare the effect of steroid hormones on both men and women, the benefits to cognition appear to be minimal or contradictory. Low plasma estradiol levels in both men and women have been associated with reduced verbal memory (136) and poor global cognitive function (136) while in women high plasma estradiol and testosterone concentrations correlated with greater verbal memory and less susceptibility to interference on the Stroop test (137). Interestingly, in men testosterone negatively correlated with verbal fluency (137). In elderly women, high estradiol levels were associated with better performance on a test for delayed visual memory and retrieval efficiency, while testosterone correlated with better verbal fluency (138). These studies suggest that estrogen and testosterone are beneficial to women but in a community-based study of 792 men and women, aged 70–79 years, testosterone levels were not associated with higher scores on the Mini-Mental State Examination test (MMSE) in either gender (136).

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Testosterone Testosterone levels also decline with age and many studies have examined the impact of androgens on cognition. In men with prostate cancer, chemical castration using androgen blockage therapy resulted in reduced performance on the Cambridge examination for mental disorders of the elderly, delayed memory (139, 140) and a slowing of visuomotor skills and impaired attention, but improved object recall (141). Further, the amount of bioavailable hormone versus total plasma hormone concentrations may be a better measure of hormone effects. Sex hormones such as estrogen and testosterone are often bound to a carrier protein called the sex-hormone binding globulin (SHBG). Serum SHBG primarily responds and regulates estrogen and testosterone and has been shown to be reduced following menopause (142, 143). In several studies, free bioavailable testosterone was associated with better visual and verbal memory and a reduced rate of decline for visual memory (144) as well as increased performance on tests of long-term storage (145) and speed and attention (146, 147), while low estradiol correlated with higher executive function (145). In a large, population-based study, the beneficial effects of testosterone in men increased with increasing age (148). However, not all studies have shown a positive correlation of testosterone with improved cognition. Bioavailable testosterone was not correlated with cognitive functioning in aged men ³55 years of age (149) nor was higher serum total testosterone associated with verbal long-term memory, verbal ability, visuospatial perception, general knowledge, and cognitive and perceptual motor processing (150). Further, in a population-based study with ages that ranged from 35 to 80 years, low free testosterone improved performance on the block-design task and draw-a-figure task (151). Several studies have also attempted to use “androgen replacement therapy” by supplementing elderly men or hypogonadal men with testosterone. These studies are complicated however by the steroid metabolism pathway itself. Testosterone can be aromatized to estrogen, so separating out the contribution of both hormones can be difficult to assess. Testosterone supplementation to men aged 50–80 for 6 weeks resulted in higher greater spatial memory, spatial ability, and verbal memory (152). In this study however, both estrogen and testosterone levels were increased making it difficult to determine which hormone contributed to these improvements. Testosterone supplementation for 3 months improved performance on a spatial cognition task in healthy older men (153). But, a negative correlation was found between plasma estradiol concentrations and spatial cognition indicating that the interplay between estrogen and testosterone may be more critical than the individual levels of these hormones (153). The difficulties in assessing the effect of testosterone is evidenced by three placebo-controlled trials in which testosterone replacement had no affect on various measures of cognitive abilities. In hypogonadal men in their late 60s, tests for recall, verbal fluency (154), speed and attention, and overall brain function (155) were not improved with 12 months of testosterone supplementation. A single dose of testosterone enanthate (250 mg) in healthy aged men also failed to improve spatial cognition or memory (156).

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Studies have also tried to examine the effectiveness of the precursor to testosterone, DHEAS, as declines in testosterone may actually be a reduction in DHEA/ DHEAS metabolism. Some studies have shown a correlation between DHEAS and higher cognitive functioning in men and women (157) or women alone (158). However, in a prospective, longitudinal study of elderly men (159), in two, randomized, double-blind, crossover studies of elderly men (160) or both elderly men and postmenopausal women (161), and a randomized, double-blind, placebo-controlled study of postmenopausal women (162) no correlation was observed between DHEAS and a battery of neuropsychological tests. In a prospective study of women with a mean age of 65 years, baseline DHEAS levels were also not associated with cognitive performance as measured by tests of executive function, dementia, and speed and attention (163). Interestingly, in a randomized study of elderly men, aged 75–85 years, who were examined every 5 years, Kahonen et al. (164) found that decreased plasma DHEAS concentrations only correlated with reduced cognitive performance when the men developed cognitive decline within the 5-year-period between examinations (164), and in another study of elderly females of the same age range, DHEA plasma levels inversely correlated with cognitive scores for the MMSE and the Test for Severe Impairment as well as for the immediate recall, copy, and recognition component of the visual reproduction subtest of the Wechsler Memory Scale-Revised test (165). These two studies, in subjects between 70 and 90 years old, suggest that in this age group, DHEA(S) actions are detrimental to cognition.

Selective Estrogen Receptor Modulators (SERMS) Selective estrogen receptor modulators (SERM) are another form of hormone therapy and are commonly used in patients with breast cancer. SERMs function as estrogen agonists or estrogen antagonists depending on the target tissue and may actually be detrimental to cognitive function. Memory problems were associated with use of the aromatase inhibitor, anastrozole (166), or the estrogen receptor antagonist, tamoxifen (166, 167). However, in a randomized, placebo-controlled trial, Yaffe et al. (168) reported that raloxifene may potentially reduce the risk of mild cognitive impairment while two other studies showed no effects when a 60 or 120 mg dose of raloxifene was used for 3 years (for review see (169)). Since it is not a feminizing agent, raloxifene treatment is also being considered for men and has yielded some positive results. In a double-blind, placebo-controlled, functional magnetic imaging study, healthy aged males treated with raloxifene for 3 months showed activation in the bilateral parietal and prefrontal areas, the anterior cingulate gyrus, and inferior prefrontal, occipital, and mediotemporal areas bilaterally during performance on a face encoding paradigm (170) and enhanced activation of the posterior parahippocampal area and right inferior prefrontal cortex following a face recognition paradigm (171). Raloxifene-treated males showed increased accuracy on the face-recognition task as compared to placebo, age-matched controls, and the

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authors hypothesized that brain activation in the reported areas correlated with the improvements observed on this cognitive task (171).

Effects of Stress and Changes in the Hypothalamic–Pituitary–Adrenal (HPA) Axis with Age One important product of interactions between the organs associated with the HPA axis is secretion of glucocorticoids such as cortisol. The highest concentration of glucocorticoid receptors have been observed in the hippocampus and projections extend from the hypothalamus to the hippocampus (for review see (172)), and in rats, glucocorticoids have been shown to inhibit postnatal neurogenesis (173, 174) and adult neurogenesis (175) suggesting that problems associated with HPA axis activity can potentially lead to cognitive impairment. This is corroborated by studies that have shown that long-term cortisol treatment is correlated with reduced hippocampal volumes and lower performance on tests of cognition (176). Further, increased cortisol concentrations have been shown to be increased in patients with cognitive impairment (177–179) and with reduced performance on cognitive tests such as the Cambridge cognitive examination in healthy, aged adults (180). Further, over a 4-year time span, both explicit memory and selective attention were negatively impacted by high increasing cortisol levels, while decreasing cortisol levels led to cognitive performance scores similar to healthy, young adults (181). The duration of cortisol concentrations may be a critical determinant in the balance between mental health and cognitive impairments. Administration of a 20 mg dose of hydrocortisone 12 h, and then again 1 h prior to cognitive testing did not affect memory, executive function, or attention in healthy, elderly patients (182) and increased salivary free cortisol following a stressful event resulted in no effects on declarative memory in healthy middle-aged women 32–68 years of age (183). Absolute levels of cortisol may be less important than the proportion of available cortisol relative to the androgenic precursor; DHEAS as an important determinant of age-related cognitive health. In demented, aged patients, nocturnal plasma cortisol increased while DHEAS decreased resulting in a higher cortisol/DHEAS ratio as compared to healthy young adults (184). Similarly, a high plasma cortisol/DHEAS ratio was observed in both male and female elderly patients as compared to young adult controls (185). Gender effects have also been reported. In a longitudinal study aged men had higher plasma DHEAS than the postmenopausal women irrespective of estrogen use and plasma cortisol increased over the 18-month study period only in postmenopausal women who were classified as estrogen nonusers (186). In two longitudinal studies, higher salivary baseline cortisol concentrations predicted verbal memory loss (187) and cognitive impairment in healthy, high-functioning elderly men and women (188). Gender differences were observed in a community-based, longitudinal study that showed women who exhibited increased cortisol excretion over a 2.5-year period were more likely to have memory deficits than men (189). In this study, increased cortisol levels did not appear to have

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permanent consequences as decreases in cortisol over this same time period in women led to improved memory (189).

Aging Diseases that May Correlate with Hormone Status Alzheimer’s Disease and Dementia Gonadal Hormones and Alzheimer’s Disease and Dementia Steroid hormones may play a critical role in protecting females from cognitive decline and dementia. Several studies have found a correlation between decreases in estradiol and/or estrone, the main derivative of estradiol in postmenopausal women, and Alzheimer’s dementia (190–192); and it may be that the proportions of estradiol, estrone, progesterone, testosterone, and cortisol influence the risk for neurodegeneration. When plasma levels of estrone, estradiol, androstenedione, testosterone, and cortisol were measured in Alzheimer’s patients, estradiol could not be measured in 37% of the patients, androstenedione and estrone were significantly elevated while the remaining hormones were not significantly different between Alzheimer’s patients and age-matched controls (191). Further, as mentioned previously, the amount of bioavailable hormone may be a better measure of hormone effects and can be measured indirectly by examining the carrier protein sex-hormone binding globulin (SHBG). Serum levels of sex-hormone binding globulin (SHBG) were increased in patients with Alzheimer’s as compared to age-matched controls and the authors suggested that this may be due to abnormalities in the regulation or production of SHBG (192). However, SHBG is not always correlated with increased dementia. In a prospective, longitudinal study conducted over 19 years, both SHBG and total testosterone were not predictors for Alzheimer’s disease; rather, decreases in free testosterone predicted Alzheimer’s disease and this hormonal decrease occurred prior to onset of the Alzheimer’s diagnosis (193). Serum gonadal hormone concentrations do not always fit with the concentrations of the hormones in the brain. SHBG-bound estrogen and testosterone is unable to cross the blood–brain barrier (194), thus studies that examine plasma concentrations of sex hormones may not be adequately measuring the amount of hormone available locally in the brain. Moreover, the brain contains aromatase and can, in essence, produce its own supply of hormones from cholesterol. In Alzheimer’s patients and healthy, aged men and women levels of brain-specific estradiol and testosterone suggested that estradiol and testosterone levels were not significantly increased from the hormone concentrations observed in control brains (88). Further, in control brains there was a gender effect in that estradiol was 3.5-fold higher in females than male brains, but testosterone was not significantly different between genders. Hormone replacement therapy (HRT) has been examined as a way to attenuate cognitive decline and dementia, and several prospective studies have suggested that

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HRT may reduce the risk of developing Alzheimer’s disease (195–197). In patients with dementia of the Alzheimer type, estrogen replacement therapy was also beneficial when a low dose was given for 5–45 months (198) or when given twice a day for 6 weeks (199), as measured by improved cognitive function, regional cerebral blood flow, EEG activity, dementia symptoms (198, 199), and improvements in daily activities as compared to pretreatment levels (198). Further, in some cases, HRT therapy has been as effective as tacrine, a cholinesterase inhibitor used to treat Alzheimer’s patients. When tacrine treatment was compared with estrogen or estrogen/progesterone replacement therapy in women who had mild-to-moderate Alzheimer’s, HRT alone showed a significant benefit on activities of daily living (200). Further, mood and cognition seemed to improve when either tacrine or HRT was used; however, in patients that were lacking the Apolipoprotein E epsilon 4 allele, an allele associated with a faster rate of cognitive decline in AD patients, tacrine was more effective (200). Other reports, however suggest that estrogen replacement is not beneficial to women with mild or moderate dementia (201). Moreover, recent studies (202, 203) reported that estrogen/progesterone replacement therapy and estrogen replacement therapy alone was associated with a higher risk of global cognitive function in women over 65 years old. These studies, when put in context of the timing of estrogen may not seem so contradictory. For example, in one study (197) the risk for dementia decreased in women who were current HRT users or had been on HRT for ³10 years, but in the most recent studies by Shumaker et al. (202) and Espeland et al. (203) where there was an increased risk for dementia with HRT, hormone therapy was not initiated until after menopause. Progestins may also be a complicating feature of hormone therapy. In one clinical study, women with Alzheimer’s disease were treated with conjugated equine estrogen for 3 weeks then switched to medroxyprogesterone acetate or norethindrone for the fourth week. Psychological assessments increased on the third week but declined following medroxyprogesterone acetate or norethindrone treatment (204). Declines in cognition following medroxyprogesterone acetate treatment were also observed in the Women’s Health Initiative Study, where conjugated equine estrogen treatment coupled with medroxyprogesterone acetate led to increased dementia (202), whereas conjugated equine estrogen therapy alone (203) led to increased mild cognitive impairment but not dementia.

