How environmental variation shapes the human cerebral cortex remains incompletely understood. Here we compare cortical brainprints - individual profiles of regional cortical morphology - in 210 twins (age 16-78 years) to distinguish prenatal, adult naturalistic and experimentally induced environmental variation from genetic contributions. Monozygotic twins showed higher brainprint similarity than dizygotic twins, consistent with genetic effects. Lasting influences of early environment were evident in lower brainprint similarity in monozygotic twin-pairs with greater birthweight discordances, driven by surface area across the cortical ribbon. In contrast, within-pair differences in adult weight and lifestyle had minimal influence. Still, a 10-week virtual-reality navigation intervention revealed training-induced changes in the gray-white interface, with curvature and area changes supported by microstructural reconfigurations. Relative brainprint similarity increased in monozygotic but diverged in dizygotic pairs following training. These findings show that distinct environmental influences acting at prenatal and adult stages are reflected in different features of the human cerebral cortex.

Alzheimer's disease (AD) neuropathological changes can be detected with blood-based biomarkers during the long preclinical phase that precedes clinical diagnosis. Tau phosphorylated at threonine 217 (p-tau217) has been found to closely correlate with brain Aβ burden. A recent large-scale cross-sectional study showed elevated p-tau217 concentrations in older individuals (Aarsland et al., 2025). This increase was higher in those with AD dementia and mild cognitive impairment (MCI), and lower in those with intact cognition and higher educational attainment. Thus, intact cognition and higher education may be associated with lower levels of AD neuropathological changes. Here we tested this hypothesis using longitudinal data from the population-based Betula study (n=1005; 1531 samples). The results revealed increases with increasing age over 10 years in p-tau217, where individuals with accelerated episodic-memory decline had the strongest increase. There were no differences in p-tau217 trajectories between individuals with lower or higher education or with well-maintained or age-typical decline in episodic memory. The lack of association with education was further replicated in the independent BioFINDER-2 cohort. These findings underscore the value of plasma p-tau217 for detecting early pathological changes in population-based settings but provide no support that individuals with well-maintained episodic memory or high educational attainment are spared from neuropathological changes.

Prevailing models typically treat cognitive aging as gradual, with rates of change evolving slowly over time and individual differences expressed mainly as variation in level, onset, and rate of decline. We test this view against an alternative in which individual trajectories are organised as extended plateaus of stability punctuated by brief episodes of accelerated loss. Harmonising 728,000 memory tests from 80,000 participants across three population cohorts, we introduce a simulation-calibrated framework to identify genuine memory stability. 10% of adults ≥ 70 showed stable performance over a decade, and in an MRI subgroup (n ≈ 2,000), these individuals exhibited lower rates of brain atrophy, linking cognitive stability to brain maintenance. However, analyses projecting trajectories backward in time showed that many late-life stable trajectories would imply implausibly low mid-life performance, indicating prior unobserved decline. Across individuals, 54% showed at least one stability period lasting 10 years, but only 0.4% maintained stability over 24 years under our criterion. Models based solely on gradual decline are insufficient to account for the observed temporal structure of memory change, and smooth population-level decline can emerge from punctuated individual dynamics. Together, the findings align with a complex-systems view where stability is often a transient state rather than a lifelong trait.

A key question in human neuroscience is to understand how individual differences in brain function relate to cognitive differences. However, the optimal condition of brain function to study between-person differences in cognition remains unclear. While many studies have developed objective biomarkers to accurately predict intelligence and general cognition, consensus on domain-specific markers has not yet emerged. Brain age has been proposed as a potential candidate, but recent research suggests that brain age offers minimal additional information on cognitive decline beyond what chronological age provides, prompting a shift toward approaches focused directly on cognitive prediction. Using a deep learning approach, we evaluated the predictive power of the functional connectome during various states (resting state, movie-watching, and n-back) on episodic memory and working memory performance. Our findings show that connectomes during tasks, especially during movie-watching, predict individual differences across cognitive domains, while resting state connectomes predict episodic memory meaningfully. Furthermore, individuals with a negative brain cognition gap (where brain predictions underestimate actual performance) exhibited lower physical activity and higher cardiovascular risk compared to those with a positive gap. This shows that knowledge of the brain cognition gap provides insights into factors contributing to cognitive resilience. Further, lower PET-derived measures of dopamine binding were linked to a greater brain cognition gap, mediated by regional functional variability. Together, our findings highlight the importance of brain state in connectome-based cognitive prediction and introduce the brain cognition gap as a potentially informative, dopamine-modulated marker of vulnerability to compromise brain function.

