The physical and social exposome affects human aging, and brain clocks may track its effects. However, most studies neglect multidomain exposures (physical, social and political) across diverse settings globally and their associations with brain aging. In this study, we characterized the associations between 73 country-level physical and social exposomal factors and multimodal brain age in 18,701 participants from 34 countries (healthy individuals and those with Alzheimer's disease, frontotemporal lobar degeneration or mild cognitive impairment). Exposome effects were assessed using generalized additive models and meta-analytic frameworks. Aggregated exposome models explained up to 15.5-fold more variance than individual exposures (delta Akaike information criterion (ΔAIC): 2,034-3,127). Physical exposome was primarily associated with accelerated structural brain aging (limbic, subcortical and cerebellar regions), whereas social exposome was more strongly associated with functional brain aging (frontotemporal and limbic networks). Exposome burden accounted for 3.3-9.1-fold higher risk of accelerated aging, exceeding effects of clinical diagnoses. Findings were out-of-sample validated in cross-sectional and longitudinal designs, remained consistent across clinical subgroups and persisted after adjustment for demographics, age correction bias, cognition, scanner type and data quality. The exposome accelerates brain aging in health and disease, underscoring the need to address physical, social and political inequities.

BACKGROUND AND OBJECTIVES:Cerebral small vessel disease (cSVD), characterized by pathologic changes in the structure and function of small brain vessels, is detectable on brain MRI in the absence of clinical symptoms. However, imaging cerebral small vessels themselves in vivo remains costly and challenging. There is growing interest in investigating whether retinal microvascular imaging features could be proxies for changes in the brain microvasculature. Using a multipronged approach, we explored the relation of retinal microvascular characteristics with MRI markers of cSVD (MRI-cSVD). METHODS:First, we explored this relationship in older community persons from the population-based 3C-Dijon cohort. MRI-cSVD was assessed on a 1.5-Tesla MRI at baseline, comprising white matter hyperintensity volume (WMHV), lacunes, and a composite extreme cSVD phenotype (WMHV extreme distributions and presence/absence of lacunes). At 10-year follow-up, participants underwent measurements of retinal microvascular features on fundus using the Singapore "I" Vessel Assessment software. To support 3C-Dijon findings, we conducted a comprehensive literature review up to July 2024 from PubMed/EMBASE and used 2-sample Mendelian randomization (MR) leveraging large-scale genome-wide association studies, to assess causality and directionality. RESULTS:In 670 3C-Dijon participants (median age 70.7, 65.7% women), multivariable analyses (adjusted for age, sex, axial length, and cardiovascular risk factors) showed a significant association of lower arteriolar fractal dimension (FDa) with extreme cSVD (odds ratio [OR] 1.68, 95% CI 1.20-2.34) after multiple testing correction (p < 0.0042), and at p < 0.05, associations of lower FDa and smaller arteriolar caliber with larger WMHV (β = 0.0534 [95% CI 0.0075-0.0569] and 0.0519 [95% CI 0.0045-0.0993]), and of greater venular tortuosity (TORTv) with lacunes (OR 1.45, 95% CI 1.05-2.00). Lower FDa was also associated with poorer executive function. The systematic review of the literature identified 12 studies (N = 7,796) that showed mostly consistent direction of effects for FDa (5/6 studies), TORTv (4/6), and arteriolar caliber (10/11), although statistical significance was observed in 5 individual studies only. Two-sample MR based on large genome-wide association studies (N = 5,292-52,798) showed evidence for a potentially causal association of greater TORTv with extreme cSVD and larger WMHV (p = 0.0017 and 0.049), with no evidence for reverse causation. DISCUSSION:We provide multimodal evidence that geometric characteristics of the retinal microvasculature are associated with increased burden of MRI-cSVD and possibly worse executive function.

Anxiety symptoms occur more frequently during adolescence and early adulthood, increasing the risk of future anxiety disorders. Neuroscientific research on anxiety has primarily focused on adulthood, employing mostly univariate approaches, discounting large-scale alterations of the brain. Indeed adolescents with trait anxiety may display similar abnormalities shown by adults in brain regions ascribed to the default mode network, associated with self-referential thinking and rumination-related processes. The present study aims to explore resting-state connectivity patterns associated with trait anxiety in a large sample of young individuals. We analyzed the rs-fMRI images of 1263 adolescents (mean age 20.55 years) and their scores on anxiety trait. A significant association between trait anxiety and resting-state functional connectivity in two networks was found, with some regions overlapping with the default mode network, such as the cingulate gyrus, the middle temporal gyri and the precuneus. Of note, the higher the trait anxiety, the lower the connectivity within both networks, suggesting abnormal self-referential processing, awareness, and emotion regulation abilities in adolescents with high anxiety trait. These findings provided a better understanding of the association between trait anxiety and brain rs-functional connectivity, and may pave the way for the development of potential biomarkers in adolescents with anxiety.