Impact of Testosterone on Alzheimer’s Disease and Dementia Testosterone levels have also been correlated with Alzheimer’s disease but may not affect both genders equally and the correlations do not hold across all studies. In men, loss of free testosterone (205) and total testosterone (206) was associated with Alzheimer’s or dementia of the Alzheimer’s type in men, respectively, while testosterone levels were not correlated with dementia in women (206). In a cross-sectional study of men over 55 years old, higher bioavailable testosterone levels correlated with reduced performance on tests of executive functioning, working memory, and attention only for those men carrying the ApoE epsilon 4 allele

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(207). In contrast, two studies found no correlation between testosterone levels and male Alzheimer’s patients (208, 209), however, increased estrogen levels were associated with increased dementia (209). DHEAS levels may also be important for development of dementia for both men and women. In a small study, aged men and women with Alzheimer’s had significantly elevated levels of serum DHEA and androstenedione (210), while another mixed-gender study found the opposite in that decreased plasma DHEAS concentrations actually correlated with dementia of the Alzheimer’s type (211). Finally, women but not men with mild-to-moderate Alzheimer’s disease showed significantly higher levels DHEA and androstenedione (212). All three studies had very low sample sizes (20–35 subjects per gender) which may account for the inconsistencies. One way to ameliorate low testosterone levels would be to administer testosterone supplementation. Testosterone supplementation could potentially benefit patients with dementia by increasing cognitive functioning or by enhancing quality of life. Cognitive improvements were not significant in men with mild-tomoderate Alzheimer’s that were treated with testosterone for 24 weeks, but significant improvements were observed for their quality of life as rated by the caregiver (213). Further improvements in verbal memory, constructional abilities (214), visuospatial abilities and the MMSE score were observed in men with mild cognitive impairment or moderate Alzheimer’s disease (215). Currently, the National Institute of Aging has begun recruiting for a new clinical trial that will examine the effects of testosterone supplementation and exercise (TEAM, NIH identifier: NCT00112151) on cognition, function, endurance, strength, and body composition in older men; thus, future use of testosterone replacement therapy is still actively being considered.

Impact of Glucocorticoids on Alzheimer’s Disease and Dementia The impact of glucocorticoids is difficult to assess. One study suggested that high cortisol and a higher cortisol/DHEAS ratio increased the risk for Alzheimer’s disease (216), and three other studies suggested that cortisol levels (217, 218) or decreased responsiveness in the HPA axis did not correlate with Alzheimer’s disease (219). One confound when considering the impact of cortisol on Alzheimer’s disease is depression. Alzheimer’s disease was associated with changes in the responsiveness of the HPA axis, and HPA axis changes resulted in increased depression and decreased hippocampal volume in the right hemisphere but these effects were not attributed to cortisol-mediated neurotoxicity (220).

Future Directions As our population ages and the incidence for obesity increases, it will be essential to find compounds that can counteract the effects of vascular events that could lead to impairments in cognition. Increasingly, the risk factors for Alzheimer’s disease

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are the same risk factors for development of cardiovascular disease. Although the current generation of statins have not been effective in preventing cognitive decline (221–223), there will likely be an increase in research to attempt to find anticholesterol drugs that reduce symptoms that lead to dementia and Alzheimer’s disease, such as amyloid beta accumulation (for review see (224)). Another area that will likely continue to expand is the use of thyroid hormone replacement therapy. With age, the incidence of hypothyroidism increases and reduced thyroid hormones may be correlated with Alzheimer’s disease (225), neuropsychiatric symptoms observed in some Alzheimer’s patients (226), or may influence episodic memory in aged men and women (227). Although, more research is necessary as a recent, population-based cohort study examining fasting and resting levels of thyroxine (T4), the main product of thyroid secretion, and triiodothyronine (T3), the product of local deiodination in peripheral tissues, found that in nondemented patients, fasting T4 and resting T3 was associated with increased hippocampal and amygdalar atrophy, but no associations were observed between thyroid hormone levels and patients with Alzheimer’s disease (228). Use of gonadotropin-releasing hormone inhibitors is also an area of active research (for review see (229)). The notion that luteinizing hormone may be a risk factor for Alzheimer’s type dementia in men has been steadily receiving more attention. In patients with Down’s syndrome, males but not females have an increased risk for Alzheimer’s-like dementia (230), and one physiological feature found in men (231, 232) but not women (231) is increased levels of luteinizing hormone. However, estrogen may play an important role as a recent study found that in women with Alzheimer’s disease, those women not taking estrogen replacement therapy also had increased gonadotropin levels (233). Further, other studies are beginning to examine the effects of gonadotropin-releasing hormone agonists on cognition in human (234) and animal models (235).

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Timing Deficits in Aging and Neuropathology Fuat Balci, Warren H. Meck, Holly Moore, and Dani Brunner*

Abstract The capacity to capture the temporal information embedded in biologically relevant events is a necessary and ubiquitous ability of higher organisms. The cognitive apparatus that supports timing is integrally entwined with those supporting other cognitive processes including memory and attention. In this chapter, we argue that timing deficits consistently occur with aging and in specific neurodegenerative disorders (i.e., Parkinson’s disorder and Huntington disease), and might depend on and reflect attentional deficits that are also characteristic of normal aging and in these clinical populations. We review the impairments in temporal information processing seen in the elderly and in neural disease, and evaluate them in relation with the structural and neurochemical brain markers. Given the good correspondence between the psychophysical properties of interval timing across nonhuman and humans, we further argue that interval timing might serve as a quantitative model for cognitive aging that offers promise in the translation from preclinical to clinical studies. Keywords Aging • cognition • divided attention • timing • time perception

The Concept of Timing From the philosophical investigations of St. Augustine (circa ac 400 (1)), through the mid-eighteenth-century early experimental work of Mach (2), and Piaget’s ideas about the genesis of the concept of time (3), up to today’s neuroimaging studies, F. Balci PsychoGenics, Tarrytown, NY, USA D. Brunner Biopsychology Department, Columbia University, New York and PsychoGenics, Tarrytown, NY, USA W.H. Meck Department of Psychology and Neuroscience and Center for Behavioral Neuroscience and Genomics, Duke University, Durham, NC, USA H. Moore Center for Neurobiology and Behavior in Psychiatry, Columbia University, New York, NY, USA J.L. Bizon, A. Woods (eds.) Animal Models of Human Cognitive Aging, DOI: 10.1007/978-1-59745-422-3_8, © Humana Press, a part of Springer Science + Business Media, LLC 2009

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fascination with the concept of time, time perception, and temporal processing has pervaded philosophy, literature, and science. Piaget argued that the sensation of time is built from the experience of two events and their speed; that is, duration is a derived quantity. Other thinkers such as Mach (2), instead, postulated an internal clock, devoted to the measurement of time. Such clocks could be started, paused, and reset. Thus, if an important event occurred that caught our attention, then our internal clock would start and allow us to obtain a temporal estimate, which could be used to immediately make a decision, or stored for later retrieval. Therefore, although we cannot directly perceive an interval of time we can trigger an internal mechanism that parallels its flow, and thus synchronizes our behavior with the physical world. As if time perception was not a difficult subject itself, we do not even agree on the nature of physical time: Is time just one more dimension in the time-space continuum? Is it the vector that explains the constant increase in entropy? And how could these intangible dimensions be sensed and perceived by organisms? Is time a property attached to objects independently of anyone’s perception of it? These are questions posed by the philosophers Kant (1724–1804) and Bergson (1859–1941) as well as the astrophysicist Eddington (1882–1944) (4–6), for which we do not have a better answer today that they did in their time. St. Augustine thought that, whereas time does not exist for God (as he exists in eternity), it is a painful experience for his creatures. Time is thus a mark of the human soul that demonstrates how far we have fallen away from God’s eternity into successive time (1). Indeed, our experience of time shapes so much of our daily life, anxieties and hopes, that dysfunction in the time apparatus, and disconnection with physical reality, could be central to a number of psychological disorders (7).

Timing Is Ubiquitous Most organisms have a system to translate or represent the basic temporal characteristics of their physical surroundings. Thus, organisms entrain their activities to physical zeitgebers such as sunlight or moonlight and/or are governed by internal circadian oscillators when activities are most efficiently organized around the time of day (8). Longer seasonal cycles exist for vital activities such as mating, or hibernating, and much shorter cycles exist in the milliseconds to seconds range for motor coordination, reflex responses, speech recognition, and other mental activities (9, 10). These latter functions are mediated through a cognitive function called “interval timing,” defined as the ability to perceive, remember, and organize behavior around periods in the range of seconds to minutes. Interval timing is present in basic daily activities such as foraging in wild animals (11), in complex decisions such as discounting future reward, choosing between reward sequences (12, 13), or simple ones such as waiting for the pot to boil or for the bus to arrive at a bus stop. The representation of time is necessary to capture environmental contingencies and estimate predictive relations between events in the environment, and between events and responses (14). Encoding of these predictions, in turn, enables us to

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make decisions about how to act within a given environment. Here the word decision does not necessarily imply a conscious process; it is, rather, a selection of a response. From this point forward in this review, our usage of the term “timing” implies an operational definition based on the requirements of our experimental tasks and the characteristics of our outcome measures. This need for operational definitions is, of course, required by our goal to translate research from animals to humans and vice versa.

Timing as a Model System to Study Cognitive Dysfunction Maybe the most common impression related to our always aging time apparatus is the feeling that physical time passes faster relative to our subjective time as we get older: whereas a year seemed to be an eternity when we were kids, now the same periods of days, weeks, and months seem to be shorter – due, in part, to the slowing of our internal clock and impaired attentional time-sharing. Temporal judgments of this sort fall into the so-called temporal life perspective subject area, which is often difficult to study in controlled settings. However we can investigate the effect of aging on timing and time perception using more controlled psychophysical studies, typically spanning much shorter event durations from milliseconds to minutes (15, 16). Although we do not have yet a consistent body of evidence that explains why life seems to accelerate with time, many findings from the laboratory are reproducible, interpretable within the context of theory, and related to changes in neurotransmitter systems and loss of plasticity in the aging brain. In this chapter we will discuss the use of interval timing as a model system of cognitive aging, will present the tasks used to study timing and time perception, the models that organize the results from these studies around the framework of an internal clock, and the neurocircuits that are proposed to underlie the different components of the timing apparatus. More often than not, differing results are associated with differing experimental details; thus it is very important to understand the different ways in which interval timing can be measured. Therefore, our aims in this chapter are to present a description of the tasks that can be successfully translated between species and discuss prominent theories of interval timing that specify its functional relation with respect to other cognitive processes. The cognitive apparatus required by interval timing involves allocation of cognitive resources to the perception and encoding of incoming temporal information, storage and retrieval of the stored temporal percept in a long-term memory, and comparison with other percepts in working memory. Central measures of interval timing are affected by those experimental and pharmacological manipulations that are known to affect other cognitive functions such as attention and memory, thus supporting a model in which these different processes interact with each other. This functional interrelation underlying interval timing thus sets an occasion for its utilization as a model of cognitive performance reflective of other aging-sensitive cognitive processes, such as divided attention, and associated neurobiological changes, such as cholinergic deficits. In this way, the measurement of interval timing can serve an important role in animal models of

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cognitive aging, as its psychophysical properties and sensitivity to aging are similar across different species (17).

