Diagnosis of Schizophrenia with Functional Connectome Data: A Graph-Based Convolutional Neural Network Approach

Previous deep learning methods have not captured graph or network representations of brain structural or functional connectome data. To address this, we developed the Brain Graph Covariance Pooling Network (BrainGCPNet) by incorporating global covariance pooling and BrainNetCNN into the self-attention mechanism. Resting-state functional magnetic resonance imaging data were obtained from 171 patients with schizophrenia spectrum disorders (SSDs) and 161 healthy controls (HCs). We conducted an ablation analysis of the proposed BrainGCPNet and quantitative performance comparisons with competing methods using the nested tenfold cross validation strategy. The performance of our model was compared with competing methods. Discriminative connections were visualized using the gradient-based explanation method and compared with the results obtained using functional connectivity analysis. The BrainGCPNet showed an accuracy of 83·13%, outperforming other competing methods. Among the top 10 discriminative connections, some were associated with the default mode network and auditory network. Interestingly, these regions were also significant in the functional connectivity analysis. Our findings suggest that the proposed BrainGCPNet can classify patients with SSDs and HCs with higher accuracy than other models. Visualization of salient regions provides important clinical information. These results highlight the potential use of the BrainGCPNet in the diagnosis of schizophrenia.