Abstract Background Accurately estimating brain age can help identify deviations linked to neurodegenerative diseases, underscoring the need for robust models that accurately perform across heterogenous cohorts. Purpose To develop an age prediction model that is interpretable and robust to demographic and technological variations in brain MRI. Materials and Methods We propose a transformer-based brain age model that analyzes 3D T1-weighted MRI. Model performance was assessed using mean absolute error (MAE). Associations between brain age gap (BAG, i.e., predicted minus chronological age) and chronological age were evaluated in cognitive normal (CN) participants. Clinical relevance was assessed by examining BAG differences across cognitive groups and correlations with Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Results We achieved an MAE 3.65 years on ADNI2 0.001) for MMSE and -0.393 (p 0.001) for MoCA, where declining scores generally signify worsening cognitive performance. The saliency map highlighted white and deep gray matter structures as key regions influenced by brain aging. Conclusion Our model effectively integrated multiview and volumetric information to achieve state-of-the-art brain age prediction, with improved generalizability, interpretability and association with cognitive function.
Kan et al. (2026) studied this question.