Neuroanatomical correlates of aging, cardiopulmonary

fitness level, and education

BRIAN A. GORDON,a,b ELENA I. RYKHLEVSKAIA,a,b CARRIE R. BRUMBACK,a,b

YUKYUNG LEE,b STERIANI ELAVSKY,c JAMES F. KONOPACK,c EDWARD MCAULEY,a,c

ARTHUR F. KRAMER,a,b STANLEY COLCOMBE,b GABRIELE GRATTON,a,b

and MONICA FABIANIa,b

aDepartment of Psychology, University of Illinois at Urbana–Champaign, Champaign, Illinois 61820, USAbBeckman Institute, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801, USAcDepartment of Kinesiology, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801, USA

Abstract

Fitness and education may protect against cognitive impairments in aging. They may also counteract age-related

structural changes within the brain. Here we analyzed volumetric differences in cerebrospinal fluid and gray and white

matter, along with neuropsychological data, in adults differing in age, fitness, and education. Cognitive performance

was correlated with fitness and education. Voxel-based morphometry was used for a whole-brain analysis of structural

magnetic resonance images. We found age-related losses in gray and white matter in medial-temporal, parietal, and

frontal areas. As in previous work, fitness within the old correlated with preserved gray matter in the same areas. In

contrast, higher education predicted preservedwhitematter in inferior frontal areas. These data suggest that fitness and

educationmay both be predictive of preserved cognitive function in aging through separable effects on brain structure.

Descriptors: Aging, Fitness, Education, Voxel-based morphometry (VBM), Grey matter, Cerebrospinal fluid (CSF),

White matter, Structural magnetic resonance imaging (MRI)

Normal aging is often accompanied by a reduction in cognitive

function. Over the life span, speed of processing, working mem-

ory, and short- and long-term memory appear to decline (Park

et al., 2002; Sliwinski & Buschke, 1999) along with correspond-

ing changes in brain function. These age-related changes are not

limited to functional outcomes, as physical degradation of gray

and white matter also occurs (Davatzikos & Resnick, 2002; Raz,

2002).

Although some age-related loss of mental abilities may be

inevitable, the decline is not uniform for all individuals, as vari-

ability in cognitive function also increases with age (Fleischman,

Wilson, Gabrieli, Bienias, & Bennett, 2004; Morse, 1993). This

suggests that it is possible to undergo successful aging with many

of one’s cognitive faculties intact. Proposed mediators of this

differential decline include physical fitness (Colcombe &

Kramer, 2003; Spirduso, 1975) and education (Richards &

Deary, 2005; Stern, 2002), as well as other factors. Only in the

last decade have studies begun to investigate these relationships

by examining the links between volumetric changes in brain

anatomy and physical fitness (Colcombe et. al., 2003, 2006),

whereas fewer studies have investigated the relationship between

education and brain anatomy (e.g., relationship between educa-

tion and white matter hyperintensities; Dufouil, Alperovitch, &

Tzourio, 2003; Nebes et al., 2006). In addition, it may be very

important to examine the effects of fitness and education within

the same sample, because these two variables are likely to be

correlated. Thus, in this study, we sought to explore the inter-

relationship between these factors, by examining the effects of

age, fitness, and education on brain anatomy and cognitive

function.

Research in the late 1970s and early 1980s laid the ground-

work for the study of the relationship between fitness and aging

(Dustman et al., 1984; Spirduso, 1975; Spirduso & Clifford,

1978). Although there is a vast literature on the cognitive and

emotional correlates of exercise and fitness, there is a dearth of

knowledge of how exercise affects the biological properties of the

human brain. Much of this knowledge is gained through work

with animal models. These studies have shown that exercise may

lead to both angiogenesis (Cotman & Berchtold, 2002) and ne-

urogenesis (van Pragg, Kemperman, & Gage, 1999; see also

Kramer, Bherer, Colcombe, Dong, & Greenough, 2004). Inter-

estingly, exercise also leads to increases in a number of nerve

growth factors, such as brain-derived neurotrophic factor (Nee-

per, Gomez-Pinilla, Choi, & Cotman, 1995) and insulin-like

Our thanks to the Image Analysis Group, FMRIB, Oxford, UK, and

their programmers and scientists who created the tools we used to do the

analysis. Thanks to Kirk Erickson for helpful suggestions on data an-

alyses. Finally we wish to show appreciation to Brad Sutton and Andrew

Webb, who answered numerous questions of a technical nature related to

MRI acquisition and protocol setup. This research was supported by

NIA grant #AG21887 to Monica Fabiani.Address reprint requests to: Monica Fabiani, Department of Psy-

chology, University of Illinois, 603 E. Daniel, Champaign, IL 61820,USA. E-mail: mfabiani@uiuc.edu

Psychophysiology, 45 (2008), 825–838. Wiley Periodicals, Inc. Printed in the USA.Copyright r 2008 Society for Psychophysiological ResearchDOI: 10.1111/j.1469-8986.2008.00676.x

825

growth factor I (Carro, Trejo, Busiguina, & Torres-Aleman,

2001), which may in some way contribute to its effects on brain

structures. These and other alterations due to exercise are

thought to contribute to improved learning and memory (An-

derson et al., 2000; Greenough,Madden, & Fleischmann, 1972),

as tested with enriched environment manipulations.

Structural changes in animals, such as increases in dendritic

length and branching (Greenough & Volkmar, 1973) and hip-

pocampal neurogenesis (Brown et al., 2003; Rossi et al., 2006)

have also resulted from environmental enrichment. This manip-

ulation typically includes an exercise component, although itmay

potentially be considered in some way an analog to increased

education in humans, due to the greater cognitive demand on the

animals.

From these animal studies, it is plausible to expect that car-

diovascular exercise will also have an impact on human brain

structure. In humans, age-related graymatter atrophy appears to

reflect primarily cell shrinkage rather than cell death (Terry,

DeTeresa&Hansen, 1987). Longitudinal interventions involving

aerobic training in humans have led to increases not only in gray

matter, but also in frontal white matter (Colcombe et al., 2006).

Because changes in gray matter are more extensive, they may be

more likely to be detected with noninvasive imaging methods

than those in white matter tracts.

