Aging as an active player in Alzheimer's disease classification: Insights from feature selection in BrainAge models.

ABSTRACT:

Background

BrainAge models estimate the biological age of the brain using neuroimaging or clinical features, making them promising tools for studying neurodegenerative diseases like Alzheimer's disease. However, the reliance of BrainAge models on neuroimaging features such as grey matter volume and hippocampal atrophy, can introduce biases linked to individuals' ages as these features are influenced both by normal aging and Alzheimer's disease progression. The potential presence of such age-biases raises a critical question: can BrainAge models trained to estimate biological brain aging make meaningful contributions to Alzheimer's diagnosis, or does any introduced age-bias conflate aging effects with disease pathology? Understanding how deliberate feature selection impacts this con

SUBMITTER: Garcia Condado J 

PROVIDER: S-EPMC12811041 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature