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REVIEW
Beta-Amyloid Deposition and the Aging Brain
Karen M. Rodrigue &Kristen M. Kennedy &
Denise C. Park
Received: 21 July 2009 / Accepted: 6 October 2009 / Published online: 12 November 2009
#Springer Science + Business Media, LLC 2009
Abstract A central issue in cognitive neuroscience of
aging research is pinpointing precise neural mechanisms
that determine cognitive outcome in late adulthood as well
as identifying early markers of less successful cognitive
aging. One promising biomarker is beta amyloid (Aβ)
deposition. Several new radiotracers have been developed
that bind to fibrillar Aβproviding sensitive estimates of
amyloid deposition in various brain regions. Aβimaging
has been primarily used to study patients with Alzheimer’s
Disease (AD) and individuals with Mild Cognitive Impair-
ment (MCI); however, there is now building data on Aβ
deposition in healthy controls that suggest at least 20% and
perhaps as much as a third of healthy older adults show
significant deposition. Considerable evidence suggests
amyloid deposition precedes declines in cognition and
may be the initiator in a cascade of events that indirectly
leads to age-related cognitive decline. We review studies of
Aβdeposition imaging in AD, MCI, and normal adults, its
cognitive consequences, and the role of genetic risk and
cognitive reserve.
Keywords Aging .Beta-amyloid .Brain .
Cognitive reserve .fMRI .PET
Beta-Amyloid Deposition and the Aging Brain
Normal aging is associated with measurable declines in
neural and cognitive systems. There is a wealth of
literature documenting age-related declines in many
aspects of cognitive behavior, including decreased speed
of information processing, working memory capacity, and
long-term memory function. At the same time, knowledge
structures are relatively preserved with age (Ghisletta and
Lindenberger 2003; Hedden and Gabrieli 2004;Parketal.
2002). Recently, significant advances have been made in
our understanding of the structure and function of the
aging brain utilizing new neuroimaging technologies (see
Park and Reuter-Lorenz 2009;ParkandGoh2009;Raz
and Kennedy 2009; Raz and Rodrigue 2006 for reviews).
At present, a central issue in cognitive neuroscience of
aging research is pinpointing the precise neural mecha-
nisms that determine cognitive outcome in late adulthood
as well as identifying markers of less successful cognitive
aging as early in life as possible. Predicting successful vs.
pathological aging trajectories requires early identification
of sensitive behavioral or neural markers prior to actual
neural pathology and cognitive decline. One such prom-
ising biomarker that has received recent attention is beta
amyloid (Aβ), a protein that is deposited on the brain in
some individuals as they age.
Significant amyloid deposition is a characteristic feature
of all patients with Alzheimer’s disease (AD). However, it
is also present in many normal adults; it is observed in
individuals with Mild Cognitive Impairment (MCI) at a
level higher than normal older adults; and it is a strong
predictive factor in conversion to AD. At the same time that
amyloid is associated with neural pathology, it is also
surprisingly common to find that neuropsychologically
normal, healthy older adults show significant neuropathol-
ogy at autopsy in the form of amyloid deposition (Dickson
et al. 1992). New imaging tracer ligands offer possibilities
for measuring Aβburden in the brain and for studying the
time course of its progression in nondemented individuals
K. M. Rodrigue :K. M. Kennedy :D. C. Park (*)
Center for BrainHealth, School of Behavioral and Brain Sciences,
The University of Texas at Dallas,
2200 W. Mockingbird Ln,
Dallas, TX 75235, USA
e-mail: denise@utdallas.edu
Neuropsychol Rev (2009) 19:436–450
DOI 10.1007/s11065-009-9118-x
as well as in AD. These radiotracer compounds bind to
amyloid deposits in the living brain when injected into the
bloodstream. When the injection is accompanied by
Positron Emission Tomography (PET) scanning, a detailed
assessment can be made of the amount of amyloid and its
distribution throughout the brain. The purpose of the
current review is to briefly explain the history of the study
of beta-amyloid, review the amyloid imaging studies of
normal aging and dementia, provide an integrative frame-
work for how beta-amyloid may play a role in normal
aging, and to offer some future directions for fruitful
avenues for the study of healthy aging using this promising
imaging technique.
Beta-Amyloid Measurement
The Beta-Amyloid Protein One common, but not univer-
sally accepted view of the pathogenesis of Alzheimer’s
Disease is the beta-amyloid hypothesis. Beta-amyloid is a
protein fragment that is deposited in the brain in the form of
sticky, starch-like plaques, in an increased manner in
individuals with AD. While the exact pathogenesis of AD
remains unknown and the role of Aβin the brain is not
entirely clear, one viewpoint is that the soluble form may
cause synaptic dysfunction (Selkoe 2002; Nordberg 2008)
as the amount of extracellular soluble Aβin the brain is a
better predictor of cognitive impairment in AD than the
amount of plaques themselves (Nordberg 2008). The beta-
amyloid protein fragments are snipped from amyloid
precursor proteins (APP). One common view is that in an
optimally-functioning brain, these protein fragments are
broken down and eliminated, but in AD the problem is that
the fragments are not broken down and accumulate in the
brain to form plaques. Another possibility is that the
plaques result from an overproduction of Aβor APP,
rather than a failure to clear these products, and this initiates
the cytotoxic effects. Figure 1illustrates varying levels of
beta-amyloid plaques in postmortem samples ranging from
sparse to moderate to frequent amyloid deposits (from
Josephs et al. 2008).
