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      Bio-psycho-social factors’ associations with brain age: a large-scale UK Biobank diffusion study of 35,749 participants

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          Abstract

          Brain age refers to age predicted by brain features. Brain age has previously been associated with various health and disease outcomes and suggested as a potential biomarker of general health. Few previous studies have systematically assessed brain age variability derived from single and multi-shell diffusion magnetic resonance imaging data. Here, we present multivariate models of brain age derived from various diffusion approaches and how they relate to bio-psycho-social variables within the domains of sociodemographic, cognitive, life-satisfaction, as well as health and lifestyle factors in midlife to old age ( N = 35,749, 44.6–82.8 years of age). Bio-psycho-social factors could uniquely explain a small proportion of the brain age variance, in a similar pattern across diffusion approaches: cognitive scores, life satisfaction, health and lifestyle factors adding to the variance explained, but not socio-demographics. Consistent brain age associations across models were found for waist-to-hip ratio, diabetes, hypertension, smoking, matrix puzzles solving, and job and health satisfaction and perception. Furthermore, we found large variability in sex and ethnicity group differences in brain age. Our results show that brain age cannot be sufficiently explained by bio-psycho-social variables alone. However, the observed associations suggest to adjust for sex, ethnicity, cognitive factors, as well as health and lifestyle factors, and to observe bio-psycho-social factor interactions’ influence on brain age in future studies.

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          Most cited references117

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          UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age

          Cathie Sudlow and colleagues describe the UK Biobank, a large population-based prospective study, established to allow investigation of the genetic and non-genetic determinants of the diseases of middle and old age.
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            FSL.

            FSL (the FMRIB Software Library) is a comprehensive library of analysis tools for functional, structural and diffusion MRI brain imaging data, written mainly by members of the Analysis Group, FMRIB, Oxford. For this NeuroImage special issue on "20 years of fMRI" we have been asked to write about the history, developments and current status of FSL. We also include some descriptions of parts of FSL that are not well covered in the existing literature. We hope that some of this content might be of interest to users of FSL, and also maybe to new research groups considering creating, releasing and supporting new software packages for brain image analysis. Copyright © 2011 Elsevier Inc. All rights reserved.
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              Image Quality Assessment: From Error Visibility to Structural Similarity

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                Author and article information

                Contributors
                Journal
                Front Psychol
                Front Psychol
                Front. Psychol.
                Frontiers in Psychology
                Frontiers Media S.A.
                1664-1078
                09 June 2023
                2023
                : 14
                : 1117732
                Affiliations
                [1] 1Department of Health and Functioning, Western Norway University of Applied Sciences , Bergen, Norway
                [2] 2Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo , Oslo, Norway
                [3] 3Mohn Medical Imaging and Visualization Center (MMIV) , Bergen, Norway
                [4] 4Department of Psychiatry, University of Oxford , Oxford, United Kingdom
                [5] 5LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital, University of Lausanne , Lausanne, Switzerland
                [6] 6Faculty of Health, Medicine and Life Sciences, Maastricht University , Maastricht, Netherlands
                [7] 7Department of Psychiatric Research, Diakonhjemmet Hospital , Oslo, Norway
                [8] 8Department of Psychology, University of Oslo , Oslo, Norway
                [9] 9Department of Radiology, Haukeland University Hospital , Bergen, Norway
                [10] 10Department of Biomedicine, University of Bergen , Bergen, Norway
                [11] 11KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo , Oslo, Norway
                Author notes

                Edited by: Helge Jörn Zöllner, Johns Hopkins Medicine, United States

                Reviewed by: Thomas Kocar, University of Ulm, Germany; Nora Bittner, Heinrich Heine University, Germany

                *Correspondence: Max Korbmacher, max.korbmacher@ 123456hvl.no ; Ivan I. Maximov, ivan.maximov@ 123456hvl.no
                Article
                10.3389/fpsyg.2023.1117732
                10288151
                37359862
                7dc3d17e-2c53-4465-8541-e581fb5a51d5
                Copyright © 2023 Korbmacher, Gurholt, de Lange, van der Meer, Beck, Eikefjord, Lundervold, Andreassen, Westlye and Maximov.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 06 December 2022
                : 27 April 2023
                Page count
                Figures: 7, Tables: 2, Equations: 8, References: 117, Pages: 19, Words: 14807
                Categories
                Psychology
                Original Research
                Custom metadata
                Neuropsychology

                Clinical Psychology & Psychiatry
                brain age,age prediction,magnetic resonance imaging,diffusion mri,health,cognition,brain variability

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