Jagust - The University of Texas at Dallas
Neuron
Review
Vulnerable Neural Systems and the Borderland of Brain Aging and Neurodegeneration
William Jagust
1 Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA 94720-3190, USA
*Correspondence: jagust@berkeley.edu
http://dx.doi.org/10.1016/j.neuron.2013.01.002
Brain aging is characterized by considerable heterogeneity, including varying degrees of dysfunction in specific brain systems, notably a medial temporal lobe memory system and a frontostriatal executive system.
These same systems are also affected by neurodegenerative diseases. Recent work using techniques for presymptomatic detection of disease in cognitively normal older people has shown that some of the late life alterations in cognition, neural structure, and function attributed to aging probably reflect early neurodegeneration. However, it has become clear that these same brain systems are also vulnerable to aging in the absence of even subtle disease. Thus, fundamental systemic limitations appear to confer vulnerability of these neural systems to a variety of insults, including those recognized as typical disease and those that are attributed to age. By focusing on the fundamental causes of neural system vulnerability, the prevention or treatment of a wide range of late-life neural dysfunction might be possible.
How does the brain age? The causes of the changes in cognition, neural function, and brain structure experienced by many older people are debated. Specifically, do the neurodegenerative diseases of older age often contribute to these problems, or is there a ‘‘pure’’ age-related form of neural decline that is clearly separate from disease? This question has occupied scientists involved in the study of aging for decades but is now becoming more tractable because of advances in techniques for studying the human brain during life.
While heuristically useful, separating age and disease also creates problems. First, what is ‘‘aging’’? There is no single mechanism that underlies age-related change other than the passage of time. Aging uncovers systemic limitations of human biology that may be susceptible to a variety of deleterious processes, some of which are related to frank disease and others not. Distinguishing age and disease has led to the concept of
‘‘normal aging,’’ which may be defined as what is left when disease is excluded. However, individuals in their seventh to ninth decades without significant disease (vascular, metabolic, or degenerative) are not statistically normal and are more aptly
described as ‘‘supernormal’’ or optimally aged ( Rowe and
). Studies of such individuals may provide insight into the intrinsic potential of human neural systems and could lead to ways of optimizing function in aging that are completely different from the methods for treating age-related disease.
Is our conceptualization of normal aging in fact contaminated by the study of normal older people who are experiencing neurodegeneration? While for decades many studies of older people may have excluded those with manifest disease, it has become increasingly clear that neurodegeneration exists in subtle preclinical forms for many years prior to symptom onset. The use of norms to exclude individuals does not ameliorate these problems since many presymptomatic individuals will test in a normal range, and the norms themselves may have been contaminated by participants with preclinical symptoms. In fact, in-depth examination of many neural systems in older people reveals alterations that have been attributed to normal aging. But are such individuals ‘‘normal’’ in the sense of being free of disease, or do age-related deficits simply reflect undetected neurodegeneration?
What is the Behavioral Phenotype of Cognitive Aging?
Over decades, considerable effort has been devoted to defining the cognitive profile of healthy (neurologically disease-free) older people. A frequent shortcoming of these studies is the use of cross-sectional as opposed to longitudinal data. Cross-sectional data do not permit the differentiation of lifelong or developmental subject characteristics from those that actually decline in older age. Nevertheless, many studies have characterized older individuals using two general approaches. One can be defined in terms of cognitive processes, while the other is more related to neural systems; these are not mutually incompatible, and both rely on examination of cognitive test performance. Regardless of the approach, there is wide agreement that older people are heterogeneous in their cognitive performance. Some older individuals have abilities typical of much younger people, while
others show marked decrements ( Wilson et al., 2002
). Furthermore, there is good agreement that some cognitive abilities, particularly semantic memory or knowledge, are relatively preserved with age (
). The cognitive abilities that most consistently decline with age are processing speed,
working memory, and episodic memory ( Schaie, 1996 ;
Park et al., 2002 ). Models of cognitive aging that attempt to define
change in terms of fundamental cognitive processes that drive general decline propose a small number of underlying cognitive processes that are responsible for decline in multiple areas; such
models generally include processing speed ( Salthouse and Ferrer-Caja, 2003
) or executive function ( Hasher and Zachs, 1988 )
as key factors. A major theory of cognitive aging attributes many aspects of cognitive decline to loss of prefrontal cortical
), although some aspects of episodic memory are not well accounted for by this model. Neural
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Figure 1. Neuropathology of Common Age-
Related Diseases
(A) A b -immunostained senile plaques (arrows).
(B) Magnified view of an A b -immunoreactive cortical senile plaque.
(C) Tau-immunostained section of the hippocampus showing phospho-tau immunoractive,
NFT-bearing neurons (arrowheads) and the granule cell layer of the hippocampus (arrows) with some tau-immunoreactive neurons.
(D) Magnified view of neuron with NFTs (arrowheads).
(E) Low-magnification view of a small-vessel infarct in the basal ganglia.
(F) Magnified basal ganglia infarct.
(G) Brainstem Lewy body (arrow) and neuromelanin, some of which is extraneuronal (arrowhead).
system-based conceptualizations define age-related cognitive decline in terms of two fundamental neural systems that support
episodic memory and executive function ( Buckner, 2004
;
). Differential decline of these cognitive processes reflects varying degrees of involvement of the medial temporal lobe memory system and a prefrontal cortex/striatal executive system. The major late-life neurodegenerative disorders affect these two neural systems differently and thereby may explain some of the heterogeneity in brain aging. In addition, there are other neural systems that are affected by age and disease that will be discussed, and there are numerous other systems that are not the focus of this Review.
Late-Life Neurodegenerative Diseases
Three common age-related diseases are the most likely culprits in producing neural alterations attributed to normal aging: Alzheimer’s disease (AD), cerebrovascular disease (CVD), and Parkinson’s disease (PD). Relationships between these disorders and aging could represent several different situations: manifest neurodegenerative disease, presymptomatic neurodegenerative disease, or a ‘‘phenocopy’’ of a neurodegenerative disease that is actually based in a different mechanism.
Alzheimer’s Disease
Alzheimer’s disease is crucial to discussions of cognitive aging simply because it is so prevalent; at least 1% of people at age
65 have AD, with prevalence rising at least to 30% by age 80
(
Hofman et al., 1991 ). These figures reflect the presence of
dementia, a progressive diminution of cognitive abilities incompatible with independent functioning. AD usually begins with anterograde amnesia, although other cognitive disturbances can occur in the initial stage. AD evolves from more subtle syndromes, often referred to as mild cognitive impairment
(MCI). While the strong age association of AD has at times resulted in the belief that it represented normative aging, it is amply clear that many individuals live to late life without dementia.
Many recent reviews have summarized the molecular pathology, clinical features,
and therapeutic approaches to AD ( Querfurth and LaFerla, 2010
;
). The disease is defined by the association of cognitive symptoms with the neuropathological findings of neuritic or senile plaques and neurofibrillary tangles (NFTs) revealed on
histological examination of the brain ( Figure 1
). Plaques are composed of the aggregated b
-amyloid (A b
) protein surrounded by dystrophic and degenerating neurites, while NFTs are composed of the microtubule-associated protein tau, which is hyperphosphorylated and aggregated as paired helical filaments. A major theory of AD causation holds that soluble forms of A b initiate the disease, leading to alterations in synaptic structure and function, followed by A b
aggregation into plaques ( Hardy,
). This is consistent with evidence that reveals limited associations between plaque A b and dementia severity but stronger associations between cognition and NFTs and synaptic number and size (
full form, this amyloid cascade hypothesis holds that early soluble A b unleashes a chain of events causing alterations in synapses and tau, and a host of downstream structural and functional neural changes that are closely related to cognitive decline.
Several issues complicate the differentiation of AD from normal aging. First, the relationship between fundamental features of AD pathology and cognition are tenuous. Recent neuropathological criteria recognize that AD pathological change can exist in the absence of symptoms (
), raising the likelihood that some individuals may be resistant to the pathology. This is a particularly confusing situation in the very old, where the relationship between AD pathological change and cognition is particularly weak (
;
Balasubramanian et al., 2012 ). Second, the pathology associated
with AD occurs in partial forms. NFTs, without A b plaques, are seen in many cognitively normal people, usually in medial temporal lobe structures, and they increase exponentially with
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Figure 2. PET Evidence of Fibrillar Brain A b
Deposition in Normal Aging
[
11
C]PiB-PET scans.
(A) A patient with AD. Hotter colors indicate tracer retention, or binding to A b
, throughout cortex. A b deposition is seen throughout cortex but particularly in medial parietal and frontal regions (arrows), areas associated with the default mode network.
(B) A normal older person with no evidence of brain A b . Cool colors indicate nonspecific uptake primarily in white matter.
(C) A normal older person with extensive A b deposition in a pattern identical to that seen in AD.
(D) Quantitative tracer retention by age in a group of young subjects (green), AD patients (blue), and
95 cognitively normal older people (red) from the
Berkeley Aging Cohort. The y axis is the distribution volume ratio (DVR), a quantitative index of total brain tracer retention indicating the brain fibrillar A b load. Dotted yellow line indicates 2 SD above the mean of young normal subjects DVR, a threshold surpassed by 28% of the older normal subjects.
advancing age (
Price and Morris, 1999 ). These NFTs particularly
affect entorhinal cortical neurons that project to dentate gyrus as the perforant pathway, functionally disconnecting the hippo-
). A b plaques also occur in ‘‘diffuse,’’ as opposed to cored or neuritic forms, and these too are
processes related to aging itself remains debated.
Cerebrovascular Disease
Stroke, or cerebral infarction, is also associated with both
advancing age and cognitive decline ( Seshadri et al., 2006
).
The most fulminant effect of stroke on cognition appears as
‘‘multi-infarct dementia’’ (
Hachinski et al., 1974 ), but the preva-
lence of this disorder is argued. At least as important are many other forms of cerebrovascular disease seen in aging, including
asymptomatic disease detected on imaging ( O’Brien et al.,
2003 ). Other manifestations of CVD include alterations in subcor-
tical white matter involving demyelination, rarefaction, and high signal intensity on MRIs, thinning of cerebral cortex and cerebral atrophy, and subclinical, silent infarction related to small vessel
occlusion ( DeCarli et al., 1999 ;
) (
).
All of these forms of CVD have been linked with cognitive dysfunction, are subtle or clinically undetectable in onset and progression, and may play a role in age-related decline.
Parkinson’s Disease
Parkinson’s disease is a disorder of the motor system with cardinal manifestations of slowing of motion (bradykinesia), tremor, rigidity, and gait and postural instability. The characteristic neuropathology involves loss of dopaminergic neurons in the pars compacta of the substantia nigra (SN). These neurons, which project to the striatum as the nigrostriatal pathway, also contain Lewy bodies, an abnormally aggregated form of the protein a
-synuclein ( Figure 1 ). PD has a strong age-associated
prevalence and is accompanied by cognitive dysfunction. Both dementia (
Aarsland et al., 1996 ) and mild cognitive impairment
( Litvan et al., 2012 ) are common in PD
patients. This type of major cognitive dysfunction is associated with widespread limbic and neocortical Lewy body pathology as well as
concomittant AD pathology ( Compta et al., 2011 ). However,
PD is also associated with a range of more subtle cognitive manifestations that may reflect dopamine deficiency.
