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Neurocognitive Aging and the
Compensation Hypothesis
Patricia A. Reuter-Lorenz and Katherine A. Cappell
University of Michigan
ABSTRACT—The most unexpected and intriguing result
from functional brain imaging studies of cognitive aging
is evidence for age-related overactivation: greater acti-
vation in older adults than in younger adults, even when
performance is age-equivalent. Here we examine the hy-
pothesis that age-related overactivation is compensatory
and discuss the compensation-related utilization of neu ral
circuits hypothesis (CRUNCH). We review evidence that
favors a compensatory account, discuss questions about
strategy differences, and consider the functions that may
be served by overactive brain areas. Future research di-
rected at neurocognitively informed training interventions
may augment the potential for plasticity that persists into
the later years of the human lifespan.
KEYWORDS—plasticity; dedifferentiation; brain imaging;
working memory
Brain imaging has become a method of great importance for
studying cognitive aging, which makes sense because the latter
presumably results from neurobiological aging. Therefore,
brain-based measurements that can be linked to cognitive pro-
cesses expand the range of questions that can be addressed
about the aging mind. The emerging answers have prompted new
ways to think about the normal aging process and about func-
tional brain organization across the lifespan. Before the advent
of brain imaging, the behavioral methods and interpretive logic
of clinical neuropsychology guided brain-based theories of
cognitive aging. This approach assumes that minimal age
differences in performance imply minimal alterations in un-
derlying cognitive mechanisms and, by extension, age-invari-
ance in the neural substrates that mediate them. In our
assessment, one of the most far-reaching discoveries to have thus
far emerged from brain imaging studies of aging is that this as-
sumption is erroneous.
The initial neuroimaging studies of cognitive aging, which
measured brain activation via the distribution of a radioactive
isotope (i.e., positron emission tomography, PET; Grady et al.,
1994), noted that older adults display activation in regions that
are not activated by younger adults performing the same tasks. In
some studies, sites of overactivation co-occur with regions that
are underactive relative to young er adults. In other studies,
regions of overactivation are the only indication that older brains
function differently than younger brains (for reviews , see Grady
& Craik, 2000; Reuter-Lorenz, 2002). The terms overactivation
and underactivation are purely relative, referring to sites that
senior adults activate more or less, respectively, than their
younger counterparts (Fig. 1). Overactivation is frequently ob-
served in prefrontal sites (Cabeza et al., 2004; Reuter-Lorenz
et al., 2000). Overactivation in seniors is often found in regions
that approximately mirror act ive sites in younger adults but in
the opposite hemisphere (e.g., Cabeza, 2002; Reuter-Lorenz
et al., 2000; see the lower left panel of Fig. 1). This pattern of
reduced asymmetry in older adults has been referred to as
hemispheric asymmetry reduction in older age, or HAROLD for
short (Cabeza, 2002).
INTERPRETING OVERACTIVATION
Age-related underactivation is typically interpreted as a sig n
of impairment due to poor or underutilized strategies or due to
structural changes such as atrophy. However, the cognitive and
neural mechanisms associated with age-specific regions of
overactivation are more ambiguous. Determining whether
overactivations are neural correlates of processes that are ben-
eficial, detrimental, or inconsequential to cognitive function is
the crux of many research efforts in the cognitive neuroscience
of aging (Reuter-Lorenz & Lustig, 2005).
Because overactivation has been found for a broad range of
tasks, across a variety of brain regions, with or without age
differences in performance, and with or without concurrent
underactivation, it is highly unlikely that all instances stem from
a single cause. Unsurprisingly, when overactivation is found in
association with poor performance, it is interp reted as impair-
Address correspondence to Patricia A. Reuter-Lorenz, Department of
Psychology, University of Michigan, 530 Church Street, Ann Arbor,
MI 48109-1043; e-mail: parl@umich.edu.
CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE
Volume 17—Number 3 177Copyright r 2008 Association for Psychological Science
ment and is typically attributed to any of several potentially
related mechanisms: the use of multiple and/or inefficient cog-
nitive strategies; disinhibition because communication between
the left and right hemispheres declines; or dedifferentiation,
whereby the specificity and selectivity of neural processors
break down.
