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Life-course exposure to risk and protective factors impacts brain macro- and micro-structure, which in turn affects cognition. The concept of brain-age gap assesses brain health by comparing an individual's neuroimaging-based predicted age with their calendar age. A higher BAG implies accelerated brain ageing and is expected to be associated with worse cognition. In this study, we comprehensively modelled mutual associations between brain health and lifestyle factors, brain age and cognition in a large, middle-aged population. For this study, cognitive test scores, lifestyle and 3T MRI data for n = 4881 participants [mean age (± SD) = 59.2 (±8.6), 50.1% male] were available from The Maastricht Study, a population-based cohort study with extensive phenotyping. Whole-brain volumes (grey matter, cerebrospinal fluid and white matter hyperintensity), cerebral microbleeds and structural white matter connectivity were calculated. Lifestyle factors were combined into an adapted LIfestyle for BRAin health weighted sum score, with higher score indicating greater dementia risk. Cognition was calculated by averaging z -scores across three cognitive domains (memory, information processing speed and executive function and attention). Brain-age gap was calculated by comparing calendar age to predictions from a neuroimaging-based multivariable regression model. Paths between LIfestyle for BRAin health tertiles, brain-age gap and cognitive function were tested using linear regression and structural equation modelling, adjusting for sociodemographic and clinical confounders. The results show that cerebrospinal fluid, grey matter, white matter hyperintensity and cerebral microbleeds best predicted brain-age gap ( R 2 = 0.455, root mean squared error = 6.44). In regression analysis, higher LIfestyle for BRAin health scores (greater dementia risk) were associated with higher brain-age gap (standardized regression coefficient β = 0.126, P < 0.001) and worse cognition ( β = -0.046, P = 0.013), while higher brain-age gap was associated with worse cognition ( β =-0.163, P < 0.001). In mediation analysis, 24.7% of the total difference in cognition between the highest and lowest LIfestyle for BRAin health tertile was mediated by brain-age gap ( β indirect = -0.049, P < 0.001; β total = -0.198, P < 0.001) and an additional 3.8% was mediated via connectivity ( β indirect = -0.006, P < 0.001; β total = -0.150, P < 0.001). Findings suggest that associations between health- and lifestyle-based risk/protective factors (LIfestyle for BRAin health) and cognition can be partially explained by structural brain health markers (brain-age gap) and white matter connectivity markers. Lifestyle interventions targeted at high-risk individuals in mid-to-late life may be effective in promoting and preserving cognitive function in the general public., Competing Interests: None to declare., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Social health factors (e.g., social activities or social support) and cognitive activity engagement have been associated with dementia risk, but their neural substrates have not been well established. This systematic review and meta-analysis summarizes the available evidence regarding the association between these factors and cerebral macro- and micro-structure. A comprehensive literature search was conducted in various databases, following predefined criteria. Heterogeneity, risk of publication bias and overall certainty of evidence were assessed using standardized scales and, whenever appropriate, random effects meta-analysis was conducted. Of 6715 identified articles, 43 were included. Overall, consistency of findings was low and methodological heterogeneity high for all outcomes. However, in some studies cognitive and social activities were positively associated with total brain, global and cortical grey matter and hippocampal volume as well as white matter microstructural integrity. Furthermore, structural social network characteristics (e.g., social network size) were associated with regional grey matter volumes, while functional social network characteristics (e.g., social support) were additionally associated with total brain volume. Meta-analyses revealed small but significant partial correlations between cognitive and social activities and hippocampal (three studies; n = 892; r z =0.07) and white matter hyperintensity volume (three studies; n = 2934; r z =-0.04). More prospective studies are needed to assess temporal associations., Competing Interests: Declaration of Competing Interest The authors declare to not have any conflicts of interest., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Introduction: Differences in brain network connectivity may reflect the capability of the neurological substrate to compensate for brain damage and preserve cognitive function (cognitive reserve). We examined the associations between white matter connectivity, brain damage markers, and cognition in a population sample of middle-aged individuals., Methods: A total of 4759 participants from The Maastricht Study (mean age = 59.2, SD = 8.7, 50.2% male) underwent cognitive testing and diffusion magnetic resonance imaging (dMRI), from which brain volume, structural connectivity, and vascular damage were quantified. Multivariable linear regression was used to investigate whether connectivity modified the association between brain damage and cognition, adjusted for demographic and cardiometabolic risk factors., Results: More atrophic and vascular brain damage was associated with worse cognition scores. Increasing connectivity moderated the negative association between damage and cognition (χ 2 = 8.64, df = 3, p ≤ 0.001); individuals with high damage but strong connectivity showed normal cognition., Discussion: Findings support the reserve hypothesis by showing that brain connectivity is associated with cognitive resilience., (© 2022 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)