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Statistical parametric map and its 3-dimensional brain rendering representation depicting areas of significant (some P<.001, some P<.01, and some P<.05, as presented in Table 4) inverse correlations between cerebral blood flow and activities score in the Alzheimer group, controlling for age and Mini-Mental State Examination score.

Statistical parametric map and its 3-dimensional brain rendering representation depicting areas of significant (some P<.001, some P<.01, and some P<.05, as presented in Table 4) inverse correlations between cerebral blood flow and activities score in the Alzheimer group, controlling for age and Mini-Mental State Examination score.

Table 1 
Items Included in the Activities Scale
Items Included in the Activities Scale
Table 2 
Demographic, Clinical, and Neuropsychologic Data for 9 Patients With Alzheimer Disease
Demographic, Clinical, and Neuropsychologic Data for 9 Patients With Alzheimer Disease
Table 3 
Demographic, Cognitive, and Functional Characteristics of the Subjects*
Demographic, Cognitive, and Functional Characteristics of the Subjects*
Table 4 
Local Maxima With Statistically Significant Inverse Association Between Cerebral Blood Flow (CBF) and Education and Between CBF and Nelson Adult Reading Test Score*
Local Maxima With Statistically Significant Inverse Association Between Cerebral Blood Flow (CBF) and Education and Between CBF and Nelson Adult Reading Test Score*
Table 5 
Local Maxima With a Statistically Significant Inverse Association Between Cerebral Blood Flow and Activity Scores*
Local Maxima With a Statistically Significant Inverse Association Between Cerebral Blood Flow and Activity Scores*
Table 6 
Local Maxima With a Statistically Significant Inverse Association Between Cerebral Blood Flow and Activity Scores*
Local Maxima With a Statistically Significant Inverse Association Between Cerebral Blood Flow and Activity Scores*
1.
Stern  Y What is cognitive reserve? theory and research application of the reserve concept.  J Int Neuropsychol Soc.2002;8:448-460.Google Scholar
2.
Stern  YGurland  BTatemichi  TKTang  MXWilder  DMayeux  R Influence of education and occupation on the incidence of Alzheimer's disease.  JAMA.1994;271:1004-1010.Google Scholar
3.
McGeer  PLKamo  HHarrop  R  et al Positron emission tomography in patients with clinically diagnosed Alzheimer's disease.  CMAJ.1986;134:597-607.Google Scholar
4.
McGeer  PLKamo  HHarrop  R  et al Comparison of PET, MRI, and CT with pathology in a proven case of Alzheimer's disease.  Neurology.1986;36:1569-1574.Google Scholar
5.
McGeer  EGMcGeer  PLHarrop  RAkiyama  HKamo  H Correlations of regional postmortem enzyme activities with premortem local glucose metabolic rates in Alzheimer's disease.  J Neurosci Res.1990;27:612-619.Google Scholar
6.
McGeer  EGPeppard  RPMcGeer  PL  et al 18Fluorodeoxyglucose positron emission tomography studies in presumed Alzheimer cases, including 13 serial scans.  Can J Neurol Sci.1990;17:1-11.Google Scholar
7.
Mielke  RSchroder  RFink  GRKessler  JHerholz  KHeiss  WD Regional cerebral glucose metabolism and postmortem pathology in Alzheimer's disease.  Acta Neuropathol.1996;91:174-179.Google Scholar
8.
Hoffman  JMWelsh-Bohmer  KAHanson  M  et al FDG PET imaging in patients with pathologically verified dementia.  J Nucl Med.2000;41:1920-1928.Google Scholar
9.
Friedland  RPBrun  ABudinger  TF Pathological and positron emission tomographic correlations in Alzheimer's disease [letter].  Lancet.1985;1:228. Google Scholar
10.
DeCarli  CAtack  JRBall  MJ  et al Post-mortem regional neurofibrillary tangle densities but not senile plaque densities are related to regional cerebral metabolic rates for glucose during life in Alzheimer's disease patients.  Neurodegeneration.1992;1:113-121.Google Scholar
11.
Stern  YAlexander  GEProhovnik  IMayeux  R Inverse relationship between education and parietotemporal perfusion deficit in Alzheimer's disease.  Ann Neurol.1992;32:371-375.Google Scholar
12.
Stern  YAlexander  GEProhovnik  I  et al Relationship between lifetime occupation and parietal flow: implications for a reserve against Alzheimer's disease pathology.  Neurology.1995;45:55-60.Google Scholar
13.
Alexander  GEFurey  MLGrady  CL  et al Association of premorbid intellectual function with cerebral metabolism in Alzheimer's disease: implications for the cognitive reserve hypothesis.  Am J Psychiatry.1997;154:165-172.Google Scholar
14.
Kondo  KNiino  MShido  K A case-control study of Alzheimer's disease in Japan: significance of life-styles.  Dementia.1994;5:314-326.Google Scholar
15.
Friedland  RPFritsch  TSmyth  KA  et al Patients with Alzheimer's disease have reduced activities in midlife compared with healthy control-group members.  Proc Natl Acad Sci U S A.2001;98:3440-3445.Google Scholar
16.
Fabrigoule  CLetenneur  LDartigues  JFZarrouk  MCommenges  DBarberger-Gateau  P Social and leisure activities and risk of dementia: a prospective longitudinal study.  J Am Geriatr Soc.1995;43:485-490.Google Scholar
17.
Wilson  RSMendes de Leon  CFBarnes  L  et al Participation in cognitively stimulating activities and risk of incident Alzheimer disease.  JAMA.2002;287:742-748.Google Scholar
18.
Scarmeas  NLevy  GTang  MManly  JStern  Y Influence of leisure activity on the incidence of Alzheimer's disease.  Neurology.2001;57:2236-2242.Google Scholar
19.
American Psychiatric Association Diagnostic and Statistical Manual of Mental Disorders, Revised Third Edition.  Washington, DC: American Psychiatric Association; 1987.
20.
McKhann  GDrachman  DFolstein  MKatzman  RPrice  DStadlan  EM Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease.  Neurology.1984;34:939-944.Google Scholar
21.
Wechsler  D Wechler Adult Intelligence Scale Revised.  New York, NY: The Psychological Corp; 1981.
22.
Grober  ESliwinski  M Development and validation of a model for estimating premorbid verbal intelligence in the elderly.  J Clin Exp Neuropsychol.1991;13:933-949.Google Scholar
23.
Nelson  HEO'Connell  A Dementia: the estimation of premorbid intelligence levels using the New Adult Reading Test.  Cortex.1978;14:234-244.Google Scholar
24.
