Reevaluating the Role of Education in Cognitive Decline and Brain Aging: Insights from Large-Scale Longitudinal Cohorts across 33 Countries

Reevaluating the Role of Education in Cognitive Decline and Brain Aging: Insights from Large-Scale Longitudinal Cohorts across 33 Countries | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Reevaluating the Role of Education in Cognitive Decline and Brain Aging: Insights from Large-Scale Longitudinal Cohorts across 33 Countries Anders Fjell, Ole Rogeberg, Øystein Sørensen, Inge Amlien, David Bartres-Faz, and 18 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5938408/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Jul, 2025 Read the published version in Nature Medicine → Version 1 posted You are reading this latest preprint version Abstract Why education is linked to higher cognitive function in aging is fiercely debated. Leading theories propose that education reduces brain decline in aging, 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 > 50 years, alongside 15.157 brain MRIs 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. While education has numerous benefits, the notion that it provides protection against cognitive or brain decline is not supported. Health sciences/Neurology/Neurological disorders/Dementia/Alzheimer's disease Scientific community and society/Social sciences/Psychology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction While the total number of people with dementia will increase massively due to population growth and ageing 1 , the incidence seems to be declining 2 , 3 , and older adults have better cognitive function today than 20 years ago 4 . One hypothesis is that this reflects broad societal and individual lifestyle changes, and that dementia incidence can be further reduced by promoting these 1 , 5 . Education has repeatedly been suggested to be one such potential protective factor 6 , 7 , in line with observations of robust associations between education and higher cognitive function in aging, as well as declines in dementia incidence with increasing population educational attainment 8 , 9 . However, results so far are heterogeneous and point in different directions, and the specific mechanisms that could explain such a causal link are widely debated 10 . We therefore suggest addressing these questions by conducting a large mega-analysis of longitudinal brain and cognitive studies covering a wider geographical distribution of samples. Education could result in better cognition in aging by contributing to a lower rate of age-normative brain decline 11 . Indeed, higher brain maintenance has been associated with better episodic memory 12 , and studies have found less brain pathology in older adults with higher education 13 , less brain decline in presymptomatic dementia 14 , and less accumulation of cerebrovascular lesions 15 . However, a recent longitudinal study investigating two independent samples did not find different rates of change in hippocampus and age-sensitive regions of the cerebral cortex in more educated participants 16 . Alternatively, education could make people more resilient to underlying brain pathology by higher cognitive reserve 17 . According to this theory, education leads to more efficient processing of cognitive tasks which in turn allows for higher performance despite age-normative levels of brain decline 18 . Although a popular theory 5 , 19 , a longitudinal study found that education did not weaken the link between hippocampal atrophy and memory change 20 . Both the maintenance and the reserve accounts of education imply that education causally influences late-life cognition by reducing or postponing age-related decline. This is controversial, however, because even though education is associated with better cognitive function among older adults, it is not clear that more educated persons show less cognitive decline when measured longitudinally 21 , 22 . An alternative perspective holds that the association between education and cognitive performance is persistent across the adult lifespan. This contrasts with the more aging-centered views presented above. Under this alternative view, if education has a positive causal effect on cognition in aging, it would be by permanently boosting cognitive function earlier in life, causing persistent differences between educational groups. Increased compulsory schooling has been shown to elevate scores on tests of memory 23 – 25 , intelligence 26 , 27 and general cognition 28 , with effects detectable decades later 29 . This perspective could also be consistent with a lack of causal effects of education on cognitive function, however, as those with higher initial cognitive functioning would be expected to reach higher levels of education than their peers. Hence, the topic of the role of education in cognitive function and brain health in aging is riddled with controversies 30 . Nonetheless, contrasting predictions can be derived from the different theories. If education improves memory in older age by shaping brain aging, we expect better preservation of memory-sensitive brain regions among individuals with higher education. If education improves cognitive reserve, we expect more tolerance to brain pathology, indexed by a lower correlation between brain decline and cognitive decline. In contrast, if the education-memory-brain relationship reflects stable individual differences, education should not correlate with either memory or brain decline. In that case, we also would expect to see selection effects, in the sense that participants with specific traits, especially higher cognitive function, are more likely to pursue further education. It is also relevant to examine whether retest effect – the tendency for performance to increase as a function of previous tests taken – is exaggerated with higher education. If more education yields cognitive reserve, this may manifest as a greater ability to take advantage of previous testing experience and to develop more efficient test taking strategies. A major challenge in addressing these questions is that we need large, representative and heterogeneous longitudinal samples with sufficient statistical power. The geographic coverage is critical, because associations between brain, cognition, and education will vary both across time 31 and societies 32 – 34 . For example, the population attributable fraction (PAF) of dementia due to low education was reported to vary from 1.7% in Argentina to 10.8% in Bolivia in a study comparing seven Latin American countries 35 . To alleviate this concern, we here compiled data from several large studies, including a total of 407.356 memory tests from 170.795 participants across 33 countries across Europe, US and Israel, with up to seven follow-up sessions per person (see Fig. 1 ). Although we do not have sufficient statistical power to systematically investigate effects of time, geography and societal differences, our approach ensures that the results are not confined to one specific time and place. Still, it is important to keep in mind that all samples come from WEIRD (Western, Educated, Industrialized, Rich, Democratic) countries, which limits generalizing conclusions to other societies. We focus on episodic memory because it is particularly sensitive to normal aging and neurodegenerative disease 36 . To address brain mechanisms, we further analyzed 15,157 brain MRIs and concurrent memory tests from 6.472 participants across seven countries. The primary data sources were the population-based, multinational SHARE (Survey of Health, Ageing and Retirement in Europe) ( https://share-eric.eu/ ) 37 and the Lifebrain consortium 38 ( https://www.lifebrain.uio.no/ ), enriched with several legacy databases. SHARE uses probability sampling to obtain sample representativity, using the best available sample frame resources in each country to achieve full probability sampling, including access to population registers for most. Although geographically spread, MRI populations will vary in representativity, and hence we chose as strategy to validate the memory-results from SHARE in the MRI samples before conducting the brain analyses. Left panel: Number of completed memory test sessions included across country across SHARE, Lifebrain and the other legacy datasets. The density plot shows sample age-distribution in SHARE. Right panel: Number of completed brain MRIs across countries. The plot shows the age-distribution for each dataset included. Note that the visual presentations of USA& Canada and Cyprus & Israel are not size-wise correct compared to the European map. Results SHARE cohort results Memory was assessed with a 10-word verbal recall test, with two conditions (immediate and 5 minutes recall), using multiple versions across waves and participants 39 . Each condition was separately included in the statistical models, yielding two observations per time point per participant. Generalized linear models with a binomial link were run using memory score as dependent variable, with the interaction between education and time since baseline as the critical term, using test type (immediate or 5-minute delay), a monotonic function of the number of previous tests taken (to control for retest effects), education, sex, country, baseline age, time since baseline, and the age × time interaction as covariates (see Methods & Protocols for the exact model specifications). Individual-specific intercepts per participant were nested within country. Z-transformed values for age and time were used in the model fitting and converted back to natural units when showing the results. A smooth function for age allowed non-linear memory trajectories. The main outputs were the odds ratios (OR) of remembering a word compared to a reference group. Memory scores were lower with higher baseline age, showing slightly accelerating trajectories (smoothing parameter for the combined sample = 45.8, CI: 20.7–81.5). Figure 2 (top left panel) revealed a perfect ordering of scores according to education level, with more education associated with higher scores across age. Compared to the education level used as reference (“upper secondary”), “no education” yielded OR = 0.54 compared to 1.55 for the highest category (“tertiary second stage”, Fig. 3 left panel; Table 1 ). Table 1 Associations between education, memory score and memory score decline. Upper secondary education is used as reference. Note that all memory scores are corrected for retest effects. Memory change (OR per year) results are presented with three decimals to allow inspection of the very weak effects. CI is 95% Education level Memory offset Odds Ratio (CI low – high) Memory change Odds Ratio (CI low – high) None 0.54 0.53–0.55 1.004 1.001–1.007 Primary 0.68 0.67–0.68 1.002 1.001–1.004 Lower secondary 0.83 0.81–0.83 1.002 1.000-1.003 Upper secondary 1 1 Post secondary non-tertiary 1.07 1.05–1.08 1.001 0.998–1.003 Tertiary 1st stage 1.31 1.29–1.32 1.001 1.000-1.003 Tertiary 2nd stage 1.55 1.49–1.60 1.004 0.999–1.010 Retest effects were substantial and thus essential to adjust for in analyses of change. ORs increased almost linearly, from 1.5 at the first follow-up to 2.5 at the fifth (Fig. 2 , top right panel). There was a small negative effect of time (one year) on memory scores (OR = 0.963, CI: 0.961–0.964), slightly increasing with age (age × time OR = 0.9981, CI: 0.9980–0.9982). We assessed whether higher education was associated with less memory decline over one year (Fig. 3 , right panel; Table 1 ). Effect sizes were negligible, with all ORs < 1.005. Further, if education is associated with the ability to benefit from previous testing experience to optimize performance, individuals with more education and cognitive reserve should be able to benefit more from repeated testing more efficiently. However, there were no systematic differences in retest effects by education (Fig. 4 , bottom row). We re-ran the analyses using education relative to birth cohort in bins of a decade (1900–1909, 1910–1919, …, 1960–1969), sex, and country as measure of interest, yielding a percentile score for each participant, while controlling for absolute level of education. This provides a test of whether the education-memory associations reflect selection effects, in the sense that people are selected into education based on some unmeasured trait, that act as a common cause, and is correlated with late-life memory scores, and partially accounts for these selection effects varying between men and women from different birth cohorts in countries with widely varying educational opportunities and experiences. As seen in Fig. 4 , including relative education in the model reduced the associations between absolute education and memory somewhat, while relative education showed an independent, positive association with memory. The effect of going from the lowest (0) to the highest (100) percentile was associated with an OR of 1.17 (CI: 1.14–1.20) compared to the reference group (“upper secondary”). Brain MRI cohort results For the brain analyses, we included 13 datasets with longitudinal MRI, memory assessments, and information about education, from seven countries across North to South of Europe, US and Canada (see Fig. 2 ). In addition to cohort-specific inclusion and exclusion criteria, participants > 50 years without cognitive impairment, neurological or psychiatric disorders were included. The initial dataset included participants with 1 to 14 MRI acquisitions with follow-up intervals spanning up to 15.8 years, and memory assessments ranging from 1 to 24 observations per participant with follow-up intervals up to 28 years. Sample characteristics are presented in Table 2, and cohort specific descriptions in Methods & Protocols. First, we tested whether the main cognitive results from SHARE replicated in the MRI cohorts. As education coding varied across samples, we could not use the same coding scheme as in SHARE, and education was hence dichotomized based on the median split for each sample, with post hoc analyses using tertiary vs. non-tertiary education as category (see Replication analyses ). A generalized additive mixed model ( GAMM ) 40 was run using memory (z-normalized based on the first observation per each dataset) as dependent variable, with education, time since baseline, sex, a dummy for retest effects as fixed effects, and baseline age as smooth term. Random intercepts were included per participant and dataset while random slopes of retest and time were included for each dataset. To test memory change, an education × time interaction term was added to the model. Exact p-values are provided down to p < .001. Table 2 Sample characteristics for samples with MRI . N: Number of unique participants. Obs: Total number of observations. Sex: M – Males/ F – females. Tertiary edu: Number of participants with tertiary or higher education. Above median edu: Number of participants with above median education. xObs: Obs per participant: Average number of test sessions per participant. eduT: Years of education. Time: Average maximum time in years from baseline to last follow-up. MRI: information for participants with available MRI only. Like the SHARE results, while high education was associated with better memory scores (β = 0.33, SE = 0.009, p < .001), the education groups showed close to parallel changes over time (Fig. 5 , panels D & E). Predicted change over 10 years was z = -0.20 for high education, compared to z = -0.26 for low education (effect of education group on memory z-score change/ year: β = 0.006, SE = 0.003, p = 0.029) (for complete results, see SI). The analysis was repeated using the alternative categorization of education (tertiary vs. non-tertiary), yielding similar results. We extracted a brain variable sensitive to memory change. For each participant, annual change in each of 166 brain regions was calculated and related to memory change by a series of linear mixed effects models, yielding 29 significant FDR-corrected significant regions (Fig. 5 , panel A). These were entered into a principal component analysis (PCA), yielding a memory-sensitive brain PC. For replication, we also used machine learning, i.e. a regularized regression model (LASSO: Least Absolute Shrinkage and Selection Operator), to predict memory based on an independent sample of 28.114 cross-sectional MRIs from UKB ( Replication analyses ). To test the association between education and brain PC score (offset effects), a GAMM was run with education, time since baseline, sex, and estimated total intracranial volume (eTIV) as fixed effects, and baseline age and sex × baseline age as smooth terms. Random intercepts were included per participant, scanner, and dataset while random slopes of time were included for each dataset. The brain PC showed the expected negative relationship to age, slightly accelerating from about 70 years (Fig. 5 , panel B), and time (β = -0.07, se = 0.008, p < .001). Estimated loss in the high education group was z = -0.68 over a decade, compared to z = -0.74 for the low group (interaction effect of education × time on brain volume: β = 0.005, se = 0.002, p = .015). This means that the difference in 10-year change was z = 0.06, and the slopes were close to parallel (Fig. 5 , panel C). Using the alternative education categorization (tertiary vs. non-tertiary) and the LASSO-derived brain measure yielded similar results. High education was also slightly positively associated with the brain PC (β = 0.04, se = 0.02, p = .049), a relationship that was numerically stronger with the alternative education classification (β = 0.06, se = 0.02, p = .003) and weaker with the LASSO-derived brain measure (β = 0.03, se = 0.04, p = .083). When removing eTIV from the model, the estimate increased to β = 0.05 (se = 0.02, p = .019) using the median split, and to 0.07 (se = 0.02, p = .0007) using the tertiary/ non-tertiary education categorization. We tested the relationship between education and eTIV which was numerically stronger for both education classifications (median split: β = 0.12, se = .002, p < .001; tertiary/non-tertiary: β = 0.13, se = 0.02, p < .001) (Fig. 6 , top left). Finally, we tested whether the brain-memory association varied as a function of education (see Table 3 for an overview). Higher brain PC was related to better memory (β = 0.073, se = 0.013, p < .001). As the brain PC was extracted from regions where brain change was related to memory change, the change-change relationship was given, but is still reported for completeness: β = 0.01, se = 0.002. More importantly, there were no significant education × brain PC (β = 0.01, se = 0.02, p = .60) or education × brain PC × time (β = 0.004, se = 0.004, p = .43) interactions. This means that the relationship between brain and memory, and between changes in the two, did not vary as a function of education (Fig. 6 , top middle & right panels). The same was found using the alternative education category and the LASSO-based brain measure. Replication analyses The main analyses were run using the alternative categorization of education (tertiary vs. non-tertiary) and brain measure (LASSO), yielding four model specifications (Table 3). Controlling for eTIV, cross-sectional education-brain associations were relatively weak, although significant at p < .05 in three models. The education × time interaction showed small effect sizes in the same three specifications, but still significant. Effect size was largest for the PC brain measure and the tertiary/ non-tertiary categorization, with an interaction coefficient of 0.008 compared to 0.005 for the two other significant specifications. The brain × education × time interaction on memory was not significant in any specification. Table 3 Replication and control analyses . Each of the main statistical models were run with two categorizations of education (median split, tertiary vs. non-tertiary) and two approaches to derive a brain component sensitive to memory (PCA based on memory-brain change-change relationship vs. LASSO applied to an independent dataset of cross-sectional MRIs). The main results are shown in the table, see SI for complete results. The random effect terms are not shown in the table (Random intercepts per participant and dataset, random slopes of time [and retest and for memory] for each dataset). P-values below .001 are written as ”<0.001”. As an additional set of control analyses, we tested whether the coefficients for the brain variables in predicting memory were affected by including education in the models (Fig. 6 , bottom panels). The coefficients changed only minimally, suggesting that the brain-memory relationships were largely independent of education (full results in SI). Discussion We found that education was only minimally associated with less age-related decline in episodic memory function, not associated with any substantial reduction in the rate of age-normative structural brain decline in memory-sensitive regions, and did not increase cognitive resilience to the observed brain changes. The small magnitude of the differences in brain and memory change across educational groups contrast with the comparatively much larger differences in level. We found, in line with previous studies, that education was associated with better episodic memory scores across the age-range, slightly larger volume of the memory-sensitive brain regions, and larger intracranial volume. These associations are likely rooted in lifelong variation in brain structure and function that originate earlier in life 30 . We also find evidence that selection effects may account for parts of the associations, in the sense that people with certain traits, such as larger brain volumes from early age as indicated by estimates of total intracranial volume and better episodic memory, tend to be selected into longer education. This selection likely varies across social and demographic dimensions as well as across features of the educational system, but it is important to note that clear patterns of associations resulted from analyses conducted on diverse samples covering a large number of WEIRD societies and age cohorts, indicating a certain degree of robustness across time and place. The implications of the results are discussed below. A role for education in brain and cognitive aging? The idea that age-related cognitive decline is reduced by higher education is based on two complementary hypotheses. According to the first, education can guard against memory decline by causally influencing lifestyle factors that preserve memory-sensitive brain regions, i.e. by increased brain maintenance. While we find support for the observation that relative absence of brain decline in terms of less atrophy is linked to better episodic memory 12 , there were, however, only minor differences in the decline trajectories of memory-sensitive brain regions across educational groups. This aligns with and extends a previous finding that educational level is not associated with differences in age-change in the brain regions most vulnerable to normal aging 16 . In sum, these results provide a neurobiological perspective for why people with different educational attainment and different levels of memory function may still show similar rates of age-related memory decline 21 , 41 - simply put, brains change across middle- and older age in very similar ways across the entire spectrum of observed differences in education. The second hypothesis is that education protects cognitive function through increased resilience to brain decline by building a “cognitive reserve” 5 , 18 , 19 . This hypothesis implies that people with more education should have higher cognitive performance than expected given their observed level of brain decline 19 . We find little support for this idea: only very small differences in the aging trajectories for memory and the memory-sensitive brain regions were observed between educational levels. Further, structural brain decline was associated with similar amounts of memory decline in more vs. less educated participants, consistent with previous research on hippocampal 20 and cortical 42 atrophy. Finally, more education was not associated with larger retest effects, which suggests that education did not come with greater ability to benefit from the specific test