A Stage-Aware Mixture of Experts Framework for Neurodegenerative Disease Progression Modelling

TL;DR

This paper introduces a stage-aware Mixture of Experts framework for modeling neurodegenerative disease progression, combining graph neural diffusion and localized reaction modules to capture complex, stage-specific brain dynamics.

Contribution

It presents a novel, dynamic model that explicitly accounts for changing mechanisms across disease stages using time-dependent expert weighting and advanced neural diffusion techniques.

Findings

Graph-related processes dominate early stages.

Model captures complex progression dynamics.

Provides clinical insights aligned with existing literature.

Abstract

The long-term progression of neurodegenerative diseases is commonly conceptualized as a spatiotemporal diffusion process that consists of a graph diffusion process across the structural brain connectome and a localized reaction process within brain regions. However, modeling this progression remains challenging due to 1) the scarcity of longitudinal data obtained through irregular and infrequent subject visits and 2) the complex interplay of pathological mechanisms across brain regions and disease stages, where traditional models assume fixed mechanisms throughout disease progression. To address these limitations, we propose a novel stage-aware Mixture of Experts (MoE) framework that explicitly models how different contributing mechanisms dominate at different disease stages through time-dependent expert weighting.Data-wise, we utilize an iterative dual optimization method to properly estimate the temporal position of individual observations, constructing a co hort-level progression trajectory from irregular snapshots. Model-wise, we enhance the spatial component with an inhomogeneous graph neural diffusion model (IGND) that allows diffusivity to vary based on node states and time, providing more flexible representations of brain networks. We also introduce a localized neural reaction module to capture complex dynamics beyond standard processes.The resulting IGND-MoE model dynamically integrates these components across temporal states, offering a principled way to understand how stage-specific pathological mechanisms contribute to progression. The stage-wise weights yield novel clinical insights that align with literature, suggesting that graph-related processes are more influential at early stages, while other unknown physical processes become dominant later on.