Nelson  HE The National Adult Reading Test (NART): Test Manual.  Windsor, England: NFER-Nelson; 1982.
25.
Folstein  MFFolstein  SEMcHugh  PR Mini-mental state: a practical method for grading the cognitive state of patients for the clinician.  J Psychiatr Res.1975;12:189-198.Google Scholar
26.
Mayeux  RStern  YRosen  JLeventhal  J Depression, intellectual impairment, and Parkinson disease.  Neurology.1981;31:645-650.Google Scholar
27.
Stern  YSano  MPaulson  JMayeux  R Modified mini-mental state examination: validity and reliability.  Neurology.1987;37 (suppl 1):179. Google Scholar
28.
Buschke  HFuld  PA Evaluating storage, retention, and retrieval in disordered memory and learning.  Neurology.1974;24:1019-1025.Google Scholar
29.
Benton  A The Visual Retention Test.  New York, NY: The Psychological Corp; 1955.
30.
Mattis  S Mental Status ecamination for organic mental syndrome inthe elderly patient.  In: Bellak  L Karasu TB, eds.  Geriatric Psychiatry. New York, NY: Grune & Stratton; 1976. Google Scholar
31.
Kaplan  EGoodglass  HWeintraub  S Boston Naming Test.  Philadelphia, Pa: Lea & Febiger; 1983.
32.
Benton  AHamsher  A Multiligual Aphasia Examination.  Iowa City: University of Iowa; 1976.
33.
Goodglass  HKaplan  D The Assessment of Aphasia and Related Disorders. 2nd ed. Philadelphia, Pa: Lea & Febiger; 1983.
34.
Not Available The Rosen Drawing Test.  Bronx NY: Veterans Administration Medical Center; 1981.
35.
Blessed  GTomlinson  BERoth  M The association between quantitative measures of dementia and of senile change in the cerebral grey matter of elderly subjects.  Br J Psychiatry.1968;114:797-811.Google Scholar
36.
Arnold  SEHyman  BTFlory  JDamasio  ARVan Hoesen  GW The topographical and neuroanatomical distribution of neurofibrillary tangles and neuritic plaques in the cerebral cortex of patients with Alzheimer's disease.  Cereb Cortex.1991;1:103-116.Google Scholar
37.
Smith  GSde Leon  MJGeorge  AE  et al Topography of cross-sectional and longitudinal glucose metabolic deficits in Alzheimer's disease: pathophysiologic implications.  Arch Neurol.1992;49:1142-1150.Google Scholar
38.
Friston  KJHolmes  APWorsley  KJPoline  JPFrackowiak  RSJ Statistical parametric maps in functional imaging: a general linear approach.  Hum Brain Mapp.1995;2:189-210.Google Scholar
39.
Lezak  M Neuropsychological Assessment.  New York, NY: Oxford University Press; 1995.
40.
Stern  YSano  MPaulson  JMayeux  R Modified mini-mental state examination: validity and reliability.  Neurology.1987;37(suppl 1):179. Google Scholar
41.
Cabeza  R Functional neuroimaging of cognitive aging.  In: Cabeza  R, Kingstone  A, eds. Handbook of Functional Neuroimaging of Cognition. Cambridge, Mass: Massachusetts Institute of Technology Press; 2001:331-377. Google Scholar
42.
Worsley  KJMarrett  SNeelin  PVandal  ACFriston  KJEvans  AC A unified statistical approach for determining significant signals in images of cerebral activation.  Hum Brain Mapp.1996;4:58-73.Google Scholar
43.
Hultsch  DFHertzog  CSmall  BJDixon  RA Use it or lose it: engaged lifestyle as a buffer of cognitive decline in aging?  Psychol Aging.1999;14:245-263.Google Scholar
44.
Gold  DPAndres  DEtezadi  JArbuckle  TSchwartzman  AChaikelson  J Structural equation model of intellectual change and continuity and predictors of intelligence in older men.  Psychol Aging.1995;10:294-303.Google Scholar
45.
Schaie  K Midlife influences upon intellectual functioning in old age.  Int J Behav Dev.1984;7:463-478.Google Scholar
46.
Li  GShen  YCChen  CHZhau  YWLi  SRLu  M A three-year follow-up study of age-related dementia in an urban area of Beijing.  Acta Psychiatr Scand.1991;83:99-104.Google Scholar
47.
Yoshitake  TKiyohara  YKato  I  et al Incidence and risk factors of vascular dementia and Alzheimer's disease in a defined elderly Japanese population: the Hisayama Study.  Neurology.1995;45:1161-1168.Google Scholar
48.
Laurin  DVerreault  RLindsay  JMacPherson  KRockwood  K Physical activity and risk of cognitive impairment and dementia in elderly persons.  Arch Neurol.2001;58:498-504.Google Scholar
49.
Broe  GACreasey  HJorm  AF  et al Health habits and risk of cognitive impairment and dementia in old age: a prospective study on the effects of exercise, smoking and alcohol consumption.  Aust N Z J Public Health.1998;22:621-623.Google Scholar
50.
van Praag  HKempermann  GGage  FH Running increases cell proliferation and neurogenesis in the adult mouse dentate gyrus.  Nat Neurosci.1999;2:266-270.Google Scholar
51.
Rogers  RLMeyer  JSMortel  KF After reaching retirement age physical activity sustains cerebral perfusion and cognition.  J Am Geriatr Soc.1990;38:123-128.Google Scholar
52.
Dustman  RERuhling  RORussell  EM  et al Aerobic exercise training and improved neuropsychological function of older individuals.  Neurobiol Aging.1984;5:35-42.Google Scholar
53.
Spirduso  WW Physical fitness, aging, and psychomotor speed: a review.  J Gerontol.1980;35:850-865.Google Scholar
54.
Kempermann  GKuhn  HGGage  FH More hippocampal neurons in adult mice living in an enriched environment.  Nature.1997;386:493-495.Google Scholar
55.
Gould  EReeves  AJGraziano  MSGross  CG Neurogenesis in the neocortex of adult primates.  Science.1999;286:548-552.Google Scholar
56.
Eriksson  PSPerfilieva  EBjork-Eriksson  T  et al Neurogenesis in the adult human hippocampus.  Nat Med.1998;4:1313-1317.Google Scholar
57.
Johansson  CBSvensson  MWallstedt  LJanson  AMFrisen  J Neural stem cells in the adult human brain.  Exp Cell Res.1999;253:733-736.Google Scholar
58.
Katzman  R Education and the prevalence of dementia and Alzheimer's disease.  Neurology.1993;43:13-20.Google Scholar
59.
Katzman  RAronson  MFuld  P  et al Development of dementing illnesses in an 80-year-old volunteer cohort.  Ann Neurol.1989;25:317-324.Google Scholar
60.
Ince  P Pathological correlates of late-onset dementia in a multicenter community-based population in England and Wales.  Lancet.2001;357:169-175.Google Scholar
61.
Goldman  WPrice  JLStorandt  M  et al Absence of cognitive impairement or decline in preclinical Alzheimer's disease.  Neurology.2001;56:361-367.Google Scholar
Original Contribution
March 2003