experience 43 . Retest effects reflect the ability to take advantage of previous testing to improve test scores. More educated participants showed greater ability to encode new information, as reflected in their higher memory scores, but this did not increase their ability to benefit from previous testing. Similar results have been found for tests of mental speed and reasoning 44 . Taken together, the results suggest that education was not associated with less decline in brain or episodic memory in aging, and that the positive associations consequently must have been established before the age of 50 years. Although the present data do not include developmental information, we can speculate that the precursors of the differences in brain and cognition observed in aging were already present early in life, as discussed further in the next section. How do associations between brain volume, cognitive function and education arise? The results revealed a robust relationship between education, higher memory function, slightly larger volumes of memory-sensitive brain regions, and larger intracranial volume. Understanding the nature of these associations is important. The most obvious explanation is that they may reflect that persons with higher cognitive abilities and larger brain volumes are more likely to select and be selected to further education 45 . Although there were unequal opportunities and clear limitations to access to education for many of the participants in the present study 46 , likely reducing the relationship between cognitive abilities and educational attainment, the existence of selection effects is well documented in previous studies. The present results suggest that this may account for at least a part of the relationship between education and memory function. Regardless of absolute educational attainment, participants with high education relative to other participants of the same sex, birth cohort and country of residence demonstrated better memory function decades later, consistent with the expectation that selection effects contribute to the observed relationship. Earlier-life cognition predicts cognitive function and brain health in aging 47 , 48 , suggesting limited opportunities for causal effects of education beyond adolescence. Instead, selection effects driven by early-life cognitive abilities and gross aspects of brain structure may explain the life-long associations between education and cognition, also consistent with recent genetic evidence 49 , 50 . Our results are also in line with a systematic review of effects of education on dementia risk, which argued that low education has a stronger association with dementia when it reflects cognitive capacity rather than privilege, and when it is associated with other risk factors across the lifespan 51 . Furthermore, cognition-education relationships can in part be explained by neuroanatomical volume differences established in early childhood 34 , also limiting the potential causal effects of later education. Brain structure may hence be a key phenotype along the causal pathway that leads from genetic variation to differences in cognitive function and educational attainment 52 . While selection effects are real, natural experiments still suggest that increased education can positively impact cognitive function 26 – 28 , including memory 23 – 25 . The results showed that taking selection effects into account reduced the association between education and memory only to a modest extent. Importantly, positive effects of increased education are due to early schooling, not reduced decline in aging 29 . Our finding of similar memory-education associations across the age-range aligns with evidence that education enhances lifelong cognitive function without affecting age-related decline. Still, most cognitive intervention studies find that positive effects on cognitive scores diminish over time 21 , 53 , so associations would be expected to be small when measured decades later. Thus, any early effect of education on cognition would likely need to be sustained by some mechanism that helps maintaining the initial effect, e.g. by increasing the likelihood of working in cognitively challenging occupations. According to the gravitational hypothesis, the stability of individual differences in cognition is caused by consistent exposure to the same environments over time, including social, educational, and economic contexts 54 , see 21 , 55 for more in-depth discussions of this topic. This is in line with studies finding ‘cognitive stimulation’ at work to be associated with lower dementia risk 56 , although this cannot explain the full association between education and less dementia 57 . Nonetheless, individuals with higher cognitive function may pursue cognitively stimulating activities irrespective of their formal education, potentially leading to spurious associations when this is not accounted for. An interesting aspect of the present results was the linear association between memory performance and educational attainment. If education caused cognitive scores to increase, one could expect diminishing marginal benefits with increasing duration, although this question has not been properly addressed by quasi-experimental methods 29 . Hence, this result could reflect that selection effects are additive across the range of educational levels, but definite evidence is currently lacking. It is also interesting that this clear pattern is identified across samples covering a large number of countries and cohorts, suggesting that this entails a certain degree of robustness to societal variations across different WEIRD societies. We observed that individuals with higher education had slightly larger volumes in memory-sensitive brain regions. Experiments have showed effects of cognitive training on both memory and relevant brain structures even in older adults 58 – 60 , and it is possible that early education could lead to increased brain volumes of a magnitude similar to that observed in the present study. However, training-induced effects on brain structure are generally even more transient than those on cognition 61 , 62 , making it less likely that direct effects of youth education on brain volume would persist into old age. Consistent with this, a recent study found no evidence of structural brain differences resulting from the increase in mandatory schooling from 15 to 16 years in the UK 50 years later 63 . Instead, intracranial volume has been shown to be more strongly related to education than gray matter volume 34 , which was also found in the current study. In fact, the association between education and intracranial volume was double the size of the association with the memory-sensitive brain component, and removing intracranial volume from the models increased the relationship between memory scores and the memory-sensitive brain PC. Since intracranial volume reaches its maximum in childhood and is unlikely to be influenced by schooling, this relationship does not reflect a causal effect of education and is a further indication that selection effects indeed play a role. Although the relationship between brain volumes and education was found to exist also independently of intracranial volume, it is most likely that the education-brain association was present early in life. Therefore, we interpret the memory-brain-education relationships observed in the present study as partly reflecting selection effects, potentially complemented by some self-reinforcing effects of early schooling. Considerations and future research First, the samples cover 33 countries, and the conclusions not confined to one specific time and place. Still, we did not attempt to detect variations in associations across time 31 and societies 32 – 35 , 64 , but another a multi-cohort, multi-national aging-study found relatively consistent associations between cognition and education 65 . Second, while SHARE used probability sampling to achieve representativity, the MRI samples are generally less representative of their respective populations (e.g. 66 ). It is difficult to estimate the influence of this, but we note that the memory-education results from SHARE were replicated in the brain imaging cohorts. Further, selective attrition and mortality may affect the longitudinal estimates, although studies addressing this have largely obtained similar estimates 21 . Third, we used memory test scores as measures of cognition. While such scores correlate with important real-life indicators, e.g. work participation and capacity for independent living, it is not clear to what extent changes in test scores imply similar changes in daily life cognitive function (for a broader discussion, see 67 ). It cannot be ruled out that education enhanced scores by increased test-taking skills or cognitive strategies with little effect on the underlying cognitive construct. Such effects could be expected to be larger for crystallized or domain knowledge-based tests, such as vocabulary or calculus, and less for fluid tests, including list recall 21 . Still, schooling can potentially increase fluid test performance by factors such as test-specific encoding strategies and test-taking skills, which may have little applicability to other aspects of life. Finally, we focused on episodic memory and structural brain changes. Causal effects of education have been identified for various cognitive measures, including fluid (such as memory), crystallized (e.g. language) and compound (e.g. the g- factor) measures of cognition 29 . One study found that the association between education and cognitive scores, when controlling for childhood cognition, comprised direct effects on specific cognitive skills, including memory, and was not mediated by the g-factor 68 . Therefore, a potential extension of the current work would be to include multiple cognitive functions and examine common versus unique associations with education and brain structure. Finally, although structural brain change is predictive of memory decline in aging 36 , other brain measures, such as those related to brain connectomics 69 , could potentially show different relationships to education. Conclusion In this large-scale, geographically spread longitudinal mega-analytic study, we find that education is robustly related to higher episodic memory function and intracranial volume, and modestly to a brain component optimized to be sensitive to memory change. However, the results do not indicate that this association is driven by slower brain aging or more resilience to structural brain change. Rather, we find evidence to suggest that the relationship is established early in life and partly is attributable to selection effects. Hence, to the extent that education may have a positive effect on episodic memory function in aging, this effect originates from earlier in life. Declarations Acknowledgement The Lifebrain consortium is funded by the EU Horizon 2020 grant agreement no. 732592 (Lifebrain). The different sub-studies are supported by different sources. LCBC is supported by the European Research Council under grant agreements no. 283634 and no. 725025 (to A.M.F.) and no. 313440 (to K.B.W.), as well as the Norwegian Research Council (325878, 262453 to A.M.F.; 325001, 301395, 239889 to K.B.W.; 249931 to A.M.F & K.B.W.; 324882 to DVP; 325415 to HG), 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. PSI2015-64227-R (AEI/FEDER, UE)); and to and to G.C and J.S.-S (grant no. PID-2022-139298OA-C22 (MCIN /AEI /10.13039/501100011033 / FEDER, UE)); by the Walnuts and Healthy Aging study (http://www.clinicaltrials.gov; grant no. NCT01634841) funded by the California Walnut Commission, Sacramento, California; and by an ICREA Academia 2019 award. BASE-II has been supported by the German Federal Ministry of Education and Research under grant nos 16SV5537, 16SV5837, 16SV5538, 16SV5536K, 01UW0808, 01UW0706, 01GL1716A and 01GL1716B and by the European Research Council under grant agreement no. 677804 (to S.K.). Dr. A. Pascual-Leone is partly supported by grants from the National Institutes of Health (R01AG076708), Jack Satter Foundation, and BrightFocus Foundation. Part of the research was conducted using the UKB resource under application no. 32048. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. LOW is funded by the South-Eastern Norway Regional Health Authorities (# 2017095), the Norwegian Health Association (#19536, #1513) and by Wellcome Leap’s Dynamic Resilience Program (jointly funded by Temasek Trust) (#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, J. Morris; NIH P50AG00561, P30NS09857781, P01AG026276, P01AG003991, R01AG043434, UL1TR000448, R01EB009352. Parts of the data collection and sharing for this project were provided by the Cambridge Centre for Ageing and Neuroscience (CamCAN). CamCAN funding was provided by the UK Biotechnology and Biological Sciences Research Council (grant number BB/H008217/1), together with support from the UK Medical Research Council and University of Cambridge, UK. Parts of the data are from VETSA, which is funded by the National Institute of Aging grants R01s AG018384, AG018386, AG050595, AG022381, AG076838. The content is the responsibility of the authors and does not necessarily represent official views of the NIA, NIH, or VA. U.S. Department of Veterans Affairs, Department of Defense; National Personnel Records Center, National Archives and Records Administration; Internal Revenue Service; National Opinion Research Center; National Research Council, National Academy of Sciences; and the Institute for Survey Research, Temple University provided invaluable assistance in the conduct of the VET Registry. The Cooperative Studies Program of the U.S. 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 VET Registry and their families. Part of the data collection and sharing was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; 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.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; 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 ( www.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 study was launched in 2010, funded by the National Institute on Aging, and is led by principal investigators Reisa A. Sperling MD and Keith A. Johnson MD at Massachusetts General Hospital/Harvard Medical School in Boston, MA. 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 N°211909, SHARE-LEAP: GA N°227822, SHARE M4: GA N°261982, DASISH: GA N°283646) and Horizon 2020 (SHARE-DEV3: GA N°676536, SHARE-COHESION: GA N°870628, SERISS: GA N°654221, SSHOC: GA N°823782, SHARE-COVID19: GA N°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 N°101052589 and EUCOVII: GA N°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 U.S. National Institute on Aging (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 from various national funding sources is gratefully acknowledged (see www.share-eric.eu). Conflicts of interest Dr. A. Pascual-Leone 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. Dr. A Pascual-Leone is 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 noninvasive brain stimulation in various neurological disorders; as well as digital biomarkers of cognition and digital assessments for early diagnosis of dementia. Methods & Protocols Samples SHARE cohort The Survey of Health, Ageing and Retirement in Europe is a research infrastructure for studying the effects of health, social, economic and environmental policies over the life-course of European citizens and beyond (https://share-eric.eu/) 37 . SHARE contains observations of individuals from 50 years of age from 28 countries, recruited to be representative of the population in each country. Data for the present analyses was extracted from easy SHARE (release 8.0.0, February 10 th 2022, doi:10.6103/SHARE.easy.800), see 70,71 for methodological details. The easy SHARE release 8.8.0 is based on SHARE Waves 1, 2, 3, 4, 5, 6, 7, and 8 (DOIs:10.6103/SHARE.w1.800, 10.6103/SHARE.w2.800, 10.6103/SHARE.w3.800, 10.6103/SHARE.w4.800, 10.6103/SHARE.w5.800, 10.6103/SHARE.w6.800, 10.6103/SHARE.w7.800, 10.6103/SHARE.w8.800) 37,72 . Participants included in the analyses participated in up to six waves of data collection. In total, we included data from 130.880 participants (mean age 64.9 years at baseline, 50.1-112.0, 59.363 males/ 71.517 females), with an average of 2.7 (SD = 1.63) waves with a mean maximum follow-up interval of 6.53 years (0.9-0-15.9, SD = 3.93). In total, 352.953 memory test sessions were included, with two test results (immediate vs. delayed recollection) for each, i.e. 705.906 memory scores went into the analyses. Respondents aged below 50 years of age (individuals recruited due to being spouses of other participants) were excluded from the sample. An overview of the age distribution per country is provided in Figure 1. Sample distribution as a function of timepoints, education category and age is provided in SI Figure 3. Memory was assessed with a 10 word verbal recall test. The word list is read out load to the participants, and then recall is tested immediately after the presentation (Recall 1) and then after a delay of approximately 5 minutes (Recall 2). Multiple versions of the lists are assigned to the respondents 39 . The response distribution is shown in SI Figure 4. There were no ceiling effects, which is important when assessing longitudinal change for the best-performing participants. There were some floor effects for recall 2, but less for recall 1, suggesting that we can estimate longitudinal chance well for most baseline levels of memory. Since education is association with differences in memory scores, ceiling and floor effects could potentially obscure real differences in change, but this is unlikely to have affected the current results given the response distribution below. Scores were lower for delayed than immediate recall (OR = 0.535, CI: 0.534 – 0.537) and females scored higher than males (OR = 1.160, CI: 1.153-1.168). In addition to the memory measures, we extracted the variables age, sex, birth year, education (based on the International Standard Classification of Education 1997), and country of current residency. Statistical analyses: SHARE Analyses were performed in R version 4.4.1 73 using the brms package’s 74 interface to the probabilistic programming language Stan 75 . To assess effects of education on memory and memory change, we ran logistic regressions with memory recall as dependent variable, yielding odds ratios as the most relevant model parameter to interpret. An odds ratio of 1 corresponds to a regression coefficient of 0. The main model was: Each memory test was used as a separate response, yielding two observations per timepoint, and the variable test represents difficulty of condition 2 relative to condition 1. To control for practice effects, a monotonic function of the number of previous tests taken was included as covariate. We used a smooth function of age to allow non-linear relationships. Individual-specific intercepts per participant were nested within country. Default priors were used for all parameters, two parallel chains of Stan’s No-U-Turn Sampler 76 were run for 1500 iterations, discarding the first 1000 as warmup. This yielded 1000 post-warmup samples. For the offset/level analyses, education (edu) was the variable of interest, while for the slope/change analyses, edu × time since baseline was the critical variable. Z-transformed variables were used in the model fitting for numerical stability, and results converted back to their natural units for easier interpretability, e.g., age and time in years. MRI cohorts We combined data from 13 datasets with longitudinal brain MRIs and memory assessments: LCBC 77 , Betula 78,79 , UB 80,81 , BASE-II 82,83 , and Cam-CAN 84 datasets (from the Lifebrain Consortium) 38 as well as the COGNORM 85 , the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (https://adni.loni.usc.edu) 86 , BBHI 87 , the Harvard Aging Brain Study (HABS) 88 , the UKB (https://www.ukbiobank.ac.uk/) 89 , PREVENT-AD 90,91 , OASIS3 (https://sites.wustl.edu/oasisbrains/) 92 , and VETSA 93 . Sample size was maximized for each analysis and hence varies due to data availability and missingness (see Table 1 for an overview). In addition to cohort-specific inclusion and exclusion criteria, participants >50 years without cognitive impairment, Alzheimer’s dementia or severe neurological or psychiatric disorders were included. Additionally, MRI data from scanners with fewer than 15 measurements were also excluded. The initial dataset included individuals with 1 to 14 MRI acquisitions with longitudinal structural MRI data spanning up to 15.8 years. Similarly, memory assessments range from 1 to 24 observations per individual with a follow-up up to 28 years. For detailed descriptions of general characteristics of each dataset, please refer to the study-specific citations above. An overview of each dataset is given in Supplementary Information (SI Table 1). The main sample descriptives are provided in Table 1, but since the exact sample size varies somewhat between analyses depending on data availability, the specific characteristics for the samples used and their age distributions used to address the different research questions are provided in SI Table 2 and SI Figure 1. Data availability and ethical approvals Each dataset has different owners. Contact information to be used for data access is specified in SI Table 3. The main project was approved by the Norwegian Regional Committee for Medical Research Ethics South (approval 8122), and each sub-study was approved by the relevant ethical review board, as specified in SI Table 3. Education in the brain imaging cohorts For each dataset, education was categorized as high or low using a mean split. We chose this approach because quantitative distributions of education were often highly non-gaussian and level-based codifications were somewhat arbitrary due to idiosyncratic reporting of years of education, and variations in schooling systems across years and country. To ensure robustness, we conducted analyses with an alternative operationalization of education, categorizing individuals with or without tertiary education. When education data was provided as qualifications or categories, these were converted to years of education based on country-specific norms. Individuals were then grouped as having high or low education based on the median. For the tertiary education categorization, the reverse process was applied, converting years of education into education qualifications. For reporting consistency, a lower cap of 6 years and an upper cap of 20 were applied to education years. An overview of education characteristics for each MRI sample is provided in SI Table 4 and SI Figure 2. Memory function in the brain imaging cohorts For each sample, we operationalized memory performance as a z-normalized score based on the first time point and the different available memory tests. When multiple scores were available, we used the first component of a Principal Component Analysis (PCA) with all measures as inputs. For each dataset, we regressed out age (as a smoothing term), sex, and one or two dummy test-retest regressors using generalized additive mixed models ( gamm4 R-package ) 40 . Individual identifiers were used as random intercepts and the number of dummy test-retest regressors depended on whether the dataset had 2 or >=3 waves with memory function data. The residuals were used as an estimate of memory function in each observation. An overview of tests included in the memory performance score for each dataset is provided in SI Table 5. Magnetic Resonance Imaging acquisition and preprocessing Structural T1-weighted (T1w) MPRAGE and FSPGR scans were collected using 1.5 and 3T MRI scanners. Information regarding scanners and scanner parameters across datasets are presented in SI Table 6. We used the longitudinal FreeSurfer v.7.1.0 stream 94 for cortical reconstruction and volumetric segmentation of the structural T1w scans 95-97 . For sessions with multiple scans, data from the scanners were averaged. Briefly, the images were processed using the cross-sectional stream, which includes the removal of nonbrain tissues, Talairach transformation, intensity correction, tissue