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      Cardiometabolic risk factors associated with brain age and accelerate brain ageing

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          Abstract

          The structure and integrity of the ageing brain is interchangeably linked to physical health, and cardiometabolic risk factors (CMRs) are associated with dementia and other brain disorders. In this mixed cross‐sectional and longitudinal study (interval mean = 19.7 months), including 790 healthy individuals (mean age = 46.7 years, 53% women), we investigated CMRs and health indicators including anthropometric measures, lifestyle factors, and blood biomarkers in relation to brain structure using MRI‐based morphometry and diffusion tensor imaging (DTI). We performed tissue specific brain age prediction using machine learning and performed Bayesian multilevel modeling to assess changes in each CMR over time, their respective association with brain age gap (BAG), and their interaction effects with time and age on the tissue‐specific BAGs. The results showed credible associations between DTI‐based BAG and blood levels of phosphate and mean cell volume (MCV), and between T1‐based BAG and systolic blood pressure, smoking, pulse, and C‐reactive protein (CRP), indicating older‐appearing brains in people with higher cardiometabolic risk (smoking, higher blood pressure and pulse, low‐grade inflammation). Longitudinal evidence supported interactions between both BAGs and waist‐to‐hip ratio (WHR), and between DTI‐based BAG and systolic blood pressure and smoking, indicating accelerated ageing in people with higher cardiometabolic risk (smoking, higher blood pressure, and WHR). The results demonstrate that cardiometabolic risk factors are associated with brain ageing. While randomized controlled trials are needed to establish causality, our results indicate that public health initiatives and treatment strategies targeting modifiable cardiometabolic risk factors may also improve risk trajectories and delay brain ageing.

          Abstract

          The structure and integrity of the ageing brain is interchangeably linked to physical health, and cardiometabolic risk factors (CMRs). We investigated CMRs and health indicators including anthropometric measures, lifestyle factors, and blood biomarkers in relation to brain structure using MRI‐based morphometry and diffusion tensor imaging (DTI). Tissue‐specific brain age prediction using machine learning revealed older‐appearing brains and accelerated ageing in people with higher cardiometabolic risk.

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          Most cited references106

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          mice: Multivariate Imputation by Chained Equations inR

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            An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

            In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.
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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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                Author and article information

                Contributors
                dani.beck@psykologi.uio.no
                l.t.westlye@psykologi.uio.no
                Journal
                Hum Brain Mapp
                Hum Brain Mapp
                10.1002/(ISSN)1097-0193
                HBM
                Human Brain Mapping
                John Wiley & Sons, Inc. (Hoboken, USA )
                1065-9471
                1097-0193
                09 October 2021
                1 February 2022
                : 43
                : 2 ( doiID: 10.1002/hbm.v43.2 )
                : 700-720
                Affiliations
                [ 1 ] NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine University of Oslo Oslo
                [ 2 ] Department of Psychology University of Oslo Oslo
                [ 3 ] Sunnaas Rehabilitation Hospital HT Nesodden
                [ 4 ] LREN, Centre for Research in Neurosciences‐Department of Clinical Neurosciences CHUV and University of Lausanne Lausanne Switzerland
                [ 5 ] Department of Psychiatry University of Oxford Oxford UK
                [ 6 ] Bjørknes College Oslo Norway
                [ 7 ] Department of Health and Functioning Western Norway University of Applied Sciences Bergen Norway
                [ 8 ] KG Jebsen Centre for Neurodevelopmental Disorders University of Oslo Oslo Norway
                [ 9 ] CatoSenteret Rehabilitation Center Son Norway
                [ 10 ] Department of Psychiatry and Psychotherapy University of Tübingen Tubingen Germany
                Author notes
                [*] [* ] Correspondence

                Dani Beck and Lars T. Westlye, Department of Psychology, University of Oslo, PO Box 1094 Blindern, Oslo 0317, Norway.

                Email: dani.beck@ 123456psykologi.uio.no (D. B.) and l.t.westlye@ 123456psykologi.uio.no (L. T. W)

                Author information
                https://orcid.org/0000-0002-0974-9304
                https://orcid.org/0000-0001-9393-5857
                https://orcid.org/0000-0001-6475-2576
                https://orcid.org/0000-0001-8644-956X
                Article
                HBM25680
                10.1002/hbm.25680
                8720200
                34626047
                c2889b9b-90b2-4618-835e-6e769726c156
                © 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 02 September 2021
                : 06 March 2021
                : 25 September 2021
                Page count
                Figures: 13, Tables: 4, Pages: 21, Words: 13685
                Funding
                Funded by: EkstraStiftelsen Helse og Rehabilitering , doi 10.13039/100009471;
                Award ID: 2015/FO5146
                Funded by: German Federal Ministry of Education and Research
                Award ID: 01ZX1904A
                Funded by: H2020 European Research Council , doi 10.13039/100010663;
                Award ID: 802998
                Award ID: 847776
                Funded by: Helse Sør‐Øst RHF , doi 10.13039/501100006095;
                Award ID: 2014097
                Award ID: 2015044
                Award ID: 2015073
                Award ID: 2016083
                Award ID: 2018037
                Award ID: 2018076
                Funded by: Norges Forskningsråd , doi 10.13039/501100005416;
                Award ID: 223273
                Award ID: 248238
                Award ID: 249795
                Award ID: 276082
                Award ID: 298646
                Funded by: Stiftelsen Kristian Gerhard Jebsen , doi 10.13039/100007793;
                Award ID: PZ00P3_193658
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                February 1, 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.7.0 mode:remove_FC converted:01.01.2022

                Neurology
                brain age,cardiometabolic risk,dti,t1 mri
                Neurology
                brain age, cardiometabolic risk, dti, t1 mri

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