TY  - CONF
AU  - Mahdipour, Mostafa
AU  - Maleki Balajoo, Somayeh
AU  - Raimondo, Federico
AU  - Nicolaisen, Eliana
AU  - More, Shammi
AU  - Hoffstaedter, Felix
AU  - Tahmasian, Masoud
AU  - Eickhoff, Simon
AU  - GENON, Sarah
TI  - Predicting Brain Aging (Brain Age Gap) from Biomedical and Lifestyle Variables
M1  - FZJ-2024-05128
PY  - 2024
AB  - 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.
T2  - Organization for Human Brain Mapping (OHBM) Annual Meeting 2024
CY  - 23 Jun 2024 - 27 Jun 2024, Seoul (South Korea)
Y2  - 23 Jun 2024 - 27 Jun 2024
M2  - Seoul, South Korea
LB  - PUB:(DE-HGF)24
DO  - DOI:10.34734/FZJ-2024-05128
UR  - https://juser.fz-juelich.de/record/1029439
ER  -