Are there individuals who resist episodic memory decline into older age? Analyzing 728,000 memory tests from 80,000 participants with at least 4 assessments, we introduce a simulation-calibrated framework to identify genuine memory stability. Across cohorts and models, ~10% of adults ≥70 years showed stable performance over a decade. In an MRI subgroup (n≈2,000), stable performers exhibited lower rates of brain atrophy across widespread regions, anchoring cognitive stability in structural brain maintenance. However, stability was often transient rather than trait-like: many individuals followed trajectories with extended plateaus of stable performance punctuated by episodes of accelerated decline. Accordingly, 54% showed at least one period of observed stability, averaging 10 years, whereas only 0.4% upheld stable performance over 24 years under the strictest definition. These findings are consistent with a complex-systems model of cognitive aging in which decline often reflects critical transitions rather than continuous erosion. ### Competing Interest Statement The authors have declared no competing interest. European Research Council, 283634, 725025, 313440 The Research Council of Norway The Research Council of Norway, https://ror.org/00epmv149, 324882 UiO:Life Science convergence environment Knut and Alice Wallenberg Foundation, https://ror.org/004hzzk67

Higher cognitive ability in late adolescence and young adulthood associate with reduced risk of dementia, but such assessments are influenced by educational selection. Whether specific cognitive abilities earlier in childhood associate with later dementia risk, independent of known associations with cardiovascular disease (CVD) and diabetes, remains unclear. We studied a Swedish population-representative birth cohort with cognitive testing at age 13 (n = 10,539 born in 1948). Dementia and somatic morbidity were ascertained from nationwide inpatient and cause-of-death registers through November 2025 (> 6 decades). Cox models estimated associations between childhood inductive reasoning, verbal and spatial ability scores and dementia, somatic morbidity, CVD and diabetes, each modelled as outcomes and as time-varying covariates in dementia models. During follow-up, 287 individuals (2.7

Some older adults show high episodic memory performance compared to same-age peers. It is not known whether their high function is caused by special brain features in aging, or whether superior memory has the same brain foundation throughout adult life. To address this, we measured hippocampal volume and atrophy, microstructural integrity by diffusion tensor imaging, and activity during an episodic memory encoding and retrieval task in cognitively healthy adults (n = 277, age 20.1–81.5 years). Atrophy was quantified by repeated MRIs (2–7 examinations, mean max follow-up time 9.3 years). Superior memory was associated with higher retrieval activity in the anterior hippocampus and less hippocampal atrophy. There were no significant age-interactions, suggesting stable correlates of superior memory function. Age-memory performance curves across the full age-range were similar for participants with high memory performance compared to those with normal and low performance. These trajectories were based on cross-sectional data but did not indicate preserved memory among the superior functioning older adults. In conclusion, the results confirm that aspects of hippocampal structure and function are related to superior memory, without evidence to suggest that the best performing older adults are characterized by special hippocampal features compared to their younger counterparts.

In neuroimaging research, tracking individuals over time is key to understanding the interplay between brain changes and genetic, environmental, or cognitive factors across the lifespan. Yet, the extent to which we can estimate the individual trajectories of brain change over time with precision remains uncertain. In this study, we estimated the reliability of structural brain change in cognitively healthy adults from multiple samples and assessed the influence of follow-up time and number of observations. Estimates of cross-sectional measurement error and brain change variance were obtained using the longitudinal FreeSurfer processing stream. Our findings showed, on average, modest longitudinal reliability with 2 years of follow-up. Increasing the follow-up time was associated with a substantial increase in longitudinal reliability, while the impact of increasing the number of observations was comparatively minor. On average, 2-year follow-up studies require ≈2.7 and ≈4.0 times more individuals than designs with follow-ups of 4 and 6 years to achieve comparable statistical power. Subcortical volume exhibited higher longitudinal reliability than cortical area, thickness, and volume. The reliability estimates were comparable with those estimated from empirical data. The reliability estimates were affected by both the cohort's age where younger adults had lower reliability of change and the preprocessing pipeline where the FreeSurfer's longitudinal stream was notably superior than the cross-sectional stream. Suboptimal reliability inflated sample size requirements and compromised the ability to distinguish individual trajectories of brain aging. This study underscores the importance of long-term follow-ups and the need to consider reliability in longitudinal neuroimaging research.

The Mirror Tracing Task (MTT) is a widely known test of motor learning. Participants are required to trace a star looking at their hand as a reflection in a mirror. In this work we introduce a new way of analysis beyond its simplicity. Three versions of the MTT were employed in this project: paper-based, online version, and an app version developed in the group, with over 3000 drawn stars and 170 participants. A generalization and standardization of the data is proposed to analyze all the possible stars and regular shapes. The proposed analysis method determines the distance between the drawn path and the mean star (the residuals), and the number of pixels drawn in a certain area (the density). These variables are measured for each unit of angle around the center of the star, providing us richer information about an MTT performance (Figure 1). Residuals and density were summarized as single values representing the overall MTT performance. The sum of the density for all the angles and the sum of the absolute value of the residuals are used, respectively. They both correlate with the number of times a person draws outside the border, becoming a good alternative to it providing, at the same time, more complex information. Together with task time, these three variables show a clear positive association with age (all p-values<0.001, see Figure 2). Through the study of the residuals and density as a function of the angle, areas of greatest difficulty for the participants can be identified. Moreover, a more refined analysis indicates that the results obtained in certain areas show a dependence on age (Figure 3). The proposed analysis provides an innovative approach to a simple task like the MTT. With this method, areas of special difficulty appear with age. These measures could be applied to the study of cognitive decline in aging or dementia, allowing us to look at the evolution of the performance with age and through the different trials.

Differences in the volumes of brain structures between individuals are often linked to various conditions, including Alzheimer's disease, schizophrenia, and overall brain health. However, it remains unclear to what extent these differences reflect individual levels present at young adulthood or diverging aging trajectories at later ages. In this study, we analyze the aging dynamics of the volume of six brain structures based on MRI scans from a large cross-cohort longitudinal sample of cognitively healthy adults (n = 8,311 with 18,520 MRIs, ages from 18 to 97 years). From general assumptions about structural brain dynamics and measurement noise, a stochastic dynamical model was fit to the data to estimate both the variability and persistence of structural changes across adulthood. Using this model, we calculated how much of the variance in individual volumetric differences can be attributed to stable levels from young adulthood versus systematic changes at older ages, as well as the theoretical sensitivity of longitudinal studies to detect individual differences in changes. The findings were as follows: 1) Before age 60 years, inter-individual differences in neuroanatomical volumes almost exclusively reflect stable differences between individuals, while the influence from systematic differences in rate-of-change increases thereafter; up to 40 % of the variation being due to differences in change at 80 years. In contrast, ventricular volume reflects differences in change from early adulthood. 2) Current brain-age models are unlikely to be sensitive to detect differences in aging trajectories. 3) Imaging studies have a low reliability to detect inter-individual brain change before age 60. After 60 years, the study reliability increases sharply with longer intervals between scans and more modestly with additional intermediate observations. In conclusion, it is critical to distinguish between stable levels from early adulthood and systematic differences in change when studying adult brain aging.

That some individuals fall below a functional threshold sooner than others, can be ascribed to differences in “brain maintenance”, slope of change, or variation in previous level, intercept. Intercept differences may be captured in the concept “brain reserve”. Searching for factors that modify outcomes, we need to distinguish how such factors associate with differences in level versus slope of brain and cognition. This necessitates longitudinal data from multiple cohorts since associations can be small and represent different conditions and covariates across samples. Unfortunately, data from longitudinal cohorts are often not readily sharable. The results of meta-and mega-analytic techniques for age-relationships of neuroimaging and other measures can be compared using data from the Lifebrain consortium (Walhovd et al. Eur Psychiatry. 2018 Jan;47:76-87). Meta-GAM, a method for meta-analysis of generalized additive models (Sørensen et al. NeuroImage 2021;224:117416), yields valid results for modeling non-linear relationships characteristic of lifespan trajectories. Using meta-GAM requires less harmonization of measures and enables well-powered cross-sample analysis by estimating the relationships of interest separately in each data location without sharing the raw data. Meta-GAM will be used to test the associations of modifiable and non-modifiable factors with intercepts and changes in brain and cognition across multiple cohorts. All cohorts will have brain MRI and measures of cognitive function, in addition to factors of interest depending on cohort, such as early life (e.g. birth weight, parental SES), genetics and adult SES and lifestyle variables (e.g. physical activity, dietary pattern). Preliminary results indicate that individual differences in the level of brain and cognition may appear more stable, larger, and more pervasive than differences in change across the lifespan (Walhovd et al. TICS,2023;27:10). New analyses will focus on the relative strength of associations between potentially modifiable factors, intercept and change of cortical volume and general cognitive ability. This initiative will allow collaboration and synergies in large-scale, multi-sample analyses of cohorts that cannot be shared due to regulatory, privacy or work burden concerns. Investigating how potentially modifiable factors are associated with both level and change may reduce the risk of ascribing undue importance to factors operating in older age.

