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Reevaluating the role of education on cognitive decline and brain aging in longitudinal cohorts across 33 Western countries

Nature Medicine volume 31, pages 2967–2976 (2025)Cite this article

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Abstract

Why education is linked to higher cognitive function in aging is fiercely debated. Leading theories propose that education reduces brain decline in aging and enhances tolerance to brain pathology or that it does not affect cognitive decline but, rather, reflects higher early-life cognitive function. To test these theories, we analyzed 407,356 episodic memory scores from 170,795 participants older than 50 years, alongside 15,157 brain magnetic resonance imaging scans from 6,472 participants across 33 Western countries. More education was associated with better memory, larger intracranial volume and slightly larger volume of memory-sensitive brain regions. However, education did not protect against age-related decline or weakened effects of brain decline on cognition. The most parsimonious explanation for the results is that the associations reflect factors present early in life, including propensity of individuals with certain traits to pursue more education. Although education has numerous benefits, the notion that it provides protection against cognitive or brain decline is not supported.

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Fig. 1: Geographical and age distribution of samples.
Fig. 2: Age, education and practice effects on memory.
Fig. 3: Associations among education, memory score and memory score decline.
Fig. 4: Sensitivity analyses.
Fig. 5: Education, brain measures and episodic memory.
Fig. 6: Relationships among brain, memory and education.

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Data availability

Each dataset has different owners. Contact information to be used for data access is specified in Supplementary Table 3. Parts of the data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (https://adni.loni.usc.edu/). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found at https://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. More information about the Vietnam Era Twin Study of Aging (VETSA), including a list of VETSA investigators, is available at https://psychiatry.ucsd.edu/research/programscenters/vetsa/index.html.

References

  1. GBD 2019 Dementia Forecasting Collaborators. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health 7, e105–e125 (2022).

    Google Scholar 

  2. Wolters, F. J. et al. Twenty-seven-year time trends in dementia incidence in Europe and the United States: the Alzheimer Cohorts Consortium. Neurology 95, e519–e531 (2020).

    PubMed  PubMed Central  Google Scholar 

  3. Chen, Y. et al. Dementia incidence trend in England and Wales, 2002–19, and projection for dementia burden to 2040: analysis of data from the English Longitudinal Study of Ageing. Lancet Public Health 8, e859–e867 (2023).

    PubMed  PubMed Central  Google Scholar 

  4. Gerstorf, D. et al. Today’s older adults are cognitively fitter than older adults were 20 years ago, but when and how they decline is no different than in the past. Psychol. Sci. 34, 22–34 (2023).

    PubMed  Google Scholar 

  5. Livingston, G. et al. Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission. Lancet 404, 572–628 (2024).

    PubMed  Google Scholar 

  6. Suemoto, C. K. et al. Risk factors for dementia in Brazil: differences by region and race. Alzheimers Dement. 19, 1849–1857 (2023).

    PubMed  Google Scholar 

  7. Lock, S. L., Chura, L. R., Dilworth-Anderson, P. & Peterson, J. Equity across the life course matters for brain health. Nat. Aging 3, 466–468 (2023).

    PubMed  Google Scholar 

  8. Ritchie, H., Samborska, V., Ahuja, N., Ortiz-Ospina, E. & Roser, M. Global Education. Our World in Data https://ourworldindata.org/global-education (2023).

  9. Opdebeeck, C., Martyr, A. & Clare, L. Cognitive reserve and cognitive function in healthy older people: a meta-analysis. Neuropsychol. Dev. Cogn. B Aging Neuropsychol. Cogn. 23, 40–60 (2016).

    PubMed  Google Scholar 

  10. Sepulcre, J. College education as a modulator of the aging brain. Nat. Aging 1, 980–981 (2021).

    PubMed  Google Scholar 

  11. Cabeza, R. et al. Maintenance, reserve and compensation: the cognitive neuroscience of healthy ageing. Nat. Rev. Neurosci. 19, 701–710 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Nyberg, L., Lovden, M., Riklund, K., Lindenberger, U. & Backman, L. Memory aging and brain maintenance. Trends Cogn. Sci. 16, 292–305 (2012).

    PubMed  Google Scholar 

  13. Arenaza-Urquijo, E. M. et al. Association between educational attainment and amyloid deposition across the spectrum from normal cognition to dementia: neuroimaging evidence for protection and compensation. Neurobiol. Aging 59, 72–79 (2017).