INTRODUCTION:White matter hyperintensities (WMHs), a major cerebral small vessel disease (cSVD) marker, may arise from different pathologies depending on their location. We explored clinical and genetic correlates of agnostically derived spatial WMH patterns in two longitudinal population-based cohorts (Three-City Study [3C]-Dijon, LIFE-Adult). METHODS:We derived seven WMH spatial patterns using Bullseye segmentation in 2878 individuals aged 65+ and explored their associations with vascular and genetic risk factors, cognitive performance, dementia and stroke incidence. RESULTS:WMHs in the frontoparietal and anterior periventricular region were associated with blood pressure traits, WMH genetic risk score (GRS), baseline and decline in general cognitive performance, incident all-cause dementia, and ischemic stroke. Juxtacortical-deep occipital WMHs were not associated with vascular risk factors and WMH GRS, but with incident all-cause dementia and intracerebral hemorrhage. DISCUSSION:Accounting for WMH spatial distribution is key to deciphering mechanisms underlying cSVD subtypes, an essential step towards personalized therapeutic approaches. HIGHLIGHTS:We studied spatial patterns of WMHs in 2878 participants. Blood pressure was associated with frontoparietal and anterior PV WMHs. Anterior PV WMHs predicted dementia and stroke risk. Juxtacortical-deep occipital WMH burden was not associated with blood pressure or WMH genetic risk. Juxtacortical-deep occipital WMH burden predicted dementia and intracerebral hemorrhage.

Anxiety is a diffuse condition ranging from milder manifestations to severe disorders, impacting individuals' lives significantly. Specific sensitive periods such as adolescence and young adulthood are particularly vulnerable to anxious states, often associated with psychological traits like impulsivity, aggression and varying coping strategies. The goal of the present study is to address the need for a comprehensive analysis of trait anxiety by employing Parallel ICA, a data fusion machine learning technique, in a sample of young individuals divided into a lower anxiety group (n = 252) and a higher anxiety group (n = 302), aiming to identify the joint grey-white matter networks characterizing higher versus lower trait anxiety. Additionally, we aim to characterize higher anxiety individuals for their usage of maladaptive coping strategies, and other affective dimensions. In higher anxious individuals, we identified a fronto-parieto-cerebellar network with decreased grey matter concentration, linked to bodily awareness and threat modulation, and a parieto-temporal network with increased white matter concentration, emphasizing insula and precuneus role. At the psychological level, we found higher stress, cognitive and motor impulsivity and avoidance/emotional coping in higher anxious individuals. These findings may enhance the understanding of anxiety's neural underpinnings in young individuals, supporting early interventions.

While previous studies have suggested the involvement of herpes simplex virus 1 (HSV-1) in the pathophysiology of Alzheimer's disease (AD), neuroimaging studies in this area are scarce. We aimed to determine the association of HSV-1 infection with neuroimaging markers of AD and to assess the impact of susceptibility factors that could modulate the deleterious effects of HSV-1. Within a subsample of the UK Biobank with both serological and MRI data, we analyzed the associations between HSV-1 seropositivity and grey matter (GM) volumes in four brain areas affected early in AD (hippocampus, amygdala, parahippocampal and entorhinal cortex), using linear regressions adjusted on potential cofounders (age, sex, APOE4 allele, education, income, smoking and alcohol intake, diabetes, hypertension, body mass index and total intracranial volume). To assess the impact of susceptibility factors, interactions of HSV-1 with cytomegalovirus (CMV), APOE4 and age were tested and stratifications on these factors were performed. CMV infection and co-infection with HSV-1 were further investigated to identify the separate and cumulative effects of each virus. Among 901 participants (mean age 64.1 (SD = 7.7) at MRI; 55.6% of women), when considering only HSV-1 we found no significant difference in GM volume in the four brain regions studied between HSV-1-infected and uninfected participants. Nevertheless, considering interaction with CMV, HSV-1 infection was associated with a lower parahippocampal volume in CMV non-infected participants only (β = -105 mm 3 , p  = 0.03). Further investigation of interactions with CMV and other susceptibility factors showed that, among APOE4 carriers ≥ 65y, i.e. the most at-risk people for AD: i) those infected only by CMV had lower amygdala volume (β = -197 mm 3 , p  = 0.04); and ii) those infected by CMV (alone or in combination with HSV-1) tended to have smaller GM volumes in three other brain regions studied ( p  < 0.20). Our study highlighted the complexity of the interactions involved when assessing associations between viral infections and radiological biomarkers of AD and the importance of considering susceptibility factors such as viral co-infection, age and APOE4 genotype to decipher the role of infections in AD.

