Brain age pre-training for prediction of Alzheimer’s disease diagnosis and mild cognitive impairment progression

Motivation: Literature suggests large multisite brain age pre-trained models (indirect models) hold significant promise for downstream prediction on small clinical samples via transfer learning. Goal(s): Our goal was to determine if such indirect models indeed outperform models trained-from-scratch (direct models), across varying training and validation set sizes, on two clinical prediction tasks: Alzheimer’s disease (AD) diagnosis, and mild cognitive impairment (MCI) progression. Approach: State-of-the-art brain age model pre-trained on n=53,542 diverse dataset was used as initialization for indirect models. Results: For AD Diagnosis, Direct model significantly outperformed feature extracted indirect model starting from 400 training and validation samples or more. Impact: The 400-training-and-validation-samples threshold encourages clinical institutions with limited computing resources and small sample sizes (n<400) to feature extract the brain age pre-trained model instead of training from scratch, potentially lowering healthcare costs and speeding up Alzheimer’s disease diagnosis and prognosis.