Experimental Assessment of Timing Several psychophysical tasks have been developed in which the subjects are asked to estimate, discriminate, produce, reproduce, synchronize with, or classify temporal intervals. A differentiation between prospective and retrospective timing has also been put forward and proposed as a fundamental difference between tasks (17). 0In prospective timing the subject is instructed to attend to the duration of the upcoming event, whereas in retrospective timing the subject needs to recall an experienced stimulus and determine its duration without knowing ahead of time that this aspect of the stimulus would be important. In animal studies retrospective and prospective timing cannot be truly separated as the animal is empirically “instructed” to estimate an upcoming duration through repeated reinforcement of an experienced interval. We present below a description of psychophysical tasks separating them, instead, according to what type of temporal stimuli are used and what type of response is required and then focus on those tasks that are important in the translation from animal preclinical to human clinical studies. Experimental procedures for the study of timing can be divided under three general categories: (1) scaling, (2) discrimination, and (3) differentiation. In scaling techniques the subject can be presented with an explicit external stimulus. These tasks take on the following forms: (1) magnitude estimation requires subjects to verbally estimate the duration; (2) categorization requires subjects to assign a stimulus to a temporal category; (3) temporal reproduction requires the subject to bracket the duration with a response; (4) ratio-setting involves the subject reporting on a duration that is a given proportion of the stimulus; (5) synchronization requires synchronizing a response with the temporal stimulus; and finally (6) temporal production is a paradigm in which the subject is verbally instructed to estimate a duration by making start/end responses (e.g., “produce a 1 min signal by pressing a key”). In discrimination tasks the temporal information is always presented as an explicit temporal signal and subjects are asked to distinguish between two given stimuli. Subclassifications of these tasks include forced choice, in which the subject is asked to identify which of two durations is the standard duration (which could be either a fixed or roving standard) and single stimulus, in which shortand long-duration standards are presented and the subject classifies a probe stimulus as one or the other. If the probe duration varies between the short and long standard, then this is the widely used bisection procedure. Here subjects are trained with two reference durations (e.g., short = 2 and long = 8 s). In probe trials intermediate durations are presented (e.g., 2.6, 3.2, 4.0, 5.0, and 6.4 s) and subjects classify the probe durations based on their similarity to the standards. The responses are plotted as psychometric functions, showing the proportion of “long” choices as a function of probe signal duration (Fig. 1). The duration that

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is half the time classified as “long” is called the bisection point (T1/2) or the point of subjective equality (PSE). In other tasks, if the probe stimuli vary from shorter than to longer than a single standard, then this is a temporal generalization task. In two other tasks, switch and time-left, the subject needs to switch from one response to another after (but not before) a certain amount of time has elapsed in order to receive reward (18–20). The time left procedure introduces an extra level of difficulty as the subject needs to estimate not only the time elapsed for one stimulus but also the time remaining (not elapsed) on other, alternative stimulus, and therefore requires a double comparison of the time elapsed with two different reference durations. In differentiation tasks the subject distributes responses over time trying to match a temporal requirement. In the fixed-interval (FI) procedure a response is reinforced if it is produced after a fixed duration has elapsed, but the task is called peak-interval (PI) procedure if some unreinforced trials are intermixed. The PI procedure is a task that has been used with humans, pigeons, starlings, rats, and mice (11, 21–23). During unreinforced trials subjects typically start responding a constant fraction before the standard duration has elapsed and stop responding a constant fraction thereafter. Thus, in a given trial the response rate resembles a step function with brisk change points at the start and stop of responding (Fig. 2A, trials 1–3). The middle point in between the start and the stop, called the peak time, and the time between starts and stops, called the spread, are taken as measures of accuracy and precision, respectively. On average, the timed responses result in a smooth Gaussian-shaped response distribution that peaks at the target duration (Fig. 2B, average response). In the final category, differential reinforcement of low or high

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response rate (DRL and DRH, respectively) requires that the subject paces its responses according to a required maximum or minimum response rate. In the DRL task the forced paced responding requires inhibition of ongoing behavior and thus it has been used to study both timing and motor impulsivity (24). The differences between all these different tasks are more than superficial. Figure 3 presents a scheme comparing time estimation, production, and reproduction. As described before, in time estimation (Fig. 3A) an event duration is explicitly presented, perceived, encoded, and transformed into a categorical duration which is later expressed to the experimenter. In production (Fig. 3B), the flow is opposite, the category is expressed by the experimenter, and encoded by the subject into a time estimate which is then translated into some physical temporal response. Temporal information expressed with symbols or other categories is qualitatively

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different from physically marked durations: categories are perfectly discriminable from each other as they belong to an ordinal scale composed of discrete items (i.e., “short” is as different from “medium” as “medium” is different from “long”). Physically signaled durations, instead, are continuous variables, which lie on a ratio scale and thus have real metric and uncertainty as some durations are truly close to each other and some are very far apart (25). The method of reproduction (Fig. 3C), or any method that requires comparison of encoded time, without intervening categories, is more amenable for modeling timing and for translational research, as the process of encoding and decoding from symbols to time does not have to be modeled theoretically or mimicked in animals (26).

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Theories and Models of Timing In accordance with Weber’s psychophysical law (27), as the standard duration increases the mean response duration and its variability also increase proportionally, so that response distributions overlap when plotted on a relative timescale. This relationship between length of an interval to be estimated and the variability of responses make to mark that duration is the “Scalar Property.” In other words, this means that the discriminability of two durations is proportional to the ratio between the two (e.g., 2 s is as hard to discriminate from 3 s as 4 s is from 6 s). These empirically derived “laws” have influenced the development of theories of perception and information processing. The Scalar Expectancy Theory (SET) was developed by the late John Gibbon in collaboration with his colleagues Russell Church and Warren Meck (22, 23, 28, 29). SET shares similarities with the internal clock model put forward in a seminal paper by Michael Treisman (30), where he postulated that there is a pacemaker that provides periodic pulses that can be accumulated as the subject estimates time (t’ in Figs. 4 and 5). The accumulator (analogous

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Fig. 4 Diagram showing a subjective representation of time in memory (y-axis) and real time (x-axis) as postulated by scalar expectancy theory (SET). As real time elapses an estimate of it accumulates in a short-term memory storage. The estimate grows according to l, the clock rate. During training the estimate of time at which reinforcement is delivered is stored in memory. As several components of the system show variability within and between trials, the memory representation is fuzzy, roughly Gaussian. During testing, in an unreinforced trial in a peak-interval procedure, for example, a sample from memory (Sa or Sb) is compared with the estimate of time elapsed in the trial (t’) using a relative difference rule (f (x, t’)). When the difference is less than a response threshold B, responding starts, when time further elapses and the differences grow to be larger than B, responding stops

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Fig. 5 A cartoon depiction of an attentional gate. An external signal triggers the accumulation of ticks. As attention is directed to the temporal signal the pacemaker closes (state = 1) and the ticks produced by the pacemaker are accumulated. At the end of the accumulation, if the signal was associated with a significant event (such as reward) the accumulated quantity can be sent to a reference memory. Accumulation stops at any time the switch opens (state = 0). Note there may be a delay between the signal onset and actual accumulation onset, a possible source of noise

to a short-term memory) gathers pulses if a switch closes with the onset of an external temporal stimulus and continues to do so until the switch opens with the offset of the stimulus (Fig. 5). The accumulated pulses may then be encoded in a reference memory, where estimates of the timed duration are stored until a comparison with another time estimate is needed. In a PI procedure, for example, it is assumed that a sample from reference memory, created during training, is accessed during testing and compared, via a “comparator” unit to an estimate of the target duration. In this situation, the start of responding occurs when the similarity between the elapsed time in the trial and the target time in memory is “sufficiently” high. The similarity between the elapsed time (t’) and a sample from memory (Sx) shown as f(x, t’) in Fig. 4. When f(x, t’) is smaller than a response threshold B, responding starts, and when it outgrows the threshold, then responding stops. In Fig. 4 two trials are represented, one in which the sample from memory was short (Sa) and resulted in an early response (Ra), and one in which the sample was average (Sb) and resulted in a wellcentered response (Rb). Of course, this representation is based on a complex model that has many other assumptions that ensure the basic psychophysical properties of time perception are robustly predicted. Changes in timing ability in aged subjects or in other cases (i.e., with pharmacological manipulation) have been proposed to be the result of a change in the speed of the clock. As we will see below, this is not a simple postulate. In a reproduction task, for example, the clock system needs to be engaged twice, once during encoding and once during decoding of the temporal signal. Thus, if the pacemaker

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is slow during encoding and decoding, the effects cancel each other and reproduction is veridical. In estimation and production tasks, instead, the clock is engaged only once. If the categorical signal retrieves a memory of the temporal stimulus that has been encoded during previous experience(s) with a “normal” clock, then changes in clock speed with aging indeed can result in changes in temporal accuracy. This example illustrates why the apparent effect of aging on timing depends on the task that is used (31). Attentional influences on the timing apparatus are postulated to modulate the opening and closing of the switch (32). If attention is directed to the timing task, then the switch is closed all the time and accumulation is maximal. For a detailed discussion about the difference between gate and pure switch models the reader should review Zakay’s and Lejeune’s arguments (33–35). The attentional account was postulated to account for the common finding that subjects engaged in nontemporal aspects of a task are more likely to underestimate the time elapsed (36, 37). It is possible to pay attention without timing and thus, consistently, we assume that attention is a general, domain-independent process that can modulate the functioning of a timing switch. This assumption is particularly clear when two or more signals are presented simultaneously, and divided attention is required to properly time each of the signals (38). As discussed later in this chapter, a deficit in divided attention may explain why aged subjects are poor in simultaneous temporal processing, but relatively normal when timing only one signal at a time (i.e., the switch itself is probably intact). The connection from the perceptual apparatus to the attentional mechanism ensures that the switch closes with the onset of an external event as illustrated in Fig. 5. Because classical clock models such as SET were designed with a focus on the psychophysical laws governing time perception, they do not account for attentional effects. The attentional gate model, in which the rate of accumulation if regulated not only by the pacemaker rate but also by the number of pulses that the switch allows to pass in a given period, can account for some effects that were originally attributed solely to pacemaker speed effects. For example, psychostimulants such as amphetamine are sometimes found to induce overestimation/underproduction of time intervals (21, 26) (see below), an effect that has been interpreted as a druginduced increase in the clock speed (see Fig. 6 for explanation). The same effect could be explained as a decrease in the threshold for pulse accumulation, not a change in pacemaker speed per se, but an increased probability of the switch being closed per unit of time. This alternative explanation, for which there is no consensus, implies that, under “normal” conditions the switch is not always closed during the timing of an interval. An alternative model described below in which the switch mechanism is not considered to be an “all-or-none phenomenon” does not lead to such counterintuitive predictions because effects on attention directly alter cortical oscillations and/or coincidence detection (39). Figure 7 shows a cartoon version of a possible gate mechanism in three different states. With high temporal awareness the switch is closed all the time (left) allowing all counts from the pacemaker to be accumulated, whereas with medium or low awareness the switch is not closed consistently, and fewer counts pass and

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Fig. 7 A cartoon depiction of an attentional gate with three different levels of temporal awareness. Note how the switch fluctuates between a closed (1) and an open (0) state. The pacemaker speed is not affected but its ticks are accumulated at a higher rate when the attention to time is more intense

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are accumulated. Note that low temporal awareness should also be associated with higher variability of temporal estimates from trial to trial as accumulator speed varies unpredictably. Some of these ideas can be captured in models based on electrophysiological properties of the neuronal systems involved, and this is described below. There is currently no formal accepted model that encompasses all aspects important for timing in aging, namely a timing apparatus, an attentional mechanism, and the effect of failing divided attention, although some attempts at formalization have been presented in slightly different context. Taatgen et al. (40) modeled time estimation in the context of an all-encompassing theory of cognition. In their model, the cognitive apparatus has access to the accumulated output of a timing module and to other non-timing modules, and direct action to the appropriate task. Attention can disrupt the content of the timing module or direct action to other nontiming tasks without disrupting the time estimate.

Neuroanatomy of Temporal Information Processing In this section we will discuss structures involved in the processing of temporal information that regulate perception of stimuli and/or the production of voluntary behaviors occurring over seconds to minutes. Although there is evidence that there are neural centers, particularly in the cerebellum that may serve to automatically control the timing of a motor-effector system, there seems to be a separate circuit involving other brain centers, namely the basal ganglia and associated cortical structures, that underlies effortful attentionally modulated temporal perception (41–43). Much of what is known about the functional neuroanatomy of time perception overlaps with what is known about the effect of elapsed time on memory retrieval or the control of a response. However, using timing paradigms such as PI and bisection procedures, changes in time perception can be measured that are not confounded by differences in mnemonic ability or response control. In the next sections we will cover pharmacological and functional anatomical studies in humans and experimental animals in which paradigms specific to time perception have been used.

