Graph Convolutional Network with Morphometric Similarity Networks for Schizophrenia Classification

There is significant interest in using neuroimaging data for schizophrenia classification. Graph convolutional networks (GCNs) provide great potential to improve schizophrenia classification using brain graphs derived from neuroimaging data. However, accurate classification of schizophrenia is still challenging due to the heterogeneity of schizophrenia and their subtle differences in neuroimaging features. This paper presents a new graph convolutional framework for population-based schizophrenia classification that leverages graph-theoretical measures of morphometric similarity networks inferred from structural MRI scans and incorporates variational edges to reinforce the learning process. Specifically, we construct individual morphometric similarity networks based on inter-regional similarity of multiple morphometric features (cortical thickness, surface area, gray matter volume, mean curvature, and Gaussian curvature) extracted from T1-weighted MRI. We then formulate an adaptive population graph where each node is represented by the topological features of individual morphometric similarity networks and each edge models the similarity between the topological features of the subjects and incorporates the phenotypic information. An encode module is devised to estimate the associations between phenotypic data of the subjects and to adaptively optimize the edge weights. Our proposed method is evaluated on a large dataset collected from nine sites, resulting in a total sample of 366 patients with schizophrenia and 590 healthy individuals. Experimental results demonstrate that our proposed method improves the classification performance over traditional machine learning algorithms, with a mean classification accuracy of 81.8%. The most salient regions contributing to classification are primarily identified in the middle temporal gyrus and superior temporal gyrus.