Effects of AD Pathology in Normal Aging
Substantial AD pathology is consistently reported in postmortem studies of older individuals who were cognitively normal prior to death. About 20%–40% of cognitively normal individuals in their eighth to ninth decades have at least intermediate levels of A b
and NFT-tau pathology on autopsy examination ( Bennett et al.,
;
Kok et al., 2009 ); many of these people meet pathological
criteria for AD. This evidence has been used to assail the amyloid cascade hypothesis, but adherents hold that such individuals were in a stage of preclinical AD that would have progressed to dementia had they lived longer. These people may remain normal because of neural compensation or reserve, which has been conceptualized in two ways: a static or passive form of reserve, often referred to as ‘‘brain reserve,’’ and a more dynamic form of reserve, described as ‘‘cognitive reserve’’ or compensation
(
Stern, 2002 ). Brain reserve implies that individuals differ in base-
line neural resources at the onset of age-related pathology, perhaps because of developmental factors or the balance of lifelong positive and negative exposures. The dynamic concept of reserve implies a more active compensatory process that could represent functional reorganization that recruits additional neural resources to maintain task performance. Individuals with AD pathology who maintain normal cognition are the focus of intense investigation because they may represent the earliest phase of
AD, and thus the types of individuals most amenable to therapy.
Such people clearly reside on the border of normal cognitive aging and neurodegeneration.
A major technological advance for studying such individuals is the development of radiolabeled tracers that bind to A b that can
be imaged with positron emission tomography (PET) ( Figure 2
). A
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11
C]PiB (or Pittsburgh compound B) has been the most widely used (
Klunk et al., 2004 ), with longer-
lived [
18
F]-labeled imaging agents recently available (
). PET amyloid imaging detects only the fibrillar aggregated forms of A b
, which is probably a later phenomenon and less pathogenic than the soluble forms. In studies of cognitively intact elderly, proportions of subjects with PET evidence of fibrillar A b deposition are congruent with the 20%–
30% rates seen with autopsy examination ( Morris et al., 2010
).
These values also agree with measurements of A b obtained in cerebrospinal fluid (CSF) (
Jagust et al., 2009 ), where lower levels
of CSF A b probably reflect increased deposition in insoluble forms in the amyloid plaque. Although both CSF and PET imaging show similar proportions of older people with evidence of brain A b
, the majority of older people do not have evidence of brain A b deposition. Nevertheless, this technology makes it possible to evaluate living people for the effects of A b on neural systems.
The ability to detect brain A b deposition in older healthy adults has resulted in intensive efforts to define cognitive performance in such people, with somewhat conflicting results. While many studies have failed to find any evidence of lower cognitive performance (
), a number have reported subtle decrements in a host of cognitive abilities in those with either imaging or pathological evidence of A b
. Most often this
is seen in episodic memory ( Bennett et al., 2006 ;
), although other cognitive domains
may be affected ( Rodrigue et al., 2012 ). Longitudinal memory
decline also appears to be greater in those with evidence of
A b
(
;
Resnick et al., 2010 ). In studies that
report such cross-sectional or longitudinal decline, deficits are quite small.
Preservation of cognitive ability could be due to reserve, or it could reflect the fact that the full cascade of negative events has not yet occurred. These downstream pathological alterations appear to follow A b in a sequence of events with prototypical ordering (
). Brain atrophy, for example, is associated with A b
deposition in normals ( Jack et al., 2009
) and hippocampal atrophy may mediate effects of A b on cognition (
Mormino et al., 2009 ) so that individuals with A
b who have not yet developed these downstream changes could be spared symptoms. Thus, older people with evidence of A b may remain normal because they have not expressed the full phenotype of
AD—they have amyloid pathology without clear evidence of neurodegeneration and will look like all other older individuals on every measure except A b
. However, mounting evidence suggests that very subtle alterations in brain function may also be present in such individuals.
A b deposition is associated with disruption of large-scale neural networks. These have been studied in the resting state using the technique of functional connectivity MRI (fcMRI), which makes use of the observation that spontaneous fluctuations in the MRI signal occur synchronously within neural systems
(
). These networks reflect sensory and motor systems as well as ensembles of brain regions brought online during tasks requiring memory, attention, or executive function
(
Damoiseaux et al., 2006 ). These may be ‘‘task-positive’’
networks activated by externally directed cognitive tasks or
‘‘task-negative’’ networks that are deactivated during externally driven cognition. The primary task-negative network, the default mode network (DMN) (
), is intrinsically disconnected in patients with AD (
); a particularly interesting observation since the pattern of A b deposition in the brain overlaps spatially with the topography of the DMN
(
). The major nodes of this network include precuneus/posterior cingulate, lateral parietal, and medial prefrontal cortices, some of which also participate in memory retrieval (
). This network is also functionally disrupted in cognitively intact older people with A b
(
) and also shows signs of atrophy (
;
). During fMRI experiments of memory encoding, older individuals deactivate the DMN less than younger individuals and the degree of deactivation is inversely proportional to the amount of A b
Increased fMRI brain activation in task-positive networks during the performance of cognitive tasks has also been re-
ported in patients with AD ( Grady et al., 2003 ), MCI ( Dickerson et al., 2004
), and those with genetic risks for AD ( Bookheimer et al., 2000
). Associations between increased activation and better performance have led to the interpretation that such changes are compensatory in nature. However, reduced activa-
tion has also been reported ( Johnson et al., 2006
) and studies may vary considerably by disease stage of participants, the cognitive task and its difficulty, and the particular brain regions examined. Two studies reported increased activation during memory encoding in older people who are amyloid positive
compared to those who are amyloid negative ( Sperling et al.,
;
), while one showed reduced activation (
Kennedy et al., 2012 ). Interestingly, looking at older
individuals as a group suggests that aging is associated with diminished activation; differentiating older people by A b deposition shows that older people who deposit A b activate more than
).
These studies point out the difficulty in generalizing about
‘‘aging’’ without also examining individuals for subtle signs of brain disease. Many features seen with A b deposition—decline in episodic memory, brain atrophy, altered resting state functional connectivity, and changes in brain activation—are commonly ascribed to normal aging. Epidemiologic cohort
studies ( Elias et al., 2000 ;
Amieva et al., 2008 ) show that subtle
cognitive decline is seen in some normal individuals up to 12 years before the diagnosis of dementia, and studies of individuals with autosomal dominant genetic causes of AD show alterations in brain structure and function 10–15 years before expected symptom onset (
;
). Longitudinal studies of aging have also shown that individuals who remain cognitively stable over time have less evidence of brain atrophy at baseline than those who subsequently decline, pointing out that studies of normal aging contain at least some individuals destined for dementia (
). Taken together, these observations make a strong case that many of the cognitive and neural effects ascribed to normal aging could be related to presymptomatic AD. A closer look, however, suggests that the situation is not so clear.
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Figure 3. Brain Activation and Deactivation during Memory Encoding
(A) Map of the topographic distribution of PiB retention in a group of older subjects.
(B) Deactivation in young subjects during the successful encoding of memories.
(C) Deactivation in older individuals without PiB retention is reduced in comparison to young.
(D) Deactivation in older individuals with PiB retention is further reduced. The patterns of PiB retention and deactivation reflect the default-mode network (DMN).
(A–D) are from
, with permission.
(E) Pattern of activation (yellow/orange) and deactivation (blue) across all young and old subjects during encoding of scenes.
(F) Differences between young and old subjects in hippocampal brain activation (arrow) during successful memory encoding, indicating reduced activation with aging.
(G) Same data as in (F) but shown as a function of PiB retention in the elderly, demonstrating higher activation in PiB+ elderly than young. (E–G) are from
Mormino et al. (2012) , with permission.
Does Presymptomatic AD Explain Cognitive Aging?
Anterograde amnesia is an early manifestation of AD, a subtle feature of A b deposition, and a common finding in normal aging.
With amyloid imaging, we can now exclude people with fibrillar
A b to examine independent effects of age on cognition. Even older individuals with no evidence of fibrillar brain A b show decline in visual memory and executive function compared to
young people ( Oh et al., 2012 ), a strong point in favor of the
argument that presymptomatic AD does not explain all frontal/ executive and medial temporal/episodic memory dysfunction.
As amyloid imaging becomes more widely applied, it is likely that we will see more cognitive investigations of normal older people who have been screened for A b
.
There are also important differences between the patterns of atrophy seen in aging and presymptomatic AD. Cross-sectional and longitudinal studies reveal whole-brain volume loss in normal individuals that includes gray and white matter and begins in middle age, with rates of loss approximately doubled
in AD patients ( Fotenos et al., 2005 ). While this global volume
loss in AD is dramatic, investigation of individuals in early stages is more revealing of specific regional vulnerabilities. Studies of either patients with MCI or normal older people who subsequently develop AD show atrophy or cortical thinning in specific
regions including the entorhinal cortex ( Stoub et al., 2005
), hippocampus (
Jack et al., 1999 ), and precuneus, angular, and
supramarginal gyri (
). These regions overlap with the brain regions that are atrophic in cognitively normal people with evidence of A b
(
;
). However, cross-sectional and longitudinal studies of aging show prominent volume loss in both
prefrontal cortex (PFC) ( Raz et al., 1997 ; Resnick et al., 2003 ;
) and striatum ( Raz et al., 2003b
), which are neither associated with A b nor implicated in the subsequent development of AD. This frontostriatal atrophy, which has been conceptualized as the substrate for the ‘‘frontal hypothesis’’ of cognitive aging, must have other explanations than AD or A b
. Furthermore,
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Figure 4. Pathological and Imaging Evidence of White Matter
Disease
Normal white matter seen on gross inspection (A) and microscopically (B).
Severe white matter pallor (C) indicating rarefaction and demyelination microscopically (D). These findings are consistent with white matter hyperintensities (E), which characteristically surround the ventricles throughout subcortical white matter (arrow). These WMHs also disrupt fiber tracts, seen in
(F) using diffusion tensor imaging tractography.
activation in the young in more ventral PFC, occipital cortex
(particularly during perceptual tasks), and hippocampus (
). These types of observations have resulted in cognitive models that have stressed compensation, particularly in PFC, as a response to age-related change (
Reuter-Lorenz and Lustig, 2005 ;
Park and Reuter-Lorenz, 2009 ). At this
point, it is difficult to assess the extent to which such activation-related changes might be due to presymptomatic AD. The constellation of reduced activation in a typically AD-affected region, the hippocampus, with increased age-related activation in a typically AD-spared region, the PFC, suggests that preclinical AD could underlie some of these findings. However, there are as yet too few studies of normal older people with measured brain A b to draw substantial conclusions. Increases in activation in aging are unlikely to have a single explanation. Future studies will need to examine interactions between task difficulty, behavioral performance, brain regions, and biomarkers of presymptomatic AD.
Normal aging, AD, and preclinical AD share the interesting features of anterograde amnesia, brain atrophy, network dysfunction, and increased cognitive activation. Based on these similarities, as well as the likelihood that normal cognitive aging studies to date have included a fair proportion of people with substantial brain A b accumulation, it is tempting to consider that many of the features of normal aging represent subtle forms of AD. However, it is important to keep in mind that most older people do not express fibrillar A b deposition. Furthermore, a small number of recent studies that compare older people without brain evidence of A b to young people show age-related alterations in cognition, atrophy, resting state networks, and brain activation. This could reflect our inability to fully exclude
AD, or it could be that the identical neural systems show the same kind of vulnerability to the different processes of aging and disease.
studies are beginning to appear that screen individuals for factors related to AD (such as genetic risks and A b
) and nevertheless show substantial age-related brain atrophy, even in
AD-related areas like entorhinal cortex (
). In the aggregate, these data do not support a unitary etiology of agerelated atrophy that is caused by preclinical AD.
Dysfunction of the DMN, while associated with A b deposition, has also been seen in studies of normal aging both as discon-
nection in resting state studies ( Damoiseaux et al., 2008 ) and
as reduced deactivation during tasks requiring semantic classification (
) and episodic memory encoding
(
). While the similarities between these aging studies and A b
-related findings are striking, older individuals with no evidence of brain A b also show reduced deactivation
of the DMN during memory encoding ( Vannini et al., 2012
) and reductions in resting state DMN connectivity (
). Thus, older people both with and without evidence of fibrillar A b share a fundamental disruption in the function of the major task-negative network. This mechanism underlying this neural ‘‘phenocopy’’ of AD in A b
-free aging is unexplained.
