Abstract
Neuroimaging studies have been instrumental in identifying sex-differences in brain structure, and there is robust evidence for age-related changes in brain structure throughout the lifespan. However, the literature on sex-differences in regional patterns of age-related changes in young adulthood remains limited. To address this, we availed of a machine learning model to obtain local brain-age-gap estimates (L-brainAGE), which reflect voxel-wise differences between an individual’s chronological and biological brain age.
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