Association of Life Activities With Cerebral Blood Flow in Alzheimer Disease: Implications for the Cognitive Reserve Hypothesis

Author Affiliations

From the Cognitive Neuroscience Division of the Taub Institute for Research in Alzheimer's Disease and the Aging Brain (Drs Scarmeas, Habeck, Hilton, and Stern, and Mr Flynn); and the Departments of Neurology (Drs Scarmeas, Marder, Bell, and Stern), Psychiatry (Drs Zarahn, Anderson, Sackeim, Moeller, and Stern), and Radiology (Drs Sackeim and Van Heertum), College of Physicians and Surgeons of Columbia University, New York, NY.

Arch Neurol. 2003;60(3):359-365. doi:10.1001/archneur.60.3.359
Abstract

Background  Regional cerebral blood flow (CBF), a good indirect index of cerebral pathologic changes in Alzheimer disease (AD), is more severely reduced in patients with higher educational attainment and IQ when controlling for clinical severity. This has been interpreted as suggesting that cognitive reserve allows these patients to cope better with the pathologic changes in AD.

Objective  To evaluate whether premorbid engagement in various activities may also provide cognitive reserve.

Design  We evaluated intellectual, social, and physical activities in 9 patients with early AD and 16 healthy elderly controls who underwent brain H215O positron emission tomography. In voxelwise multiple regression analyses that controlled for age and clinical severity, we investigated the association between education, estimated premorbid IQ, and activities, and CBF.

Results  In accordance with previous findings, we replicated an inverse association between education and CBF and IQ and CBF in patients with AD. In addition, there was a negative correlation between previous reported activity score and CBF in patients with AD. When both education and IQ were added as covariates in the same model, a higher activity score was still associated with more prominent CBF deficits. No significant associations were detected in the controls.

Conclusions  At any given level of clinical disease severity, there is a greater degree of brain pathologic involvement in patients with AD who have more engagement in activities, even when education and IQ are taken into account. This may suggest that interindividual differences in lifestyle may affect cognitive reserve by partially mediating the relationship between brain damage and the clinical manifestation of AD.

THE COGNITIVE reserve (CR) hypothesis suggests that there are individual differences in the ability to cope with the pathologic changes in Alzheimer disease (AD).1 Innate intelligence or aspects of life experience may supply reserve in the form of a set of skills or repertoires that allow some people to cope with the pathologic changes better than others. Educational and occupational attainments are considered such aspects of life experience.

