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001     1029439
005     20240801202956.0
024 7 _ |a 10.34734/FZJ-2024-05128
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037 _ _ |a FZJ-2024-05128
100 1 _ |a Mahdipour, Mostafa
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111 2 _ |a Organization for Human Brain Mapping (OHBM) Annual Meeting 2024
|c Seoul
|d 2024-06-23 - 2024-06-27
|w South Korea
245 _ _ |a Predicting Brain Aging (Brain Age Gap) from Biomedical and Lifestyle Variables
260 _ _ |c 2024
336 7 _ |a Conference Paper
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520 _ _ |a 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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