Deviations from normative brain ageing trajectories are linked to a wide range of adverse health outcomes. A number of brain age prediction models have been developed, based on various neuroimaging modalities, machine learning algorithms, training samples, and age ranges. However, it remains unknown whether these models converge on a shared genetic liability, and whether capturing this shared signal could provide a more sensitive marker of brain health than any single model alone. We first conducted a new brain age gap (BAG) GWAS in a sample of 60,735 individuals across 29 cohorts worldwide, and then applied genomic structural equation modelling to examine the shared genetic variance between five prior BAG GWASs and our new analysis, using a single latent BAG factor (30 cohorts overall). All six BAG GWASs loaded onto a single factor, explaining 63% of the total genetic variance. We identified 19 independent SNPs associated with the BAG factor, including four novel associations. Genetically, the BAG factor was positively correlated with multiple traits, with blood pressure, smoking, longevity, autism, and sleep showing putatively causal effects. A polygenic score (PGS) for the BAG factor showed associations with phenotypic BAGs already in childhood, with stronger links observed in adulthood. Phenome-wide association analyses indicated that BAG factor PGS captured associations with more health traits than individual BAG PGSs. Our findings underscore the importance of considering the shared variance across different BAG constructs to identify robust correlates of poor brain health.

BACKGROUND:Adverse childhood experiences (ACEs) have been associated with volume alterations of stress-related brain structures among aging and clinical populations, however, existing studies have predominantly assessed only one type of ACE, with small sample sizes, and it is less clear if these associations exist among a general population of young adults. OBJECTIVE:The aims were to describe structural hippocampal volumetric differences by ACEs exposure and investigate the association between ACEs exposure and left and right hippocampal volume in a student sample of young adults. METHODS:959 young adult students (18-24 years old) completed an online questionnaire on ACEs, mental health conditions, and sociodemographic characteristics. Magnetic resonance imaging (MRI) was used to measure left and right hippocampal volume (mm3). We used linear regression to explore the differences of hippocampal volumes in university students with and without ACEs. RESULTS:Two thirds of students (65.9%) reported ACEs exposure. As ACEs exposure increased there were significant volumetric reductions in left (p < 0.0001) and right hippocampal volume (p = 0.001) and left (p = 0.0023) and right (p = 0.0013) amygdala volume. After adjusting for intracranial brain volume, sex, age, and depression diagnosis there was a negative association between ACEs exposure and left (β = -22.6, CI = -44.5, -0.7, p = 0.0412) but not right hippocampal volume (β = -18.3, CI = -39.2, 2.6, p = 0.0792). After adjusting for intracranial volume there were no associations between ACEs exposure and left (β = -9.2, CI = -26.2, 7.9 p = 0.2926) or right (β = -5.6, CI = -19.9,8.8 p = 0.4466) amygdala volume. CONCLUSIONS:Hippocampal volume varied by ACEs exposure in young adult students. ACEs appear to contribute to neuroanatomic differences in young adults from the general population.