and volumetric segmentation, cortical surface reconstruction, and cortical parcellation. Next, an unbiased within-subject template space based on all cross-sectional images was created for each participant, using robust, inverse-consistent registration (Reuter et al., 2010). The processing of each time point was then reinitialized with common information from the within-subject template, to increase reliability and statistical power. Except for the BETULA dataset, all data was preprocessed on the Colossus processing cluster, part of the Services for Sensitive Data (TSD) (https://www.uio.no/tjenester/it/forskning/sensitiv/), University of Oslo. Memory-sensitive brain measures for each observation were derived using regional loadings based on the Destrieux (cortical) 98 and aseg (subcortical) atlases 99 . Memory-sensitive brain measures We computed two complimentary measures of brain structure sensitive to memory, capturing different aspects of memory function in older age. The primary measure was defined as a longitudinal brain component sensitive to memory changes inspired by Vidal-Pineiro et al. ( in preparation ). The second measure, for the purpose of assessing the robustness of the results, was trained on independent scans to detect cross-sectional brain-memory relationships in aging. The components were highly correlated (r = .71), both decrease with age (r = -.67, r = -.64, respectively) and include partially overlapping set of brain regions. The first measure (brain PC) is optimized to be sensitive to memory changes in aging, while the second (brain LASSO) is optimized to detect also offset, i.e. baseline, associations. See below for a full description of both methods. Brain PC: Change based, memory-sensitive measure: This measure was derived from a sample largely overlapping with that used for the statistical analyses and the AIBL in the present work but included participants down to age > 18 years. Brain PC is based on a principal component (PC) of longitudinal change in 20 cortical thickness and 9 subcortical volume regions. Brain regions were harmonized using a normative modelling framework 100,101 with the PCNtoolkit (0.30.post2), in Python3 environment 102 (version 3.9.5). This framework offers several advantages as i) it is run independently across sites, ii) can isolate site-effects from other sources of variance associated with it, and iii) produces site-agnostic deviation scores ( z -statistics) adjusted for age, and sex. PCNtoolkit uses a Hierarchical Bayesian Regression (HBR) technique 103 and pretrained models from 82 different datasets, including UKB and CamCan data. To avoid losing longitudinal observations, we performed this step recursively by iteratively (n = 100) holding out a calibrating sample and computing the estimates on the remaining data. The average scores of all iterations were used as the standardized scores for each observation. Scanners contributing with < 12 unique individuals or 12 and = 32 unique individuals, we used, in each iteration, a held-out sample of 30 individuals while estimates were applied on the rest. Next, we selected individuals with at least 2 observations and a minimum follow-up of 1.5 years. For both MRI and memory preprocessed data, we estimated yearly change for each subject, by regressing data on follow-up time. Change data was then fed into separate linear mixed models as implemented in lme4, lmerTest 104,105 ,one per brain region . Note that here we used estimates of change, and there was only one observation per individual. For each region, we predicted memory change by brain change, using dataset as random intercepts. Additionally, we used weights to account for potential heteroskedasticity. That is, individuals with short follow-up periods and less observations contribute with more unreliable, high-variance data and thus should produce an unequal spread of residuals. We used the square of reliability as weights as estimated in 106 . Longitudinal reliability is a function of variance in change and mean measurement error for a given region, and number of observations and total follow-up time for a given individual. After False Discovery Rate (FDR)-correction (p < .05), 29 regions showed significant associations between brain change and memory change, including 9 volumetric subcortical (bilateral amygdala, hippocampus, and thalamus, left lateral and inferior lateral ventricle, right accumbens area) and 20 cortical thickness regions (left G cingul-Post-dorsal, G cingul-Post-ventral, G insular_short, G oc-temp_med-Parahip, G front_inf-Opercular, G front_inf-Triangul, G subcallosal, S temporal_sup; right G Ins lg&S cent_ins, S circular_insula_ant, S oc-temp_med&Lingual, S suborbital; bilateral G temp_sup-Plan_polar, S orbital-H_Shaped, S front_middle, S circular_insula_inf). These regions were entered into the PCA to extract the PC of the memory-sensitive brain regions, yielding a brain measure sensitive to episodic memory change in aging. All regions except the ventricles showed positive loadings with the brain PC. Brain LASSO: Cross-sectional-based, memory-sensitive measure: The alternative brain measure was derived by predicting cross-sectional memory function by cross-sectional brain structure features on an independent sample of UKB individuals not included in other brain analyses. Prediction was performed with a Least Absolute Shrinkage and Selection Operator (LASSO) machine learning algorithm as implemented in the glmnet package 107 . LASSO is a regression technique that performs variable selection and regularization by adding a penalty term, reducing overfitting, and simplifying the model. Lambda was selected as the maximum value within one standard error from minimum lambda, using a cross-validated approach with K = 10 folds (λ = .0143; MSE = .943). LASSO coefficients are provided in SI Table 7. The sample consisted of 28,114 individuals from UKB aged 65.05 years (SD = 7.60) (range 47.32 – 82.78), without longitudinal MRI data, and not included in the main brain analyses. Age was not regressed out allowing prediction to capture both offset and level effects of brain structure on memory function as well as indirect effects due to the unaccounted correlation of age with both MRI features and memory function. We used the Paired associate learning (PAL) (#20197 UKB field) at the first MRI timepoint as index of memory function. MRI data included 337 features; subcortical regions and global brain measures from the aseg atlas and cortical area, and thickness regions from the Destrieux atlas. Both brain and memory indices were z-standardized, and outliers were considered as values >5 SD apart from the mean. Individuals with outlier values for memory were excluded while brain outlier values were recoded as 0. Statistical analyses: MRI cohorts All the analyses were performed using the R environment (version 4.2.1) 73 . Visualizations were made with ggplot2 108 and ggseg 109 R-packages . Memory, brain variables, and estimated intracranial volume (eTiv) were Z-standardized before inclusion in the models. Outlier values defined as values >5 SD from the mean, were removed from the analyses. Analyses were run using gamm models as implemented in the gamm4 R-package 40 , unless otherwise specified. Memory score was modeled as a function of education, time since baseline, sex, and a dummy regressor for test-retest effects as fixed effects. Baseline age by sex was included as a smooth term. Random intercepts were modeled per participant and dataset, with random slopes of retest effects and time from baseline at a dataset level. To test the effects on memory change, the model was re-run with an additional education × time interaction term. Education was operationalized either as mean-split or based on tertiary education in separate models. Brain structure was modeled as a function of education, time since baseline, sex, and eTiv as fixed effects. Baseline age by sex was included as a smooth term. Random intercepts were modeled per participant, scanner, and dataset with random slopes of time included at a dataset level. To test effects on brain change, the model was re-run with an additional education × time interaction term. As control analyses, we reran the gamm models without eTiv as covariate. Additionally, we ran a linear mixed model as implemented in lme4, with eTiv being modeled as a function of education, sex, and baseline age as fixed effects, while site and dataset were included as random intercepts. Only the first observation of each participant was included, as eTiv and education are time-invariant variables. Alternative operationalizations of education and brain structure were tested in separate, but otherwise identical, models. We used a fuzzy join algorithm, as implemented in fuzzyjoin 110 to link pairwise MRI and cognitive observations as these were not necessarily collected on the same day. MRI observations were matched with the closest cognitive observations within a maximum time gap of 1 year. Unlinked observations were excluded from the analyses. The relationship between brain, memory level, and education was assessed with several models. Brain level and memory level: Memory was modeled by brain structure, sex, time, eTiv, and a dummy regressor for test-retest effects as fixed effects. Baseline age by sex was introduced as a smooth term. Random intercepts were modeled per participant, scanner, and dataset with random slopes of retest and time modeled at a dataset level. Brain change and memory change: An additional brain × time term was added to the model. Moderating effect of education on level – level associations: Additional terms for education and education × brain were added in the first model. Moderating effect of education on change – change associations: A triple interaction term (brain × time × education) as well as its lower order components were added in the first model. Control analyses: A main education term – without any interaction – was added to the models to assess level – level and change – change associations between brain and memory, to test whether the strength of these associations was affected by education level. As with other analyses, alternative operationalizations of education and memory-sensitive brain structure were tested in separate but comparable models. References Collaborators, G. B. D. D. F. 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). https://doi.org/10.1016/S2468-2667(21)00249-8 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). https://doi.org/10.1212/WNL.0000000000010022 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). https://doi.org/10.1016/S2468-2667(23)00214-1 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). https://doi.org/10.1177/09567976221118541 Livingston, G. et al. Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission. Lancet 404, 572–628 (2024). https://doi.org/10.1016/S0140-6736(24)01296-0 Suemoto, C. K. et al. Risk factors for dementia in Brazil: Differences by region and race. Alzheimers Dement 19, 1849–1857 (2023). https://doi.org/10.1002/alz.12820 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). https://doi.org/10.1038/s43587-023-00413-1 Ritchie, H., Samborska, V., Ahuja, N., Ortiz-Ospina, E. & Roser, M. (Published online at OurWorldinData.org. Retrieved from: ' https://ourworldindata.org/global-education' [Online Resource], 2023). 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). https://doi.org/10.1080/13825585.2015.1041450 Sepulcre, J. College education as a modulator of the aging brain. Nat Aging 1, 980–981 (2021). https://doi.org/10.1038/s43587-021-00131-6 Cabeza, R. et al. Maintenance, reserve and compensation: the cognitive neuroscience of healthy ageing. Nat Rev Neurosci 19, 701–710 (2018). https://doi.org/10.1038/s41583-018-0068-2 Nyberg, L., Lovden, M., Riklund, K., Lindenberger, U. & Backman, L. Memory aging and brain maintenance. Trends Cogn Sci 16, 292–305 (2012). https://doi.org/10.1016/j.tics.2012.04.005 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). https://doi.org/10.1016/j.neurobiolaging.2017.06.016 Gazzina, S. et al. Education modulates brain maintenance in presymptomatic frontotemporal dementia. J Neurol Neurosurg Psychiatry 90, 1124–1130 (2019). https://doi.org/10.1136/jnnp-2019-320439 Del Ser, T., Hachinski, V., Merskey, H. & Munoz, D. G. An autopsy-verified study of the effect of education on degenerative dementia. Brain 122 (Pt 12), 2309–2319 (1999). https://doi.org/10.1093/brain/122.12.2309 Nyberg, L. et al. Educational attainment does not influence brain aging. Proc Natl Acad Sci U S A 118 (2021). https://doi.org/10.1073/pnas.2101644118 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). https://doi.org/10.1016/j.neurobiolaging.2019.03.022 Stern, Y. What is cognitive reserve? Theory and research application of the reserve concept. J Int Neuropsychol Soc 8, 448–460 (2002). Stern, Y. et al. A framework for concepts of reserve and resilience in aging. Neurobiol Aging 124, 100–103 (2023). https://doi.org/10.1016/j.neurobiolaging.2022.10.015 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). https://doi.org/10.1016/j.nbas.2023.100082 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). https://doi.org/10.1177/1529100620920576 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). https://doi.org/10.1016/j.arr.2019.101005 Schneeweis, N., Skirbekk, V. & Winter-Ebmer, R. Does education improve cognitive performance four decades after school completion? Demography 51, 619–643 (2014). https://doi.org/10.1007/s13524-014-0281-1 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). https://doi.org/10.1136/jech.2006.059469 Gorman, E. Does Schooling Have Lasting Effects on Cognitive Function? Evidence From Compulsory Schooling Laws. Demography 60, 1139–1161 (2023). https://doi.org/10.1215/00703370-10875853 Brinch, C. N. & Galloway, T. A. Schooling in adolescence raises IQ scores. Proc Natl Acad Sci U S A 109, 425–430 (2012). https://doi.org/10.1073/pnas.1106077109 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). https://doi.org/10.1093/ije/dyw093 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). https://doi.org/10.1136/jech-2018-211746 Ritchie, S. J. & Tucker-Drob, E. M. How Much Does Education Improve Intelligence? A Meta-Analysis. Psychol Sci 29, 1358–1369 (2018). https://doi.org/10.1177/0956797618774253 Walhovd, K. B., Lovden, M. & Fjell, A. M. Timing of lifespan influences on brain and cognition. Trends Cogn Sci 27, 901–915 (2023). https://doi.org/10.1016/j.tics.2023.07.001 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). https://doi.org/10.1038/s41598-023-44605-6 Calandri, I. L. et al. Sex and Socioeconomic Disparities in Dementia Risk: A Population Attributable Fractions Analysis in Argentina. Neuroepidemiology (2024). https://doi.org/10.1159/000536524 Aman, Y. Modifiable risk factors of dementia in Latin America. Nat Aging (2024). https://doi.org/10.1038/s43587-024-00766-1 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). https://doi.org/10.1093/cercor/bhab248 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). https://doi.org/10.1016/S2214-109X(24)00275-4 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). https://doi.org/10.1016/j.pneurobio.2014.02.004 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). https://doi.org/10.1093/ije/dyt088 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). https://doi.org/10.1016/j.eurpsy.2017.12.006 Mehrbrodt, T., Gruber, S. & Wagner, M. Scales and Multi-Item Indicators in the Survey of Health, Ageing and Retirement in Europe. (Germany, 2019). gamm4: Generalized Additive Mixed Models using 'mgcv' and 'lme4'. R package version 0.2-6, https://CRAN.R-project.org/package=gamm4 . (2020). 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). https://doi.org/10.1159/000452276 Walhovd, K. B. et al. Brain aging differs with cognitive ability regardless of education. Sci Rep 12, 13886 (2022). https://doi.org/10.1038/s41598-022-17727-6 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). https://doi.org/10.1080/13854046.2010.526785 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). https://doi.org/10.1037/a0014012 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). https://doi.org/10.1348/000709905X50906 Eurostat. Eurostat regional yearbook. (2024). 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). https://doi.org/10.1177/0956797613486487 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 U S A 121, e2412017121 (2024). https://doi.org/10.1073/pnas.2412017121 Thorp, J. G. et al. Genetic evidence that the causal association of educational attainment with reduced risk of Alzheimer's disease is driven by intelligence. Neurobiol Aging 119, 127–135 (2022). https://doi.org/10.1016/j.neurobiolaging.2022.07.011 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). https://doi.org/10.1093/ije/dyz280 Sharp, E. S. & Gatz, M. Relationship between education and dementia: an updated systematic review. Alzheimer Dis Assoc Disord 25, 289–304 (2011). https://doi.org/10.1097/WAD.0b013e318211c83c Grasby, K. L. et al. The genetic architecture of the human cerebral cortex. Science 367 (2020). https://doi.org/10.1126/science.aay6690 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 Brown University, 2020). 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). https://doi.org/10.1111/j.1467-8624.1993.tb02896.x 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). https://doi.org/10.1037/a0035893 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). https://doi.org/10.1212/WNL.0000000000209353 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). https://doi.org/10.1136/bmj.n1804 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). https://doi.org/10.3233/JAD-131889 Engvig, A. et al. Effects of memory training on cortical thickness in the elderly. Neuroimage 52, 1667–1676 (2010). https://doi.org/10.1016/j.neuroimage.2010.05.041 Lovden, M. et al. Spatial navigation training protects the hippocampus against age-related changes during early and late adulthood. Neurobiol Aging 33, 620 e629-620 e622 (2012). https://doi.org/10.1016/j.neurobiolaging.2011.02.013 Brathen, A. C. S. et al. Cognitive and hippocampal changes weeks and years after memory training. Sci Rep 12, 7877 (2022). https://doi.org/10.1038/s41598-022-11636-4 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). https://doi.org/10.1093/cercor/bhy003 Judd, N. & Kievit, R. No effect of additional education on long-term brain structure – a preregistered natural experiment in over 30,000 individuals. bioRxiv (2024). https://doi.org/https://doi.org/10.1101/2024.05.17.594682 Bonsang, E., Skirbekk, V. & Staudinger, U. M. As You Sow, So Shall You Reap: Gender-Role Attitudes and Late-Life Cognition. Psychol Sci 28, 1201–1213 (2017). https://doi.org/10.1177/0956797617708634 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). https://doi.org/10.1371/journal.pmed.1002261 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). https://doi.org/10.1093/aje/kwx246 Ackerman, P. L. Adult Intelligence: The Construct and the Criterion Problem. Perspect Psychol Sci 12, 987–998 (2017). https://doi.org/10.1177/1745691617703437 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). https://doi.org/10.1037/a0038981 Chan, M. Y. et al. Long-term prognosis and educational determinants of brain network decline in older adult individuals. Nat Aging 1, 1053–1067 (2021). https://doi.org/10.1038/s43587-021-00125-4 Gruber, S., Hunkler, C. & Stuck, S. Generating easySHARE: guidelines, structure, content and programming. (Munich: MEA, Max Planck Institute for Social Law and Social Policy, 2014). 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.. (Munich: MEA, Max Planck Institute for Social Law and Social Policy, 2019). Börsch-Supan, A. & Gruber, S. easySHARE. Release version: 8.0.0. SHARE-ERIC. (2022). https://doi.org/10.6103/SHARE.easy.800 R: A language and environment for statistical computing. (R Foundation for Statistical Computing, Vienna, Austria., 2024). Bürkner, P.-C. brms: An R Package for Bayesian Multilevel Models Using Stan. Journal of Statistical Software 80(1), 1–28 (2017). https://doi.org/doi:10.18637/jss.v080.i01 Carpenter, B. et al. Stan: A Probabilistic Programming Language. Journal of Statistical Software 76, 1–32 (2017). https://doi.org/https://doi.org/10.18637/jss.v076.i01 Hofman, M. D. & Gelman, A. The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research 15, 1593–1623 (2014). Walhovd, K. B. et al. Neurodevelopmental origins of lifespan changes in brain and cognition. Proc Natl Acad Sci U S A 113, 9357–9362 (2016). https://doi.org/10.1073/pnas.1524259113 Nilsson, L. G. et al. The Betula prospective cohort study: Memory, health, and aging. Aging, Neuropsychology and Cognition 4, 1–32 (1997). https://doi.org/10.1080/13825589708256633 Nilsson, L.-G. et al. Betula: A prospective cohort study on memory, health and aging. Aging, Neuropsychology, and Cognition.11, pp (2004). https://doi.org/10.1080/13825580490511026 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). https://doi.org/10.3389/fnagi.2016.00333 Vidal-Pineiro, 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). https://doi.org/10.1016/j.brs.2013.12.016 Bertram, L. et al. Cohort profile: The Berlin Aging Study II (BASE-II). Int J Epidemiol 43, 703–712 (2014). https://doi.org/10.1093/ije/dyt018 Gerstorf, D. et al. Editorial. Gerontology 62, 311–315 (2016). https://doi.org/10.1159/000441495 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). https://doi.org/10.1186/s12883-014-0204-1 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). https://doi.org/10.1016/j.neurobiolaging.2020.04.002 Mueller, S. G. et al. The Alzheimer's disease neuroimaging initiative. Neuroimaging Clin N Am 15, 869–877, xi-xii (2005). https://doi.org/10.1016/j.nic.2005.09.008 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). https://doi.org/10.3389/fnagi.2018.00321 Dagley, A. et al. Harvard Aging Brain Study: Dataset and accessibility. Neuroimage 144, 255–258 (2017). https://doi.org/10.1016/j.neuroimage.2015.03.069 Miller, K. L. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci 19, 1523–1536 (2016). https://doi.org/10.1038/nn.4393 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). https://doi.org/10.14283/jpad.2016.121 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). https://doi.org/10.1016/j.nicl.2021.102733 LaMontagne, P. J. et al. OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease. medRxiv (2019). https://doi.org/https://doi.org/10.1101/2019.12.13.19014902 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). https://doi.org/10.1017/thg.2019.125 Reuter, M., Schmansky, N. J., Rosas, H. D. & Fischl, B. Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61, 1402–1418 (2012). https://doi.org/10.1016/j.neuroimage.2012.02.084 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). https://doi.org/10.1006/nimg.1998.0396 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). https://doi.org/10.1002/(sici)1097-0193(1999)8:43.0.co;2-4 Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179–194 (1999). https://doi.org/10.1006/nimg.1998.0395 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). https://doi.org/10.1016/j.neuroimage.2010.06.010 Fischl, B. et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002). https://doi.org/10.1016/s0896-6273(02)00569-x Rutherford, S. et al. Charting brain growth and aging at high spatial precision. Elife 11 (2022). https://doi.org/10.7554/eLife.72904 Rutherford, S. et al. The normative modeling framework for computational psychiatry. Nat Protoc 17, 1711–1734 (2022). https://doi.org/10.1038/s41596-022-00696-5 Van Rossum, G. & Drake, F. L. (CreateSpace, Scotts Valley, CA, 2009). Kia, S. M. et al. Closing the life-cycle of normative modeling using federated hierarchical Bayesian regression. PLoS One 17, e0278776 (2022). https://doi.org/10.1371/journal.pone.0278776 Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software 67, 1–48 (2015). https://doi.org/https://doi.org/10.18637/jss.v067.i01 Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: Tests in Linear Mixed Effects Models. Journal of Statistical Software 82, 1–26 (2017). Vidal-Piñeiro, D. et al. Reliability of structural brain change in cognitively healthy adult samples. bioRxiv (2024). https://doi.org/https://doi.org/10.1101/2024.06.03.592804 Friedman, J. H., Hastie, T. & Tibshirani, R. Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software 33, 1–22 (2010). Wickham, H. Elegant Graphics for Data Analysis, 2nd ed, Use R! , (Springer International Publishing, 2016). Mowinckel, A. M. & Vidal-Piñeiro, D. Visualization of Brain Statistics With R Packages ggseg and ggseg3d. Advances in Methods and Practices in Psychological Science 3, 466–483 (2020). https://doi.org/https://doi.org/10.1177/2515245920928009 Robinson, D., 2020. fuzzyjoin: Join Tables Together on Inexact Matching. (2020). Additional Declarations Yes there is potential Competing Interest. We have no real conflict of interest, but co-author Alvaro Pascual-Leone has declared the following (included in a paragraph at the end of the manuscript): Dr. A. Pascual-Leone 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. Dr. A Pascual-Leone is 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 noninvasive brain stimulation in various neurological disorders; as well as digital biomarkers of cognition and digital assessments for early diagnosis of dementia. Supplementary Files FjelletalSupplementalInformation.docx Supplemental Information Cite Share Download PDF Status: Published Journal Publication published 28 Jul, 2025 Read the published version in Nature Medicine → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Oslo","correspondingAuthor":false,"prefix":"","firstName":"Marie","middleName":"","lastName":"Stromstad","suffix":""},{"id":412253192,"identity":"f37ae1bd-020f-47b9-af50-cb7a30301a39","order_by":20,"name":"Leiv Watne","email":"","orcid":"https://orcid.org/0000-0003-0441-6700","institution":"University of Oslo","correspondingAuthor":false,"prefix":"","firstName":"Leiv","middleName":"","lastName":"Watne","suffix":""},{"id":412253193,"identity":"42864ee3-0fb4-47a9-9f23-ff8c3923ebf2","order_by":21,"name":"Kristine Beate Walhovd","email":"","orcid":"","institution":"University of Oslo","correspondingAuthor":false,"prefix":"","firstName":"Kristine","middleName":"Beate","lastName":"Walhovd","suffix":""},{"id":412253194,"identity":"ac7a74aa-473a-42f0-95ca-044b878733b7","order_by":22,"name":"Didac Vidal","email":"","orcid":"","institution":"University of Oslo","correspondingAuthor":false,"prefix":"","firstName":"Didac","middleName":"","lastName":"Vidal","suffix":""}],"badges":[],"createdAt":"2025-01-31 19:00:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5938408/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5938408/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41591-025-03828-y","type":"published","date":"2025-07-28T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":75896763,"identity":"07a99bf0-bf54-4287-9cca-fd79bb6243e1","added_by":"auto","created_at":"2025-02-10 10:37:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":441020,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eGeographical and age distribution of samples\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLeft panel: Number of completed memory test sessions included across country across SHARE, Lifebrain and the other legacy datasets. The density plot shows sample age-distribution in SHARE. Right panel: Number of completed brain MRIs across countries. The plot shows the age-distribution for each dataset included. Note that the visual presentations of USA\u0026amp; Canada and Cyprus \u0026amp; Israel are not size-wise correct compared to the European map.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5938408/v1/3159cfb0730b45ba1b39a05c.png"},{"id":75898317,"identity":"34fe7007-7bf2-49ab-b7f1-59c516575381","added_by":"auto","created_at":"2025-02-10 10:45:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":295887,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAge, education and practice effects on memory\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e. Top left: Memory score trajectory as a function of baseline age. The y-axis is on the logit scale, illustrating how the linear predictor changes with varying baseline age for each education category. The legend is organized from the highest (“tertiary 2\u003c/em\u003e\u003csup\u003e\u003cem\u003end\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e stage”) to the lowest (“none”) level of educational attainment. Top right: Retest effects, expressed as odds ratio (y-axis) with first test session as reference and number of previous tests at the x-axis. Bottom left: Retest effects plotted for each education group. Bottom right: Comparing retest effects for each education group to the reference group by calculating Odds ratio for the given education / Odds ratio for “Upper Secondary” illustrated by the dotted horizontal line. Shaded areas denote 95% CI.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5938408/v1/8d1abda703974a5b8a6269a8.png"},{"id":75896766,"identity":"4a0f2677-fc46-4733-ba09-ab1bda2af09e","added_by":"auto","created_at":"2025-02-10 10:37:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":79451,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAssociations between education, memory score and memory score decline\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e. Left: Associations between education and memory offset scores. Right: Associations between education and decline in memory scores. ”Upper secondary” education (pink color) is used as reference, illustrated with the dashed line. Note that all memory scores are corrected for retest effects. Error bars denote 95% CI.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5938408/v1/8f93fdb74ffc704a873a106b.png"},{"id":75896770,"identity":"e172aaae-b877-4ff4-b6b1-56a071046712","added_by":"auto","created_at":"2025-02-10 10:37:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":167499,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAssociations between memory, absolute and relative education\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e. Effects of each education category on memory compared to the reference group (“upper secondary”), illustrated with the dashed line. Models were run with (corrected) or without (base) relative education level included. Relative education was calculated as education relative to birth cohort, sex, and country, yielding a percentile score for each participant. The top row (yellow color) shows the effect of going from 0 to the 100\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e percentile in relative education, when controlling for the influence of absolute education. Error bars denote 95% CI.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5938408/v1/d148e6d1c63fcac03ab52e49.png"},{"id":75898322,"identity":"0c455d86-bf78-42fa-94ce-412a81ffcd57","added_by":"auto","created_at":"2025-02-10 10:45:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1065838,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eEducation, brain measures and episodic memory. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eA: Regions where brain changes and memory changes are related (FDR \u0026lt; .05) are color coded by loadings on the principal component (“brain PC”). Nucleus Accumbens and left inferior lateral ventricle are not shown. B: Age-plot of the memory-sensitive PC (residuals) after accounting for sample differences. Shaded areas depict 95% CI. C: Brain change as a function of education was calculated for each education group and plotted over 3 years. Brain volumes are slightly larger for the high (green) than the low (orange) education group, but the slopes of decline are almost parallel.\u003c/em\u003e \u003cem\u003eShaded areas depict SE of the subject-level predictions.\u003c/em\u003e \u003cem\u003eD: Age-plot of episodic memory (residuals) after accounting for sample differences. Shaded areas depict 95% CI. E: Episodic memory change as a function of education was calculated for each education group and plotted over 3 years. Scores are higher for the high (green) than the low (orange) education group, but the slope lines are close to parallel. SE of the subject-level predictions\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5938408/v1/fa190ce7b6b7c6f5dffff3a1.png"},{"id":75896777,"identity":"291a10d3-bdeb-42aa-96ed-bba42578135f","added_by":"auto","created_at":"2025-02-10 10:37:55","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":209786,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRelationships between brain, memory, and education\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTop row\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Left: Estimated total intracranial volume (eTiv) in the high vs. low education group. Mean eTiv was significantly larger in the high education group. Middle: Relationship between brain PC at baseline and memory score separately for the high and the low education group. The brain-memory relationships are positive, but did not differ between groups. Right: Change in memory over time as a function of brain PC. More memory decline is seen for lower values of brain PC, but this relationship did not differ between education groups. Shaded areas around the lines depict SE of the subject-level predictions. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eBottom row: \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eTesting whether including education in the statistical models reduced the coefficients for the brain variables in predicting memory, across two brain measures and two education categorizations. Error bars depict SE. Blue: Education not included in the model. Orange/ green: Education included in the model.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5938408/v1/1b3b71848df71a21f8798d46.png"},{"id":87797362,"identity":"703d733e-f782-4e31-aa38-a7f962ecbf5a","added_by":"auto","created_at":"2025-07-29 07:12:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3614960,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5938408/v1/ffae6f4c-9a78-4ca3-a9b6-116c7ead8584.pdf"},{"id":75896767,"identity":"8ebbb39a-3bff-4b4e-abb0-fb4b98c7af67","added_by":"auto","created_at":"2025-02-10 10:37:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":982197,"visible":true,"origin":"","legend":"Supplemental Information","description":"","filename":"FjelletalSupplementalInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-5938408/v1/886c5b5d4e4ff1d3bedf967e.docx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nWe have no real conflict of interest, but co-author Alvaro Pascual-Leone has declared the following (included in a paragraph at the end of the manuscript): \r\nDr. A. Pascual-Leone 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. Dr. A Pascual-Leone is 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 noninvasive brain stimulation in various neurological disorders; as well as digital biomarkers of cognition and digital assessments for early diagnosis of dementia.","formattedTitle":"Reevaluating the Role of Education in Cognitive Decline and Brain Aging: Insights from Large-Scale Longitudinal Cohorts across 33 Countries","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWhile the total number of people with dementia will increase massively due to population growth and ageing \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, the incidence seems to be declining\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, and older adults have better cognitive function today than 20 years ago \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. One hypothesis is that this reflects broad societal and individual lifestyle changes, and that dementia incidence can be further reduced by promoting these \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Education has repeatedly been suggested to be one such potential protective factor \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, in line with observations of robust associations between education and higher cognitive function in aging, as well as declines in dementia incidence with increasing population educational attainment \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, results so far are heterogeneous and point in different directions, and the specific mechanisms that could explain such a causal link are widely debated \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. We therefore suggest addressing these questions by conducting a large mega-analysis of longitudinal brain and cognitive studies covering a wider geographical distribution of samples.\u003c/p\u003e \u003cp\u003eEducation could result in better cognition in aging by contributing to a lower rate of age-normative brain decline \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Indeed, higher \u003cem\u003ebrain maintenance\u003c/em\u003e has been associated with better episodic memory\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, and studies have found less brain pathology in older adults with higher education\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, less brain decline in presymptomatic dementia \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, and less accumulation of cerebrovascular lesions \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. However, a recent longitudinal study investigating two independent samples did not find different rates of change in hippocampus and age-sensitive regions of the cerebral cortex in more educated participants \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Alternatively, education could make people more resilient to underlying brain pathology by higher \u003cem\u003ecognitive reserve\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. According to this theory, education leads to more efficient processing of cognitive tasks which in turn allows for higher performance despite age-normative levels of brain decline \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Although a popular theory \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, a longitudinal study found that education did not weaken the link between hippocampal atrophy and memory change \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Both the maintenance and the reserve accounts of education imply that education causally influences late-life cognition by reducing or postponing age-related decline. This is controversial, however, because even though education is associated with better cognitive function among older adults, it is not clear that more educated persons show less cognitive decline when measured longitudinally \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAn alternative perspective holds that the association between education and cognitive performance is persistent across the adult lifespan. This contrasts with the more aging-centered views presented above. Under this alternative view, if education has a positive causal effect on cognition in aging, it would be by permanently boosting cognitive function earlier in life, causing persistent differences between educational groups. Increased compulsory schooling has been shown to elevate scores on tests of memory \u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, intelligence \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e and general cognition \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, with effects detectable decades later \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. This perspective could also be consistent with a lack of causal effects of education on cognitive function, however, as those with higher initial cognitive functioning would be expected to reach higher levels of education than their peers. Hence, the topic of the role of education in cognitive function and brain health in aging is riddled with controversies \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNonetheless, contrasting predictions can be derived from the different theories. If education improves memory in older age by shaping brain aging, we expect better preservation of memory-sensitive brain regions among individuals with higher education. If education improves cognitive reserve, we expect more tolerance to brain pathology, indexed by a lower correlation between brain decline and cognitive decline. In contrast, if the education-memory-brain relationship reflects stable individual differences, education should not correlate with either memory or brain decline. In that case, we also would expect to see selection effects, in the sense that participants with specific traits, especially higher cognitive function, are more likely to pursue further education. It is also relevant to examine whether retest effect \u0026ndash; the tendency for performance to increase as a function of previous tests taken \u0026ndash; is exaggerated with higher education. If more education yields cognitive reserve, this may manifest as a greater ability to take advantage of previous testing experience and to develop more efficient test taking strategies.\u003c/p\u003e \u003cp\u003eA major challenge in addressing these questions is that we need large, representative and heterogeneous longitudinal samples with sufficient statistical power. The geographic coverage is critical, because associations between brain, cognition, and education will vary both across time \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and societies \u003csup\u003e\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. For example, the population attributable fraction (PAF) of dementia due to low education was reported to vary from 1.7% in Argentina to 10.8% in Bolivia in a study comparing seven Latin American countries \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. To alleviate this concern, we here compiled data from several large studies, including a total of 407.356 memory tests from 170.795 participants across 33 countries across Europe, US and Israel, with up to seven follow-up sessions per person (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Although we do not have sufficient statistical power to systematically investigate effects of time, geography and societal differences, our approach ensures that the results are not confined to one specific time and place. Still, it is important to keep in mind that all samples come from WEIRD (Western, Educated, Industrialized, Rich, Democratic) countries, which limits generalizing conclusions to other societies. We focus on episodic memory because it is particularly sensitive to normal aging and neurodegenerative disease \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. To address brain mechanisms, we further analyzed 15,157 brain MRIs and concurrent memory tests from 6.472 participants across seven countries. The primary data sources were the population-based, multinational SHARE (Survey of Health, Ageing and Retirement in Europe) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://share-eric.eu/\u003c/span\u003e\u003cspan address=\"https://share-eric.eu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e and the Lifebrain consortium \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.lifebrain.uio.no/\u003c/span\u003e\u003cspan address=\"https://www.lifebrain.uio.no/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), enriched with several legacy databases. SHARE uses probability sampling to obtain sample representativity, using the best available sample frame resources in each country to achieve full probability sampling, including access to population registers for most. Although geographically spread, MRI populations will vary in representativity, and hence we chose as strategy to validate the memory-results from SHARE in the MRI samples before conducting the brain analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eLeft panel: Number of completed memory test sessions included across country across SHARE, Lifebrain and the other legacy datasets. The density plot shows sample age-distribution in SHARE. Right panel: Number of completed brain MRIs across countries. The plot shows the age-distribution for each dataset included. Note that the visual presentations of USA\u0026amp; Canada and Cyprus \u0026amp; Israel are not size-wise correct compared to the European map.\u003c/em\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eSHARE cohort results\u003c/h2\u003e\n \u003cp\u003eMemory was assessed with a 10-word verbal recall test, with two conditions (immediate and 5 minutes recall), using multiple versions across waves and participants \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Each condition was separately included in the statistical models, yielding two observations per time point per participant. Generalized linear models with a binomial link were run using memory score as dependent variable, with the interaction between education and time since baseline as the critical term, using test type (immediate or 5-minute delay), a monotonic function of the number of previous tests taken (to control for retest effects), education, sex, country, baseline age, time since baseline, and the age \u0026times; time interaction as covariates (see Methods \u0026amp; Protocols for the exact model specifications). Individual-specific intercepts per participant were nested within country. Z-transformed values for age and time were used in the model fitting and converted back to natural units when showing the results. A smooth function for age allowed non-linear memory trajectories. The main outputs were the odds ratios (OR) of remembering a word compared to a reference group.\u003c/p\u003e\n \u003cp\u003eMemory scores were lower with higher baseline age, showing slightly accelerating trajectories (smoothing parameter for the combined sample\u0026thinsp;=\u0026thinsp;45.8, CI: 20.7\u0026ndash;81.5). Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e (top left panel) revealed a perfect ordering of scores according to education level, with more education associated with higher scores across age. Compared to the education level used as reference (\u0026ldquo;upper secondary\u0026rdquo;), \u0026ldquo;no education\u0026rdquo; yielded OR\u0026thinsp;=\u0026thinsp;0.54 compared to 1.55 for the highest category (\u0026ldquo;tertiary second stage\u0026rdquo;, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e left panel; Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssociations between education, memory score and memory score decline.\u003c/strong\u003e Upper secondary education is used as reference. Note that all memory scores are corrected for retest effects. Memory change (OR per year) results are presented with three decimals to allow inspection of the very weak effects. CI is 95%\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMemory offset\u003c/p\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003cp\u003e(CI low \u0026ndash; high)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMemory change\u003c/p\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003cp\u003e(CI low \u0026ndash; high)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.53\u0026ndash;0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.001\u0026ndash;1.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u0026ndash;0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.001\u0026ndash;1.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u0026ndash;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000-1.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpper secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost secondary non-tertiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.05\u0026ndash;1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.998\u0026ndash;1.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTertiary 1st stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.29\u0026ndash;1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000-1.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTertiary 2nd stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.49\u0026ndash;1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.999\u0026ndash;1.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eRetest effects were substantial and thus essential to adjust for in analyses of change. ORs increased almost linearly, from 1.5 at the first follow-up to 2.5 at the fifth (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, top right panel). There was a small negative effect of time (one year) on memory scores (OR\u0026thinsp;=\u0026thinsp;0.963, CI: 0.961\u0026ndash;0.964), slightly increasing with age (age \u0026times; time OR\u0026thinsp;=\u0026thinsp;0.9981, CI: 0.9980\u0026ndash;0.9982). We assessed whether higher education was associated with less memory decline over one year (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, right panel; Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Effect sizes were negligible, with all ORs\u0026thinsp;\u0026lt;\u0026thinsp;1.005. Further, if education is associated with the ability to benefit from previous testing experience to optimize performance, individuals with more education and cognitive reserve should be able to benefit more from repeated testing more efficiently. However, there were no systematic differences in retest effects by education (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, bottom row).\u003c/p\u003e\n \u003cp\u003eWe re-ran the analyses using education relative to birth cohort in bins of a decade (1900\u0026ndash;1909, 1910\u0026ndash;1919, \u0026hellip;, 1960\u0026ndash;1969), sex, and country as measure of interest, yielding a percentile score for each participant, while controlling for absolute level of education. This provides a test of whether the education-memory associations reflect selection effects, in the sense that people are selected into education based on some unmeasured trait, that act as a common cause, and is correlated with late-life memory scores, and partially accounts for these selection effects varying between men and women from different birth cohorts in countries with widely varying educational opportunities and experiences. As seen in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, including relative education in the model reduced the associations between absolute education and memory somewhat, while relative education showed an independent, positive association with memory. The effect of going from the lowest (0) to the highest (100) percentile was associated with an OR of 1.17 (CI: 1.14\u0026ndash;1.20) compared to the reference group (\u0026ldquo;upper secondary\u0026rdquo;).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eBrain MRI cohort results\u003c/h3\u003e\n\u003cp\u003eFor the brain analyses, we included 13 datasets with longitudinal MRI, memory assessments, and information about education, from seven countries across North to South of Europe, US and Canada (see Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). In addition to cohort-specific inclusion and exclusion criteria, participants\u0026thinsp;\u0026gt;\u0026thinsp;50 years without cognitive impairment, neurological or psychiatric disorders were included. The initial dataset included participants with 1 to 14 MRI acquisitions with follow-up intervals spanning up to 15.8 years, and memory assessments ranging from 1 to 24 observations per participant with follow-up intervals up to 28 years. Sample characteristics are presented in Table\u0026nbsp;2, and cohort specific descriptions in Methods \u0026amp; Protocols.\u003c/p\u003e\n\u003cp\u003eFirst, we tested whether the main cognitive results from SHARE replicated in the MRI cohorts. As education coding varied across samples, we could not use the same coding scheme as in SHARE, and education was hence dichotomized based on the median split for each sample, with post hoc analyses using tertiary vs. non-tertiary education as category (see \u003cem\u003eReplication analyses\u003c/em\u003e). A generalized additive mixed model (\u003cem\u003eGAMM\u003c/em\u003e) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e was run using memory (z-normalized based on the first observation per each dataset) as dependent variable, with education, time since baseline, sex, a dummy for retest effects as fixed effects, and baseline age as smooth term. Random intercepts were included per participant and dataset while random slopes of retest and time were included for each dataset. To test memory change, an education \u0026times; time interaction term was added to the model. Exact p-values are provided down to p\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;2 Sample characteristics for samples with MRI\u003c/strong\u003e. \u003cem\u003eN: Number of unique participants. Obs: Total number of observations. Sex: M \u0026ndash; Males/ F \u0026ndash; females. Tertiary edu: Number of participants with tertiary or higher education. Above median edu: Number of participants with above median education. xObs: Obs per participant: Average number of test sessions per participant. eduT: Years of education. Time: Average maximum time in years from baseline to last follow-up. MRI: information for participants with available MRI only.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cimg 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\"\u003e\u003c/em\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eLike the SHARE results, while high education was associated with better memory scores (\u0026beta;\u0026thinsp;=\u0026thinsp;0.33, SE\u0026thinsp;=\u0026thinsp;0.009, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), the education groups showed close to parallel changes over time (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, panels D \u0026amp; E). Predicted change over 10 years was z = -0.20 for high education, compared to z = -0.26 for low education (effect of education group on memory z-score change/ year: \u0026beta;\u0026thinsp;=\u0026thinsp;0.006, SE\u0026thinsp;=\u0026thinsp;0.003, p\u0026thinsp;=\u0026thinsp;0.029) (for complete results, see SI). The analysis was repeated using the alternative categorization of education (tertiary vs. non-tertiary), yielding similar results.\u003c/p\u003e\n\u003cp\u003eWe extracted a brain variable sensitive to memory change. For each participant, annual change in each of 166 brain regions was calculated and related to memory change by a series of linear mixed effects models, yielding 29 significant FDR-corrected significant regions (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, panel A). These were entered into a principal component analysis (PCA), yielding a memory-sensitive brain PC. For replication, we also used machine learning, i.e. a regularized regression model (LASSO: Least Absolute Shrinkage and Selection Operator), to predict memory based on an independent sample of 28.114 cross-sectional MRIs from UKB (\u003cem\u003eReplication analyses\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eTo test the association between education and brain PC score (offset effects), a GAMM was run with education, time since baseline, sex, and estimated total intracranial volume (eTIV) as fixed effects, and baseline age and sex \u0026times; baseline age as smooth terms. Random intercepts were included per participant, scanner, and dataset while random slopes of time were included for each dataset. The brain PC showed the expected negative relationship to age, slightly accelerating from about 70 years (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, panel B), and time (\u0026beta; = -0.07, se\u0026thinsp;=\u0026thinsp;0.008, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Estimated loss in the high education group was z = -0.68 over a decade, compared to z = -0.74 for the low group (interaction effect of education \u0026times; time on brain volume: \u0026beta;\u0026thinsp;=\u0026thinsp;0.005, se\u0026thinsp;=\u0026thinsp;0.002, p\u0026thinsp;=\u0026thinsp;.015). This means that the difference in 10-year change was z\u0026thinsp;=\u0026thinsp;0.06, and the slopes were close to parallel (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, panel C). Using the alternative education categorization (tertiary vs. non-tertiary) and the LASSO-derived brain measure yielded similar results. High education was also slightly positively associated with the brain PC (\u0026beta;\u0026thinsp;=\u0026thinsp;0.04, se\u0026thinsp;=\u0026thinsp;0.02, p\u0026thinsp;=\u0026thinsp;.049), a relationship that was numerically stronger with the alternative education classification (\u0026beta;\u0026thinsp;=\u0026thinsp;0.06, se\u0026thinsp;=\u0026thinsp;0.02, p\u0026thinsp;=\u0026thinsp;.003) and weaker with the LASSO-derived brain measure (\u0026beta;\u0026thinsp;=\u0026thinsp;0.03, se\u0026thinsp;=\u0026thinsp;0.04, p\u0026thinsp;=\u0026thinsp;.083). When removing eTIV from the model, the estimate increased to \u0026beta;\u0026thinsp;=\u0026thinsp;0.05 (se\u0026thinsp;=\u0026thinsp;0.02, p\u0026thinsp;=\u0026thinsp;.019) using the median split, and to 0.07 (se\u0026thinsp;=\u0026thinsp;0.02, p\u0026thinsp;=\u0026thinsp;.0007) using the tertiary/ non-tertiary education categorization. We tested the relationship between education and eTIV which was numerically stronger for both education classifications (median split: \u0026beta;\u0026thinsp;=\u0026thinsp;0.12, se\u0026thinsp;=\u0026thinsp;.002, p\u0026thinsp;\u0026lt;\u0026thinsp;.001; tertiary/non-tertiary: \u0026beta;\u0026thinsp;=\u0026thinsp;0.13, se\u0026thinsp;=\u0026thinsp;0.02, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, top left).\u003c/p\u003e\n\u003cp\u003eFinally, we tested whether the brain-memory association varied as a function of education (see Table 3 for an overview). Higher brain PC was related to better memory (\u0026beta;\u0026thinsp;=\u0026thinsp;0.073, se\u0026thinsp;=\u0026thinsp;0.013, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). As the brain PC was extracted from regions where brain change was related to memory change, the change-change relationship was given, but is still reported for completeness: \u0026beta;\u0026thinsp;=\u0026thinsp;0.01, se\u0026thinsp;=\u0026thinsp;0.002. More importantly, there were no significant education \u0026times; brain PC (\u0026beta;\u0026thinsp;=\u0026thinsp;0.01, se\u0026thinsp;=\u0026thinsp;0.02, p\u0026thinsp;=\u0026thinsp;.60) or education \u0026times; brain PC \u0026times; time (\u0026beta;\u0026thinsp;=\u0026thinsp;0.004, se\u0026thinsp;=\u0026thinsp;0.004, p\u0026thinsp;=\u0026thinsp;.43) interactions. This means that the relationship between brain and memory, and between changes in the two, did not vary as a function of education (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, top middle \u0026amp; right panels). The same was found using the alternative education category and the LASSO-based brain measure.\u003c/p\u003e\n\u003ch3\u003eReplication analyses\u003c/h3\u003e\n\u003cp\u003eThe main analyses were run using the alternative categorization of education (tertiary vs. non-tertiary) and brain measure (LASSO), yielding four model specifications (Table\u0026nbsp;3). Controlling for eTIV, cross-sectional education-brain associations were relatively weak, although significant at p\u0026thinsp;\u0026lt;\u0026thinsp;.05 in three models. The education \u0026times; time interaction showed small effect sizes in the same three specifications, but still significant. Effect size was largest for the PC brain measure and the tertiary/ non-tertiary categorization, with an interaction coefficient of 0.008 compared to 0.005 for the two other significant specifications. The brain \u0026times; education \u0026times; time interaction on memory was not significant in any specification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;3 Replication and control analyses\u003c/strong\u003e. \u003cem\u003eEach of the main statistical models were run with two categorizations of education (median split, tertiary vs. non-tertiary) and two approaches to derive a brain component sensitive to memory (PCA based on memory-brain change-change relationship vs. LASSO applied to an independent dataset of cross-sectional MRIs). The main results are shown in the table, see SI for complete results. The random effect terms are not shown in the table (Random intercepts per participant and dataset, random slopes of time [and retest and for memory] for each dataset). P-values below .001 are written as \u0026rdquo;\u0026lt;0.001\u0026rdquo;.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cimg 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\"\u003e\u003c/em\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eAs an additional set of control analyses, we tested whether the coefficients for the brain variables in predicting memory were affected by including education in the models (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, bottom panels). The coefficients changed only minimally, suggesting that the brain-memory relationships were largely independent of education (full results in SI).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe found that education was only minimally associated with less age-related decline in episodic memory function, not associated with any substantial reduction in the rate of age-normative structural brain decline in memory-sensitive regions, and did not increase cognitive resilience to the observed brain changes. The small magnitude of the differences in brain and memory change across educational groups contrast with the comparatively much larger differences in level. We found, in line with previous studies, that education was associated with better episodic memory scores across the age-range, slightly larger volume of the memory-sensitive brain regions, and larger intracranial volume. These associations are likely rooted in lifelong variation in brain structure and function that originate earlier in life \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. We also find evidence that selection effects may account for parts of the associations, in the sense that people with certain traits, such as larger brain volumes from early age as indicated by estimates of total intracranial volume and better episodic memory, tend to be selected into longer education. This selection likely varies across social and demographic dimensions as well as across features of the educational system, but it is important to note that clear patterns of associations resulted from analyses conducted on diverse samples covering a large number of WEIRD societies and age cohorts, indicating a certain degree of robustness across time and place. The implications of the results are discussed below.\u003c/p\u003e\n\u003ch3\u003eA role for education in brain and cognitive aging?\u003c/h3\u003e\n\u003cp\u003eThe idea that age-related cognitive decline is reduced by higher education is based on two complementary hypotheses. According to the first, education can guard against memory decline by causally influencing lifestyle factors that preserve memory-sensitive brain regions, i.e. by increased brain maintenance. While we find support for the observation that relative absence of brain decline in terms of less atrophy is linked to better episodic memory \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, there were, however, only minor differences in the decline trajectories of memory-sensitive brain regions across educational groups. This aligns with and extends a previous finding that educational level is not associated with differences in age-change in the brain regions most vulnerable to normal aging \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In sum, these results provide a neurobiological perspective for why people with different educational attainment and different levels of memory function may still show similar rates of age-related memory decline \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e - simply put, brains change across middle- and older age in very similar ways across the entire spectrum of observed differences in education.\u003c/p\u003e \u003cp\u003eThe second hypothesis is that education protects cognitive function through increased resilience to brain decline by building a \u0026ldquo;cognitive reserve\u0026rdquo; \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. This hypothesis implies that people with more education should have higher cognitive performance than expected given their observed level of brain decline \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. We find little support for this idea: only very small differences in the aging trajectories for memory and the memory-sensitive brain regions were observed between educational levels. Further, structural brain decline was associated with similar amounts of memory decline in more vs. less educated participants, consistent with previous research on hippocampal \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e and cortical \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e atrophy. Finally, more education was not associated with larger retest effects, which suggests that education did not come with greater ability to benefit from the specific test experience \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Retest effects reflect the ability to take advantage of previous testing to improve test scores. More educated participants showed greater ability to encode new information, as reflected in their higher memory scores, but this did not increase their ability to benefit from previous testing. Similar results have been found for tests of mental speed and reasoning \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Taken together, the results suggest that education was not associated with less decline in brain or episodic memory in aging, and that the positive associations consequently must have been established before the age of 50 years. Although the present data do not include developmental information, we can speculate that the precursors of the differences in brain and cognition observed in aging were already present early in life, as discussed further in the next section.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eHow do associations between brain volume, cognitive function and education arise?\u003c/h2\u003e \u003cp\u003eThe results revealed a robust relationship between education, higher memory function, slightly larger volumes of memory-sensitive brain regions, and larger intracranial volume. Understanding the nature of these associations is important. The most obvious explanation is that they may reflect that persons with higher cognitive abilities and larger brain volumes are more likely to select and be selected to further education \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Although there were unequal opportunities and clear limitations to access to education for many of the participants in the present study \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, likely reducing the relationship between cognitive abilities and educational attainment, the existence of selection effects is well documented in previous studies. The present results suggest that this may account for at least a part of the relationship between education and memory function. Regardless of absolute educational attainment, participants with high education relative to other participants of the same sex, birth cohort and country of residence demonstrated better memory function decades later, consistent with the expectation that selection effects contribute to the observed relationship. Earlier-life cognition predicts cognitive function and brain health in aging \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, suggesting limited opportunities for causal effects of education beyond adolescence. Instead, selection effects driven by early-life cognitive abilities and gross aspects of brain structure may explain the life-long associations between education and cognition, also consistent with recent genetic evidence \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Our results are also in line with a systematic review of effects of education on dementia risk, which argued that low education has a stronger association with dementia when it reflects cognitive capacity rather than privilege, and when it is associated with other risk factors across the lifespan \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, cognition-education relationships can in part be explained by neuroanatomical volume differences established in early childhood \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, also limiting the potential causal effects of later education. Brain structure may hence be a key phenotype along the causal pathway that leads from genetic variation to differences in cognitive function and educational attainment \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile selection effects are real, natural experiments still suggest that increased education can positively impact cognitive function \u003csup\u003e\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, including memory \u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The results showed that taking selection effects into account reduced the association between education and memory only to a modest extent. Importantly, positive effects of increased education are due to early schooling, not reduced decline in aging \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Our finding of similar memory-education associations across the age-range aligns with evidence that education enhances lifelong cognitive function without affecting age-related decline. Still, most cognitive intervention studies find that positive effects on cognitive scores diminish over time \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, so associations would be expected to be small when measured decades later. Thus, any early effect of education on cognition would likely need to be sustained by some mechanism that helps maintaining the initial effect, e.g. by increasing the likelihood of working in cognitively challenging occupations. According to the gravitational hypothesis, the stability of individual differences in cognition is caused by consistent exposure to the same environments over time, including social, educational, and economic contexts \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, see \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e for more in-depth discussions of this topic. This is in line with studies finding \u0026lsquo;cognitive stimulation\u0026rsquo; at work to be associated with lower dementia risk \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, although this cannot explain the full association between education and less dementia \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Nonetheless, individuals with higher cognitive function may pursue cognitively stimulating activities irrespective of their formal education, potentially leading to spurious associations when this is not accounted for.\u003c/p\u003e \u003cp\u003eAn interesting aspect of the present results was the linear association between memory performance and educational attainment. If education caused cognitive scores to increase, one could expect diminishing marginal benefits with increasing duration, although this question has not been properly addressed by quasi-experimental methods \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Hence, this result could reflect that selection effects are additive across the range of educational levels, but definite evidence is currently lacking. It is also interesting that this clear pattern is identified across samples covering a large number of countries and cohorts, suggesting that this entails a certain degree of robustness to societal variations across different WEIRD societies.