A variety of cognitive tasks have been associated with increased fMRI evidence of neural activation in PFC in normal older adults compared to younger adults. This is often accompanied by reduced activation elsewhere in the brain and a tendency for older people to activate both hemispheres when younger people activate unilaterally (
Cabeza et al., 1997 ). A widely
hypothesized explanation for this effect involves PFC compensation for failing neural function elsewhere, such as the hippocampus (
). A recent meta-analysis that considered both task demands and performance reported significant trends for increases in activation in dorsolateral
PFC in older compared to younger individuals, with greater
Cerebrovascular Disease and Cognitive Aging
Studies of cognitively normal older individuals can exclude participants with overt or symptomatic CVD by careful clinical examination since such individuals usually have neurological symptoms affecting motor and sensory systems that are straightforward to detect. However, 20%–30% of older people also have asymptomatic cerebrovascular disease such as
infarction, microbleeds, and microinfarcts ( Vermeer et al.,
;
). Another category of vascular pathology frequently seen on MR scans of older individuals is white matter hyperintensities (WMHs) (
increased MR signal seen on T2 and FLAIR images have been strongly associated with a host of vascular risk factors and the pathological changes of white matter rarefaction and demyelination (
). Although MR scanning could exclude most such individuals from studies of normal aging, microinfarcts pose challenges. Many normal aging studies do not exclude such individuals because WMHs and similar findings might be considered part of normal aging, because microinfarcts are undetectable, or because MR scans may not have been performed.
There is ample evidence that these vascular pathologies affect cognition in normal older people without dementia or MCI.
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WMHs predominate in the frontal lobes ( Wen and Sachdev,
2004 ) and regardless of their location tend to have effects on
glucose metabolism that preferentially affect prefrontal cortex, and behavioral effects on executive function (
;
).
WMHs are associated with a typical age-related cognitive deficit, failure of cognitive control, as well as with failure to activate PFC
during tasks requiring cognitive control ( Mayda et al., 2011
).
Even the risk factors for CVD affect PFC structure and function.
Higher scores on the Framingham Cardiovascular Risk Profile are associated with reductions in glucose metabolism in PFC
(
Kuczynski et al., 2009 ) and hypertension is associated with
reductions in prefrontal cortical volume and decline in executive function tasks (
). A strong chain of evidence thus links CVD and cerebrovascular risk, PFC atrophy, and decline in prefrontal cognition in aging, suggesting that at least part of the frontal hypothesis of aging could be mediated by cerebrovascular disease.
It is likely, however, that the neural mechanisms linking CVD to behavior are even more pervasive, as WMHs are not only associated with decline in executive function but also memory
(
Gunning-Dixon and Raz, 2000 ). While memory decline in aging
could reflect involvement of PFC in strategic processing, there is ample evidence for direct hippocampal effects. Hypertension is associated with hippocampal atrophy (
WMHs are associated with reduced activation in the medial temporal lobes during memory encoding (
).
CVD thus affects both major neural systems that have been associated with cognitive aging: the PFC/executive system and the medial temporal lobe memory system.
Manifestations of cerebrovascular disease can be generalized, subtle, and temporally remote. For example, levels of blood pressure at midlife are associated with greater cerebral
atrophy in late life ( DeCarli et al., 1999 ). The precise mechanisms
mediating relationships between vascular disease or risk factors and volume loss are unknown but could involve loss of white matter volume, microbleeds, or microinfarcts. A role for chronic ischemia in the etiology of WMHs or cortical atrophy has been proposed but never proven. Reduced white matter integrity, measured with the MR technique of diffusion tensor imaging
(DTI), is also related to a crucial cognitive phenomenon in aging, loss of processing speed (
). Decreases in functional connectivity within the DMN in older subjects are also
these DTI findings is unclear. Associations between DTI changes and WMHs and between DTI changes and some measures of cerebrovascular disease (
suggest that vascular factors may play a role. Whether these
DTI changes reflect a vascular etiology of age-associated cognitive decline responsible for processing speed and network alterations or whether they reflect disease-free aging remains to be determined.
The evidence is thus overwhelming that CVD, like AD, exists in subtle forms in many older people. Interestingly, exclusion of normal subjects with either cerebrovascular risk factors or overt cerebrovascular disease attenuates (though does not remove) both age-related cognitive decline and longitudinal brain atrophy
(
). CVD affects both neural systems that decline with age and is thereby implicated in decline of executive and memory function. Based on this evidence, it is reasonable to conclude that subtle forms of cerebrovascular disease have contributed to our conceptualization of aging, and that this group of disorders and risk factors may be at least partly responsible for both frontal and medial temporal theories of cognitive aging. Most importantly, many factors associated with CVD such as hypertension, obesity, and diabetes are treatable, an argument against the inevitability of at least some aspects of cognitive aging.
Parkinson’s Disease and Cognitive Aging
While PD is characteristically a disorder of movement, cognitive decline and dementia are common symptoms in PD patients that are most often associated at postmortem with widely distributed
Lewy bodies or concommitant AD pathology ( Jellinger, 2013 ).
However, these pathological processes fail to account for
many cases of PD-associated cognitive decline ( Libow et al.,
). In fact, the essential neurochemical disturbances associated with PD provide a compelling explanation for some forms of age-related cognitive decline. In addition to degeneration of the
SN, neurons in the ventral tegmental area (VTA) also degenerate in PD. Both the VTA and SN contain neurons that project to
limbic and cortical regions involved in cognition ( Bjo¨rklund and
). Cognitive deterioration in PD is associated with loss of both VTA neurons and neurons in the medial part of the
SN that project to the dorsal caudate ( Rinne et al., 1989
;
Zweig et al., 1993 ). These findings have implications for a neurochem-
ical basis of age-related cognitive decline.
The dopaminergic deficits of PD provide a useful model for understanding the neurochemical changes that underlie cognitive aging. PD patients show dysfunction in cognitive processes that rely upon dorsolateral PFC abilities and include planning,
temporal sequencing, delayed response, and set shifting ( Pillon et al., 1986
;
). These neuropsychological deficits have been investigated using PET to study in vivo measures of neurochemistry. Such studies have made use of dopamine precursor molecules such as 18 F-labeled Fluorodopa
(FDOPA) and fluorometatyrosine (FMT,
mine synthesis in the presynaptic neuron and also a variety of radio-labeled ligands that bind to the dopamine transporter
(DAT) on the presynaptic neuron. Such studies have shown
relationships between lower caudate FDOPA uptake ( Bru¨ck et al., 2001 ), reduced DATs (
), and impaired cognition in PD patients. The loss of nigral dopaminergic input to the dorsolateral caudate probably plays a key role in prefrontal cognitive dysfunction because caudate is anatomically linked to PFC through the striato-thalamo-cortical func-
tional loops ( Alexander et al., 1986
). There is also in vivo PET evidence for loss of extrastriatal dopamine (
and for reduced PFC glucose metabolism (
and neural activation ( Carbon et al., 2004
) that correlates with dopamine loss.
Could these strong links between midbrain dopaminergic neurodegeneration, striatal dopamine loss, and failure on prefrontal cognitive tasks in PD find a correlate in normal cognitive aging?
The link between dopamine and prefrontal cognition is well
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Figure 5. In Vivo Dopamine Imaging and Relationships to Brain Function
(A) Uptake of the dopamine synthesis tracer [
18
F]-fluorometatyrosine (FMT) in a normal individual. Hotter colors indicate tracer uptake in presynaptic neurons in striatum (yellow arrows) and brainstem (red arrow).
(B) Correlation between performance on the listening span test, a test of working memory, and dopamine synthesis measured with FMT. Hot-colored voxels
(indicated with red arrow) are regions in which greater dopamine function is associated with better working memory performance in a group of older individuals.
(C) Correlation of caudate dopamine synthesis with brain activation in the left middle frontal gyrus. Higher dopamine synthesis was associated with greater activation during the delay phase of a working memory task. (B) and (C) are from
, with permission.
(D) Regions in which binding potential at the D1 receptor was reduced in young people performing the multisource interference task. Reduced binding potential reflects release of endogenous dopamine.
(E) Changes in binding potential by age in the same experiment as (D). Younger individuals show evidence of dopamine release, while older individuals do not. (D) and (E) are from
, with permission.
established through decades of human and animal research
(
Arnsten, 2011 ). In addition, there are compelling similarities
between the neurochemical and motoric aspects of PD and aging. Nigral dopaminergic loss is a feature of aging itself.
Studies of postmortem tissue have revealed loss of nigral dopa-
minergic neurons and DATs at a rate of 5%–8%/decade ( Fearnley and Lees, 1991 ;
). These postmortem measures are paralleled by multiple in vivo PET studies showing age-associated loss of DATs (
), vesicular monoamine transporters (a marker of presynaptic dopaminergic neurons) (
Frey et al., 1996 ), and D2 dopamine receptors (
Volkow et al., 1998 ) in the striatum. Although the high density of dopami-
nergic terminals and receptors in the striatum make that region most amenable to reliable PET measurement, recent methods for investigating this system in extrastriatal regions have shown age-associated loss of D2 receptors in PFC and MTL (
Kaasinen et al., 2000 ) consistent with degeneration of mesocortical and
mesolimbic projections. Community studies that have examined the prevalence of parkinsonism note that at least 15%–30% of older individuals show motor impairment similar to but less
severe than those with full-fledged PD ( Bennett et al., 1996
;
Uemura et al., 2011 ). A community-based study with autopsy
follow-up, in fact, linked the presence of such parkinsonian symptoms in normal older people to loss of nigral neurons
(
Ross et al., 2004 ). Finally, in addition to the well-recognized
association between frontal atrophy and aging, one of the brain regions showing the strongest and most consistent age-associated shrinkage is the striatum (
Raz et al., 2003b ). Although this
has not been directly related to loss of dopaminergic input, the rate of striatal volume loss with advancing age parallels the rate of dopamine loss in aging.
Straightforward evidence of a dopamine-deficiency substrate of cognitive aging comes from studies that have used PET measurements of dopamine function to explain individual differences in the cognitive performance of older people. This work has shown associations between D2 receptors or DATs and
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spatial cognition ( Volkow et al., 1998
;
Erixon-Lindroth et al., 2005 ;
). There are strong relationships between dopamine synthesis and working
memory ( Landau et al., 2009 ) and between D1 receptors and
variability in cognitive performance in aging (
2012 ). Some evidence suggests that older people upregulate
dopamine synthesis in the striatum as a potential compensatory mechanism (
Braskie et al., 2008 ). Another powerful approach
involves measuring actual dopamine release by using a PET receptor ligand that can be displaced by endogenous dopamine.
In one such study, younger individuals showed evidence of dopamine release in the striatum during an interference task, while dopamine release was not detectable in the older group
A few studies have paired PET measurement of dopamine with fMRI measures of brain activation to link neurochemistry to neural activity in aging. Caudate dopamine synthesis is associated with activation in PFC in older people during the
delay period of a working memory task ( Landau et al., 2009
), findings that parallel dopamine-dependent PFC delay period neural activity in primate electrophysiological studies of work-
ing memory ( Goldman-Rakic, 1996
). Functional connectivity between PFC and caudate during the delay period was greater in younger than older subjects, and greater connectivity was associated with both better working memory performance and lower dopamine synthesis, supporting the observation that increased synthesis might reflect compensatory processes in
a deficient system ( Klostermann et al., 2012
). Dopamine may also affect allocation of cognitive resources by modulating deactivation during cognition. Greater dopamine synthesis is associated with more DMN deactivation in young people, but
this relationship is lost in older individuals ( Braskie et al., 2011
).
Similar relationships between dopamine, brain activation, and cognitive performance in aging have been seen in studies that used PET to examine dopamine receptors. Numerous studies have shown an age-related blunting of the load-related increase in brain activation during working memory tasks that could indicate failure of neural recruitment in the face of a more demanding task. Dopamine D1 receptor density in the caudate explains a substantial proportion of the variance in BOLD response in
PFC in this situation (
Ba¨ckman et al., 2011 ). This underrecruit-
ment could be reproduced in younger subjects given a D1
receptor antagonist ( Fischer et al., 2010
).
