Published Ahead of Print on March 19, 2008 as 10.1212/01.wnl.0000304345.14212.38
Processing resources reduce the effect of
Alzheimer pathology on other cognitive
systems
P.A. Boyle, PhD
R.S. Wilson, PhD
J.A. Schneider, MD
J.L. Bienias, ScD
D.A. Bennett, MD
Address correspondence and
reprint requests to Dr. Patricia
Boyle, Rush Alzheimer’s
Disease Center, Rush University
Medical Center, 600 S. Paulina,
1020B, Chicago, IL 60612
Patricia_Boyle@Rush.edu
ABSTRACT
The cognitive abilities of older persons vary greatly, even among those with similar amounts of
Alzheimer disease (AD) pathology, suggesting differences in neural reserve. Although its neurobiologic basis is not well understood, reserve may reflect differences in the ability to compensate
for the deleterious effects of pathology by recruiting alternative or additional brain networks to
perform a specific task. If this is an effective compensatory strategy, then involvement of additional cognitive systems may help maintain function in other cognitive systems despite the accumulation of pathology.
Objective: We tested the hypothesis that processing resources, specifically perceptual speed and
working memory, modify the associations of AD pathology with other cognitive systems.
Method: A total of 103 older participants of the Rush Memory and Aging Project underwent
detailed annual clinical evaluations and brain autopsy. Five cognitive systems including perceptual speed, working memory, semantic memory, visuospatial abilities, and episodic memory were
assessed proximate to death, and AD pathology including tau tangles and amyloid load were
quantified postmortem.
Results: In multiple regression models adjusted for age, sex, and education, processing resources
reduced the associations of tangles with other cognitive systems, such that persons with higher
levels of perceptual speed and working memory performed better on semantic memory and visuospatial abilities despite the burden of tangles. Perceptual speed also reduced the associations of
amyloid with semantic memory, visuospatial abilities, and episodic memory.
Conclusion: These findings suggest that processing resources may help compensate for the deleterious effects of Alzheimer disease pathology on other cognitive systems in older persons.
Neurology® 2008;70:1534–1542
GLOSSARY
AD ⫽ Alzheimer disease.
The concept of reserve applies to many physiologic systems and is an important determinant of health outcomes. Reserve also applies to brain function, including cognition.1
Numerous clinical–pathologic studies of older persons have reported only modest associations between the degree of neuropathology and the level of impairment prior to
death.2-5 Many persons without clinical dementia meet pathologic criteria for Alzheimer
disease (AD)2,3 and AD pathology is frequently observed in persons known to have had
only mild or no cognitive impairment prior to death,5-8 suggesting that some form of
reserve protects the brain from expressing pathology as impairment or clinically evident
disease.1
The neurobiologic basis of reserve is not well understood. Neural reserve may reflect
differences in brain reserve capacity or the threshold of susceptibility to pathology.9
e-Pub ahead of print at www.neurology.org.
From Rush Alzheimer’s Disease Center (P.A.B., R.S.W., J.A.S., D.A.B.), Department of Behavioral Sciences (P.A.B., R.S.W.), Department of
Neurological Sciences (R.S.W., J.A.S., D.A.B.), Rush Institute for Healthy Aging (J.L.B.), and Department of Internal Medicine (J.L.B.),
Rush University Medical Center, Chicago, IL.
Supported by National Institute on Aging grants R01AG17917 and K23 AG23040.
Disclosure: The authors report no conflicts of interest.
1534
Copyright © 2008 by AAN Enterprises, Inc.
Thus, persons with larger brains or more
neurons or synapses may be able to withstand a greater amount of pathology prior
to the manifestation of cognitive impairment. A related idea is that neural reserve
reflects differences in the ability of the brain
to actively respond to challenges.1,10-13 For example, there may be differences in neural efficiency, such that some persons have highly
efficient and flexible neural networks that
are less susceptible to disruption from
pathology. Alternatively, there may be differences in the ways in which brains compensate in response to pathology. The
compensation hypothesis10,11 posits that
some persons actively recruit alternative or
additional neural networks not normally
involved in a specific task to compensate
for the disruption of other networks by pathology. In prior work from our group using this and another cohort, we identified
several factors that modified the relation of
AD pathology to cognition, consistent with
this hypothesis12,13 (e.g., education and social networks).