Human Neuroimaging Studies The human neuroimaging studies on temporal cognition using functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) support a role for the frontal cortex, particularly inferior gyrus, basal ganglia, and cerebellum in timing and motor sequencing, whereas parahippocampal cortices have been more specifically implicated in the retrieval of stimulus properties required for timing (44–57). In addition, each sensory modality activates specific cortical regions for discrimination of fast frequencies involved in stimulus discrimination (58). It should

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be noted that aging may change the particular circuits recruited in timing as studies using both fMRI and PET imaging in humans (and neurochemical measures in rats) have detected both decreases and increases with age in some local regional activity, possibly reflecting loss of processing efficiency and functional compensation by the recruitment of additional brain regions, respectively (59, 60, 89). Nenadic et al. (54) reported a distinct pattern of cerebral activity evoked during one or two tasks: (1) the participants’ estimation of whether a probe tone was the same duration as the 1 s standard tone or (2) discrimination between two tones based on frequency (mean frequency = 1,000 Hz). Estimation of duration versus frequency activated a similar network of structures, including auditory encoding regions of the superior temporal gyrus (bilaterally), insular cortex (bilateral), middle frontal gyrus (right side), the anterior, supragenual cingulate (or “medial frontal”) cortex (bilaterally), dorsolateral prefrontal cortex (bilaterally), anterior or mediodorsal thalamus (right), caudate nucleus (right), and the putamen (bilaterally). Only the putamen was specifically activated by duration (i.e., not frequency) estimation. Other recent studies have shown that activation of structures within this network may be more selectively related to encoding events (“temporal markers”) versus the processing or retrieval of temporal (duration) information. For example, Harrington et al. (46) showed that during the encoding phase of a trial, activation was observed in the right caudate nucleus, right inferior parietal cortex, and left cerebellum. On the other hand, encoding-related activity in the right parahippocampus and hippocampus correlated more with time estimation and retrieval involved in discriminating time intervals. In the same study, difficult discriminations between intervals recruited regions associated with working memory (frontal and parietal regions), response control (e.g., middle-frontal and parietal cortex) and mode-specific rehearsal (e.g., superior temporal for auditory stimuli). In contrast, studies comparing regions recruited by temporal versus ordinal processing (where the temporal task required to produce an eight-interval rhythm on one key, and the ordinal required participants to use eight keys to reproduce tones in the correct order) show that frontal cortex, basal ganglia, and cerebellum are important for encoding ordinal information, whereas additional activation of the inferior frontal gyrus, superior temporal cortex, and motor cortex are seen in temporal processing tasks (45). The cerebellum, although activated during the encoding and retrieval of the order of stimuli or responses, is also very sensitive to the regularity of time intervals, regardless of ordinal complexity (45). In studies where participants were required to classify stimuli according to either temporal order or membership category, both types of responses activated the middle frontal gyrus and inferior frontal gyri, with differences in lateralization between the tasks (46). Jahanshahi et al. (61) observed participants while they were being tested with a reproduction of short (500 ms) and long (2,000 ms) temporal intervals. As a control, they also tested participants with a non-timing reaction-time task. Using PET, they reported timing-specific activation of left substantia nigra and the left premotor cortex. They further reported that left caudate was more active for sub-second range and the right putamen was more active for supra-second range reproductions. In addition, Stevens et al. (62) used spatial independent component analysis (ICA)

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of fMRI data to isolate a timekeeping circuit comprising the right middle frontal gyrus, left cingulate, supplementary motor area (superior frontal gyrus), right superior temporal gyrus and supramarginal gyrus, bilateral insula, bilateral caudate, bilateral putamen, bilateral globus pallidus, and bilateral thalamus in several different types of discrete timing tasks. Overall, their analysis revealed activation of a frontostriatal neural timing circuit independent of whether or not the timing task had an explicit motor component. Although small areas of the right cerebellum were also activated, the patterns of activation suggested that it was not the primary substrate of interval timing in these tasks. Several review papers have compared imaging studies in terms of the timing tasks used and the experimental manipulations explored. Lewis and Miall (63) concluded that distinct areas can be clustered into two different groups. One group is comprised of those areas activated in tasks that require automatic processing with some involvement of motor function and timing of short durations. The regions in this network include the left sensorimotor cortex, right cerebellum, left thalamus, basal ganglia, and right superior temporal gyrus. Another network is activated by tasks considered to be attentionally driven or “cognitively controlled,” with minimal motor involvement and longer target durations. This network includes the left cerebellum and prefrontal and parietal cortex, areas also associated with working memory recall and attention. A few areas (lateral premotor and bilateral supplementary motor area) were activated in both types of tasks. This distinction implies that these different circuits need to be incorporated into a universal model of timing in order to bridge empirical research and theory, as the type of timing task used and the particular experimental details can determine the neural circuits involved and accessory processes engaged (in particular, attention).

Animal Lesion and Electrophysiological Studies The roles of the frontal cortex, hippocampus, striatum, and cerebellum in timing and memory have been extensively reviewed (41–43). This section will summarize major concepts from these reviews and selected data papers. Based on known neuroanatomical and lesion studies, Meck (64) initially proposed that the pacemaker resides in the substantia nigra and that pulses are transmitted to the striatum, when an attentional gate governed by cortical structures allows it, where they are accumulated. This correspondence between neuroanatomical sites and components of the clock model is supported by the empirical data showing that rats with striatal lesions show severe deficits in interval timing, not ameliorated by treatment with l-dopa. On the other hand, lesions of the substantia nigra pars compacta produce timing deficits that can be rescued with l-dopa treatment (65). Studies in humans and other animals have supported the notion that frontal cortical and hippocampal efferents (particularly to the basal ganglia) are critical for controlling temporal processing that may, in turn, contribute to duration discrimination. In their seminal studies, Meck, Church, and Olton (66) revealed the role of hippocampal pathways in time perception and how this relates to working memory.

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Fig. 8 Lesions of the hippocampus in mice produced a shift in the response curve in the peakinterval procedure. Note the average response curve does not reach 100% as different mice reach the maximum in different time bins (Redrawn from Brucato, F. H., Kogan, J. H., McNicholas, K. L., Fedorov, N. B., Rose, G. M., Klitenick, M., Liu, D., Poon, P. and Brunner, D. (2004) Pretraining lesions of the dorsal hippocampus impair both spatial and non-spatial memory in mice. Poster presented at the Society for Neuroscience. San Diego)

In their studies, lesions of the fimbria fornix produced a leftward shift in the peak responding of rats estimating a 20 s interval. Figure 8 shows that the response rate in a PI procedure of mice with hippocampal lesions presents the same pattern (Brunner et al., DB; 2008, unpublished data), with a clear shift to the left due to a shortening of both the start and stop of responding in each trial (not shown). In the studies by Meck and his colleagues, the most marked effect of the fimbria-fornix lesion or related hippocampal damage was the complete loss in lesioned rats of the ability to sum signal durations across a gap or break in the stimulus presentation. Subsequently, numerous studies have supported the role of the hippocampus in “holding” a representation across a gap in time, so that it can be later integrated with a continuation of the same stimulus or so that it can be associated with events subsequent to the gap (66, 67). The critical role of the hippocampus in trace conditioning also illustrates the possible role of this area as a short-term memory buffer for temporal intervals (68). However, it is also possible that the ability to sum durations over a gap depends on hippocampal involvement in attention rather than on its role as a working memory buffer. This view is supported by the fact that a reduction in the intensity of the gap signal helps summing the durations before and after the interruption, and an increase in the saliency of the gap results in the resetting of the internal clock (69). If this is the case, then the hippocampus is acting as a signal detector and participates in divided attention, and thus loss of hippocampal modulation then would result in behavioral inflexibility, but not necessarily in loss of timing performance in simple tasks.

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The striatum appears to have a more central role in the timekeeping system. Lesions of the dorsal striatum obliterate the temporal control of behavior; this is a regionally specific effect as lesions of the nucleus accumbens leave timing abilities intact (65). Consistent with pharmacologic studies of dopamine (DA) receptors (discussed below), lesions of the DA inputs to the striatum shift time estimation to the right (an apparent slowing of the clock) in time reproduction tasks, an effect reversed by l-dopa treatment (64, 65). The membrane properties of striatal medium spiny neurons and the tonically firing interneurons make the striatum a structure with the ideal neural properties for contributing to interval timing. The cortical inputs to the striatum synapse on the projection neurons, the medium spiny neurons, with about 10,000–30,000 separate axons synapsing onto each medium spiny neuron (70). Medium spiny neurons have a resting state of −85 mV which can be depolarized to −60 mV if enough coincidence activation of its cortical inputs (>150 inputs) is received. The medium spiny neurons, thus, act as coincidence detectors of synaptic input from distinct cortical and thalamic glutamatergic inputs and the dopaminergic inputs from the substantia nigra pars compacta modulate this process (39). The striatum contains two distinct sets of interneurons that are characterized by fast tonic spike firing: the cholinergic interneurons and parvalbumin/GABAergic interneurons which fire at rates of approximately 10 and 40 Hz, respectively (71). Both these populations receive collateral inputs from the cortical projections to the medium spiny projection neurons. Moreover, both are potently modulated by dopaminergic inputs. These neuronal populations appear to signal salient environmental changes with interruptions of their tonic firing. The tonically active cholinergic interneurons have been better characterized in this respect. These neurons display a tonic spike firing pattern (5–40 Hz), and in response to an environmental event of motivational significance show a transient depression, followed by a rebound increase, in spike firing. This response coincides with and is, in part, produced by DA signaling at these neurons (72). Conversely, the interruption in the cholinergic signal likely has a significant impact on DA release (73, 74). Consistent with the role of the striatum and DA in timing, the firing rate of this population is highly regulated in situations when time intervals must be used to predict biologically important events. Indeed, different striatal neuron populations seem to respond to different temporal requirements in tasks that require a common motor output (75). Thus, tonically active cholinergic neurons in the striatum have been postulated to be important for monitoring “temporal relationships between environmental events” (76). In turn, the acetylcholine released by these neurons increases excitability and state-stability in the projection neurons (77–79), changing their responsivity to cortical and thalamic inputs signaling salient events. Whereas neurons in the striatum seem to be involved in the timing of individual durations, most neurons in the prefrontal cortex of rats preferentially respond to the presentation of compound signals corresponding to two or more durations, rather than to the individual signals (80). This finding suggests that this the prefrontal cortex is involved in higher-order processes such as the control of divided attention when the task involves the simultaneous temporal processing of multiple signal durations (38, 67).

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The roles of the hippocampus and cerebellum in interval timing have been revealed in studies of Pavlovian conditioning in which a response in not necessary to collect a reward. Such studies are helpful in that they are not confounded by the potential role of brain structures involved in motor control. Such studies have shown that lesions that cause disruption of cholinergic inputs to the hippocampus, or cerebellar cortical lesions can cause the conditioned response to be less temporally accurate. Interestingly, the cerebellum, midbrain, and brainstem are sufficient for retention of interval information in conditioned eyeblink which involves much shorter durations (81). Consistently, lesions or disease in the cerebellum affect automatic estimation of time intervals in the millisecond range, rather than timing behavior that depends additionally on stimulus–response associations, which also require the hippocampus and basal ganglia (41, 42). The ability of cerebellar Purkinje neurons to fire at 20–50 Hz with a very high degree of regularity is consistent with the contribution of the cerebellar cortex in the automatic estimation of sub-second durations, which in turn supports the formation of Pavlovian stimulus–stimulus and stimulus–response occurring across short intervals (82).

