Morphometric similarity network-based graph convolutional networks for schizophrenia classification.

ABSTRACT: 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,

SUBMITTER: Park HW 

PROVIDER: S-EPMC12532806 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature