Morphometric similarity network-based graph convolutional networks for schizophrenia classification

Schizophrenia is a complex neuropsychiatric disorder characterized by significant heterogeneity, posing a challenge for accurate classification using neuroimaging data. Graph convolutional networks (GCNs) have emerged as a promising approach for leveraging the inherent graph structure of brain connectivity to discriminate between patients with schizophrenia and healthy controls. However, existing GCN-based methods often struggle to capture the subtle neuroimaging differences associated with the disorder. To address these limitations, we propose a novel GCN framework (MSN-GCN) that integrates morphometric similarity networks (MSN) derived from structural MRI scans. Our method involves constructing individual brain graphs based on multiple morphometric features, including cortical thickness, surface area, gray matter volume, mean curvature, and Gaussian curvature. These individual graphs are then combined into a population-level graph that incorporates both topological and phenotypic information. By employing a variational edge learning approach, our model adaptively optimizes the edge weights to capture the complex relationships between brain structure and schizophrenia. We evaluated our proposed method on a large, multi-site dataset comprising 377 schizophrenia patients and 590 healthy controls. Experimental results demonstrate superior classification performance compared to state-of-the-art methods, achieving a mean accuracy of 80.85%. Notably, the superior temporal gyrus emerged as a key region contributing to classification. Significant differences in the clustering coefficient of the superior temporal gyrus, postcentral gyrus, and lateral occipital cortex between patients and healthy controls, and their correlations with negative symptoms were detected in post-hoc analyses. These findings demonstrate the potential of MSN-GCN for accurate schizophrenia detection and provide valuable insights into the neural correlates of the disorder.