Until the development of the amyloid-sensitive ligands,
there have been a variety of other techniques used to
measure amyloid plaque accumulation, including methods
that indirectly estimated levels of brain amyloid plaques
from Aβlevels in plasma or cerebral spinal fluid (CSF).
The significance of amyloid deposits for disease specificity
is uncertain, as deposits are often found in the cortex of
non-demented older adults at autopsy, although these
estimates are influenced by the age of the cohort sampled
and the method of defining disease pathology (Bennett et
al. 2006; Braak and Braak 1996;Thaletal.2006).
Postmortem studies, previously the only method of exam-
ining Aβ, have found that 25–30% of individuals with no
clinical symptoms of dementia have levels of Aβequal to
the diagnostic level for AD (Katzman et al. 1988).
Beta-amyloid levels can also be measured in CSF and
interestingly, higher levels of beta-amyloid deposits in the
brain are correlated with lower levels of beta-amyloid in the
CSF (Grimmer et al. 2009; Strozyk et al. 2003). This
finding supports the notion that as Aβplaques become less
soluble and clearance of beta-amyloid declines, an aggre-
gation of plaque is formed in the brain and less is broken
down and observed in the circulating CSF. Further support
for this process of Aβdeposition and clearance comes from
studies that indicate CSF amyloid beta levels correlate
positively with cognitive performance (Nordlund et al.
2008) and negatively with AD symptom severity (Samuels
et al. 1999), predict conversion from MCI to AD
(Andreasen et al. 1999; Diniz et al. 2008) and correlate
positively with brain volume and negatively with ventric-
ular size in persons with AD, suggesting that CSF Aβ
levels drop as AD progresses (Tapiola et al. 2000; Wahlund
and Blennow 2003).
PET Imaging of Beta Amyloid In addition to measuring Aβ
levels in the CSF, more recently beta amyloid deposits have
become measureable using PET and radiotracer ligands that
bind to the aggregated fibrillar form of Aβ. Radiotracers
with a high affinity for amyloid (in extracellular Aβ
plaques) have been developed recently for use in humans
(Ichise et al. 2008; Klunk et al. 2004; Shoghi-Jadid et al.
2002). For example, Fig. 2illustrates the binding pattern of
a radiotracer in a patient with Alzheimer’s Disease and in a
healthy control subject. Higher uptake rates, as evidenced
by the warmer red and yellow color scale, can be seen in
the patient compared to the normal older adult. These
tracers are labeled with either carbon-11 or fluorine-18. The
most important difference between these labeling isotopes
is their half-lives. The half-life of an isotope refers to the
amount of time that it takes for half of the atoms in the
radioactive substance to decay. The half-life then depends
on the rate of decay, which is exponential (see Fig. 3).
Carbon-11 has a half-life of approximately 20 min, there-
fore every 20 min there is 50% less tracer available.
Fluorine-18 has a half-life of approximately 110 min,
allowing a longer time window before radioactive decay
of the tracer.
The three most common ligands in use to image Aβ
deposition with PET are the 11C-labeled PET tracer 6-OH-
BTA also known as Pittsburgh compound B or PIB (Klunk
et al. 2004), the 18F-labeled tracer FDDNP (Shoghi-Jadid
et al. 2002), and 18Florpiramine, also known as 18F-AV-45
(Ichise et al. 2008; Zhang et al. 2007). While none of these
compounds are FDA approved for clinical use, these
amyloid imaging agents have been received with great
Neuropsychol Rev (2009) 19:436–450 437
interest in the research community and are in use in a
number of clinical trials. The FDA has already approved
the use of all three compounds as biomarkers to test the
mechanisms of several putative amyloid-lowering drugs
(Skovronsky 2008). All three ligands show higher amyloid
uptake in the cortex of patients with AD compared to the
cortex of healthy controls, reflecting the elevated accumu-
lation of Aβpathology and consequent binding of amyloid
in the cortex of patients with AD (Lopresti et al. 2005;
Skovronsky et al. 2008). Further, all three ligands demon-
strate good test-retest reliability, especially when using the
optimal method of correcting for cerebellar reference tissue
uptake (Tolboom et al. 2009b).
The first ligand developed to image amyloid using
carbon-11 was 11C-PIB (Klunk et al. 2004) and it has
since been the most widely studied. Because this compound
relies on the production of carbon-11 labeled tracers, which
has only a 20-minute half life, the use of PIB requires an
on-site cyclotron, available at less than 10% of PET scanner
sites (Klunk and Mathis 2008). Therefore the availability of
this compound for research use is limited to only those sites
with a cyclotron. In contrast to PIB, fluorine-18 is used to
Fig. 2
18
F AV-45 uptake in an AD subject (left panel top) and a Healthy Control (left panel bottom). The right panel displays normalized signal uptake
value (SUVr) in patients with AD and healthy controls in the precuneus and neocortex
Fig. 1 Illustration of beta-amyloid plaques in fixed tissue at autopsy (from Josephs et al. 2008). ashows an example of sparse Aβburden; b
illustrates a brain with moderate Aβ; and cis an example of frequent Aβdeposition in the brain
438 Neuropsychol Rev (2009) 19:436–450
label FDDNP and AV-45. Because 18F has a radioactive
half-life of 110 min, it is quite feasible for regional
preparation of the compound to occur with shipping of
doses to research sites up to 4 h away (i.e., 22% of the
radiotracer would remain and doses are adjusted accord-
ingly). For this reason, more sites have begun using 18F
labeled agents. Thus far, however, the vast majority of
published studies have used PIB (Aizenstein et al. 2008;
Buckner et al. 2005; Jack et al. 2008).