In many studies, however, overactivation is accompanied by
age-equivalent performance, raising the possibility that the
additional activity serves a beneficial, compensatory function
without which performance decrements would result (see Fig. 1).
Regardless of whether performance matching is achieved by
selecting younger and older subgroups that perform at equiva-
lent levels, providin g different amounts of training, adopting
age-tailored stimulus parameters, or otherwise altering task
demands for each age group, overactivation has been foun d
consistently across perceptual, motoric, mnemonic, verbal, and
spatial domains. The compensation hypothesis predicts that,
even while performance is matched at the group level, overac-
tivation across individuals should be correlated with higher
performance in the older group. Although significant correla-
tions may sometimes be lacking due to insufficient variability or
a lack of statistical power, positive activation–performance
correlations have been reported, lending support to the com-
pensatory account of age-specific overactivations (Fig. 1; Ca-
beza et al., 2004; Reuter-Lorenz & Lustig, 2005).
Establishing that overactive sites in older adults contribute to
and are necessary for successful performance would provide
especially strong support for the compensation hypothesis.
Transcranial magnetic stimulation (TMS) is a technique that
applies a series of focally directed magnetic pulses to the scalp to
stimulate the underlying neural tissue. TMS can be applied in
either a deactivating or an activating mode. In the deactivating
mode, TMS temporarily disrupts the underlying neural signals,
producing a virtual, transient lesion. Using this mode, Rossi
et al. (2005) showed that overactive sites in seniors contributed
to performance success: Older adults, who typically show bi-
lateral prefrontal activation during recognition memory, were
impaired by TMS to either hemisphere, suggesting that recog-
nition relies on both sides. Younger adults, who activate uni-
laterally during recognition memory, were impaired by TMS to
only one side. When used in the activating mode, TMS increases
the contribution of the underlying tissue. Another study found
that, when TMS was applied prefrontally in the activating mode,
a group of low-performing elderly showed improvement; fur-
thermore, functional magnetic resonance imaging (fMRI)
showed their brain activation to be unilateral before TMS and
bilateral after TMS, in association with their improved perfor-
mance (Sole-Padulles et al., 2006).
COMPENSATION FOR WHAT?
The compensation hypothesis assumes that overactive sites in
older adult brains are ‘‘working harder’’ than the corresponding
regions in their younger counterparts. In the aging brain, a
network may work harder, and thus overactivate, to make up
either for its own declining efficiency or for processing defi-
ciencies elsewhere in the brain. Although definitive support for
the first possibility is currently lacking , such support could come
from work using multiple measures to assess structural and
functional integrity within the same subjects. For example,
volumetric measures could reveal age-related atrophy in a re-
gion that also displays overactivation. When also coupled with
preserved performance, such a pattern would suggest that in-
creased recruitment compensates for decline (cf., Persson et al.,
2006).
Alternatively, a network may need to work harder and thus
becomes overactive because the input it receives is degraded or
compromised. By this account, overactivation is compensating
for functional declines elsewhere. We see three types of evidence
as being consistent with this possibility. First, several studies
Fig. 1. Results typically referred to as ‘‘underactivation’’ (top) and
‘‘overactivation’’ (bottom). When older adults activate a brain region at
lower levels or show a smaller extent of activation compared to younger
adults, as illustrated in the top pair of images, the results are often inter-
preted to indicate that the older group is functionally deficient in the
processing operations mediated by this region. The overactivation pattern
in the bottom pair of images illustrates the hemispheric asymmetry re-
duction in older age (or HAROLD) effect: Younger adults show activation
that is lateralized to the left hemisphere, whereas the older adults are
activating homologous brain regions in the opposite hemisphere also. See
Reuter-Lorenz and Lustig (2005) for examples of studies reporting these
age-specific activation patterns.
178 Volume 17—Number 3
Neurocognitive Aging and the Compensation Hypothesis
report overactive sites accompanied by, and in some cases in-
versely correlated with, sites of underactivation (Reuter-Lorenz
& Lu stig, 2005). For exampl e, in a study of incidental memory
for complex scenes, Gutchess et al. (2005) compared the neural
correlates of successfully remembered items to those of forgotten
items in younger and older adults. Compared to the older group,
successful memory in younger adults was associated with greater
activation in medial temporal lobe (MTL) regions. In contrast,
when older adults were successful, the prefrontal cortex was
overactivated and was inversely correlated with MTL activation.
Prefrontal activity appears to compensate for MTL declines to
support successful memory with age. Likewise, in a recent study
using functional connectivity analyses to measure intercorre-
lations between brain regions (Daselaar, Fleck, Dobbins, Mad-
den, & Cabeza, 2006), younger adults were more confident in
their memory performance and showed greater connectivity
between the hippocampus and a posterior, midline network as-
sociated with detailed, contextual memory; older adults with
equal but less confident memories showed more activation and
greater connectivity in a different network that included the
prefrontal cortex and was associated with familiarity. A tendency
to rely more on familiarit y signals in the aging brain presumably
serves to compensate for decreased availability of hippocam-
pally mediated context memory.
Second, overactivation may compensate for increased ‘‘noise’’
or the declining precision of perceptual processes. For example,
representational codes or receptive field properties may be less
specific in senior adults. Single-uni t recordings in aged animals
and brain-imaging studies in older huma ns reveal broader tun-
ing curves and declining precision of attribute- and category-
specific coding in posterior cortices (Reut er-Lorenz & Lustig,
2005). Consequently, higher cogni tive operations that utilize
these codes may have to ‘‘work harder’’ to perform the same
tasks. Consistent with this possibility, Denise Park’s group has
shown that deficient domain selectivity in ventral visual asso-
ciation cortex in older adults is associated with overactive pre-
frontal cortex (Payer, Marshuetz, Sutton, Hebrank, Welsh, &
Park, 2006) in a visual working memory task (see also Cabeza
et al., 2004; Grady et al., 1994; Madden et al., 1996). Likewise,
greater noise or interference may result from age-related d iffi-
culties suppressing or inhibiting irrelevant information due to
alterations in top-down, controlled processing (e.g., Gazzaley,
Cooney, Rissman, & D’Esposito, 2005).
Finally, the aging brain may also have to contend with noise
from nonperceptual processes. A growing body of evidence in-
dicates that older adults have difficulty attenuating activity in
the so-called ‘‘default network.’’ This network shows correlated
activations during nontask periods (e.g., passive fixation base-
line or rest) and deactivations during engagement in experi-
mental tasks. The default network is thought to mediate
unconstrained self-referential thought about past and future
events that occurs when cognition is not being dictated by ex-
ternal demands, such as those imposed by an experimental task.
Activity in task-related regions is inversely correlated with de-
fault-network activity, suggesting that default-network quies-
cence focuses neura l and cognitive resources on the task at
hand: More demanding cognitive tasks are associated wit h
greater levels of deactivation. However, across a variety of
cognitive tasks, older adults show less deactivation of the default
network than their younger counterparts do. Moreover, age
differences in deactivation magnitudes increase at higher levels
of task demand (Persson, Lustig, Nelson, & Reuter-Lorenz,
2007), indicating that older brains are particularly deficient at
silencing the default network when faced with tasks requiring
greater cognitive effort.
ARE OLDER BRAINS SIMPLY YOUNGER BRAINS
WORKING HARDER?
Are patterns of overactivation observed in the aging brain
‘‘equivalent’’ to those found when the younger brain contends
with increased task demand? Although this model is surely
incomplete, it may accurately characterize some aspects of
neurocognitive aging. In our lab, using variable verbal working
memory loads, we found that older adults activated regions of the
dorsolateral prefrontal cortex at lower loads, whereas younger
adults activated these same sites only at higher loads. Impor-
tantly, at the lower loads, age differences in performance were
minimal. At the higher loads, activation in the younger grou p
exceeded that observed in the older group, and elderly perfor-
mance was relatively deficient as well (Cappell, Gmeindl, &
Reuter-Lorenz, 2006). Mattay et al. (2006) report a similar result
using a different verbal task. At the lowest level of task demand,
senior adults overactivate the prefrontal cortex while performing
equivalently to younger adults. With increasing demand, this
prefrontal site becomes underactivated in seniors, and perfor-
mance becomes impaired.
