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

2026-03-20

AI Summary

This work addresses the challenge of modeling irregularly sampled and visit-missing longitudinal fMRI data in Alzheimer’s disease by proposing the SDE-HGNN framework. It uniquely integrates stochastic differential equations (SDEs) with a dynamic hypergraph neural network to reconstruct continuous latent trajectories and capture high-order interactions among brain regions through dynamically constructed hypergraphs. The framework further incorporates a time-interval-conditioned parameter evolution mechanism and sparse importance learning to enable adaptive connectivity modeling tailored to disease stages. Evaluated on the OASIS-3 and ADNI datasets, the proposed method consistently outperforms existing graph- and hypergraph-based models, achieving significant performance gains in disease progression prediction tasks.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• irregular longitudinal fMRI
• Alzheimer's disease
• connectome modeling
• temporal representation
• missing visits

Innovation

• Stochastic Differential Equation (SDE)
• Hypergraph Neural Network
• Irregular Longitudinal fMRI
• Dynamic Brain Connectivity
• Alzheimer's Disease Progression