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The available evidence suggests that dynamic functional connectivity (dFC)can capture time-varying abnormalities in brain activity in resting-statecerebral functional magnetic resonance imaging (rs-fMRI) data and has a naturaladvantage in uncovering mechanisms of abnormal brain activity inschizophrenia(SZ) patients. Hence, an advanced dynamic brain network analysismodel called the temporal brain category graph convolutional network(Temporal-BCGCN) was employed. Firstly, a unique dynamic brain network analysismodule, DSF-BrainNet, was designed to construct dynamic synchronizationfeatures. Subsequently, a revolutionary graph convolution method, TemporalConv,was proposed, based on the synchronous temporal properties of feature. Finally,the first modular abnormal hemispherical lateralization test tool in deeplearning based on rs-fMRI data, named CategoryPool, was proposed. This studywas validated on COBRE and UCLA datasets and achieved 83.62% and 89.71% averageaccuracies, respectively, outperforming the baseline model and otherstate-of-the-art methods. The ablation results also demonstrate the advantagesof TemporalConv over the traditional edge feature graph convolution approachand the improvement of CategoryPool over the classical graph pooling approach.Interestingly, this study showed that the lower order perceptual system andhigher order network regions in the left hemisphere are more severelydysfunctional than in the right hemisphere in SZ and reaffirms the importanceof the left medial superior frontal gyrus in SZ. Our core code is available at:https://github.com/swfen/Temporal-BCGCN.

Temporal Dynamic Synchronous Functional Brain Network for Schizophrenia Diagnosis and Lateralization Analysis