    CAS  PubMed  Google Scholar 

  14. Gazzina, S. et al. Education modulates brain maintenance in presymptomatic frontotemporal dementia. J. Neurol. Neurosurg. Psychiatry 90, 1124–1130 (2019).

    PubMed  Google Scholar 

  15. Del Ser, T., Hachinski, V., Merskey, H. & Munoz, D. G. An autopsy-verified study of the effect of education on degenerative dementia. Brain 122, 2309–2319 (1999).

    PubMed  Google Scholar 

  16. Nyberg, L. et al. Educational attainment does not influence brain aging. Proc. Natl Acad. Sci. USA 118, e2101644118 (2021).

  17. Stern, Y., Barnes, C. A., Grady, C., Jones, R. N. & Raz, N. Brain reserve, cognitive reserve, compensation, and maintenance: operationalization, validity, and mechanisms of cognitive resilience. Neurobiol. Aging 83, 124–129 (2019).

    PubMed  PubMed Central  Google Scholar 

  18. Stern, Y. What is cognitive reserve? Theory and research application of the reserve concept. J. Int. Neuropsychol. Soc. 8, 448–460 (2002).

    PubMed  Google Scholar 

  19. Stern, Y. et al. A framework for concepts of reserve and resilience in aging. Neurobiol. Aging 124, 100–103 (2023).

    PubMed  Google Scholar 

  20. Lovden, M. et al. No moderating influence of education on the association between changes in hippocampus volume and memory performance in aging. Aging Brain 4, 100082 (2023).

    PubMed  PubMed Central  Google Scholar 

  21. Lovden, M., Fratiglioni, L., Glymour, M. M., Lindenberger, U. & Tucker-Drob, E. M. Education and cognitive functioning across the life span. Psychol. Sci. Public Interest 21, 6–41 (2020).

    PubMed  PubMed Central  Google Scholar 

  22. Seblova, D., Berggren, R. & Lovden, M. Education and age-related decline in cognitive performance: systematic review and meta-analysis of longitudinal cohort studies. Ageing Res. Rev. 58, 101005 (2020).

    CAS  PubMed  Google Scholar 

  23. Schneeweis, N., Skirbekk, V. & Winter-Ebmer, R. Does education improve cognitive performance four decades after school completion? Demography 51, 619–643 (2014).

    PubMed  Google Scholar 

  24. Glymour, M. M., Kawachi, I., Jencks, C. S. & Berkman, L. F. Does childhood schooling affect old age memory or mental status? Using state schooling laws as natural experiments. J. Epidemiol. Community Health 62, 532–537 (2008).

    CAS  PubMed  Google Scholar 

  25. Gorman, E. Does schooling have lasting effects on cognitive function? Evidence from compulsory schooling laws. Demography 60, 1139–1161 (2023).

    PubMed  Google Scholar 

  26. Brinch, C. N. & Galloway, T. A. Schooling in adolescence raises IQ scores. Proc. Natl Acad. Sci. USA 109, 425–430 (2012).

    CAS  PubMed  Google Scholar 

  27. Lager, A., Seblova, D., Falkstedt, D. & Lovden, M. Cognitive and emotional outcomes after prolonged education: a quasi-experiment on 320 182 Swedish boys. Int. J. Epidemiol. 46, 303–311 (2017).

    PubMed  Google Scholar 

  28. Courtin, E. et al. Long-term effects of compulsory schooling on physical, mental and cognitive ageing: a natural experiment. J. Epidemiol. Community Health 73, 370–376 (2019).

    PubMed  Google Scholar 

  29. Ritchie, S. J. & Tucker-Drob, E. M. How much does education improve intelligence? A meta-analysis. Psychol. Sci. 29, 1358–1369 (2018).

    PubMed  PubMed Central  Google Scholar 

  30. Walhovd, K. B., Lovden, M. & Fjell, A. M. Timing of lifespan influences on brain and cognition. Trends Cogn. Sci. 27, 901–915 (2023).

    PubMed  Google Scholar 

  31. Van Hootegem, A., Rogeberg, O., Bratsberg, B. & Lyngstad, T. H. Correlation between cognitive ability and educational attainment weakens over birth cohorts. Sci. Rep. 13, 17747 (2023).

    PubMed  PubMed Central  Google Scholar 

  32. Calandri, I. L. et al. Sex and socioeconomic disparities in dementia risk: a population attributable fractions analysis in Argentina. Neuroepidemiology 58, 264–275 (2024).