Cerebral small vessel disease (cSVD) is a leading cause of stroke and dementia. Genetic risk loci for white matter hyperintensities (WMH), the most common MRI-marker of cSVD in older age, were recently shown to be significantly associated with white matter (WM) microstructure on diffusion tensor imaging (signal-based) in young adults. To provide new insights into these early changes in WM microstructure and their relation with cSVD, we sought to explore the genetic underpinnings of cutting-edge tissue-based diffusion imaging markers across the adult lifespan. We conducted a genome-wide association study of neurite orientation dispersion and density imaging (NODDI) markers in young adults (i-Share study: N = 1 758, (mean[range]) 22.1[18-35] years), with follow-up in young middle-aged (Rhineland Study: N = 714, 35.2[30-40] years) and late middle-aged to older individuals (UK Biobank: N = 33 224, 64.3[45-82] years). We identified 21 loci associated with NODDI markers across brain regions in young adults. The most robust association, replicated in both follow-up cohorts, was with Neurite Density Index (NDI) at chr5q14.3, a known WMH locus in VCAN. Two additional loci were replicated in UK Biobank, at chr17q21.2 with NDI, and chr19q13.12 with Orientation Dispersion Index (ODI). Transcriptome-wide association studies showed associations of STAT3 expression in arterial and adipose tissue (chr17q21.2) with NDI, and of several genes at chr19q13.12 with ODI. Genetic susceptibility to larger WMH volume, but not to vascular risk factors, was significantly associated with decreased NDI in young adults, especially in regions known to harbor WMH in older age. Individually, seven of 25 known WMH risk loci were associated with NDI in young adults. In conclusion, we identified multiple novel genetic risk loci associated with NODDI markers, particularly NDI, in early adulthood. These point to possible early-life mechanisms underlying cSVD and to processes involving remyelination, neurodevelopment and neurodegeneration, with a potential for novel approaches to prevention.

The value of normative models in research and clinical practice relies on their robustness and a systematic comparison of different modelling algorithms and parameters; however, this has not been done to date. We aimed to identify the optimal approach for normative modelling of brain morphometric data through systematic empirical benchmarking, by quantifying the accuracy of different algorithms and identifying parameters that optimised model performance. We developed this framework with regional morphometric data from 37 407 healthy individuals (53% female and 47% male; aged 3-90 years) from 87 datasets from Europe, Australia, the USA, South Africa, and east Asia following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The multivariate fractional polynomial regression (MFPR) emerged as the preferred algorithm, optimised with non -linear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3000 study participants. This model can inform about the biological and behavioural implications of deviations from typical age-related neuroanatomical changes and support future study designs. The model and scripts described here are freely available through CentileBrain.

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (), an open-science, web-based platform for individualized neuroimaging metrics. In this work, we developed and empirically validated sex-specific brain-age models to cover most of the human lifespan (5-90 years). Specifically, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals. The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals and for longitudinal consistency in a further independent sample comprising 377 healthy individuals. image

INTRODUCTION: We tested the association of brain artery diameters with dementia and stroke risk in three distinct population-based studies using conventional T2-weighted brainmagnetic resonance imaging (MRI) images. METHODS: We included 8420 adults > 40 years old from three longitudinal population-based studies with brain MRI scans. We estimated and meta-analyzed the hazard ratios (HRs) of the brain and carotids and basilar diameters associated with dementia and stroke. RESULT: Overall and carotid artery diameters > 95th percentile increased the risk for dementia by 1.74 (95% confidence interval [CI], 1.13-2.68) and 1.48 (95% CI, 1.12-1.96) fold, respectively. For stroke, meta-analyses yielded HRs of 1.59 (95% CI, 1.04-2.42) for overall arteries and 2.11 (95% CI, 1.45-3.08) for basilar artery diameters > 95th percentile. DISCUSSION: Individuals with dilated brain arteries are at higher risk for dementia and stroke, across distinct populations. Our findings underline the potential value of T2-weighted brain MRI-based brain diameter assessment in estimating the risk of dementia and stroke.

The size of the human head is highly heritable, but genetic drivers of its variation within the general population remain unmapped. We performa genome-wide association study on head size (N = 80,890) and identify 67 genetic loci, of which 50 are novel. Neuroimagingstudies showthat 17 variants affect specificbrain areas, butmost have widespread effects. Gene set enrichment is observed for various cancers and the p53, Wnt, and ErbB signaling pathways. Genes harboring lead variants are enriched for macrocephaly syndrome genes (37-fold) and high-fidelity cancer genes (9-fold), which is not seen for human height variants. Head size variants are also near genes preferentially expressed in intermediate progenitor cells, neural cells linked to evolutionary brain expansion. Our results indicate that genes regulating early brain and cranial growth incline to neoplasia later in life, irrespective of height. This warrants investigation of clinical implications of the link between head size and cancer.