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.

Neurodevelopmental origins of functional variation through the lifespan are acknowledged, but pathways need to be identified. The objectives of the project Set-to-change is to test whether and how early life environmental factors and genetic makeup regulate brain and cognition and its change, as well as neurocognitive plasticity in response to training through the lifespan. Preliminary analyses for the first months are presented. We investigate differences in brain and cognition and their change, in adult mono- (MZ) and dizygotic (DZ) twins (total n ∼220 individuals, age range 16-79 years, mean 36), with varying degrees of prenatal environmental variance, as indexed by their extent of discordance in birth weight (BW). Half of the sample trained in a novel navigation intervention utilizing true locomotion in virtual reality, assessed with brain MRI and cognitive measures at pre- and post- 10 weeks intervention in an AB/BA crossover design. Effects of training on hippocampus volume are analyzed using generalized additive mixed models (GAMM4) accounting for condition (pre-train, post-train, post-rest), sex, age (smooth), time, and time x age. Anatomical brainprints from T1 and T2W scans are used to assess effects of genetic (MZ/DZ) and early environmental (birth weight; BW) differences. Preliminary analyses indicate a positive effect of training on hippocampal volumes. Differences in behavioral level reached in training, related to differences in BW among MZ twins, but the higher BW twin tended to train more. Using “brainprints” (Valizadeh et al. Sci Rep 2018, 8, 5611) from multicontrast MRI data, we observed that across 10 weeks, self-similarity was high and could be used for perfect identification of one self and MZ twin, DZ-similarity also identifiable. Brainprint similarity at baseline varied as a function of BW in MZ twins (Figure 1). BW discordance in MZ twins (n for analysis: 27 pairs) related to degree of functional plasticity, as measured by more whole brain connectome change after training being higher with higher BW. Twin designs sampling early and current life environmental conditions experimentally, may further enhance our understanding of the impact of early life factors on current outcomes and in adulthood.

The Delis Kaplan Executive Functions system (D-KEFS) Color-Word Interference Test (CWIT) is a neuropsychological test of executive function. The CWIT, an expanded version of the Stroop test, includes contrast measures created by subtracting lower-level conditions from higher-level conditions. These measures do not have available Norwegian norms and have been criticized for their low reliability. For this reason, we present normative data for regression-based derived measures based on a large sample of healthy Norwegians aged 20-85 years and compare the regression measures to conventional contrast measures. The study sample comprised 1011 healthy participants between ages 20 and 85, pooled from three cohorts: the Dementia Disease Initiation cohort (DDI), the Oslo MCI cohort, and the Lifespan Changes in Brain and Cognition cohort (LCBC). Multiple regression analyses were used to model the effects of the lower-level reading or naming conditions on higher-level inhibition or inhibition/switching conditions. Conventional contrasts and regression-based derived measures were assessed for residual effects of demographics and lower-level CWIT conditions. Stability over time was examined in a sub-sample with available follow-up data. Conventional contrasts showed residual effects of demographics and basic functions. Analyses of stability over time showed ICCs in the poor to moderate range (range = 0.39-0.56) for conventional contrasts and somewhat higher estimates for the regression measures (range = 0.50-0.65). As such, the regression-based derived measures adequately adjust for demographics and lower-level conditions and show better stability over time compared to conventional D-KEFS contrast measures. To fully understand the clinical applicability of regression-based derived measures, future work should include clinical samples.

