Brain-age models with lower age prediction accuracy have higher sensitivity for disease detection.

ABSTRACT: This study critically reevaluates the utility of brain-age models within the context of detecting neurological and psychiatric disorders, challenging the conventional emphasis on maximizing chronological age prediction accuracy. Our analysis of T1 MRI data from 46,381 UK Biobank participants reveals that simpler machine learning models, and notably those with excessive regularization, demonstrate superior sensitivity to disease-relevant changes compared to their more complex counterparts, despite being less accurate in chronological age prediction. This counterintuitive discovery suggests that models traditionally deemed inferior might, in fact, offer a more meaningful biomarker for brain health by capturing variations pertinent to disease states. These findings challenge the assumption th

SUBMITTER: Schulz MA 

PROVIDER: S-EPMC12633945 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature