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001029439 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-05128
001029439 037__ $$aFZJ-2024-05128
001029439 1001_ $$0P:(DE-Juel1)187159$$aMahdipour, Mostafa$$b0$$eCorresponding author
001029439 1112_ $$aOrganization for Human Brain Mapping (OHBM) Annual Meeting 2024$$cSeoul$$d2024-06-23 - 2024-06-27$$wSouth Korea
001029439 245__ $$aPredicting Brain Aging (Brain Age Gap) from Biomedical and Lifestyle Variables
001029439 260__ $$c2024
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001029439 520__ $$aThe Brain Age Gap (BAG) can be considered as an indicator of the brain health [1, 2]. BAG is defi ned as thedifference between an individual's chronological age and the age predicted by a machine learning (ML)algorithm based on individual brain features. While some studies demonstrated univariate associationsbetween the BAG and several lifestyle and biomedical variables [2, 3], a substantial gap persists inunderstanding the multivariate association between these factors and BAG. Here, we addressed this questionby using a wide range of biomedical, lifestyle, and sociodemographic variables conjointly to predict the BAG ina large population of the UK Biobank.
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001029439 7001_ $$0P:(DE-Juel1)177823$$aMore, Shammi$$b4$$ufzj
001029439 7001_ $$0P:(DE-Juel1)131684$$aHoffstaedter, Felix$$b5$$ufzj
001029439 7001_ $$0P:(DE-Juel1)188400$$aTahmasian, Masoud$$b6$$ufzj
001029439 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b7$$ufzj
001029439 7001_ $$0P:(DE-Juel1)161225$$aGENON, Sarah$$b8$$ufzj
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