There is reason to think that some cognitive systems, such as processing resources, may also alter the relation of AD
pathology to other cognitive systems. Cognitive function reflects the output of several relatively dissociable neural networks
that subserve different aspects of cognition. Whereas some cognitive systems are
relatively functionally and neuroanatomically specific, others are required for multiple cognitive functions and are subserved
by widely distributed neural networks.14 If
recruitment of alternative or additional
brain networks is an effective compensatory mechanism, then the recruitment of
alternative or additional cognitive systems
may help compensate for the deleterious
effects of pathology on other cognitive systems. For example, processing resources
such as perceptual speed and working
memory enhance virtually all other cognitive systems and reflect the output of extensive neural networks.15-17 Processing
resources therefore may buffer or reduce
the negative effect of pathology on other
systems. Although processing resources are
considered an important determinant of
cognitive aging,16-19 we are not aware of
any prior study that has examined whether
processing resources provide a compensatory benefit by altering the association of
pathology with other cognitive systems.
We used data from the Rush Memory and
Aging Project,20 a large longitudinal
clinical-pathologic investigation of common conditions of old age, to test the hypothesis that processing resources modify
the association of AD pathology with other
cognitive systems.
METHODS Participants and procedures. Participants
were from the Rush Memory and Aging Project,20 an ongoing longitudinal clinical-pathologic study of common
chronic conditions of old age. Participants were recruited
from approximately 40 senior residential facilities and social
service agencies throughout the Chicago metropolitan area
and provided written informed consent in accordance with
the Declaration of Helsinki and an anatomic gift act. The
study was approved by the Institutional Review Board of
Rush University Medical Center. More than 1,100 persons
have completed the baseline clinical assessment. The overall
annual follow-up rate of survivors exceeds 90% and the autopsy rate exceeds 85%. These analyses were based on the
first 103 persons (38 without cognitive impairment, 36 with
mild cognitive impairment, and 29 with AD) for whom postmortem data were available.
All participants underwent structured clinical assessments including medical history interviews, neurologic examination, and neuropsychological assessment, as
previously described.20-22 Annual follow-up assessments were
identical in all essential details to the baseline and were conducted by examiners shielded to previously collected data.
Assessment of cognitive function. Cognitive function
was assessed via a battery of 21 tests, as previously described. 20,23 Scores on 19 tests were used to create summary
indices of the following five specific cognitive domains: episodic memory, semantic memory, working memory, perceptual speed, and visuospatial ability. The cognitive tests were
grouped, a priori, to represent five cognitive systems. We
then conducted a factor analysis with varimax rotation of
the 19 tests. Loadings of 0.50 or greater were assigned to a
group. The relations between the hypothesized groupings
and the empirically derived groupings were examined using
the Rand statistic.24 Our data yielded a Rand statistic of 0.79
(p ⬍ 0.001), indicating a good fit consistent with the hypothesized cognitive systems. Next, in order to combine the
scores from different tests, we converted the raw scores on
each of the tests to z-scores using the baseline mean and SD
of the entire cohort and averaged the z-scores of tests within
a given system to yield a domain score.