Epidemiologic data supporting the CR hypothesis include observations that higher educational and occupational attainment is associated with decreased risk for incident dementia.2 Functional imaging studies have also provided support for the concept of CR. Considering cerebral blood flow (CBF) as an indirect index of pathologic changes in disease3-10 (lower blood flow indicating more advanced pathologic changes in AD) studies have shown that patients with higher educational11 or occupational12 attainment as well as those with a higher premorbid IQ13 have more prominent flow deficits when controlling for clinical severity. These flow deficits were located in the brain regions typically associated with reduced CBF in AD. Again, these observations support the prediction that individuals with more CR can tolerate more pathologic changes. Factors other than education and occupation might also provide reserve against pathologic changes in AD. Both cross-sectional and prospective longitudinal studies have suggested that engaging in various social, intellectual, and leisure activities is associated with reduced risk of prevalent or incident AD.14-18

The present study was designed to use the functional imaging approach just described to clarify the role of reported activities in CR. We evaluated intellectual, social, and physical activities in patients with AD who underwent brain H215O positron emission tomography (PET). A role for such activities in CR would predict that, when controlling for disease severity, patients with higher activity scores would have more advanced pathologic changes in AD. Thus, using CBF as an indirect indicator of pathologic changes in disease would show a negative correlation between activity scores and CBF in areas of the brain that typically show reduced CBF in AD.

Methods
Subjects

Nine patients who met Diagnostic and Statistical Manual of Mental Disorders, Revised Third Edition (DSM-III-R) criteria for dementia19 and the criteria of the National Institute of Neurological and Communicative Disorders and Stroke and Alzheimer's Disease and Related Disorders Association for probable AD20 and 16 healthy elderly controls participated in this study. The patients underwent extensive neuropsychologic evaluation, including the Wechsler Adult Intelligence Scale–Revised,21 the American version of the Nelson Adult Reading Test (NART) estimated IQ,22-24 the modified Mini-Mental State examination (mMMS),25-27 and tests of memory (short- and long-term verbal28 and nonverbal28,29), orientation, abstract reasoning (verbal21 and nonverbal30), language (naming,31 verbal fluency,32,33 comprehension,33 and repetition33), and construction (copying34 and matching29). In addition, the Blessed Dementia Rating Scale (part I, sections A and B)35 was administered. Magnetic resonance imaging (MRI) was used to rule out patients with vascular diseases or tumors. Other causes of dementia were excluded with appropriate laboratory tests. The diagnosis of AD was reached at a consensus diagnostic conference of physicians and neuropsychologists. Positron emission tomography results did not play any role in the diagnostic process.

Activities

Before the PET scan, an interview with the patient and the informant or the healthy subject assessed activities engaged in during the last 6 months. The questionnaire was an expanded version of a scale administered to a community of 1772 elderly subjects in a prospective incidence dementia study.18 Participation in 18 activities was recorded (Table 1). The sum of the points over the 18 activities was calculated for each patient and this leisure score was used as a predictor in subsequent analyses. It was also recorded whether the amount of time patients had spent doing each activity had decreased, remained the same, or increased during the previous 10 years.

Pet scan acquisition and processing

Scans were collected while the subject was at rest with eyes closed. For each scan, a bolus of 30 mCi (1110 MBq) of intravenous H215O was injected. Using an EXACT 47 PET camera (Siemens, Knoxville, Tenn), three 30-second scan frames were acquired in 2-dimensional mode beginning 20 seconds after tracer administration. After measured attenuation correction (15-minute transmission scan) and reconstruction by filtered back-projection, image resolution was 10 mm full width at half-maximum (FWHM). Arterial blood sampling was not conducted; thus, nonquantitative count images (and not absolute CBF measures) were obtained.

Using modules from the statistical parametric mapping program (SPM99; Wellcome Department of Cognitive Neurology, London, England), the following steps were performed in turn for each subject: (1) The 3 PET frames were realigned to each other and summed. (2) A T1-weighted MRI was coregistered to this summed PET image. Magnetic resonance images were acquired using a 1.5-Tesla MR scanner. A T1 3-dimensional spoiled-gradient-recalled echo sequence (repetition time [TR] = 34 ms; echo time [TE] = 5 ms; flip angle = 45°) was used to acquire T1-weighted images with an in-plane resolution of 0.859 mm × 0.859 mm (256 × 256 matrix; 22 cm2 field of view). One hundred twenty four –1.5-mm transaxial slices were acquired. The z-dimension was down-sampled to a final dimension of 6 mm per slice. (3) The coregistered MRI was spatially transformed to the coordinates defined by the Montreal Neurological Institute template brain provided by SPM99. (4) The spatial normalization parameters were applied to the summed PET image, which was resliced using sinc interpolation to a final voxel size of 2 mm × 2 mm × 2 mm. (5) The spatially normalized PET image was intensity normalized by its average perirolandic count value (given that both pathologic and quantitative PET CBF imaging data suggest that the perirolandic cortex is typically spared by pathologic changes in disease8,10,36,37). (6) The spatially and intensity normalized PET image was spatially smoothed with an isotropic Gaussian kernel (FWHM = 8 mm).

Statistical analyses

Various voxelwise multiple regression analyses38 were performed on the intensity normalized images (subsequently referred to as "CBF" with the understanding that they are not in physiologic units). Values of the CBF were used as the dependent variable in all of the multiple regression models.

Several regression models were separately estimated. Putative CR variables in these various models included education, estimates of premorbid IQ, and the activity score. Word-reading ability as assessed by NART was used to estimate premorbid IQ. Performance on this task remains relatively preserved in mild AD and is considered a good estimate of premorbid abilities.39 The score on the mMMS was used as an index of the clinical severity of dementia.25,26,40

Our strategy for the regression analyses was to first assess the association between CBF and each putative CR variable individually. We controlled for age41 and mMMS score (ie, clinical severity) in all of the models. We first introduced years of education and NART score (in 2 separate models) as independent variables to replicate previous findings. We then used the activity score as an independent variable in a separate model. We finally sought to explore whether there was a relationship between CBF and activity score while accounting for the other variables (education and NART score).

