Physics-Guided Multi-View Graph Neural Network for Schizophrenia Classification via Structural-Functional Coupling

TL;DR

This paper introduces a physics-guided multi-view graph neural network that models the coupling between brain structural and functional connectivity to improve schizophrenia classification accuracy.

Contribution

It presents a novel deep learning framework combining neural oscillation models with multi-view GNNs to better understand SC-FC relationships in schizophrenia.

Findings

Enhanced classification performance on clinical datasets

Robustness of the proposed approach demonstrated

Effective modeling of SC-FC coupling

Abstract

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.