In recent years, a number of experiments have provided fur-

ther support for the role of physical fitness in human aging by

showing that structural changes are also associated with im-

proved cognitive function (Colcombe & Kramer, 2003, 2006;

Hillman, Belopolsky, Snook, Kramer, & McAuley, 2004;

Kramer et al., 1999; McAuley, Kramer, & Colcombe, 2004).

For example, Colcombe and colleagues (2004) examined brain

activations during an Eriksen flanker task (Botvinick, Nystrom,

Fissel, Carter, & Cohen, 1999; Eriksen & Eriksen, 1974). In both

cross-sectional and clinical studies, higher levels of fitness were

implicated in improved attentional control.

Similarly to physical fitness, education has been suggested as a

possible factor in successful aging (Vaillant & Mukamal, 2001).

For example, higher levels of education may contribute to pre-

served memory performance through increased brain activity at

frontal sites in old age (Czernochowski et al., in press). Education

serves to improve cognitive function and may also delay mental

decline in later years by creating a reserve capacity (Stern, 2002).

The ‘‘cognitive reserve’’ hypothesis suggests that individuals can

cope with advancing brain pathology through either a set of

acquired skills or inherent abilities. This theoretical standpoint

has been operationally translated into an examination of specific

lifestyle factors, including level of education, IQ, and occupa-

tional status (Habeck et al., 2003; Richards & Deary, 2005;

Scarmeas et al., 2003). In fact, the concept of cognitive reserve

was initially invoked to account for the imperfect coupling be-

tween the degree of brain pathology and loss of cognitive ca-

pacity. For example, one study reported that a substantial

number of individuals without behavioral symptoms of Alzhei-

mer’s disease during life were found to have histological signs of

the disease upon postmortem examination (Ince, 2001). This

behavioral sparing may be mediated by high levels of education

or IQ (Snowdon, Greiner, & Markesbery, 2000). Several neuro-

imaging studies have recently been conducted to examine the

brain substrates supporting cognitive reserve (Habeck et al.,

2003; Scarmeas et al., 2003). However the possible role played by

education is still not determined. Due to its suggested role in

creating a cognitive reserve, it is possible that years of education

may affect the brain structurally. However, as mentioned, the

examination of structural effects of education concurrently with

those due to aerobic fitness has not yet been carried out. Edu-

cation could have a direct effect due to ‘‘use,’’ as seen with other

factors such as psychomotor activity (Draganski et al., 2004), or

an indirect effect through its influence on life habits such as nu-

trition, exercise, and health. Although we predict potential struc-

tural changes in older adults to be moderated by both aerobic

fitness and years of education, these effects could manifest in

different ways and affect different brain structures.

In contrast to the effect of aerobic fitness, it is not yet estab-

lished how an increased brain use due to education may lead to

structural effects. However, there is some evidence that education

may influence white matter integrity (Dufouil et al., 2003; Nebes

et al., 2006; however, see alsoChristensen et al., 2007). A possible

mechanism is that axonal function (through use) may influence

myelination (Colello, Devey, Imperato, & Pot, 1995), but other

mechanisms are also possible. Coffey, Saxton, Ratcliff, Bryan,

and Lucke (1999) found reduced cortical atrophy (indexed by

sulcal CSF volume) to be correlated with education in a large

sample of older adults. However, these data were not replicated

by Christensen et al. (2007), who, however, used a younger and

more restricted group of subjects (60–64 years of age).

In summary, although this latter group of studies suggests

effects of education onwhite and possibly gray matter, two issues

remain unresolved. First, all of these studies used global volu-

metric assessments, whereas it is well known that aging affects

frontal and temporal areasmore than sensory ormotor areas (for

reviews, see Colcombe et al., 2003; Kemper, 1994; see also Esiri,

1994). Second, none of these studies controlled for physical fit-

ness, and, as we have alreadymentioned, it is likely that these two

variables could be confounded.

Within the field of human aging research, there are compar-

atively few studies that have assessed the presence of systematic

volumetric changes in the whole brain. Until recently, quantita-

tive measurements of the brain in vivo would have been impos-

sible. However, the advent of high-resolution structural magnetic

resonance imaging (MRI) has made in vivo brain volumetric

assessment possible. Volumetric measures have been used, for

example, in studies of Alzheimer’s disease (Jack, Petersen,

O’Brien, & Tangalos, 1992; Kohler et al., 1998) and schizophre-

nia (Sachdev, Brodaty, Cheang, & Cathcart, 2000; Wright et al.,

1995) as well as in aging (Gunning-Dixon & Raz, 2003; Raz,

Gunning-Dixon, Head, Dupuis, & Acker, 1998). The gold stan-

dard used in volumetric studies is the manual tracing of specific

areas on high-resolution images by trained neuroanatomists.

However, with manual tracing, the number of subjects and/or

brain regions investigated is limited, because the work is time

intensive and requires extensive neuroanatomical knowledge.

Furthermore, although interrater reliability within a laboratory

can be quite high, reproducibility is not guaranteed, which may

limit the generalizability of results.

It is possible to avoid these problems through the use of voxel-

based morphometry (VBM; Ashburner & Friston, 2000, 2001).

VBManalysis is based on the computation of the probability that

each voxel in a subject’s structural image is classified as a par-

ticular tissue type: cerebrospinal fluid, gray matter, or white

matter. These classifications can then be statistically compared

between groups of subjects or regressed on a continuous variable

within a group. VBM allows for a whole-brain analysis in a semi-

automated fashion. Thus, it is easily reproducible and does not

require expert-level anatomical knowledge.

826 B.A. Gordon et al.

The use of VBM as a tool for studying structural differences

between groups presents some controversial issues. In particular,

there is concern that potential misalignment during the registra-

tion step could negatively impact results (Bookstein, 2001).

However, when used appropriately, VBM is a robust and accu-

rate tool (Ashburner & Friston, 2000). The method has proved

to be reliable in the study of a variety of populations, including,

but not limited to, schizophrenic patients (Wright et al., 1995),

gender differences (Good et al., 2001a), obsessive-compulsive

disorder (Pujol et al., 2004), diabetes (Musen et al., 2006), and

aging (Colcombe et al., 2003).