Does PD explain cognitive aging? From the perspective of typical PD—that is, a movement disorder associated with the deposition of brainstem Lewy bodies and dopamine deficiency—PD seems an unlikely explanation for much of cognitive aging. However, subtle changes in motor function are common in aging as is loss of dopaminergic neurons and many dopaminergic neurochemical measures. There is also strong evidence linking these dopamine losses to decline in cognitive function with age and specifically to alterations in both the frontostriatal executive system and the DMN. While this dopamine deficiency probably does not necessarily reflect incipient PD, it shares behavioral and neural similarities implicating dopamine loss as a fundamental mechanism of cognitive aging.
Differentiating Age and Disease in the Brain: Animal
Models
If studies of cognitive aging have included people with subtle forms of AD, CVD, and PD, is there any way to define mechanisms of brain aging that are independent of these disorders?
While the careful application of techniques for characterizing older subjects, such as amyloid imaging or structural brain imaging offers considerable promise, animal models of naturally occurring aging phenomena can also be helpful in establishing aging phenotypes. Although some old animals, including dogs and monkeys, show evidence of amyloid plaque accumulation
(
Selkoe et al., 1987 ), they do not show full-blown evidence of
AD or other neurodegenerative disorders that complicate the study of aging. Rodents and primates both exhibit signs of aging that are similar to those seen in humans including behavioral deficits, structural and ultrastructural alterations, and changes
in electrophysiology ( Yeoman et al., 2012 ). These deficits may
or may not be driven by mechanisms identical to those that underlie human aging, as there is no a priori theory that necessitates common cross-species mechanisms of aging. Nevertheless, these animal models may generate approaches to human aging that offer the potential for revealing fundamental ageassociated mechanisms of decline.
Studies of aged monkeys have conclusively demonstrated age-associated performance decrements on a variety of behavioral tasks that reflect function of both dorsolateral PFC and medial temporal lobe systems. Modern anatomic studies have failed to detect substantial neuronal loss in either of these regions (
). In the dlPFC, the morphological changes that appear best correlated to declining performance with age involve alterations in synapses (
Peters et al., 2008 ). This includes loss of synapse
density and apical dendritic branching that particularly affects
the small, thin spines most involved in plasticity ( Dumitriu et al.,
). These spines may be affected by disinhibition of cAMP signaling; a recent study indicated that inhibition of cAMP signaling in old monkeys restored neuronal activity during the delay period of a working memory task (
). These findings unify structural and neurochemical theories of PFC aging using animal models that do not reflect the common neurodegenerative diseases like AD and PD.
Age-related change in the primate medial temporal lobe involves loss of spine and synapse density in the subiculum
(
Uemura, 1985a , 1985b ), as well as alterations in the proportions
of multiple-synapse boutons and nonsynaptic boutons in the
outer molecular layer of the dentate gyrus (DG) ( Hara et al.,
). In rodents, aging is also associated with loss of synapses in DG (
Geinisman et al., 1992 ) and loss of synaptophysin in DG
) that is attributable to loss of DG input via the perforant pathway. A multimodal/multispecies imaging approach used fMRI in monkeys to measure cerebral blood volume (CBV), a correlate of neuronal function, and Arc gene expression in rats, an immediate early gene whose expression is
related to neuronal activity ( Small et al., 2004 ). Results indicated
age-associated loss of CBV and arc gene expression in only the DG in both species. Furthermore, CBV was related to behavioral performance on the delayed nonmatch to sample task, a test of memory that draws upon medial temporal lobe resources.
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Similarities between human brain aging and the nonhuman primate are clear. There is extensive evidence for synaptic changes throughout neocortex in association with human aging.
This includes reduction in dendritic spine density, volume, and length seen across the lifespan in multiple cortical regions
(
Benavides-Piccione et al., 2012 ). Neuronal
loss, historically considered a major aspect of brain aging, is probably minimal, while the synaptic alterations predominate.
Nevertheless, loss of specific neuronal types may play important roles. This is particularly true in the medial temporal lobe, where differential vulnerability to age and disease has been noted. The entorhinal cortex is susceptible to AD pathology, with involvement by NFTs and loss of the perforant pathway providing input from the entorhinal cortex to the dentate gyrus (
1984 ). Age, in contrast, appears to be associated with neuronal
also associated with neuronal loss in the hilus and subiculum,
CA1 is an interesting region in demonstrating considerable
AD-related neuronal loss without showing age effects ( West et al., 1994
;
Taken together, these data suggest two types of age-related morphological alterations distinct from neurodegeneration that occur in animals and humans. One is loss of synaptic density, particularly in PFC. The other is neuronal loss in the DG that is related to age and not disease and that may reflect loss of synaptic input, via the perforant pathway. Conversely, loss in
CA1 and entorhinal cortex is more likely to reflect the presence of AD. These findings can inform investigations of age-related cognitive decline in humans that may reflect aging, as opposed to neurodegenerative, phenotypes.
Are There Nondegenerative Aging Phenotypes of Neural
Function?
While standard clinical approaches to the investigation of memory in aging have been very helpful in defining relationships between age and disease, more mechanistic behavioral approaches offer additional promise in this regard. For example, the dual-process theory of episodic memory contrasts recollection as a relatively precise reconstruction of the details of an event, while familiarity is conceptualized as a more vague sense of ‘‘knowing’’ or previous experience. Studies consistently find age-related failure of recollection but less consistent failure of
familiarity ( Jennings and Jacoby, 1993
), while patients with AD show failure in both processes. One controversial model posits that recollection is related to hippocampal function and familiarity to extrahippocampal medial temporal lobe structures.
This dissociation has been seen in studies that have used MRI to measure the volumes of these structures in aging and dementia (
Yonelinas et al., 2007 ; Wolk et al., 2011
). These studies provide some evidence for a distinction between aging and AD based in hippocampal anatomy, since AD effects on both hippocampus (CA1) and entorhinal cortex may have their behavioral correlate in loss of familiarity and recollection, while aging is better related to hippocampally mediated (i.e., DG) deficits in recollection.
Another behavioral approach to the study of memory has relied upon computational models of hippocampal function emphasizing pattern separation and pattern completion.
Reviewed elsewhere ( Yassa and Stark, 2011
), this approach compares how the hippocampus manages representations that share features to either match degraded representations to stored templates (i.e., pattern completion) or to differentiate representations with similar features from previously stored information (i.e., pattern separation). These functions permit, respectively, the processes of generalization and the ability to encode novel information as distinct from prior, similar information. Considerable evidence links the DG to performance of pattern separation and, predictably, older adults show deficits on a task requiring the separation of spatial patterns (
Stark et al., 2010 ). However, such deficits share features of AD since
they occur in individuals with evidence of greater age-related impairment, and because they are also seen in patients with
MCI (
Studies using imaging approaches have recently begun to investigate subregions of the hippocampus with both structural and functional methods. High-field MRI permits higher resolution and contrast and therefore differentiation of hippocampal fields. In general, AD tends to involve volume loss in the subic-
ulum and CA1 ( Fouquet et al., 2012 ) and age affects volume
in CA3/DG (which cannot be differentiated on these images)
(
), although some studies have found that age may also be associated with loss in CA1
(
) and subiculum (
). Memory performance in older people has been linked to CA3/DG volume, while atrophy in CA1 was related to the presence of cerebrovascular risk factors (
This again suggests a relative specificity of some subregions for age and others for disease, in this case of a vascular nature. It is important to keep in mind the limitation of these studies, particularly limits in resolution that may make independent measures of neighboring regions difficult, and the fact that older individuals could still be in preclinical stages of AD.
However, a search for regional specificity in the hippocampus, especially if paired with measures of A b and cerebrovascular disease, offers potential for defining age-specific morphological alterations.
These morphological alterations have functional correlates as well. Initial studies used a form of resting fMRI data acquisition to investigate steady-state blood flow as an index of basal hippocampal function (
), demonstrating age-related decline in hippocampal function in the DG and subiculum as distinct from entorhinal cortex. Application of the pattern separation paradigm has also produced interesting results; older individuals show greater activation in CA3/DG than younger subjects performing this task while also showing a tendency
for pattern completion, rather than separation ( Yassa et al.,
). These findings have been linked to a loss of DG input
DG related to perforant pathway damage is seen in pathological processes common to animal models of aging, older humans, and the NFT pathology of AD. This is an excellent example of how a single neural system is vulnerable to a variety of insults that may reflect both age-related and neurodegenerative processes.
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Figure 6. Human Hippocampus in Cross-
Section and MR Images
(A) Coronal cross-section of human temporal lobe.
DG, dentate gryrus; S, subiculum; PrS, presubiculum; EC, entorhinal cortex; cs, collateral sulcus; FG, fusiform gyrus; its, inferior temporal sulcus; ITG, inferior temporal gyrus; mts, middle temporal sulcus; MTG, middle temporal gyrus; sts, superior temporal sulcus; STG, superior temporal gyrus.
(B) Coronal MRI view indicating location of hippocampus (arrow).
(C and D) High-field MRI images showing hippocampus and medial temporal lobe segmented into
CA3/DG (brown), CA1-2 transition (yellow), CA1
(blue), Subiculum (green), and entorhinal cortex
(red).
Older adults also show reductions in dorsolateral PFC activation compared to younger adults during the response phase
of a working memory task ( Rypma and D’Esposito, 2000 ), with
faster response time in the older subjects associated with greater activation. Studies of cognitive control have also been productive in establishing unique age-related mechanisms of impairment. Such studies have shown reductions in suppression of task-irrelevant information by PFC in older, compared to younger people (
Gazzaley et al., 2005 ), as well as failure to shift
between functional brain networks after interruption (
2011 ). These functional alterations reflect predominant age-
related effects on PFC, which could reflect some of the synaptic alterations seen in aging, although the actual mechanism of these changes is unknown.
Summary and Conclusions
The behavioral and neural alterations associated with aging affect the medial temporal and frontostriatal systems that underlie episodic memory and executive function. In addition, there is considerable evidence for age-related disruption of the
DMN and, within the medial temporal lobe, dysfunction in the
DG/perforant pathway system. Deposition of brain A b has substantial effects on both medial temporal and neocortical systems that are involved in memory function. NFT pathology affects the entorhinal cortex and perforant pathway. Loss of dopamine has effects on the frontostriatal/executive system.
CVD affects both medial temporal and frontostriatal systems.
The DMN is affected by age, A b
, CVD, and dopamine deficiency.
Is cognitive aging just the aggregation of these disorders, or something else?
We can draw important inferences from the small number of studies that find many of the AD-like age-related changes persist in older people screened for fibrillar A b
. It remains possible that such subjects have still not been adequately screened to rule out early neurodegeneration like AD. This is an important concept that reflects our current ignorance about what truly constitutes
AD. Is AD simply fibrillar A b that can be detected with PET? Or does it reflect soluble A b
, diffuse A b plaques, NFTs (without
A b
), or even synaptic loss? It is impossible to completely differentiate age and AD until we know what AD is. Similar concerns apply to the other age-related neurodegenerations.
However, the observation that the same neural systems can be affected by more than one process also raises fundamental questions about our understanding of aging itself. If the episodic memory system is affected in individuals with and without A b
, and the frontostriatal system is affected by conditions as varied as CVD, dopamine deficiency, and age-related synaptic loss, perhaps the question is not whether disease affects these systems, but rather how such systems are vulnerable to so many different processes. In other words, these neural systems are highly susceptible to multiple factors, including those that can be clearly ascribed to our current conceptualizations of disease, and those that do not fit such neat categories. This explanation highlights aging as a process that reflects systemic vulnerabilities to a variety of deleterious factors that accumulate with the passage of time. Some of these factors are disease related and others are not.