Oscillators, Frequencies, and a Coincidence Detection Model Recent neurobiological modeling of interval timing, based on the models described above, proposes that neural inputs that constitute the clock pulses arise from the neural activity of large areas of the cortex (39). The cortex contains neurons that oscillate at different rates. Striatal spiny neurons receive most of their synaptic input from the cortex and can monitor the oscillatory patterns of cortical neural activity – although the particular contributions of a, g, and q oscillatory rhythms must still be disentangled. According to this striatal beat frequency (SBF) model, coincidence detection in the striatum results in the identification of a pattern of oscillatory firings or beats (i.e., similar to a musical chord) among other beats that represent noise or unrelated information. The probability that a particular “chord” will be identified as a signal increases as the number of detectors that simultaneously respond to such beats increases (similar to what in electronics is called a “lock-in amplifier”). According to the SBF model, signal durations are translated into a particular cortical pattern or “chord” formed by the firing of multiple neurons with different rates of oscillations. Such an encoding scheme ensures that a large number of specific temporal intervals can be produced by the integration of a limited number of primitives represented by different oscillation rates in the cortex. In comparison with the pacemaker/accumulator model where DA is assumed to be the neurobiological substrate of the pacemaker pulses, in the SBF model the role of DA is assumed to act as a “start gun” by indicating the onset of a relevant signal – leading to the synchronization of cortical oscillators and the resetting of the membrane properties of the striatal spiny neurons. Consequently, this initial DA pulse coincides with the

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“closing of the switch” to begin timing and later, at the end of the signal, a second DA pulse co-occurring with the delivery of reward serves to strengthen synaptic connections that are active within the striatum at the time of reward – thereby building a “coincidence detector” for a specific signal duration(s) (39).

Neurotransmitter Function in Timing Cholinergic Function and Timing Scopolamine and other muscarinic antagonists have long been known to disrupt event encoding, producing severe apparent anterograde amnesia. The septohippocampal cholinergic system regulates hippocampal neuronal activity, specifically neural network patterning such as the q rhythm (83–85). Such neuronal network activity patterns have been shown to be critical in the encoding and short-term retention of stimulus representations. In the context of the role of the hippocampus in timing behavior, cholinergic agonists and antagonists have been shown to affect timing behavior via changing the animal’s sensitivity to the duration of delays between the presentation of a stimulus and retrieval of information for time estimation (86). This may be directly related to the role of hippocampal cholinergic transmission in representation retention over the durations associated with timing behavior. In addition, attentional allocation to the duration of a stimulus recruits inferior frontal gyrus and motor preparation (premotor) regions of the frontal cortex (49). Cortical acetylcholine (ACh) has been shown to be essential for attentional allocation (87, 88). To put it simply, it is difficult to estimate the duration of an event if the representation of the event cannot be maintained or if the attention required to integrate temporal information cannot be allocated. In the striatum, the fast-firing cholinergic neurons mark events with rapid changes in firing rate. As reviewed above, cholinergic neurons in the striatum can serve to both signal the duration of an event and affect the sensitivity of striatal projection neurons to their cortical excitatory inputs – thereby having the potential to distort the synaptic weights reflecting the content of temporal memory (64, 89). Drugs that increase the effective levels of ACh (e.g., physostigmine) gradually shift the peak times (in the PI procedure) and PSEs (in the bisection procedure) leftward, whereas drugs that block cholinergic function (e.g., atropine) result in gradual rightward shifts in peak times and PSEs that were not compensated for with repeated training (64, 86, 90). The muscarinic antagonist scopolamine also impairs temporal control of responding in a PI procedure acutely, as shown by flattened response curves that suggest loss of temporal control (21). The flatten response curve was predominantly due to an increase in the variability of the “stop” response threshold. Curiously, physostigmine did not rescue the impairment of the response curve and the increase in “stop” variability caused by scopolamine, but reduced variability in the timing of both the

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“start” and “stop” response thresholds, and shortened the “stops” when injected alone. Thus, although the typical result of blockade of scopolamine’s action by physostigmine was not found, at least at the doses tested, these results clearly implicate cholinergic function in timing and suggest that the effect may be due to impairment in attention or memory. The anticholinergic effects of scopolamine have been proposed as a model of aging based on studies that indicate a decreased cholinergic system function in humans with age. The cognitive impairment caused by scopolamine in younger participants is similar to some aspects of the impairment which occurs in normal aging, in particular vigilance/attention (88). Consistently, some studies suggest that cholinergic neurons in the frontal cortex are involved in the storage of reference temporal information and participate in the timing of multiple intervals, i.e., divided attention for time (67, 80). Cholinergic function in cortical areas seems to also be involved, especially in aged subjects, in a correction mechanism that senses the discrepancy between a remembered and a new estimated duration (89). We have reviewed evidence that suggests a major role of cholinergic function in the cognitive decline seen with aging as measured by performance in timing tasks. Other neurotransmitter systems, however, also play a role in timing. We briefly review dopaminergic and serotonergic effects in the sections below.

DA Function and Timing The DA transporter (DAT) knockdown, which has elevated levels of DA in the synaptic cleft (91), not only had higher response rate but also a shift of the response curve to the left in a PI task (92) (Fig. 9). The shift was proportional to the interval being timed, suggesting more than a decreased response latency. As explained

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above, an increased clock speed in these mutant mice should not result in maintained leftward shifts as these subjects have been trained and tested under the same conditions. A possible mechanism, due to hyperdopaminergic function, could be a reduced variability in the encoding and decoding process (e.g., decreased variability in clock speed between trials) due to a tightly controlled attentional gate, resulting in a shifted and narrower memory distribution. This implies the counterintuitive hypothesis that normal mice are less attentive and slightly more distractible than the knockdowns, or, in other terms, that coincidence detection of cortical inputs is less effective in wild type than in knockdown mice. These results, however, have not been observed in DAT-KO +/− mice trained on the PI procedure (93) (although a similar effect has been reported for the duration bisection procedure (94)), suggesting a more complex scenario affecting the timing of “start” and “stop” response thresholds. Overall, these results are consistent with the effects of low to moderate doses of DA psychostimulants such as amphetamine and methamphetamine which produce shifts compatible with an increase in the accumulator speed (21, 64, 90, 93, 95–97). For example, in two different strains of mice, Abner et al. (21) showed that whereas high doses of amphetamine disrupted timing and produced flat response curves, the lowest doses produced the expected sharpening of the curve with a slight shift to the left, a finding replicated by others (93, 97). A trial-by-trial analysis of the same data focusing on the “starts” and “stops” of responding showed that there were significant shifts to the left in all doses, although higher doses dramatically increased variability. This analysis suggested that average response curves do not allow a complete assessment of drug effects and, second, that shifts to the left may be accompanied by increases in variability, creating a pattern that cannot be reconciled with a simple increase in clock speed. This upper end of an inverted U-shaped dose–response function may reflect a hyperactive state with the loss of temporal control (97), indicating that it is important to separate the effects of drugs on performance and timing (93). As indicated above, proportional leftward horizontal shifts of the psychometric functions flow increased dopaminergic activation are not a universal finding. In addition to the inverted U-shaped dose–response function contributing to the loss of executive or attentional control at higher drug doses, there is also evidence for the development of habit formation and/or automatic processing that is relatively insensitive to dopaminergic manipulations. These adjustments have been described for a variety of experimental situations (98), and have been demonstrated in timing tasks following extended training (99). Moreover, lesions of the frontal cortex abolish (100) and changes in glutamatergic cortical input reinstate (101) the left-shift effects of dopaminergic drugs in overtrained rats, suggesting that a transition between controlled and automatic timing may explain some of these discrepant drug effects (102). In contrast with DA activation, DA receptor antagonists, instead, shift the psychometric functions to the right in a proportional manner, indicative of a decrease in clock speed (64, 90, 95, 103–105). The magnitude of this rightward shift is correlated with the drug’s binding affinity for the D2 receptor rather than to other

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aminergic receptors (D1, D3, the a-noradrenergic receptor, S1, and S2) (106). This suggests that D2 receptor might have a major role in timing, in particular related to the accumulator rate, or, alternatively, as a vehicle for attentional effects. Blockage of D2 receptors, of course results in inhibition of motor function, so as before performance and timing effects need to be separated. D1 receptors, on the other hand, do not appear to have a major role in timing (103), although shifts to the left of the psychometric functions in some timing tasks caused by d-amphetamine seem to be reversible by D1 antagonists (107, 108).

Serotonin Function and Timing Studies on the effect of serotonin (5-HT) manipulations have not shown a direct involvement of this neuromodulator on temporal processing, although significant effects in timing tasks have been reported. The 5-HT2A receptor agonist, 2,5-dimethoxy-4-iodoamphetamine (DOI) and quipazine, a nonselective 5-HT receptor agonist, shifted the PSE of the psychometric functions to the right (108–114). DOI also reduced peak time and increased the spread in the PI procedure (109). In a bisection procedure quipazine shifted the PSE rightward suggesting that probe durations were judged as shorter under the influence of the drug, but also flattened the psychometric function, which shows reduced temporal control of behavior (110). The shift in the position of the psychometric function, however, seems to depend on the details of the procedure. Overall, results from lesions of the ascending serotonergic pathways and DOI studies suggest that timing does not depend on serotonergic function, but can be affected by acute stimulation of the 5HT2A receptor (111–114). Overall, manipulation of serotonergic function in studies of timing has been shown to have effects more consistent with changes in impulsivity and response control rather than in time perception per se (115).

Importance of Interval Timing as a Construct in Neuropathology Dysfunction of timing seems to be present in a wide range of pathological conditions from developmental disorders such as schizophrenia (116–122) and ADHD (7, 123–125), to neurodegenerative disorders such as Parkinson’s disease (PD) (126–129) and Huntington’s disease (HD) (55), and age-related disorders such as mild cognitive deficit, Alzheimer’s disease (AD), other forms of senile dementia (130–132). Despite presenting different neuropathologies it is likely that many of these disorders share a basic timing dysfunction resulting from the common neural circuits underlying timing, namely the corticostriatal loop. Disruption of normal timing with aging include increased variability (decreased precision) of judgments of duration (15, 16, 133, 134), less accuracy and precision

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particularly with simultaneously timed intervals (135), proportional overproduction (89, 136), and overestimation of short and underestimation of long duration in time reproduction in delayed recall of concurrent schedules (137). An age-associated timing dysfunction may show varying severity. For instance, aged populations may include individuals at risk or in the early phases of a disorder, such as AD, HD, or PD, and thus might show disruptions in timing due to an underlying pathology (137, 138). Heterogeneity in some of the results of timing studies may therefore reflect heterogeneity in the population with regards to underlying ongoing neuropathology. Socioeconomic factors and IQ, which has been negatively associated with variability in time estimates (139), may also contribute to inter-subject variability. Disorders that cause disrupted interval timing seem to involve dysfunction in the DA or ACh systems and mainly dysfunction of the thalamo–cortical–striatal loop. The neurobiological resemblances between the patient and aged population and qualitative similarities between these populations in the timing impairments suggest that mouse models for disorders that involve impairment of these systems can also serve as aging models for timing studies (140).

Timing in Aging Normal aging is associated with a gradual decline in cholinergic and dopaminergic inputs to forebrain centers involved in time perception (141–143). Loss of cholinergic inputs to hippocampal and parahippocampal structures or primary pathology in these regions would be expected to disrupt encoding of events as temporal markers and contribute to a high sensitivity to interruptions (gaps) in estimating the cumulative duration of a stimulus. Loss of striatal DA, on the other hand, may be expected to lead to a slowing down of the internal clock (43). Block and colleagues conducted a meta-analysis of many of the papers dealing with timing in humans (31). They concluded that older adults produced verbal estimates (Fig. 3A) that were longer than those produced by younger participants and also made shorter productions. Similarly, Coelho et al. (144) reported longer verbal estimates for older adults compared to younger adults (although level of literacy accounted for much of the variance). As divided attention and working memory seem to fail with aging (135, 145–149), these effects have been explained as increased temporal attention and increased accumulation of ticks during time estimation (encoding as in Fig. 7 left attention panel) and thus in a larger verbal estimates during decoding (145). In a production task, instead, an interval given as a number elicits the same associated counts from reference memory in all participants (Fig. 3B), assumed to have been encoded with a veridical scale, but older participants reach the counts by accumulating ticks more rapidly during decoding, if they have higher temporal awareness. In reproduction tasks (Fig. 3C) the difference in speed of accumulation during encoding and decoding would cancel out and produce veridical estimates in both groups, which is consistent with Block’s metaanalysis (31, 145).

