What provides cerebral reserve?

What provides cerebral reserve?

Roger T. Staff,1 Alison D. Murray,2 Ian J. Deary4 and Lawrence J. Whalley3

1Department of Bio-Medical Physics and Bioengineering,

University of Aberdeen and Aberdeen Royal In®rmary,2Department of Radiology, University of Aberdeen,3Clinical Research Centre, Department of Mental Health,

Aberdeen and 4Department of Psychology, University of

Edinburgh, Edinburgh, UK

Correspondence to: Roger T. Staff, Department of

Bio-Medical Physics and Bioengineering, University of

Aberdeen, Foresterhill, Aberdeen AB25 2ZD, UK

E-mail: [email protected]

SummaryThe cerebral reserve hypothesis is a heuristic conceptused to explain apparent protection from the onset ofcerebral disease and/or cognitive decline in old age. Asigni®cant obstacle when investigating the reservehypothesis is the absence of baseline data with which tocompare current cognitive status. We tested the in¯u-ence of three hypothesized proxies of reserve (educa-tion, head size and occupational attainment [OCC]) in92 volunteers born in 1921, whose cognitive functionwas measured at age 11 and 79 years, and who under-went brain MRI. The association between each proxyand old age cognitive function was tested, adjusting forvariance contributed by childhood mental ability anddetrimental age-related pathological changes measured

using MRI. The results showed that education andOCC, but not total intracranial volume (TICV), con-tribute to cerebral reserve and help retain cognitivefunction in old age. Education was found to contributebetween 5 and 6% of the variance found in old agememory function but was found to have no signi®cantassociation with reasoning abilities. OCC was found tocontribute around 5% of the variance found in old agememory function and between 6 and 8% of the vari-ance found in old age reasoning abilities. We concludethat the intellectual challenges experienced during life,such as education and occupation, accumulate reserveand allow cognitive function to be maintained in oldage.

Keywords: cerebral reserve; education; occupation; head size; childhood intelligence

Abbreviations: AVLT = Auditory Verbal Learning Test; BF = brain fraction; GM = grey matter; MHT = Moray House

Test; OCC = occupational attainment; RPM = Raven's Progressive Matrices; TICV = total intracranial volume; WM =

white matter; WMH = white matter hyperintensities

Received August 21, 2003. Revised January 6, 2004. Accepted January 10, 2004. Advance Access publication March 26, 2004

IntroductionThe extent to which cognitive performance is retained in the

face of brain ageing is of great importance in clinical

neuroscience. Many studies of cognitive ageing and dementia

show large cognitive variations between individuals in the

presence of brain structural changes that appear similar in

location and extent (Fazekas et al., 1987; Katzman et al.,

1989; Satz 1993; Leaper et al., 2001; Meguro et al., 2001;

Bigio et al., 2002). To date, little is known of the sources of

these inter-individual variations. The largest contributor to

cognitive function in old age is cognitive function in youth,

accounting for just under half of the variance (Deary et al.,

2000). Some reports stress the importance of environmental

in¯uences on cognitive ageing, especially nutritional, during

life (Bowirrat et al., 2002; Perls et al., 2002). Others have

emphasized the importance of genetics in individual differ-

ences in cognitive ageing, to which polymorphisms in the

apolipoprotein E allele make a small but signi®cant contri-

bution (Abate et al., 1998; Bowirrat et al., 2002; Deary et al.,

2002).

The fact that substantial differences in cognitive impair-

ment exist in individuals with an identical amount of brain

ageing may be accounted for by the hypothesized protective

effect of `cerebral reserve'. Stern (Stern, 2002) made analysis

of cerebral reserve more tractable by: (i) distinguishing

between active and passive components of reserve; and

(ii) identifying the burden imposed on reserve by age-related

brain pathology. Cerebral reserve may be a passive capacity

of brain available to withstandÐup to a ®nite thresholdÐ

brain ageing, after which de®cits appear. For example, if

brain size is a proxy of passive cerebral reserve, the cerebral

reserve hypothesis predicts that those with a larger intracra-

nial capacity are able to withstand brain ageing better than

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those with a smaller intracranial capacity; it would take more