The important question therefore is not whether a disease accounts for the change we see with age, but whether we can unravel the fundamental mechanisms that produce changes in the neural systems under discussion. If we can indeed identify fundamental mechanisms responsible for the degeneration of these systems, we may be on the road to developing methods that increase brain health in the face of many different diseases and even age itself. Answering these questions will no doubt be enhanced by the dramatic improvement in our ability to detect biomarkers of disease in asymptomatic people so that we may define how our current understanding of disease relates to our current understanding of aging. While this may not provide an ultimate accounting of mechanisms underlying age-related change, it may speed therapeutic approaches not only to what we now consider overt disease but to age-related decline that is based in fundamental mechanisms of neural vulnerability.
ACKNOWLEDGMENTS
This work was partially supported by NIH grant AG034570. I thank Susan
Landau, Gil Rabinovici, John Flannery, and Mike Yassa for critical feedback.
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Figures were generously contributed by Harry Vinters, Owen Carmichael, Susanne Mueller, and David Amaral.
REFERENCES
Aarsland, D., Tandberg, E., Larsen, J.P., and Cummings, J.L. (1996).
Frequency of dementia in Parkinson disease. Arch. Neurol.
53 , 538–542.
Aizenstein, H.J., Nebes, R.D., Saxton, J.A., Price, J.C., Mathis, C.A., Tsopelas,
N.D., Ziolko, S.K., James, J.A., Snitz, B.E., Houck, P.R., et al. (2008). Frequent amyloid deposition without significant cognitive impairment among the elderly.
Arch. Neurol.
65
, 1509–1517.
Alexander, G.E., DeLong, M.R., and Strick, P.L. (1986). Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu. Rev.
Neurosci.
9
, 357–381.
Amaral, D.G. (1993). Morphological analyses of the brains of behaviorally characterized aged nonhuman primates. Neurobiol. Aging 14 , 671–672.
Amieva, H., Le Goff, M., Millet, X., Orgogozo, J.M., Pe´re`s, K., Barberger-
Gateau, P., Jacqmin-Gadda, H., and Dartigues, J.F. (2008). Prodromal
Alzheimer’s disease: successive emergence of the clinical symptoms. Ann.
Neurol.
64 , 492–498.
Andrews-Hanna, J.R., Snyder, A.Z., Vincent, J.L., Lustig, C., Head, D.,
Raichle, M.E., and Buckner, R.L. (2007). Disruption of large-scale brain systems in advanced aging. Neuron
56
, 924–935.
Arnsten, A.F. (2011). Catecholamine influences on dorsolateral prefrontal cortical networks. Biol. Psychiatry 69 , e89–e99.
Ba¨ckman, L., Ginovart, N., Dixon, R.A., Wahlin, T.B., Wahlin, A., Halldin, C., and Farde, L. (2000). Age-related cognitive deficits mediated by changes in the striatal dopamine system. Am. J. Psychiatry 157 , 635–637.
Ba¨ckman, L., Karlsson, S., Fischer, H., Karlsson, P., Brehmer, Y., Rieckmann,
A., MacDonald, S.W., Farde, L., and Nyberg, L. (2011). Dopamine D(1) receptors and age differences in brain activation during working memory. Neurobiol.
Aging
32
, 1849–1856.
Balasubramanian, A.B., Kawas, C.H., Peltz, C.B., Brookmeyer, R., and Corrada, M.M. (2012). Alzheimer disease pathology and longitudinal cognitive performance in the oldest-old with no dementia. Neurology
79
, 915–921.
Bateman, R.J., Xiong, C., Benzinger, T.L., Fagan, A.M., Goate, A., Fox, N.C.,
Marcus, D.S., Cairns, N.J., Xie, X., Blazey, T.M., et al.; Dominantly Inherited
Alzheimer Network. (2012). Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N. Engl. J. Med.
367 , 795–804.
Becker, J.A., Hedden, T., Carmasin, J., Maye, J., Rentz, D.M., Putcha, D.,
Fischl, B., Greve, D.N., Marshall, G.A., Salloway, S., et al. (2011). Amyloidb associated cortical thinning in clinically normal elderly. Ann. Neurol.
69
,
1032–1042.
Benavides-Piccione, R., Fernaud-Espinosa, I., Robles, V., Yuste, R., and
Defelipe, J. (2012). Age-based comparison of human dendritic spine structure using complete three-dimensional reconstructions. Cereb. Cortex. Published online June 17, 2012. http://dx.doi.org/10.1093/cercor/bhs154.
Bennett, D.A., Beckett, L.A., Murray, A.M., Shannon, K.M., Goetz, C.G.,
Pilgrim, D.M., and Evans, D.A. (1996). Prevalence of parkinsonian signs and associated mortality in a community population of older people. N. Engl. J.
Med.
334 , 71–76.
Bennett, D.A., Schneider, J.A., Arvanitakis, Z., Kelly, J.F., Aggarwal, N.T.,
Shah, R.C., and Wilson, R.S. (2006). Neuropathology of older persons without cognitive impairment from two community-based studies. Neurology 66 ,
1837–1844.
Biswal, B., Yetkin, F.Z., Haughton, V.M., and Hyde, J.S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar
MRI. Magn. Reson. Med.
34
, 537–541.
Bjo¨rklund, A., and Dunnett, S.B. (2007). Dopamine neuron systems in the brain: an update. Trends Neurosci.
30 , 194–202.
Bookheimer, S.Y., Strojwas, M.H., Cohen, M.S., Saunders, A.M., Pericak-
Vance, M.A., Mazziotta, J.C., and Small, G.W. (2000). Patterns of brain activation in people at risk for Alzheimer’s disease. N. Engl. J. Med.
343 , 450–456.
Braskie, M.N., Wilcox, C.E., Landau, S.M., O’Neil, J.P., Baker, S.L., Madison,
C.M., Kluth, J.T., and Jagust, W.J. (2008). Relationship of striatal dopamine synthesis capacity to age and cognition. J. Neurosci.
28 , 14320–14328.
Braskie, M.N., Landau, S.M., Wilcox, C.E., Taylor, S.D., O’Neil, J.P., Baker,
S.L., Madison, C.M., and Jagust, W.J. (2011). Correlations of striatal dopamine synthesis with default network deactivations during working memory in younger adults. Hum. Brain Mapp.
32 , 947–961.
Breteler, M.M.B., van Swieten, J.C., Bots, M.L., Grobbee, D.E., Claus, J.J., van den Hout, J.H.W., van Harskamp, F., Tanghe, H.L.J., de Jong, P.T.V.M., van
Gijn, J., et al. (1994). Cerebral white matter lesions, vascular risk factors, and cognitive function in a population-based study: the Rotterdam Study.
Neurology
44
, 1246–1252.
Bru¨ck, A., Portin, R., Lindell, A., Laihinen, A., Bergman, J., Haaparanta, M.,
Solin, O., and Rinne, J.O. (2001). Positron emission tomography shows that impaired frontal lobe functioning in Parkinson’s disease is related to dopaminergic hypofunction in the caudate nucleus. Neurosci. Lett.
311 , 81–84.
Buckner, R.L. (2004). Memory and executive function in aging and AD: multiple factors that cause decline and reserve factors that compensate. Neuron 44 ,
195–208.
Buckner, R.L., Snyder, A.Z., Shannon, B.J., LaRossa, G., Sachs, R., Fotenos,
A.F., Sheline, Y.I., Klunk, W.E., Mathis, C.A., Morris, J.C., and Mintun, M.A.
(2005). Molecular, structural, and functional characterization of Alzheimer’s disease: evidence for a relationship between default activity, amyloid, and memory. J. Neurosci.
25
, 7709–7717.
Burgmans, S., van Boxtel, M.P., Vuurman, E.F., Smeets, F., Gronenschild,
E.H., Uylings, H.B., and Jolles, J. (2009). The prevalence of cortical gray matter atrophy may be overestimated in the healthy aging brain. Neuropsychology 23 ,
541–550.
Burgmans, S., Gronenschild, E.H., Fandakova, Y., Shing, Y.L., van Boxtel,
M.P., Vuurman, E.F., Uylings, H.B., Jolles, J., and Raz, N. (2011). Age differences in speed of processing are partially mediated by differences in axonal integrity. Neuroimage 55 , 1287–1297.
Cabeza, R., Grady, C.L., Nyberg, L., McIntosh, A.R., Tulving, E., Kapur, S.,
Jennings, J.M., Houle, S., and Craik, F.I. (1997). Age-related differences in neural activity during memory encoding and retrieval: a positron emission tomography study. J. Neurosci.
17
, 391–400.
Carbon, M., Ma, Y., Barnes, A., Dhawan, V., Chaly, T., Ghilardi, M.F., and
Eidelberg, D. (2004). Caudate nucleus: influence of dopaminergic input on sequence learning and brain activation in Parkinsonism. Neuroimage 21 ,
1497–1507.
Clapp, W.C., Rubens, M.T., Sabharwal, J., and Gazzaley, A. (2011). Deficit in switching between functional brain networks underlies the impact of multitasking on working memory in older adults. Proc. Natl. Acad. Sci. USA 108 ,
7212–7217.
Clark, C.M., Schneider, J.A., Bedell, B.J., Beach, T.G., Bilker, W.B., Mintun,
M.A., Pontecorvo, M.J., Hefti, F., Carpenter, A.P., Flitter, M.L., et al.; AV45-
A07 Study Group. (2011). Use of florbetapir-PET for imaging beta-amyloid pathology. JAMA
305
, 275–283.
Compta, Y., Parkkinen, L., O’Sullivan, S.S., Vandrovcova, J., Holton, J.L.,
Collins, C., Lashley, T., Kallis, C., Williams, D.R., de Silva, R., et al. (2011).
Lewy- and Alzheimer-type pathologies in Parkinson’s disease dementia: which is more important? Brain 134 , 1493–1505.
Damoiseaux, J.S., Rombouts, S.A., Barkhof, F., Scheltens, P., Stam, C.J.,
Smith, S.M., and Beckmann, C.F. (2006). Consistent resting-state networks across healthy subjects. Proc. Natl. Acad. Sci. USA 103 , 13848–13853.
Damoiseaux, J.S., Beckmann, C.F., Arigita, E.J., Barkhof, F., Scheltens, P.,
Stam, C.J., Smith, S.M., and Rombouts, S.A. (2008). Reduced resting-state brain activity in the ‘‘default network’’ in normal aging. Cereb. Cortex
18
,
1856–1864.
Daselaar, S.M., Fleck, M.S., Dobbins, I.G., Madden, D.J., and Cabeza, R.
(2006). Effects of healthy aging on hippocampal and rhinal memory functions: an event-related fMRI study. Cereb. Cortex 16 , 1771–1782.
Davatzikos, C., Xu, F., An, Y., Fan, Y., and Resnick, S.M. (2009). Longitudinal progression of Alzheimer’s-like patterns of atrophy in normal older adults: the
SPARE-AD index. Brain 132 , 2026–2035.
230 Neuron
77
, January 23, 2013
ª
2013 Elsevier Inc.
Neuron
Review
DeCarli, C., Miller, B.L., Swan, G.E., Reed, T., Wolf, P.A., Garner, J., Jack, L., and Carmelli, D. (1999). Predictors of brain morphology for the men of the
NHLBI twin study. Stroke 30 , 529–536.
DeKosky, S.T., and Scheff, S.W. (1990). Synapse loss in frontal cortex biopsies in Alzheimer’s disease: correlation with cognitive severity. Ann. Neurol.
27 ,
457–464.
Dickerson, B.C., Salat, D.H., Bates, J.F., Atiya, M., Killiany, R.J., Greve, D.N.,
Dale, A.M., Stern, C.E., Blacker, D., Albert, M.S., and Sperling, R.A. (2004).
Medial temporal lobe function and structure in mild cognitive impairment.
Ann. Neurol.
56 , 27–35.
Dickerson, B.C., Stoub, T.R., Shah, R.C., Sperling, R.A., Killiany, R.J., Albert,
M.S., Hyman, B.T., Blacker, D., and Detoledo-Morrell, L. (2011). Alzheimersignature MRI biomarker predicts AD dementia in cognitively normal adults.