In the present study, we used VBM to test a series of hy-

potheses on a sample of younger and older adults. VBM allows

for a whole-brain comparison between younger and older adults

as well as for analyses of fitness and education effects within the

older adults. The initial comparison based on age should lead to

robust differences between the two groups as previously reported

(Good et al., 2001a; Raz, 2002). We predicted that, in older

adults, fitness and education may result in somewhat different

profiles of tissue sparing (with possible overlap). The mitigating

effects of aerobic fitness have shown their greatest effects on

frontal andmedio-temporal gray matter as well as anterior white

matter (Colcombe et al., 2006). Although there is no previous

work indicating where the effects of education should manifest,

studies of general aging have indicated greater anterior decline in

white matter while showing relative preservation in other areas

(for a review, see Kemper, 1994). To measure potential func-

tional correlates of structural changes, a battery of neuropsy-

chological tests was administered to all subjects. Analyses

examining the effects of age, education, and fitness were per-

formed on these data. These behavioral measures provide a par-

allel to the structural data.

In summary, although these analyses draw upon previous

research, they provide a new and important examination of the

differential contributions of age, cardiovascular fitness, and

years of formal education on brain structure. In particular, be-

cause both fitness and education have been suggested to serve as

protective factors against cognitive aging, it is important to

compare their relative contributions within the same sample with

a method that allows for the independent and parallel evaluation

of gray and white matter effects.

Methods

Participants

Participantswere drawn froma study that investigated the effects

of differences in physical fitness on neurovascular coupling

(Fabiani et al., 2004).1 The final sample comprised 20 younger

adults (age 20–28, 10 women, recruited from the University

of Illinois student population through postings in university

buildings and by word of mouth) and 40 older adults (age 65–81,

23 women, recruited through ads in the local newspaper, cam-

pus-wide e-mailings, and postings at area gyms, retirement

homes, and community centers). Participants were all right-

handed and fluent in English. Prior to graded maximal exercise

testing, all participants completed a personal health history form

and obtained medical clearance from their personal physician.

The demographic characteristics of the participants are

summarized in Table 1.

Screening Procedures

Potential participants were screened with a phone interview prior

to their entering the study. During this phone assessment, po-

tential participants were excluded if they regularly took medica-

tions known to directly affect the central nervous system or the

cardiovascular system (e.g., beta blockers, CNS stimulants, an-

tidepressants, anti-psychotics, sedating antihistamines, or mi-

graine medication) or had other serious or chronic medical

conditions. Screenings for these medications would eliminate

participants being treated for common health problems such as

hypertension. Participants who passed the phone screening then

underwent a session of screening and neuropsychological testing.

To continue in the experiment, individuals had to score at least 51

on the modified Mini-Mental Status exam (mMMS; Mayeux,

Stern, Rosen, & Leventhal, 1981) and show no signs of depres-

sion on Beck’s Depression Scale (Beck, Steer, & Brown, 1996; a

score of 12 or greater was exclusionary).

Neuropsychological Assessment

Participants who were within the limits of our screening proce-

dures also received an extensive battery of neuropsychological

tests, designed to assess the integrity of their cognitive functions.

In addition to themMMS test, which was used to examine global

cognitive competency, and the forward and backward digit span,

used to assess short-term memory, participants were adminis-

tered the Vocabulary subtest of the Wechsler Adult Intelligence

Scale–Revised (WAIS-R; Wechsler, 1981) to assess verbal skills,

the full version of the Wisconsin Card Sorting Test (WCST;

Heaton, 1981), the verbal fluency (CFL version) of the Con-

trolled Oral Word Association Test (Benton & Hamsher, 1976),

and the Alternate Category Test (Benton & Hamsher, 1976) to

assess flexibility and frontal lobe function, the short version of

the Raven’s Progressive Matrices (Engle, Tuholski, Laughlin, &

Conway, 1999; Raven, Court, & Raven, 1977) to evaluate fluid

intelligence, the operation word span task (OSPAN; Engle et al.,

1999) to evaluate working memory and attention control, the

digit copying test (Kendrick &Watts, 1999), the timed box com-

pletion of the WAIS-R (Wechsler, 1981), and the Trail Making

Tests A and B (Corrigan & Hinkeldey, 1987) to assess motor

speed, the digit-symbol copying test, and the three-, six-, and

nine-letter comparison tests (Salthouse, 1992) to assess process-

ing speed.

Fitness Assessment

Cardiovascular fitness for the older adults was assessed by a

maximal graded exercise test on a motor-driven treadmill using a

modified Balke protocol (American College of Sports Medicine,

1991). The goal of the test is to find a participant’s maximal rate

of oxygen uptake (VO2max). This measure is accepted as the cri-

terion measure of cardiorespiratory fitness (American College of

SportsMedicine, 1991). The protocol involves walking at a speed

of 3mphwith increasing grade increments of 2% every 2min and

was supervised by a physician, exercise test technologist, and a

registered nurse certified in Advanced Cardiac Life Support.

Expired gases were sampled at 30-s intervals and analyzed using

a Parvomedics Inc. TrueMax2400 metabolic system. The test

termination criteria included any of the following: (a) objective

evidence that VO2max had been attained, (b) volitional

exhaustion on the part of the participant, or (c) cardiovascular

Anatomical correlates of aging, fitness, and education 827

1Note that this sample was newly assessed and independent fromthose previously reported by Kramer and colleagues.

abnormalities or other symptoms indicating ischemia or abnor-

mal test responses. Individuals with abnormal responses were

excluded from the study. Heart rate was assessed at each work

stage via continuous 12-lead electrocardiographic monitoring.

Blood pressure was monitored by auscultation and sphygmo-

manometer. As is common practice within fitness literature,

when serving as controls, young participants were considered as

being relatively high fit as compared to the older adults.

Structural Imaging

All participants included in this study underwent a high-resolu-

tion structuralMRI scan in a Siemens 3-TeslaMagnetomAllegra

MR Headscanner. Using an MPRAGE sequence, a 144-slice

scan with a 1.2-mm slice thickness was obtained either in the

sagittal or axial plane.2 The pulse parameters used in MR re-

cording included a repetition time of 1800 ms, an echo time of

4.38 ms, and a flip angle of 81. The field of view was 240 � 240 �172.8 mmwith matrix dimensions of 192 � 256 � 144 and voxel

size of 1.25 � 0.938 � 1.2 mm.

