From ‘loose fitting’ to high-performance, uncertainty-aware brain-age modelling

In brain-age modelling, a machine learning model is trained on a normative, usually healthy group of individuals to predict chronological age from neuroimaging data. This model is then applied to new data and the difference between predicted and chronological age —termed the brain-age gap (BAG)—is taken as a measure of deviation from ‘normal ageing’. This new area of research has generated large interest over the last decade and accelerated ageing has been associated with many different disorders and pathologies (Franke and Gaser, 2019).
Source: Brain - Category: Neurology Source Type: research