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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Alzheimers Dis. Author manuscript; available in PMC 2010 May 13.
Published in final edited form as:
PMCID: PMC2868929

Education Attenuates the Effect of Medial Temporal Lobe Atrophy on Cognitive Function in Alzheimer’s Disease: The MIRAGE Study


Functional imaging and neuropathological studies suggest that individuals with higher education have better cognitive performance at the same level of brain pathology than less educated subjects. No in vivo studies are available that directly test how education modifies the effect of structural pathology on cognition in Alzheimer’s disease (AD). The present study therefore aimed to measure this effect using data from a large multi-center study. 270 patients with AD underwent cognitive testing using the Mini Mental State Examination (MMSE), apolipoprotein E (APOE) genotyping, and cerebral magnetic resonance imaging. A linear regression analysis was used to examine the relation of medial temporal lobe atrophy (MTA), as a proxy of AD pathology, to MMSE score, adjusting for age, gender, APOE, cerebrovascular disease, ethnicity, education, and disease duration. An interaction term for MTA and education was introduced to test the hypothesis that education modifies the effect of MTA on cognition. There was a significant inverse association between MTA and cognition. Most interestingly, the interaction term between education and MTA was significant suggesting that education modifies the relation of MTA to cognition. At any level of pathology, cognition remained higher for better educated individuals.

Keywords: Alzheimer’s disease, cognition, cognitive reserve, dementia, education, hippocampus, magnetic resonance imaging, medial temporal lobe atrophy


The medial temporal lobe, which includes the hippocampus and parahippocampal gyrus (the latter includes the entorhinal cortex), is preferentially affected by Alzheimer’s disease (AD) pathology, including neurofibrillary tangle formation [1], amyloid-β deposition [2], neuronal loss, and volume reduction [3]. Although magnetic resonance imaging (MRI) findings show some heterogeneity regarding their neuropathological basis [4], MRI medial temporal lobe atrophy (MTA) is a sensitive marker for pathologic AD stage [5]; MRI is able to detect MTA at early clinical stages of AD [6] and track its progression as the disease advances [7]. Furthermore, MTA is associated with cognitive impairment and decline over time and predicts AD in individuals with minor cognitive impairment [8]. The relationship between AD pathology and clinical symptoms, however, is not tight [9]. Elderly individuals may show a sufficient number of amyloid-β containing plaques and neurofibrillary tangles at autopsy to warrant a neuropathological diagnosis of AD but exhibit no symptoms of dementia during life [10]. The disjunction between pathology and symptoms is thought to indicate a variable capacity among individuals to withstand pathological change, which is referred to as brain reserve [11] or cognitive reserve (CR) [12,13]. Studies relating plaque counts at postmortem examination [14], regional blood flow [15-17], or metabolism [18-25] to clinical symptoms and biographical variables have consistently demonstrated that patients with higher pre-morbid intelligence, longer education, or greater occupational attainment have better cognitive performance at the same level of disease severity. Koepsell and colleagues [26], however, found no evidence of larger education-related differences in cognitive function in patients with more advanced AD neuropathology. The neurobiological substrate of CR is not known but may involve structural factors such as brain size, neuron numbers, and synaptic density as well as functional components including efficiency of neural networks and brain connectivity [27]. Only one study has explored the association between in vivo structural indices of AD pathology, education, and cognition so far. Kidron et al. [28] reported that education was a significant predictor of parietal atrophy, controlling for cognitive impairment, disease duration, age, and sex. There are, however, no other published reports that directly test whether educational attainment modifies the relationship between structural indices of AD pathology, such as MRI-based assessments of brain atrophy, and clinical symptoms. If such an effect were present, it would suggest that the influence of CR is powerful enough to offset significant amounts of brain tissue loss. The present study was undertaken to test the hypothesis that education modifies the association between MTA and cognitive performance in AD, taking into account other variables that are known to impact on cognitive ability, including age [29], apolipoprotein E (APOE) genotype [30], head size [31], cerebrovascular lesion burden [32], and duration of disease [33].