Data Analyses

MR data were processed using tools in the FMRIB Software

Library (Image Analysis Group, FMRIB, Oxford, UK; Smith

et al., 2004). AVBMmethodwas used to determine the impact of

age, fitness level, and education on brain tissues. Before applying

this technique, the MR images underwent a series of automated

preprocessing steps. The purposes of these steps were to (1) re-

move all nonbrainmatter from theMR images (Smith, 2002); (2)

align different individuals to a common template in MNI space,

which is achieved through a series of affine transformations

(Jenkinson, Bannister, Brady, & Smith, 2002; Jenkinson &

Smith, 2001); (3) for each participant, assign to each voxel partial

volume estimates describing the probabilities that that voxel

contains cerebrospinal fluid, white matter, or gray matter (‘‘seg-

mentation’’; Zhang, Brady, & Smith, 2001)Fseparate maps for

each participant and tissue type are thus derived; and (4) smooth

the tissue maps with an 8-mm full-width at half-maximum

Gaussian kernel. (For further descriptions of VBM methodol-

ogies and template see Ashburner & Firston, 2000; Good et al.,

2001b; Pujol et al., 2004).

In all the analyses presented here we used a template (20

young, 10 female, 20 old, 10 female), selected from a pool of

participants acquired from other studies in our laboratory. The

template was composed of an equal numbers of older and

younger participants and gender balanced to make it as repre-

sentative as possible of the participants examined. By balancing

the template on both age and gender, we removed any potential

systematic registration bias that could have occurred.

Within the segmentation processes, the tissue maps were

multiplied by their Jacobian determinants obtained from the

registration step to account for the changes in voxel size due to

the spatial normalization. This partial volume correction proce-

dure makes it possible to describe the results of the analysis in

terms of actual volumes rather than tissue densities. Within the

tissue segmentation step of preprocessing, there was also a B1

inhomogeneity correction.

The final outputs of the processing were three parametric

maps representing partial volume estimates for each of the tissue

typesFgray matter, white matter, and CSFFfor each partic-

ipant. For the comparison between younger and older adults, a

group comparison was then carried out on each of the three sets

of parametric maps. The effect of age within the older population

was estimated by means of linear regression analyses in a voxel-

wise fashion for each map type. The regression analyses using

log-transformed VO2max scores as predictors were conducted

solely on the parametric maps of partial volume estimates of the

older adults. A third set of regressionswas run on the older adults

to examine if there were structural changes associated solely with

education. Within each of these analyses, the statistics were ad-

justed formultiple comparisons. Each comparison generated two

t-statistic maps, corresponding to the opposite directional con-

trastsFa possible increase or decrease for each tissue type with

age, fitness, or education. Because education and fitness were

correlated, we used each of them as a covariate in the analysis of

the other. Thus, the results presented here indicate only their

independent contributions. Similarly, gender was used as a

828 B.A. Gordon et al.

Table 1. Mean (SD) Demographic Characteristics of the Younger and Older Adults, and Separately for High- and Low-Fit Older Adults

Measure Young (n5 20) Old (n5 40) Old low-fit (n5 20) Old high-fit (n5 20) Old-low vs.old-high:t(38), p

Age (years) 22.5 (2.1) 71.5 (4.7) 72.7 (4.9) 70.4 (4.4) 1.53, n.s.Range 20–28 65–81 65–81 65–79

Education (years) 16.5 (1.4) 16.7 (2.8) 15.1 (2.4) 18.3 (2.2) 4.31,o.001Range 15–20 12–20 12–20 13–20

VO2max (mL kg� 1 min) F 25.1 (7.8) 18.7 (2.4) 31.4 (5.9) 8.87,o.001Range 10.3–44.7 10.3–21.3 24.3–44.7

BP systolic F 141.2 (16.2) 143.5 (21.4) 139.8 (12.5) 0.59, n.s.Range 120–190 124–190 120–172

BP diastolic F 83.2 (7.6) 86.9 (9.3) 80.9 (5.5) 2.20,o.05Range 70–104 78–104 70–92

HR resting F 68.4 (15.0) 68.8 (12.3) 68.2 (16.7) 0.11, n.s.Range 42–109 55–95 42–109

HR peak F 158.4 (14.9) 152.6 (19.2) 162.0 (10.6) 1.70, n.s.Range 104–180 104–179 136–180

BDI score 1.3 (1.2) 2.0 (2.4) 2.3 (2.4) 1.6 (2.3) 0.94, n.s.Range 0–3 0–9 0–7 0–9

Notes. Ranges are also provided, as well as the results of a comparison of high- and low-fir older adults. The blood pressure measurements (BP) weretaken at the time of VO2 max assessment. BDI: Beck’s Depression Inventory; HR: heart rate.

2A small number of scans (o4) were acquired in the axial rather thanthe saggital plane. This was due to wrapping issues on the scans. Therewas no effect of this difference.

covariate in all analyses to avoid possible biases and eliminate its

effects. Finally, age was also used as a covariate to evaluate its

effects within the older adult group.

Results

Neuropsychological Tests

The results of the neuropsychological tests are summarized in

Tables 2 through 5. Tables 2 and 3 report mean data separately

for the younger and older adults, whereas Tables 4 and 5 report

results on these same tests for the older adults only, grouped by

fitness level using a median split. The majority of neuropsycho-

logical measures showed significant differences as a function of

age. A similar patternwas present when older adults were divided

into high- and low-fit groups (Tables 4 and 5).

Correlations between the neuropsychological test scores and

either aerobic fitness or education within the older adult group

also yielded multiple significant effects (see Table 6, first two

columns). Both years of education and aerobic fitness were cor-

related with higher performance on a range of neuropsycholog-

ical tests. However, some of these effects may be correlated or

confounded with other factors, such as age and gender. There-

fore, to evaluate their independent contributions we ran a mul-

tiple regression analysis for each neuropsychological variables

using education, VO2max, age, and gender as predictors. The

results of this analysis (beta weights and multiple correlations)

are also reported in Table 6.

