SDE-Driven Spatio-Temporal Hypergraph Neural Networks for Irregular Longitudinal fMRI Connectome Modeling in Alzheimer's Disease

TL;DR

This paper introduces SDE-HGNN, a novel neural network framework that models irregular longitudinal fMRI data in Alzheimer's disease using stochastic differential equations to reconstruct continuous trajectories and capture dynamic brain connectivity.

Contribution

The paper presents a new SDE-driven hypergraph neural network that effectively handles irregular sampling and missing data in longitudinal neuroimaging for AD progression modeling.

Findings

Outperforms state-of-the-art methods in AD progression prediction

Effectively reconstructs continuous brain trajectories from irregular data

Identifies salient brain regions and connectivity patterns

Abstract

Longitudinal neuroimaging is essential for modeling disease progression in Alzheimer's disease (AD), yet irregular sampling and missing visits pose substantial challenges for learning reliable temporal representations. To address this challenge, we propose SDE-HGNN, a stochastic differential equation (SDE)-driven spatio-temporal hypergraph neural network for irregular longitudinal fMRI connectome modeling. The framework first employs an SDE-based reconstruction module to recover continuous latent trajectories from irregular observations. Based on these reconstructed representations, dynamic hypergraphs are constructed to capture higher-order interactions among brain regions over time. To further model temporal evolution, hypergraph convolution parameters evolve through SDE-controlled recurrent dynamics conditioned on inter-scan intervals, enabling disease-stage-adaptive connectivity modeling. We also incorporate a sparsity-based importance learning mechanism to identify salient brain regions and discriminative connectivity patterns. Extensive experiments on the OASIS-3 and ADNI cohorts demonstrate consistent improvements over state-of-the-art graph and hypergraph baselines in AD progression prediction. The source code is available at https://anonymous.4open.science/r/SDE-HGNN-017F.