\u003c/p\u003e \u003cp\u003eWe observed that individuals with higher education had slightly larger volumes in memory-sensitive brain regions. Experiments have showed effects of cognitive training on both memory and relevant brain structures even in older adults \u003csup\u003e\u003cspan additionalcitationids=\"CR59\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, and it is possible that early education could lead to increased brain volumes of a magnitude similar to that observed in the present study. However, training-induced effects on brain structure are generally even more transient than those on cognition \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e, making it less likely that direct effects of youth education on brain volume would persist into old age. Consistent with this, a recent study found no evidence of structural brain differences resulting from the increase in mandatory schooling from 15 to 16 years in the UK 50 years later \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Instead, intracranial volume has been shown to be more strongly related to education than gray matter volume \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, which was also found in the current study. In fact, the association between education and intracranial volume was double the size of the association with the memory-sensitive brain component, and removing intracranial volume from the models increased the relationship between memory scores and the memory-sensitive brain PC. Since intracranial volume reaches its maximum in childhood and is unlikely to be influenced by schooling, this relationship does not reflect a causal effect of education and is a further indication that selection effects indeed play a role. Although the relationship between brain volumes and education was found to exist also independently of intracranial volume, it is most likely that the education-brain association was present early in life. Therefore, we interpret the memory-brain-education relationships observed in the present study as partly reflecting selection effects, potentially complemented by some self-reinforcing effects of early schooling.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConsiderations and future research\u003c/h3\u003e\n\u003cp\u003eFirst, the samples cover 33 countries, and the conclusions not confined to one specific time and place. Still, we did not attempt to detect variations in associations across time \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and societies \u003csup\u003e\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e, but another a multi-cohort, multi-national aging-study found relatively consistent associations between cognition and education \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Second, while SHARE used probability sampling to achieve representativity, the MRI samples are generally less representative of their respective populations (e.g. \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e). It is difficult to estimate the influence of this, but we note that the memory-education results from SHARE were replicated in the brain imaging cohorts. Further, selective attrition and mortality may affect the longitudinal estimates, although studies addressing this have largely obtained similar estimates \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThird, we used memory test scores as measures of cognition. While such scores correlate with important real-life indicators, e.g. work participation and capacity for independent living, it is not clear to what extent changes in test scores imply similar changes in daily life cognitive function (for a broader discussion, see \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e). It cannot be ruled out that education enhanced scores by increased test-taking skills or cognitive strategies with little effect on the underlying cognitive construct. Such effects could be expected to be larger for crystallized or domain knowledge-based tests, such as vocabulary or calculus, and less for fluid tests, including list recall \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Still, schooling can potentially increase fluid test performance by factors such as test-specific encoding strategies and test-taking skills, which may have little applicability to other aspects of life.\u003c/p\u003e \u003cp\u003eFinally, we focused on episodic memory and structural brain changes. Causal effects of education have been identified for various cognitive measures, including fluid (such as memory), crystallized (e.g. language) and compound (e.g. the \u003cem\u003eg-\u003c/em\u003efactor) measures of cognition \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. One study found that the association between education and cognitive scores, when controlling for childhood cognition, comprised direct effects on specific cognitive skills, including memory, and was not mediated by the g-factor \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Therefore, a potential extension of the current work would be to include multiple cognitive functions and examine common versus unique associations with education and brain structure. Finally, although structural brain change is predictive of memory decline in aging \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, other brain measures, such as those related to brain connectomics \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e, could potentially show different relationships to education.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this large-scale, geographically spread longitudinal mega-analytic study, we find that education is robustly related to higher episodic memory function and intracranial volume, and modestly to a brain component optimized to be sensitive to memory change. However, the results do not indicate that this association is driven by slower brain aging or more resilience to structural brain change. Rather, we find evidence to suggest that the relationship is established early in life and partly is attributable to selection effects. Hence, to the extent that education may have a positive effect on episodic memory function in aging, this effect originates from earlier in life.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Lifebrain consortium is funded by the EU Horizon 2020 grant agreement no. 732592 (Lifebrain). The different sub-studies are supported by different sources. LCBC is supported by the European Research Council under grant agreements no. 283634 and no. 725025 (to A.M.F.) and no. 313440 (to K.B.W.), as well as the Norwegian Research Council (325878, 262453 to A.M.F.; 325001, 301395, 239889 to K.B.W.; 249931 to A.M.F \u0026amp; K.B.W.; 324882 to DVP; 325415 to HG), the National Association for Public Health\u0026rsquo;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. PSI2015-64227-R (AEI/FEDER, UE)); and to\u0026nbsp;and to G.C and J.S.-S (grant no. PID-2022-139298OA-C22 (MCIN /AEI /10.13039/501100011033 / FEDER, UE));\u0026nbsp;by the Walnuts and Healthy Aging study (http://www.clinicaltrials.gov; grant no. NCT01634841) funded by the California Walnut Commission, Sacramento, California; and by an ICREA Academia 2019 award. BASE-II has been supported by the German Federal Ministry of Education and Research under grant nos 16SV5537, 16SV5837, 16SV5538, 16SV5536K, 01UW0808, 01UW0706, 01GL1716A and 01GL1716B and by the European Research Council under grant agreement no. 677804 (to S.K.). Dr. A. Pascual-Leone is partly supported by grants from the National Institutes of Health (R01AG076708), Jack Satter Foundation, and BrightFocus Foundation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePart of the research was conducted using the UKB resource under application no. 32048. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. LOW is funded by the South-Eastern Norway Regional Health Authorities (# 2017095), the Norwegian Health Association (#19536, #1513) and by Wellcome Leap\u0026rsquo;s Dynamic Resilience Program (jointly funded by Temasek Trust) (#104617).\u003c/p\u003e\n\u003cp\u003eParts of the data used in preparation of this article were obtained from the Pre-Symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer\u0026apos;s Disease (PREVENT-AD) program. Data were provided [in part] by OASIS-3\u0026rdquo; \u0026quot;OASIS-3: Principal Investigators: T. Benzinger, D. Marcus, J. Morris; NIH P50AG00561, P30NS09857781, P01AG026276, P01AG003991, R01AG043434, UL1TR000448, R01EB009352.\u003c/p\u003e\n\u003cp\u003eParts of the data collection and sharing for this project were provided by the Cambridge Centre for Ageing and Neuroscience (CamCAN). CamCAN funding was provided by the UK Biotechnology and Biological Sciences Research Council (grant number BB/H008217/1), together with support from the UK Medical Research Council and University of Cambridge, UK.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParts of the data are from VETSA, which is funded by the National Institute of Aging grants R01s AG018384, AG018386, AG050595, AG022381, AG076838. The content is the responsibility of the authors and does not necessarily represent official views of the NIA, NIH, or VA. U.S. Department of Veterans Affairs, Department of Defense; National Personnel Records Center, National Archives and Records Administration; Internal Revenue Service; National Opinion Research Center; National\u003c/p\u003e\n\u003cp\u003eResearch Council, National Academy of Sciences; and the Institute for Survey Research, Temple University provided invaluable assistance in the conduct of the VET Registry. The Cooperative Studies Program of the U.S. 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 VET\u003c/p\u003e\n\u003cp\u003eRegistry and their families.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePart of the data collection and sharing was funded by the Alzheimer\u0026apos;s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer\u0026rsquo;s Association; Alzheimer\u0026rsquo;s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; 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 \u0026amp; Development, LLC.; Johnson \u0026amp; Johnson Pharmaceutical Research \u0026amp; Development LLC.; Lumosity; Lundbeck; Merck \u0026amp; Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; 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 ( www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer\u0026rsquo;s Therapeutic Research Institute at the University of Southern \u0026nbsp;California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParts 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 study was launched in 2010, funded by the National Institute on Aging, and is led by principal investigators Reisa A. Sperling MD and Keith A. Johnson MD at Massachusetts General Hospital/Harvard Medical School in Boston, MA.\u003c/p\u003e\n\u003cp\u003eThe 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 N\u0026deg;211909, SHARE-LEAP: GA N\u0026deg;227822, SHARE M4: GA N\u0026deg;261982, DASISH: GA N\u0026deg;283646) and Horizon 2020 (SHARE-DEV3: GA N\u0026deg;676536, SHARE-COHESION: GA N\u0026deg;870628, SERISS: GA N\u0026deg;654221, SSHOC: GA N\u0026deg;823782, SHARE-COVID19: GA N\u0026deg;101015924) and by DG Employment, Social Affairs \u0026amp; Inclusion through VS 2015/0195, VS 2016/0135, VS 2018/0285, VS 2019/0332, VS 2020/0313, SHARE-EUCOV: GA N\u0026deg;101052589 and EUCOVII: GA N\u0026deg;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 U.S. National Institute on Aging (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 from various national funding sources is gratefully acknowledged (see www.share-eric.eu).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDr. A. Pascual-Leone 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. Dr. A Pascual-Leone is 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 noninvasive brain stimulation in various neurological disorders; as well as digital biomarkers of cognition and digital assessments for early diagnosis of dementia.\u003c/p\u003e"},{"header":"Methods \u0026 Protocols","content":"\u003cp\u003e\u003cem\u003eSamples\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eSHARE cohort\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Survey of Health, Ageing and Retirement in Europe is a research infrastructure for studying the effects of health, social, economic and environmental policies over the life-course of European citizens and beyond (https://share-eric.eu/) \u003csup\u003e37\u003c/sup\u003e. SHARE contains observations of individuals from 50 years of age from 28 countries, recruited to be representative of the population in each country. Data for the present analyses was extracted from \u003cem\u003eeasy\u003c/em\u003eSHARE (release 8.0.0, February 10\u003csup\u003eth\u003c/sup\u003e 2022, doi:10.6103/SHARE.easy.800), see \u003csup\u003e70,71\u003c/sup\u003e for methodological details. The \u003cem\u003eeasy\u003c/em\u003eSHARE release 8.8.0 is based on SHARE Waves 1, 2, 3, 4, 5, 6, 7, and 8 (DOIs:10.6103/SHARE.w1.800, 10.6103/SHARE.w2.800, 10.6103/SHARE.w3.800, 10.6103/SHARE.w4.800, 10.6103/SHARE.w5.800, 10.6103/SHARE.w6.800, 10.6103/SHARE.w7.800, 10.6103/SHARE.w8.800) \u003csup\u003e37,72\u003c/sup\u003e. Participants included in the analyses participated in up to six waves of data collection. In total, we included data from 130.880 participants (mean age 64.9 years at baseline, 50.1-112.0, 59.363 males/ 71.517 females), with an average of 2.7 (SD = 1.63) waves with a mean maximum follow-up interval of 6.53 years (0.9-0-15.9, SD = 3.93). \u0026nbsp;In total, 352.953 memory test sessions were included, with two test results (immediate vs. delayed recollection) for each, i.e. 705.906 memory scores went into the analyses. Respondents aged below 50 years of age (individuals recruited due to being spouses of other participants) were excluded from the sample. An overview of the age distribution per country is provided in Figure 1. Sample distribution as a function of timepoints, education category and age is provided in SI Figure 3.\u003c/p\u003e\n\u003cp\u003eMemory was assessed with a 10 word verbal recall test. The word list is read out load to the participants, and then recall is tested immediately after the presentation (Recall 1) and then after a delay of approximately 5 minutes (Recall 2). Multiple versions of the lists are assigned to the respondents \u003csup\u003e39\u003c/sup\u003e. The response distribution is shown in SI Figure 4. There were no ceiling effects, which is important when assessing longitudinal change for the best-performing participants. There were some floor effects for recall 2, but less for recall 1, suggesting that we can estimate longitudinal chance well for most baseline levels of memory. Since education is association with differences in memory scores, ceiling and floor effects could potentially obscure real differences in change, but this is unlikely to have affected the current results given the response distribution below. Scores were lower for delayed than immediate recall (OR = 0.535, CI: 0.534 \u0026ndash; 0.537) and females scored higher than males (OR = 1.160, CI: 1.153-1.168).\u003c/p\u003e\n\u003cp\u003eIn addition to the memory measures, we extracted the variables age, sex, birth year, education (based on the International Standard Classification of Education 1997), and country of current residency.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical analyses: SHARE\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAnalyses were performed in R version 4.4.1 \u003csup\u003e73\u003c/sup\u003e using the brms package\u0026rsquo;s \u003csup\u003e74\u003c/sup\u003e interface to the probabilistic programming language Stan \u003csup\u003e75\u003c/sup\u003e. To assess effects of education on memory and memory change, we ran logistic regressions with memory recall as dependent variable, yielding odds ratios as the most relevant model parameter to interpret. An odds ratio of 1 corresponds to a regression coefficient of 0. The main model was:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" alt=\"image\" width=\"601\" /\u003e\u003c/p\u003e\n\u003cp\u003eEach memory test was used as a separate response, yielding two observations per timepoint, and the variable \u003cem\u003etest\u003c/em\u003e represents difficulty of condition 2 relative to condition 1. To control for practice effects, a monotonic function of the number of previous tests taken was included as covariate. We used a smooth function of age to allow non-linear relationships. Individual-specific intercepts per participant were nested within country. Default priors were used for all parameters, two parallel chains of Stan\u0026rsquo;s No-U-Turn Sampler \u003csup\u003e76\u003c/sup\u003e were run for 1500 iterations, discarding the first 1000 as warmup. This yielded 1000 post-warmup samples. For the offset/level analyses, education (edu) was the variable of interest, while for the slope/change analyses, edu\u0026nbsp;\u0026times;\u0026nbsp;time since baseline was the critical variable. Z-transformed variables were used in the model fitting for numerical stability, and results converted back to their natural units for easier interpretability, e.g., age and time in years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eMRI cohorts\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe combined data from 13 datasets with longitudinal brain MRIs and memory assessments: LCBC \u003csup\u003e77\u003c/sup\u003e, Betula \u003csup\u003e78,79\u003c/sup\u003e, UB \u003csup\u003e80,81\u003c/sup\u003e, BASE-II \u003csup\u003e82,83\u003c/sup\u003e, and Cam-CAN \u003csup\u003e84\u003c/sup\u003e datasets (from the Lifebrain Consortium) \u003csup\u003e38\u003c/sup\u003e as well as the COGNORM \u003csup\u003e85\u003c/sup\u003e, the Alzheimer\u0026rsquo;s Disease Neuroimaging Initiative (ADNI) database (https://adni.loni.usc.edu) \u003csup\u003e86\u003c/sup\u003e, BBHI \u003csup\u003e87\u003c/sup\u003e, the Harvard Aging Brain Study (HABS) \u003csup\u003e88\u003c/sup\u003e, the UKB (https://www.ukbiobank.ac.uk/) \u003csup\u003e89\u003c/sup\u003e, PREVENT-AD \u003csup\u003e90,91\u003c/sup\u003e, OASIS3 (https://sites.wustl.edu/oasisbrains/) \u003csup\u003e92\u003c/sup\u003e, and VETSA \u003csup\u003e93\u003c/sup\u003e. Sample size was maximized for each analysis and hence varies due to data availability and missingness (see Table 1 for an overview). In addition to cohort-specific inclusion and exclusion criteria, participants \u0026gt;50 years without cognitive impairment, Alzheimer\u0026rsquo;s dementia or severe neurological or psychiatric disorders were included. Additionally, MRI data from scanners with fewer than 15 measurements were also excluded. The initial dataset included individuals with 1 to 14 MRI acquisitions with longitudinal structural MRI data spanning up to 15.8 years. Similarly, memory assessments range from 1 to 24 observations per individual with a follow-up up to 28 years. For detailed descriptions of general characteristics of each dataset, please refer to the study-specific citations above. An overview of each dataset is given in Supplementary Information (SI Table 1). The main sample descriptives are provided in Table 1, but since the exact sample size varies somewhat between analyses depending on data availability, the specific characteristics for the samples used and their age distributions used to address the different research questions are provided in SI Table 2 and SI Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData availability and ethical approvals\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEach dataset has different owners. Contact information to be used for data access is specified in SI Table 3. The main project was approved by the Norwegian Regional Committee for Medical Research Ethics South (approval 8122), and each sub-study was approved by the relevant ethical review board, as specified in SI Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEducation in the brain imaging cohorts\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor each dataset, education was categorized as high or low using a mean split. We chose this approach because quantitative distributions of education were often highly non-gaussian and level-based codifications were somewhat arbitrary due to idiosyncratic reporting of years of education, and variations in schooling systems across years and country. To ensure robustness, we conducted analyses with an alternative operationalization of education, categorizing individuals with or without tertiary education. When education data was provided as qualifications or categories, these were converted to years of education based on country-specific norms. Individuals were then grouped as having high or low education based on the median.\u0026nbsp;For the tertiary education categorization, the reverse process was applied, converting years of education into education qualifications. For reporting consistency, a lower cap of 6 years and an upper cap of 20 were applied to education years. An overview of education characteristics for each MRI sample is provided in SI Table 4 and SI Figure 2.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMemory function in the brain imaging cohorts\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor each sample, we operationalized memory performance as a z-normalized score based on the first time point and the different available memory tests. When multiple scores were available, we used the first component of a Principal Component Analysis (PCA) with all measures as inputs. For each dataset, we regressed out age (as a smoothing term), sex, and one or two dummy test-retest regressors using generalized additive mixed models (\u003cem\u003egamm4\u003c/em\u003e \u003cem\u003eR-package\u003c/em\u003e) \u003csup\u003e40\u003c/sup\u003e. Individual identifiers were used as random intercepts and the number of dummy test-retest regressors depended on whether the dataset had 2 or \u0026gt;=3 waves with memory function data. The residuals were used as an estimate of memory function in each observation. An overview of tests included in the memory performance score for each dataset is provided in SI Table 5.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMagnetic Resonance Imaging acquisition and preprocessing\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eStructural T1-weighted (T1w) MPRAGE and FSPGR scans were collected using 1.5 and 3T MRI scanners. Information regarding scanners and scanner parameters across datasets are presented in SI Table 6. We used the longitudinal FreeSurfer v.7.1.0 stream \u003csup\u003e94\u003c/sup\u003e for cortical reconstruction and volumetric segmentation of the structural T1w scans \u003csup\u003e95-97\u003c/sup\u003e. For sessions with multiple scans, data from the scanners were averaged. Briefly, the images were processed using the cross-sectional stream, which includes the removal of nonbrain tissues, Talairach transformation, intensity correction, tissue and volumetric segmentation, cortical surface reconstruction, and cortical parcellation. Next, an unbiased within-subject template space based on all cross-sectional images was created for each participant, using robust, inverse-consistent registration \u0026nbsp;(Reuter et al., 2010). The processing of each time point was then reinitialized with common information from the within-subject template, to increase reliability and statistical power. Except for the BETULA dataset, all\u0026nbsp;data was preprocessed on the Colossus processing cluster, part of the Services for Sensitive Data (TSD) (https://www.uio.no/tjenester/it/forskning/sensitiv/), University of Oslo. Memory-sensitive brain measures for each observation were derived using regional loadings based on the \u003cem\u003eDestrieux\u003c/em\u003e (cortical)\u0026nbsp;\u003csup\u003e98\u003c/sup\u003e and \u003cem\u003easeg\u003c/em\u003e (subcortical) atlases\u0026nbsp;\u003csup\u003e99\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMemory-sensitive brain measures\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe computed two complimentary measures of brain structure sensitive to memory, capturing different aspects of memory function in older age. The primary measure was defined as a longitudinal brain component sensitive to memory changes inspired by Vidal-Pineiro et al. (\u003cem\u003ein preparation\u003c/em\u003e). The second measure, for the purpose of assessing the robustness of the results, was trained on independent scans to detect cross-sectional brain-memory relationships in aging. The components were highly correlated (r = .71), both decrease with age (r = -.67, r = -.64, respectively) and include partially overlapping set of brain regions. The first measure (brain PC) is optimized to be sensitive to memory changes in aging, while the second (brain LASSO) is optimized to detect also offset, i.e. baseline, associations. See below for a full description of both methods.