    PubMed  Google Scholar 

  33. Walhovd, K. B. et al. Education and income show heterogeneous relationships to lifespan brain and cognitive differences across European and US cohorts. Cereb. Cortex 32, 839–854 (2022).

    PubMed  Google Scholar 

  34. Paradela, R. S. et al. Population attributable fractions for risk factors for dementia in seven Latin American countries: an analysis using cross-sectional survey data. Lancet Glob. Health 12, e1600–e1610 (2024).

    CAS  PubMed  Google Scholar 

  35. Gross, A. L. et al. Harmonisation of later-life cognitive function across national contexts: results from the Harmonized Cognitive Assessment Protocols. Lancet Healthy Longev. 4, e573–e583 (2023).

    PubMed  PubMed Central  Google Scholar 

  36. Tulving, E. Episodic memory: from mind to brain. Annu. Rev. Psychol. 53, 1–25 (2002).

    PubMed  Google Scholar 

  37. Nyberg, L. in The Sage Handbook of Cognitive and Systems Neuroscience Vol. 1 (eds Boyle, G. J. et al.) 47–60 (Sage Publications, 2023).

  38. Nyberg, L. et al. Biological and environmental predictors of heterogeneity in neurocognitive ageing: evidence from Betula and other longitudinal studies. Ageing Res. Rev. 64, 101184 (2020).

    CAS  PubMed  Google Scholar 

  39. Borsch-Supan, A. et al. Data resource profile: the Survey of Health, Ageing and Retirement in Europe (SHARE). Int. J. Epidemiol. 42, 992–1001 (2013).

    PubMed  PubMed Central  Google Scholar 

  40. Walhovd, K. B. et al. Healthy minds 0–100 years: optimising the use of European brain imaging cohorts (‘Lifebrain’). Eur. Psychiatry 50, 47–56 (2018).

    PubMed  Google Scholar 

  41. Mehrbrodt, T., Gruber, S. & Wagner, M. Scales and Multi-item Indicators in the Survey of Health, Ageing and Retirement in Europe (SHARE-ERIC, 2019); https://share-eric.eu/fileadmin/user_upload/SHARE_Working_Paper/WP_Series_45_2019.pdf

  42. Zhang, Y. S. et al. Educational attainment and later-life cognitive function in high- and middle-income countries: evidence from the Harmonized Cognitive Assessment Protocol. J. Gerontol. B Psychol. Sci. Soc. Sci. 79, gbae005 (2024).

  43. Wood, S. N. & Scheipl, F. gamm4: generalized additive mixed models using ‘mgcv’ and ‘lme4’. R package version 0.2-6 https://CRAN.R-project.org/package=gamm4 (2020).

  44. Cadar, D. et al. An international evaluation of cognitive reserve and memory changes in early old age in 10 European countries. Neuroepidemiology 48, 9–20 (2017).

    PubMed  Google Scholar 

  45. Walhovd, K. B. et al. Brain aging differs with cognitive ability regardless of education. Sci. Rep. 12, 13886 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Heilbronner, R. L. et al. Official position of the American Academy of Clinical Neuropsychology on serial neuropsychological assessments: the utility and challenges of repeat test administrations in clinical and forensic contexts. Clin. Neuropsychol. 24, 1267–1278 (2010).

    PubMed  Google Scholar 

  47. Tucker-Drob, E. M., Johnson, K. E. & Jones, R. N. The cognitive reserve hypothesis: a longitudinal examination of age-associated declines in reasoning and processing speed. Dev. Psychol. 45, 431–446 (2009).

    PubMed  PubMed Central  Google Scholar 

  48. Strand, S., Deary, I. J. & Smith, P. Sex differences in cognitive abilities test scores: a UK national picture. Br. J. Educ. Psychol. 76, 463–480 (2006).

    PubMed  Google Scholar 

  49. Eurostat Regional Yearbook, 2024 Edition (Publications Office of the European Union, 2024); https://ec.europa.eu/eurostat/web/products-flagship-publications/w/ks-ha-24-001

  50. Evans, M. D. R., Kelley, J., Sikora, J. & Treiman, D. J. Family scholarly culture and educational success: books and schooling in 27 nations. Res. Soc. Stratif. Mobil. 28, 171–197 (2010).

    Google Scholar 

  51. Deary, I. J., Pattie, A. & Starr, J. M. The stability of intelligence from age 11 to age 90 years: the Lothian birth cohort of 1921. Psychol. Sci. 24, 2361–2368 (2013).