Grid cells are spatially modulated cells in the entorhinal cortex (EC) that fire in a hexagonally patterned grid which tiles the environment. These cells are assumed important in human spatial navigation. The EC is vulnerable to neurodegenerative processes in both normal aging and Alzheimer’s disease and decline in grid cell function may be a key factor in understanding age-related navigational decline. Recent work suggests that conjunctive grid and head direction cells can allow for the detection of grid-like activity based on movement direction. If moving in alignment with a hexagonal grid, EC activation should be higher than movement not aligned with the grid. The present study attempts replicate findings from previous studies of grid-like signals detected through fMRI. The sample included 64 (40 female, 24 male) adults, ages ranging from 18 to 78 (M = 37.67). Participants were subject to a fMRI grid cell paradigm, tasked to passively navigate through a room and remember the location of objects. The movement directions in the virtual environment were used to find the mean grid orientation for each participant set through a general linear model with the sixfold pattern as a regressor, and this was used as a regressor in a second GLM to calculate the grid magnitude, a measure of the stability of the grid codes. A one-sample t-test showed no significant grid code magnitudes (M = 0.002, SD = 0.091) compared to zero for a sixfold symmetry t(63) = 0.19, p = .85. For age-related analysis, the sample was divided into a younger and older sample through a median split using the median of 30.5 years. A Two Sample t-test did not show any difference between the younger (M = 0.021, SD = 0.089) and older (M = -0.017, SD = 0.090) groups, t(61.99) = -1.68, p = .097. Figure 1 shows the mean grid code magnitudes for both age groups. Preliminary results suggest no evidence for stable grid-like representations in the present sample. The present study is reasonably powered relative to the previous study, and possible reasons and limitations hindering identification of grid-like representations should be discussed and further researched.

Various studies have reported an association between physical activity and grey matter volumes. Some studies have suggested that this relationship may be moderated by sex, yet the direction is still under debate. Focusing on hippocampus and dorsolateral prefrontal cortex (dlPFC), we tested whether the association between regional grey matter volumes and self-reported physical activity differs between women and men. We examined this interaction in five European cohorts from the Lifebrain consortium (n = 1809; age range: 18-88 years). Effect sizes were first determined by linear models run separately for each cohort, then pooled across datasets in a random-effects meta-analysis. Contrary to our hypotheses, there was no evidence of a relationship between physical activity and hippocampal or dlPFC volumes, nor was there a moderation by sex. Our null findings raise the question of whether self-report questionnaires of physical activity, which commonly feature in big datasets, are sufficiently sensitive to capture a-presumably modest-association between physical activity levels and grey matter outcomes. We conclude that the reliance on self-report questionnaires of physical activity is sub-optimal for brain-behaviour analyses.

It is a widely held opinion that sleep is important for human brain health. Here we examine the evidence for this view, focusing on normal variations in sleep patterns. We discuss the functions of sleep and highlight the paradoxical implications of theories seeing sleep as an adaptive capacity versus the theory that sleep benefits clearance of metabolic waste from the brain. We also evaluate the proposition that sleep plays an active role in consolidation of memories. Finally, we review research on possible effects of chronic sleep deprivation on brain health. We find that the evidence for a causal role of sleep in human brain health is surprisingly weak relative to the amount of attention to sleep in science and society. While there are well-established associations between sleep parameters and aspects of brain health, results are generally not consistent across studies and measures, and it is not clear to what extent alterations in sleep patterns represent symptoms or causes. Especially, the proposition that long sleep (>8 hours) in general is beneficial for long-term brain health in humans seems to lack empirical support. We suggest directions for future research to establish a solid foundation of knowledge about a role of sleep in brain health based on longitudinal studies with frequent sampling, attention to individual differences, and more ecologically valid intervention studies.

An emerging biomarker of blood-brain barrier (BBB) permeability is the time of exchange (Tex) of water from the blood to tissue, as measured by multi-echo arterial spin labeling (ASL) MRI. This new non-invasive sequence, already tested in mice, has recently been adapted to humans and optimized for clinical scanning time. In this study, we studied the normal variability of Tex over age and sex, which needs to be established as a reference for studying changes in neurological disease. We evaluated Tex, cerebral blood flow (CBF) and arterial transit time (ATT) in 209 healthy adults between 26 and 87 years, over age and sex, using general linear models in gray matter, white matter, and regionally in cerebral lobes. After QC, 194 participants were included in the main analysis, and the results demonstrated that both gray matter (GM) and white matter (WM) BBB permeability was higher with higher age (Tex lower by 0.47 ms per year in GM [p < 0.05], and by 0.49 ms in WM, for females; no significant for males), with the largest Tex difference in the frontal lobes (0.64 ms decrease per year, p = 0.011, population average). CBF was lower with higher age in the GM (-0.71 mL/min/100g per year, p < 0.001, for females; -0.31 mL/min/100g per year, p < 0.05, for males). When correcting Tex models for CBF and ATT, effect of age on Tex disappears in the GM, but not in the WM (β=-0.28, p = 0.08). The CBF findings of this study are in line with previous studies, demonstrating the validity of the new sequence. The BBB water permeability variation over age and sex described in this study provides a reference for future BBB research.