Neuropathologic evaluation. Brains were removed in
standard fashion and cut coronally using a Plexiglas jig into
1-cm slabs, as previously described.12,13,20 Slabs from one
hemisphere were fixed in 4% paraformaldehyde for 72
Neurology 70
April 22, 2008
1535
hours. Multiple tissue blocks from entorhinal cortex proper,
hippocampus (CA1/subiculum), superior frontal cortex,
dorsolateral prefrontal cortex, inferior temporal cortex, angular gyrus cortex, anterior cingulate cortex, and calcarine
cortex were embedded in paraffin and cut into 20 ␮m sections. Amyloid-␤ was labeled with an N-terminus directed
monoclonal antibody (10D5, courtesy Elan Pharmaceuticals;
1:1,000), and immunohistochemistry was performed as previously described.12,13 PHF tau was labeled with an antibody
specific for phosphorylated tau, AT8 (Innogenetics, San
Ramon, CA, 1:1,000). Quantification of amyloid-␤ load was
done via an automated, multistage computational image
analysis protocol that uses an algorithm with the addition of
a defective pixel removal procedure to exclude faulty pixels,
as previously described.12,13 Mean fraction (% area) per region
and per subject were computed. Quantification of tangle density per mm2 was performed, as previously described.12,13
Hypothesis testing required the use of continuous quantitative measures of amyloid and tangles with adequate metric properties. Thus, for these analyses, we used composite
measures which were computed by averaging the amyloid
measures and the tangle counts across all regions. The approach is supported by Cronbach coefficient ␣, factor analysis, and intercorrelations, as described previously in this and
another cohort.12,13
Statistical analysis. We first examined the simple associations of the measures of AD pathology with cognitive function via linear regression analyses; these and all subsequent
models controlled for age, sex, and education. Next, multiple regression analyses were used to test examine whether
perceptual speed and working memory modified the associations of AD pathology with the other cognitive systems. The
initial models included terms for the main effects of the relevant measure of AD pathology and perceptual speed or
working memory, respectively. Then, to explicitly test the
hypothesis that perceptual speed and working memory modify (or, more specifically, reduce) the associations of AD pathology with other cognitive systems, we repeated the initial
models with additional terms for the interaction of each processing resource measure with the relevant measure of AD
pathology. In these tests of effect modification,25 the interaction term directly examines the extent to which a unit increase in the processing resource reduces the effect of a unit
of pathology on the level of function in another cognitive
system; thus, interaction terms are the primary focus of the results. In secondary analyses, we examined the extent to which
the other cognitive systems modified the associations of AD pathology with each other and with the processing resource measures. Unadjusted regression coefficients are presented in data
tables. Models were validated graphically and analytically and
analyses were carried out using SAS/STAT software version 8.26
The mean age of participants was
about 87 years at the time of death, and the sample was predominantly female, white, and nonHispanic (table 1). The average interval from the
last clinical interview to autopsy was 6.8 months
(SD ⫽ 4.2). The mean amyloid load was 3.7%
(SD ⫽ 4.2) and the mean number of neurofibrillary tangles per mm2 was 6.4 (SD ⫽ 8.2).
RESULTS
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Neurology 70
April 22, 2008
Table 1
Sample characteristics of deceased
participants
Characteristic
Sample mean
(n ⫽ 103)
Age at death, y (SD)
87.4 (5.9)
Women, %
55
Education, y (SD)
14.3 (3.3)
Postmortem interval, h (SD)
8.8 (9.6)
% White, non-Hispanic
98
Clinical interval,* mo (SD)
6.8 (4.2)
Amyloid, % burden (SD)
3.7 (4.2)
2
Tangles, n/mm (SD)
6.4 (8.2)
Perceptual speed
⫺0.8 (1.2
Working memory
⫺0.3 (1.3)
Semantic memory
⫺0.5 (1.3)
Visuospatial ability
⫺0.2 (1.2)
Episodic memory
⫺0.6 (1.2)
*Interval between last clinical evaluation and death.
Associations of AD pathology with cognitive function. We first examined the associations of AD
pathology with the level of function in the cognitive systems that serve as the outcomes in our core
models; these and all subsequent models controlled for age, sex, and education. In separate linear regression models, a greater burden of tangle
and amyloid pathology was associated with a lower
level semantic memory (for tangles ⫽ ⫺0.082, p ⬍
0.001, for amyloid ⫽ ⫺0.063, p ⫽ 0.028) and episodic memory (for tangles ⫽ ⫺0.096, p ⬍ 0.001, for
amyloid ⫽ ⫺0.063, p ⫽ 0.002). A greater burden of
tangle pathology was associated with a lower level
of visuospatial abilities ( ⫽ ⫺0.053, p ⬍ 0.001) but
the association of amyloid with visuospatial abilities
was not significant ( ⫽ ⫺0.021, p ⫽ 0.464).
