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.

翻译：临床研究表明，神经精神疾病（如精神分裂症）患者的大脑结构连接与功能连接存在紊乱。传统方法可能因功能数据有限而仅依赖结构连接，忽视了复杂的结构-功能相互作用，从而阻碍了对精神分裂症患者认知与行为障碍的理解。为解决这一挑战，我们提出了一种新颖的物理引导深度学习框架，该框架利用神经振荡模型来描述一组通过分布于大脑结构中的神经纤维相互连接的神经振荡器的动态特性。我们提出的框架从系统动力学视角出发，通过学习结构-功能耦合，利用结构连接同步生成功能连接。此外，该框架采用一种新颖的多视图图神经网络，结合联合损失函数，执行基于相关性的结构-功能融合及精神分裂症患者分类。在临床数据集上进行的实验显示出性能提升，证明了所提方法的鲁棒性。