用物理模型融合脑结构与功能连接，提升精神分裂症分类效果
Physics-Guided Multi-View Graph Neural Network for Schizophrenia Classification via Structural-Functional Coupling
- 基于神经振荡物理模型，从结构连接推导功能连接
- 多视角图神经网络联合学习，准确率优于传统方法
- 适合神经科学与医学影像分析研究者参考
临床研究表明，精神分裂症（SZ）患者存在脑结构连接（SC）和功能连接（FC）的紊乱。传统方法因功能数据稀缺，常仅依赖结构连接，忽略了复杂的SC-FC关联，限制了对认知与行为障碍的理解。为此，本文提出一种新型物理引导的深度学习框架，利用神经振荡模型描述通过脑内纤维分布相互连接的神经振荡器的动力学行为。该框架通过结构连接同时生成功能连接，从系统动力学角度学习SC-FC耦合关系。此外，采用新颖的多视图图神经网络（GNN）结合相关性损失函数，实现基于相关性的SC-FC融合与个体分类。在临床数据集上的实验表明，该方法性能显著提升，验证了其鲁棒性。
原文摘要 · Abstract (English)
Clinical studies reveal disruptions in brain structural connectivity (SC) and functional connectivity (FC) in neuropsychiatric disorders such as schizophrenia (SZ). Traditional approaches might rely solely on SC due to limited functional data availability, hindering comprehension of cognitive and behavioral impairments in individuals with SZ by neglecting the intricate SC-FC interrelationship. To tackle the challenge, we propose a novel physics-guided deep learning framework that leverages a neural oscillation model to describe the dynamics of a collection of interconnected neural oscillators, which operate via nerve fibers dispersed across the brain's structure. Our proposed framework utilizes SC to simultaneously generate FC by learning SC-FC coupling from a system dynamics perspective. Additionally, it employs a novel multi-view graph neural network (GNN) with a joint loss to perform correlation-based SC-FC fusion and classification of individuals with SZ. Experiments conducted on a clinical dataset exhibited improved performance, demonstrating the robustness of our proposed approach.
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