Genome-wide association studies (GWAS) have advanced the quest to understand how specific genetic variants influence human brain structure and function. Recent work has identified hundreds of common variants associated with subcortical brain volumes, sparking interest in how these genetic markers overlap across brain networks. While this can be estimated by hierarchical clustering of the genetic correlation matrix to identify modular patterns of shared architecture, no brain-wide maps of these effects are available. To address this, we computed polygenic scores (PGS) from loci associated with ten brain volume regions of interest (ROIs): nine major subcortical structures and intracranial volume, with each locus weighted by its association with regional volume. In an independent sample from the discovery GWAS, we performed large-scale segmentation of 3D volumetric T1-weighted MRI scans using voxel-based morphometry (VBM) to map 3D profile of regions where gray matter volume (GMV) was associated with each PGS. We found statistically significant, localized effects for PGS defined for the amygdala, thalamus, and basal ganglia, but PGS for brainstem volume was associated with widespread differences throughout the brain. These brain-wide maps reveal patterns consistent with both localized and distributed genetic influences, offering a novel approach to interpret the genomic architecture of brain structure.

Physical activity is believed to positively influence brain health and cognition and is considered a modifiable lifestyle factor that may protect against cognitive decline and neurodegeneration. In this observational study, we investigated the cross-sectional and longitudinal effects of self-reported total and moderate-to-vigorous physical activity on cognitive scores on the Trail Making Test (TMT-A and TMT-B), hippocampal volume, and Brain Age Gap Estimate (BrainAGE) in a large population-based cohort from the LIFE-Adult Study (n=2576). Furthermore, we examined the effect of objectively measured physical activity on brain structure in a subgroup with available accelerometry data (n=227). Multiple linear regression analyses did not show any positive effects of self-reported or objectively measured physical activity on hippocampal volume or processing speed and executive function. Longitudinal path analyses suggested a potential for reverse causation, where a higher BrainAGE at baseline was associated with lower physical capacity at follow-up. Additionally, we observed an age-related bias in the self-reporting of physical activity, indicating that older individuals tend to overestimate their level of activity. Future interventions targeting middle-aged adults may be necessary to raise awareness of potential misperception and encourage increased physical activity.

Background: Reliable staging of early memory decline is essential for identifying individuals at risk for Alzheimer’s disease. The Stages of Objective Memory Impairment (SOMI) framework provides a clinically scalable tool for characterizing episodic memory loss, yet its neurobiological validity remains underexplored. Recent advances in plasma biomarkers (e.g., p-tau217, p-tau181, Aβ42/40) offer emerging blood-based alternatives to CSF and PET, although their diagnostic implementation continues to evolve. Here, we examine whether SOMI stages reflect widespread brain aging, as indexed by BrainAGE, a structural MRI–based biomarker quantifying deviation from normative aging trajectories. While previous studies have linked SOMI to hippocampal atrophy and tau pathology, no study to date has examined its association with a global MRI-derived biomarker of systemic brain aging. Methods: In a well-characterized cohort of 119 older adults on the Alzheimer’s disease continuum, we evaluated whether higher SOMI stages were linked to elevated BrainAGE scores and examined whether this association remained significant after adjusting for age, sex, education, and hippocampal volume. Results: Higher SOMI stages were robustly associated with elevated BrainAGE scores, indicating accelerated neurobiological aging. This relationship remained significant after adjusting for covariates and was confirmed in sensitivity analyses. Notably, a marked discontinuity in BrainAGE emerged between SOMI stages 0–2 and 3–5, aligning with the theoretical transition from retrieval to storage impairment, long recognized as a turning point in prodromal Alzheimer’s disease. Conclusions: These findings validate SOMI as a low-cost, non-invasive behavioral marker of systemic brain health. By linking cognitive staging to global neuroimaging biomarkers, our study supports SOMI’s translational utility for large-scale screening, clinical trial stratification, and early intervention planning in Alzheimer’s disease.