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eBrain PC: Change based, memory-sensitive measure:\u0026nbsp;\u003c/u\u003eThis measure was derived from a sample largely overlapping with that used for the statistical analyses and the AIBL in the present work but included participants down to age \u0026gt; 18 years. Brain PC is based on a principal component (PC) of longitudinal change in 20 cortical thickness and 9 subcortical volume regions. Brain regions were harmonized using a normative modelling framework \u003csup\u003e100,101\u003c/sup\u003e with the \u003cem\u003ePCNtoolkit\u0026nbsp;\u003c/em\u003e(0.30.post2), in \u003cem\u003ePython3\u003c/em\u003e environment \u0026nbsp;\u003csup\u003e102\u003c/sup\u003e (version 3.9.5). This framework offers several advantages as i) it is run independently across sites, ii) can isolate site-effects from other sources of variance associated with it, and iii) produces site-agnostic deviation scores (\u003cem\u003ez\u003c/em\u003e-statistics) adjusted for age, and sex. \u003cem\u003ePCNtoolkit\u003c/em\u003e uses a Hierarchical Bayesian Regression (HBR) technique \u003csup\u003e103\u003c/sup\u003e and pretrained models from 82 different datasets, including UKB and CamCan data. To avoid losing longitudinal observations, we performed this step recursively by iteratively (n = 100) holding out a calibrating sample and computing the estimates on the remaining data. The average scores of all iterations were used as the standardized scores for each observation. Scanners contributing with \u0026lt; 12 unique individuals or \u0026lt; 25 observations were excluded. For scanners contributing \u0026gt; 12 and \u0026lt; 32 unique individuals, we used a calibration sample consisting of all but 2 participants and estimate the harmonized scores in these two. For scanners with \u0026gt;= 32 unique individuals, we used, in each iteration, a held-out sample of 30 individuals while estimates were applied on the rest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNext, we selected individuals with at least 2 observations and a minimum follow-up of 1.5 years. For both MRI and memory preprocessed data, we estimated yearly change for each subject, by regressing data on follow-up time. Change data was then fed into separate linear mixed models as implemented in \u003cem\u003elme4, lmerTest\u0026nbsp;\u003c/em\u003e\u003csup\u003e104,105\u003c/sup\u003e,one per brain region\u003cem\u003e.\u0026nbsp;\u003c/em\u003eNote that here we used estimates of change, and there was only one observation per individual. For each region, we predicted memory change by brain change, using dataset as random intercepts. Additionally, we used weights to account for potential heteroskedasticity. That is, individuals with short follow-up periods and less observations contribute with more unreliable, high-variance data and thus should produce an unequal spread of residuals. We used the square of reliability as weights as estimated in \u003csup\u003e106\u003c/sup\u003e. Longitudinal reliability is a function of variance in change and mean measurement error for a given region, and number of observations and total follow-up time for a given individual. After False Discovery Rate (FDR)-correction (p \u0026lt; .05), 29 regions showed significant associations between brain change and memory change, including 9 volumetric subcortical\u0026nbsp;(bilateral amygdala, hippocampus, and thalamus, left lateral and inferior lateral ventricle, right accumbens area) and 20 cortical thickness regions (left G cingul-Post-dorsal, G cingul-Post-ventral, G insular_short, G oc-temp_med-Parahip, G front_inf-Opercular, G front_inf-Triangul, G subcallosal, S temporal_sup; right G Ins lg\u0026amp;S cent_ins, S circular_insula_ant, S oc-temp_med\u0026amp;Lingual, S suborbital; bilateral G temp_sup-Plan_polar, S orbital-H_Shaped, S front_middle, S circular_insula_inf). These regions were entered into the PCA to extract the PC of the memory-sensitive brain regions, yielding a brain measure sensitive to episodic memory change in aging. All regions except the ventricles showed positive loadings with the brain PC.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eBrain LASSO: Cross-sectional-based, memory-sensitive measure:\u003c/u\u003e The alternative brain measure was derived by predicting cross-sectional memory function by cross-sectional brain structure features on an independent sample of UKB individuals not included in other brain analyses. Prediction was performed with a Least Absolute Shrinkage and Selection Operator (LASSO) machine learning algorithm as implemented in the \u003cem\u003eglmnet\u0026nbsp;\u003c/em\u003epackage\u003csup\u003e107\u003c/sup\u003e. LASSO is a regression technique that performs variable selection and regularization by adding a penalty term, reducing overfitting, and simplifying the model. Lambda was selected as the maximum value within one standard error from minimum lambda, using a cross-validated approach with K = 10 folds (\u0026lambda; = .0143; MSE = .943). LASSO coefficients are provided in SI Table 7. The sample consisted of 28,114 individuals from UKB aged 65.05 years (SD = 7.60) (range 47.32 \u0026ndash; 82.78), without longitudinal MRI data, and not included in the main brain analyses. Age was not regressed out allowing prediction to capture both offset and level effects of brain structure on memory function as well as indirect effects due to the unaccounted correlation of age with both MRI features and memory function. We used the Paired associate learning (PAL) (#20197 UKB field) at the first MRI timepoint as index of memory function. MRI data included 337 features; subcortical regions and global brain measures from the \u003cem\u003easeg\u003c/em\u003e atlas and cortical area, and thickness regions from the \u003cem\u003eDestrieux\u003c/em\u003e atlas. Both brain and memory indices were z-standardized, and outliers were considered as values \u0026gt;5 SD apart from the mean. Individuals with outlier values for memory were excluded while brain outlier values were recoded as 0.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical analyses: MRI cohorts\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll the analyses were performed using the R environment (version 4.2.1) \u003csup\u003e73\u003c/sup\u003e. Visualizations were made with \u003cem\u003eggplot2\u0026nbsp;\u003c/em\u003e\u003csup\u003e108\u003c/sup\u003e and \u003cem\u003eggseg\u0026nbsp;\u003c/em\u003e\u003csup\u003e109\u003c/sup\u003eR-packages\u003cem\u003e.\u003c/em\u003e Memory, brain variables, and estimated intracranial volume (eTiv) were Z-standardized before inclusion in the models. Outlier values defined as values \u0026gt;5 SD from the mean, were removed from the analyses. Analyses were run using \u003cem\u003egamm\u0026nbsp;\u003c/em\u003emodels as implemented in the \u003cem\u003egamm4\u003c/em\u003e \u003cem\u003eR-package\u003c/em\u003e \u003csup\u003e40\u003c/sup\u003e, unless otherwise specified.\u003c/p\u003e\n\u003cp\u003eMemory score was modeled as a function of education, time since baseline, sex, and a dummy regressor for test-retest effects as fixed effects. Baseline age by sex was included as a smooth term. Random intercepts were modeled per participant and dataset, with random slopes of retest effects and time from baseline at a dataset level. To test the effects on memory change, the model was re-run with an additional education\u0026nbsp;\u0026times; time interaction term. Education was operationalized either as mean-split or based on tertiary education in separate models.\u003c/p\u003e\n\u003cp\u003eBrain structure was modeled as a function of education, time since baseline, sex, and eTiv as fixed effects. Baseline age by sex was included as a smooth term. Random intercepts were modeled per participant, scanner, and dataset with random slopes of time included at a dataset level. To test effects on brain change, the model was re-run with an additional education\u0026nbsp;\u0026times; time interaction term. As control analyses, we reran the \u003cem\u003egamm\u003c/em\u003e models without eTiv as covariate. Additionally, we ran a linear mixed model as implemented in \u003cem\u003elme4,\u0026nbsp;\u003c/em\u003ewith eTiv being modeled as a function of education, sex, and baseline age as fixed effects, while site and dataset were included as random intercepts. Only the first observation of each participant was included, as eTiv and education are time-invariant variables. Alternative operationalizations of education and brain structure were tested in separate, but otherwise identical, models. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe used a fuzzy join algorithm, as implemented in \u003cem\u003efuzzyjoin\u0026nbsp;\u003c/em\u003e\u003csup\u003e110\u003c/sup\u003eto link pairwise MRI and cognitive observations as these were not necessarily collected on the same day. MRI observations were matched with the closest cognitive observations within a maximum time gap of 1 year. Unlinked observations were excluded from the analyses. The relationship between brain, memory level, and education was assessed with several models. \u003cem\u003eBrain level and memory level:\u0026nbsp;\u003c/em\u003eMemory was modeled\u0026nbsp;by brain structure, sex, time, eTiv,\u0026nbsp;and a dummy regressor for test-retest effects\u0026nbsp;as fixed effects. Baseline age by sex was introduced as a smooth term. Random intercepts were modeled per participant, scanner, and dataset with random slopes of retest and time modeled at a dataset level.\u0026nbsp;\u003cem\u003eBrain change and memory change:\u0026nbsp;\u003c/em\u003eAn additional brain\u0026nbsp;\u0026times;\u0026nbsp;time term was added to the model. \u003cem\u003eModerating effect of education on level \u0026ndash; level associations:\u003c/em\u003e Additional terms for\u0026nbsp;education and education\u0026nbsp;\u0026times;\u0026nbsp;brain were added in the first model. \u003cem\u003eModerating effect of education on change \u0026ndash; change associations:\u0026nbsp;\u003c/em\u003eA triple interaction term (brain\u0026nbsp;\u0026times; time \u0026times; education)\u0026nbsp;as well as its lower order components were added in the first model. \u003cem\u003eControl analyses:\u0026nbsp;\u003c/em\u003eA main education term \u0026ndash; without any interaction \u0026ndash; was added to the models to assess level \u0026ndash; level and change \u0026ndash; change associations between brain and memory, to test whether the strength of these associations was affected by education level.\u0026nbsp;As with other analyses, alternative operationalizations of education and memory-sensitive brain structure were tested in separate but comparable models.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCollaborators, G. B. D. D. F. 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). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2468-2667(21)00249-8\u003c/span\u003e\u003cspan address=\"10.1016/S2468-2667(21)00249-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolters, F. J. \u003cem\u003eet al.\u003c/em\u003e Twenty-seven-year time trends in dementia incidence in Europe and the United States: The Alzheimer Cohorts Consortium. Neurology 95, e519-e531 (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1212/WNL.0000000000010022\u003c/span\u003e\u003cspan address=\"10.1212/WNL.0000000000010022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, Y. \u003cem\u003eet al.\u003c/em\u003e 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). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2468-2667(23)00214-1\u003c/span\u003e\u003cspan address=\"10.1016/S2468-2667(23)00214-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGerstorf, D. \u003cem\u003eet al.\u003c/em\u003e 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\u0026ndash;34 (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/09567976221118541\u003c/span\u003e\u003cspan address=\"10.1177/09567976221118541\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLivingston, G. \u003cem\u003eet al.\u003c/em\u003e Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission. Lancet 404, 572\u0026ndash;628 (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(24)01296-0\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(24)01296-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuemoto, C. K. \u003cem\u003eet al.\u003c/em\u003e Risk factors for dementia in Brazil: Differences by region and race. Alzheimers Dement 19, 1849\u0026ndash;1857 (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/alz.12820\u003c/span\u003e\u003cspan address=\"10.1002/alz.12820\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLock, S. L., Chura, L. R., Dilworth-Anderson, P. \u0026amp; Peterson, J. Equity across the life course matters for brain health. Nat Aging 3, 466\u0026ndash;468 (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s43587-023-00413-1\u003c/span\u003e\u003cspan address=\"10.1038/s43587-023-00413-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRitchie, H., Samborska, V., Ahuja, N., Ortiz-Ospina, E. \u0026amp; Roser, M. (Published online at OurWorldinData.org. Retrieved from: '\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ourworldindata.org/global-education'\u003c/span\u003e\u003cspan address=\"https://ourworldindata.org/global-education'\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [Online Resource], 2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOpdebeeck, C., Martyr, A. \u0026amp; Clare, L. Cognitive reserve and cognitive function in healthy older people: a meta-analysis. Neuropsychol Dev Cogn B Aging Neuropsychol Cogn 23, 40\u0026ndash;60 (2016). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/13825585.2015.1041450\u003c/span\u003e\u003cspan address=\"10.1080/13825585.2015.1041450\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSepulcre, J. College education as a modulator of the aging brain. Nat Aging 1, 980\u0026ndash;981 (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s43587-021-00131-6\u003c/span\u003e\u003cspan address=\"10.1038/s43587-021-00131-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCabeza, R. \u003cem\u003eet al.\u003c/em\u003e Maintenance, reserve and compensation: the cognitive neuroscience of healthy ageing. Nat Rev Neurosci 19, 701\u0026ndash;710 (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41583-018-0068-2\u003c/span\u003e\u003cspan address=\"10.1038/s41583-018-0068-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNyberg, L., Lovden, M., Riklund, K., Lindenberger, U. \u0026amp; Backman, L. Memory aging and brain maintenance. Trends Cogn Sci 16, 292\u0026ndash;305 (2012). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tics.2012.04.005\u003c/span\u003e\u003cspan address=\"10.1016/j.tics.2012.04.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArenaza-Urquijo, E. M. \u003cem\u003eet al.\u003c/em\u003e 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\u0026ndash;79 (2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neurobiolaging.2017.06.016\u003c/span\u003e\u003cspan address=\"10.1016/j.neurobiolaging.2017.06.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGazzina, S. \u003cem\u003eet al.\u003c/em\u003e Education modulates brain maintenance in presymptomatic frontotemporal dementia. J Neurol Neurosurg Psychiatry 90, 1124\u0026ndash;1130 (2019). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/jnnp-2019-320439\u003c/span\u003e\u003cspan address=\"10.1136/jnnp-2019-320439\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDel Ser, T., Hachinski, V., Merskey, H. \u0026amp; Munoz, D. G. An autopsy-verified study of the effect of education on degenerative dementia. Brain 122 (Pt 12), 2309\u0026ndash;2319 (1999). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/brain/122.12.2309\u003c/span\u003e\u003cspan address=\"10.1093/brain/122.12.2309\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNyberg, L. \u003cem\u003eet al.\u003c/em\u003e Educational attainment does not influence brain aging. Proc Natl Acad Sci U S A 118 (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.2101644118\u003c/span\u003e\u003cspan address=\"10.1073/pnas.2101644118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStern, Y., Barnes, C. A., Grady, C., Jones, R. N. \u0026amp; Raz, N. Brain reserve, cognitive reserve, compensation, and maintenance: operationalization, validity, and mechanisms of cognitive resilience. Neurobiol Aging 83, 124\u0026ndash;129 (2019). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neurobiolaging.2019.03.022\u003c/span\u003e\u003cspan address=\"10.1016/j.neurobiolaging.2019.03.022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStern, Y. What is cognitive reserve? Theory and research application of the reserve concept. J Int Neuropsychol Soc 8, 448\u0026ndash;460 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStern, Y. \u003cem\u003eet al.\u003c/em\u003e A framework for concepts of reserve and resilience in aging. Neurobiol Aging 124, 100\u0026ndash;103 (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neurobiolaging.2022.10.015\u003c/span\u003e\u003cspan address=\"10.1016/j.neurobiolaging.2022.10.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLovden, M. \u003cem\u003eet al.\u003c/em\u003e No moderating influence of education on the association between changes in hippocampus volume and memory performance in aging. Aging Brain 4, 100082 (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.nbas.2023.100082\u003c/span\u003e\u003cspan address=\"10.1016/j.nbas.2023.100082\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLovden, M., Fratiglioni, L., Glymour, M. M., Lindenberger, U. \u0026amp; Tucker-Drob, E. M. Education and Cognitive Functioning Across the Life Span. Psychol Sci Public Interest 21, 6\u0026ndash;41 (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1529100620920576\u003c/span\u003e\u003cspan address=\"10.1177/1529100620920576\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeblova, D., Berggren, R. \u0026amp; 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). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.arr.2019.101005\u003c/span\u003e\u003cspan address=\"10.1016/j.arr.2019.101005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchneeweis, N., Skirbekk, V. \u0026amp; Winter-Ebmer, R. Does education improve cognitive performance four decades after school completion? \u003cem\u003eDemography\u003c/em\u003e 51, 619\u0026ndash;643 (2014). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13524-014-0281-1\u003c/span\u003e\u003cspan address=\"10.1007/s13524-014-0281-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlymour, M. M., Kawachi, I., Jencks, C. S. \u0026amp; 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\u0026ndash;537 (2008). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/jech.2006.059469\u003c/span\u003e\u003cspan address=\"10.1136/jech.2006.059469\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGorman, E. Does Schooling Have Lasting Effects on Cognitive Function? Evidence From Compulsory Schooling Laws. Demography 60, 1139\u0026ndash;1161 (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1215/00703370-10875853\u003c/span\u003e\u003cspan address=\"10.1215/00703370-10875853\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrinch, C. N. \u0026amp; Galloway, T. A. Schooling in adolescence raises IQ scores. Proc Natl Acad Sci U S A 109, 425\u0026ndash;430 (2012). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1106077109\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1106077109\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLager, A., Seblova, D., Falkstedt, D. \u0026amp; Lovden, M. Cognitive and emotional outcomes after prolonged education: a quasi-experiment on 320 182 Swedish boys. Int J Epidemiol 46, 303\u0026ndash;311 (2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ije/dyw093\u003c/span\u003e\u003cspan address=\"10.1093/ije/dyw093\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCourtin, E. \u003cem\u003eet al.\u003c/em\u003e Long-term effects of compulsory schooling on physical, mental and cognitive ageing: a natural experiment. J Epidemiol Community Health 73, 370\u0026ndash;376 (2019). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/jech-2018-211746\u003c/span\u003e\u003cspan address=\"10.1136/jech-2018-211746\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRitchie, S. J. \u0026amp; Tucker-Drob, E. M. How Much Does Education Improve Intelligence? A Meta-Analysis. Psychol Sci 29, 1358\u0026ndash;1369 (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0956797618774253\u003c/span\u003e\u003cspan address=\"10.1177/0956797618774253\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalhovd, K. B., Lovden, M. \u0026amp; Fjell, A. M. Timing of lifespan influences on brain and cognition. Trends Cogn Sci 27, 901\u0026ndash;915 (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tics.2023.07.001\u003c/span\u003e\u003cspan address=\"10.1016/j.tics.2023.07.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Hootegem, A., Rogeberg, O., Bratsberg, B. \u0026amp; Lyngstad, T. H. Correlation between cognitive ability and educational attainment weakens over birth cohorts. Sci Rep 13, 17747 (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-023-44605-6\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-44605-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalandri, I. L. \u003cem\u003eet al.\u003c/em\u003e Sex and Socioeconomic Disparities in Dementia Risk: A Population Attributable Fractions Analysis in Argentina. \u003cem\u003eNeuroepidemiology\u003c/em\u003e (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1159/000536524\u003c/span\u003e\u003cspan address=\"10.1159/000536524\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAman, Y. Modifiable risk factors of dementia in Latin America. Nat Aging (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s43587-024-00766-1\u003c/span\u003e\u003cspan address=\"10.1038/s43587-024-00766-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalhovd, K. B. \u003cem\u003eet al.\u003c/em\u003e Education and Income Show Heterogeneous Relationships to Lifespan Brain and Cognitive Differences Across European and US Cohorts. Cereb Cortex 32, 839\u0026ndash;854 (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/cercor/bhab248\u003c/span\u003e\u003cspan address=\"10.1093/cercor/bhab248\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParadela, R. S. \u003cem\u003eet al.\u003c/em\u003e 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). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2214-109X(24)00275-4\u003c/span\u003e\u003cspan address=\"10.1016/S2214-109X(24)00275-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFjell, A. M. \u003cem\u003eet al.\u003c/em\u003e 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\u0026ndash;40 (2014). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.pneurobio.2014.02.004\u003c/span\u003e\u003cspan address=\"10.1016/j.pneurobio.2014.02.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorsch-Supan, A. \u003cem\u003eet al.\u003c/em\u003e Data Resource Profile: the Survey of Health, Ageing and Retirement in Europe (SHARE). Int J Epidemiol 42, 992\u0026ndash;1001 (2013). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ije/dyt088\u003c/span\u003e\u003cspan address=\"10.1093/ije/dyt088\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalhovd, K. B. \u003cem\u003eet al.\u003c/em\u003e Healthy minds 0-100 years: Optimising the use of European brain imaging cohorts (\"Lifebrain\"). Eur Psychiatry 50, 47\u0026ndash;56 (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eurpsy.2017.12.006\u003c/span\u003e\u003cspan address=\"10.1016/j.eurpsy.2017.12.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehrbrodt, T., Gruber, S. \u0026amp; Wagner, M. Scales and Multi-Item Indicators in the Survey of Health, Ageing and Retirement in Europe. (Germany, 2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003egamm4: Generalized Additive Mixed Models using 'mgcv' and 'lme4'. R package version 0.2-6, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=gamm4\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=gamm4\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCadar, D. \u003cem\u003eet al.\u003c/em\u003e An International Evaluation of Cognitive Reserve and Memory Changes in Early Old Age in 10 European Countries. Neuroepidemiology 48, 9\u0026ndash;20 (2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1159/000452276\u003c/span\u003e\u003cspan address=\"10.1159/000452276\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalhovd, K. B. \u003cem\u003eet al.