Discovering Heterogeneous Neurodegenerative Disease Patterns From MRI Data for Improved Prediction

Abstract

Neurodegenerative diseases exhibit substantial heterogeneity, complicating both diagnosis and prognosis. Identifying clinically meaningful subtypes is crucial for understanding disease mechanisms and can also improve diagnostic precision and prognostic accuracy. Existing subtyping approaches primarily rely on unsupervised learning of patient data for capturing inter-individual variability, often failing to uncover subtypes that are informative for diagnosis or prognosis. To address this limitation, we propose a novel mixture-of-experts (MoE) framework that integrates predictive modeling with subtype identification. Unlike traditional subtyping methods, our approach learns a router to assign individuals to specialized expert networks, each corresponding to a distinct subtype, to improve predictive accuracy. This MoE framework ensures that the discovered subtypes 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 for predicting MCI subjects progression to Alzheimers disease while identifying distinct clinically meaningful MCI subtypes. Code is available at https://github.com/Kateridge/MoESubtyping.