Abstract Cross-sectional studies suggest associations between physical exercise and white matter in older adults, but evidence from randomized controlled trials is scarce. Neurite orientation dispersion and density imaging indices are biophysically informed metrics of white matter microstructure. Here, we tested whether a remotely delivered, 8-week multicomponent physical exercise intervention impacts neurite density (NDI) and orientation dispersion (ODI) across major white matter tracts in older adults. This secondary analysis of a randomized controlled trial included participants with available diffusion MRI data (n = 66; age: 66.4 ± 3.6 y; 43 females). Participants were randomized to a multicomponent exercise (PAG, n = 34) or an active control (CON, n = 32) intervention. Intervention effects on NDI/ODI were tested using linear mixed-effects and Bayesian multilevel models adjusted for age and sex. A significant Timepoint × Group interaction was observed for NDI (p = 0.003) but not for ODI (p = 0.785), further confirmed in Bayesian analyses for 22 white matter tracts, indicating a greater increase in NDI in the PAG. The standardized composite VO 2 max score increased from pre- to post-intervention within the PAG, although the Timepoint × Group interaction was not significant (p = 0.079). Across all participants, pre-to-post changes in mean NDI were positively correlated with changes in VO 2 max, but this association did not differ between groups. Our results indicate that white matter microstructure remains responsive to short-term, multicomponent physical exercise in older adults. Significance statement White matter microstructure deteriorates with aging. Given the ample health benefits of physical exercise, we investigated whether it can also improve white matter microstructural indices. In this randomized controlled trial, an 8-week remotely delivered multicomponent exercise program increased an MRI-based index of white matter tissue density across major tracts in older adults. These changes were not observed in an active control group. An index of white matter tissue orientation did not show intervention-related changes. Our findings provide experimental evidence that white matter microstructure remains sensitive and responsive to short-term multicomponent physical exercise in later life.

Positive and negative schizotypy reflect distinct patterns of subclinical traits in the general population associated with neurodevelopmental and schizophrenia-spectrum pathologies. Yet, a comprehensive characterization of the unique and shared neuroanatomical signatures of these schizotypy dimensions is lacking. Leveraging 3D brain MRI data from 2730 unmedicated healthy individuals, we identified neuroanatomical profiles of positive and negative schizotypy and systematically compared them with disorder-specific, microarchitectural, neurotransmitter-level, and connectome measures. Positive and negative schizotypy were associated with distinct cortical signatures, of predominantly thinner frontal and thicker paralimbic cortical areas, respectively. These cortical signatures of positive and negative schizotypy were differentially linked to brain-wide cortical patterns of schizophrenia-spectrum (clinical high-risk for psychosis, schizophrenia) and neurodevelopmental conditions (ADHD, autism spectrum disorder and 22q11.2 deletion syndrome). Additionally, the positive and negative schizotypy-related cortical profiles mapped onto different local attributes of gene expression, cortical myelination, D1, and histamine receptor distributions. Network models further showed that positive and negative schizotypy cortical signatures were spatially associated with cortical hubs, suggesting that highly interconnected regions are more vulnerable to the morphological differences associated with both schizotypy dimensions. Finally, predominantly sensorimotor-to-association and paralimbic areas emerged as epicenters with connectivity profiles significantly linked to the schizotypy-related cortical patterns. Collectively, this study identified cortical signatures of positive and negative schizotypy traits that are embedded along multiple scales of cortical organization and neuropsychiatric pathologies. Our work yields novel insights into how neurobiology and brain architecture may guide neuroanatomical vulnerability and resilience to psychopathology in the general population.

Magnetic Resonance Imaging (MRI) derived brain age varies substantially between individuals, but it remains unclear whether early deviations from normal brain ageing precede future cognitive decline and whether they provide predictive value beyond conventional MRI measures. Here, we investigated whether MRI-derived brain age gap estimation (BrainAGE) identifies early structural brain ageing differences among cognitively normal individuals who later develop mild cognitive impairment (MCI) or dementia. We analysed longitudinal structural MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and replicated the main findings in the population-based Kuopio Osteoporosis Risk Factor and Prevention Study (OSTPRE). Individuals who later converted to MCI or dementia had higher BrainAGE values several years before diagnosis and, in ADNI, showed steeper longitudinal increases than stable individuals. Elevated BrainAGE values were also associated with increased risk of future conversion to MCI in cognitively healthy individuals and faster subsequent memory decline. Cross-sectional differences and the association between BrainAGE and risk of future conversion were replicated in OSTPRE. Importantly, adding BrainAGE to models including demographic, APOE4, cognitive, and MRI-derived measures consistently improved prediction of future cognitive outcomes, with the greatest benefit observed for individuals who converted after longer follow-up. These findings show that structural brain ageing begins to diverge years before the onset of MCI. BrainAGE captures this early divergence, providing complementary information beyond conventional structural MRI measures that may improve the early identification of cognitively normal individuals at increased risk of future cognitive decline when integrated with other biomarkers.

