Reconsidering Brain Age: Why Age-Prediction Models Fail as Measures of Brain Aging

Abstract

Brain age models - machine-learning predictions of chronological age from brain imaging - are widely interpreted as markers of accelerated brain aging. Here we show that this interpretation cannot be supported. Because these models are trained to predict chronological age, they prioritize features that change similarly across people and actively downweight features that capture differences in individual trajectories, precisely the property an aging-rate biomarker must have. In effect, brain age models are optimized to ignore the very signal they are used to study, thereby risking converting stable between-person differences into apparent accelerated aging. Using theoretical analysis, simulations, and longitudinal MRI, we confirm both predicted failure modes: brain age models indicated "accelerated aging" in participants with low birth weight despite no longitudinal evidence, while a single hippocampal volume measurement was more sensitive than the brain age gap to tau-related neurodegeneration. Across much of the brain age literature, it is therefore not possible to determine whether reported effects reflect brain aging or stable anatomical differences, and the brain age gap should not be interpreted as a marker of brain aging or brain health. We propose alternative strategies that reorient prediction targets from shared age-related patterns to individual differences in change.