Subcortical brain structures are involved in developmental, psychiatric and neurological disorders. Here we performed genome-wide association studies meta-analyses of intracranial and nine subcortical brain volumes (brainstem, caudate nucleus, putamen, hippocampus, globus pallidus, thalamus, nucleus accumbens, amygdala and the ventral diencephalon) in 74,898 participants of European ancestry. We identified 254 independent loci associated with these brain volumes, explaining up to 35% of phenotypic variance. We observed gene expression in specific neural cell types across differentiation time points, including genes involved in intracellular signaling and brain aging-related processes. Polygenic scores for brain volumes showed predictive ability when applied to individuals of diverse ancestries. We observed causal genetic effects of brain volumes with Parkinson’s disease and attention-deficit/hyperactivity disorder. Findings implicate specific gene expression patterns in brain development and genetic variants in comorbid neuropsychiatric disorders, which could point to a brain substrate and region of action for risk genes implicated in brain diseases. Genome-wide association analyses of intracranial and nine subcortical brain volumes in 74,898 participants of European ancestry identify 254 independent loci and yield polygenic scores accounting for brain variation across ancestries.

It is unclear whether brain artery diameters measured on conventional T2-weighted brain MRI images relate to dementia and stroke outcomes across distinct populations. We aimed this study to evaluate the association of T2-weighted brain artery luminal diameters with dementia and stroke in three distinct population-based studies. Three longitudinal population-based studies with 8420 adults >40 years old (Northern Manhattan Study [NOMAS] from the United States, and the Rotterdam Study [RS], from the Netherlands, and Three-City, from France) with brain MRI scans obtained between 1999 and 2015. The median follow-up time for clinical events ranged between 7 and 12.5 years. We tested our hypothesis in each cohort separately due to local data-sharing regulations. The exposure variable was brain carotid and basilar artery luminal diameters measured on MRI axial T2-weighted scans. Multivariable hazard ratios (HRs) and their 95% confidence intervals (CI) expressed the risk of dementia and stroke (primary outcomes) associated with the lowest (<5 th ) and highest (>95 th ) percentiles of the rank-normalized brain artery diameters compared to a reference group defined as the diameters distributed between the 5 th and 95 th percentiles. Secondary outcomes included total and vascular mortality, and fatal and nonfatal cardiovascular and coronary end points. Among the three cohorts (mean age ranged from 65 to 73 y, ≥57% women), 335 participants developed dementia and 331 strokes. Compared with the reference group, participants with arterial diameters >95 th percentile had a higher risk of dementia (HR range 1.15-4.50) and any stroke (HR range 1.29-2.03). For secondary outcomes, participants with arterial diameters >95 th percentile had a consistent higher risk of coronary outcomes, vascular mortality and a composite of any vascular events. The results were less supportive of a higher risk of events among participants with arterial diameters <5 th percentile except for vascular mortality. Individuals with dilated brain arteries are at higher risk of dementia and vascular events. Our findings were consistency across distinct populations in spite of using a non-enhanced, conventional T2-weighted MRI sequence. Understanding the underlying physiopathology of the reported associations, particularly with dementia and stroke, might reveal novel vascular contributions to dementia

Given the anatomical and functional similarities between the retina and the brain, the retina could be a "window" for viewing brain structures. We investigated the association between retinal nerve fiber layers (peripapillary retinal nerve fiber layer, ppRNFL; macular ganglion cell-inner plexiform layer, GC-IPL; and macular ganglion cell complex, GCC), and brain magnetic resonance imaging (MRI) parameters in young health adults. We included 857 students (mean age: 23.3 years, 71.3% women) from the i-Share study. We used multivariate linear models to study the cross-sectional association of each retinal nerve layer thickness assessed by spectral-domain optical coherence tomography (SD-OCT) with structural (volumes and cortical thickness), and microstructural brain markers, assessed on MRI globally and regionally. Microstructural MRI parameters included diffusion tensor imaging (DTI) and Neurite Orientation Dispersion and Density Imaging (NODDI). On global brain analysis, thicker ppRNFL, GC-IPL and GCC were all significantly associated with patterns of diffusion metrics consistent with higher WM microstructural integrity. In regional analyses, after multiple testing corrections, our results suggested significant associations of some retinal nerve layers with brain regional gray matter occipital volumes and with diffusion MRI parameters in a region involved in the visual pathway and in regions containing associative tracts. No associations were found with global volumes or with global or regional cortical thicknesses. Results of this study suggest that some retinal nerve layers may reflect brain structures. Further studies are needed to confirm these results in young subjects.