A number of significant beta weights were present for edu-

cation and a smaller number for VO2max. To avoid redundancy

and improve our understanding of the relationships between ed-

ucation, VO2max, and neuropsychological variables, we also ran

a factor analysis (based on a principal component analysis fol-

lowed by a varimax rotation) to group the latter. Both the

Kaiser’s criterion (eigenvalue 41) and a scree test converged

into selecting eight factors (accounting for 83.9%of the variance)

for the rotation. The factor analysis grouped the variables in a

way that was consistent with our expectations: most factors

(seven out of eight) appeared to correspond to the constructs we

expected to be assessed by the neuropsychological tests. Another

multiple regression was then run for each of these factors, using

age, gender, VO2max, and education as predictors and the factor

scores as dependent variables. The factor loadings and the results

of this multiple regression analysis are reported in Table 7. They

indicate that education was a significant predictor of Factor 1

(mainly related to several of the WCST scales, which could be

tentatively interpreted as representing cognitive flexibility) and

that VO2max was a significant predictor of Factor 7 (mainly

Anatomical correlates of aging, fitness, and education 829

Table 2. Mean (SD) Scores for Each Neuropsychological Test for Participants Grouped by Age

Test Young Old p value Directiona

mMMS 55.8 (1.5) 54.8 (1.7) n 1WAIS-R Vocabulary scaled 13.4 (1.8) 13.4 (2.7) 1Forward Digit Span 7.2 (1.4) 6.9 (1.1) 1Backwards Digit Span 6.0 (1.4) 5.1 (1.6) n 1Boxes 52.4 (11.5) 43.7 (12.7) n 1

Trail Making A 12.7 (4.2) 20.8 (11.0) n �Trail Making B 20.0 (6.5) 35.5 (18.3) nn �Digit Copying 79.5 (5.9) 65.7 (13.3) nn 1Digit Symbol 49.3 (5.3) 34.4 (5.8) nn 1Letter Completion 3 23.6 (2.7) 15.6 (3.1) nn 1Letter Completion 6 11.1 (2.9) 8.4 (1.9) nn 1

Letter Completion 9 7.3 (1.6) 6.2 (1.5) n 1Raven’s Prog. Matrices 11.1 (2.5) 5.3 (2.6) nn 1OSPAN 21.6 (0.8) 12.7 (6.5) n 1Verbal Fluency (CFL)% 80.5 (20.4) 60.2 (29.6) n 1Alternate Category 10.4 (2.0) 7.7 (2.4) n 1

aA plus sign means a higher score indicates higher performance on the test; a minus sign means a lower score indicates higher performance on the test.no.05; nno.001.

Table 3. Mean (SD) for the Wisconsin Card Sorting Test Subscores for Participants Grouped by Age

Young Old p value Directiona

Categories completed 5.7 (0.8) 4.3 (1.90) n 1Number of trials 76.9 (7.0) 101.3 (22.3) nn �Correct 64.6 (4.9) 68.1 (14.4) 1Errors 12.3 (5.8) 33.2 (20.9) nn �Perseverative responses 7.2 (4.3) 18.0 (13.6) nn �Perseverative errors 6.8 (3.5) 16.2 (11.7) nn �Nonperseverative errors 5.6 (3.3) 16.9 (12.4) nn �% perseverative errors 8.7 (4.2) 15.0 (8.6) n �Completion first category 17.4 (22.8) 16.6 (11.1) �% conceptual level resp. 81.6 (8.5) 61.1 (21.2) nn 1Failure to maintain set 0.3 (0.6) 1.1 (1.2) n �Learning to learn � 1.5 (5.2) � 6.7 (9.9) n 1

aA plus sign means a higher score indicates higher performance on the test; a minus sign means a lower score indicates higher performance on the test.no.05; nno.001.

related tomMMS andWAIS-R vocabulary score and tentatively

interpreted as an index of global cognitive competence). These

data suggest that the effects of VO2max and education, although

largely overlapping, also have some unique contributions.

Age Effects on Brain Volumes

The tissue density maps for the young and old adults are pre-

sented in Figure 1A (with Figure 1B showing 3D renderings of

the same effects). Statistical comparisons showed a number of

significant areas of change for all three tissue types (Z41.96,

po.05). The analysis of cerebrospinal fluid showed an increase

with age along the midline and in tissue bordering the lateral

ventricles and cerebral aqueduct (first row). There were signifi-

cant decreases in gray matter density in extensive temporal, pa-

rietal, and prefrontal regions as a function of age (second row). A

significant age-related decline inwhitematter volumewas evident

in anteriormedial areas near the lateral and third ventricles (third

row). The white matter showed relative preservation in posterior

regions, especially parietal and occipital areas. These results are

in line with previous findings of age-related volumetric declines

(Colcombe et al., 2003; Raz et al., 1998). No areas showed a

reduction of CSF or an increase in gray matter as a function of

age. However, older adults showed increases in white matter in

one section posterior and one superior to the corpus callosum

(fourth row). This suggests that some of the apparent reduction

of periventricular white matter may not be due solely to atrophy,

but also to white matter tissue displacement in older adults into

regions normally occupied by gray matter in younger adults.

A second analysis, based on a regression with age, was per-

formed within the older adult group. The rationale for this ap-

proach was that, although the range of ages within the old group

was somewhat limited (65–81 years), if tissue deterioration con-

tinues over time, then differences may become detectable even

within this age group. This prediction was confirmed by the re-

sults presented in Figure 2A (with 3D renderings of the same

effects in Figure 2B). Smaller clusters of voxels showed signifi-

cant effects in midline cerebrospinal fluid (first row) and poste-

rior whitematter (third row) (Z41.96, po.05). Significant tissue

loss with increasing age was found for gray matter in the medial

temporal lobe (Z41.96, po.05) (second row). This type of effect

has been previously found along with effects in frontal cortex

(Colcombe et al., 2003).

Fitness Effects on Brain Volumes

The comparisons based on the VO2max scores within the older

adults (with gender, age, and education as covariates) yielded

significant results for gray matter (Figure 3A, first row; 3D ren-

derings in Figure 3B). In particular, there were significant pos-

itive associations between gray matter volume and aerobic fitness

in medial-temporal, anterior parietal, and inferior frontal areas.

No significant relationships were found in the white matter (but

see Colcombe et al., 2003, 2006, for white matter changes and

differences with fitness), whereas a marginally negative associ-

ation was found with CSF, with more CSF for low-fit older

adults.

Education Effects on Brain Volume

An analysis of the three tissue types based upon years of edu-

cation was conducted within the older population, with age,

gender, and fitness as covariates. This comparison yielded a sig-

nificant positive effect in anterior white matter. Specifically, sig-

nificant effects were found in the rostrum of the corpus callosum

as well as in inferior frontal cortex. No other significant effects

were found for education. These results are shown in Figure 3A

(second row; 3D renderings in Figure 3B).

