用随机微分方程建模阿尔茨海默病脑连接组的不规则纵向变化
SDE-Driven Spatio-Temporal Hypergraph Neural Networks for Irregular Longitudinal fMRI Connectome Modeling in Alzheimer's Disease
- 基于SDE重建不规则采样下的连续脑区轨迹
- 动态超图捕捉脑区间高阶时序交互，提升预测准确率
- 可识别关键脑区与疾病特征连接模式，适合神经退行性研究
纵向神经影像对阿尔茨海默病（AD）进展建模至关重要，但不规则采样和缺失随访带来挑战。本文提出SDE-HGNN，一种基于随机微分方程（SDE）的时空超图神经网络，用于不规则纵向fMRI脑连接组建模。该框架首先通过SDE驱动的重建模块，从不规则观测中恢复连续潜在轨迹；基于重构表示构建动态超图，捕捉脑区间随时间演化的高阶交互；同时，超图卷积参数通过受访间隔条件控制的SDE递归动态演化，实现疾病阶段自适应连接建模。此外，引入基于稀疏性的重要性学习机制，识别显著脑区与判别性连接模式。在OASIS-3和ADNI队列上的实验表明，该方法在AD进展预测任务中持续优于当前最优的图与超图基线模型。
原文摘要 · Abstract (English)
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-visit 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.
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