brain ageing before age-related cognitive decline was

detectable. The second component addresses the active

processes that are available to ameliorate the effects of

brain ageing. These active processes are of two types. One is

the relative ability to switch to alternative cognitive para-

digms to overcome the effects of brain ageing. The other is

the ability to recruit compensatory neuronal structures to

replace processing pathways damaged by ageing. It is

impossible to quantify the diverse positive and negative

in¯uences on brain development. However, appropriate

proxies of active reserve are education and occupation,

which are generally available. To test the cerebral reserve

hypothesis requires these passive and active indicators of

cerebral reserve, and a measure of brain ageing. To quantify

the burden imposed by brain ageing, we used an estimation of

white matter hyperintensities, which occur more frequently in

the presence of risk factors for vascular disease and are

associated with impaired cognitive function (Leaper et al.,

2001; Deary et al., 2003).

Testing the cerebral reserve hypothesis is dif®cult, because

it demands a set of measured variables that are rarely

available in a single cohort. Ideally, one requires records of

mental ability in youth and old age to measure age-related

cognitive change; measures of brain ageing to assess the

burden placed on the brain; and measures of passive (e.g.

brain size) and active (e.g. education, occupational category)

cerebral reserve that might ameliorate this burden somewhat.

Precise tests of cerebral reserve hypotheses have hitherto not

been feasible because data on mental ability from youth are

rarely available for those older people now at risk of cognitive

decline. The use of education- or occupation-related variables

by some researchers as proxies for childhood mental ability is

unsatisfactory, because these are themselves reserve factors

contributing to the active model of cerebral reserve.

Here we use cognitive test scores at age 11 years and at age

78±80 years in the same subjects to obtain a measure of age-

related cognitive change. We use a white matter hyperinten-

sity score detected on MRI, which has previously been shown

to correlate with old age cognition (Leaper et al., 2001), as a

quanti®able example of age-related brain burden. In the

active reserve model, we were also able to use brain fraction

(BF) as an additional measure of burden, which has been

shown to reduce with age (Blatter et al., 1995) and correlates

with intelligence (Willerman et al., 1990). The BF is the ratio

of the total white and grey matter volumes to the total

intracranial volume (TICV). This measure is reproducible

and suitable for a cross-sectional study such as this (Chard

et al., 2002). In the passive cerebral reserve hypothesis, TICV

is a putative index of cerebral reserve, and in the active model

education and/or occupational status are indicators of reserve.

We formulated tests of the reserve hypotheses as follows: if

the cerebral reserve hypothesis is correct, then the measure of

reserve should account for signi®cant variance in the

cognitive outcomes in old age after adjusting for variance

contributed by childhood mental ability and burden. In other

words, possessing some reserve means that one's cognitive

score is greater than would be predicted from the person's

childhood ability and the amount of overt, accumulated

burden.

MethodSampleThe 1932 Scottish Mental Survey tested the cognitive ability

of most of the eligible Scottish children born in 1921

attending school on 1 June 1932 using a version of the Moray

House Test (MHT) (Scottish Council for Research in

Education, 1933). With the approval of the local ethics

committee, 235 of the local residents who took part in the

1932 survey were recruited into a prospective, longitudinal

study of brain ageing and health. From this sample, 144

subjects were randomly invited to undergo brain MRI

examination. Medical contraindications prevented 17 from

participating, 21 refused, and 106 volunteers (65 male and 41

female) were imaged. Eight were removed from the study (six

male and two female) because of excess movement during

acquisition of volumetric MRI data. Satisfactory MRI data

were obtained in a total of 98 volunteers. A sample derivation

¯ow chart is shown in Fig. 1.

MRI acquisitionSubjects were imaged using a 1 T Impact MRI scanner

(Siemens, Erlangen, Germany) and a RF head coil. The MRI

protocol consisted of two acquisitions: ®rst a T1-weighted

sequence, which was used to calculate the brain volume

measures, and then a T2-weighted sequence, which was used

to quantify white matter hyperintensities. The T1-weighted

sequence was a 3D-MP-RAGE (3D magnetization-prepared

rapid acquisition gradient echo) sequence. This sequence

produced 128 sagittal slices of the head with an effective

thickness of 1.41 mm. The repetition time was 11.4 ms, the

echo time was 4.4 ms, the RF ¯ip angle was 15°, the ®eld of

view was 250 mm, the slab thickness was 180 mm, and the

acquisition time was 6 min 7 s. The T2-weighted sequence

Fig. 1 A schematic ¯ow chart showing how our sample wasobtained from the original Scottish mental health survey of 1932.