Cognitive reserve (CR) and Brain Maintenance (BR) are constructs defined at a theoretical level (Stern et al., Neurobiol Aging, 2021 Apr, 124:100-103). Our aim was to propose a reproducible procedure to compare CR-like of BR-like mechanisms underlying interindividual differences in memory stability. Leveraging data from the Lifebrain consortia (Walhovd et al. Eur Psychiatry. 2018 Jan;47:76-87) we gathered information regarding 1) episodic memory (EM) stability, defined as those subjects showing no negative memory changes across two time point assessments (i.e., change ≥0), 2) brain structure and 3) brain functionality (resting-state functional magnetic resonance imaging [MRI]) changes). We designed a unified approach where at each step an analysis between EM and multimodal MRI-based measures provides a general metric, referring to either associations between EM and hippocampal (HC) volume changes, or to EM and fMRI connectivity changes (see Figure). This aimed to distinguish a ‘BM – Pathway 1’ identifying those subjects presenting a correspondence between both stability of EM and of HC volumes (i.e x≥0 & y≥0 quadrants of the upper left scatterplot) and a, ‘BM - pathway 2’ reflecting a correspondence between stability of EM and HPC fMRI functionality (i.e x≥0 & y≥0 quadrants of the bottom right scatterplot). Finally, a CR - pathway (‘deviant cases’ in Figure) was defined for subjects where a discrepancy between EM stability and their neural substrates was found (i.e. x>0 and y<0 quadrants in both scatterplots). Findings obtained from N = 532 participants (67.8 years at baseline, 294 women) revealed that N = 275 exhibited memory stability over time. Present analyses show that from those, N = 166 (60%) could be classified as BM -pathway 1 at the first step, whereas N = 109 (40%) cases were classified as CR - pathway. Further results from a subsample, indicate that CR - pathway cases exhibited a positive change in EM and negative in HC volumes, evidencing reductions in functional connectivity between HC and the Medial Prefrontal node from the dorsal Default Mode Network. The present approach combining structural and functional MRI to study CR and BM constructs shall provide new relevant empirical data helping to clarify the conceptual boundaries between these categories.

Brain atrophy is a key factor behind episodic memory loss in aging, but the nature and ubiquity of this relationship remains poorly understood. This study leveraged 13 longitudinal datasets, including 3,737 cognitively healthy adults (10,343 MRI scans; 13,460 memory assessments), to determine whether brain change-memory change associations are more pronounced with age and genetic risk for Alzheimer's Disease. Both factors are associated with accelerated brain decline, yet it remains unclear whether memory loss is exacerbated beyond what atrophy alone would predict. Additionally, we assessed whether memory decline aligns with a global pattern of atrophy or stems from distinct regional contributions. Our mega-analysis revealed a nonlinear relationship between memory decline and brain atrophy, primarily affecting individuals with above-average brain structural decline. The associations were stronger in the hippocampus but also spread across diverse cortical and subcortical regions. The associations strengthened with age, reaching moderate associations in participants in their eighties. While APOE ε4 carriers exhibited steeper brain and memory loss, genetic risk had no effect on the change-change associations. These findings support the presence of common biological macrostructural substrates underlying memory function in older age which are vulnerable to multiple age-related factors, even in the absence of overt pathological changes.

Current models of AD posit neurodegeneration and cognitive decline occur downstream in a pathophsyiological cascade initiated by amyloid (Aβ), yet lifespan research suggests the brain regions and cognitive functions impacted most by AD exhibit the steepest, steady decline rates across life. We hypothesised adult lifespan neurodegeneration in AD-vulnerable brain regions would predict memory decline rates detectable in healthy adults as they age, independent of Aβ. We combined MRI scans across three large longitudinal cohorts of cognitively healthy adults (age 30-96 years) to estimate brain change relative to the change expected given a person’s age (2-14 timepoints; 4125 scans of 1027 individuals; cohorts: LCBC, the Berkeley Aging Cohort Study [BACS]; ADNI [stable cognitively healthy]). We similarly estimated episodic memory change, as measured by the California Verbal Learning Test (LCBC and BACS; 2356 observations of 601 individuals). Using a set of features we previously determined classifies AD patients from controls (AUC=.952), we ran multivariate models to test associations between brain structure change and memory change. Specifically, we calculated the principal component of change across different subsets of brain features using a sliding window through AD feature importance, iteratively testing its association with memory change. In a subset with PET data (537 Aβ observations of 262 individuals), we tested whether brain-memory change associations were independent of Aβ. Finally, we tested the spatial correlation between adult lifespan brain change rates in only Aβ-negative individuals (adults >40 years confirmed Aβ-negative at every timepoint) and an AD-control effect size based on change. Associations between brain structure change and memory change were detectable in healthy adult lifespan data (p=7.0e -10 ), present in comparatively young adults (in 30–65-year-olds), and strongest in regions changing faster in AD. Correcting for Aβ did not attenuate brain-memory change associations, though Aβ was independently associated with more memory decline (p=2.7 -3 ). The spatial correlation between adult lifespan change rates (in Aβ-negative) and accelerated change in AD was r=.74, p=10 -63 . Our results show neurodegeneration in AD-sensitive regions exists independently of Aβ, and that such changes are not benign, but track with more memory decline across the healthy adult lifespan.