For each regression model, voxelwise relationships between the scaled count values and the independent variables of interest were calculated and statistically assessed with t values.38 The false-positive rate was controlled at α = .05 per map via Bonferroni correction for the number of statistically independent resolution elements (resels) across which regressions were calculated (number of resels = total volume of map / product of FWHM across x, y, and z dimensions).42

We used the tissue density values from subjects' MRI results (in voxels where significant associations were noted in the previous analyses) as covariates in the regression analyses to explore the possibility that any associations between the CR variables and CBF are mediated by atrophy. The atrophy analyses did not change the results.

Results
Demographics, leisure scores, and cr summary measures

Demographic, clinical, and neuropsychologic variables for each of the AD subjects are presented in Table 2. The same data are presented for both patients with AD and controls in Table 3. There were 5 men and 4 women in the AD group and 6 men and 10 women in the control group.

Although all of the test subjects had only mild AD, it could be argued that current activities do not accurately reflect premorbid lifestyle. We therefore examined the reported change in the amount of time spent doing each activity during the last 10 years. For 5 patients, the time spent on each activity was reported to have either increased or remained stable over the last 10 years. Four patients reported decreased time devoted to 2 of 18, 3 of 18, 4 of 18, and 6 of 18 activities, respectively. Since these activities represented only a small fraction of the overall score, with most of the activities exercised at a stable rate or more frequently, we considered the activity score to be a reasonable estimate of activities, at least during the decade preceding the study.

Correlations between education and NART score were r = 0.59, P<.002 (AD: r = 0.60, P<.09; control: r = 0.67, P<.004); between education and activities, r = 0.29, P<.15 (AD: r = 0.60, P<.08; control: r = −0.15, P<.59); and between NART score and activities, r = 0.53, P<.007 (AD: r = 0.81, P<.008; control: r = −0.05, P<.85). The nonsignificant correlations may indicate that the variables are not truly associated. Nevertheless, it is hard to draw many conclusions from this given the very small number of subjects (9 patients with AD and 16 controls), which may have resulted in low power to detect existing significant correlations.

Regression analyses

In accordance with previous findings,11 education was inversely associated with CBF (Table 4). Similarly, in line with previous reports,13 there was a negative association between premorbid IQ (as measured by the NART) and CBF (Table 4).

The activity score was also inversely correlated with CBF when controlling for age and mMMS score (Table 5 and Figure 1). Significant associations were localized mainly to the temporal lobe but also in temporal-parietal-occipital association areas. When simultaneously controlling for age, mMMS score, education, and NART score, activity score was still negatively correlated with CBF (Table 6).

With the exception of 2 voxels (1 in the cerebellum [Table 5], and 1 in the lentiform nucleus [Table 6]), no significant associations were detected for the controls. We did not detect positive associations in any of the analyses, neither in the AD nor in the control group.

Comment

Consistent with the prediction of the CR hypothesis, we found that activities had a negative correlation with CBF in patients with AD when controlling for disease severity. This relationship was seen primarily in voxels located in the temporal but also in temporal-occipital-parietal association cortices, the areas in which CBF changes are typically noted in AD. This inverse association was still present when both education and IQ were included in the model. This indicates that there is a unique relationship between activities and CBF over and above the relationship of CBF to education and IQ.

A previous study noted an inverse association between educational attainment and CBF (controlling for disease severity) in parietal areas.11 In our study, we observed a similar negative correlation between education and CBF, localizing to parietal, frontal, and temporal regions.

Another study found an inverse association between premorbid IQ and CBF in prefrontal, premotor, orbitofrontal, superior parietal, thalamic, and anterior cingulate areas.13 In our study, similar results (in terms of directionality of the relationship between IQ and CBF) were obtained. Significant correlations included thalamic, temporal, parietal, frontal, and cingulate areas in our study.

The observed differences in localization between previous studies and the present one could derive from the different types of functional imaging modalities used (xenon and fluorodeoxyglucose in previous studies vs oxygen 15 here), different covariates reflecting disease severity used in the analysis, different denominators used for intensity normalization, or different measures of IQ. Most notable, however, is the different methods of analysis: both previous studies used a region-of-interest analysis when they examined the associations between education and CBF and IQ and CBF,11,13 while we elected to use a voxelwise analysis. Because we were able to replicate the directionality and, to a certain extent, the localization of these earlier findings with a voxelwise analysis, the validity of these previous observations is strengthened.

We detected significant associations in different areas of the brain depending on whether education or IQ or activities were used in the analyses. At first approximation, this may suggest that different aspects of CR mediate clinical protection in an anatomically specific way: ie, patients with high educational attainment can be maintained at similar clinical severity status when the pathologic changes AD affect certain areas of the brain, while patients with high IQ or leisure activity scores can be maintained when other brain regions are affected. Nevertheless, it is also possible that different aspects of CR are not region-specific and that the locations reported in our analyses generally co-localize to the same extended association areas usually affected by AD. The combination of the limited number of subjects included in our analyses and our conservative approach for controlling for type I error might have limited our power to detect significant associations in the whole spectrum of affected areas of the brain. In addition, the locations reported in our analyses represent only local maxima of more extended areas of the brain (Figure 1). Maintaining intellectual and social engagement through participation in everyday activities seems to buffer healthy individuals against cognitive decline in later life.43-45

We recently reported the results of a longitudinal epidemiologic study in northern Manhattan, NY.18 Thirteen leisure activities (intellectual, social, and physical in character) were assessed in 1772 healthy elderly subjects who were prospectively followed for up to 7 years. Even when multiple potentially confounding factors were controlled for, subjects with high leisure activity scores had 38% less risk of developing dementia. There are at least 2 other large prospective cohorts that have reported a protective effect for leisure16 and cognitive activities17 in relationship to incident dementia.