Does perceptual speed modify the associations of
AD pathology with other cognitive systems? We
constructed a series of models to examine cognition as a function of each measure of AD pathology and perceptual speed. For these analyses, we
first examined the main effects of the relevant
measure of pathology and perceptual speed, and
subsequently added a term for their interaction.
The interaction term directly examines the extent
to which a unit increase in perceptual speed reduces the effect of a unit of pathology on the level
of function in the cognitive system. Semantic
memory was the outcome in the first analysis. In
the initial model, each tangle per mm2 was associated with about a 0.04 (p ⬍ 0.001) unit lower semantic memory score, and each unit increase in
perceptual speed was associated with about a 0.6
(p ⬍ 0.001) unit increase in semantic memory (ta-
ble 2, semantic memory, model 1, tangles). We
then added a term for the interaction of tangles
with perceptual speed. In this model, the effect of
each tangle on the level of semantic memory was
reduced by about 50% (p ⫽ 0.023) for a one unit
increase in perceptual speed (table 2, semantic
memory, model 2, tangles). Next, we repeated the
models above but replaced tangles with amyloid.
In the initial model, each 1% increase in amyloid
was associated with about a 0.04 (p ⫽ 0.065) unit
lower semantic memory score, and each unit increase in perceptual speed was associated with
about a 0.7 (p ⬍ 0.001) unit increase in semantic
memory (table 2, semantic memory, model 1,
amyloid). In the model with the additional interaction term, the effect of a percent increase in
amyloid on the level of semantic memory was reduced by more than 100% (p ⫽ 0.006) for a one
unit increase in perceptual speed (table 2, semantic memory, model 2, amyloid). These findings
suggest that perceptual speed reduces the association of tangles and amyloid with semantic memory, such that persons with higher levels of
perceptual speed function better on semantic
memory despite the burden of AD pathology.
A similar series of analyses was conducted
with visuospatial abilities as the outcome. In the
first model, each tangle was associated with
about a 0.02 (p ⫽ 0.089) unit lower visuospatial
ability score, and perceptual speed was associated
with about a 0.5 (p ⬍ 0.001) unit increase in
visuospatial abilities (table 2, visuospatial abilities model 1, tangles). In the model with the additional interaction term, the effect of each tangle
on level of visuospatial abilities was reduced by
more than 100% (p ⬍ 0.001) for a one unit increase in perceptual speed (table 2, visuospatial
abilities, model 2, tangles). The corresponding
models for amyloid are also presented in table 2.
Although the interaction term with amyloid also
was significant (p ⫽ 0.010), the finding must be
interpreted with caution since the basic association of amyloid with visuospatial abilities was not
significant.
Finally, we repeated these analyses with episodic memory as the outcome. Each tangle was
associated with about a 0.07 (p ⬍ 0.001) unit
lower episodic memory score, and each unit increase in perceptual speed was associated with
about a 0.4 (p ⬍ 0.001) unit increase in episodic
memory (table 2, episodic memory, model 1, tangles). However, the interaction of tangles with
perceptual speed was not significant (table 2, episodic memory, model 2, tangles). In the initial
corresponding model for amyloid, each 1% in-
crease in amyloid was associated with about a
0.09 (p ⬍ 0.001) unit lower episodic memory
score and each unit increase in perceptual speed
was associated with about a 0.6 (p ⬍ 0.001) unit
increase in episodic memory (table 2, episodic
memory, model 1, amyloid). In the model with
the additional interaction term, the effect of a percent increase in amyloid on the level of episodic
memory was reduced by about 50% (p ⫽ 0.009)
for a one unit increase in perceptual speed (table
2, episodic memory, model 2, amyloid).