Discussion

The data presented here showed structural and behavioral

changes associated with age, fitness level, and education. The

comparison between the independent influence of fitness and

education shows different patterns of effects: Fitness effects were

mainly found in gray matter whereas education effects were

found for frontal white matter connections.

Our analysis of tissue density as a function of age yielded

results that are in accord with those previously observed (Col-

combe et al., 2003; Raz et al., 1998). Decreases of gray and white

matter in the frontal and medial temporal lobes are also consis-

830 B.A. Gordon et al.

Table 4.Mean (SD) for the Neuropsychological Test Scores of Old

Adults Grouped by Fitness Level

TestHigh-fit

oldLow-fit

old p value Directiona

mMMS 55.6 (1.2) 54.1 (1.9) nn 1WAIS-R vocabularyscaled

14.7 (2.9) 12.1 (1.7) n 1

Forward Digit Span 7.1 (1.1) 6.8 (1.1) 1Backwards Digit Span 5.4 (1.6) 4.9 (1.6) 1Boxes 46.7 (12.6) 40.8 (12.5) 1Trail Making A 19.2 (9.6) 22.5 (12.2) �Trail Making B 30.5 (9.6) 40.5 (23.3) n �Digit Copying 71.3 (14.8) 60.2 (8.9) n 1Digit Symbol 36.1 (6.2) 32.7 (5.0) n 1Letter Completion 3 16.6 (3.5) 14.6 (2.4) n 1Letter Completion 6 8.9 (1.9) 7.9 (1.7) n 1Letter Completion 9 6.6 (1.4) 5.9 (1.6) 1OSPAN 14.2 (7.0) 10.9 (6.6) 1Raven’s Prog. Matrices 6.2 (2.2) 4.5 (2.7) n 1

Verbal Fluency (CFL)% 57.5 (29.1) 62.9 (30.6) 1Alternate Category 8.9 (2.1) 6.6 (2.2) nn 1

aA plus sign means a higher score indicates higher performance on thetest; a minus sign means a lower score indicates higher performance onthe test.no.05; nno.001.

Table 5. Mean (SD) for the Wisconsin Card Sorting Test Scores

for Old Adults Grouped by Fitness Level

SubtestHigh-fit

oldLow-fit

old p value Directiona

Categories completed 5.3 (1.2) 3.4 (2.0) nn 1Number of trials 88.5 (16.3) 113.4 (20.5) nn �Correct 68.8 (12.8) 67.5 (16.1) 1Errors 19.7 (11.2) 45.9 (20.1) nn �Perseverative responses 10.9 (6.7) 24.7 (15.1) nn �Perseverative errors 10.1 (5.7) 22.1 (13.0) nn �Nonperseverative errors 9.7 (7.0) 23.8 (12.7) nn �% perseverative errors 11.3 (6.6) 18.5 (9.0) n �Completion first category 12.3 (2.8) 20.8 (14.2) n �% conceptual level resp. 73.9 (14.4) 49.0 (19.6) nn 1Failure to maintain set 0.8 (0.9) 1.4 (1.4) n �Learning to learn � 3.6 (6.9) � 10.5 (11.3) n 1

aA plus sign means a higher score indicates higher performance on thetest; a minus sign means a lower score indicates higher performance onthe test.no.05; nno.001.

tent with extensive behavioral evidence showing impairments in

tasks of executive control and memory for which these areas are

considered to be important (Park et al., 2002; West, 1996).

Cerebrospinal fluid increase in the parietal lobe is probably due

to decrease in the density or volume of the white matter sur-

rounding the ventricles, whereas increased amounts of cerebro-

spinal fluid in the longitudinal fissure and around the outer brain

surface can be attributed to a general reduction of the brain

volume.

The fitness data presented here support the idea that the be-

havioral effects observed in both the exercise and aging literature

may have a counterpart in brain anatomy. We also showed that,

within the older adults, higher fitness levels are associated with

relative sparing of tissue in brain areas typically vulnerable to the

effects of aging. This suggests that higher levels of fitness may

stave off some of the deleterious effects of aging on the brain and

possibly prevent or delay ensuing brain pathologies. This is con-

sistent with research in animal models as well as in humans

(Colcombe et al., 2003, 2004; Neeper et al., 1995; van Pragg

et al., 1999).

According to the cognitive reserve hypothesis, higher educa-

tion could potentially lead to, or be associated with, a surplus of

tissue. Specifically, our results are consistent with those indicat-

ing decreased occurrence of white matter hyperintensities in peo-

ple with higher education (Dufouil et al., 2003; Nebes et al.,

2006) and do suggest preferential sparing of white matter in this

group. The spatial localization of the education effect is very

similar to that observed in previous work after exercise inter-

vention (Colcombe et al., 2006). It is known that anterior white

matter is selectively sensitive to age-related declines, so any effect

of education on white matter was expected to show the greatest

effect in this area. Previous work on exercise interventions in-

dicates that it is possible to partially reverse this decline, whereas

the current study shows that education may be related to the

preservation of this area.

Due to the fact that the formal education used here as a pre-

dictor is completed long before adults reach an old age, longi-

tudinal studies are difficult to perform. Therefore, it is not easy to

determine whether education may be responsible for the pre-

served white matter integrity we reported or whether the pro-

pensity for more established frontal connections provides for

greater scholastic aptitude. Current work on cognitive interven-

tion on at-risk, low-socioeconomic status older adults (Carlson

et al., in press) suggests that improvements in neuropsychological

performance can be gained longitudinally. It would be useful to

expand this work to include structural brain measures to assess

whether white matter sparing and/or recovery accompanies these

functional changes.

It is also important to consider that higher levels of education

could be a proxy for a host of other influences that may affect

Anatomical correlates of aging, fitness, and education 831

Table 6. Correlations and Beta Weights of Neuropsychological Measures with Fitness and Years of Education for the Older Adults

Variable

Raw correlations

VO2max Education VO2max b Weight Education b Weight Multiple Correl.