Subjects and data collection

The MIRAGE Study was designed as a family-based multi-center study of genetic and environmental risk factors for AD, the details of which, regarding data collection and reliabilities of questionnaires, are published elsewhere [34-36]. Briefly, participants included in this investigation were ascertained through research registries or specialized memory clinics at 17 sites in the USA (14), Canada (1), Germany (1), and Greece (1) between February 2002 and November 2006. All individuals were diagnosed with probable AD according to the National Institute of Neurological and Communication Disorders and Stroke/Alzheimer’s Disease and Related Disorders Association (NINCDS/ADRDA) criteria [37]. Medical history, risk factor information, blood samples for genetic analyses, and cranial MRI scans were collected from all study participants. The patients’ educational level was dichotomized according to the highest level attained (low education: less than high school graduate; high education: high school graduate or higher), because the MIRAGE Study assesses levels of educational attainment which are not interval-scaled. A combination of informed written consent by patient and informed consent by proxy was obtained. Procedures involving experiments on human subjects were done in accord with the Helsinki Declaration of 1975. Cognitive ability was assessed in all patients using the Mini-Mental Status Examination (MMSE) [38]. For the present study only patients with an MMSE score lower than 26 were used to ensure diagnostic accuracy [39]. No other exclusion criteria were applied.

Acquisition of MRI scans

The MRI scanning procedures and analysis protocols have been described previously [40]. In brief, double spin echo, fluid-attenuated inversion recovery, and high resolution T1 images were acquired from each individual according to exactly the same protocol. All MRI were acquired on 1.5 T scanners and the sequences were modified to suit differences in machine manufacturers and operating systems. Qualitative rating scales were applied, which, by their simplicity, are relatively insensitive to measures at multiple sites [41]. In addition, all data were analyzed by a single rater (C.D.), who was blind to all clinical and genetic data, to reduce inter-rater variance [42]. The amount of MTA was determined from the high resolution T1 scans using a semi-quantitative visual scale [43], ranging from 0 (no atrophy) to 4 (most severe atrophy) that discriminates well between individuals with AD and cognitively healthy subjects, and has a high degree of inter-rater reliability [44]. Wahlung and colleagues [45] furthermore reported a high correlation between the visual rating and time-consuming volumetric procedures, and the visual rating had a higher diagnostic accuracy in the differentiation between patients with AD and healthy control subjects than the volumetric assessment. White matter hyperintensities (WMH) were rated from fluid-attenuated inversion recovery images on a 100 mm visual analogue scale, on which 0 stood for the total absence of WMH and 100 for the most severe degree of WMH. Examples of quantified abnormalities were incorporated as landmarks in the rating process. Finally, the presence or absence of MRI infarction (INF) was determined from the size, location, and imaging characteristics of the lesion, using information from all available scans according to a previously described standard protocol [46]. An overall rating of cerebrovascular disease (CVD) was created using a combination of WMH and INF data to describe the additive effects of both lesion types. CVD stands for the summed severity of WMH and INF; e.g., in the absence of INF, CVD equals WMH severity, whereas in the presence of accompanying INF, the CVD rating is obtained by summing the single scores for WMH and INF. Previous work found that MRI ratings of WMH and INF are associated with cerebrovascular abnormalities but not with AD pathology [4]. Wu et al. [47] reported a high correlation between the semi-quantitative visual rating and an automated quantitative rating on segmented brains.

APOE genotyping

APOE genotyping was performed using a standard polymerase chain reaction as reported elsewhere [48]. For the purpose of the present study, subjects were classified as APOE ε4 (−) or ε4 (+).

Measurement of head circumference

Head circumference was measured in a standardized manner by placing a measuring tape over the eyebrows and passing it around the head to fit snugly over the most posterior protuberance of the occiput [49].

Statistical analyses

Data were analyzed using the Statistical Package for Social Sciences (SPSS), v16.0 (SPSS Inc., Chicago, IL, USA). All p-values shown are two-sided and subject to a significance level of 0.05. Correlations (Pearson product-moment or Spearman’s rank correlation coefficients) were calculated in order to explore dependencies in the dataset. More precisely, correlations were computed between the MMSE score and the MTA rating, education, and the CVD rating; and between age and the MTA, and the CVD ratings. The association of MTA and cognitive function was examined using multiple linear regression analysis with the MMSE score as the dependent variable. MTA score and other variables with a putative effect on cognitive function including age, education, gender, head circumference, APOE genotype, CVD rating, and duration of disease were considered as predictors. The regression model also included a trichotomous classification variable for ethnicity (Caucasian, African-American, and Asian-American) with Caucasian as the referent to control for ethnic differences in educational attainment. To control for differences in scanner sensitivity for WMH at the different study centers, variables for the main effect of study center and the interaction between center and WMH were also included in the regression analysis.