Physical activity may enhance cognition in older adults, yet evidence from randomized controlled trials (RCTs) on mechanistic pathways remains inconclusive. This single-blinded RCT examined the effects of an 8-week, online-guided, multicomponent physical activity intervention on cognitive function, resting-state functional brain connectivity (rsFC), and the gut microbiome in 92 healthy older adults (M age = 66.35). Participants were randomized to a physical activity group performing moderate-to-vigorous-intensity aerobic, coordination, and balance exercises, or to an active control group engaging in progressive muscle relaxation and listening to aging-related podcasts. The primary outcome was change in visual processing speed (items/s) from pre- to post-assessment. Secondary outcomes included changes in additional cognitive measures, rsFC, cardiorespiratory fitness (CRF), and the gut microbiome. The primary outcome showed no significant between-group differences. However, exploratory analyses revealed potential improvements in inhibition (η2 = 0.061; p = 0.025) and visual memory (η2 = 0.047; p = 0.040) in the physical activity group. This group also showed a potential increase in rsFC between visual and dorsal attention networks (η2 = 0.101; p = 0.009). Visual memory gains correlated with improvements in rsFC (p = 0.013). No between-group differences were observed in CRF or gut microbiome composition. While the primary outcome (visual processing speed) and predefined mechanistic pathways (e.g., gut microbiome composition) remained unaffected, this may partly reflect the sample’s high baseline fitness, which likely limited observable improvements. Exploratory findings suggest potential cognitive and associated rsFC benefits in memory and attention-related networks. The online format enabled a structured, scalable intervention while minimizing potential confounding from social interaction. https://drks.de/search/de/trial/DRKS00028022 (date of registration: 14.02.2022).

MRI-visible perivascular spaces (PVS) are increasingly recognised as markers of compromised brain health, but their quantitative use requires segmentation methods that remain reliable beyond the datasets on which they were developed. Whether current methods meet this requirement remains unclear. To address this gap, we organised the Domain Randomisation PVS (DoRA-PVS) Challenge, a no-data-shared benchmark designed to evaluate out-of-sample PVS segmentation in two settings: an open method track for previously published methods, and a domain randomisation track for new strategies trained exclusively on synthetic data. The testbed comprised 285 images from 12 cohorts, capturing substantial heterogeneity in scanner vendors, field strengths, sequences, imaging protocols, image quality, and participant characteristics. Four teams competed in the first DoRA-PVS Challenge: two in the open method track, one in the domain-randomisation track, and one in both tracks. Preliminary results show that external generalisation is achievable. Across tracks, most methods achieved AUPRC values above random-classifier performance on unseen data, indicating that they could segment PVS beyond their development cohorts. However, this generalisation was not consistent: performance varied substantially across cohorts and evaluation metrics. These preliminary findings shift the central question from whether PVS segmentation methods can generalise at all to whether they can generalise reliably across heterogeneous data. The DoRA-PVS Challenge therefore establishes a rigorous benchmark for assessing out-of-sample robustness and provides a framework for developing PVS segmentation methods that are more consistent across acquisition protocols, populations, and anatomical regions.

