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@INPROCEEDINGS{Mahdipour:1029439,
author = {Mahdipour, Mostafa and Maleki Balajoo, Somayeh and
Raimondo, Federico and Nicolaisen, Eliana and More, Shammi
and Hoffstaedter, Felix and Tahmasian, Masoud and Eickhoff,
Simon and GENON, Sarah},
title = {{P}redicting {B}rain {A}ging ({B}rain {A}ge {G}ap) from
{B}iomedical and {L}ifestyle {V}ariables},
reportid = {FZJ-2024-05128},
year = {2024},
abstract = {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.},
month = {Jun},
date = {2024-06-23},
organization = {Organization for Human Brain Mapping
(OHBM) Annual Meeting 2024, Seoul
(South Korea), 23 Jun 2024 - 27 Jun
2024},
subtyp = {Invited},
cin = {INM-7},
cid = {I:(DE-Juel1)INM-7-20090406},
pnm = {5251 - Multilevel Brain Organization and Variability
(POF4-525) / 5252 - Brain Dysfunction and Plasticity
(POF4-525)},
pid = {G:(DE-HGF)POF4-5251 / G:(DE-HGF)POF4-5252},
typ = {PUB:(DE-HGF)24},
doi = {10.34734/FZJ-2024-05128},
url = {https://juser.fz-juelich.de/record/1029439},
}