To determine whether education modified the effect of MTA on cognitive ability, an interaction term between education and MTA was added to the regression model. In this test of effect modification, the interaction term directly examines the extent to which education changes the effect of MTA on cognition. Thus, the interaction term is the primary focus of the analysis. In addition, to compare the distribution of the variable MMSE score with the normal distribution, a normal P-P plot of regression standardized residuals was generated, which compares the cumulative proportions of standardized residuals of the MMSE score with the cumulative proportions of the respective normal distribution. If the normality assumption is not violated, points are clustered around a straight line.


A description of the study sample is given in Table 1. A total of 270 patients with AD were included who had an average age of 75 years, a mean MMSE score of 17 (median 19, range 0–25, kurtosis 0.28, skewness 0.89), and a mean MTA rating of 2.5 (median 3, range 0–4, kurtosis 0.80, skewness 0.42). Approximately 60% of the subjects were female, APOE ε4 allele carriers, and high school graduates. Correlation analysis revealed some plausible significant associations. In particular, a higher MMSE score was associated with a less severe MTA (r = −0.31, p < 0.001), and older age was correlated with both higher MTA (r = 0.35, p < 0.001) and CVD (r = 0.36, p < 0.001) ratings. There was no significant correlation between disease severity as indicated by the MMSE score as well as the MTA rating, education, and the CVD rating (MMSE: r = 0.08, p = 0.43; MTA: r = 0.09, p = 0.16; CVD: r = 0.02, p = 0.76).

Table 1
Description of the patient sample

In the linear regression analysis, MTA (p < 0.001) and age (p = 0.03) were inversely associated with cognitive performance (indicated by a negative β) (Table 2). The other independent variables were not significant (gender: p = 0.47; APOE genotype: p = 0.92; head circumference: p = 0.74; CVD: p = 0.78; education: p = 0.35; Asian-American ethnicity: p = 0.15; African-American ethnicity: p = 0.07; duration of disease: p = 0.07; study center: p = 0.36, study center * WMH: 0.52).

Table 2
Linear regression models examining the relation of MTA and education to global cognitive function

Most interestingly, in the model with an added interaction term between MTA and education, the interaction term showed a statistically significant inverse association with the MMSE score (p = 0.03), indicating that education attenuated the impact of MTA on cognitive performance (again, indicated by a negative β). In this model, age (p = 0.02) and education (p = 0.02) were significant predictors of cognitive performance (Table 2). MTA and the other independent variables did not show significant effects (MTA: p = 0.18; gender p = 0.42; APOE genotype: p = 0.90; head circumference: p = 0.67; CVD: p = 0.62; Asian-American ethnicity: p = 0.11, African-American ethnicity: p = 0.09; duration of disease: p = 0.09; study center: p = 0.48, study center * WMH: 0.43). The normal P-P plot of regression standardized residuals supported the normality assumption (Fig. 1).

Fig. 1
Normal P-P Plot of regression standardized residuals (dependent variable: MMSE).


The present study suggests that educational attainment modifies the association between ratings of MTA and cognitive performance in patients with AD, taking into account other factors which may have an impact on cognition, including age, gender, APOE genotype, head size, and cerebrovascular lesion burden. In well-educated patients, the effect of MTA on cognition was weaker than in less-educated subjects. This finding is consistent with the concept of CR [12,13]. It is also in line with previous studies relating functional and structural indicators of neurodegeneration, including metabolism, cerebral blood flow or brain atrophy, with cognitive ability and education as a measure of CR. These studies have consistently demonstrated that the association between in vivo pathological indices and cognitive impairment was weaker in better educated individuals with AD [15,16,18,21,28], dementia with Lewy bodies [22], frontotemporal dementia [50], and non-fluent progressive aphasia [20]. In addition, clinico-pathological studies have suggested that not only functional alterations but also morphological brain changes have a less negative effect on cognitive ability shortly before death in patients with greater CR [51]. In line with these studies, our findings suggest that the effect of CR, whatever its nature, is robust enough to offset the consequences of brain tissue loss on cognitive ability.