\u003c/em\u003e Brain aging differs with cognitive ability regardless of education. Sci Rep 12, 13886 (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-022-17727-6\u003c/span\u003e\u003cspan address=\"10.1038/s41598-022-17727-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeilbronner, R. L. \u003cem\u003eet al.\u003c/em\u003e 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\u0026ndash;1278 (2010). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/13854046.2010.526785\u003c/span\u003e\u003cspan address=\"10.1080/13854046.2010.526785\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTucker-Drob, E. M., Johnson, K. E. \u0026amp; Jones, R. N. The cognitive reserve hypothesis: a longitudinal examination of age-associated declines in reasoning and processing speed. Dev Psychol 45, 431\u0026ndash;446 (2009). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/a0014012\u003c/span\u003e\u003cspan address=\"10.1037/a0014012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrand, S., Deary, I. J. \u0026amp; Smith, P. Sex differences in cognitive abilities test scores: a UK national picture. Br J Educ Psychol 76, 463\u0026ndash;480 (2006). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1348/000709905X50906\u003c/span\u003e\u003cspan address=\"10.1348/000709905X50906\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEurostat. Eurostat regional yearbook. (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeary, I. J., Pattie, A. \u0026amp; Starr, J. M. The stability of intelligence from age 11 to age 90 years: the Lothian birth cohort of 1921. Psychol Sci 24, 2361\u0026ndash;2368 (2013). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0956797613486487\u003c/span\u003e\u003cspan address=\"10.1177/0956797613486487\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBratsberg, B., Fjell, A. M., Rogeberg, O. J., Skirbekk, V. F. \u0026amp; 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 U S A 121, e2412017121 (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.2412017121\u003c/span\u003e\u003cspan address=\"10.1073/pnas.2412017121\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThorp, J. G. \u003cem\u003eet al.\u003c/em\u003e Genetic evidence that the causal association of educational attainment with reduced risk of Alzheimer's disease is driven by intelligence. Neurobiol Aging 119, 127\u0026ndash;135 (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neurobiolaging.2022.07.011\u003c/span\u003e\u003cspan address=\"10.1016/j.neurobiolaging.2022.07.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnderson, E. L. \u003cem\u003eet al.\u003c/em\u003e Education, intelligence and Alzheimer's disease: evidence from a multivariable two-sample Mendelian randomization study. Int J Epidemiol 49, 1163\u0026ndash;1172 (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ije/dyz280\u003c/span\u003e\u003cspan address=\"10.1093/ije/dyz280\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharp, E. S. \u0026amp; Gatz, M. Relationship between education and dementia: an updated systematic review. Alzheimer Dis Assoc Disord 25, 289\u0026ndash;304 (2011). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/WAD.0b013e318211c83c\u003c/span\u003e\u003cspan address=\"10.1097/WAD.0b013e318211c83c\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrasby, K. L. \u003cem\u003eet al.\u003c/em\u003e The genetic architecture of the human cerebral cortex. Science 367 (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.aay6690\u003c/span\u003e\u003cspan address=\"10.1126/science.aay6690\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, W. \u003cem\u003eet al.\u003c/em\u003e 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 Brown University, 2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSameroff, A. J., Seifer, R., Baldwin, A. \u0026amp; Baldwin, C. Stability of intelligence from preschool to adolescence: the influence of social and family risk factors. Child Dev 64, 80\u0026ndash;97 (1993). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1467-8624.1993.tb02896.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1467-8624.1993.tb02896.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTucker-Drob, E. M. \u0026amp; 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\u0026ndash;979 (2014). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/a0035893\u003c/span\u003e\u003cspan address=\"10.1037/a0035893\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdwin, T. H. \u003cem\u003eet al.\u003c/em\u003e Trajectories of Occupational Cognitive Demands and Risk of Mild Cognitive Impairment and Dementia in Later Life: The HUNT4 70\u0026thinsp;+\u0026thinsp;Study. Neurology 102, e209353 (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1212/WNL.0000000000209353\u003c/span\u003e\u003cspan address=\"10.1212/WNL.0000000000209353\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKivimaki, M. \u003cem\u003eet al.\u003c/em\u003e Cognitive stimulation in the workplace, plasma proteins, and risk of dementia: three analyses of population cohort studies. BMJ 374, n1804 (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bmj.n1804\u003c/span\u003e\u003cspan address=\"10.1136/bmj.n1804\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEngvig, A. \u003cem\u003eet al.\u003c/em\u003e Effects of cognitive training on gray matter volumes in memory clinic patients with subjective memory impairment. J Alzheimers Dis 41, 779\u0026ndash;791 (2014). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3233/JAD-131889\u003c/span\u003e\u003cspan address=\"10.3233/JAD-131889\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEngvig, A. \u003cem\u003eet al.\u003c/em\u003e Effects of memory training on cortical thickness in the elderly. Neuroimage 52, 1667\u0026ndash;1676 (2010). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2010.05.041\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2010.05.041\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLovden, M. \u003cem\u003eet al.\u003c/em\u003e Spatial navigation training protects the hippocampus against age-related changes during early and late adulthood. Neurobiol Aging 33, 620 e629-620 e622 (2012). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neurobiolaging.2011.02.013\u003c/span\u003e\u003cspan address=\"10.1016/j.neurobiolaging.2011.02.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrathen, A. C. S. \u003cem\u003eet al.\u003c/em\u003e Cognitive and hippocampal changes weeks and years after memory training. Sci Rep 12, 7877 (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-022-11636-4\u003c/span\u003e\u003cspan address=\"10.1038/s41598-022-11636-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Lange, A. G., Brathen, A. C. S., Rohani, D. A., Fjell, A. M. \u0026amp; Walhovd, K. B. The Temporal Dynamics of Brain Plasticity in Aging. Cereb Cortex 28, 1857\u0026ndash;1865 (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/cercor/bhy003\u003c/span\u003e\u003cspan address=\"10.1093/cercor/bhy003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJudd, N. \u0026amp; Kievit, R. No effect of additional education on long-term brain structure \u0026ndash; a preregistered natural experiment in over 30,000 individuals. \u003cem\u003ebioRxiv\u003c/em\u003e (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1101/2024.05.17.594682\u003c/span\u003e\u003cspan address=\"10.1101/2024.05.17.594682\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonsang, E., Skirbekk, V. \u0026amp; Staudinger, U. M. As You Sow, So Shall You Reap: Gender-Role Attitudes and Late-Life Cognition. Psychol Sci 28, 1201\u0026ndash;1213 (2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0956797617708634\u003c/span\u003e\u003cspan address=\"10.1177/0956797617708634\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLipnicki, D. M. \u003cem\u003eet al.\u003c/em\u003e 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). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pmed.1002261\u003c/span\u003e\u003cspan address=\"10.1371/journal.pmed.1002261\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFry, A. \u003cem\u003eet al.\u003c/em\u003e Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population. Am J Epidemiol 186, 1026\u0026ndash;1034 (2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/aje/kwx246\u003c/span\u003e\u003cspan address=\"10.1093/aje/kwx246\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAckerman, P. L. Adult Intelligence: The Construct and the Criterion Problem. Perspect Psychol Sci 12, 987\u0026ndash;998 (2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1745691617703437\u003c/span\u003e\u003cspan address=\"10.1177/1745691617703437\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRitchie, S. J., Bates, T. C. \u0026amp; Deary, I. J. Is education associated with improvements in general cognitive ability, or in specific skills? Dev Psychol 51, 573\u0026ndash;582 (2015). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/a0038981\u003c/span\u003e\u003cspan address=\"10.1037/a0038981\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan, M. Y. \u003cem\u003eet al.\u003c/em\u003e Long-term prognosis and educational determinants of brain network decline in older adult individuals. Nat Aging 1, 1053\u0026ndash;1067 (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s43587-021-00125-4\u003c/span\u003e\u003cspan address=\"10.1038/s43587-021-00125-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGruber, S., Hunkler, C. \u0026amp; Stuck, S. Generating easySHARE: guidelines, structure, content and programming. (Munich: MEA, Max Planck Institute for Social Law and Social Policy, 2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBergmann, M., Kneip, T., De Luca, G. \u0026amp; Scherpenzeel, A. Survey participation in the Survey of Health, Ageing and Retirement in Europe (SHARE), Wave 1\u0026ndash;7. Based on Release 7.0.0.. (Munich: MEA, Max Planck Institute for Social Law and Social Policy, 2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB\u0026ouml;rsch-Supan, A. \u0026amp; Gruber, S. easySHARE. Release version: 8.0.0. SHARE-ERIC. (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.6103/SHARE.easy.800\u003c/span\u003e\u003cspan address=\"10.6103/SHARE.easy.800\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR: A language and environment for statistical computing. (R Foundation for Statistical Computing, Vienna, Austria., 2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB\u0026uuml;rkner, P.-C. brms: An R Package for Bayesian Multilevel Models Using Stan. Journal of Statistical Software 80(1), 1\u0026ndash;28 (2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi:10.18637/jss.v080.i01\u003c/span\u003e\u003cspan address=\"doi:10.18637/jss.v080.i01\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarpenter, B. \u003cem\u003eet al.\u003c/em\u003e Stan: A Probabilistic Programming Language. Journal of Statistical Software 76, 1\u0026ndash;32 (2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.18637/jss.v076.i01\u003c/span\u003e\u003cspan address=\"10.18637/jss.v076.i01\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHofman, M. D. \u0026amp; Gelman, A. The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research 15, 1593\u0026ndash;1623 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalhovd, K. B. \u003cem\u003eet al.\u003c/em\u003e Neurodevelopmental origins of lifespan changes in brain and cognition. Proc Natl Acad Sci U S A 113, 9357\u0026ndash;9362 (2016). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1524259113\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1524259113\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNilsson, L. G. \u003cem\u003eet al.\u003c/em\u003e The Betula prospective cohort study: Memory, health, and aging. Aging, Neuropsychology and Cognition 4, 1\u0026ndash;32 (1997). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/13825589708256633\u003c/span\u003e\u003cspan address=\"10.1080/13825589708256633\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNilsson, L.-G. \u003cem\u003eet al.\u003c/em\u003e Betula: A prospective cohort study on memory, health and aging. Aging, Neuropsychology, and Cognition.11, pp (2004). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/13825580490511026\u003c/span\u003e\u003cspan address=\"10.1080/13825580490511026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajaram, S. \u003cem\u003eet al.\u003c/em\u003e The Walnuts and Healthy Aging Study (WAHA): Protocol for a Nutritional Intervention Trial with Walnuts on Brain Aging. Front Aging Neurosci 8, 333 (2016). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnagi.2016.00333\u003c/span\u003e\u003cspan address=\"10.3389/fnagi.2016.00333\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVidal-Pineiro, D. \u003cem\u003eet al.\u003c/em\u003e Task-dependent activity and connectivity predict episodic memory network-based responses to brain stimulation in healthy aging. Brain Stimul 7, 287\u0026ndash;296 (2014). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.brs.2013.12.016\u003c/span\u003e\u003cspan address=\"10.1016/j.brs.2013.12.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBertram, L. \u003cem\u003eet al.\u003c/em\u003e Cohort profile: The Berlin Aging Study II (BASE-II). Int J Epidemiol 43, 703\u0026ndash;712 (2014). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ije/dyt018\u003c/span\u003e\u003cspan address=\"10.1093/ije/dyt018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGerstorf, D. \u003cem\u003eet al.\u003c/em\u003e Editorial. Gerontology 62, 311\u0026ndash;315 (2016). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1159/000441495\u003c/span\u003e\u003cspan address=\"10.1159/000441495\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShafto, M. A. \u003cem\u003eet al.\u003c/em\u003e 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). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12883-014-0204-1\u003c/span\u003e\u003cspan address=\"10.1186/s12883-014-0204-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIdland, A. V. \u003cem\u003eet al.\u003c/em\u003e Biomarker profiling beyond amyloid and tau: cerebrospinal fluid markers, hippocampal atrophy, and memory change in cognitively unimpaired older adults. Neurobiol Aging 93, 1\u0026ndash;15 (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neurobiolaging.2020.04.002\u003c/span\u003e\u003cspan address=\"10.1016/j.neurobiolaging.2020.04.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMueller, S. G. \u003cem\u003eet al.\u003c/em\u003e The Alzheimer's disease neuroimaging initiative. \u003cem\u003eNeuroimaging Clin N Am\u003c/em\u003e 15, 869\u0026ndash;877, xi-xii (2005). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.nic.2005.09.008\u003c/span\u003e\u003cspan address=\"10.1016/j.nic.2005.09.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCattaneo, G. \u003cem\u003eet al.\u003c/em\u003e The Barcelona Brain Health Initiative: A Cohort Study to Define and Promote Determinants of Brain Health. Front Aging Neurosci 10, 321 (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnagi.2018.00321\u003c/span\u003e\u003cspan address=\"10.3389/fnagi.2018.00321\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDagley, A. \u003cem\u003eet al.\u003c/em\u003e Harvard Aging Brain Study: Dataset and accessibility. Neuroimage 144, 255\u0026ndash;258 (2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2015.03.069\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2015.03.069\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiller, K. L. \u003cem\u003eet al.\u003c/em\u003e Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci 19, 1523\u0026ndash;1536 (2016). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nn.4393\u003c/span\u003e\u003cspan address=\"10.1038/nn.4393\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBreitner, J. C. S., Poirier, J., Etienne, P. E. \u0026amp; 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\u0026ndash;242 (2016). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.14283/jpad.2016.121\u003c/span\u003e\u003cspan address=\"10.14283/jpad.2016.121\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTremblay-Mercier, J. \u003cem\u003eet al.\u003c/em\u003e Open science datasets from PREVENT-AD, a longitudinal cohort of pre-symptomatic Alzheimer's disease. Neuroimage Clin 31, 102733 (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.nicl.2021.102733\u003c/span\u003e\u003cspan address=\"10.1016/j.nicl.2021.102733\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaMontagne, P. J. \u003cem\u003eet al.\u003c/em\u003e OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease. \u003cem\u003emedRxiv\u003c/em\u003e (2019). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1101/2019.12.13.19014902\u003c/span\u003e\u003cspan address=\"10.1101/2019.12.13.19014902\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKremen, W. S., Franz, C. E. \u0026amp; Lyons, M. J. Current Status of the Vietnam Era Twin Study of Aging (VETSA). Twin Res Hum Genet 22, 783\u0026ndash;787 (2019). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/thg.2019.125\u003c/span\u003e\u003cspan address=\"10.1017/thg.2019.125\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReuter, M., Schmansky, N. J., Rosas, H. D. \u0026amp; Fischl, B. Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61, 1402\u0026ndash;1418 (2012). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2012.02.084\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2012.02.084\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFischl, B., Sereno, M. I. \u0026amp; Dale, A. M. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 9, 195\u0026ndash;207 (1999). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1006/nimg.1998.0396\u003c/span\u003e\u003cspan address=\"10.1006/nimg.1998.0396\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFischl, B., Sereno, M. I., Tootell, R. B. \u0026amp; Dale, A. M. High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum Brain Mapp 8, 272\u0026ndash;284 (1999). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/(sici)1097-0193(1999)8:4\u0026lt;272::aid-hbm10\u0026gt;3.0.co;2-4\u003c/span\u003e\u003cspan address=\"10.1002/(sici)1097-0193(1999)8:4%3C272::aid-hbm10%3E3.0.co;2-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDale, A. M., Fischl, B. \u0026amp; Sereno, M. I. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179\u0026ndash;194 (1999). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1006/nimg.1998.0395\u003c/span\u003e\u003cspan address=\"10.1006/nimg.1998.0395\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDestrieux, C., Fischl, B., Dale, A. \u0026amp; Halgren, E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 53, 1\u0026ndash;15 (2010). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2010.06.010\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2010.06.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFischl, B. \u003cem\u003eet al.\u003c/em\u003e Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341\u0026ndash;355 (2002). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s0896-6273(02)00569-x\u003c/span\u003e\u003cspan address=\"10.1016/s0896-6273(02)00569-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRutherford, S. \u003cem\u003eet al.\u003c/em\u003e Charting brain growth and aging at high spatial precision. Elife 11 (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7554/eLife.72904\u003c/span\u003e\u003cspan address=\"10.7554/eLife.72904\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRutherford, S. \u003cem\u003eet al.\u003c/em\u003e The normative modeling framework for computational psychiatry. Nat Protoc 17, 1711\u0026ndash;1734 (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41596-022-00696-5\u003c/span\u003e\u003cspan address=\"10.1038/s41596-022-00696-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Rossum, G. \u0026amp; Drake, F. L. (CreateSpace, Scotts Valley, CA, 2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKia, S. M. \u003cem\u003eet al.\u003c/em\u003e Closing the life-cycle of normative modeling using federated hierarchical Bayesian regression. PLoS One 17, e0278776 (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0278776\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0278776\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBates, D., M\u0026auml;chler, M., Bolker, B. \u0026amp; Walker, S. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software 67, 1\u0026ndash;48 (2015). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.18637/jss.v067.i01\u003c/span\u003e\u003cspan address=\"10.18637/jss.v067.i01\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuznetsova, A., Brockhoff, P. B. \u0026amp; Christensen, R. H. B. lmerTest Package: Tests in Linear Mixed Effects Models. Journal of Statistical Software 82, 1\u0026ndash;26 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVidal-Pi\u0026ntilde;eiro, D. \u003cem\u003eet al.\u003c/em\u003e Reliability of structural brain change in cognitively healthy adult samples. \u003cem\u003ebioRxiv\u003c/em\u003e (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1101/2024.06.03.592804\u003c/span\u003e\u003cspan address=\"10.1101/2024.06.03.592804\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriedman, J. H., Hastie, T. \u0026amp; Tibshirani, R. Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software 33, 1\u0026ndash;22 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWickham, H. \u003cem\u003eElegant Graphics for Data Analysis, 2nd ed, Use R!\u003c/em\u003e, (Springer International Publishing, 2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMowinckel, A. M. \u0026amp; Vidal-Pi\u0026ntilde;eiro, D. Visualization of Brain Statistics With R Packages ggseg and ggseg3d. Advances in Methods and Practices in Psychological Science 3, 466\u0026ndash;483 (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1177/2515245920928009\u003c/span\u003e\u003cspan address=\"10.1177/2515245920928009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobinson, D., 2020. fuzzyjoin: Join Tables Together on Inexact Matching. (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5938408/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5938408/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWhy education is linked to higher cognitive function in aging is fiercely debated. Leading theories propose that education reduces brain decline in aging, 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\u0026thinsp;\u0026gt;\u0026thinsp;50 years, alongside 15.157 brain MRIs 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. While education has numerous benefits, the notion that it provides protection against cognitive or brain decline is not supported.\u003c/p\u003e","manuscriptTitle":"Reevaluating the Role of Education in Cognitive Decline and Brain Aging: Insights from Large-Scale Longitudinal Cohorts across 33 Countries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-10 10:37:50","doi":"10.21203/rs.3.rs-5938408/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"nm","sideBox":"Learn more about [Nature Medicine](http://www.nature.com/nm/)","snPcode":"","submissionUrl":"","title":"Nature Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e65adb83-d030-4c46-a5f2-8a73552789ac","owner":[],"postedDate":"February 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":43963328,"name":"Health sciences/Neurology/Neurological disorders/Dementia/Alzheimer's disease"},{"id":43963329,"name":"Scientific community and society/Social sciences/Psychology"}],"tags":[],"updatedAt":"2025-07-29T07:12:33+00:00","versionOfRecord":{"articleIdentity":"rs-5938408","link":"https://doi.org/10.1038/s41591-025-03828-y","journal":{"identity":"nature-medicine","isVorOnly":false,"title":"Nature Medicine"},"publishedOn":"2025-07-28 04:00:00","publishedOnDateReadable":"July 28th, 2025"},"versionCreatedAt":"2025-02-10 10:37:50","video":"","vorDoi":"10.1038/s41591-025-03828-y","vorDoiUrl":"https://doi.org/10.1038/s41591-025-03828-y","workflowStages":[]},"version":"v1","identity":"rs-5938408","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5938408","identity":"rs-5938408","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}