OpenMAP-BrainAge: generalizable and interpretable brain age predictor from MRI

Accurately estimating brain age can help identify deviations linked to neurodegenerative diseases, underscoring the need for robust models that accurately perform across heterogenous cohorts.

To develop an age prediction model that is interpretable and robust to demographic and technological variations in brain MRI.

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, ie, 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).

We achieved an MAE 3.65 years on ADNI2 & 3 and OASIS3 test sets, and a high generalizability of MAE of 3.54 years on AIBL. In dementia, a notable increase in brain age gap (BAG) along with cognitive decline, with a mean of 0.15 years (95% CI: [-0.22, 0.51]) in CN, 2.55 years ([2.40, 2.70]) in mild cognitive impairment (MCI), and 6.12 years ([5.82, 6.43]) is noted. Negative correlation between BAG and cognitive scores was observed after adjustment for covariates, with r = -0.397 (P < 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.

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.