    PubMed  Google Scholar 

  52. Bratsberg, B., Fjell, A. M., Rogeberg, O. J., Skirbekk, V. F. & Walhovd, K. B. Differences in cognitive function at 18 y of age explain the association between low education and early dementia risk. Proc. Natl Acad. Sci. USA 121, e2412017121 (2024).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Sharp, E. S. & Gatz, M. Relationship between education and dementia: an updated systematic review. Alzheimer Dis. Assoc. Disord. 25, 289–304 (2011).

    PubMed  PubMed Central  Google Scholar 

  54. Fürtjes, A. E. et al. Measurement characteristics and genome-wide correlates of lifetime brain atrophy estimated from a single MRI. Nat. Commun. (in the press).

  55. van Loenhoud, A. C., Groot, C., Vogel, J. W., van der Flier, W. M. & Ossenkoppele, R. Is intracranial volume a suitable proxy for brain reserve? Alzheimers Res. Ther. 10, 91 (2018).

    PubMed  PubMed Central  Google Scholar 

  56. Engvig, A. et al. Effects of cognitive training on gray matter volumes in memory clinic patients with subjective memory impairment. J. Alzheimers Dis. 41, 779–791 (2014).

    PubMed  Google Scholar 

  57. Engvig, A. et al. Effects of memory training on cortical thickness in the elderly. Neuroimage 52, 1667–1676 (2010).

    PubMed  Google Scholar 

  58. Lovden, M. et al. Spatial navigation training protects the hippocampus against age-related changes during early and late adulthood. Neurobiol. Aging 33, 620.e9–620.e22 (2012).

    PubMed  Google Scholar 

  59. Grasby, K. L. et al. The genetic architecture of the human cerebral cortex. Science 367, eaay6690 (2020).

  60. Brathen, A. C. S. et al. Cognitive and hippocampal changes weeks and years after memory training. Sci. Rep. 12, 7877 (2022).

    PubMed  PubMed Central  Google Scholar 

  61. de Lange, A. G., Brathen, A. C. S., Rohani, D. A., Fjell, A. M. & Walhovd, K. B. The temporal dynamics of brain plasticity in aging. Cereb. Cortex 28, 1857–1865 (2018).

    PubMed  PubMed Central  Google Scholar 

  62. Judd, N. & Kievit, R. No effect of additional education on long-term brain structure—a preregistered natural experiment in thousands of individuals. eLife 13, RP101526 (2025).

    PubMed  PubMed Central  Google Scholar 

  63. Anderson, E. L. et al. Education, intelligence and Alzheimer’s disease: evidence from a multivariable two-sample Mendelian randomization study. Int. J. Epidemiol. 49, 1163–1172 (2020).

    PubMed  PubMed Central  Google Scholar 

  64. Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Ackerman, P. L. Adult intelligence: the construct and the criterion problem. Perspect. Psychol. Sci. 12, 987–998 (2017).

    PubMed  Google Scholar 

  67. Li, W. et al. Timing in Early Childhood Education: How Cognitive and Achievement Program Impacts Vary by Starting Age, Program Duration, and Time since the End of the Program (Annenberg Institute for School Reform at Brown University, 2020); https://eric.ed.gov/?id=ED610271

  68. Sameroff, A. J., Seifer, R., Baldwin, A. & Baldwin, C. Stability of intelligence from preschool to adolescence: the influence of social and family risk factors. Child Dev. 64, 80–97 (1993).

    CAS  PubMed  Google Scholar 

  69. Tucker-Drob, E. M. & Briley, D. A. Continuity of genetic and environmental influences on cognition across the life span: a meta-analysis of longitudinal twin and adoption studies. Psychol. Bull. 140, 949–979 (2014).

    PubMed  PubMed Central  Google Scholar 

  70. Edwin, T. H. et al. Trajectories of occupational cognitive demands and risk of mild cognitive impairment and dementia in later life: the HUNT4 70+ study. Neurology 102, e209353 (2024).

    PubMed  PubMed Central  Google Scholar 

  71. Kivimaki, M. et al. Cognitive stimulation in the workplace, plasma proteins, and risk of dementia: three analyses of population cohort studies. BMJ 374, n1804 (2021).

    PubMed  PubMed Central  Google Scholar 

  72. Lipnicki, D. M. et al. Age-related cognitive decline and associations with sex, education and apolipoprotein E genotype across ethnocultural groups and geographic regions: a collaborative cohort study. PLoS Med. 14, e1002261 (2017).