Environmental factors, such as nutrition, influence brain physiology and health throughout the life course. While research has focused on the extremes of the age spectrum, less is known about early adulthood, yet a critical period for the consolidation of brain maturation and the building of adult behaviors. Nutrition may impact late maturational changes in the post-adolescent brain and contribute to the degree of brain reserve that minimizes the risk to develop dementia. We took advantage of a large sample of young adults to evaluate the association of dietary behavior with brain structure characteristics. This cross-sectional study included 1721 university students (18-35 years-old) from the French i-Share cohort, who underwent brain MRI. A 12-item online Food Frequency Questionnaire was used to evaluate consumption in major food groups and to determine dietary patterns from Principal Component Analysis (PCA). Multivariable-adjusted linear regressions were used to estimate the association of PCA scores with brain structure (cortical thickness and surface area, grey matter and white matter volumes, diffusion parameters). The first PCA component (explained variance, 17%) contrasted a healthy diet (higher fruit and vegetable intakes) to a poor diet (higher fast food, sugary drink and snack consumptions). In models adjusted for total intracranial volume, age, sex, physical activity, body mass index, alcohol and tobacco consumptions, a higher PCA score (reflecting healthier diet) was associated with lower total gray matter volume (β for 1 point score = –0.17 [95%CI, –0.31; –0.03] cm 3 ), in various brain areas ( Figure ). Healthy diet was also associated with lower global white matter volume (β = –0.18 [–0.32; –0.03] cm 3 ). No association was found with diffusion tensor imaging, and neurite orientation dispersion and density imaging metrics in the white matter. In this large sample of young adults, healthier diet was associated with lower brain volumes, independent of multiple potential confounders. Gray matter volume loss during post-adolescence is a marker of brain maturation; thus, environmental factors emphasizing this loss may benefit maturation. Our results raise the hypothesis that a healthy diet may favor and a poor diet may hamper brain maturation; an assumption which deserves further investigation in other populations.

Background: The long-term effects of traumatic brain injury (TBI) with loss of consciousness (LOC) on magnetic resonance imaging (MRI) markers of brain health and on dementia risk are still debated. Objective: To investigate the associations of history of TBI with LOC with incident dementia and neuroimaging markers of brain structure and small vessel disease lesions. Methods: The analytical sample consisted in 4,144 participants aged 65 and older who were dementia-free at baseline from the Three City - Dijon study. History of TBI with LOC was self-reported at baseline. Clinical Dementia was assessed every two to three years, up to 12 years of follow-up. A subsample of 1,675 participants <80 years old underwent a brain MRI at baseline. We investigated the associations between history of TBI with LOC and 1) incident all cause and Alzheimer's disease (AD) dementia using illness-death models, and 2) neuroimaging markers at baseline. Results: At baseline, 8.3% of the participants reported a history of TBI with LOC. In fully-adjusted models, participants with a history of TBI with LOC had no statistically significant differences in dementia risk (HR = 0.90, 95% CI = 0.60-1.36) or AD risk (HR = 1.03, 95% CI = 0.69-1.52), compared to participants without TBI history. History of TBI with LOC was associated with lower white matter volume (beta = -4.58, p = 0.048), but not with other brain volumes, white matter hyperintensities volume, nor covert brain infarct. Conclusion: This study did not find evidence of an association between history of TBI with LOC and dementia or AD dementia risks over 12-year follow-up, brain atrophy, or markers of small vessel disease.

Abstract Anxiety is a diffuse condition that can range from mild to more severe manifestations, including proper anxiety disorders. Specific sensitive periods such as adolescence and young adulthood are particularly vulnerable to anxious states and may favour the onset of future anxiety disorders. Until now, neuroanatomical research on anxiety has focused mainly on adults, employed univariate inference-based approaches, and considered one single neuroimaging modality, thus leading to an incomplete picture. The aim of the present study is to characterize the joint GM-WM contribution in high versus low trait anxiety, in a large sample of young individuals, exploiting a data fusion machine learning technique known as Parallel ICA, and to build a predictive model of trait anxiety based on a Random Forest classifier. Additionally, we aimed to characterize high anxiety individuals for their usage of maladaptive coping strategies, and other affective dimensions such as anger, impulsivity, and stress, and to test their relevance in predicting new cases of high trait anxiety. At the neural level we found a fronto-parieto-cerebellar network to have a decrease gray matter concentration in high anxious individuals, and a parieto-temporal network to have an increase white matter concentration in high anxious individuals. Additionally, at the psychological level, individuals with high anxiety are characterized by higher stress, cognitive and motor impulsivity, and avoidance/emotional coping. Lastly, the Random Forest classifier robustly confirmed the goodness of the morphometric and psychological factors in predicting new cases of trait anxiety. As such, these findings may pave the road for the creation of an early biomarker of trait anxiety in young individuals, contributing to an early intervention to prevent the future development of anxiety disorders.