Studies conducted to date suggest that 18F-AV-45 labels
amyloid plaques in a manner similar to PIB. Like PIB, AV-
45 exhibits high affinity specific binding to amyloid
plaques. In-vitro autoradiography studies further confirm
that when applied at tracer concentrations AV-45 labels Aβ
amyloid plaques in sections from patients with pathologi-
cally confirmed AD (Skovronsky et al. 2008; Wong et al.
2008; Ichise et al. 2008). The non-radioactive version of
AV-45 can be prepared at high concentrations and shows
very low to no affinity for all other central nervous system
and cardiovascular receptors tested. Similarly, 18F-FDDNP
also successfully labels amyloid plaques (Small et al.
2006), but may do so in a different manner than PIB
(Tolboom et al. 2009a). For instance, in a study that imaged
normal elderly, individuals with MCI, and AD patients,
FDDNP had a higher binding level in MCI than did PIB,
and thus, did not discriminate between MCI and AD groups
as well as PIB. The regional uptake pattern differed
between the two ligands as well, with almost no medial
temporal lobe (MTL) binding with PIB, but a high affinity
for MTL was found with FDDNP (Tolboom et al. 2009a),
likely due to the binding of FDDNP to tau in the abundant
neurofibrillary tangles found in the hippocampus in persons
with AD (Schmidt et al. 1990). Although global brain
uptake is moderately correlated (r= .45) between FDDNP
and PIB, these two ligands likely capture related but
different characteristics of AD. Some of this discrepancy
may be explained by the fact that FDDNP binds to both Aβ
and tau proteins as well as to prion proteins found in Aβ
plaques in AD (Jagust 2009a).
Imaging Studies on Beta-Amyloid Deposition
Before reviewing the literature on studies of normal aging,
it is helpful as a comparison to briefly summarize the
findings of Aβdeposition in persons with Alzheimer’s
Disease and Mild Cognitive Impairment.
Amyloid Deposition in AD and MCI There is strong
evidence that amyloid deposition (as measured by PIB,
FDDNP, or AV-45) is found at a high level in essentially all
patients with a clinical diagnosis of Alzheimer’s disease.
Indeed, dementia in the absence of a high level of amyloid
at autopsy would lead to exclusion of Alzheimer’sasa
diagnosis and consideration of frontal-temporal dementia or
some other cause for the observed cognitive symptoms. All
studies using PIB, FDDNP, or AV-45 find evidence for
greater amyloid deposition in AD patients (see Fig. 2for an
illustration; Buckner et al. 2005;Jacketal.2008;
Linazasoro 2008; Kemppainen et al. 2006; Rowe et al.
2008; Skovronsky et al. 2008; Small et al. 2006; Wong et
al. 2008). Generally, in individuals with AD, Aβbinding is
widely distributed across the cortex. Buckner et al. (2005)
reported PIB binding prominently in the frontal regions
along the midline and extending laterally, as well as medial
and lateral posterior parietal cortex and precuneus extend-
ing into posterior cingulate and retrosplenial cortex. Braskie
et al. (2008) reported FDDNP binding in frontal and
parietal cortices, but also in lateral temporal regions.
Similarly, Edison et al. (2007) found increased PIB uptake
Fig. 3 An illustration of the
timecourse of the rate of the
decay of a radiotracer. As each
half-life is passed the radioactive
material is reduced by 50% and
follows an exponential function
Neuropsychol Rev (2009) 19:436–450 439
in AD relative to controls in frontal, cingulate, parietal,
temporal and occipital regions, indicating a widespread
distribution of Aβin this population. The most consistent
and uniform finding across AD studies is that early in the
disease there is binding to the precuneus/posterior cingulate
cortex (Jagust 2009b).
The other group that has been commonly imaged is
individuals with mild cognitive impairment. MCI is a
somewhat arbitrary category used to describe a population
of individuals who do not display cognitive impairment
severe enough for diagnosis of a neurodegenerative
disorder, but who do display a mild level of cognitive
impairment that normal older adults do not evidence
(Petersen et al. 1999). It is typical to find a bivariate
distribution for amyloid in the MCI population, with
approximately half of the subjects evidencing a high level
of amyloid that looks like the AD patients and half showing
lower Aβlevels more similar to the healthy controls (Jack
et al. 2008; Kemppainen et al. 2007; Li et al. 2008; Rowe et
al. 2007).
1
Level of Aβbinding in the MCI subjects with
high levels is a strong predictor for later conversion to AD
(Forsberg et al. 2008; Jack et al. 2008; Mormino et al.
2009; Pike et al. 2007). Small et al. (2009) recently reported
that MCI subjects had greater FDDNP binding in the
frontal, temporal, parietal, and posterior cingulate cortex
than normal controls. Similarly, using principal component
analysis, Fripp et al. (2008) found patterns of variation in
Aβdeposition in MCI (low and high uptake) that
distinguished between the normal controls and AD subjects.
Forsberg et al. (2008) reported that the seven MCI
converters evidenced greater PIB uptake than the six
controls in the frontal, parietal, and temporal cortices,
suggesting that a specific regional marker of normal-to-
MCI has yet to be specified, although larger sample sizes
may be more informative. Figure 4(adapted from Pike et
al. 2007) displays typical Aβbinding across different
groups and stages of pathology. It can be seen in this figure
that from normal aging to pathology there is an increase in
Aβdeposition, where blue and purple regions represent
little Aβdeposition and yellow and red areas evidence the
highest levels of deposition.
