  The available evidence suggests that dynamic functional connectivity (dFC)
can capture time-varying abnormalities in brain activity in resting-state
cerebral functional magnetic resonance imaging (rs-fMRI) data and has a natural
advantage in uncovering mechanisms of abnormal brain activity in
schizophrenia(SZ) patients. Hence, an advanced dynamic brain network analysis
model called the temporal brain category graph convolutional network
(Temporal-BCGCN) was employed. Firstly, a unique dynamic brain network analysis
module, DSF-BrainNet, was designed to construct dynamic synchronization
features. 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 deep
learning based on rs-fMRI data, named CategoryPool, was proposed. This study
was validated on COBRE and UCLA datasets and achieved 83.62% and 89.71% average
accuracies, respectively, outperforming the baseline model and other
state-of-the-art methods. The ablation results also demonstrate the advantages
of TemporalConv over the traditional edge feature graph convolution approach
and the improvement of CategoryPool over the classical graph pooling approach.
Interestingly, this study showed that the lower order perceptual system and
higher order network regions in the left hemisphere are more severely
dysfunctional than in the right hemisphere in SZ and reaffirms the importance
of the left medial superior frontal gyrus in SZ. Our core code is available at:
https://github.com/swfen/Temporal-BCGCN.