Does working memory modify the associations of
AD pathology with other cognitive systems? To test
the hypothesis that working memory may also
modify the association of AD pathology with
other cognitive systems, we repeated the models
above but replaced perceptual speed with working memory. Notably, the interaction model
showed that the effect of each tangle on the level
of semantic memory was reduced by about 50%
(p ⫽ 0.003) for a one unit increase in working
memory (table 3, semantic memory, model 2, tangles), suggesting that persons with higher levels of
working memory perform better on semantic
memory despite the burden of tangles. No such
effect was found for amyloid.
When examining visuospatial abilities as the
outcome, the interaction model showed that effect of each tangle on the level of visuospatial
abilities was reduced by about 75% (p ⫽ 0.022)
for a one unit increase in working memory (table
3, visuospatial abilities, model 2, tangles). The
corresponding model for amyloid is presented in
table 3 but should be interpreted with caution
since the basic association of amyloid with visuospatial abilities was not significant.
Finally, in the models with episodic memory as
the outcome (table 3, episodic memory, model 1,
tangles and amyloid), the interactions of working
memory with tangles and amyloid were not significant (table 3, episodic memory, model 2, tangles and amyloid).
Examining the effects for other cognitive systems. In
order to determine whether the findings were specific for processing resources or reflected more
general associations between cognitive systems,
we next examined whether the level of function in
semantic memory, visuospatial abilities, and episodic memory modified the associations of AD
pathology with each other and with processing
resources (table 4). Episodic memory reduced the
associations of tangles with semantic memory (p
⬍ 0.001), visuospatial abilities (p ⬍ 0.001), and
working memory (p ⫽ 0.002), and amyloid with
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April 22, 2008
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Neurology 70
April 22, 2008
⫺0.004 (0.023)
0.500 (0.107)
0.065
⬍0.001
⫺0.012 (0.015)
0.473 (0.082)
⬍0.001
⬍0.001
⫺0.043 (0.011)
0.571 (0.122)
0.47
0.47
0.007 (0.017)
⬍0.001
⫺0.006 (0.022)
0.609 (0.074)
Model 2
⫺0.004 (0.023)
0.784
Model 1
Based on linear regression models adjusted for age, sex, and education.
Adjusted R
2
Amyloid ⫻ working memory
Working memory
Amyloid
Amyloid
0.019 (0.006)
0.67
0.64
Tangles ⫻ working memory
Adjusted R2
Working memory
0.572 (0.071)
Model 2
Model 1
ˆ (SE)
␤
Tangles
p
Semantic
ˆ (SE)
memory, ␤
Tangles
0.744
0.006
⬍0.001
0.865
0.023
⬍0.001
p
0.36
0.527 (0.098)
⫺0.003 (0.025)
Model 1
0.45
0.494 (0.098)
⫺0.023 (0.013)
Model 1
Visuospatial
ˆ (SE)
abilities, ␤
0.089
⬍0.001
0.908
⬍0.001
p
0.41
0.057 (0.022)
0.282 (0.132)
0.036 (0.028)
Model 2
0.52
0.037 (0.010)
0.023 (0.114)
0.041 (0.021)
Model 2
ˆ (SE)
␤
0.597
0.691
⬍0.001
0.869
0.003
⬍0.001
p
0.41
0.510 (0.087)
0.024 (0.025)
Model 1
0.49
0.473 (0.081)
⫺0.022 (0.012)
Model 1
Visuospatial
ˆ (SE)
abilities, ␤
0.084
⬍0.001
0.329
⬍0.001
p
0.41
0.008 (0.020)
0.466 (0.140)
0.027 (0.026)
Model 2
0.52
0.016 (0.007)
0.351 (0.095)
0.004 (0.017)
Model 2
ˆ (SE)
␤
Cognitive performance as a function of Alzheimer disease pathology, working memory, and their interactions
Terms
Table 3
Based on linear regression models adjusted for age, sex, and education.