Genetics of Amyloid Deposition Risk There is a large
literature on the risk conveyed by carrying the APOE-ε4
allele for age-related decline in cognitive function in both
normal adults and AD patients and this is the best
characterized genetic polymorphism associated with AD
(Corder et al. 1993; Farrer et al. 1997). Homozygosity for
APOE-ε4 occurs in 1–2% of the population and confers a
serious risk of AD, but even ε3/4 heterozygosity, which
occurs in 25% of the population, results in an increased
risk. In fact, inheritance of a single APOEε4 allele is
associated with temporal and frontal brain atrophy in
cognitively intact adults (Wishart et al. 2006). Further,
APOE-ε4 homozygotes are at 10 to 30 times the risk of
developing AD by the age of 75 compared to those with no
ε4 alleles. Although the mechanism for this increased
genetic risk is not clear, it is thought that it is due to an
interaction with Aβ(Jiang et al. 2008). It has been reported
that 80% of amyloid positive adults with MCI are APOE-ε4
carriers (Pike et al. 2007) and 40% of persons with MCI are
homozygous for APOE-ε4 (Farlow et al. 2004). At liberal
estimates, half of MCI individuals eventually convert to AD
in 5 years (Grundman et al. 2002). Recent studies indicate
that the APOE-ε4 genotype is a strong risk factor for
amyloid deposition even in non-demented elderly individ-
uals (Morris et al. 2009). Importantly, APOE-ε4 carriers
have been found to have higher Aβbinding than the non-
carriers (Rowe et al. 2008). Drzezga et al. (2009) found
significantly increased amyloid deposition in temporopar-
ietal and frontal cortex in AD patients who were APOE-ε4
carriers than AD patients who were non-carriers, suggesting
that there is a genetic exacerbation even within Alzheimer’s
disease. Small et al. (2009) recently compared MCI and
normal adults’FDDNP binding with respect to their APOE-
ε4 genotype. Differential patterns of Aβdeposition were
found among the groups, where MCI subjects who were ε4
carriers evidenced greater FDDNP binding in the medial
temporal cortex, whereas normal elderly ε4 carriers
displayed greater binding in the frontal cortex. Recently,
Reiman et al. (2009) reported a gene dose-response effect
for APOE-ε4 alleles, where higher Aβburden was
associated most highly with individuals possessing two
copies of the ε4 allele, followed by those with one copy,
compared to non-carriers in frontal, precuneus/posterior
cingulate, temporal and parietal regions. However, there are
older adults with high levels of Aβwho do not carry the
APOE-ε4 genotype, indicating that this is a risk factor, but
certainly not a determinant for Aβdeposition. Figure 5
illustrates the association between APOE genotype and PIB
binding in the brain in cognitively normal older adults
(from Reiman et al. 2009). There is only small to moderate
PIB binding in the ε4 heterozygotes compared to non-
carriers, whereas there is a greater increase in PIB binding
in ε4 homozygotes compared to non-carriers, and a dose-
related association of greater PIB binding with increasing
ε4 alleles.
Beta-Amyloid and Normal Aging Less is known about the
role of Aβin normal aging compared to the literature
1
This bimodal distribution may serve to highlight the arbitrary nature
of a categorical MCI group and emphasizes the continuum of
cognitive performance from normal to AD, with MCI being a mid-
point of this continuum.
440 Neuropsychol Rev (2009) 19:436–450
examining memory impairment or dementia. However, a
few studies have measured amyloid deposition with respect
to normal aging and the findings indicate the presence of
deposits in frontal, cingulate, and parietal areas, with
primary sensory/visual areas relatively protected from
amyloid deposition. Studies with normal aging adult
samples consistently report that approximately 20 to 30%
of healthy older controls show significant amyloid deposi-
tion. This individual variability is illustrated in Fig. 6,
which depicts a normal older adult with very high Aβ
deposition and an adult with low deposition, both of whom
are cognitively normal (Jack et al. 2008).
Of 20 healthy older controls (66–86 years old), four
individuals (20%) evidenced PIB uptake equal to that of the
AD subjects of similar age (Mintun et al. 2006). This
increased uptake was primarily seen in the precuneus, a
region that displays Aβat an early stage in AD patients.
Buckner et al. (2005) reported 2 of 8 healthy controls
(25%) were PIB positive. Fotenos et al. (2008) reported that
of 58 normal controls (aged 47–86), 9 individuals (16%)
were classified as PIB positive, and these individuals
evidenced smaller whole brain volumes than the PIB
negative subjects. Nelissen et al (2007) found that 3 of 16
healthy controls (19%) had PIB uptake at the level of their
AD patients. Of 34 elderly participants from the Melbourne
Healthy Aging Study (Villemagne et al. 2008), 10 subjects
were classified as “clinically declining”over time and these
subjects showed a greater instance of Aβbinding than did
the non-decliners (70% vs. 17%). In this study, Aβburden
was correlated with memory performance in the declining,
but not the non-declining group.
The findings with respect to the relationship between
amyloid deposition and measures of cognitive decline are
somewhat mixed when amyloid is detected in a healthy
older adult. For example, Rowe et al. (2007) reported that 6
(22%) of 27 older adult subjects were amyloid positive, but
cognitive symptoms in this group were absent. Jack et al.
(2008) tested 20 normal subjects and reported that 6
subjects (30%) were amyloid positive. No association
between Aβpositivity and cognitive function was found,
although some evidence for mediation of cognitive symp-
toms by hippocampal volume was reported (Jack et al.
