Discovering Heterogeneous Neurodegenerative Disease Patterns From MRI Data for Improved Prediction

Neurodegenerative diseases exhibit substantial heterogeneity, complicating both diagnosis and prognosis. Identifying clinically meaningful subtypes is crucial for understanding disease mechanisms and improving diagnostic precision and prognostic accuracy. Existing subtyping approaches primarily rely on unsupervised learning of patient data for capturing inter-individual variability, but they may fail to uncover subtypes that are directly informative for diagnosis or prognosis. To address this limitation, we propose a novel mixture-of-experts (MoE) framework that integrates predictive modeling with subtype discovery. Unlike traditional subtyping methods, our approach learns a router to assign individuals to specialized expert networks, each corresponding to a distinct subtype, for improving predictive accuracy. By coupling subtype identification with prediction, the proposed MoE framework encourages the discovery of subtypes that are not only statistically distinct but also clinically informative. We evaluate the framework on a real-world dataset of mild cognitive impairment (MCI) subjects and a semi-simulated dataset, demonstrating superior performance in predicting progression from MCI to Alzheimer's disease while revealing distinct, clinically meaningful MCI subtypes. Code is available at https://github.com/Kateridge/MoESubtyping.