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Lustig (145) attributed these findings to the discrepancy in the attentional demands of the experimental environment and daily life. Lustig argued that, whereas in the laboratory older participants direct their poor attentional resources to the temporal signal, in daily life they will be forced to allocate their limited attention to other competing incoming signals. Thus, limited divided attention may result in opposite findings depending on the complexity of the tasks (149). In particular, experiments in which divided attention is required for successful performance should reveal larger effects in the older than the younger participants. In modeling terms, an attentionally modulated accumulator seems to bridge the gap between attentional and clock speed accounts of timing results. A simple clock-speed model, instead, would have to account for these results by proposing an accelerated clock in aged participants, which is contrary to the general idea that physiological processes and cognitive processing speed decreases with age (15). In line with this reasoning, Craik and Hay (149) investigated the interaction of task complexity and age asking participants to both verbally estimate and to produce temporal intervals, while an attention-demanding task was being performed. Older participants produced shorter verbal estimates and longer productions compared to younger participants. The complexity of the perceptual task had an effect when the interval being judged was the longest, producing shorter verbal estimates and longer productions as expected, although this effect did not interact with age, contrary what was expected. On the other hand, Lustig (145) interpreted the lack of effect of complexity level as prioritization of the timing task over the perceptual one, supported by the decrease in accuracy of the perceptual judgments with the complexity of the task. This decline was more pronounced for older participants compared to younger ones. Briefly, according to this interpretation the temporal accuracy was spared in expense of visual–perceptual accuracy in older participants. In contrast, the idea that task requirements will have stronger effects when divided attention is impaired, as in aging, has received empirical support from other studies (150). Vanneste and Pouthas (134), for example, tested young and old participants in a time reproduction task involving presentation of one, two, or three durations, but requiring the reproduction of only one of them. Figure 10 shows the absolute percent deviation from the target duration (|T − R|/T × 100) for the three durations used (6, 8, and 10 s) combined (note that this measure does not indicate the direction of the error). Older participants showed errors that increased with the complexity of the task in a greater extent than younger ones, as expected. In a time reproduction task, with the features used in this study, the prediction from an attentional gate model will be that reproductions will be quite veridical when the stimulus is simple but as complexity increases and attention cannot cope with the encoding of more than one stimulus, counts will be missed during encoding, and the durations will be under-reproduced during decoding. Older participants produced somehow shorter durations for all stimuli during the simple task, but seriously underproduced them when the task was complex. Younger participants showed a more complex pattern in their deviations, as they were quite accurate in simple tasks, but overestimated the shorter stimuli (6 s) and underestimated the longer stimuli (10 s) in the complex task conditions (a temporal “migration” effect).

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Another line of research investigated the age-dependent differences in temporal information processing, utilizing the phenomenon of differential timing of durations signaled through different modalities (visual and auditory). In one of these studies, Lustig and Meck (135) tested young and older participants in a duration bisection procedure. Each participant was asked to classify probe durations as “short” or “long” (relative to two anchor durations) and was tested with a single as well as both modalities. In the bimodal case, visual and auditory stimuli, presented with different onset latencies, signaled different durations, and participants had to classify both durations simultaneously, thus requiring divided attention. In this task, younger participants were equally sensitive to signal duration in single and compound modality conditions, but older participants showed a large decrease in sensitivity to time in compound – compared to single-modality conditions. Age also correlated with shorter temporal judgments for visual versus auditory stimulus, with older participants showing a stronger modality effect. The interactive effects of modality and task complexity on temporal control can both be explained by the demands on attentional time-sharing, affecting the rate of accumulation more in aged participants than in younger individuals (151–153). The difficulty of an interval-timing task can be increased by manipulating the probability and validity of the feedback given to the participant regarding the accuracy and precision of their temporal judgments (7, 154, 155). For example, Rakitin et al. (155) tested young and aged participants in the PI procedure using two different target durations (6 and 17 s) with and without feedback. Once training was complete, a session of probe trials without feedback was conducted (free-recall session). Note that during free recall this task resembles a duration production task,

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as the duration to be reproduced is no longer provided by the experimenter, but must be recalled from memory. Older participants showed more sensitivity to the difficulty imposed with free recall showing larger differences between their baseline and their free-recall performance, especially in the long-duration testing block. Figure 11 depicts a relative error measure ((Response − Duration)/Duration × 100) that shows an increase in the overestimation of the short duration in both age groups. Both groups overproduced the short duration during baseline and underproduced the long duration (a temporal migration effect). But under the more difficult free-recall conditions, the migration effect was exaggerated, especially in the aged group, which showed such a large migration effect that switched to underestimation of the 17 s interval. Although a migration effect is not directly predicted by an attentional model, it is possible that during interval-timing tasks that require divided attention, an accumulator devoted to one particular interval may receive pulses that should have been received by an alternate accumulator, that is, pulses are miscounted. In the extreme, if there is no ability to divide attention at all, an accumulator may count pulses independently of the requirement to attend to a particular duration, and thus all intervals will result in a similar estimate, i.e., regression to the mean. One should note, however, that such an account suggests that attention plays a role in keeping track of the identity of different intervals, while updating information relevant to their representations in working memory (156, 157). This is an additional role that is independent (but not necessarily mutually exclusive) of the involvement of attention in mediating the perception of time through modulation of the switch/gate mechanisms associated with the clock stage of information processing (34, 43).

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This study replicates previous findings by the same research group (128) in an important way. Aged participants, as do PD patients, show a migration effect during free recall. The two studies point to several important hypotheses. First, the absence of feedback during retest brings about a different type of result than other standard tests paradigms in which feedback is given in proximity to the probe trial. Second, the intersession interval between training and testing may be exacerbating the migration effect seen in the aged participants and PD patients. Third, the same participant performing in the same task may show overproduction or underproduction of time, depending on whether he or she is being tested under training (feedback present) or free recall. McCormack et al. (136) investigated the same problem by imposing a different form of task complexity during temporal judgments. They trained young and old participants to categorize nine tones based on their duration (in a range of 250– 2,039 ms) and pitch. Whereas there were no differences in pitch categorization, elderly participants were less accurate than younger participants and identified the test duration as shorter than their actual duration during training. Although McCormack et al. attributed this effect to the distorted long-term memory representation of temporal intervals (64, 89, 90), the results can also be interpreted as decreased divided attention as described above. This also points out that perception of some dimensions such as pitch may be less affected in older participants as encoding may be faster and more resilient to lapses of attention than for the temporal dimension. As previously discussed, there are no strong differences across younger and older adults when testing requires the reproduction of a single interval. Sometimes, however, slight underestimation of duration intervals is observed (128), which has been attributed to fatigue, boredom, and loss of attention, of particular importance for long durations and in the older population (139, 145). Different age groups tested with temporal generalization (131) and bisection methods (135) support this interpretation. Age-dependent decreases in temporal precision, measured as the variability across responses, are not as common as decreases in temporal accuracy (over- or underestimation), although studies have reported a flatter generalization gradient in a temporal generalization task in older participants, but not in bisection and production tasks (131, 145, 158, 159). Another measure of temporal precision, the justnoticeable difference (the shortest difference between two intervals that can be discriminated with a given precision) between cross-modal temporal intervals has been shown to be higher for older than for younger participants (149). Further, Perbal et al. (160) tested young and old adults in both temporal production and reproduction tasks with either a concurrent counting or reading task. Each participant was also tested with a reaction-time task and a battery of neuropsychological memory tasks. The counting condition resulted in higher temporal precision compared to the reading condition in both timing tasks and both age groups, suggesting that counting may have been used as an aid to timing. In the reading condition, however, both age groups reproduced shorter and produced longer durations with respect to the reference durations. Importantly, this effect was more pronounced in

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the elderly group. Although there were no major age-related differences in temporal precision, the coefficient of variations in the reproduction task in the concurrent reading condition revealed that older participants had somehow lower temporal precision compared to younger participants. The study further showed correlations between the degree of under-reproduction and working memory, and between overproduction and information-processing speed. Consequently, these findings suggest a decrease in temporal precision with increasing age that may be over and above the associated attentional deficits. As we can deduce from these examples, specific results not only depend on the type of timing task used (estimation, reproduction, production, or discrimination), but also on the details of the experimental protocol followed. Much of the inconsistency in the field, as in so many other disciplines, comes from the use of protocols that may differ in minute, but crucial experimental details. Nonetheless, seemingly consistent findings suggest that when the difficulty in temporal judgments is increased with the requirement of simultaneous temporal judgments, secondary tasks, and/or lack of feedback, aged participants tend to exhibit impairments in temporal accuracy and precision relative to younger participants.

Interval Timing Studies in Aged Animals The effect of aging on interval timing has also been studied in animals. In one of these studies, aged rats trained in a PI procedure produced response functions that were shifted to the right relative to those of younger rats (89, 161). Note, once again, that this rightward shift cannot be easily attributed to a decreased clock speed because the rats learned the target interval with the same clock speed (64). Although the slowed response of aged rats trained in the PI procedure may result in an indirect lengthening of the experienced intervals – thus increasing the time estimates of memory stored in memory – this does not appear to account for the observed effects. Rather, a decrease in the speed of memory storage as affected by cholinergic-sensitive theta rhythms appears to account for the durations being remembered as proportionally longer than they actually are (64, 80, 86, 89, 162). Consistent with the idea that timing deficits seen in aged rats relate to a cholinergic deficit, rats exposed perinatally to a choline-deficient diet (DEF) show an enhanced rightward shift relative to untreated control rats (CON), with perinatally cholinesupplemented rats (SUP) showing virtually no age-related changes in temporal memory. In the case of the CON and DEF rats, the age-related rightward shifts in peak time were proportional to the target durations used in the PI procedure (e.g., 15 and 30 s) and were associated with deficits in divided attention when the rats were required to time both durations simultaneously (163). In line with these findings, Meck et al. (164) reported changes in cholinergic function accompanied by increases in the remembered time of reinforcement and broadening of the PI response distributions in aged rats compared to younger rats, seemingly similar to that shown in by Liu et al. (165).

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The decrease in temporal precision with aging was also reported in an earlier study in which Campbell and Haroutunian (166) trained 6-, 12-, and 26-month-old rats to bar press in a 60 s FI schedule for eight sessions. Training was followed by a 16-day retention interval at the end of which all rats were tested. The 26-month-old rats exhibited a flatter scallop in their response distributions compared to the response distributions of 6-month-old rats, even when compared to their response distributions on the last day of their training.

Interval Timing Studies in Neurodegenerative Disorders: PD and HD Dysfunction of temporal processing has been shown to be an early symptom of certain neurodegenerative disorders such as PD and HD (41, 55). These two disorders share a profound striatal dysfunction, although of possibly very different pathological cause. Whereas in PD dopaminergic neurons in the substantia nigra are lost, which results in lower levels of DA in striatum (167), in HD the dysfunction seems to start with a loss of BDNF signal from the cortex to the striatum and maybe best characterized as a dysfunction of the frontostriatal circuit and not of the striatum per se, at least in the initial stages (168). Further comparative studies of these two disorders may prove of much heuristic value for the understanding the contribution of different areas and circuits to timing function. As reported earlier, intact dopaminergic functioning in the striatum underlies normal interval timing and thus any impairment in this system would impact temporal processing. We will here briefly review experimental studies of interval timing in PD and HD. Malapani et al. (128) studied interval timing in patients with PD both with levodopa + apomorphine medication (ON) and no medication (OFF), who were compared to healthy young and aged participants in a PI procedure. Participants were trained with two different target durations (8 and 21 s) and asked to reproduce them. Healthy aged participants had increased variability in their temporal reproductions compared to young ones, although the scalar property was not violated. PD patients evaluated on ‘ON’ medication showed performance equal to, or better than, healthy aged participants, but showed a temporal migration effect such as that described above when ‘OFF’ medication. The scalar property was also violated, i.e., 21 s peak functions were significantly sharper than 8 s peak functions when plotted on a relative timescale. Malapani et al. attributed this effect to the distortion in the memory encoding processes rather than to clock rate, as the distortion remained despite corrective feedback. The migration effect could be due to memories being coupled in memory or during recall, although the latter would predict bimodality in the response distributions, which was not observed in this study. In other words, if in a given trial the participant was sampling one or the other of two reference durations, their average response distributions would have two peaks, at the short and long durations. On the other hand, if the memories were coupled, the response distributions for the two durations would look more and more similar to each other, which is what was