1192 R. T. Staff et al.




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was a fast spin echo sequence with a repetition time of

4000 ms and an echo time of 96 ms. This sequence produced

axial images of the head with a section thickness of 5 mm and

an intersection gap of 1.5 mm. The ®eld of view was 230 mm,

the slab thickness was 140 mm and the acquisition time was

1 min 55 s.

Image processingThe T1 imaging data were processed using SPM99

(Statistical Parametric Mapping 99) image processing pack-

age (Wellcome Department of Cognitive Neurology,

London). Each data set was automatically segmented into

images representing the probability that any voxel contained

grey matter (GM), white matter (WM), CSF or other tissue.

The segmentation algorithm was described by and uses an a

priori estimate of the distribution of GM, WM and CSF and

cluster analysis to estimate the distribution within individuals

(Ashburner and Friston., 1997). As part of the segmentation

algorithm, the data were corrected for the effect of hetero-

geneity attributable to magnetic ®eld non-uniformity (Sled

et al., 1998). The resultant images were inspected for

adequate segmentation into the different tissue types.

Using GM and WM probability maps, a bit maps mask

representing the parenchymal brain was created using the

software feature available within the SPM99 software

package. This involves erosion and dilation of the probability

maps and extracts the brain from the initial segmentation

maps. Mutually exclusive bit maps for the GM and WM were

then classi®ed using the tissue type with greatest probability

within the parenchymal brain. After manual editing, a CSF bit

map was created by including those voxels that had a

probability of containing CSF greater than 0.5. The three bit

maps obtained were then adjusted by classifying any

con¯icting voxels as CSF so that these comprised mutually

exclusive data sets. Axial slices inferior to the cerebellum

were not included in the calculations. The results were

expressed as fractions of the TICV, calculated as the sum of

the GM, WM and CSF. The parenchymal BF was calculated

as the ratio of the parenchymal brain to the TICV. This ratio

has previously been used to estimate whole brain shrinkage

(Rudick et al., 1999) and thus may offer an index of the brain

ageing burden.

Subcortical and deep white matter hyperintensities (WMH)

on the T2-weighted MRIs were analysed using a semiquanti-

tative rating scale (Fazekas et al., 1987). Each image was

scored by three independent observers on a scale ranging from

0 to 3 of increasing severity (0 normal, 1 punctate, 2 coalescing

and 3 con¯uent) and mean scores were used in the analyses.

We have previously shown this scale to be reliable in terms of

intra- and interobserver variability (Leaper et al., 2001).

Cognitive testingMoray House Test scores from age 11 were available for all

subjects from the records of the Scottish Mental Survey 1932.

Mental test scores from old age were obtained by a trained

psychologist who administered Raven's Progressive Matrices

(RPM) (Raven, 1960), a non-verbal reasoning measure of

¯uid-type intelligence, and the Auditory Verbal Learning

Test (AVLT) (Rey, 1958), a measure of immediate and

delayed verbal memory. Memory and reasoning are cognitive

functions known to decline with age (Salthouse, 2003). The

RPM test was used since it is one of the best individual tests

with respect to general cognitive ability (Raven, 2000) and

the AVLT is a common tool for measuring memory

impairment (Flowers et al., 1984). Verbal memory tests are

especially sensitive to the early stages of pathological

cognitive decline (Howieson et al., 2003). A total of 92

volunteers satisfactorily completed all MRI and cognitive

testing.

Demographic measuresYears of education and occupational classi®cation of each

participant were recorded at interview. Occupational attain-

ment (OCC) was categorized using the UK's Of®ce of

Population Statistics classi®cation (Great Britain: Of®ce of

Population Censuses and Surveys, 1990). Their educational

experience was typical of the UK in the 1930s, with most

people leaving school by the age of 14 years (65.2%). More

than 95% of the group had no full-time education after

secondary school. The median duration of education in years

for males and females was 9 years. Three individuals had a

university education. The split between blue- and white-

collar occupations was approximately 50 : 50. The distribu-

tion of occupational level within the group was as follows:

managerial 9, professional 10, lesser professional 3, secre-

tarial 24, skilled manual 21, semi-skilled ii 5, semi-skilled i 8,

unskilled ii 8, and unskilled i 7. These values represented the

highest ever occupational level obtained by an individual.