Neurology
76
, 1395–1402.
Dumitriu, D., Hao, J., Hara, Y., Kaufmann, J., Janssen, W.G., Lou, W., Rapp,
P.R., and Morrison, J.H. (2010). Selective changes in thin spine density and morphology in monkey prefrontal cortex correlate with aging-related cognitive impairment. J. Neurosci.
30
, 7507–7515.
Elias, M.F., Beiser, A., Wolf, P.A., Au, R., White, R.F., and D’Agostino, R.B.
(2000). The preclinical phase of alzheimer disease: A 22-year prospective study of the Framingham Cohort. Arch. Neurol.
57 , 808–813.
Erixon-Lindroth, N., Farde, L., Wahlin, T.B., Sovago, J., Halldin, C., and Ba¨ckman, L. (2005). The role of the striatal dopamine transporter in cognitive aging.
Psychiatry Res.
138 , 1–12.
Fearnley, J.M., and Lees, A.J. (1991). Ageing and Parkinson’s disease: substantia nigra regional selectivity. Brain 114 , 2283–2301.
Fischer, H., Nyberg, L., Karlsson, S., Karlsson, P., Brehmer, Y., Rieckmann, A.,
MacDonald, S.W., Farde, L., and Ba¨ckman, L. (2010). Simulating neurocognitive aging: effects of a dopaminergic antagonist on brain activity during working memory. Biol. Psychiatry
67
, 575–580.
Fjell, A.M., Westlye, L.T., Amlien, I., Espeseth, T., Reinvang, I., Raz, N., Agartz,
I., Salat, D.H., Greve, D.N., Fischl, B., et al. (2009). High consistency of regional cortical thinning in aging across multiple samples. Cereb. Cortex
19
, 2001–
2012.
Fjell, A.M., Westlye, L.T., Grydeland, H., Amlien, I., Espeseth, T., Reinvang, I.,
Raz, N., Dale, A.M., and Walhovd, K.B.; for the Alzheimer Disease Neuroimaging Initiative. (2012). Accelerating cortical thinning: unique to dementia or universal in aging? Cereb. Cortex. Published online December 12, 2012.
http://dx.doi.org/10.1093/cercor/bhs379.
Fotenos, A.F., Snyder, A.Z., Girton, L.E., Morris, J.C., and Buckner, R.L.
(2005). Normative estimates of cross-sectional and longitudinal brain volume decline in aging and AD. Neurology 64 , 1032–1039.
Fouquet, M., Desgranges, B., La Joie, R., Rivie`re, D., Mangin, J.F., Landeau,
B., Me´zenge, F., Pe´lerin, A., de La Sayette, V., Viader, F., et al. (2012). Role of hippocampal CA1 atrophy in memory encoding deficits in amnestic Mild
Cognitive Impairment. Neuroimage 59 , 3309–3315.
Frey, K.A., Koeppe, R.A., Kilbourn, M.R., Vander Borght, T.M., Albin, R.L.,
Gilman, S., and Kuhl, D.E. (1996). Presynaptic monoaminergic vesicles in
Parkinson’s disease and normal aging. Ann. Neurol.
40
, 873–884.
Gazzaley, A., Cooney, J.W., Rissman, J., and D’Esposito, M. (2005). Top-down suppression deficit underlies working memory impairment in normal aging.
Nat. Neurosci.
8
, 1298–1300.
Geinisman, Y., deToledo-Morrell, L., Morrell, F., Persina, I.S., and Rossi, M.
(1992). Age-related loss of axospinous synapses formed by two afferent systems in the rat dentate gyrus as revealed by the unbiased stereological dissector technique. Hippocampus
2
, 437–444.
Golde, T.E., Schneider, L.S., and Koo, E.H. (2011). Anti-a b therapeutics in
Alzheimer’s disease: the need for a paradigm shift. Neuron 69 , 203–213.
Goldman-Rakic, P.S. (1996). Regional and cellular fractionation of working memory. Proc. Natl. Acad. Sci. USA 93 , 13473–13480.
Grady, C.L., McIntosh, A.R., Beig, S., Keightley, M.L., Burian, H., and Black,
S.E. (2003). Evidence from functional neuroimaging of a compensatory prefrontal network in Alzheimer’s disease. J. Neurosci.
23 , 986–993.
Greicius, M.D., Srivastava, G., Reiss, A.L., and Menon, V. (2004). Defaultmode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc. Natl. Acad. Sci. USA 101 , 4637–4642.
Gunning-Dixon, F.M., and Raz, N. (2000). The cognitive correlates of white matter abnormalities in normal aging: a quantitative review. Neuropsychology
14 , 224–232.
Gunning-Dixon, F.M., and Raz, N. (2003). Neuroanatomical correlates of selected executive functions in middle-aged and older adults: a prospective
MRI study. Neuropsychologia 41 , 1929–1941.
Hachinski, V.C., Lassen, N.A., and Marshall, J. (1974). Multi-infarct dementia.
A cause of mental deterioration in the elderly. Lancet
2
, 207–210.
Hara, Y., Park, C.S., Janssen, W.G., Punsoni, M., Rapp, P.R., and Morrison,
J.H. (2011). Synaptic characteristics of dentate gyrus axonal boutons and their relationships with aging, menopause, and memory in female rhesus monkeys.
J. Neurosci.
31
, 7737–7744.
Hardy, J. (2009). The amyloid hypothesis for Alzheimer’s disease: a critical reappraisal. J. Neurochem.
110
, 1129–1134.
Hasher, L., and Zachs, R.T. (1988). Working memory, comprehension, and aging: A review and a new view. In The Psychology of Learning and Motivation,
22 (New York: Academic Press), pp. 193–225.
Head, D., Raz, N., Gunning-Dixon, F., Williamson, A., and Acker, J.D. (2002).
Age-related differences in the course of cognitive skill acquisition: the role of regional cortical shrinkage and cognitive resources. Psychol. Aging 17 , 72–84.
Hedden, T., and Gabrieli, J.D.E. (2004). Insights into the ageing mind: a view from cognitive neuroscience. Nat. Rev. Neurosci.
5 , 87–96.
Hedden, T., Van Dijk, K.R., Becker, J.A., Mehta, A., Sperling, R.A., Johnson,
K.A., and Buckner, R.L. (2009). Disruption of functional connectivity in clinically normal older adults harboring amyloid burden. J. Neurosci.
29 , 12686–12694.
Hof, P.R., Nimchinsky, E.A., Young, W.G., and Morrison, J.H. (2000). Numbers of meynert and layer IVB cells in area V1: a stereologic analysis in young and aged macaque monkeys. J. Comp. Neurol.
420
, 113–126.
Hofman, A., Rocca, W.A., Brayne, C., Breteler, M.M., Clarke, M., Cooper, B.,
Copeland, J.R., Dartigues, J.F., da Silva Droux, A., Hagnell, O., et al.; Eurodem
Prevalence Research Group. (1991). The prevalence of dementia in Europe: a collaborative study of 1980-1990 findings. Int. J. Epidemiol.
20
, 736–748.
Hyman, B.T., Van Hoesen, G.W., Damasio, A.R., and Barnes, C.L. (1984). Alzheimer’s disease: cell-specific pathology isolates the hippocampal formation.
Science 225 , 1168–1170.
Hyman, B.T., Phelps, C.H., Beach, T.G., Bigio, E.H., Cairns, N.J., Carrillo,
M.C., Dickson, D.W., Duyckaerts, C., Frosch, M.P., Masliah, E., et al. (2012).
National Institute on Aging-Alzheimer’s Association guidelines for the neuropathologic assessment of Alzheimer’s disease. Alzheimers Dement.
8 , 1–13.
Jack, C.R., Jr., Petersen, R.C., Xu, Y.C., O’Brien, P.C., Smith, G.E., Ivnik, R.J.,
Boeve, B.F., Waring, S.C., Tangalos, E.G., and Kokmen, E. (1999). Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment.
Neurology 52 , 1397–1403.
Jack, C.R., Jr., Lowe, V.J., Weigand, S.D., Wiste, H.J., Senjem, M.L.,
Knopman, D.S., Shiung, M.M., Gunter, J.L., Boeve, B.F., Kemp, B.J., et al.;
Alzheimer’s Disease Neuroimaging Initiative. (2009). Serial PIB and MRI in normal, mild cognitive impairment and Alzheimer’s disease: implications for sequence of pathological events in Alzheimer’s disease. Brain
132
, 1355–
1365.
Jack, C.R., Jr., Knopman, D.S., Jagust, W.J., Shaw, L.M., Aisen, P.S., Weiner,
M.W., Petersen, R.C., and Trojanowski, J.Q. (2010). Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol.
9 , 119–128.
Jacobs, B., Driscoll, L., and Schall, M. (1997). Life-span dendritic and spine changes in areas 10 and 18 of human cortex: a quantitative Golgi study. J.
Comp. Neurol.
386 , 661–680.
Jagust, W.J., Zheng, L., Harvey, D.J., Mack, W.J., Vinters, H.V., Weiner, M.W.,
Ellis, W.G., Zarow, C., Mungas, D., Reed, B.R., et al. (2008). Neuropathological basis of magnetic resonance images in aging and dementia. Ann. Neurol.
63 ,
72–80.
Neuron
77
, January 23, 2013
ª
2013 Elsevier Inc.
231
Neuron
Review
Jagust, W.J., Landau, S.M., Shaw, L.M., Trojanowski, J.Q., Koeppe, R.A., Reiman, E.M., Foster, N.L., Petersen, R.C., Weiner, M.W., Price, J.C., and Mathis,
C.A.; Alzheimer’s Disease Neuroimaging Initiative. (2009). Relationships between biomarkers in aging and dementia. Neurology 73 , 1193–1199.
Jeerakathil, T., Wolf, P.A., Beiser, A., Hald, J.K., Au, R., Kase, C.S., Massaro,
J.M., and DeCarli, C. (2004). Cerebral microbleeds: prevalence and associations with cardiovascular risk factors in the Framingham Study. Stroke 35 ,
1831–1835.
Jellinger, K.A. (2013). Mild cognitive impairment in Parkinson disease: heterogenous mechanisms. J. Neural Transm.
120 , 157–167.
Jennings, J.M., and Jacoby, L.L. (1993). Automatic versus intentional uses of memory: aging, attention, and control. Psychol. Aging
8
, 283–293.
Johnson, S.C., Schmitz, T.W., Trivedi, M.A., Ries, M.L., Torgerson, B.M.,
Carlsson, C.M., Asthana, S., Hermann, B.P., and Sager, M.A. (2006). The influence of Alzheimer disease family history and apolipoprotein E epsilon4 on mesial temporal lobe activation. J. Neurosci.
26
, 6069–6076.
Kaasinen, V., Vilkman, H., Hietala, J., Na˚gren, K., Helenius, H., Olsson, H.,
Farde, L., and Rinne, J. (2000). Age-related dopamine D2/D3 receptor loss in extrastriatal regions of the human brain. Neurobiol. Aging 21 , 683–688.
Karlsson, S., Nyberg, L., Karlsson, P., Fischer, H., Thilers, P., Macdonald, S.,
Brehmer, Y., Rieckmann, A., Halldin, C., Farde, L., and Ba¨ckman, L. (2009).
Modulation of striatal dopamine D1 binding by cognitive processing. Neuroimage 48 , 398–404.
Kennedy, K.M., Rodrigue, K.M., Devous, M.D., Sr., Hebrank, A.C., Bischof,
G.N., and Park, D.C. (2012). Effects of beta-amyloid accumulation on neural function during encoding across the adult lifespan. Neuroimage 62 , 1–8.
Klein, J.C., Eggers, C., Kalbe, E., Weisenbach, S., Hohmann, C., Vollmar, S.,
Baudrexel, S., Diederich, N.J., Heiss, W.D., and Hilker, R. (2010). Neurotransmitter changes in dementia with Lewy bodies and Parkinson disease dementia in vivo. Neurology
74
, 885–892.