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actually observed by Malapani et al. (128). In addition, as argued before, a failure in divided attention could also explain the migration effect as a failure to encode to separate independent memories, or to decode them (43). The fact that training with a single target duration (21 s) resulted overestimation, not underestimation, for PD patients in the OFF condition is consistent with a slowing of clock speed and demonstrates that the migration effect is linked to the presentation of multiple intervals during training and not to a simple perception deficit. A subsequent study by Malapani et al. (169)again used multiple target durations to demonstrate that migration occurs when PD patients are OFF medication during decoding, but not during encoding. When encoding occurs in the OFF condition, but decoding occurs in the ON condition, the target durations are overestimated. Consistent with the previous finding, the scalar property was violated when the reproductions migrated toward each other while it held when the intervals were overestimated. The authors suggested that different neural circuits may be needed for storage, an excitatory corticostriatal circuit, and decoding of multiple stimuli, perhaps an inhibitory, striato-pallidal circuit. Note that attentional dysfunction during encoding (equivalent to less pulses) should have resulted in underestimation in the OFF–ON condition, and thus cannot readily account for these results. Shea-Brown et al. (170) recently developed a firing-rate model to explain the temporal migration and overestimation results in the multiple timing experiments reported by Malapani et al. (128, 169). In their model, influenced by, but different from, SET, they assumed curvilinear accumulation of firing rates of the underlying neural population activity with recurrent excitation. Two main parameters of this model are strength of the neural feedback and the input rate received by the neural population. Different values for these parameters allowed them to address different disruptions of interval timing in PD patients (171). The model, however, could not account for the opposite effects seen for the longer stimulus (21 s) in the single versus the multiple timing tasks, suggesting the necessity of some type of “leaky” gate through which information about differing stimuli can be mixed during recall. Relative to PD, deficits in interval timing in HD have received less scientific attention. To our knowledge, there is only one published study that investigated interval timing in pre-HD population. In their work, Paulsen et al. (55) tested three groups of participants; two of these groups were constituted of participants who were expected to develop HD later on in life. One group was expected to show symptoms in less than 12 years (“close” group), a second group in more than 12 years (“far” group), and a third group of participants served as a control group. Participants were asked to decide if a stimulus (1,200 ± 60 ms) was shorter or longer than a standard interval of 1,200 ms. The “close” group accuracy was worse than both the “far” and control groups. This impairment in temporal discrimination showed a correlation with differences in striatal function as assessed with fMRI. Compared to the “far” and control groups, the “close” group was found to have reduced bilateral caudate volume, which is an area critical for interval timing as lesions of this structure obliterate timing behavior. This emphasizes the parallelism between the structural changes in the HD and aging brain, since one can argue that the cognitive decline in aging might be due to changes in frontostriatal circuit, specifically a decline in the caudate nucleus rather than the changes in the frontal

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lobe given that lesions in both areas result in decline in the same cognitive functions. The “close” group was found to have less activation in basal ganglia, thalamus, and pre-supplementary motor area/cingulate compared to the “far” and control groups, all brain areas previously implicated in timing (41, 62). The “far” group was found to have hyperactivation of the pre-SMA and caudate nucleus compared to other groups. These results suggest that the deficits in temporal discrimination and neurobiological changes that underlie this dysfunction can be early markers of HD.

Sleep and Interval Timing in Neurodegenerative Disorders Sleep architecture may also share neural substrates with time perception. Durations related to REM sleep are similar to that involved in time perception, and REM sleep is characterized by phasic activation of the ascending cholinergic inputs to thalamus, and neocortex (172–174). Moreover, during REM sleep, spike activity patterns of hippocampal neurons “recapitulate” patterns shown during encoding of spatiotemporal maps (175, 176). Consistent with this parallel, diseases such as PD and HD, which are characterized by disruptions of striatal-dependent motor timing (177), also show marked deficits in timing, as discussed above, and sleep architecture (178, 179). Sleep architecture, of course, changes drastically with age as well (180).

Timing in Other Disorders: Schizophrenia The neuropathological basis of schizophrenia (SZ) has been alternatively assigned to changes of dopaminergic or glutamatergic function. If it is true that the timing apparatus crucially depends on interactions between dopaminergic and glutaminergic systems (101, 181), then it would be expected that schizophrenic patients exhibit some timing deficits. Indeed, several researchers report deficits in the timing of temporal intervals that range between less than 100 ms to several minutes. For instance, Densen(121) found that schizophrenic patients verbally overestimated a duration (5 s) devoted to a particular activity as compared to normal subjects. Similarly, Wahl and Sieg (182) found that schizophrenic patients overestimated, and were less accurate, when verbally estimating short intervals in the seconds range and reported underproduction when patients were asked to mention when a standard duration had elapsed. Tysk (183) tested schizophrenic patients with adjustment to a metronome, verbal estimation, and production tasks and reported overproduction of temporal intervals compared to control groups (that included schizotypal personality). There was no difference in terms of overestimation of temporal intervals across different types of SZ suffered by the participants. Furthermore, a series of studies by Elvevag et al.(184, 185) used temporal generalization and bisection procedures with brief durations (the standard was 500 ms in temporal generalization and 200 and 800 ms in the bisection method) to test

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schizophrenic patients (on medication) and healthy participants. They found that in both tasks schizophrenic patients showed flatter generalization gradients and psychometric functions in both procedures than control participants, which suggests less precise temporal information processing in this population. This effect was more prominent in temporal generalization than in bisection procedures. There were also qualitative differences across groups. For instance, generalization gradients were found to be more asymmetric, and PSEs larger, in schizophrenic than control participants. These differences were interpreted as indicating longer durations stored in the reference memory of schizophrenic patients. This interpretation was partly derived from the lack of correlation between digit-span task (targeting working memory) and the performance in the timing tasks. More recently, Penney et al. (118) studied interval timing in participants who were in high genetic risk for SZ and normal controls using the bisection procedure. Temporal intervals were presented both with auditory and visual stimuli with anchor durations of 3 and 6 s. The difference between the timing of auditory and visual stimuli has been reported in earlier studies: the duration of visual signals is normally judged to be shorter than auditory signals (152). Penney et al. found that this difference is larger in participants under risk for SZ compared to normal participants. Specifically, they found that although the psychometric functions for visual stimuli shifted farther to the right in participants under risk compared to control group(s), the location of psychometric functions for auditory stimulus did not differ across groups. This indicates that there was not a consistent overestimation of durations as reported in earlier findings. According to Penney et al. (118), this particular finding rules out the role of memory and clock speed difference (across groups) as possible explanation of their finding. They instead explain their findings in terms of attentional deficits in under-risk participants. Lapses in attention seem more likely with visual signals than with auditory signals. Fewer pulses therefore would be accumulated during visual than auditory signals, for the same duration. Thus the same signal duration will be judged as shorter if the signal was a light then if it was a sound. Interestingly, similar effects have been reported for the timing of sub-second durations which may not be under attentional control (118), and suggest separate dopaminergic effects on clock speed and attention for time (186).

Conclusion In this chapter, we reviewed different information-processing models of interval timing and neurobiological substrates that are thought to implement temporal cognition. The adoption of this framework sets the occasion for identifying and understanding the nature of cognitive deficits in aging because interval timing entails multiple cognitive processes such as learning, short- and long-term memory, attention, and decision-making (187). Taken together, temporal cognition structures one’s actions in terms of the temporal properties of the environment (188). Given

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that one can dissect the role of different cognitive processes from the behavioral data gathered in timing paradigms, one can identify the nature of specific cognitive deficits related to aging and particular neurodegenerative disorders. Surely, this depends on the development of comprehensive and accurate models of interval timing that spans different cognitive and sensorimotor functions (189). As it has been presented in this chapter, the literature is not unequivocal about the contribution of different cognitive processes involved in interval timing. Thus, the use of different theoretical approaches for interpreting the effects of neurobiological changes/pharmacological agents on various aspects of cognition has slowed consensus. One such controversy relates to the role(s) of attention and clock speed in accounting for the changes in temporally controlled responses. Particularly, based on the data gathered with different age groups in tasks with different complexities (requiring different levels of cognitive resources), it is clear that attention is an important component of interval timing that mediates the shortening/lengthening of time perception (accuracy) and variability in the memory representation of these intervals (precision) (37, 150, 186). In addition to the correspondences between the general decline in the cognitive processes and interval-timing abilities, there are also neurobiological correspondences between the aging brain and neural substrate underlying interval timing. Considering the neurobiological resemblances, interval timing constitutes a sound construct to study the cognitive effects of aging brain. On the other hand, this also depends on the neurobiological models for interval timing. In parallel to the lack of consensus regarding the various cognitive models of interval timing, there are various views regarding the role of different brain structures and neurotransmitters in the process of interval timing. For instance, one view suggests that DA constitutes the neuron-chemical substrate for pulses that are hypothetical units of subjective time (64), while more recent views attribute it a role signaling the occurrence of important events for coincidence detection of cortical oscillations (39, 41, 43). Further, we have discussed that different disorders such as PD, HD, and SZ have their own signatures regarding measures of interval timing. These signatures were also identified in the groups that were in the risk group of developing the disorder at some point in their life and in the subpopulation of elderly, healthy individuals. If an aging population is viewed as heterogonous in terms of the risk of developing different disorders related to aging, interval timing might be successfully used as a behavioral screen for early diagnosis of these disorders. Interval timing is a special area of studying cognitive aging also because it is a process that can be studied comparatively in humans and other animals (26, 189, 190). In other words, because interval timing is a very basic function that is observed in a large group of organisms, animal models can be used to understand the effect of aging and neurodegenerative disorders on cognitive processes. We believe the next steps in this area will involve modeling of the different attentional processes seemingly involved during encoding and decoding of temporal information and will incorporate realistic modeling of the population activity of the neural circuits underlying temporal information processing.

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Index

A Abnormal aging. See Alzheimer’s disease (AD); Mild cognitive impairment (MCI) Adrenal hormones, effect on cognitive functions, 36 Adrogen receptor, localization and impacts, 133 Age-related brain changes, in animal models, 32–33, 43–44 Age-related cognitive decline, 2 attention, 182–183 and free-recall performance, 184–185 interaction of task complexity, 183 in temporal precision, 183–186 and timing, 182–187 All-or-none phenomenon, 170 Alzheimer’s disease (AD), 1, 59, 64, 92. See also β-Amyloid peptide (Aβ ); Tau animal models, 117, 119–122 biology, 114 brain changes, 11–12 causes amyloid hypothesis, 13 tau hypothesis, 14 changes in HPA axis, 146 cognitive functions in, 2 ER α, 132 familial, 114 genetics, 114–115 gonadal hormone status, 144–145 health aspects, 16–17 impact of glucocorticoids, 146 impact of testosterone, 145–146 neurodegeneration in, 132–133 nonsteroidal anti-inflammatory drugs (NSAIDs) and, 17 pathological features, 114 progression and devastating consequences, 9 risk factors, 15

age, 16 apolipoprotein E (ApoE) gene, 16 cholesterol metabolism, 16 and timing, 181 in vivo imaging studies and postmortem studies, 10–11 A β monomers, 116 Amphetamine, 170, 180 Amyloid cascade hypothesis, 114, 120 β-Amyloid peptide (Aβ ), 32, 38 animal models, 119–120 3xTg-AD, 121–122 and cognitive impairment, 120 hypothesis, 13 production of, 115–117 toxicity, 34 β−Amyloid precursor protein (APP), 114 models, 119–120 3xTg-AD, 121–122 processing of, 116 Anastrozole, 142 Androgen blockage therapy, 141 Androgen replacement therapy, 141 Androgens, effect on cognitive functions, 33, 35 Animal models, of AD, 117 Animal models, of aging, 7–8. See also Nonhuman primates models, of cognitive aging cytoarchitectonic arguments, 60 interval timing studies, 187–188 rodents, 60 Anterior corpus callosum, 5 A β oligomerization, 116 Apolipoprotein E (ApoE) gene, 115 Apolipoprotein E epsilon 4 allele, 145 Atrophy, of brain, 2, 4, 6 and exercise, 19 Attentional set-shifting, 64–67 Autobiographical memory, 4 203

204 B BACE cleavage, 115 Basal ganglia, decline of, 2 Bisection point, 164 Bisection procedure, 164 BN rat strain, 83–84 Brain-derived neurotrophic factor (BDNF), 20 Brain volume and aging, 4