Some limitations of the study should be considered in the interpretation of the results. First, our patient sample was generally well-educated and was recruited from memory clinics or similar institutions, so that the results may not be generalizable. This may be one of the reasons for the underrepresentation of CVD in the study sample. Particularly, cerebral infarction was rather rare, so that the CVD rating predominantly represents WMH. Therefore, effects of CVD on cognition may have been underestimated. Second, MTA was assessed using a visual rating procedure which may not be sensitive to minor or non-linear changes. Therefore, the analysis might be improved by volumetric MTA measurements. Third, we considered education level as a dichotomous outcome and may not have captured non-linear effects of years of schooling or identified a level of education that is optimal for assessing the effect of education on the association of MTA with cognitive performance. Furthermore, it has to be noted that education might not be the ideal proxy for CR, although it has been used as such in most studies. Other demographic factors, such as intelligence [18], lifetime occupation [17], leisure activities [15], or social networks [52] may also contribute to CR in a way that is yet to be understood. Forth, the MMSE was used to rate cognitive impairment in the MIRAGE study. Although it is a reliable assessment scale in AD, more sensitive tests may have further improved the results.

In conclusion, the present study strengthens the concept of CR by demonstrating that manifest morphological brain changes have a less negative effect on cognition in patients with AD and greater educational attainment. Therefore, education is not only associated with a cognitive advantage such that well-educated individuals have better cognitive function and require more pathology to reach any given level of cognitive impairment; education also modifies the association between pathology and cognition at any given level of brain damage. Future studies using more precise volumetric measures of MTA in a larger sample are needed to refine and extend the results of the present study.


The MIRAGE Study Group members are Drs Lindsay A. Farrer, Robert C. Green, L. Adrienne Cupples, Clinton T. Baldwin, Kathryn L. Lunetta, Mark Logue and Sanford Auerbach (Boston University); Drs Abimbola Akomolafe, Angela Ashley, Lorin Freedman and Elizabeth Ofili (Morehouse School of Medicine); Dr Helena Chui (University of Southern California); Dr Charles DeCarli (University of California–Davis); Dr Ranjan Duara (Mt Sinai Medical Center, Miami); Drs Tatiana Foroud and Martin Farlow (Indiana University School of Medicine); Dr Robert Friedland (Case Western Reserve University); Dr Rodney Go (University of Alabama-Birmingham); Dr Alexander Kurz (Technische Universität München, Munich, Germany); Dr Thomas Obisesan (Howard University); Drs Helen Petrovitch and Lon White (Pacific Health Research Institute); Dr Marwan Sabbagh (Sun Health Research Institute); Dr Dessa Sadovnick (University of British Columbia) and Dr Magda Tsolaki (University of Aristotle, Thessaloniki, Greece). We are grateful to Michael Wake for project coordination, Irene Simkin for laboratory work, John Farrell for database programming and electronic data capturing support, and Jianping Guo for data management. We are also indebted to the MIRAGE site coordinators and the study participants. This work was supported in part by National Institute on Aging grants R01-AG09029, R01-HG/AG02213, K24-AG027841, and P30-AG13846. The sponsor of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The authors wish to thank Dorottya Ruisz for proofreading.

Dr. Perneczky received lecture fees from Janssen-Cilag and Pfizer. No other authors had anything to disclose.


[1] Braak H, Braak E. Evolution of the neuropathology of Alzheimer’s disease. Acta Neurol Scand Suppl. 1996;165:3–12. [PubMed]
[2] Thal DR, Rub U, Orantes M, Braak H. Phases of A beta-deposition in the human brain and its relevance for the development of AD. Neurology. 2002;58:1791–1800. [PubMed]
[3] Bobinski M, Wegiel J, Wisniewski HM, Tarnawski M, Reisberg B, De Leon MJ, Miller DC. Neurofibrillary pathology-correlation with hippocampal formation atrophy in Alzheimer disease. Neurobiol Aging. 1996;17:909–919. [PubMed]
[4] Jagust WJ, Zheng L, Harvey DJ, Mack WJ, Vinters HV, Weiner MW, Ellis WG, Zarow C, Mungas D, Reed BR, Kramer JH, Schuff N, DeCarli C, Chui HC. Neuropathological basis of magnetic resonance images in aging and dementia. Ann Neurol. 2008;63:72–80. [PMC free article] [PubMed]
[5] Jack CR, Jr., Dickson DW, Parisi JE, Xu YC, Cha RH, O’Brien PC, Edland SD, Smith GE, Boeve BF, Tangalos EG, Kokmen E, Petersen RC. Antemortem MRI findings correlate with hippocampal neuropathology in typical aging and dementia. Neurology. 2002;58:750–757. [PMC free article] [PubMed]
[6] De-Leon MJ, Mosconi L, Blennow K, DeSanti S, Zinkowski R, Mehta PD, Pratico D, Tsui W, Saint-Louis LA, Sobanska L, Brys M, Li Y, Rich K, Rinne J, Rusinek H. Imaging and CSF studies in the preclinical diagnosis of Alzheimer’s disease. Ann N Y Acad Sci. 2007;1097:114–145. [PubMed]
[7] Ridha BH, Barnes J, Bartlett JW, Godbolt A, Pepple T, Rossor MN, Fox NC. Tracking atrophy progression in familial Alzheimer’s disease: a serial MRI study. Lancet Neurol. 2006;5:828–834. [PubMed]
[8] Visser PJ, Verhey FR, Hofman PA, Scheltens P, Jolles J. Medial temporal lobe atrophy predicts Alzheimer’s disease in patients with minor cognitive impairment. J Neurol Neurosurg Psychiatry. 2002;72:491–497. [PMC free article] [PubMed]
[9] Rothschild D. Alzheimer’s disease. A clinicopathologic study of five cases. Am J Psychiatry. 1934;91:485–519.
[10] Riley KP, Snowdon DA, Markesbery WR. Alzheimer’s neurofibrillary pathology and the spectrum of cognitive function: findings from the Nun Study. Ann Neurol. 2002;51:567–577. [PubMed]
[11] Katzman R, Terry R, DeTeresa R, Brown T, Davies P, Fuld P, Renbing X, Peck A. Clinical, pathological, and neurochemical changes in dementia: a subgroup with preserved mental status and numerous neocortical plaques. Ann Neurol. 1988;23:138–144. [PubMed]
[12] Stern Y. What is cognitive reserve? Theory and research application of the reserve concept. J Int Neuropsychol Soc. 2002;8:448–460. [PubMed]
[13] Mortimer J. Important role of brain reserve in lowering the risk of Alzheimer’s disease. Future Neurol. 2009;4:1–4.
[14] Bennett DA, Wilson RS, Schneider JA, Evans DA, Mendes de Leon CF, Arnold SE, Barnes LL, Bienias JL. Education modifies the relation of AD pathology to level of cognitive function in older persons. Neurology. 2003;60:1909–1915. [PubMed]
[15] Scarmeas N, Zarahn E, Anderson KE, Habeck CG, Hilton J, Flynn J, Marder KS, Bell DL, Sackeim HA, Van-Heertum RL, Moeller JR, Stern Y. Association of life activities with cerebral blood flow in Alzheimer’s disease. Implications for the cognitive reserve hypothesis. Arch Neurol. 2003;60:359–365. [PMC free article] [PubMed]
[16] Stern Y, Alexander GE, Prohovnik I, Mayeux R. Inverse relationship between education and parietotemporal perfusion deficit in Alzheimer’s disease. Ann Neurol. 1992;32:371–375. [PubMed]
[17] Stern Y, Alexander GE, Prohovnik I, Stricks L, Link B, Lennon MC, Mayeux R. Relationship between lifetime occupation and parietal flow: Implications for a reserve against Alzheimer’s disease pathology. Neurology. 1995;45:55–60. [PubMed]
[18] Alexander GE, Furey ML, Grady CL, Pietrini P, Brady DR, mentis MJ, Shapiro MB. 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. [PubMed]