As neuroimaging analysis shifts toward large-scale, multi-site studies, managing the unwanted variability introduced by combining heterogeneous datasets has become a critical challenge. Although tools such as ComBat and its neuroimaging extensions are widely used to address this variability, they only permit the modeling of categorical site effects and cannot account for continuous sources of confounding, such as image quality, head motion, and acquisition parameters. We introduce ComCat, an extension of the ComBat framework that preserves biologically relevant covariates while removing the effects of categorical site indicators and continuous nuisance variables. The latter are modeled as smooth nonlinear functions via B-spline basis expansion. ComCat is applicable to a broad range of brain analysis tasks, including voxel- and surface-based morphometry, normative modeling, and machine learning-based prediction. To demonstrate its capabilities, we evaluated ComCat on brain age prediction across five datasets covering complementary multi-site harmonization scenarios: ON-Harmony (10 subjects x 6 scanners; n = 80); the Buchert traveling-phantom dataset (1 subject x 116 scanners; n = 531); the Tohoku single-scanner, varying-acquisition dataset (n = 121); MR-ART (148 subjects with varying motion levels); and an ABIDE subset comprising 229 control subjects and 208 individuals with autism spectrum disorder across 14 scanners. Using image quality measures derived from CAT12 as continuous nuisance variables, ComCat reduced the mean absolute error (MAE) in brain age prediction relative to ComBat-GAM in all five datasets, including the two scenarios where site information was unavailable or uninformative. In the ABIDE dataset, ComCat improved harmonization while preserving the difference between the control and ASD groups, demonstrating that scanner-related variance can be removed without affecting biologically meaningful signals. ComCat can operate with or without site labels and is agnostic to the source of image quality metrics.

Abstract Human brain function emerges from dynamic reconfigurations of large-scale neural networks. While population-level reference charts have transformed the study of static brain structure and connectivity, an equivalent normative framework for intrinsic brain dynamics has been lacking. This gap has limited our ability to characterize individual variability, development, ageing, and mental health conditions at scale. Here, we establish a population-level normative reference for large-scale human brain dynamics using resting-state fMRI data from more than 10,000 individuals spanning the lifespan and 91 scanning sites. We derive a compact set of recurring brain-state configurations that are reproducible across scanners and acquisition paradigms and that generalize to previously unseen cohorts. Anchoring these dynamic states to normative lifespan models enables the quantification of individual deviations relative to population reference distributions. We show that intrinsic brain dynamics undergo systematic reorganization across development and ageing, with pronounced changes before early adulthood and more gradual modulation thereafter. Applying this framework across multiple mental health conditions reveals disorder-specific and highly heterogeneous deviations in brain dynamics that are not captured by static neuroimaging measures. Robust transfer to independent cohorts and longitudinal analyses demonstrate that normative brain dynamics can be reliably assessed out of distribution. These results delineate a population-scale dynamic architecture of the human brain and extend normative brain mapping from static phenotypes to the temporal domain, providing a reference framework for studying brain function across the lifespan in health and disease.

Background Early task‐specific rehabilitation is critical for recovery after stroke, yet it remains unclear whether combining established rehabilitative training principles into an early multimodal motor training (EMT) paradigm improves recovery by engaging only local peri‐infarct adaptation or a broader, longitudinally detectable pattern of structural brain remodeling. Methods Male rats were assigned to stroke+EMT, stroke, EMT, or control groups. Focal photothrombotic stroke was induced in the right sensorimotor cortex. EMT began 2 days after stroke and consisted of an obstacle‐based training paradigm with variable and progressively increasing difficulty delivered over 8 weeks. Skilled locomotion was assessed using the ladder‐rung test. Longitudinal T2‐weighted magnetic resonance imaging and deformation‐based morphometry were used to quantify structural brain alterations. Significant loci from voxel‐wise group×time interaction F‐contrast maps (family‐wise error‐corrected, P<0.05) were summarized as structural events and analyzed according to their spatial and temporal organization. Results EMT improved poststroke motor recovery and was associated with broader structural remodeling than stroke or EMT alone (82 versus 15 and 10 structural events, respectively). These alterations extended beyond peri‐infarct tissue, were bilaterally distributed, and mapped to both sensorimotor and higher‐order brain systems, with frequent homotopic occurrence (74.4%) and multinetwork assignment (50.0%). Structural events were distributed across 6 temporal profiles, indicating distinct early‐to‐late remodeling courses rather than a single monotonic process. Conclusions These findings support a shift in how early poststroke rehabilitation is conceptualized: not as a local process confined to isolated brain regions, but as a driver of distributed, bilateral, and temporally organized structural remodeling across multiple brain systems.