    PubMed  PubMed Central  Google Scholar 

  73. Fry, A. et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am. J. Epidemiol. 186, 1026–1034 (2017).

    PubMed  PubMed Central  Google Scholar 

  74. Ritchie, S. J., Bates, T. C. & Deary, I. J. Is education associated with improvements in general cognitive ability, or in specific skills? Dev. Psychol. 51, 573–582 (2015).

    PubMed  PubMed Central  Google Scholar 

  75. Fjell, A. M. et al. What is normal in normal aging? Effects of aging, amyloid and Alzheimer’s disease on the cerebral cortex and the hippocampus. Prog. Neurobiol. 117, 20–40 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Gruber, S., Hunkler, C. & Stuck, S. Generating easySHARE: Guidelines, Structure, Content and Programming (SHARE-ERIC, 2014); https://share-eric.eu/fileadmin/user_upload/SHARE_Working_Paper/SHARE_WP_Series_17_2014.pdf

  77. Bergmann, M., Kneip, T., De Luca, G. & Scherpenzeel, A. Survey Participation in the Survey of Health, Ageing and Retirement in Europe (SHARE), Wave 1-7: Based on Release 7.0.0 (SHARE-ERIC, 2019); https://share-eric.eu/fileadmin/user_upload/SHARE_Working_Paper/WP_Series_41_2019_Bergmann_et_al.pdf

  78. Börsch-Supan, A. & Gruber, S. easySHARE. Release version: 8.0.0. SHARE-ERIC https://doi.org/10.6103/SHARE.easy.800 (2022).

  79. Walhovd, K. B. et al. Neurodevelopmental origins of lifespan changes in brain and cognition. Proc. Natl Acad. Sci. USA 113, 9357–9362 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Nilsson, L. G. et al. The Betula prospective cohort study: memory, health, and aging. Aging Neuropsychol. Cogn. 4, 1–32 (1997).

    Google Scholar 

  81. Nilsson, L.-G. et al. Betula: a prospective cohort study on memory, health and aging. Aging Neuropsychol. Cogn. 11, 134–148 (2004).

    Google Scholar 

  82. Rajaram, S. et al. The Walnuts and Healthy Aging Study (WAHA): protocol for a nutritional intervention trial with walnuts on brain aging. Front. Aging Neurosci. 8, 333 (2016).

    PubMed  Google Scholar 

  83. Vidal-Piñeiro, D. et al. Task-dependent activity and connectivity predict episodic memory network-based responses to brain stimulation in healthy aging. Brain Stimul. 7, 287–296 (2014).

    PubMed  PubMed Central  Google Scholar 

  84. Bertram, L. et al. Cohort profile: the Berlin Aging Study II (BASE-II). Int. J. Epidemiol. 43, 703–712 (2014).

    PubMed  Google Scholar 

  85. Gerstorf, D. et al. Editorial. Gerontology 62, 311–315 (2016).

    PubMed  Google Scholar 

  86. Shafto, M. A. et al. The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing. BMC Neurol. 14, 204 (2014).

    PubMed  PubMed Central  Google Scholar 

  87. Idland, A. V. et al. Biomarker profiling beyond amyloid and tau: cerebrospinal fluid markers, hippocampal atrophy, and memory change in cognitively unimpaired older adults. Neurobiol. Aging 93, 1–15 (2020).

    CAS  PubMed  Google Scholar 

  88. Mueller, S. G. et al. The Alzheimer’s Disease Neuroimaging Initiative. Neuroimaging Clin. N. Am. 15, 869–877 (2005).

    PubMed  PubMed Central  Google Scholar 

  89. Cattaneo, G. et al. The Barcelona Brain Health Initiative: a cohort study to define and promote determinants of brain health. Front. Aging Neurosci. 10, 321 (2018).

    PubMed  PubMed Central  Google Scholar 

  90. Dagley, A. et al. Harvard Aging Brain Study: dataset and accessibility. Neuroimage 144, 255–258 (2017).

    PubMed  Google Scholar 

  91. Miller, K. L. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci. 19, 1523–1536 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Breitner, J. C. S., Poirier, J., Etienne, P. E. & Leoutsakos, J. M. Rationale and structure for a new Center for Studies on Prevention of Alzheimer’s Disease (StoP-AD). J. Prev. Alzheimers Dis. 3, 236–242 (2016).

    CAS  PubMed  Google Scholar 

  93. Tremblay-Mercier, J. et al. Open science datasets from PREVENT-AD, a longitudinal cohort of pre-symptomatic Alzheimer’s disease. Neuroimage Clin. 31, 102733 (2021).

    PubMed  PubMed Central  Google Scholar 

  94. LaMontagne, P. J. et al. OASIS-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease. Preprint at medRxiv https://doi.org/10.1101/2019.12.13.19014902 (2019).

  95. Kremen, W. S., Franz, C. E. & Lyons, M. J. Current status of the Vietnam Era Twin Study of Aging (VETSA). Twin Res. Hum. Genet. 22, 783–787 (2019).

    PubMed  Google Scholar 

  96. Reuter, M., Schmansky, N. J., Rosas, H. D. & Fischl, B. Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61, 1402–1418 (2012).

    PubMed  Google Scholar 

  97. Fischl, B., Sereno, M. I. & Dale, A. M. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 9, 195–207 (1999).

    CAS  PubMed  Google Scholar 

  98. Fischl, B., Sereno, M. I., Tootell, R. B. & Dale, A. M. High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp. 8, 272–284 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  99. Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179–194 (1999).

    CAS  PubMed  Google Scholar 

  100. Destrieux, C., Fischl, B., Dale, A. & Halgren, E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 53, 1–15 (2010).

    PubMed  Google Scholar 

  101. Fischl, B. et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002).

    CAS  PubMed  Google Scholar 

  102. Rutherford, S. et al. Charting brain growth and aging at high spatial precision. eLife 11, e72904 (2022).

  103. Rutherford, S. et al. The normative modeling framework for computational psychiatry. Nat. Protoc. 17, 1711–1734 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. Van Rossum, G. & Drake, F. L. Python 3 Reference Manual (CreateSpace, 2009).

  105. Kia, S. M. et al. Closing the life-cycle of normative modeling using federated hierarchical Bayesian regression. PLoS ONE 17, e0278776 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

    Google Scholar 

  107. Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).

    Google Scholar 

  108. Vidal-Piñeiro, D. et al. Reliability of structural brain change in cognitively healthy adult samples. Imaging Neurosci. 3, imag_a_00547 (2025).

  109. R: a language and environment for statistical computing. R Foundation for Statistical Computing https://www.r-project.org/ (2024).

  110. Bürkner, P.-C. brms: an R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).

    Google Scholar 

  111. Carpenter, B. et al. Stan: a probabilistic programming language. J. Stat. Softw. 76, 1–32 (2017).

    PubMed  PubMed Central  Google Scholar 

  112. Hofman, M. D. & Gelman, A. The No-U-Turn Sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. J. Mach. Learn. Res. 15, 1593–1623 (2014).

    Google Scholar 

  113. Wickham, H. ggplot2: Elegant Graphics for Data Analysis 2nd edn (Springer, 2016).

  114. Mowinckel, A. M. & Vidal-Piñeiro, D. Visualization of brain statistics with R packages ggseg and ggseg3d. Adv. Methods Pract. Psychol. Sci. 3, 466–483 (2020).

    Google Scholar 

  115. Robinson, D. fuzzyjoin: join tables together on inexact matching. https://cran.r-project.org/web/packages/fuzzyjoin/index.html (2020).

Download references

Acknowledgements

The Lifebrain consortium is funded by EU Horizon 2020 grant agreement number 732592 (Lifebrain). The different substudies are supported by different sources. LCBC is supported by the European Research Council under grant agreement numbers 283634 and 725025 (to A.M.F.) and number 313440 (to K.B.W.) as well as the Norwegian Research Council (325878 and 262453 to A.M.F.; 325001, 301395 and 239889 to K.B.W.; 249931 to A.M.F. and K.B.W.; 324882 to D.V.-P.; 325415 to H.G.); the National Association for Public Health’s Dementia Research Program, Norway (to A.M.F.); and the University of Oslo through the UiO:Life Science convergence environment (to A.M.F.). Betula is supported by a scholar grant from the Knut and Alice Wallenberg Foundation to L.N. Barcelona is partially supported by a Spanish Ministry of Economy and Competitiveness grant to D.B.-F. (grant no. PID2022–137234OB-100 (AEI/FEDER, UE)) and to G.C. and J.S. (grant no. PID-2022–139298OA-C22 (MCIN /AEI /10.13039/501100011033 / FEDER, UE)); by the Walnuts and Healthy Aging Study (grant no. NCT01634841), funded by the California Walnut Commission, Sacramento, California; and by ICREA Academia 2019 and 2024 awards. BASE-II has been supported by the German Federal Ministry of Education and Research under grant numbers 16SV5537, 16SV5837, 16SV5538, 16SV5536K, 01UW0808, 01UW0706, 01GL1716A and 01GL1716B and by the European Research Council under grant agreement number 677804 (to S.K.). A.P.-L. is partly supported by grants from the National Institutes of Health (NIH) (R01AG076708), the Jack Satter Foundation and the BrightFocus Foundation. Part of the research was conducted using the UK Biobank resource under application number 32048. The funders had no role in study design, data collection and analysis, preparation of the manuscript or decision to publish. L.O.W. is funded by the South-Eastern Norway Regional Health Authorities (no. 2017095), by the Norwegian Health Association (no. 19536 and no. 1513) and by Wellcome Leap’s Dynamic Resilience Program (jointly funded by Temasek Trust) (no. 104617). Parts of the data used in preparation of this article were obtained from the Pre-Symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer’s Disease (PREVENT-AD) program. Data were provided, in part, by OASIS-3 (OASIS-3 principal investigators: T. Benzinger, D. Marcus and J. Morris; NIH P50AG00561, P30NS09857781, P01AG026276, P01AG003991, R01AG043434, UL1TR000448 and R01EB009352). Parts of the data collection and sharing for this project were provided by the Cambridge Centre for Ageing and Neuroscience (Cam-CAN). Cam-CAN funding was provided by the UK Biotechnology and Biological Sciences Research Council (grant no. BB/H008217/1), together with support from the UK Medical Research Council and the University of Cambridge. Parts of the data are from VETSA, which is funded by National Institute of Aging (NIA) R01 grants AG018384, AG018386, AG050595, AG022381 and AG076838. The content is the responsibility of the authors and does not necessarily represent the official views of the NIA, the NIH, the US Department of Veterans Affairs, the US Department of Defense, the National Personnel Records Center, the National Archives and Records Administration, the Internal Revenue Service, the National Opinion Research Center, the National Research Council, the National Academy of Sciences or the Institute for Survey Research. Temple University provided invaluable assistance in the conduct of the Vietnam Era Twin Registry. The Cooperative Studies Program of the US Department of Veterans Affairs provided financial support for development and maintenance of the Vietnam Era Twin Registry. We would also like to acknowledge the continued cooperation and participation of the members of the Vietnam Era Twin Registry and their families. Part of the data collection and sharing was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (NIH grant U01 AG024904) and DOD ADNI (Department of Defense award no. W81XWH-12–2–0012). The ADNI is funded by the NIA and the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: AbbVie, the Alzheimer’s Association; the Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol Myers Squibb; CereSpir, Inc.; Cogstate; Eisai, Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche, Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO, Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development, LLC; Lumosity; Lundbeck; Merck & Co., Inc.; MesoScale Diagnostics, LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals; Pfizer, Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (https://fnih.org/). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Parts of the data used in the preparation of this article were obtained from the Harvard Aging Brain Study (HABS - P01AG036694; https://habs.mgh.harvard.edu). The HABS was launched in 2010, funded by the NIA, and is led by principal investigators R. A. Sperling and K. A. Johnson at Massachusetts General Hospital/Harvard Medical School. The SHARE data collection has been funded by the European Commission, DG RTD through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006-028812), FP7 (SHARE-PREP: GA no. 211909, SHARE-LEAP: GA no. 227822, SHARE M4: GA no. 261982, DASISH: GA no. 283646) and Horizon 2020 (SHARE-DEV3: GA no.; 676536, SHARE-COHESION: GA no. 870628, SERISS: GA no. 654221, SSHOC: GA no. 823782, SHARE-COVID19: GA no. 101015924) and by DG Employment, Social Affairs & Inclusion through VS 2015/0195, VS 2016/0135, VS 2018/0285, VS 2019/0332, VS 2020/0313, SHARE-EUCOV: GA no. 101052589 and EUCOVII: GA no. 101102412. Additional funding from the German Federal Ministry of Education and Research (01UW1301, 01UW1801, 01UW2202), the Max Planck Society for the Advancement of Science, the NIA (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553-01, IAG_BSR06-11, OGHA_04-064, BSR12-04, R01_AG052527-02, R01_AG056329-02, R01_AG063944, HHSN271201300071C, RAG052527A) and various national funding sources is gratefully acknowledged (see https://www.share-eric.eu/).