Statistical analysisThe statistical comparisons were performed using SSP10

(Statistical Package for the Social Sciences 10; Chicago, IL,

USA).

We used general linear modelling analysis of covariance

(ANCOVA) to test the active and passive brain reserve

hypotheses. In both the active and passive models the

outcome variables were cognitive test scores at age 79.

Separate models were tested for verbal memory (AVLT) and

non-verbal reasoning (RPM). Moray House Test scores at age

11 were covariates. Measures of brain ageing burden and

reserve factors were also covariates. Sex was entered as a

between-subjects ®xed factor.

ResultsThe means (standard deviation) for each variable are

shown in Table 1. Years of education, TICV, brain fraction

and AVLT differed between sexes, with women spending

Proxies of reserve and old age cognitive function 1193




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longer in education (P = 0.036) and scoring higher on AVLT

(P = 0.042). Women had smaller intracranial volumes (P <

0.001).

Testing the passive cerebral reserve hypothesisThe simplest model of cerebral reserve is that it is passive,

and that possessing more of a reserve factor protects an

individual to some extent from intellectual decline. Brain size

before the onset of ageing is estimated here from total

intracranial volume, and is used as a reserve factor in the

passive model. This reasoning implies that a person with a

larger brain prior to brain ageing is capable of withstanding a

larger degree of brain ageing burden, and should show less

decline in cognitive function than a person with similar

childhood mental ability, the same amount of brain ageing

burden, and a smaller brain. In other words, to test the passive

reserve hypothesis, we ask whether there is a relationship

between original brain size (inferred from total intracranial

volume) and a measure of cognitive function in later life,

when individual differences in childhood intelligence and a

measure of brain ageing burden (hereinafter just burden) are

controlled. First, we used memory scores (AVLT) as the

dependent variable, with childhood mental ability (MHT),

burden (WMH) and TICV as the covariates, and sex as a ®xed

factor. Table 2 indicates that brain size makes no signi®cant

contribution to memory at age 79. This analysis also showed

that sex and extent of burden (estimated from WMH score)

also made no signi®cant contribution to verbal memory

(AVLT), whereas childhood MHT score contributed signi®-

cantly (P = 0.021), accounting for 6% of the variance

(Table 3). Similarly, we repeated the analysis with non-verbal

reasoning (RPM) as our dependent variable and again found

that TICV made no signi®cant contribution to cognitive score

at age 79. This analysis also showed that sex also made no

signi®cant contribution to non-verbal reasoning (RPM), but

childhood intelligence and degree of burden (WMH) con-

tributed 14% (P < 0.001) and 13% (P < 0.001) of the variance,

respectively (Table 4).

It would be statistically invalid to test for a similar

relationship using BF as our estimate of burden in the passive

reserve model, because TICV is used to calculate brain

fraction. In summary, the passive reserve hypothesis is not

supported in this sample.

Table 1 Volunteer group characteristics for each measured variable

Male Female P Total Max. Min.(n = 53) (n = 39) (n = 92)

MHT score at age 11 63.0 17.0Mean 41.7 42.2 0.827 41.9SD 11.8 9.6 10.995% CI (upper, lower) 45.0, 38.4 45.3, 39.1 44.2, 39.7

EDU (years) 17 8Mean 9.51 10.2 0.036 9.80SD 1.10 2.00 1.5795% CI (upper, lower) 9.81, 9.21 10.85, 9.56 10.1, 9.48

OCC 9 1Mean 4.75 4.82 0.893 4.78SD 2.39 2.22 2.3195% CI (upper, lower) 5.41, 4.10 5.54, 4.10 5.26, 4.31

TICV (cm3) 1934.3 1128.1Mean 1574.4 1387.7 <0.001 1495.3SD 140.9 128.0 163.795% CI (upper, lower) 1613.2, 1535.6 1429.3, 1346.3 1529.2, 1461.4

BF (%) 82.3 65.4Mean 72.0 73.9 0.012 72.8SD 3.5 3.7 3.795% CI (upper, lower) 72.9, 71.0 75.1, 72.7 73.5, 72.0