Age � .25 � .16 F F FGender � .32n .02 F F FVO2 1.00 .56n F F FEducation .56n 1.00 F F FmMMS .42n .04 .58n � .31+ .50n

WAIS-R Vocab. .44n .52n .21 .38n .55n

Forward Digit .10 � .01 .13 � .08 .13Backward Digit .06 .18 � .04 .23 .24Boxes .15 .29+ .02 .29 .31Trail-A � .13 � .05 � .13 .01 .15Trail-B � .28 � .32+ � .07 � .29 .37Digit Copying .30+ .29+ .26 .10 .43Digit Symbol .26 .28 .33 .08 .48+

Letter Comp. (3, 6, 9) .28+ .29+ .18 .16 .36OSPAN .19 .19 .28 .01 .36Raven’s .32n .18 .19 .11 .49n

Verbal Fluency (CFL) % � .11 .12 � .14 .21 .32Alternate .39n .33+ .29 .17 .41WCSTCategories completed .34n .23 .15 .13 .44Number of trials � .40n � .46n � .20 � .34+ .50n

Correct .02 � .06 � .16 .00 .48+

Errors � .44n � .45n � .10 � .37n .61n

Perseverative resp. � .32n � .40n � .01 � .39n .48+

Perseverative errors � .33n � .43n .03 � .44n .53n

Nonpers. errors � .43n � .36n � .19 � .21 .56n

% perseverative errors � .25 � .37n .12 � .44n .50n

Completion first category � .30+ � .29+ � .18 � .19 .34% conceptual level resp. .40n .38n .07 .30+ .58n

Failure to maintain set � .22 .00 � .46n .24 .38Learning to learn .20 .06 � .13 � .02 .20

Notes. Age and gender are included in the model as predictors. Alternate: Alternate Category test. Letter Comp (3, 6, 9): Letter completion (combinedscore for 3,6,9). WCST: Wisconsin Card Sorting Test.npo.05; +po.10 (note that these correlations would all be significant if one-tailed tests were used, whichwould be justified here given that all our a priorihypotheses were unidirectional).

brain function and structure, such as improved access to health

care, better diet, and less stressful living environments. Irrespec-

tive of the possible origins of these effects, the identification of

structural correlates of higher education is an intriguing result.

The neuropsychological tests provide a behavioral parallel to

the structural analyses. Our data indicate that high fitness scores

predicted improved performance on a wide range of cognitive

tasks (Tables 4–6, raw correlations). This evidence supports pre-

viously published human behavioral data indicating that higher

cognitive performance is associated with higher aerobic fitness

(Churchill et al., 2002; Colcombe & Kramer, 2003; Hall, Smith,

& Keele, 2001). These data are also consistent with work done

within the animal literature providing evidence for improved

learning and memory with enriched environments, which lead

animals to engage in increased motor and exploratory behaviors

(Anderson et al., 2000; Churchill et al., 2002; Greenough et al.,

1972). In a manner similar to aerobic fitness, higher levels of

education were associated with better performance on several

neuropsychological tests (Tables 4–6).

Even though fitness and years of education were significantly

correlated, the volumetric analyses controlled for the other vari-

able, as well as for age and gender. Therefore, it was possible to

investigate the independent structural contributions of the two

factors. In the same way, the neuropsychological test results also

supported the existence of dissociations between the effects of

fitness and education, when the other factors were controlled for

(Tables 6 and 7, regression analyses). Specifically, the behavioral

data indicate that the independent effects of education are most

evident in tests highlighting flexibility (the WCST scales) and

semantic knowledge (WAIS-R vocabulary). These scales may be

closely related to the concept of cognitive reserve, which is

thought to be increased as a function of education (Stern, 2002).

In contrast, the greatest influence of fitness appears to be on the

mMMS scale, a measure of overall cognitive performance in

older adults. This suggests that, whereas education may boost

potential resources, fitness may in fact influence the way in which

these cognitive resources are actually deployed in everyday tasks.

This would suggest that these two factors may exert a comple-

mentary role in the preservation of cognitive function with ag-

ingFand that intervention strategies emphasizing both may be

particularly beneficial.

This study does have a number of potential limitations that

should be taken into account when interpreting and/or general-

izing our findings. For example, the cross-sectional nature of this

study may help to explain differences in results between this and

previous reports (Colcombe et al., 2006) that have shown aerobic

effects on both white and gray matter. The within-subject design

used by Colcombe and colleagues provides greater power to

832 B.A. Gordon et al.

Table 7. Summary of the Factor Analysis of the Neuropsychological Tests and of the Related Multiple Regression Analyses

Loadings 4.50Factor

Variable 1 2 3 4 5 6 7 8

mMMS 0.87WAIS-R Vocab. 0.56Forward Digit 0.80Backward Digit 0.83Boxes 0.82Trail-A � 0.63Trail-B 0.63 0.55Digit Copying 0.83Digit Symbol 0.81Letter Comp 3 0.67Letter Comp 6 0.75Letter Comp 9 0.73OSPAN 0.68Raven’s � 0.87Verbal Fluency (CFL) %AlternateWCSTCategories completed 0.84Number of trials � 0.63Correct 0.77Errors � 0.91Perverative resp. � 0.93Perseverative errors � 0.94Nonperseverative errors � 0.68 � 0.51% perseverative errors � 0.94Completion first category � 0.53% conceptual level resp. 0.89Failure to maintain set 0.78Learning to learn 0.89

Beta weightsAge � 0.12 � 0.19 0.10 0.10 � 0.32 � 0.04 � 0.10 � 0.24Gender � 0.30 0.20 0.14 � 0.02 � 0.44 0.20 0.12 0.43

VO2 � 0.16 0.22 � 0.03 � 0.17 � 0.32 0.19 0.58 0.02Education 0.48 � 0.01 0.01 0.23 � 0.03 � 0.02 � 0.22 � 0.32

Multiple R 0.52 0.33 0.20 0.20 0.54 0.23 0.48 0.52

Notes. Beta weights in bold indicate po.05, and in italics po.10.

detect relatively small anatomical changes. Thus a longitudinal

study would be able to detect relatively localized but small

changes in white matter whereas a cross-sectional study would

have more difficulty.

In the examination of the effects of fitness, secondary medical

conditions such as mild hypertension could influence the results.