2008). Thus, Jack et al. suggested that amyloid deposition
is an early symptom of Alzheimer’s disease that is followed
by shrinkage of the hippocampus.
In contrast to the aforementioned studies, Pike et al.
(2007) reported in an early paper a significant link between
amyloid deposition and cognitive function in apparently
healthy elderly. Greater amyloid deposition was negatively
related to episodic memory performance in 22% of 32
healthy controls who were amyloid positive. However, this
group recently reported that in a larger sample (n= 89), this
association between PIB positivity and memory was
nonsignificant in the normal control group (Bourgeat et al.
2009). Mormino et al. (2009) tested 37 normal controls and
rather than simply characterizing subjects as amyloid
positive or negative, they utilized amyloid deposition as a
continuous variable. Increased amyloid deposition was
related to both decreased hippocampal volume and poorer
episodic memory. They suggested, in accord with Jack et al.
(2008) that the critical event in determining cognitive health
in late adulthood is amyloid deposition which results in a
Fig. 4 Illustration of typical Aβdeposition across varying groups and
stages of pathology. Examples are from left to right, images of a PIB
negative healthy older adult, a PIB positive healthy older adult, a PIB
negative MCI subject, a PIB positive MCI subject, and an AD patient.
Blues and purples represent little Aβdeposition and yellow and red
the highest deposition (from Pike et al. 2007)
Neuropsychol Rev (2009) 19:436–450 441
cascade of events—with amyloid initiating hippocampal
shrinkage, followed by episodic memory decline and
ultimately, over time, a diagnosis of Alzheimer’s Disease.
Although, given the crucial role of the entorhinal cortex in
the pathogenesis of AD, it is important for future studies to
measure the volume of this structure. Interestingly, even
though hippocampal volume measures from MRI are
associated with AD and Aβdeposition, a recent study
showed that overall brain atrophy measured at postmortem
did not correlate with amount of Aβplaques in the brain
(Josephs et al. 2008), suggesting that rate of overall volume
loss is not significantly influenced by amount of Aβ
plaques. The effect may be specific to localized brain
regions, such as the hippocampus or entorhinal cortex. For
example, in a preliminary study Dickerson et al. (2009)
compared cortical thickness of 9 PIB positive nondemented
older adults to 35 PIB negative nondemented older adults
and found thinner temporal pole cortex in the PIB positive
adults and a nonsignificant trend for thinner superior frontal
cortex. When pooling all regions to form an average
Fig. 5 Illustration of association
between APOE genotype and
Aβdeposition in the brain in
cognitively normal older adults
(from Reiman et al. 2009). a
displays a small to moderate
increase in PIB binding in the
individuals heterozygous for ε4
compared with non-carriers; b
illustrates the greater increased
PIB binding in individuals ho-
mozygous for ε4 compared with
non-carriers; cdepicts the asso-
ciation of greater PIB binding
and APOE- ε4 dose for the
whole sample
442 Neuropsychol Rev (2009) 19:436–450
thinning index, this did not differ significantly between the
PIB positive and negative groups (p= .09). In a longitudinal
study, Scheinin et al. (2009) report that PIB uptake at
baseline predicted longitudinal change in hippocampus,
precuneus, and temporal cortex volume in the group of
normal control subjects (n= 13) but not in the AD group (p
=.07; n=14). Obviously, far larger samples are necessary to
further test these relations.
There is some debate as to whether Aβdeposition
proceeds in a linear or an accelerated fashion (Ingelsson et
al. 2004; Jack et al. 2009). It seems clear that amyloid
burden accumulates well before clinical symptoms. Then
this deposition either plateaus with little to no further
accumulation over time, or continues to slowly accumulate
throughout the duration of the lifespan (Jack et al. 2009).
Jack et al. (2009) found no difference in longitudinal
change in Aβprogression among AD, MCI, and normal
control groups, suggesting a linear decline, that when
extrapolating back, would suggest that Aβaccumulation
would have to begin in the 40s. Ingelsson et al. (2004)
posits that Aβdeposits early and rapidly, and then slows
with age. A theoretical model of Aβdeposition over the
time course of normal or asymptomatic aging, MCI and AD
is illustrated in Fig. 7(from Jack et al. 2009).
One of the earliest studies designed to investigate normal
elderly was conducted by Aizenstein et al. (2008)on43
normal elderly (aged 65–88). They reported that 21% of
these subjects had significant amyloid and that amyloid
positive subjects performed similarly on a cognitive battery
to amyloid negative subjects. One theoretical explanation
for these puzzling results is the notion of cognitive reserve
(Stern 2002). The cognitive reserve hypothesis posits that
some individuals are better equipped to cope with the
physiological challenges to neural structure and function
that occur with aging and/or disease processes, and is
generally attributed to higher levels of education, socioeco-
nomic status and intelligence in those individuals (Stern
2002). Thus, the negative cognitive consequences that are
typically associated with declining structural integrity and
reduced efficiency of neural function with aging are
attenuated, delayed or masked in those with greater
cognitive reserve capacity (Stern 2009). In accord with this
hypothesis, the Aizenstein et al. (2008) study examined
whether the amyloid positive subjects would have higher
premorbid intelligence and greater levels of education that
protected them from expressing negative cognitive symp-
toms. Contrary to predictions from the cognitive reserve
hypothesis, the authors found the opposite result—that the
amyloid negative individuals had higher levels of premor-
bid intelligence than the amyloid positive group, and the
amyloid negative group (with higher premorbid intelligence
and no amyloid) actually had lower episodic memory
scores than the amyloid positive group. Other studies,
however, are in agreement with the cognitive reserve
hypothesis. For example, Kemppainen et al. (2008)
compared AD patients with high (n=12) and low (n=
13) levels of education and found that the high education
group had greater Aβbinding in the lateral frontal cortex
than the lower education group, as well as lower glucose
metabolism in the temporal-parietal cortex, demonstrating
that the more highly educated group harbored greater
levels of brain pathology at the same degree of cognitive
decline as lower educated AD patients. For non-demented
elderly, greater levels of education/socioeconomic status
has been associated with smaller whole brain volumes
and accelerated volume loss over time, suggesting that
Fig. 7 Proposed model relating imaging, pathology and clinical
presentation over an individual’s adult lifetime (from Jack et al. 2009).