0.048 (0.017)
0.61
0.58
Amyloid ⫻ perceptual speed
0.711 (0.079)
⫺0.038 (0.020)
0.63
Model 2
Model 1
0.61
0.021 (0.009)
0.480 (0.106)
⬍0.001
0.628 (0.087)
Adjusted R2
Perceptual speed
Amyloid
Amyloid
Adjusted R
2
Tangles ⫻ perceptual speed
Perceptual speed
Model 2
⫺0.006 (0.020)
⬍0.001
ˆ (SE)
␤
⫺0.044 (0.012)
p
Model 1
Tangles
Tangles
Semantic
ˆ (SE)
memory, ␤
Cognitive performance as a function of Alzheimer disease pathology, perceptual speed, and their interactions
Terms
Table 2
0.044
0.051
0.797
0.689
⬍0.001
0.299
0.022
⬍0.001
p
0.010
0.036
0.202
⬍0.001
p
0.47
0.523 (0.077)
⫺0.057 (0.023)
Model 1
0.63
0.393 (0.066)
⫺0.069 (0.010)
Model 1
Episodic
ˆ (SE)
memory, ␤
0.53
0.583 (0.079)
⫺0.087 (0.021)
Model 1
0.61
0.402 (0.076)
⫺0.070 (0.010)
Model 1
Episodic
ˆ (SE)
memory, ␤
⬍0.001
0.016
⬍0.001
⬍0.001
p
⬍0.001
⬍0.001
⬍0.001
⬍0.001
p
0.48
⫺0.006 (0.018)
0.554 (0.128)
⫺0.059 (0.024)
Model 2
0.63
⫺0.008 (0.006)
0.453 (0.079)
⫺0.082 (0.014)
Model 2
ˆ (SE)
␤
0.56
0.046 (0.017)
0.376 (0.107)
⫺0.054 (0.024)
Model 2
0.62
⫺0.008 (0.008)
0.046 (0.094)
⫺0.085 (0.018)
Model 2
ˆ (SE)
␤
0.759
⬍0.001
0.017
0.181
⬍0.001
⬍0.001
p
0.009
0.008
0.026
0.326
⬍0.001
⬍0.001
p
0.826
0.795
0.006 (0.024)
0.916
0.002 (0.023)
—
0.535
⫺0.012 (0.019)
0.002 (0.023)
Amyloid ⫻ visuospatial abilities
0.933
0.001 (0.019)
Based on linear regression models adjusted for the main effects of the relevant measure of Alzheimer disease pathology, the cognitive system, and age, sex, and education.
0.098
0.040 (0.024)
⫺0.005 (0.022)
0.055
0.162
0.236
0.036 (0.025)
—
Amyloid ⫻ episodic memory
Amyloid ⫻ semantic memory
0.956
—
0.062 (0.018)
⬍0.001
⫺0.001 (0.027)
0.162
0.022 (0.022)
0.971
⫺0.038 (0.019)
0.657
⫺0.000 (0.008)
0.303
⫺0.008 (0.007)
⫺0.003 (09.017)
0.007
⫺0.021 (0.006)
0.288
0.008 (0.007)
—
0.349
–
0.007 (0.007)
0.052
⫺0.014 (0.007)
⬍0.001
⫺0.022 (0.005)
Tangles ⫻ visuospatial abilities
—
Tangles ⫻ episodic memory
Tangles ⫻ semantic memory
p
0.002
0.040 (0.010)
0.214
0.012 (0.010)
⬍0.001
0.039 (0.010)
⬍0.001
0.043 (0.008)
Working
ˆ (SE)
memory, ␤
p
Perceptual
ˆ (SE)
speed, ␤
p
Visuospatial
ˆ (SE)
abilities, ␤
p
Semantic
ˆ (SE)
memory, ␤
p
Episodic
ˆ (SE)
memory, ␤
Interaction terms
Cognitive performance as a function of the interactions of Alzheimer disease pathology with episodic memory, semantic memory and visuospatial abilities
Table 4
semantic memory (p ⬍ 0.001). By contrast, semantic memory and visuospatial abilities did not
reduce the associations of tangles or amyloid with
each other or with the processing resources.