Abstract Genome-wide association studies (GWAS) have identified hundreds of common genetic variants associated with regional brain volumes, enabling the construction of polygenic scores (PGS) that summarize genetic predisposition for variation in specific neuroanatomical traits. To investigate how these genetic influences are exerted spatially throughout the brain, we computed PGS for ten brain volume phenotypes, including nine major subcortical structures and intracranial volume. Each locus was weighted by its estimated GWAS effect size on regional volume in the original GWAS. In an independent, non-overlapping sample of 2,830 UK Biobank participants, we performed whole-brain voxel-based morphometry (VBM) analyses of 3D volumetric brain MRI to reveal voxel-wise associations between each PGS and modulated gray matter volume (GMV). To probe genetic effects across multiple spatial scales, analyses were repeated across Gaussian smoothing kernels ranging from 2-mm to 12-mm full-width at half-maximum (FWHM). Several PGS demonstrated highly significant associations with GMV, including localized effects in the hippocampus, amygdala, thalamus, and basal ganglia, whereas the brainstem PGS showed more widespread associations throughout the brain. For most of the PGS, the fraction of voxels surviving the false discovery rate (FDR) correction increased with increasing FWHM. Peak voxel-wise significance was often strongest at intermediate smoothing levels. Hippocampal significance maps showed progressively larger regions of significant signal at higher smoothing levels, and subsampling showed that detectable signal remained present even with substantial reductions in sample size. These findings suggest that genetic influences on brain morphology are expressed across multiple spatial scales, with consequences that may help to guide the design of deep learning methods to discover genomic loci associated with brain structure and brain diseases.

When planning longitudinal magnetic resonance imaging (MRI) studies, it is advisable to consider various (confounding) factors that could influence brain structural changes over time. The goal of this study was to identify factors that contribute to intraindividual variability of brain structure within a short period of time. We employed multilevel sparse partial least squares regression to investigate the changes in regional gray matter volume in the longitudinal Day2day MRI dataset. The findings suggest that the changes in regional GM volume estimations were primarily driven by image quality, while the outdoor temperature and time since baseline appeared as the main predictors of volumetric changes in insular and diencephalic brain regions. We additionally investigated factors associated with variability in image quality. The findings underscore the importance of maintaining adequate participant arousal during scanning.

Reliable staging of early memory decline is essential for identifying individuals at risk for Alzheimer’s disease. The Stages of Objective Memory Impairment (SOMI) framework provides a clinically scalable tool for characterizing episodic memory loss, yet its neurobiological validity remains underexplored. Recent advances in plasma biomarkers (e.g., p-tau217, p-tau181, Aβ42/40) offer emerging blood-based alternatives to CSF and PET, although their diagnostic implementation continues to evolve. Here, we examine whether SOMI stages reflect widespread brain aging, as indexed by BrainAGE, a structural MRI–based biomarker quantifying deviation from normative aging trajectories. While previous studies have linked SOMI to hippocampal atrophy and tau pathology, no study to date has examined its association with a global MRI-derived biomarker of systemic brain aging. In a well-characterized cohort of 119 older adults on the Alzheimer’s disease continuum, we evaluated whether higher SOMI stages were linked to elevated BrainAGE scores and examined whether this association remained significant after adjusting for age, sex, education, and hippocampal volume. Higher SOMI stages were robustly associated with elevated BrainAGE scores, indicating accelerated neurobiological aging. This relationship remained significant after adjusting for covariates and was confirmed in sensitivity analyses. Notably, a marked discontinuity in BrainAGE emerged between SOMI stages 0–2 and 3–5, aligning with the theoretical transition from retrieval to storage impairment, long recognized as a turning point in prodromal Alzheimer’s disease. These findings validate SOMI as a low-cost, non-invasive behavioral marker of systemic brain health. By linking cognitive staging to global neuroimaging biomarkers, our study supports SOMI’s translational utility for large-scale screening, clinical trial stratification, and early intervention planning in Alzheimer’s disease.