The lifetime course of progression from presymptomatic, prodromal
(MCI), and dementia (AD) phases is plotted. Neurodegeneration,
detected by MRI, is indicated by a dashed line. Cognitive function is
indicated by a dot-dash line. Amyloid deposition is indicated by a
solid line late in life (i.e. that portion of the disease for which data
currently exist). The time course of amyloid deposition early in life is
represented as two possible theoretical trajectories (dotted lines),
reflecting uncertainty about the time course trajectory of early Aβ
deposition
Fig. 6 Illustration of normal variation in magnitude and extent of Aβ
deposition in healthy controls (from Jack et al. 2008). ais the scan
from the subject with the highest PIB retention in the study (despite
being a normal control and cognitively normal) and bis the scan from
a healthy control subject with low PIB retention, who was cognitively
normal, but scored lower than the subject depicted in panel (a)
Neuropsychol Rev (2009) 19:436–450 443
better educated, more privileged older adults may harbor
premorbid dementia, or can hide the signs of brain
changes longer than less privileged older adults (Fotenos
et al. 2008). Other research has shown that in a group of
MCI subjects, those who converted to AD had lower
education. Moreover, the converters with the highest
education had more severe amyloid pathology (as mea-
suredinCSF)butperformedequallywellonneuropsy-
chological tests compared to MCI converters with lower
education (Rolstad et al. 2009). This finding again
suggests that greater brain pathology was associated with
cognitively “privileged”older adults, in line with the
cognitive reserve hypothesis.
To summarize this complex literature, early evidence
tentatively suggests that greater reserve may stave off the
cognitive expression of neural decline (i.e., amyloid burden),
but the mechanisms though which this occurs are unknown.
It is plausible that the apparent protection conferred by
cognitive reserve is simply a masking effect where individ-
uals with more developed cognitive abilities have a delayed
behavioral expression of pathology. Additionally, a potential
alternative explanation that has yet to be ruled out is that
people with higher education perform better on neuropsy-
chological tests at all stages (before, during and after they are
diagnosed with dementia). Because dementia is diagnosed
using neuropsychological tests, a diagnosis would come later
to these higher performing, better educated individuals.
Further, similar neuropsychological tests are used to both
diagnosis dementia and to quantify cognitive reserve,
possibly introducing a confound among diagnostic group,
cognitive reserve score, and test performance. Finally, it is
also possible that beta-amyloid plaques detected with PIB
are not actually neurotoxic and are entirely independent (at a
given timepoint) of cognitive preservation or decline, or are
too early an event to be predictive of cognitive performance.
Longitudinal studies will help disentangle these complicated
relations. We suggest, in line with the STAC theory proposed
by Park and Reuter-Lorenz (2009), that cognitive reserve
may have an actual neural substrate in enhanced white
matter connectivity and/or the propensity for more successful
functional reorganization of the cortex which may compen-
sate for neural insults such as accumulation of amyloid
plaques.
Definitively evaluating cognitive performance in healthy
adults in relation to Aβdeposition is difficult at this point,
as the few extant studies have found inconsistent results.
While finding no consistent differences in cognitive
performance between normal elderly with high and low
amyloid deposition, nor between high and low deposition
in MCI subjects, Jack et al. (2008) did find moderate
correlations (r=−.34 to −.36) with PIB binding and
cognitive measures in the whole sample across three indices
of memory performance. Pike et al. (2007) found stronger
associations between episodic memory performance and
Aβbinding in MCI (r=−.60) than in healthy older adults
(r=−.38). Mormino et al. (2009) further found that the
relation between memory performance and Aβis mediated
by hippocampal volume, suggesting an order for the
cascade of events that build to affect memory decline.
Aizenstein et al. (2008), however found that in general,
most cognitive functions were unrelated to amyloid burden
with the only significant finding being lower scores on a
delayed recall task, but this was for the amyloid negative
group. In a longitudinal study, in initially healthy elderly,
those who declined clinically showed a significant associ-
ation of memory performance and amyloid deposition
(Fripp et al. 2008). Braskie et al. (2008) found in
cognitively normal older adults higher FDDNP binding in
the frontal and parietal cortex was associated with poorer
cognitive performance, although in this study test perfor-
mance from multiple domains was collapsed into one
composite measure. Overall, it appears that at least memory
performance may be negatively associated with Aβ
deposition, even in relatively healthy older adults, although
new, but growing evidence suggests that Aβ’s effects are
carried out via structural and functional degradation, rather
than a direct effect of amyloid deposition per se. Clearly,
further research is needed to determine the specific
cognitive effects of increased beta-amyloid deposition and
by which markers of neural health these effects are
mediated.