Author information

Authors and Affiliations

  1. Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway

    Anders M. Fjell, Øystein Sørensen, Inge K. Amlien, Håkon Grydeland, Athanasia M. Mowinckel, Lars Nyberg, Markus H. Sneve, Marie Strømstad, Kristine B. Walhovd & Didac Vidal-Piñeiro

  2. Computational Radiology and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway

    Anders M. Fjell, Inge K. Amlien & Kristine B. Walhovd

  3. Ragnar Frisch Centre for Economic Research, Oslo, Norway

    Ole Rogeberg

  4. Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain

    David Bartrés-Faz & Cristina Solé-Padullés

  5. Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Barcelona, Spain

    David Bartrés-Faz, Gabriele Cattaneo & Javier Solana

  6. Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain

    David Bartrés-Faz & Cristina Solé-Padullés

  7. Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany

    Andreas M. Brandmaier, Sandra Düzel & Ulman Lindenberger

  8. Department of Psychology, MSB Medical School Berlin, Berlin, Germany

    Andreas M. Brandmaier

  9. Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany

    Andreas M. Brandmaier, Simone Kühn & Ulman Lindenberger

  10. Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Barcelona, Spain

    Gabriele Cattaneo

  11. MRC Cognition and Brain Sciences Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK

    Richard N. Henson

  12. Center for Environmental Neuroscience, Max Planck Institute for Human Development, Berlin, Germany

    Simone Kühn

  13. Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Simone Kühn

  14. Department of Sociology and Human Geography, University of Oslo, Oslo, Norway

    Torkild Hovde Lyngstad

  15. Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden

    Lars Nyberg

  16. Department of Medical and Translational Biology, Umeå University, Umeå, Sweden

    Lars Nyberg

  17. Department of Diagnostics and Intervention, Umeå University, Umeå, Sweden

    Lars Nyberg

  18. Hinda and Arthur Marcus Institute for Aging Research, Deanna and Sidney Wolk Center for Memory Health, Hebrew SeniorLife, Boston, MA, USA

    Alvaro Pascual-Leone

  19. Department of Neurology, Harvard Medical School, Boston, MA, USA

    Alvaro Pascual-Leone

  20. Oslo Delirium Research Group, Institute of Clinical Medicine, Campus Ahus, University of Oslo, Oslo, Norway

    Leiv Otto Watne

  21. Department of Geriatric Medicine, Akershus University Hospital, Lørenskog, Norway

    Leiv Otto Watne

Authors
  1. Anders M. Fjell
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  2. Ole Rogeberg
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  3. Øystein Sørensen
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  23. Didac Vidal-Piñeiro
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Contributions

A.M.F., K.B.W., O.R. and D.V.-P. conceptualized the study. A.M.F., O.R., D.V.-P. and Ø.S. analyzed the data. A.M.D. wrote the paper. All authors critically revised the paper and approved the final version.

Corresponding author

Correspondence to Anders M. Fjell.

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Competing interests

A.P.-L. serves as a paid member of the scientific advisory boards for Neuroelectrics, Magstim Inc., TetraNeuron, Skin2Neuron, MedRhythms and AscenZion. He is co-founder of TI Solutions and co-founder and chief medical officer of Linus Health. A.P.-L. is also listed as an inventor on several issued and pending patents on the real-time integration of transcranial magnetic stimulation with electroencephalography and magnetic resonance imaging and applications of non-invasive brain stimulation in various neurological disorders as well as digital biomarkers of cognition and digital assessments for early diagnosis of dementia. The other authors declare no competing interests.

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Nature Medicine thanks Sarah Ackley, Jasmine Mah and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Jerome Staal, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Table 1 Associations among education, memory score and memory score decline
Full size table
Extended Data Table 2 Sample characteristics for samples with MRI
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Supplementary information

41591_2025_3828_MOESM1_ESM.pdf

Supplementary Tables 1–9 and Figs. 1–11. MRI samples, Data availability, Education in the MRI cohorts, Memory testing in the MRI cohorts, MRI acquisition and preprocessing, SHARE sample distributions, Memory testing in SHARE and Sensitivity analyses of cognition.

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Fjell, A.M., Rogeberg, O., Sørensen, Ø. et al. Reevaluating the role of education on cognitive decline and brain aging in longitudinal cohorts across 33 Western countries. Nat Med 31, 2967–2976 (2025). https://doi.org/10.1038/s41591-025-03828-y

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