PURPOSE:Congenital infection with human Cytomegalovirus (hCMV) is a common cause of severe neurodevelopmental disability, while postnatal infection of a term-born infant will usually not lead to an adverse neurodevelopmental outcome. In preterm-born infants, long-term consequences of an early postnatal hCMV infection (usually via breast milk) are still controversial. This is highly relevant as preventative measures exist. METHODS:Data of 37 preterm-born children (PT; ≤ 32 weeks of gestation and/or weighing ≤ 1500 g) was included. Of these, 14 acquired an early postnatal infection with hCMV (PT hCMV+), while 23 did not (PT hCMV-). Further, 38 healthy term-born participants (FT) were included. Overall median age was 13.6 years (range 7.9-17.8 years). Global and local tissue volumes and brain surface parameters were analyzed. Consequences of prematurity were detected by comparing FT and PT, and sequelae of hCMV infection by comparing PT hCMV- and PT hCMV+. FINDINGS:Compared to FT, PT showed lower global gray matter (GM); interestingly, PT hCMV+ showed a trend toward higher global GM than PT hCMV-. Several clusters of local GM differed in volume between PT and FT, but none as a function of hCMV infection. Surface analyses between PT and FT identified predominantly right-hemispheric regions of lower cortical thickness in PT. Unexpectedly, widespread clusters of higher cortical thickness were found bilaterally in predominantly frontal brain regions in PT hCMV+ compared to PT hCMV-, demonstrating a lasting effect of hCMV infection. CONCLUSION:We found lower global and local GM volumes due to of prematurity. Additionally, we demonstrate long-term effects of early postnatal hCMV infection on brain structure in PT, markedly different from those resulting from prematurity alone. This suggests distinct long-term cerebral consequences of early postnatal hCMV infection in former preterm-born children above and beyond those attributable to prematurity. Consequently, efforts to avoid HCMV infection in preterm-born infants should be implemented.

BACKGROUND AND OBJECTIVES:High-dose methylprednisolone (MP) is the global standard for treating pregnancy-associated relapses in multiple sclerosis (MS). Given that glucocorticoids cross the placenta and may interfere with fetal brain development, concerns remain about their long-term safety. This study assessed whether in utero MP exposure as part of MS relapse therapy affects neurodevelopment in school-aged children. METHODS:In this cross-sectional, 2-center study, term-born children with prenatal exposure to MP due to maternal MS relapse treatment were compared with a nonexposed reference group of children, all born to mothers with MS. Participants were primarily identified using the German MS and Pregnancy Registry and assessed at tertiary MS centers. The primary outcome was global cognitive ability, measured using a standardized intelligence test. Secondary outcomes included attention, behavior, motor performance, and electrocortical activity at rest. Structural brain development was assessed using high-resolution MRI, including voxel-based and surface-based morphometry. Deviations from chronological brain age were quantified using a machine learning-based framework. Statistical associations were examined using linear regression models. RESULTS:The MP-exposed group (n = 30; mean age 9.6 years; 37% female) and the reference group (n = 30; mean age 10.0 years; 40% female) were comparable with respect to demographic and perinatal characteristics. The median cumulative MP dose was 5 g (Q1-Q3: 3-7.5), predominantly administered during the second trimester. Global IQ did not differ between groups (MP: 103.0; 95% CI: 99.2-106.8 vs reference: 101.5; 95% CI: 97.6-105.3). After correction for multiple comparisons, no group differences emerged in secondary neuropsychological outcomes or electrocortical parameters. MRI analyses revealed no differences in gray matter volume, cortical thickness, gyrification, or chronological brain age. DISCUSSION:In spite of theoretical concerns that MP exposure during pregnancy might lead to alterations in neurodevelopment, this was not found to be the case in this cohort, with most exposures occurring during the second trimester. However, this study was not powered to detect subtle associations in secondary analyses or to draw definitive conclusions regarding potential dose-response relationships. Given the remaining uncertainties, MP should be used with caution at the lowest effective dose until larger follow-up studies provide further clarity.

Episodic memory, the ability to recall past events, is particularly vulnerable to ageing. A decline in episodic memory performance is generally considered part of ageing. However, the episodic memory performance of superagers —defined as individuals aged 80+ years old with episodic memory of people 30 years younger— is superior to that typical of their chronological age. The aim of this study was to determine whether the discrepancy between the superager's episodic memory and chronological age is also evident in their brain age. A BrainAGE (Brain Age Gap Estimation) approach, a multidimensional computational neuroanatomical method that uses structural neuroimaging data to estimate biological brain age, was applied. The study population comprised 64 superagers (mean age = 81.9 ± 1.9) and 55 age-matched typical older adults (82.4 ± 1.9). Cross-sectional analyses revealed a negative BrainAGE score for superagers (mean = -0.95 ± 2.36) indicating a deceleration of the ageing process. By contrast, typical older adults showed an average score close to zero (0.05 ± 3.03) consistent with their chronological age. The BrainAGE score of superagers was found to be lower relative to typical older adults, and the progression over a 5-year follow-up period was slower in superagers, in keeping with their youthful memory ability. Therefore, superagers have a younger brain than those of typical older adults, suggesting that their ageing mechanisms may involve resistance to age-related brain structural changes. However, despite a 30-year gap in episodic memory, their BrainAGE score differed by only one year, indicating that factors beyond brain structure contribute to the superager phenotype.