C C99, 115 Callithrix jacchus (common marmoset), 40–41 Cambridge cognitive examination, 143 Categorization, 164 C57BL/6 strain, study of cognitive aging, 108–110 Cebus species (capuchins), 39 Cerebellar neurons, 7 Chlorocebus aethiops (vervet monkey), 38 Cholinergic functions and timing, 178–179 Cognitive Abilities Screening Instrument examination, 140 Cognitive continuum, 9 Cognitive domains, 3 Cognitive functions, 1 in AD, 2 in aging, 2 Cognitive information processing architecture, 3 Cognitive reserve, 2 Compensatory strategies, 2, 6 Conjugated equine estrogen (CEE) replacement therapy, 34, 139 Cortical acetylcholine (ACh), 178 Cortical basal degeneration (CBD), 117 Cortical networks, 2 Corticosteroid receptor, localization and impacts, 134–135 Cortisol treatment, 143

D DA function and timing, 179–181 Declarative/explicit memory, 78–80 Dehydroepiandrosterone, 36 Delayed nonmatching to sample (DNMS) task, 40, 62 Delayed Recognition Span Test (DRST), 34 Delayed response (DR) task, 35 Dementia, 1, 78 gonadal hormone status, 144–145 impact of glucocorticoids, 146 impact of testosterone, 145–146

Index DHEA(S) and cognitive impairment, 36, 41 Differentiation tasks, 165 Digit-span task, 3 Discrimination deficits, assessment of, 89–94 Discrimination reversal learning, 63–64 Discrimination tasks, 164 Donepezil, 92–93 Dopaminergic depletion, 5 Dorsolateral prefrontal lesions, 62 Dyslipidemia, 7

E Emotional processing, 4 Encoding, 3 Episodic memory, 3 ER α knockout mice, 131 Estradiol-replaced aged monkeys, 35 17β-Estradiol therapy, 139 Estrogen affects, on cognitive function, 17, 139–140 Estrogen receptor, localization and impacts, 131–133 Estrogen receptor-beta (ER β), 131 Estrogen replacement therapy (ERT), 34 and cognitive functioning, 17–18 Executive function, defined, 3 Exercise direct effects on brain, 20 health benefits, 18–19 protection from negative effects, 19–20 Extradimensional/intradimensional (ED/ID) shifting, in learning, 65–66 psychological interpretation, 66 Extradimensional shift (EDS), 88–89

F Familial AD (FAD), 114 F344BNF1 rat strain, 83–84 Fixed-interval (FI) procedure, 165 Frontal-striatal neural system, 3, 5 Frontotemporal dementia, 59 Frontotemporal dementia with parkinsonism (FTD), 117 F344 strain and Gallagher’s protocol for the water maze, 80–83 spatial learning deficits, 83 Functional magnetic resonance imaging (MRI), 5

Index G Gallagher’s protocol for water maze, 79 evaluation of set-shifting tasks, 87–89 evaluation of spatial memory, 84–87 F344 strain, 80–83 olfactory detection and discrimination deficits, 89–94 Gallagher’s Spatial Learning Index (SLI), 79 Gate mechanism, 170–171 Glucocorticoids, 36 Goal-oriented tasks, 3 Gonadal hormones, effect on cognitive functions, 33–36 Gonadotropin-releasing hormone (GnRH) agonist, 139 Go–No-Go odor–reward association task, 90 Gorilla, 43 Grey matter changes, during aging, 4–5

H Heterogeneity, 182 Hippocampal-dependent learning tasks, 68 Hippocampal long-term potentiation (LTP), 8 Hippocampal volume, 6 Homology between species, 60–61 Hormonal influences, on aging cognitions, impact on, 139–144 effect on cholinergic projections, 135–136 learning and memory, impact on, 130, 136–139 neural development and differentiation, impacts, 130 and neurodegenerative diseases, 144–146 sex hormones, 130 status with age, 136 steroid hormones, 130–135 Hormone replacement therapy (HRT), 7, 17–18, 133, 139–145 Human aging. See Specific topics ailments, 7 brain’s structural and functional integrity changes compensatory strategies, 6 executive function deficits, 5–6 grey and white matter, 4–5 memory decline, 6 cardiovascular capacity, 7 susceptibility of brain regions, 7 Huntington’s disease (HD), 181 interval timing studies in, 188–190 HVS Image, 104 Hylobates (gibbon and siamang), 43 Hyperglycemia, 7

205 Hypertension, 7 Hypoglycemia, 7 Hypothalamic–Pituitary–Adrenal (HPA) axis changes, with age, 143–144 Hypoxia, 7

I Impulsivity, 105 Insulin insensitivity, 7 Insulin-like growth factor-1 (IGF-1), 20 Interval timing central measures, 163 significance in neuropathology, 181–191 Intradimensional shift (IDS), 88 Ischemia, 7

J J20 mice, 122

L Learning unimpaired vs. learning impaired aged rats, 84 LE model of natural aging, 80 Life expectancy, 75 Lifestyle factors and cognitive functioning, 2, 7, 17 Lifestyle interventions, for improving brain functions exercise, 18–20 hormone replacement therapy, 7, 17–18, 133, 139–145 Long-term memory, 3–4

M Macaca fuscata (Japanese macaque), 31 Macaca mulatta (rhesus monkey), 31 Macaca nemestrina (pig-tailed macaque), 31 Macaca radiata (bonnet macaque), 31 Magnitude estimation, 164 MAPT gene, 117 Medial temporal lobe (MTL), 2–3 Mediodorsal thalamus (MD), 61 Medroxyprogesterone acetate (MPA) therapy, 34, 140, 145 Memory capacity, 3 Memory decline, 6 Menopause, 17 Methamphetamine, 180 Microcebus murinus, 39–40

206 Mild cognitive impairment (MCI), 9, 89, 139 brain changes, 11–12 incidence rate, 10 in vivo imaging studies and postmortem studies, 10–11 Mild memory decline, 1 Mini-Mental State Examination test (MMSE), 140, 146 Morris water maze (MWM) test, 104, 121 Mouse models. See also Rat models, of aging anatomical, 103–104 discrimination tasks, 106–107 Morris water maze test, 104 neural substrates, 108–110 search strategy, 104–105 spatial perception, 106–107 vs. rat strains, 105–106 MTL neural system, 6

N Natural aging progression, in humans, 80 Neurofibrillary tangles (NFTs), 114 Non-declarative memory, 3 Nonhuman primates models, of cognitive aging. See also Rat models, of aging baboon species, 37–38 capuchins (Cebus species), 39 great apes advantages and drawbacks, 44–45 changes in brain anatomy and function, 43–44 cognitive deficits, 42–43 endocrine changes, 44 macaque advantages and drawbacks, 36–37 changes in brain anatomy and function, 32–33 cognitive deficits, 31–32 endocrine influences, 33–36 squirrel monkeys (Saimiri sciureus ), 38–39 usefulness of small primate species as common marmoset (Callithrix jacchus), 40–41 cotton-top tamarin (Saguinus oedipus ), 41 Strepsirrhini, 39–40 vervet monkey (Chlorocebus aethiops ), 38 Nonsteroidal anti-inflammatory drugs (NSAIDs) and AD, 17 Noradrenaline decline, 5

Index O Occipital cortex, 4 Odor information, processing of, 89 Olfactory detection, assessment, 89–94 Orbital prefrontal damage, 64

P P. hamadryas, 37 Pan spp., 43 Papio spp., 37 Parkinson’s disease (PD), 5, 181 interval timing studies in, 188–190 Peak-interval (PI) procedure, 165, 168, 171 Physostigmine, 178 Point of subjective equality (PSE), 164 Pongo (orangutan), 43 Positron-emission tomography (PET), 5, 84 Posterior cingulate, 2 Prefrontal cortex (PFC), 3–6, 104 cognitive functions animal models vs. human studies, 67–68 attentional set-shifting, 64–67 discrimination reversal learning, 63–64 spatial working memory, 61–63 decline, 2 dysfunction, 59 Presenilin-1 (PS1), 114 Presenilin-2 (PS2), 114 Procedural memory. See Non-declarative memory Processing speed, 3 Progesterone receptors localization and impacts, 133–134 PR-A, 131 PR-B, 131 Progestins, 145 Progestrone affects, on cognitive function, 139–140 Progressive supranuclear palsy (PSP), 117

R Raloxifene, 142 Ratio-setting, 164 Rat models, of aging. See also Animal models, of aging; Mouse models; Nonhuman primates models, of cognitive aging assessments age-related sex differences in working memory, 86–87 age-related working memory deficits, 86

Index of deficits in spatial reference memory, 83 impairments in spatial working memory, 85–86 medial temporal lobe (MTL) functions, 78 olfactory detection and discrimination deficits, 89–94 set-shifting tasks, 87–89 spatial/hidden platform water maze task, 78–79 working memory task protocols, 87 behavioral tasks to assess age-related cognitive decline, 78–80 declarative/explicit memory, 78–80 Gallagher’s Spatial Learning Index (SLI), 80–94 choice of strain biological differences between strains, 75–76 BN and hybrid F344BNF1 rat strains, 83–84 Brown Norway (BN) rats, 76 F344 strain, 76, 80–83 F344 x BN hybrid (F344BNF1) rats, 76 hybrid, 75 Long Evans (LE) rats, 76–77 NIA strains, 76 non-NIA strains, 76–77 outbred vs. inbred, 74–75 Sprague-Dawley (SD) rats, 77 Wistar rats, 77 life expectancy of strain, 77 Reasoning, 3 Regulated intramembranous proteolysis, 116 REM sleep, 190 Retrosplenial cortex, 2

S Saguinus fuscicollis, 41 Saguinus oedipus (cotton-top tamarin), 41 Saimiri sciureus (squirrel monkeys), 38–39 SAPP β, 115 Scalar Expectancy Theory (SET), 167–168 Scaling techniques, 164 Schizophrenia, 190–191 Scopolamine, 178 β-Secretase, 115 Selective estrogen receptor modulators (SERM), 142–143 Semantic knowledge, 4 Serotonin decline, 5 Serotonin function and timing, 181 Set-shifting tasks, evaluation of, 87–89

207 Sex-hormone binding globulin (SHBG), 141, 144 Short-term memory. See Working memory Spatial ability, 3 Spatial delayed response task, 61 Spatial memory impairments, 62 Spatial working memory, 61–63 evaluation of, 84–87 young adults vs. elderly, 85 Species differences anatomical, 103–104 discrimination tasks, 106–107 Morris water maze test, 104 neural substrates, 108–110 search strategy, 104–105 spatial perception, 106–107 strains, 105–106 Spread, 165 Statins, 7 Steroid hormones effect on cholinergic projections, 135–136 impact on Alzheimer’s disease and dementia, 144–146 localization adrogen receptor, 133 corticosteroid receptor, 134–135 estrogen receptor, 131–133 progesterone receptor, 133–134 status with age, 136 Strepsirrhini, 39–40 Striatal uptake, in elderly patients, 5 Striatum, 3, 5, 7 Stroop test, 140 defined, 3 Sulfate DHEA(S), 36 Synaptic loss, 2, 8 Synchronization, 164

T Tau animal models, 120 3xTg-AD, 121–122 genetic mutations of gene, 117 hypothesis, 14 Temporal generalization task, 164 Temporal life perspective subject area, 163 Temporal lobe syndrome, 68 Temporal production, 164 Testosterone levels and cognitive functioning, 34, 141–142 impact on Alzheimer’s disease and dementia, 145–146 Testosterone supplementation therapy, 141

208 Thalamo–cortical–striatal loop dysfunction, 182 Time left procedure, 164 Timing in aging, 182–187 assessment of, 163–167 attentional influences, 169–170 concept and representation, 161–162 functional neuroanatomy of human neuroimaging studies on temporal cognition, 172–174 oscillatory rhythms and frequencies, 177 roles of the frontal cortex, hippocampus, striatum, and cerebellum, 174–177 interval, 162 significance in neuropathology, 181–191 as model system to study cognitive dysfunction, 162–163 in neurodegenerative disorders, 188–190 neurotransmitter functions cholinergic functions, 178–179 DA function, 179–181 serotonin function, 181 in schizophrenia, 190–191 theories and models of, 167–172

Index T-maze test, 62–63, 68 Transdermal estradiol therapy, in women, 139

V Vascular endothelial-derived growth factor (VEGF), 20 Volumetric changes, with aging. See Atrophy, of brain

W Weber’s psychophysical law, 167 White matter changes, during aging, 4–5 WinTrack, 104 Wisconsin card-sorting task (WCST), 64–66, 87 Wisconsin General Testing Apparatus (WGTA), 31 Women and aging, 17–18 and DHEAS levels, 142–143 effect of steroid hormones, 140 Working memory defined, 3 testing of, 2

X 3xTg-AD mouse model, 121–122