A limitation of the present study is that activities were assessed in patients with mild dementia and that the extent to which they participated in these activities might have been affected by the disease. Optimally, activities should have been recorded during presymptomatic periods. However, as described in the "Results" section, our patients were at early stages of AD and the reported changes in their activities over the last 10 years were ostensibly negligible. We therefore used the recorded activity score as an estimate of lifestyle during the 10 years before study enrollment.

To further explore this potential bias, we repeated the analyses using reconstructed scores for the subjects who reported decreased time devoted to activities, adding in points for the activities they reported to exercise less over the last 10 years. The results for all models were essentially unchanged. We elected to use the contemporaneous activity score for the analyses because it is less prone to recollection bias. The fact that the activities information was corroborated by the informant also added to the face validity of the scores.

The activity scale items collected in this study reflected not only intellectual and social activities but also physical ones. Epidemiologic evidence that physical exercise may delay cognitive impairment is equivocal. While high levels of physical activity were associated with reduced risk of dementia in at least 4 prospective studies,18,46-48 no effect of exercise on dementia and cognitive impairment risk was reported from other cohorts.17,49 Additionally, there is basic research evidence that environmental enrichment in the form of voluntary wheel running is associated with enhanced neurogenesis in the adult mouse dentate gyrus.50 It has also been shown that physical activity sustains cerebral blood flow51 and may improve aerobic capacity and cerebral nutrient supply.52,53 Therefore, although it is conceivable that physical activity may merely be a nonspecific marker of good health indirectly related to dementia (or not related to dementia at all), it is also possible that it has a direct physiologic association with brain disease.

Recent evidence indicates that certain areas of the brain retain the capability to generate new neurons into adulthood, not only in rodents54 and primates55 but also in humans.56,57 Thus, it is possible that the stimulation provided by everyday intellectual and social activities facilitates the maintenance of general cognitive skills in a manner that is analogous to physical exercise for musculoskeletal and cardiovascular functions.43 This reserve could be the result of a physiologic process involving increased synaptic density in the neocortical association cortex acquired by stimulation.58

This study does not indicate that more engagement in activities affords some kind of immunity to the neuropathologic aspect of the AD process. On the contrary, we assume that the pathologic changes in AD progress independently of life activities. The concept of CR was developed in an attempt to explain interindividual differences in the degree of pathologic changes in the brain necessary for the clinical expression of disease.59 This has been confirmed in multiple studies, which have found that a significant proportion of clinically nondemented individuals manifest neuropathologic changes consistent with AD at autopsy.60,61 More engagement in activities may supply a reserve that allows an individual to cope longer before AD is clinically expressed. Aspects of life experience, such as social, intellectual, and physical activities, could modify the paradigms used by the brain to mediate a task by making individuals more efficient or resilient in the face of pathologic changes in the brain or by recruitment of alternate networks.

The activity score may simply represent innate rather than acquired abilities. However, activities, education, and IQ seem to have a unique association to CBF, which supports the concept that aspects of life experience may modulate reserve. The hypothesized contribution of life experiences, styles, and activities to the ability to cope with the pathologic changes in AD suggests the possibility of interventions that might delay the onset of the clinical symptoms of the disease.

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Corresponding author and reprints: Nikolaos Scarmeas, MD, Columbia Presbyterian Medical Center, P&S Mailbox 16, PH 19th Floor, 630 W 168th St, New York, NY 10032 (e-mail: ns257@columbia.edu).

Accepted for publication October 23, 2002.

Author contributions: Study concept and design (Drs Scarmeas, Anderson, Sackeim, Van Heertum, and Stern); acquisition of data (Drs Scarmeas, Anderson, Hilton, Marder, Bell, Van Heertum, and Stern, and Mr Flynn); analysis and interpretation of data (Drs Scarmeas, Zarahn, Habeck, Hilton, Sackeim, Van Heertum, Moeller, and Stern, and Mr Flynn); drafting of the manuscript (Drs Scarmeas and Van Heertum); critical revision of the manuscript for important intellectual content (Drs Zarahn, Anderson, Habeck, Hilton, Marder, Bell, Sackeim, Van Heertum, Moeller, and Stern, and Mr Flynn); statistical expertise (Drs Scarmeas, Zarahn, Habeck, Hilton, Sackeim, Moeller, and Stern, and Mr Flynn); obtained funding (Drs Sackeim and Stern); administrative, technical, and material support (Drs Sackeim, Van Heertum, and Stern); study supervision (Drs Scarmeas, Anderson, Van Heertum, and Stern).

This research was supported by federal grants AG 14671 and RR 00645.