Amyloid Burden and Functional Brain Imaging The data
relating amyloid deposition to functional imaging are very
limited but expanding. There is some evidence that patients
with amyloid deposition compensate for the amyloid
burden through functional reorganization of neural activity,
directing neural activity away from sites of amyloid
deposits into healthier areas of the brain. Specifically,
Nelissen et al. (2007) demonstrated in a fMRI study that
AD subjects showed lesser activation than controls in
superior temporal cortex, and this activation was associated
with greater Aβload in the same region. For the
contralateral hemisphere, however, the pattern was reversed
with greater activation in the AD subjects vs. controls, and
this contralateral recruitment was positively correlated with
cognitive measures, demonstrating that at least in early
stage AD, functional reorganization can help overcome
negative effects of Aβdeposition.
An alternative view comes from studies of default
network activity. The default network consists of regions
of the brain that are more active than others when the brain
is at “rest”(i.e., periods when task-related activity are not
required) compared to when the brain is engaged in active
tasks. A core set of regions comprise the default network
(Buckner et al. 2008; Greicius et al. 2003) that include the
444 Neuropsychol Rev (2009) 19:436–450
ventral medial and dorsal medial prefrontal, posterior
cingulate/retrosplenial, inferior parietal, and lateral tempo-
ral cortices. Young adults suppress default activity when
faced with cognitive challenge, but older adults display
difficulty shifting out of the default mode to task-relevant
modes when confronted with a cognitive task. Buckner et
al. (2005) hypothesized that sustained engagement across
the lifespan in default network activity might actually lead
to amyloid deposition. Aβdeposition correlated with
regions of deactivation in the default network, suggesting
that regions used more frequently are the same ones that
show increased Aβdeposition implying that repetitive use
of these regions might promote plaque deposition. Support
for this wear-and-tear hypothesis also comes from evi-
dence that both Aβproduction and amyloid precursor
protein regulation are activity-dependent (Kamenetz et
al. 2003; Nitsch et al. 1993), and therefore would be
produced and expressed in greater quantities in the most
metabolically active brain regions (Buckner et al. 2005).
In a later study out of this lab, Andrews-Hanna et al.
(2007) isolated nine older adults who were amyloid free.
These individuals also showed default network disruption
as well as white matter connectivity reduction, suggesting
that the disruption of the default network is not solely
related to amyloid pathology.
More recently, Buckner et al. (2009) have identified
associative cortex regions that served as “cortical hubs”
involved in both active and resting states during functional
scanning. These hubs showed higher levels of Aβ
deposition in AD patients compared to controls, consistent
with the notion that “overuse”of the same regions of the
brain is metabolically expensive and the resulting wear-
and-tear may lead to adverse consequences such as
increased Aβdeposition and cognitive decline or even
development of AD. In their newest paper, Sperling et al
(2009) investigated PIB- (n= 22) vs PIB+ (n= 13) groups in
their patterns of overlapping fMRI default activity and PIB
uptake. They found increased PIB binding in the precuneus/
posterior cingulate and medial prefrontal cortices (both
default network areas) in 30% of their older adults and PIB
uptake in the precuneus/posterior cingulate correlated with
increased default activity in this region. This pattern is
similar to that found in AD patients and suggests that even
in cognitively normal older adults, there is a failure to
deactivate the regions of the default network necessary for
memory function, perhaps due to increased Aβburden in
this system. PIB uptake did not, however correlate with
memory performance on the functional task. The absolute
number of observations in these studies was small,
however, and the relationship of amyloid to functional
imaging is still in its early stages, especially with regard to
patterns of task-activation and functional reorganization.
Resolving the nature of these associations will be a major
goal of future research in the areas of both normal aging
and AD.
Integrating Amyloid Deposition with a Model
of Neurocognitive Aging
Thus far, it is clear from the literature that amyloid
deposition in cognitively healthy individuals from age
65–90 is roughly 20–30%. Second, some studies find
evidence that amyloid deposition is related to decreased
hippocampal volume or decreased episodic memory, but the
relationship is tentative, as a number of studies have failed
to confirm this. Third, the limited data suggest that high
premorbid ability may buffer against the impact of amyloid
deposition, but much more research is required in this area.
Fourth, while there is a high likelihood of mildly impaired
individuals converting to AD over time, it appears that
significant deposition of amyloid by no means guarantees
conversion to AD. Finally, numerous authors indicate the
importance of understanding whether amyloid deposition is
occurring earlier in patients and whether amyloid deposi-
tion at a relatively young age is a harbinger of Alzheimer’s
Disease at a later age. The issue of when amyloid
deposition appears in healthy aging, particularly the
changes that occur during middle age, becomes critically
important as interventions become available to stave off
both normal and pathological age-related cognitive decline
(Linazasoro 2008; Small et al. 2008). At this time, there are
no published lifespan studies of amyloid deposition. Thus,
knowledge of the timecourse, extent and distribution of
amyloid in normal adults remains limited, making this a
much needed active area of future study.
The Scaffolding Theory of Aging and Cognition (STAC)
Given the central role that amyloid deposition appears to
play in brain health and cognition in normally aging adults,
it is helpful to think about Aβdeposition within an
integrative model of neurocognitive aging, STAC (Park
and Reuter-Lorenz 2009). The STAC model (Park and
Reuter-Lorenz 2009) is a broad integration of the behav-
ioral and neural data presently available with respect to
cognitive aging and can be conceptualized as a means to
theoretically integrate the amyloid data with the existing
neurocognitive literature. The model is presented in Fig. 8.
STAC posits that cognitive function in older adults can be
understood in terms of the magnitude of neural insults that
the brain has sustained (both structural and functional) as
well as the compensatory neural activities (“scaffolding”)
that operate to maintain cognitive behavior. According to
this model, scaffolding is conceptualized as the recruitment
of additional circuitry to compensate for declining struc-
tures whose functioning has become noisy, inefficient, or
Neuropsychol Rev (2009) 19:436–450 445
both. The pervasive finding of increased prefrontal activa-
tion in older adults across many different cognitive tasks
reflects the engagement of “compensatory scaffolding.”The
scaffolding is a direct response to amyloid deposition,
regional brain shrinkage, white matter changes, as well as
functional dedifferentiation of ventral visual cortex and
deficient default network activity. STAC also provides for
mechanisms that can enhance the development of compen-
satory scaffolding. The STAC model views amyloid depo-
sition as one type of neural insult that will result in
compensatory functional reorganization and decreased cog-
nition. The data reviewed here are consistent with the
predictions of STAC, but are increasingly suggestive that
amyloid deposition may be an initiating event that leads to
the hippocampal shrinkage (Rodrigue and Raz 2004)and
decline in fMRI subsequent memory performance previ-
ously documented in aging studies (e.g., Gutchess et al.
2005).
Future Directions: What We Need to Know
To summarize, we believe that the existing evidence
suggests that amyloid is a critical initiating event in a
cascade of events that ultimately leads to cognitive decline.
Because amyloid deposition is putatively the first event in
this negative cascade, many normal old adults harbor
amyloid burden but behave within normal cognitive limits.
This may occur for two reasons. First, the pathology has yet
to be expressed behaviorally because this is an early
neuropathological event that has its detrimental effects via
functional and structural degradation, and second, we
hypothesize that the brain responds to the initial pathology
by reorganizing and compensating functionally for the
amyloid deposition to sustain normal cognitive perfor-
mance. Given the likely importance of amyloid in deter-
mining an individual’s course of aging, filling in the
knowledge gap about the presence and consequences of
amyloid in normal populations of adults (particularly
middle-aged participants) will advance our understanding
of healthy aging and of disease progression. As larger
samples of healthy adults are being amassed for the study
of amyloid deposition, the middle age range needs to also
be included to chart the timecourse of deposition. So far,
there are only a handful of studies that integrate amyloid
imaging with functional imaging and most of these focus
on the default network system. Reasons for the sparcity of
data are primarily the recency of the technology to conduct
this type of research, the high cost of amyloid imaging, and
the short half-life of the ligands, which makes it essential
that a cyclotron be on site (for the PIB ligand) or within a
few hours drive (for the AV-45 or FDDNP ligand). We
believe that increasing amounts of data on this topic will
facilitate the field’s ability to identify individuals early who
are in need of interventions, as finding amyloid-reducing
mechanisms is a joint goal of academic and pharmaceutical
researchers. It appears increasingly likely that effective
interventions for Alzheimer’sDisease(whentheyare
developed) may need to occur in seemingly healthy
middle-aged and elderly adults who harbor latent pathology.
Adding amyloid imaging to cognitive neuroscience of
aging studies to predict structural, neural, and cognitive
Fig. 8 A conceptual model of
the scaffolding theory of aging
and cognition (STAC; adapted
from Park and Reuter-Lorenz
2009)
446 Neuropsychol Rev (2009) 19:436–450
function will yield valuable information to the research
community and will provide significant insight into the role
of subtle neural pathology in predicting changes in
cognitive function. What is missing from this early body
of literature to date is a characterization of beta-amyloid
deposition across the entire adult lifespan and large-scale
longitudinal follow-up of its development and progression.
These studies will allow us to assess across each decade
how amyloid deposition, neural structure and neural
function predict cognition, as well as to better understand
the role of amyloid deposition in predicting patterns of
neuronal function. This approach will open up new
possibilities for understanding critical dissociations be-
tween normal and pathological aging.
Conclusion
Several new radiotracers have been developed that bind to
fibrillar amyloid beta providing sensitive estimates of
amyloid deposition in various brain regions. Aβimaging
has been primarily used to study patients with AD and
MCI; however, there is now building, but limited data on
Aβdeposition in healthy controls, and these Aβimaging
studies suggest that approximately 20–30% of healthy older
adults show significant deposition. The most consistent and
uniform finding across AD studies is that early in the
disease there is binding to the precuneus/posterior cingulate
cortex and early evidence suggests that this also seems to
be a prevalent region for deposition in nondemented elderly
in addition to medial prefrontal regions. There is consider-
able evidence suggesting that amyloid deposition precedes
declines in cognition and may be the initiator in a cascade
of other neural events that ultimately lead to age-related
cognitive decline. What is less clear is why there is a
relative lack of association between amyloid binding and
cognitive performance in these studies. It seems likely that
Aβdeposition is such an early event in the cascade that its
ultimate detrimental effect on cognitive performance is
carried out via mediating effects of structural and functional
disruption. Adding amyloid imaging to cognitive neurosci-
ence of aging studies to predict structural, neural, and
cognitive function will enable us to disentangle these
associations and understand the mediating neural effects
and will provide significant insight into the role of subtle
neural pathology in predicting distal changes in cognitive
function. This approach will open up new possibilities for
understanding critical dissociations between normal and
pathological aging. Detailed characterization of the most
salient predictors of successful versus unsuccessful aging
will provide much needed information for the development
of preventative approaches to optimize cognitive health in
late adulthood.
Acknowledgements Preparation of this paper was supported by
National Institutes of Health grant AG-006265-23 to Denise Park.
Disclosures The authors have no financial disclosures to report.
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