Additional analyses. Because the measures of AD
pathology were skewed and we were concerned
about the potential influence of outliers, we conducted several secondary analyses to further examine the robustness of these findings. First, we
repeated all models using a square-root transformation of the pathology variables. Second, we repeated
all models after excluding those persons with the
top 2.5%, 5%, and 10%, respectively, of values on
each of the pathology measures. The main findings
persisted in both sets of analyses and estimates were
essentially unchanged (data not shown).
In more than 100 well-characterized
older persons in whom multiple cognitive abilities
were assessed proximate to death and AD pathology was quantified postmortem, we found that processing resources modified the associations of AD
pathology with other cognitive systems. Specifically,
perceptual speed and working memory reduced the
associations of tangles with semantic memory and
visuospatial abilities. Perceptual speed also reduced
the associations of amyloid with semantic memory,
visuospatial abilities, and episodic memory. In secondary analyses, episodic memory reduced the associations of tangles with semantic memory,
visuospatial abilities, and working memory, and
amyloid with semantic memory. These findings suggest that processing resources and, to some degree,
episodic memory help compensate for the deleterious effects of AD pathology in older persons by reducing the effect of pathology, especially
neurofibrillary tangles, on other cognitive systems.
It is widely recognized that the modest association between the degree of neuropathology and
the level of cognitive impairment suggests individual differences in neural reserve.2-5 Neural reserve
may reflect the threshold of susceptibility to injury, the ability to use pre-existing neural networks efficiently, or the ability of the brain to
actively compensate for pathology by recruiting
alternative or additional neural networks to perform a specific task.9-13,27,28 Support for a compensatory response comes from neuroimaging studies
which have shown that older persons activate
larger brain volumes than younger persons to perform cognitive tasks.29-31 Often, the degree of brain
activation is related to task proficiency,13,28,31-33 suggesting that increased activation is associated with
maintenance of cognitive function. Activation of regions not typically involved in a task may represent
DISCUSSION
Neurology 70
April 22, 2008
1539
a functional reorganization of the brain in an attempt to compensate in response to pathology.
Importantly, although neuroimaging data provide strong evidence of a potential compensatory
response, neuropathology cannot yet be directly
measured via neuroimaging. Clinical–pathologic
studies have the potential to complement imaging
studies by examining whether involvement of alternative functional neural systems promotes
maintenance of cognitive function as pathology
accumulates. We are unaware of any prior study
that has attempted to examine neural reserve in
this manner. Our hypothesis was that processing
resources, which are integral to most other cognitive functions and reflect the output of distributed
brain networks, reduce the association of AD pathology with other cognitive systems. Processing
resources are considered an important determinant of cognitive aging.16-18,34-36 Perceptual speed
refers to the speed with which mental comparisons are made, and working memory involves the
ability to hold and manipulate information in
short-term memory stores.15,17,18,34-36 Both are essential for information processing and are considered indicators of mental capacity.19,34,36 We
suspect that processing resources optimize other
cognitive systems because they promote efficient
information transfer and reduce demand on other
systems. Additionally, processing resources appear to rely on complex brain networks that involve multiple brain regions.14,34-36 Processing
resources therefore may be available to support
function in other systems that have been compromised by pathology.
In addition to processing resources, we also
found that episodic memory reduced the association of AD pathology with semantic memory,
visuospatial abilities, and working memory.
Some data suggest that episodic memory may
support other cognitive functions.14,37 Our findings may suggest that it provides compensatory
benefit when preserved.
Notably, the beneficial effects of processing resources and episodic memory were most evident
in analyses with tangles as compared to amyloid.
Tangles are more strongly associated with cognitive function,38 and the modifying effects may
have been greatest for tangles for this reason. We
have found similar dissociations with other factors that modify the associations of amyloid but
not tangles with cognition in a separate cohort.13
In addition, it is possible that the temporal and
regional pattern of the progression of AD pathology affects the extent to which specific cognitive
systems are able to provide compensatory benefit
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April 22, 2008
in the face of accumulating pathology. AD pathology is thought to begin in the mesial temporal
region and affect the neocortex later. Thus, semantic memory and visuospatial abilities, primarily subserved by domain-specific neocortical
association areas, are relatively less profoundly
affected by AD pathology and may be more amenable to facilitation by a higher level of processing resources, particularly early in the disease. By
contrast, the hippocampal formation, a small region highly vulnerable to AD pathology early in
the disease, is critical to the formation of episodic
memory and may be less amenable to facilitation by
processing resources once compromised. Future
studies that compare persons with differing degrees
of cognitive impairment or across diagnostic groups
may provide important information regarding how
the regional and temporal progression of AD pathology affects neural reserve. In addition, it will be
important to further examine how other AD-related
pathologic changes and diverse pathologies that degrade cognitive function in aging influence reserve.
Although the results of this study are consistent with compensation, this may not be the only
mechanism by which processing resources are
linked to neural reserve. Persons with greater processing resources may benefit from a higher
threshold of brain reserve capacity and have more
efficient neural networks, which may increase
their ability to maintain function despite pathology. It is difficult to directly study the neurobiologic basis of reserve, as the factors that
contribute to reserve are not mutually exclusive.
In addition, processing resources themselves are
considered indicators of fluid ability.39 Although
we did not directly assess fluid ability in this
study, if we reconceptualize processing resources
as indicators of fluid ability in the present study
and semantic memory resources as indicators of
crystallized ability, then our data suggest that
fluid ability serves as a better modifier of the effect of AD pathology on other cognitive systems
than crystallized ability. Thus, processing resources or fluid ability may show greater plasticity and offer particular compensatory benefit in
response to the accumulation of AD pathology in
aging. Most notably, however, our findings support those of imaging studies in which recruitment of additional brain regions is associated
with maintenance of cognitive function and suggest that some form of compensation also works
at the level of the cognitive systems.
This study has several strengths, including the
linking of quantitative measures of AD pathology
with cognition proximate to death in more than
100 well-characterized community-based persons. In addition, data come from a single cohort
with high rates of follow-up and autopsy, and
uniform structured procedures were followed
with blinding of postmortem data to clinical data.
9.
10.
11.
ACKNOWLEDGMENT
The authors thank the Illinois residents from the following facilities
for participating in the Memory and Aging Project: Fairview Village,
Downers Grove; The Holmstead, Batavia; Covenant Village, Northbrook; Wyndemere, Wheaton; Francis Manor, Des Plaines; Friendship Village, Schaumberg; Peace Village, Palos Park; Washington
Jane Smith, Chicago; Garden House Apartments, Calumet City; Victorian Village, Homer Glen; King Bruwaert, Burr Ridge, The Breakers of Edgewater; The Imperial, Chicago; Victory Lakes,
Lindenhurst; Windsor Park Manor, Carol Stream; Fanciscan Village,
Lemont; Renaissance, Chicago; Alden of Waterford, Aurora; Elgin
Housing Authority; The Oaks, Oak Park; Bethlehem Woods, LaGrange Park; Luther Village, Arlington Heights; St. Paul’s Home,
Chicago; Marion Village, Home Glen; Holland Home, South Holland; Village Woods, Crete; Laurence Manor, Matteson; Trinity,
Chicago; St. Andrew’s Phoenix, Phoenix; The Moorings, Arlington
Heights; Mayslake, Oak Brook; The Birches, Clarendon Hills; Cedar
Village, Arlington Heights; Kingston Manor, Chicago; Community
Renewal Senior Ministry, Chicago; and the residents of the Chicago
metropolitan area. The authors also thank Traci Colvin, MPH,
Tracy Hagman, and Tracy Nowakowski for study coordination,
Pam Smith, Barbara Eubeler, Karen Lowe Graham, and Mary Futrell
for study recruitment, George Dombrowski and Greg Klein for data
management, Zhaotai Cui for statistical programming, and the staff
of the Rush AD Center.
Received July 23, 2007. Accepted in final form December 5,
2007.
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