Lower episodic memory capability, as seen in development and aging compared with younger adulthood, may partly depend on lower brain network segregation. Here, our objective was twofold: (1) test this hypothesis using within- and between-network functional connectivity (FC) during episodic memory encoding and retrieval, in two independent samples (n = 734, age 7-82 years). (2) Assess associations with age and the ability to predict memory comparing task-general FC and memory-modulated FC. In a multiverse-inspired approach, we performed tests across multiple analytic choices. Results showed that relationships differed based on these analytic choices and were mainly present in the largest dataset,. Significant relationships indicated that (i) memory- modulated FC predicted memory performance and associated with memory in an age-invariant manner. (ii) In line with the so-called neural dedifferentiation view, task-general FC showed lower segregation with higher age in adults which was associated with worse memory performance. In development, although there were only weak signs of a neural differentiation, that is, gradually higher segregation with higher age, we observed similar lower segregation-worse memory relationships. This age-invariant relationships between FC and episodic memory suggest that network segregation is pivotal for memory across the healthy lifespan.

Structural brain changes underlie cognitive changes and interindividual variability in cognition in older age. By using structural MRI data-driven clustering, we aimed to identify subgroups of cognitively unimpaired older adults based on brain change patterns and assess how changes in cortical thickness, surface area, and subcortical volume relate to cognitive change. We tested (1) which brain structural changes predict cognitive change (2) whether these are associated with core cerebrospinal fluid (CSF) Alzheimer's disease biomarkers, and (3) the degree of overlap between clusters derived from different structural modalities in 1899 cognitively healthy older adults followed up to 16 years. We identified four groups for each brain feature, based on the degree of a main longitudinal component of decline. The minimal overlap between features suggested that each contributed uniquely and independently to structural brain changes in aging. Cognitive change and baseline cognition were associated with cortical area change, whereas higher baseline levels of phosphorylated tau and amyloid-beta related to changes in subcortical volume. These results may contribute to a better understanding of different aging trajectories.

Human cortical morphology is genetically programmed but also influenced by environment both in development and adulthood. Determining the timing of these influences across the lifespan is a key challenge. Here we test what makes genetically identical brains differ and converge. Mono- (MZ) and dizygotic (DZ) twins (n = 206, age 16-79 yrs) with known extent of birth weight (BW) discordance, had MRIs pre- and post- 10 weeks immersive virtual reality navigation training in a train-rest-rest/rest-train-rest-design, or as passive controls. As a measure of between-twin similarity, we calculated "brainprints" from 272 structural cortical features, to assess effects of genetic (MZ/DZ) and environmental variation at early (BW discordance) and later life stages (training status). Baseline brainprint similarity was higher in MZ than DZ twins, but greater BW discordance yielded less similarity in MZ (r = -.54, p < .0001), dominated by cortical area effects (t = -6.748, p < .0001). In contrast, training increased brainprint similarity of MZ relative to DZ twins (zygosity x training; t = -2.864, p =.0046), mostly by cortical curvature (t = -4.401, p<.0001). Follow-up analyses indicated training increased white matter curvature and surface area. The findings demonstrate that in adulthood, early life environmental difference persistently contributes to make the brains of genetically identical twins deviate, while concurrent environmental influence in the form of training still can cause their brainprints to converge at the grey-white-matter boundary. This indicates how these early and later environmental influences on the cortex can be distinguished, and how cortical characteristics can be modified. ### Competing Interest Statement The authors have declared no competing interest.