These studies have several important implications. First,
some sites of overactivation displayed by older adults are neu-
rologically ‘‘normal’’ in that they are also activated by younger
adults. Older adults merely recruit them sooner in the load-ac-
tivation function. Some overactivations may therefore reflect the
brain’s response to increased task difficulty by which ‘‘reserve’’
resources are recruited (Stern et al., 2005). Second, at lower
levels of task demand, region-specific overactivation in seniors
is associated with good performance and presumably is com-
pensatory because performance differences are minimal despite
activation differences. Third, beyond a certain level of task
demand, the senior brain falls short of sufficient activation lev-
els, and performance declines relative to the younger group. We
(e.g., Reuter-Lorenz & Lustig, 2005; Cappell et al., 2006) have
referred to this tradeoff as the compensation-related utilization
of neural circuits hypothesis (or CRUNCH; see Fig. 2). Ac-
cording to CRUNCH, processing inefficiencies cause the aging
brain to recruit more neural resources to achieve computational
output equivalent to that of a younger brain. The resulting
Volume 17—Number 3 179
Patricia A. Reuter-Lorenz and Katherine A. Cappell
compensatory activation is effective at lower levels of task de-
mand, but as dem and increases, a resourc e ceiling is reached,
leading to insufficient processing and age-related decrements
for harder tasks. Training, exercise, and other interventions
applied in older age or throughout the life course (Reuter-Lorenz
& Mikels, 2006) may increase available resources and com-
pensatory potential (e.g., cognitive reserve, Stern et al., 2005).
Conversely, sleep deprivation, neurological damage, or genetic
vulnerabilities may lower the resource ceiling, leading to un-
deractivation and performance decrements.
COGNITIVE ‘‘CORRELATES’’ OF OVERACTIVATION
What cognitive operations are supported by sites of age-related
overactivation? Do older adults engage different cognitive
strategies than younger adults do, especially where the ‘‘younger
brain working harder’’ model will not suffice? Prefrontal regions
show the greatest evidence for age-related atrophy, and yet,
paradoxically, these are the sites where overactivation and evi-
dence for compensation tend to be most pronounced. Executive
control functions mediated by lateral and inferior prefrontal sites
(e.g., attentional selection, inhibition, rule switching, mainte-
nance, and context processing) can be recruited adaptively to
meet the challenges of changing environmental and task de-
mands. Executive recruitment may also be the primary means by
which the brain adapts both neurally and cognitively to its own
aging. Executive processes account for a wide range of indi-
vidual differences and are a likely source of age-related varia-
tions as well.
Yet, evidence linking age-related changes in activation to
specific age differences in cognitive strategies remains sparse.
Some tasks are not amenable to strategy analyses, and some
studies that have attempted to relate age differences in activa-
tion to differences in strategy have failed to find such links. One
example (Fera et al., 2005) comes from a weather-prediction task
that permitted strategy analysis. Despite pronounced age-re-
lated activation differences, including parietal overactivation
that correlated positively with performance in seniors, no age
differences in accuracy or strategy use were found.
Nevertheless, assuming that neural indices have cognitive
correlates, there must be some yet unidentified cognitive
differences that distinguish younger and older approaches to the
same tasks. Available imaging methods can be better utilized to
characterize age differences in neural activity and examine the
possibility of age-altered strategy use. New fMRI task designs
that can distinguish activation patterns sustained over a block of
trials from transient, within-trial changes in activity may be
successful in identifying age differences in strategies. As in the
study by Daselaar et al. (2006; see above), functional-connec-
tivity analyses can reveal dysfunctional or compensatory net-
works that can be linked to age-related changes in reliance on
different psychological processes.