References
1.
Stern  Y What is cognitive reserve? theory and research application of the reserve concept.  J Int Neuropsychol Soc.2002;8:448-460.Google Scholar
2.
Stern  YGurland  BTatemichi  TKTang  MXWilder  DMayeux  R Influence of education and occupation on the incidence of Alzheimer's disease.  JAMA.1994;271:1004-1010.Google Scholar
3.
McGeer  PLKamo  HHarrop  R  et al Positron emission tomography in patients with clinically diagnosed Alzheimer's disease.  CMAJ.1986;134:597-607.Google Scholar
4.
McGeer  PLKamo  HHarrop  R  et al Comparison of PET, MRI, and CT with pathology in a proven case of Alzheimer's disease.  Neurology.1986;36:1569-1574.Google Scholar
5.
McGeer  EGMcGeer  PLHarrop  RAkiyama  HKamo  H Correlations of regional postmortem enzyme activities with premortem local glucose metabolic rates in Alzheimer's disease.  J Neurosci Res.1990;27:612-619.Google Scholar
6.
McGeer  EGPeppard  RPMcGeer  PL  et al 18Fluorodeoxyglucose positron emission tomography studies in presumed Alzheimer cases, including 13 serial scans.  Can J Neurol Sci.1990;17:1-11.Google Scholar
7.
Mielke  RSchroder  RFink  GRKessler  JHerholz  KHeiss  WD Regional cerebral glucose metabolism and postmortem pathology in Alzheimer's disease.  Acta Neuropathol.1996;91:174-179.Google Scholar
8.
Hoffman  JMWelsh-Bohmer  KAHanson  M  et al FDG PET imaging in patients with pathologically verified dementia.  J Nucl Med.2000;41:1920-1928.Google Scholar
9.
Friedland  RPBrun  ABudinger  TF Pathological and positron emission tomographic correlations in Alzheimer's disease [letter].  Lancet.1985;1:228. Google Scholar
10.
DeCarli  CAtack  JRBall  MJ  et al Post-mortem regional neurofibrillary tangle densities but not senile plaque densities are related to regional cerebral metabolic rates for glucose during life in Alzheimer's disease patients.  Neurodegeneration.1992;1:113-121.Google Scholar
11.
Stern  YAlexander  GEProhovnik  IMayeux  R Inverse relationship between education and parietotemporal perfusion deficit in Alzheimer's disease.  Ann Neurol.1992;32:371-375.Google Scholar
12.
Stern  YAlexander  GEProhovnik  I  et al Relationship between lifetime occupation and parietal flow: implications for a reserve against Alzheimer's disease pathology.  Neurology.1995;45:55-60.Google Scholar
13.
Alexander  GEFurey  MLGrady  CL  et al Association of premorbid intellectual function with cerebral metabolism in Alzheimer's disease: implications for the cognitive reserve hypothesis.  Am J Psychiatry.1997;154:165-172.Google Scholar
14.
Kondo  KNiino  MShido  K A case-control study of Alzheimer's disease in Japan: significance of life-styles.  Dementia.1994;5:314-326.Google Scholar
15.
Friedland  RPFritsch  TSmyth  KA  et al Patients with Alzheimer's disease have reduced activities in midlife compared with healthy control-group members.  Proc Natl Acad Sci U S A.2001;98:3440-3445.Google Scholar
16.
Fabrigoule  CLetenneur  LDartigues  JFZarrouk  MCommenges  DBarberger-Gateau  P Social and leisure activities and risk of dementia: a prospective longitudinal study.  J Am Geriatr Soc.1995;43:485-490.Google Scholar
17.
Wilson  RSMendes de Leon  CFBarnes  L  et al Participation in cognitively stimulating activities and risk of incident Alzheimer disease.  JAMA.2002;287:742-748.Google Scholar
18.
Scarmeas  NLevy  GTang  MManly  JStern  Y Influence of leisure activity on the incidence of Alzheimer's disease.  Neurology.2001;57:2236-2242.Google Scholar
19.
American Psychiatric Association Diagnostic and Statistical Manual of Mental Disorders, Revised Third Edition.  Washington, DC: American Psychiatric Association; 1987.
20.
McKhann  GDrachman  DFolstein  MKatzman  RPrice  DStadlan  EM Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease.  Neurology.1984;34:939-944.Google Scholar
21.
Wechsler  D Wechler Adult Intelligence Scale Revised.  New York, NY: The Psychological Corp; 1981.
22.
Grober  ESliwinski  M Development and validation of a model for estimating premorbid verbal intelligence in the elderly.  J Clin Exp Neuropsychol.1991;13:933-949.Google Scholar
23.
Nelson  HEO'Connell  A Dementia: the estimation of premorbid intelligence levels using the New Adult Reading Test.  Cortex.1978;14:234-244.Google Scholar
24.
Nelson  HE The National Adult Reading Test (NART): Test Manual.  Windsor, England: NFER-Nelson; 1982.
25.
Folstein  MFFolstein  SEMcHugh  PR Mini-mental state: a practical method for grading the cognitive state of patients for the clinician.  J Psychiatr Res.1975;12:189-198.Google Scholar
26.
Mayeux  RStern  YRosen  JLeventhal  J Depression, intellectual impairment, and Parkinson disease.  Neurology.1981;31:645-650.Google Scholar
27.
Stern  YSano  MPaulson  JMayeux  R Modified mini-mental state examination: validity and reliability.  Neurology.1987;37 (suppl 1):179. Google Scholar
28.
Buschke  HFuld  PA Evaluating storage, retention, and retrieval in disordered memory and learning.  Neurology.1974;24:1019-1025.Google Scholar
29.
Benton  A The Visual Retention Test.  New York, NY: The Psychological Corp; 1955.
30.
Mattis  S Mental Status ecamination for organic mental syndrome inthe elderly patient.  In: Bellak  L Karasu TB, eds.  Geriatric Psychiatry. New York, NY: Grune & Stratton; 1976. Google Scholar
