Poster (Invited)

FZJ-2024-05128

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Predicting Brain Aging (Brain Age Gap) from Biomedical and Lifestyle Variables

Mahdipour, M. (Corresponding author)FZJ* ; Maleki Balajoo, S.FZJ* ; Raimondo, F.FZJ* ; Nicolaisen, E.FZJ* ; More, S.FZJ* ; Hoffstaedter, F.FZJ* ; Tahmasian, M.FZJ* ; Eickhoff, S.FZJ* ; GENON, S.FZJ*

2024

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Please use a persistent id in citations: doi:10.34734/FZJ-2024-05128

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.

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Contributing Institute(s):

1. Gehirn & Verhalten (INM-7)

Research Program(s):

1. 5251 - Multilevel Brain Organization and Variability (POF4-525) (POF4-525)
2. 5252 - Brain Dysfunction and Plasticity (POF4-525) (POF4-525)

Appears in the scientific report 2024

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Database coverage:

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