Also, despite the low temporal resolution of fMRI, time-course
information alone or combined with event-related potential
methods can reveal the timing of age-related activation differ-
ences, thereby providing some insight into what mental opera-
tions they mediate. A recent study (Velanova, Lustig, Jaccoby, &
Buckner, 2006) demonstrated that prefrontal overactivity in
CRUNCH Activation Predictions
0
0.5
1
1.5
2
2.5
3
3.5
123
Level of Task Demand
Change from Baseline
(arbitrary units)
CRUNCH Performance Predictions
50
55
60
65
70
75
80
85
90
95
100
123
Level of Task Demand
Percent Accuracy
Younger
Older
Younger
Older
Fig. 2. Patterns of activation (in arbitrary units; left graph) and performance levels (right graph)
predicted by the compensation-related utilization of neural circuits hypothesis (CRUNCH). Increased
recruitment in response to increasing task demand is a ‘‘normal’’ neural response, evident in younger
adults; what varies with age, according to CRUNCH, is the slope of the function relating activation to
demand and the level at which activation asymptotes. The left graph shows how, relative to younger
adults, older adults progress from overactivation at lower levels of task demand to underactivation at
higher levels of task demand within the same region of interest. According to CRUNCH, compensatory
recruitment at low demand maintains seniors’ performance at levels that are equivalent to or mini-
mally different from younger adult levels. The right graph shows how, as task demands increase, older
adults reach a resource ceiling, and performance levels drop, especially in comparison to those of
younger groups. At peak levels of demand, errors may be sufficiently frequent that the task is met with
frustration or approached with ineffective strategies, or other factors may prevail that lead to un-
deractivation of this region compared to younger groups (cf. Mattay et al., 2006).
180 Volume 17—Number 3
Neurocognitive Aging and the Compensation Hypothesis
older adults was most evident in the latter part of a trial, sug-
gesting a shift in strategic, effortful processing operations from
earlier to later stages of the task. This temporal pattern suggests
that prefrontally mediated processes may be recruited ‘‘reac-
tively,’’ as if to perform clean-up operations due to failure to exert
adequate control in a proactive manner (Braver, Gray, & Bur-
gess, 2007).
FUTURE DIRECTIONS
As a dynamic biological process, aging reveals an enduring
neural capacity for functional reorganization or redistribution
of reso urces in response to metabolic and neurobiological de-
clines. As such, brain aging may share compensatory principles
with other neurobiological perturbations, including epilepsy
and stroke, developmental disorders such as attention-deficit-
hyperactivity disorder, and sleep deprivation. The brain is ex-
ceedingly clever, not only in the social, affective, and cognitive
states it supports, but in the neural strategies it invokes to de-
velop and maintain these states effectively over the lifespan. A
major research frontier concerns the neural effects of training
and practice of cognitive skills early in life, throughout life, and
late in life (e.g., Eriksen et al., 2007; Persson & Reuter-Lorenz,
in press). Can we foster cognitive success and resilience in later
life by discovering ways to forestal l or reverse declines, and
otherwise optimize the brain’s response to its own aging?
Recommended Reading
Cabeza, R., Nyberg, L., & Park, D. (2005). The cognitive neuroscience of
aging. Oxford, UK: Oxford University Press. An edited volume
that includes comprehensive contributions from major researchers
using neuroscientific approaches to cognitive aging.
Greenwood, P. (2007). Functional plasticity in cognitive aging: Review
and hypothesis. Neuropsychology, 21, 657–673. A thorough, up-to-
date treatment of the interplay between decline and compensation.
Hedden, T., & Gabrieli, J.D. (2005). Healthy and pathological processes
in adult development: New evidence from neuroimaging of the
aging brain. Current Opinion in Neurology, 18, 740–747. A com-
prehensive review of research addressing functional, structural,
pharmacological and genetic factors that characterize the fine line
between healthy and pathological aging.
Reuter-Lorenz, P.A., & Lustig, C. (2005). (See References). A highly
accessible overview of research on the cognitive neuroscience of
aging.
Reuter-Lorenz, P.A., & Mikels, J.A. (2006). (See References). A further
discussion of CRUNCH within a broad treatment of cognitive
aging that takes an interdisciplinary approach to analyze the
reciprocal interactions of cultural, psychological and biological
influences on development, decline, and compensation across the
lifespan.
Acknowledgments—We thank the members of the Reuter-
Lorenz, Lustig, and Jonides Laboratories at the University of
Michigan for useful discussion of ideas presented in this paper.
Preparation was supported by National Institutes of Health
Grant AG18286.
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