WMH score 3 0Mean 1.06 1.27 0.165 1.15SD 0.67 0.70 0.6995% CI (upper, lower) 1.25, 0.88 1.49, 1.03 1.29, 1.00

RPM score 45 10Mean 30.1 27.2 0.122 28.9SD 7.8 9.7 8.795% CI (upper, lower) 32.2, 29.7 30.4, 24.1 30.7, 27.1

AVLT score 66 17Mean 37.2 41.6 0.042 39.1SD 10.2 10.0 10.395% CI (upper, lower) 40.0, 34.4 44.8, 38.4 41.2, 37.0

Calculated P values are given after testing for differences between sexes. EDU = education.





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Testing the active cerebral reserve hypothesisThe active model of cerebral reserve was tested in the same

way. For each potential measure of reserve, a general linear

model was used to examine its association with the dependent

variable (AVLT or RPM) adjusting for childhood mental

ability and one of the brain ageing burden measures (WMH or

BF), with sex as a ®xed factor. Table 2 shows that occupation

contributes signi®cant variance to verbal memory (AVLT)

and non-verbal reasoning (RPM) scores (i.e. occupation acts

as a reserve factor) when BF is used as an estimate of burden,

and acts as a signi®cant reserve factor for non-verbal

reasoning, but not verbal memory, when WMH is used as

an estimate of brain ageing burden. Education acts as a

reserve factor for verbal memory but not non-verbal reason-

ing when either measure of the brain ageing burden is used.

Table 2 also shows that occupation contributes approximately

5% of the variance in memory function at age 79, and

between 5 and 8 % of the variance in non-verbal reasoning

Table 3 Effect of childhood intelligence on cognitive ability aged 79 years

Memory (AVLT) Reasoning (RPM)

Model Proxy of reserve Measure of brainburden

P Partial h2 P Partial h2

Passive TICV WMH 0.021 0.059 0.000 0.140Active OCC WMH 0.090 0.033 0.004 0.086

BF 0.255 0.015 0.042 0.044EDU WMH 0.045 0.045 0.000 0.138

BF 0.122 0.029 0.002 0.097

The table shows the effect of childhood intelligence on old-age cognition when tested using different combinations of burden, reserveproxy and old-age cognitive ability. The values were calculated using the general linear model (univariate analysis) with sex as a ®xedfactor. The partial h2 is an estimate of the effect size, which is the proportion of the variance that can be explained by the variable.EDU = education.

Table 4 The effect of brain burden on cognitive ability aged 79 years


Model Proxy of reserve Measure ofbrain burden



BF 0.034 0.051 0.024 0.054EDU WMH 0.065 0.039 0.000 0.149

BF 0.048 0.044 0.076 0.034

The table shows the effect of brain burden on old-age cognition when tested using different combinations of reserve proxy and old-agecognitive ability. The values were calculated using the general linear model (univariate analysis) with childhood intelligence as anadditional covariate and gender as a ®xed factor. The partial h2 is an estimate of the effect size, which is the proportion of the variancethat can be explained by the variable. EDU = education.

Table 2 The effect of each proxy of reserve on cognitive ability aged 79 years


Model Proxy of reserve Measure ofbrain burden



BF 0.031 0.052 0.006 0.078EDU WMH 0.024 0.057 0.828 0.001

BF 0.019 0.062 0.925 0.000

The table shows the effect of each proxy of reserve on old-age cognition when tested using different combinations of burden and old-agecognitive ability. The values were calculated using the general linear model (univariate analysis) with childhood intelligence as anadditional covariate and sex as a ®xed factor. The partial h2 is an estimate of the effect size, which is the proportion of the variance thatcan be explained by the variable. EDU = education.





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function at that age. Similarly, education contributes 6% of

the memory variance but has no signi®cant effect on non-

verbal reasoning. In summary, the active reserve hypothesis is

supported and its effect size is moderate.