However, our stringent screening process and the medical release

requirements should have limited some of these potential

Anatomical correlates of aging, fitness, and education 833

A

8.4Z-score

Z-score

Z-score

CSF: O>Y

e 2.3 7.5Grey: Y>O

White: Y>O - 2.3 7.0

2.3 5.1Z-scoreWhite: O>Y

B

Grey: Y>O

CSF: O>Y

White: Y>O

White: O>Y

2.3

Figure 1.A: Partial-volumemaps depicting the comparisons between younger and older adults. Row 1: CSF; colors identify regions

in which CSF partial volume increases as a function of age. Row 2: Grey matter; colors identify regions in which gray matter partial

volume decreases as a function of age. Rows 3 and 4: White matter; colors identify regions in which white matter partial volume

decreases (row 3) or increases (row 4) as a function of age. All images are presented in radiological convention. Y: young; O: old.

Saggital slices: MNI coordinates x: 20 (right), 0 (medial), � 20 (left); Coronal slices: y: � 38 (posterior), � 18 (middle), 2 (anterior);

Horizontal slices: z: 2 (inferior), 18 (middle), 38 (superior). B: Three-dimensional renderings of the effects in A, also shown in

radiological convention.

confounds. In fact, these screening and recruiting procedures

are likely to reduce the sizes of possible fitness or education

effects, as both variables have reduced ranges in our samples

compared to the overall population. Notwithstanding these

reduced ranges, however, significant contributions of both fac-

tors were found.

The relatively small sample size in this study also poses an-

other limitation, weakening the power of statistical inference.

834 B.A. Gordon et al.

Age Effects Within the Old Adults

A

Z-score 1.96CSF

Age Effects Within the Old Adults

B

CSF

Grey

White

4.90

Z-score 1.96Grey 4.30

Z-score 1.96White 4.10

Figure 2. A: Partial-volume maps depicting the analysis of age-effects within the older population. Row 1: CSF; colors identify

regions in which CSF partial volume increases as a function of age. Row 2: Grey matter; colors identify regions in which gray matter

partial volume decreases as a function of age. Row 3: White matter;: colors identify regions in which white matter partial volume

decreases as a function of age. All images are presented in radiological convention. Saggital slices: MNI coordinates x: 20 (right), 0

(medial), � 20 (left); Coronal slices: y: � 38 (posterior), � 18 (middle), 2 (anterior); Horizontal slices: z: 2 (inferior), 18 (middle), 38

(superior). B: Three-dimensaional renderings of the effects in A, also shown in radiological convention.

The number of individuals included is not as large as other stud-

ies (Good et al., 2001b). However, a sample size smaller than 40

subjects is not uncommon in the literature (Buskova, Vaneckova,

Sonka, Seidl, & Nevsimalova, 2006; Holzapfel, Barnea-Goraly,

Eckert, Kesler, & Reiss, 2006). Although the current sample size

is more than sufficient to detect robust differences, it may lead to

a small reduction in statistical power compared to some previous

work (Colcombe et al., 2003, 2006). In the current study, the

Anatomical correlates of aging, fitness, and education 835

Unique Effects of Fitness and Education Within the Old AdultsA

Fitness with age, education and gender removed

Education with age, fitness and gender removed

2.3 4.5Z-score

2.3 4.5Z-score

Unique Effects of Fitness and Education Within the Old Adults B

Fitness with age, education and gender removed

Grey

Education with age, fitness and gender removed

White

Figure 3.A:Graymatter partial-volumemaps depicting the unique contributions of fitness (row 1)Fcolors identify regions inwhich

gray matter increases as a function of fitness across all older adults, with education, gender, and age as variables in the modelFand

education (row 2)Fcolors identify regions in which white matter increases as a function of education across the older adults, with

fitness, gender, and age as variables in the model. All images are presented in radiological convention. Saggital slices: MNI

coordinates x: 20 (right), 0 (medial), � 20 (left); Coronal slices: y: � 38 (posterior), � 18 (middle), 2 (anterior); Horizontal slices: z:

2 (inferior), 18 (middle), 38 (superior). B: Three-dimensional renderings of the effects in A, also shown in radiological convention.

effect of fitness on gray matter found by Colcombe and col-

leagues (2003, 2006) was replicated, but the fitness effects on

white matter may have remained subthreshold. Further investi-

gation and generalization of the effects of fitness and education

will benefit from a larger sample size. Notwithstanding these

possible limitations, the results presented in this article advance

our understanding of the aging brain. This work reaffirms the

behavioral and volumetric effects associated with aerobic fitness.

It expands upon this work to also show that education impacts

not only cognitive measures, but also white matter integrity.

Although it has much support in the literature (Habeck et al.,

2003; Richards & Deary, 2005; Scarmeas et al., 2003) the ‘‘cog-

nitive reserve’’ hypothesis is still debated (Christensen et al., 2007).

These results add support to this idea that higher educationmay be

predictive of preserved brain tissue. In the future we hope to di-

rectly examine the interaction of such factors as socioeconomic

status, verbal IQ, as well as years of education. A composite score

based upon these three would potentially allow us to directly ex-

amine the cognitive reserve hypothesis in a volumetric manner.

This work goes beyond purely structural implications. For

example, it may be possible to directly correlate age-related

structural changes to changes in patterns of blood flow in fMRI

and other functional imaging studies (Buckner et al., 2004). The

anatomical differences between the higher- and lower-fit older

adults, as well as the effects of education, suggest that the changes

in functional activation observed in older adults may be related,

at least in part, to specific structural changes in the brain. Re-

search has shown that, compared to less fit older adults, aero-

bically fit older adults exhibit greater activation in brain areas

related to attention and reduced activation in brain areas related

to conflict (Colcombe et al., 2004). Similarly, Czernochowski

and colleagues (in press) showed that increased brain activity in

highly educated older adults, compared to older adults with

lower education, was associated with preserved memory perfor-

mance.

In conclusion, this article takes a novel approach to exam-

ining the effects of both education and fitness in aging. Simul-

taneous comparison of the effects of these two factors allowed us

to differentiate their influence on brain anatomy and cognitive

performance. Further research efforts may be directed toward

establishing cause–effect relationships between cardiovascular

fitness, brain anatomy, and function through fitness intervention

studies and to further clarify the potential effects of education

through cognitive intervention studies.

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(Received May 4, 2007; Accepted October 22, 2007)

838 B.A. Gordon et al.