Klostermann, E.C., Braskie, M.N., Landau, S.M., O’Neil, J.P., and Jagust, W.J.
(2012). Dopamine and frontostriatal networks in cognitive aging. Neurobiol.
Aging
33
, 623, e15–e24.
Klunk, W.E., Engler, H., Nordberg, A., Wang, Y., Blomqvist, G., Holt, D.P.,
Bergstro¨m, M., Savitcheva, I., Huang, G.F., Estrada, S., et al. (2004). Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Ann.
Neurol.
55
, 306–319.
Knopman, D.S., Parisi, J.E., Salviati, A., Floriach-Robert, M., Boeve, B.F.,
Ivnik, R.J., Smith, G.E., Dickson, D.W., Johnson, K.A., Petersen, L.E., et al.
(2003). Neuropathology of cognitively normal elderly. J. Neuropathol. Exp.
Neurol.
62 , 1087–1095.
Kok, E., Haikonen, S., Luoto, T., Huhtala, H., Goebeler, S., Haapasalo, H., and
Karhunen, P.J. (2009). Apolipoprotein E-dependent accumulation of Alzheimer disease-related lesions begins in middle age. Ann. Neurol.
65 , 650–657.
Kuczynski, B., Jagust, W., Chui, H.C., and Reed, B. (2009). An inverse association of cardiovascular risk and frontal lobe glucose metabolism. Neurology
72 , 738–743.
La Joie, R., Fouquet, M., Me´zenge, F., Landeau, B., Villain, N., Mevel, K.,
Pe´lerin, A., Eustache, F., Desgranges, B., and Che´telat, G. (2010). Differential effect of age on hippocampal subfields assessed using a new high-resolution
3T MR sequence. Neuroimage
53
, 506–514.
Landau, S.M., Lal, R., O’Neil, J.P., Baker, S., and Jagust, W.J. (2009). Striatal dopamine and working memory. Cereb. Cortex
19
, 445–454.
Libow, L.S., Frisina, P.G., Haroutunian, V., Perl, D.P., and Purohit, D.P. (2009).
Parkinson’s disease dementia: a diminished role for the Lewy body. Parkinsonism Relat. Disord.
15 , 572–575.
Litvan, I., Goldman, J.G., Tro¨ster, A.I., Schmand, B.A., Weintraub, D.,
Petersen, R.C., Mollenhauer, B., Adler, C.H., Marder, K., Williams-Gray,
C.H., et al. (2012). Diagnostic criteria for mild cognitive impairment in
Parkinson’s disease: Movement Disorder Society Task Force guidelines.
Mov. Disord.
27 , 349–356.
Lustig, C., Snyder, A.Z., Bhakta, M., O’Brien, K.C., McAvoy, M., Raichle, M.E.,
Morris, J.C., and Buckner, R.L. (2003). Functional deactivations: change with age and dementia of the Alzheimer type. Proc. Natl. Acad. Sci. USA 100 ,
14504–14509.
Ma, S.Y., Ciliax, B.J., Stebbins, G., Jaffar, S., Joyce, J.N., Cochran, E.J.,
Kordower, J.H., Mash, D.C., Levey, A.I., and Mufson, E.J. (1999). Dopamine transporter-immunoreactive neurons decrease with age in the human substantia nigra. J. Comp. Neurol.
409 , 25–37.
MacDonald, S.W., Karlsson, S., Rieckmann, A., Nyberg, L., and Ba¨ckman, L.
(2012). Aging-related increases in behavioral variability: relations to losses of dopamine D1 receptors. J. Neurosci.
32 , 8186–8191.
Marchant, N.L., Reed, B.R., DeCarli, C.S., Madison, C.M., Weiner, M.W., Chui,
H.C., and Jagust, W.J. (2012). Cerebrovascular disease, b -amyloid, and cognition in aging. Neurobiol. Aging 33 , 1006, e25–e36.
Marie´, R.M., Barre´, L., Dupuy, B., Viader, F., Defer, G., and Baron, J.C. (1999).
Relationships between striatal dopamine denervation and frontal executive tests in Parkinson’s disease. Neurosci. Lett.
260
, 77–80.
Mayda, A.B., Westphal, A., Carter, C.S., and DeCarli, C. (2011). Late life cognitive control deficits are accentuated by white matter disease burden. Brain
134
, 1673–1683.
Miller, S.L., Celone, K., DePeau, K., Diamond, E., Dickerson, B.C., Rentz, D.,
Pihlajama¨ki, M., and Sperling, R.A. (2008). Age-related memory impairment associated with loss of parietal deactivation but preserved hippocampal activation. Proc. Natl. Acad. Sci. USA
105
, 2181–2186.
Mormino, E.C., Kluth, J.T., Madison, C.M., Rabinovici, G.D., Baker, S.L., Miller,
B.L., Koeppe, R.A., Mathis, C.A., Weiner, M.W., and Jagust, W.J.; Alzheimer’s
Disease Neuroimaging Initiative. (2009). Episodic memory loss is related to hippocampal-mediated beta-amyloid deposition in elderly subjects. Brain
132 , 1310–1323.
Mormino, E.C., Brandel, M.G., Madison, C.M., Marks, S., Baker, S.L., and Jagust, W.J. (2012). A b Deposition in aging is associated with increases in brain activation during successful memory encoding. Cereb. Cortex 22 , 1813–1823.
Morris, J.C., Roe, C.M., Xiong, C., Fagan, A.M., Goate, A.M., Holtzman, D.M., and Mintun, M.A. (2010). APOE predicts amyloid-beta but not tau Alzheimer pathology in cognitively normal aging. Ann. Neurol.
67 , 122–131.
Mueller, S.G., and Weiner, M.W. (2009). Selective effect of age, Apo e4, and
Alzheimer’s disease on hippocampal subfields. Hippocampus 19 , 558–564.
Nelson, P.T., Alafuzoff, I., Bigio, E.H., Bouras, C., Braak, H., Cairns, N.J., Castellani, R.J., Crain, B.J., Davies, P., Del Tredici, K., et al. (2012). Correlation of
Alzheimer disease neuropathologic changes with cognitive status: a review of the literature. J. Neuropathol. Exp. Neurol.
71 , 362–381.
Nitkunan, A., Charlton, R.A., McIntyre, D.J., Barrick, T.R., Howe, F.A., and
Markus, H.S. (2008). Diffusion tensor imaging and MR spectroscopy in hypertension and presumed cerebral small vessel disease. Magn. Reson. Med.
59
,
528–534.
Nordahl, C.W., Ranganath, C., Yonelinas, A.P., Decarli, C., Fletcher, E., and
Jagust, W.J. (2006). White matter changes compromise prefrontal cortex function in healthy elderly individuals. J. Cogn. Neurosci.
18
, 418–429.
O’Brien, J.T., Erkinjuntti, T., Reisberg, B., Roman, G., Sawada, T., Pantoni, L.,
Bowler, J.V., Ballard, C., DeCarli, C., Gorelick, P.B., et al. (2003). Vascular cognitive impairment. Lancet Neurol.
2
, 89–98.
Oh, H., Mormino, E.C., Madison, C., Hayenga, A., Smiljic, A., and Jagust, W.J.
(2011).
b -Amyloid affects frontal and posterior brain networks in normal aging.
Neuroimage 54 , 1887–1895.
Oh, H., Madison, C., Haight, T.J., Markley, C., and Jagust, W.J. (2012). Effects of age and b -amyloid on cognitive changes in normal elderly people. Neurobiol. Aging 33 , 2746–2755.
Park, D.C., and Reuter-Lorenz, P. (2009). The adaptive brain: aging and neurocognitive scaffolding. Annu. Rev. Psychol.
60 , 173–196.
Park, D.C., Lautenschlager, G., Hedden, T., Davidson, N.S., Smith, A.D., and
Smith, P.K. (2002). Models of visuospatial and verbal memory across the adult life span. Psychol. Aging 17 , 299–320.
Perrotin, A., Mormino, E.C., Madison, C.M., Hayenga, A.O., and Jagust, W.J.
(2012). Subjective cognition and amyloid deposition imaging: a Pittsburgh
232 Neuron
77
, January 23, 2013
ª
2013 Elsevier Inc.
Neuron
Review
Compound B positron emission tomography study in normal elderly individuals. Arch. Neurol.
69 , 223–229.
Peters, A., Leahu, D., Moss, M.B., and McNally, K.J. (1994). The effects of aging on area 46 of the frontal cortex of the rhesus monkey. Cereb. Cortex
4
, 621–635.
Peters, A., Sethares, C., and Luebke, J.I. (2008). Synapses are lost during aging in the primate prefrontal cortex. Neuroscience 152 , 970–981.
Pike, K.E., Ellis, K.A., Villemagne, V.L., Good, N., Che´telat, G., Ames, D.,
Szoeke, C., Laws, S.M., Verdile, G., Martins, R.N., et al. (2011). Cognition and beta-amyloid in preclinical Alzheimer’s disease: data from the AIBL study.
Neuropsychologia
49
, 2384–2390.
Pillon, B., Dubois, B., Lhermitte, F., and Agid, Y. (1986). Heterogeneity of cognitive impairment in progressive supranuclear palsy, Parkinson’s disease, and Alzheimer’s disease. Neurology 36 , 1179–1185.
Polito, C., Berti, V., Ramat, S., Vanzi, E., De Cristofaro, M.T., Pellicano`, G.,
Mungai, F., Marini, P., Formiconi, A.R., Sorbi, S., and Pupi, A. (2012).
Interaction of caudate dopamine depletion and brain metabolic changes with cognitive dysfunction in early Parkinson’s disease. Neurobiol. Aging
33
,
206, e29–e39.
Postle, B.R., Jonides, J., Smith, E.E., Corkin, S., and Growdon, J.H. (1997).
Spatial, but not object, delayed response is impaired in early Parkinson’s disease. Neuropsychology 11 , 171–179.
Price, J.L., and Morris, J.C. (1999). Tangles and plaques in nondemented aging and ‘‘preclinical’’ Alzheimer’s disease. Ann. Neurol.
45
, 358–368.
Price, J.L., Ko, A.I., Wade, M.J., Tsou, S.K., McKeel, D.W., and Morris, J.C.
(2001). Neuron number in the entorhinal cortex and CA1 in preclinical
Alzheimer disease. Arch. Neurol.
58 , 1395–1402.
Querfurth, H.W., and LaFerla, F.M. (2010). Alzheimer’s disease. N. Engl. J.
Med.
362
, 329–344.
Raichle, M.E., MacLeod, A.M., Snyder, A.Z., Powers, W.J., Gusnard, D.A., and
Shulman, G.L. (2001). A default mode of brain function. Proc. Natl. Acad. Sci.
USA 98 , 676–682.
Raz, N., Gunning, F.M., Head, D., Dupuis, J.H., McQuain, J., Briggs, S.D.,
Loken, W.J., Thornton, A.E., and Acker, J.D. (1997). Selective aging of the human cerebral cortex observed in vivo: differential vulnerability of the prefrontal gray matter. Cereb. Cortex
7
, 268–282.
Raz, N., Rodrigue, K.M., and Acker, J.D. (2003a). Hypertension and the brain: vulnerability of the prefrontal regions and executive functions. Behav. Neurosci.
117 , 1169–1180.
Raz, N., Rodrigue, K.M., Kennedy, K.M., Head, D., Gunning-Dixon, F., and
Acker, J.D. (2003b). Differential aging of the human striatum: longitudinal evidence. AJNR Am. J. Neuroradiol.
24
, 1849–1856.
Raz, N., Lindenberger, U., Rodrigue, K.M., Kennedy, K.M., Head, D., Williamson, A., Dahle, C., Gerstorf, D., and Acker, J.D. (2005). Regional brain changes in aging healthy adults: general trends, individual differences and modifiers.