31.
Kaplan  EGoodglass  HWeintraub  S Boston Naming Test.  Philadelphia, Pa: Lea & Febiger; 1983.
32.
Benton  AHamsher  A Multiligual Aphasia Examination.  Iowa City: University of Iowa; 1976.
33.
Goodglass  HKaplan  D The Assessment of Aphasia and Related Disorders. 2nd ed. Philadelphia, Pa: Lea & Febiger; 1983.
34.
Not Available The Rosen Drawing Test.  Bronx NY: Veterans Administration Medical Center; 1981.
35.
Blessed  GTomlinson  BERoth  M The association between quantitative measures of dementia and of senile change in the cerebral grey matter of elderly subjects.  Br J Psychiatry.1968;114:797-811.Google Scholar
36.
Arnold  SEHyman  BTFlory  JDamasio  ARVan Hoesen  GW The topographical and neuroanatomical distribution of neurofibrillary tangles and neuritic plaques in the cerebral cortex of patients with Alzheimer's disease.  Cereb Cortex.1991;1:103-116.Google Scholar
37.
Smith  GSde Leon  MJGeorge  AE  et al Topography of cross-sectional and longitudinal glucose metabolic deficits in Alzheimer's disease: pathophysiologic implications.  Arch Neurol.1992;49:1142-1150.Google Scholar
38.
Friston  KJHolmes  APWorsley  KJPoline  JPFrackowiak  RSJ Statistical parametric maps in functional imaging: a general linear approach.  Hum Brain Mapp.1995;2:189-210.Google Scholar
39.
Lezak  M Neuropsychological Assessment.  New York, NY: Oxford University Press; 1995.
40.
Stern  YSano  MPaulson  JMayeux  R Modified mini-mental state examination: validity and reliability.  Neurology.1987;37(suppl 1):179. Google Scholar
41.
Cabeza  R Functional neuroimaging of cognitive aging.  In: Cabeza  R, Kingstone  A, eds. Handbook of Functional Neuroimaging of Cognition. Cambridge, Mass: Massachusetts Institute of Technology Press; 2001:331-377. Google Scholar
42.
Worsley  KJMarrett  SNeelin  PVandal  ACFriston  KJEvans  AC A unified statistical approach for determining significant signals in images of cerebral activation.  Hum Brain Mapp.1996;4:58-73.Google Scholar
43.
Hultsch  DFHertzog  CSmall  BJDixon  RA Use it or lose it: engaged lifestyle as a buffer of cognitive decline in aging?  Psychol Aging.1999;14:245-263.Google Scholar
44.
Gold  DPAndres  DEtezadi  JArbuckle  TSchwartzman  AChaikelson  J Structural equation model of intellectual change and continuity and predictors of intelligence in older men.  Psychol Aging.1995;10:294-303.Google Scholar
45.
Schaie  K Midlife influences upon intellectual functioning in old age.  Int J Behav Dev.1984;7:463-478.Google Scholar
46.
Li  GShen  YCChen  CHZhau  YWLi  SRLu  M A three-year follow-up study of age-related dementia in an urban area of Beijing.  Acta Psychiatr Scand.1991;83:99-104.Google Scholar
47.
Yoshitake  TKiyohara  YKato  I  et al Incidence and risk factors of vascular dementia and Alzheimer's disease in a defined elderly Japanese population: the Hisayama Study.  Neurology.1995;45:1161-1168.Google Scholar
48.
Laurin  DVerreault  RLindsay  JMacPherson  KRockwood  K Physical activity and risk of cognitive impairment and dementia in elderly persons.  Arch Neurol.2001;58:498-504.Google Scholar
49.
Broe  GACreasey  HJorm  AF  et al Health habits and risk of cognitive impairment and dementia in old age: a prospective study on the effects of exercise, smoking and alcohol consumption.  Aust N Z J Public Health.1998;22:621-623.Google Scholar
50.
van Praag  HKempermann  GGage  FH Running increases cell proliferation and neurogenesis in the adult mouse dentate gyrus.  Nat Neurosci.1999;2:266-270.Google Scholar
51.
Rogers  RLMeyer  JSMortel  KF After reaching retirement age physical activity sustains cerebral perfusion and cognition.  J Am Geriatr Soc.1990;38:123-128.Google Scholar
52.
Dustman  RERuhling  RORussell  EM  et al Aerobic exercise training and improved neuropsychological function of older individuals.  Neurobiol Aging.1984;5:35-42.Google Scholar
53.
Spirduso  WW Physical fitness, aging, and psychomotor speed: a review.  J Gerontol.1980;35:850-865.Google Scholar
54.
Kempermann  GKuhn  HGGage  FH More hippocampal neurons in adult mice living in an enriched environment.  Nature.1997;386:493-495.Google Scholar
55.
Gould  EReeves  AJGraziano  MSGross  CG Neurogenesis in the neocortex of adult primates.  Science.1999;286:548-552.Google Scholar
56.
Eriksson  PSPerfilieva  EBjork-Eriksson  T  et al Neurogenesis in the adult human hippocampus.  Nat Med.1998;4:1313-1317.Google Scholar
57.
Johansson  CBSvensson  MWallstedt  LJanson  AMFrisen  J Neural stem cells in the adult human brain.  Exp Cell Res.1999;253:733-736.Google Scholar
58.
Katzman  R Education and the prevalence of dementia and Alzheimer's disease.  Neurology.1993;43:13-20.Google Scholar
59.
Katzman  RAronson  MFuld  P  et al Development of dementing illnesses in an 80-year-old volunteer cohort.  Ann Neurol.1989;25:317-324.Google Scholar
60.
Ince  P Pathological correlates of late-onset dementia in a multicenter community-based population in England and Wales.  Lancet.2001;357:169-175.Google Scholar
61.
Goldman  WPrice  JLStorandt  M  et al Absence of cognitive impairement or decline in preclinical Alzheimer's disease.  Neurology.2001;56:361-367.Google Scholar
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