Hemispheric brain asymmetries emerge in early life but continue to change over time. However, there is no consensus on whether asymmetries become weaker or stronger with age or which brain regions are most affected. Here, we set out to further explore age-related changes in brain asymmetry, with a particular focus on voxel-wise gray matter asymmetry. For this purpose, we selected a sample of 2,322 participants (1,150 women/1,172 men), aged between 47 and 80 years (mean 62.3 years), from the UK Biobank. Each participant was scanned twice; with an interval between baseline and follow-up scans ranging between 1 and 7 years (mean 2.4 years). Significant changes in asymmetry were observed, particularly in the temporal and occipital lobe, as well as the cerebellum. Overall, decreases in asymmetry were more prominent than increases, but with hemisphere-specific effects (i.e., leftward asymmetries decreased more than increased, while rightward asymmetries increased more than decreased). Changes in asymmetry were not significantly associated with chronological age or biological sex, suggesting that these changes neither accelerate nor decelerate with increasing age, and do not differ between the sexes. Follow-up research – potentially incorporating additional morphometric measures, different stages of life, and/or clinical populations – is necessary, not only to replicate the current findings but also to investigate changes over longer timeframes.

Anorexia nervosa (AN), a severe eating disorder marked by extreme weight loss and malnutrition, leads to significant alterations in brain structure. This study used machine learning (ML) to estimate brain age from structural MRI scans and investigated brain-predicted age difference (brain-PAD) as a potential biomarker in AN. Structural MRI scans were collected from female participants aged 10-40 years across two institutions (Boston, USA, and Jena, Germany), including acute AN (acAN; n=113), weight-restored AN (wrAN; n=35), and age-matched healthy controls (HC; n=90). The ML model was trained on 3487 healthy female participants (ages 5-45 years) from ten datasets, using 377 neuroanatomical features extracted from T1-weighted MRI scans. The model achieved strong performance with a mean absolute error (MAE) of 1.93 years and a correlation of r = 0.88 in HCs. In acAN patients, brain age was overestimated by an average of +2.25 years, suggesting advanced brain aging. In contrast, wrAN participants showed significantly lower brain-PAD than acAN (+0.26 years, p=0.0026) and did not differ from HC (p=0.98), suggesting normalization of brain age estimates following weight restoration. A significant group-by-age interaction effect on predicted brain age (p<0.001) indicated that brain age deviations were most pronounced in younger acAN participants. Brain-PAD in acAN was significantly negatively associated with BMI (r = -0.291, pfdr = 0.005), but not in wrAN or HC groups. Importantly, no significant associations were found between brain-PAD and clinical symptom severity. These findings suggest that acute AN is linked to advanced brain aging during the acute stage, and that may partially normalize following weight recovery.

Meditation is thought to promote healthy aging by improving mental health, preserving brain integrity and reducing Alzheimer’s disease risk. We examined the impact of long-term meditation expertise and an 18-month meditation training on brain aging in older adults using machine learning. We included 25 Older Expert Meditators (OldExpMed) with > 20 years of practice and 135 Cognitively Unimpaired Older Adults (CUOA) from the Age-Well randomized controlled trial. CUOA were randomized (1:1:1) into an 18-month meditation training, a non-native language training, and a no intervention group. Brain age was predicted using a machine learning model trained on gray and white matter volume and glucose metabolism data from ADNI and replicated with a second model. Brain Predicted Age Difference (BrainPAD) was computed as the gap between predicted and chronological age. We assessed meditation expertise effects on BrainPAD, its links with meditation hours, cognitive, and affective measures, and the impact of 18-month training. Compared to CUOA, OldExpMed exhibited significantly lower/more negative BrainPAD, linked to meditation hours, mental imagery, and prosocialness. No significant effect of 18-month training was observed. Results were consistent across the replication model. Long-term meditation is associated with younger brain age, but 18-month training has no effect, emphasizing the need for sustained practice to support healthy brain aging.