Cognitive aging is a highly heterogeneous process, with some individuals preserving stable cognitive performance across the lifespan while others exhibiting pronounced decline. This marked interindividual variability indicates that chronological age alone is a poor predictor of cognitive health. Rather than reflecting uniform degeneration, cognitive aging emerges from divergent biological trajectories spanning molecular, cellular, and network levels. In this review, we synthesize emerging biological hallmarks of healthy cognitive aging, emphasizing studies that characterize longitudinal cognitive trajectories in humans or distinguish aged individuals who retain learning capacity from those who do not. We focus on the medial temporal lobe, a region critical for episodic memory and spatial navigation, and examine how variability in its integrity contributes to distinct cognitive outcomes. Across species, convergent evidence suggests that cognitive decline is more closely linked to alterations in network regulation and synaptic plasticity than to overt neuronal loss. We identify key mechanisms shaping individual trajectories, including large-scale network organization, excitation-inhibition balance, neuromodulatory tone, glial and vascular regulation, adult hippocampal neurogenesis, and cellular homeostasis. These processes operate within an interconnected system in which disruptions in core regulatory mechanisms can propagate across levels of organization. Together, this synthesis supports a system-level framework in which cognitive resilience depends on the preservation of coordinated network dynamics. We advocate for longitudinal, multidimensional approaches to identify early shifts in regulatory balance and inform strategies to maintain cognitive function across the lifespan.

Several aging-related brain changes have been associated with unsuccessful cognitive aging, including dopamine decline, increased astrocyte reactivity, and cerebral small-vessel disease (SVD). We hypothesized that dopamine decline is exacerbated in older adults with higher measures of astrocyte reactivity and cerebral SVD, and that reduced dopamine integrity would be the strongest predictor of lower cognitive performance. Healthy adults (n = 55, ages: 60-79 years) underwent positron emission tomography with ligands 18F-FE-PE2I to estimate levels of dopamine transporters (DAT) and 11C-L-deprenyl-D2 to estimate levels of monoamine oxidase B (MAO-B)-a protein expressed to some degree by neurons but mainly by astrocytes. Cerebral SVD was assessed by white matter lesion volumes from magnetic resonance images. General cognition was evaluated via tests of episodic memory, working memory, and perceptual speed. Contrary to expectations, increased MAO-B levels (indicative of astrocyte reactivity) were associated with higher DAT availability (r = 0.53, p < 0.001) and reduced white matter lesion volumes (r = -0.33, p = 0.021). Reduced DAT availability was more strongly related to reduced MAO-B (r = 0.47, p < 0.001) than white matter lesion volumes (r = -0.22, p > 0.05), and only DAT was a significant predictor of cognition (r = 0.36, p = 0.032). These findings underscore the critical role of dopamine for cognition and indicate reduced glial function to underlie dopaminergic losses.