[19] Perneczky R, Diehl-Schmid J, Förstl H, Drzezga A, Kurz A. Brain reserve capacity in frontotemporal dementia: A voxel-based (18)F-FDG PET study. Eur J Nucl Med Mol Imaging. 2007;34:1082–1087. [PubMed]
[20] Perneczky R, Diehl-Schmid J, Pohl C, Drzezga A, Kurz A. Non-fluent progressive aphasia: cerebral metabolic patterns and brain reserve. Brain Res. 2007;1133:178–185. [PubMed]
[21] Perneczky R, Drzezga A, Diehl-Schmid J, Schmid G, Wohlschläger A, Kars S, Grimmer T, Wagenpfeil S, Monsch A, Kurz A. Schooling mediates brain reserve in Alzheimer’s disease: findings of fluoro-deoxy-glucose-positron emission tomography. J Neurol Neurosurg Pychiatry. 2006;77:1060–1063. [PMC free article] [PubMed]
[22] Perneczky R, Häussermann P, Diehl-Schmid J, Boecker H, Förstl H, Drzezga A, Kurz A. Metabolic correlates of brain reserve in dementia with Lewy bodies: An FDG PET study. Dement Geriatr Cogn Disord. 2007;23:316–322. [PubMed]
[23] Mosconi L, Tsui WH, Herholz K, Pupi A, Drzezga A, Lucignani G, Reiman EM, Holthoff V, Kalbe E, Sorbi S, Diehl-Schmid J, Perneczky R, Clerici F, Caselli R, Beuthien-Baumann B, Kurz A, Minoshima S, de Leon MJ. Multicenter standardized 18F-FDG PET diagnosis of mild cognitive impairment, Alzheimer’s disease, and other dementias. J Nucl Med. 2008;49:390–398. [PMC free article] [PubMed]
[24] Perneczky R, Häussermannn P, Drzezga A, Boecker H, Granert O, Förstl H, Kurz A. Fluoro-deoxy-glucose positron emission tomography correlates of impaired activities of daily living in dementia with lewy bodies: implications for cognitive reserve. Am J Geriatr Psychiatry. in press. [PubMed]
[25] Garibotto V, Borroni B, Kalbe E, Herholz K, Salmon E, Holtoff V, Sorbi S, Cappa SF, Padovani A, Fazio F, Perani D. Education and occupation as proxies for reserve in aMCI converters and AD: FDG-PET evidence. Neurology. 2008;71:1342–1349. [PubMed]
[26] Koepsell TD, Kurland BF, Harel O, Johnson EA, Zhou XH, Kukull WA. Education, cognitive function, and severity of neuropathology in Alzheimer disease. Neurology. 2008;70:1732–1739. [PubMed]
[27] Stern Y, Zarahn E, Hilton HJ, Flynn J, DeLaPaz R, Rakitin B. Exploring the neural basis of cognitive reserve. J Clin Exp Neuropsychol. 2003;25:691–701. [PubMed]
[28] Kidron D, Black SE, Stanchev P, Buck B, Szalai JP, Parker J, Szekely C, Bronskill MJ. Quantitative MR volumetry in Alzheimer’s disease. Topographic markers and the effects of sex and education. Neurology. 1997;49:1504–1512. [PubMed]
[29] Singer T, Verhaeghen P, Ghisletta P, Lindenberger U, Baltes PB. The fate of cognition in very old age: six-year longitudinal findings in the Berlin Aging Study (BASE) Psychol Aging. 2003;18:318–331. [PubMed]
[30] Martins CA, Oulhaj A, de Jager CA, Williams JH. APOE alleles predict the rate of cognitive decline in Alzheimer disease: a nonlinear model. Neurology. 2005;65:1888–1893. [PubMed]
[31] Witelson SF, Beresh H, Kigar DL. Intelligence and brain size in 100 postmortem brains: sex, lateralization and age factors. Brain. 2006;129:386–398. [PubMed]
[32] Jellinger KA. The enigma of vascular cognitive disorder and vascular dementia. Acta Neuropathol. 2007;113:349–388. [PubMed]
[33] Swanwick GR, Coen RF, Maguire CP, Kirby M, Walsh JB, O’Neill D, Coakley D, Lawlor BA. The association between demographic factors, disease severity and the duration of symptoms at clinical presentation in elderly people with dementia. Age Ageing. 1999;28:295–299. [PubMed]
[34] Demissie S, Green RC, Mucci L, Tziavas S, Martelli K, Bang K, Coons L, Bourque S, Buchillon D, Johnson K, Smith T, Sharrow N, Lautenschlager N, Friedland R, Cupples LA, Farrer LA. Reliability of information collected by proxy in family studies of Alzheimer’s disease. Neuroepidemiology. 2001;20:105–111. [PubMed]
[35] Farrer LA, Cupples LA, Blackburn S, Kiely DK, Auerbach S, Growdon JH, Connor-Lacke L, Karlinsky H, Thibert A, Burke JR, et al. Interrater agreement for diagnosis of Alzheimer’s disease: the MIRAGE study. Neurology. 1994;44:652–656. [PubMed]
