Ramon Casanova, Keenan A Walker, Jamie N Justice, Andrea Anderson, Michael R Duggan, Jenifer Cordon, Ryan T Barnard, Lingyi Lu, Fang-Chi Hsu, Sanaz Sedaghat, Anna Prizment, Stephen B Kritchevsky, Lynne E Wagenknecht, Timothy M Hughes
Machine learning models are increasingly being used to estimate "brain age" from neuroimaging data. The gap between chronological age and the estimated brain age gap (BAG) is potentially a measure of accelerated and resilient brain aging. Brain age calculated in this fashion has been shown to be associated with mortality, measures of physical function, health, and disease. Here, we estimate the BAG using a voxel-based elastic net regression approach, and then, we investigate its associations with mortality, cognitive status, and measures of health and disease in participants from Atherosclerosis Risk in Communities (ARIC) study who had a brain MRI at visit 5 of the study...
March 4, 2024: GeroScience