Cognitive reserve (CR) and brain maintenance (BM) reflect better than expected cognition despite brain pathology and minimal age-related brain changes that explain stable cognition, respectively. Despite being commonly used, joint quantification of these concepts has been limited; our aim is to derive longitudinal CR and BM measures and investigate CR’s relationship with education and functional connectivity. We analyzed longitudinal data from 451 participants (241 female, agemean = 68.5 years, follow-upmean = 4.2 years). From a hippocampus change-memory change regression, we modeled BM as memory stability relative to hippocampal stability and CR as better-than-expected memory stability relative to hippocampal atrophy. We examined whether education and resting-state functional connectivity (Default Mode, Executive Control and anterior Salience Network) (1) moderated the hippocampal-memory relationship using linear mixed effects models (LMEs), (2) moderated the hippocampus change-memory change relationship using linear regressions, and (3) were associated with our CR measure using LMEs. Analyses adjusted for demographics and MRI scanner/cohort, using false discovery rate (FDR) for multiple comparisons. From a significant hippocampus change-memory change relationship (β = 0.130, pFDR = 0.016, r2 = 0.282), we derived continuous CR and BM measures, providing subject-level estimates. Salience Network connectivity moderated the hippocampal-memory relationship (β = − 0.070, pFDR = 0.046, marginal r2 = 0.179). No variable moderated the hippocampus change-memory change relationship or correlated with our CR measure, consistent with the measure’s longitudinal definition. Our CR and BM measures refine phenotyping of aging trajectories. CR may be expressed through increased Salience Network connectivity, preserving memory level in the presence of hippocampal atrophy. Future work should delineate longitudinal CR mechanisms that decouple memory change from hippocampal atrophy.

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

ObjectiveGiven the clinical heterogeneity of Parkinson’s disease (PD), identification of early -stage subgroups with shared non-motor symptom (NMS) profiles may clarify its pathophysiology. This study used latent-profile analyses (LPA) to define subgroups based on sleep disturbances, cognitive performance and neuropsychiatric symptoms, and examined dopaminergic function and brain volume differences between them.MethodsWe analyzed data from 51 cognitively normal non-PD older adults and 105 early-stage PD participants from the iPARK trial, including 19 who underwent [11C]-raclopride PET/MR. Participants completed the Hospital Anxiety and Depression Scale, the short version of the Karolinska Sleep Questionnaire and a battery of neuropsychological tests. LPA were used in PD to identify subgroups based on NMS profiles, which were then characterized and examined in relation to dopaminergic integrity and brain morphology.ResultsLPA identified a two-cluster solution as the best fit. Group 1 (N = 49) showed poorer working memory, executive function and processing speed along with greater daytime sleepiness, depression and anxiety. Group 2 (N = 56) exhibited less affected cognitive function and minimal NMS. Groups were similar in demographics, disease duration, motor symptom severity and medication, but differed on UPDRS-1 NMS. Group 1 demonstrated significantly reduced [11C]-raclopride binding potential compared to Group 2 in the left putamen at both ROI- and voxel-wise analysis.ConclusionThese findings indicate clinically distinct subgroups in early-stage PD. Greater NMS burden is linked to impaired dopaminergic integrity, suggesting a potential neurobiological signature. Early identification of such subgroups may improve understanding of disease heterogeneity and support personalized management and interventions.Clinical trial registrationhttps://clinicaltrials.gov/study/NCT03680170?id=NCT03680170&rank=1, identifier (NCT03680170).

Individual differences in the volumes of brain structures 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 from young adulthood or diverging aging trajectories from later ages. In this study, we analyze the aging dynamics of the volumes of six brain structures based on magnetic resonance imaging (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 fitted 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 of volumetric differences between individuals 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 change. 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 50% 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 low reliability in detecting inter-individual brain changes before age 60 years. After 60 years, the study reliability increases sharply with longer intervals between scans and more modestly with additional intermediate observations. In conclusion, our results reinforce the view that it is critical to distinguish stable early adulthood levels from systematic differences in change when studying adult brain aging.

Age-related loss of dopamine (DA) integrity has been linked to cognitive decline. Relatedly, education and leisure activity engagement have been highlighted as neurocognitive protective factors, but their associations with DA integrity remain poorly understood. Using Bayesian structural equation modeling, we analyzed longitudinal data from the Cognition, Brain, and Aging (COBRA) prospective cohort study with 181 older adults at baseline to examine correlations among DA D2-like receptor (DRD2) availability in the caudate and putamen, measured using [11C]raclopride positron emission tomography (PET), cognition (working memory, episodic memory, and perceptual speed), education, and self-reported physical, cognitive, and social leisure activity measures. Our research questions target whether (i) education or leisure activities are associated with baseline levels or 5-year changes in DRD2 availability; (ii) changes in leisure activities covary with DRD2 changes; and (iii) education or leisure activities moderate DRD2-cognition change-change correlations. Results showed declines in DRD2 availability in the caudate and putamen, with weak overall DRD2-cognition change-change correlations. For both baseline levels and changes in DRD2 availability, the associations with education and leisure activities were uniformly negligible or small and not strongly supported. Neither education nor leisure activities moderated DRD2-cognition change-change correlations.