The principal results in this paper concern the contribution

of the reserve factors to mental test scores in old age. It is

informative also to describe the contributions of childhood

mental ability and brain ageing burden. These are provided in

Tables 3 and 4, respectively and a schematic diagram

representing the in¯uence of each factor on old age cognitive

function is shown in Fig. 2. Childhood intelligence contrib-

utes to non-verbal reasoning abilities at age 79. However, the

contribution to old-age verbal memory is less consistent, only

one of the four tests showing a signi®cant result (P < 0.05)

when the active model is tested. A similar pattern of results is

shown when the effect of brain ageing burden is assessed: it

clearly contributes to non-verbal reasoning ability in old age,

the effect on memory being less clear (Table 4). Gender

in¯uenced reasoning and memory in old age only when

occupation was the proxy of reserve. Gender contributed

4.6% (P = 0.039) of the reasoning variance when BF was the

measure of burden, males producing a higher RPM score than

females. Gender also contributed 6% (P = 0.021) of the

memory variance when WMH was the measure of burden,

females producing a higher AVLT score than males.

DiscussionTo our knowledge, this is the most comprehensive test of the

active and passive components of the brain reserve hypothesis

as articulated by Satz (Satz, 1993) and Stern (Stern, 2002).

We used actual childhood measures of cognitive ability,

measures of brain burden, and widely suggested parameters

of brain reserve to account for variance in cognitive abilities

in non-demented old people. We investigated passive reserve

by using TICV, and active reserve by using education and

occupation. The ®ndings support the active but not the

passive version of the brain reserve hypothesis. More

education and a more cognitively complex occupation predict

higher cognitive ability in old age than would be expected for

Fig. 2 A schematic ¯ow chart representing the relationship between the parameters explored in this study. The partial calculated h2 valuesare shown. The partial h2 represent the level of independent variance in the old-age cognitive ability accounted for by each variable.These values are the equivalent to the square of a correlation type measure. In cases where we calculated more than three values of h2, therange is quoted. Note that not all of the h2 values are representative of analyses that achieve signi®cance. Those values that describesigni®cant associations (P < 0.05) are shown in bold typeface. MH = white matter hyperintensities; BF = brain fraction; OCC =occupational attainment; EDU = education; MHT = Moray House Test; TICV= total intracranial volume; AVLT = Auditory VerbalLearning Test; RPM = Raven's Progressive Matrices.





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a person's childhood ability and accumulated brain burden.

Possession of a bigger brain in earlier life conferred no such

reserve.

The passive model, with head size as the potential proxy of

reserve, has been investigated with con¯icting results (Graves

et al., 1996; Jack et al., 1997; Mori et al., 1997; Scho®eld

et al., 1997; Jenkins et al., 2000). Scho®eld and colleagues

(Scho®eld et al., 1997) reported that old people with smaller

head sizes were more likely to have Alzheimer's disease,

whereas Jenkins and colleagues (Jenkins et al., 2000) found

that larger head size did not delay the onset of Alzheimer's

disease. These reports were not able to control for measured

premorbid cognitive ability, and do not attempt to assess the

effect of brain burden as a potential confounding variable. We

used white matter hyperintensity lesion score as a measure of

burden. These lesions are often associated with changes in

brain blood vessels. Numerous reports (Double et al., 1996;

Fox and Freeborough, 1997; Mouton et al., 1998; Leaper

et al., 2001) have found relationships between the measures

of burden used here (WMH and BF) and cognitive ability in

old age. Currently, a complex model of the summative effects

of this cerebrovascular pathology, the density of plaques and

tangles of Alzheimer's disease, and the amount of reserve

seems best placed to explain the threshold between brain

ageing and dementia (Esiri et al., 2001; Whalley, 2002). We

have found that bigger brains do not act as a proxy of reserve

in terms of the passive model for reserve mediation. The

result does not refute the passive model as a whole but shows

that TICV is not a proxy for passive reserve in this sample.

Alternative proxies could include synaptic count, which is not

necessarily re¯ected in total or regional brain size. It may be

that the size of speci®c brain structures, such as the

hippocampus, could be a proxy of passive reserve and these

should be considered in the design of future studies. An

alternative explanation for TICV not acting as a proxy of

reserve is that head size is a poor predictor of performance.

Further, in the largest study of brain size and cognition

conducted to date, the association between memory and brain

size was to overall size and not to speci®c regions. Also, the

memory contribution operated via the general cognitive

factor (MacLullich et al., 2002).

Investigations into the relationship between demographic

measures and cognitive function in later life have show

signi®cant correlations (Snowdon et al., 1989; Plassman et al.,

1995; Kidron et al., 1997; Coffey et al., 1999). Studies have

suggested that education and occupational status contribute

independently to reserve (Mortel et al., 1995). This study

shows relationships between memory (AVLT) and education,

memory and occupation, reasoning (RPM) and occupation,

but not reasoning and education. This raises the possibility

that different life experiences may contribute to the reserve of

different cognitive domains. It may also re¯ect the nature of

the education received in the 1920s and 1930s, when there

was a greater emphasis on verbatim learning rather than

problem-solving. The neurobiological correlates of higher

educational attainment and occupational complexity are

uncertain but neurodevelopmental studies in animals suggest

that exposure to an enriched or more stimulating environment

before completion of sexual maturation is associated with

greater synaptic density and more complex neocortical

interneuronal connections (Rapp and Bachevalier, 2003). In

later life, synaptic density falls in the frontal cortex and this

fall is greater in early Alzheimer's disease (Small et al.,

2001). It is plausible, therefore, that the detection of roles for

education and occupation in this study points to differences in

synaptic function as the underlying source of variance in

active reserve.

A possible explanation for the correlation between demo-

graphic measures such as education and occupation and

decline in old age is that those of lower of socio-economic

status might have an increased risk of toxic or environmental

exposures, nutritional de®ciencies and/or perinatal insult

(Katzman, 1993). These issues are confounded by the well-

established roles of education and occupation as risk factors

for many multifactorial, late-onset diseases, especially

ischaemic vascular disease (Hemingway and Marmot, 1999)

and Alzheimer's disease (Stern et al., 1994). Many reports

suggest that low educational attainment is associated with

increased dementia risk and there are clear socio-economic

gradients with risk of ischaemic vascular disease (Katzman,

1993; Mortimer and Graves. 1993; Mortel et al., 1995; Qiu

et al., 2001). These associations point to complex inter-

relations occurring at several levels between late life

cognitive function, cerebrovascular disease and differences

in education and occupation. Controlling for all potential risk

factors is a dif®cult undertaking. In this study education had

signi®cant predictive value after we controlled for estimates

of the brain burden of cerebrovascular pathology, the latter

possibly recording life-long environmental exposure.

Potential criticisms of this study are related to sample size

and bias, particularly survival bias; that is, only those who

survived to 1999±2000 were able to participate. Our MRI

sample was drawn from the larger 1921 Aberdeen sample.

The fact that we investigated healthy survivors creates an

unavoidable bias in the imaged group. The general population

would contain those ageing less successfully who potentially

have a greater degree of brain burden. There is an additional

potential for bias attributable to those excluded for medical

contraindications such as the presence of a cardiac pace-

maker, a tremor involving the head and neck and other

conditions that would not allow them to be imaged. Arguably,

this group has a greater potential for vascular disease and

would exhibit a larger level of brain burden. Similarly, those

who refused to be imaged or who were eliminated due to

excessive movement during acquisition potentially have a

greater degree of burden, which would bias our results. The

net result would be to lower the effect sizes with regard to

those expected in a more representative sample. The imaging

protocol, processing and analysis tools used in this study are

straightforward and the software used is available in the

public domain. It is possible that the use of different imaging

protocols of more elaborate forms of processing and image





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classi®cation techniques may produce results that are more

sensitive.

ConclusionThe maintenance of cognitive function in old age has become

increasingly desirable to society with an ageing population.

This work investigates a range of potential factors that

contribute to cognitive function in old age, based around the

concept of the reserve hypothesis. We found no evidence of

reserve being implemented through a passive process, with

TICV as a measure of reserve capacity. The results show that

education and occupation can be considered as proxies of

active reserve. Our results suggest that these two measures of

active reserve in¯uence different cognitive domains to

varying extents.

AcknowledgementsThe authors would like to thank S. A. Leaper, H. A. Lemmon,

A. Lyburn, L. Frisch and J. Walker for their help during this

project. The project was funded by a grant from the Chief

Scientist Of®ce of the Scottish Executive. I.J.D. is the

recipient of a Royal Society-Wolfson Research merit award.

L.J.W. holds a Career Development Award from the

Wellcome Trust.

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What provides cerebral reserve?