[36] Lautenschlager NT, Cupples LA, Rao VS, Auerbach SA, Becker R, Burke J, Chui H, Duara R, Foley EJ, Glatt SL, Green RC, Jones R, Karlinsky H, Kukull WA, Kurz A, Larson EB, Martelli K, Sadovnick AD, Volicer L, Waring SC, Growdon JH, Farrer LA. Risk of dementia among relatives of Alzheimer’s disease patients in the MIRAGE study: What is in store for the oldest old? Neurology. 1996;46:641–650. [PubMed]
[37] McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan 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. [PubMed]
[38] Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatric Res. 1975;12:189–198. [PubMed]
[39] Perneczky R, Wagenpfeil S, Komossa K, Grimmer T, Diehl J, Kurz A. Mapping scores onto stages: mini-mental state examination and clinical dementia rating. Am J Geriatr Psychiatry. 2006;14:139–144. [PubMed]
[40] Lunetta KL, Erlich PM, Cuenco KT, Cupples LA, Green RC, Farrer LA, Decarli C. Heritability of magnetic resonance imaging (MRI) traits in Alzheimer disease cases and their siblings in the MIRAGE study. Alzheimer Dis Assoc Disord. 2007;21:85–91. [PubMed]
[41] van Straaten EC, Harvey D, Scheltens P, Barkhof F, Petersen RC, Thal LJ, Jack CR, Jr., DeCarli C. Periventricular white matter hyperintensities increase the likelihood of progression from amnestic mild cognitive impairment to dementia. J Neurol. 2008;255:1302–1308. [PMC free article] [PubMed]
[42] DeCarli C, Frisoni GB, Clark CM, Harvey D, Grundman M, Petersen RC, Thal LJ, Jin S, Jack CR, Jr., Scheltens P. Qualitative estimates of medial temporal atrophy as a predictor of progression from mild cognitive impairment to dementia. Arch Neurol. 2007;64:108–115. [PubMed]
[43] Scheltens P, Leys D, Barkhof F, Huglo D, Weinstein HC, Vermersch P, Kuiper M, Steinling M, Wolters EC, Valk J. Atrophy of medial temporal lobes on MRI in “probable” Alzheimer’s disease and normal ageing: diagnostic value and neuropsychological correlates. J Neurol Neurosurg Psychiatry. 1992;55:967–972. [PMC free article] [PubMed]
[44] Scheltens P, Launer LJ, Barkhof F, Weinstein HC, van Gool WA. Visual assessment of medial temporal lobe atrophy on magnetic resonance imaging: interobserver reliability. J Neurol. 1995;242:557–560. [PubMed]
[45] Wahlund LO, Julin P, Lindqvist J, Scheltens P. Visual assessment of medical temporal lobe atrophy in demented and healthy control subjects: correlation with volumetry. Psychiatry Res. 1999;90:193–199. [PubMed]
[46] DeCarli C, Massaro J, Harvey D, Hald J, Tullberg M, Au R, Beiser A, D’Agostino R, Wolf PA. Measures of brain morphology and infarction in the framingham heart study: establishing what is normal. Neurobiol Aging. 2005;26:491–510. [PubMed]
[47] Wu CC, Mungas D, Petkov CI, Eberling JL, Zrelak PA, Buonocore MH, Brunberg JA, Haan MN, Jagust WJ. Brain structure and cognition in a community sample of elderly Latinos. Neurology. 2002;59:383–391. [PubMed]
[48] Wenham PR, Price WH, Blandell G. Apolipoprotein E genotyping by one-stage PCR. Lancet. 1991;337:1158–1159. [PubMed]
[49] Cameron N. The methods of auxological antropometry. In: Falkner F, Tanner J, editors. Human growth: Vol. 2 Postnatal growth. Plenum press; New York: 1978.
[50] Perneczky R, Diehl-Schmid J, Drzezga A, Kurz A. Brain reserve capacity in frontotemporal dementia: a voxel-based 18F-FDG PET study. Eur J Nucl Med Mol Imaging. 2007;34:1082–1087. [PubMed]
[51] Bennett DA, Schneider JA, Wilson RS, Bienias JL, Arnold SE. Education modifies the association of amyloid but not tangles with cognitive function. Neurology. 2005;65:953–955. [PubMed]
[52] Bennett DA, Schneider JA, Tang Y, Arnold SE, Wilson RS. The effect of social networks on the relation between Alzheimer’s disease pathology and level of cognitive function in old people: a longitudinal cohort study. Lancet Neurol. 2006;5:406–412. [PubMed]