A key question in human neuroscience is to understand how individual differences in brain function relate to cognitive differences. However, the optimal condition of brain function to study between-person differences in cognition remains unclear. While many studies have developed objective biomarkers to accurately predict intelligence and general cognition, consensus on domain-specific markers has not yet emerged. Brain age has been proposed as a potential candidate, but recent research suggests that brain age offers minimal additional information on cognitive decline beyond what chronological age provides, prompting a shift toward approaches focused directly on cognitive prediction. Using a deep learning approach, we evaluated the predictive power of the functional connectome during various states (resting state, movie-watching, and n-back) on episodic memory and working memory performance. Our findings show that connectomes during tasks, especially during movie-watching, predict individual differences across cognitive domains, while resting state connectomes predict episodic memory meaningfully. Furthermore, individuals with a negative brain cognition gap (where brain predictions underestimate actual performance) exhibited lower physical activity and higher cardiovascular risk compared to those with a positive gap. This shows that knowledge of the brain cognition gap provides insights into factors contributing to cognitive resilience. Further, lower PET-derived measures of dopamine binding were linked to a greater brain cognition gap, mediated by regional functional variability. Together, our findings highlight the importance of brain state in connectome-based cognitive prediction and introduce the brain cognition gap as a potentially informative, dopamine-modulated marker of vulnerability to compromise brain function.

Substantial heterogeneity in cognitive ageing is well documented. Such heterogeneity has been attributed to individual differences in brain maintenance - i.e., the relative preservation of neural resources in ageing. However, large-scale longitudinal evidence is currently lacking. In this study, we pooled data from three longitudinal population-based Swedish cohorts (total N = 1 356, 60-93 years at baseline, maximum follow-up duration: 7 years) to assess whether global brain maintenance is associated with better preserved cognition in ageing, and to identify lifestyle predictors of brain maintenance. In each cohort, global brain integrity was indexed by the volume of the lateral ventricles, and general cognitive function based on a principal component analysis of four age-sensitive cognitive domains. Participants were classified into subgroups of low (i.e., 'aged') versus high (i.e., 'youth-like') brain integrity based on comparison to ventricular volume estimates from a younger reference sample (N = 60, 25-55 years). Across cohorts, 881 individuals (64.97%) were classified into the high brain integrity subgroup at baseline and 409 individuals (49.82%) over the follow-up. Maintenance of more youth-like brain integrity was associated with better baseline cognition and less cognitive decline longitudinally. Moreover, lower cardiovascular disease risk and the absence of diabetes predicted brain maintenance at baseline and over the follow-up. These findings underscore general brain maintenance as a key determinant of cognitive ageing and highlight the importance of managing cardiovascular and metabolic disease risk factors for promotion of brain and cognitive health in later life.

We still know relatively little about how the human brain supports intelligence. I this personal view I argue that adopting the framework of neurocognitive component processes (NCP) might advance the current state of knowledge. Integration of information processing across distributed brain regions is proposed as a potential NCP, and some possible clinical implications of adopting the NCP framework are outlined.

Normal aging is associated with decline in dopamine function. Factors associated with individual differences in dopamine decline rates remain unclear but are important to map to spare dopamine-related functions, such as cognition. Here we focused on manifestations of cerebral small-vessel disease from magnetic resonance imaging (white-matter lesions, lacunes, and perivascular space dilation) and vascular risk factors (e.g., hypertension, body mass index (BMI), and hyperlipidemia). We assessed striatal dopamine D2-like receptor (DRD2) reductions across five years in healthy, older adults (n = 129, ages: 64-68 years at baseline) using 11C-raclopride/positron emission tomography. Manifestations of confluent lesions and lacunes at baseline had additive effects on DRD2 decline. Individuals with both manifestations showed fastest DRD2 decline rates (∼ -4 %), followed by those with one manifestation (∼ -2 %), whereas individuals spared of confluent lesions and lacunes showed stable DRD2 levels over time (∼ 0 % change). Furthermore, individuals with confluent lesions or lacunes showed more marked decline in perceptual speed performance, as compared to individuals spared of these manifestations (p < 0.05). Higher systolic blood pressure and lower BMI at baseline were associated with faster 5-year DRD2 decline in the putamen (r = -0.17, p < 0.05) and caudate (r = 0.23, p < 0.05), respectively. Together, confluent lesions and lacunes explained up to 8 % of striatal DRD2 change, and up to 10 % when adding hypertension and BMI to the model. These findings suggest that hallmarks of SVD and certain vascular risk factors predispose faster DRD2 decline in aging and may thus serve as factors to consider in future interventions.

Long-term exposure to ambient air pollution has recently been highlighted as a modifiable risk factor for dementia. However, the mechanisms underlying these associations still remain unclear. The goal of this study was to investigate the associations between air pollution and neuroimaging correlates in a sample of middle-aged and older adults. This study used data from the Betula Project, which is a longitudinal study on aging, memory and dementia in Umeå, Northen Sweden. Participants were aged 50-81 years old. For air pollution measures, data were available for local and total fine ambient particulate matter (PM 2.5 , PM 10 ) and black carbon (BC) from vehicle exhaust and wood-smoke. Participants were scanned in a 3T Discovery 750 (General Electric) MR-scanner. All T1-images were processed through a longitudinal pipeline in Freesurfer ver 7.11. MRI outcomes included total grey volume, cortex thickness, ventricle volume, and hippocampus volume. After removing participants due to incorrect hippocampus segmentation or missing values in air pollution or nuisance regressors 249 participants were included in the analyses. Partial correlations were performed, adjusting for age, sex and education. Additional analyses were conducted, stratifying by APOE4 status and sex. The analyses showed that smaller hippocampal volume was associated with higher levels of all three air pollution measures (PM 2.5 (r = -0.159, p = 0.012), PM 10 (r = -0.125, p = 0.048), and BC (r = -0.148, p = 0.019)). In the APOE4 non-carrier group, smaller hippocampal volume was associated with higher PM2.5 (r = -159, p = 0.034), whereas this association was not significant among the APOE4 carriers (r = -0.161, p = 0.178). The association between smaller hippocampal volume and PM2.5 was more pronounced among women (r = -0.168, p = 0.052) compared to men (r = -0.150, p = 0.112) This study shows that ambient air pollution is associated with smaller hippocampal volume, and this association appears to be more pronounced among APOE4 non-carriers and women. These results shed light on the mechanism through which air pollution may increase the risk for cognitive decline and dementia, with important implications for risk reduction initiatives.

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.

Although age differences in the dopamine system have been suggested to contribute to age-related cognitive decline based on cross-sectional data, recent large-scale cross-sectional studies reported only weak evidence for a correlation among aging, dopamine receptor availability, and cognition. Regardless, longitudinal data remain essential to make robust statements about dopamine losses as a basis for cognitive aging. We present correlations between changes in D2/3 dopamine receptor availability and changes in working memory measured over 5 yr in healthy, older adults (n = 128, ages 64 to 68 yr at baseline). Greater decline in D2/3 dopamine receptor availability in working memory-relevant regions (caudate, middle frontal cortex, hippocampus) was related to greater decline in working memory performance in individuals who exhibited working memory reductions across time (n = 43; caudate: rs = 0.494; middle frontal cortex: rs = 0.506; hippocampus; rs = 0.423), but not in individuals who maintained performance (n = 41; caudate: rs = 0.052; middle frontal cortex: rs = 0.198; hippocampus; rs = 0.076). The dopamine-working memory link in decliners was not observed in the orbitofrontal cortex, which does not belong to the core working memory network. Our longitudinal analyses support the notion that aging-related changes in the dopamine system contribute to working memory decline in aging.