Animal Models of Human Cognitive Aging (Aging Medicine)

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Animal Models of Human Cognitive Aging

“This page left intentionally blank.”

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.


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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Preface

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

xii

Contributors

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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N.C. Berchtold and C.W. Cotman

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-

Normal and Pathological Aging: From Animals to Humans

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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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N.C. Berchtold and C.W. Cotman 60

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 (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 (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. 6 (A) Diagram showing the decoding process and response production in the peak-interval procedure. The tick line shows a normal accumulator speed (l) used during encoding and normal decoding. Hypothetical change in rate (l’) in a probe trial results in a shift to the left of the response distribution. (B) The bisection procedure showing encoding of the short and long standards and classification of a middle probe (T1/2) with a normal accumulator rate (l) and with a fast rate (l’). When the same probe is encoded with the fast rate it produces a higher number of pulses, which in comparison with the normally encoded standards result in a higher probability of a “long” response, translated in a left-shifted bisection function

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

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