Cereb. Cortex 15 , 1676–1689.
Reeves, S.J., Grasby, P.M., Howard, R.J., Bantick, R.A., Asselin, M.C., and
Mehta, M.A. (2005). A positron emission tomography (PET) investigation of the role of striatal dopamine (D2) receptor availability in spatial cognition.
Neuroimage
28
, 216–226.
Reiman, E.M., Quiroz, Y.T., Fleisher, A.S., Chen, K., Velez-Pardo, C., Jimenez-
Del-Rio, M., Fagan, A.M., Shah, A.R., Alvarez, S., Arbelaez, A., et al. (2012).
Brain imaging and fluid biomarker analysis in young adults at genetic risk for autosomal dominant Alzheimer’s disease in the presenilin 1 E280A kindred: a case-control study. Lancet Neurol.
11 , 1048–1056.
Resnick, S.M., Pham, D.L., Kraut, M.A., Zonderman, A.B., and Davatzikos, C.
(2003). Longitudinal magnetic resonance imaging studies of older adults: a shrinking brain. J. Neurosci.
23
, 3295–3301.
Resnick, S.M., Sojkova, J., Zhou, Y., An, Y., Ye, W., Holt, D.P., Dannals, R.F.,
Mathis, C.A., Klunk, W.E., Ferrucci, L., et al. (2010). Longitudinal cognitive decline is associated with fibrillar amyloid-beta measured by [11C]PiB.
Neurology 74 , 807–815.
Reuter-Lorenz, P.A., and Lustig, C. (2005). Brain aging: reorganizing discoveries about the aging mind. Curr. Opin. Neurobiol.
15 , 245–251.
Rinne, J.O., Rummukainen, J., Palja¨rvi, L., and Rinne, U.K. (1989). Dementia in
Parkinson’s disease is related to neuronal loss in the medial substantia nigra.
Ann. Neurol.
26 , 47–50.
Rodrigue, K.M., Kennedy, K.M., Devous, M.D., Sr., Rieck, J.R., Hebrank, A.C.,
Diaz-Arrastia, R., Mathews, D., and Park, D.C. (2012).
b -Amyloid burden in healthy aging: regional distribution and cognitive consequences. Neurology
78 , 387–395.
Ross, G.W., Petrovitch, H., Abbott, R.D., Nelson, J., Markesbery, W., Davis,
D., Hardman, J., Launer, L., Masaki, K., Tanner, C.M., and White, L.R.
(2004). Parkinsonian signs and substantia nigra neuron density in decendents elders without PD. Ann. Neurol.
56 , 532–539.
Rowe, J.W., and Kahn, R.L. (1987). Human aging: usual and successful.
Science
237
, 143–149.
Rypma, B., and D’Esposito, M. (2000). Isolating the neural mechanisms of agerelated changes in human working memory. Nat. Neurosci.
3
, 509–515.
Salthouse, T.A., and Ferrer-Caja, E. (2003). What needs to be explained to account for age-related effects on multiple cognitive variables? Psychol. Aging
18
, 91–110.
Savva, G.M., Wharton, S.B., Ince, P.G., Forster, G., Matthews, F.E., and
Brayne, C.; Medical Research Council Cognitive Function and Ageing Study.
(2009). Age, neuropathology, and dementia. N. Engl. J. Med.
360
, 2302–2309.
Schaie, K.W. (1996). Intellectual Development in Adulthood: The Seattle
Longitudinal Study (Cambridge, MA: Cambridge University Press).
Selkoe, D.J., Bell, D.S., Podlisny, M.B., Price, D.L., and Cork, L.C. (1987).
Conservation of brain amyloid proteins in aged mammals and humans with
Alzheimer’s disease. Science 235 , 873–877.
Seshadri, S., Beiser, A., Kelly-Hayes, M., Kase, C.S., Au, R., Kannel, W.B., and
Wolf, P.A. (2006). The lifetime risk of stroke: estimates from the Framingham
Study. Stroke 37 , 345–350.
Shing, Y.L., Rodrigue, K.M., Kennedy, K.M., Fandakova, Y., Bodammer, N.,
Werkle-Bergner, M., Lindenberger, U., and Raz, N. (2011). Hippocampal subfield volumes: age, vascular risk, and correlation with associative memory.
Front Aging Neurosci 3 , 2.
Small, S.A., Tsai, W.Y., DeLaPaz, R., Mayeux, R., and Stern, Y. (2002). Imaging hippocampal function across the human life span: is memory decline normal or not? Ann. Neurol.
51 , 290–295.
Small, S.A., Chawla, M.K., Buonocore, M., Rapp, P.R., and Barnes, C.A.
(2004). Imaging correlates of brain function in monkeys and rats isolates a hippocampal subregion differentially vulnerable to aging. Proc. Natl. Acad.
Sci. USA
101
, 7181–7186.
Smith, T.D., Adams, M.M., Gallagher, M., Morrison, J.H., and Rapp, P.R.
(2000). Circuit-specific alterations in hippocampal synaptophysin immunoreactivity predict spatial learning impairment in aged rats. J. Neurosci.
20
,
6587–6593.
Sperling, R.A., Laviolette, P.S., O’Keefe, K., O’Brien, J., Rentz, D.M., Pihlajamaki, M., Marshall, G., Hyman, B.T., Selkoe, D.J., Hedden, T., et al. (2009).
Amyloid deposition is associated with impaired default network function in older persons without dementia. Neuron
63
, 178–188.
Spreng, R.N., Wojtowicz, M., and Grady, C.L. (2010). Reliable differences in brain activity between young and old adults: a quantitative meta-analysis across multiple cognitive domains. Neurosci. Biobehav. Rev.
34 , 1178–1194.
Stark, S.M., Yassa, M.A., and Stark, C.E. (2010). Individual differences in spatial pattern separation performance associated with healthy aging in humans. Learn. Mem.
17 , 284–288.
Stern, Y. (2002). What is cognitive reserve? Theory and research application of the reserve concept. J. Int. Neuropsychol. Soc.
8 , 448–460.
Storandt, M., Mintun, M.A., Head, D., and Morris, J.C. (2009). Cognitive decline and brain volume loss as signatures of cerebral amyloid-beta peptide deposition identified with Pittsburgh compound B: cognitive decline associated with Abeta deposition. Arch. Neurol.
66 , 1476–1481.
Neuron
77
, January 23, 2013
ª
2013 Elsevier Inc.
233
Neuron
Review
Stoub, T.R., Bulgakova, M., Leurgans, S., Bennett, D.A., Fleischman, D.,
Turner, D.A., and deToledo-Morrell, L. (2005). MRI predictors of risk of incident
Alzheimer disease: a longitudinal study. Neurology 64 , 1520–1524.
Tullberg, M., Fletcher, E., DeCarli, C., Mungas, D., Reed, B.R., Harvey, D.J.,
Weiner, M.W., Chui, H.C., and Jagust, W.J. (2004). White matter lesions impair frontal lobe function regardless of their location. Neurology
63
, 246–253.
Uemura, E. (1985a). Age-related changes in the subiculum of Macaca mulatta: dendritic branching pattern. Exp. Neurol.
87
, 412–427.
Uemura, E. (1985b). Age-related changes in the subiculum of Macaca mulatta: synaptic density. Exp. Neurol.
87 , 403–411.
Uemura, Y., Wada-Isoe, K., Nakashita, S., and Nakashima, K. (2011). Mild parkinsonian signs in a community-dwelling elderly population sample in
Japan. J. Neurol. Sci.
304
, 61–66.
Vannini, P., Hedden, T., Huijbers, W., Ward, A., Johnson, K.A., and Sperling,
R.A. (2012). The ups and downs of the posteromedial cortex: age- and amyloid-related functional alterations of the encoding/retrieval flip in cognitively normal older adults. Cereb. Cortex. Published online May 14, 2012.
http://dx.doi.org/10.1093/cercor/bhs108.
Vermeer, S.E., Prins, N.D., den Heijer, T., Hofman, A., Koudstaal, P.J., and
Breteler, M.M. (2003). Silent brain infarcts and the risk of dementia and cognitive decline. N. Engl. J. Med.
348
, 1215–1222.
Volkow, N.D., Fowler, J.S., Wang, G.J., Logan, J., Schlyer, D., MacGregor, R.,
Hitzemann, R., and Wolf, A.P. (1994). Decreased dopamine transporters with age in health human subjects. Ann. Neurol.
36 , 237–239.
Volkow, N.D., Gur, R.C., Wang, G.J., Fowler, J.S., Moberg, P.J., Ding, Y.S.,
Hitzemann, R., Smith, G., and Logan, J. (1998). Association between decline in brain dopamine activity with age and cognitive and motor impairment in healthy individuals. Am. J. Psychiatry
155
, 344–349.
Wagner, A.D., Shannon, B.J., Kahn, I., and Buckner, R.L. (2005). Parietal lobe contributions to episodic memory retrieval. Trends Cogn. Sci.
9 , 445–453.
Wang, M., Gamo, N.J., Yang, Y., Jin, L.E., Wang, X.J., Laubach, M., Mazer,
J.A., Lee, D., and Arnsten, A.F. (2011). Neuronal basis of age-related working memory decline. Nature 476 , 210–213.
Wen, W., and Sachdev, P. (2004). The topography of white matter hyperintensities on brain MRI in healthy 60- to 64-year-old individuals. Neuroimage 22 ,
144–154.
West, R.L. (1996). An application of prefrontal cortex function theory to cognitive aging. Psychol. Bull.
120
, 272–292.
West, M.J., Coleman, P.D., Flood, D.G., and Troncoso, J.C. (1994). Differences in the pattern of hippocampal neuronal loss in normal ageing and
Alzheimer’s disease. Lancet 344 , 769–772.
Wilson, R.S., Beckett, L.A., Barnes, L.L., Schneider, J.A., Bach, J., Evans,
D.A., and Bennett, D.A. (2002). Individual differences in rates of change in cognitive abilities of older persons. Psychol. Aging 17 , 179–193.
Wolk, D.A., Dunfee, K.L., Dickerson, B.C., Aizenstein, H.J., and DeKosky, S.T.
(2011). A medial temporal lobe division of labor: insights from memory in aging and early Alzheimer disease. Hippocampus
21
, 461–466.
Yassa, M.A., and Stark, C.E. (2011). Pattern separation in the hippocampus.
Trends Neurosci.
34 , 515–525.
Yassa, M.A., Stark, S.M., Bakker, A., Albert, M.S., Gallagher, M., and Stark,
C.E. (2010). High-resolution structural and functional MRI of hippocampal
CA3 and dentate gyrus in patients with amnestic Mild Cognitive Impairment.
Neuroimage 51 , 1242–1252.
Yassa, M.A., Lacy, J.W., Stark, S.M., Albert, M.S., Gallagher, M., and
Stark, C.E. (2011a). Pattern separation deficits associated with increased hippocampal CA3 and dentate gyrus activity in nondemented older adults.
Hippocampus
21
, 968–979.
Yassa, M.A., Mattfeld, A.T., Stark, S.M., and Stark, C.E. (2011b). Age-related memory deficits linked to circuit-specific disruptions in the hippocampus.
Proc. Natl. Acad. Sci. USA
108
, 8873–8878.
Yeoman, M., Scutt, G., and Faragher, R. (2012). Insights into CNS ageing from animal models of senescence. Nat. Rev. Neurosci.
13 , 435–445.
Yonelinas, A.P., Widaman, K., Mungas, D., Reed, B., Weiner, M.W., and Chui,
H.C. (2007). Memory in the aging brain: doubly dissociating the contribution of the hippocampus and entorhinal cortex. Hippocampus
17
, 1134–1140.
Zweig, R.M., Cardillo, J.E., Cohen, M., Giere, S., and Hedreen, J.C. (1993). The locus ceruleus and dementia in Parkinson’s disease. Neurology 43 , 986–991.
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77
, January 23, 2013
ª
2013 Elsevier Inc.
