%0 Conference Paper
%A Mahdipour, Mostafa
%A Maleki Balajoo, Somayeh
%A Raimondo, Federico
%A Nicolaisen, Eliana
%A More, Shammi
%A Hoffstaedter, Felix
%A Tahmasian, Masoud
%A Eickhoff, Simon
%A GENON, Sarah
%T Predicting Brain Aging (Brain Age Gap) from Biomedical and Lifestyle Variables
%M FZJ-2024-05128
%D 2024
%X The 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.
%B Organization for Human Brain Mapping (OHBM) Annual Meeting 2024
%C 23 Jun 2024 - 27 Jun 2024, Seoul (South Korea)
Y2 23 Jun 2024 - 27 Jun 2024
M2 Seoul, South Korea
%F PUB:(DE-HGF)24
%9 Poster
%R 10.34734/FZJ-2024-05128
%U https://juser.fz-juelich.de/record/1029439