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.

翻译：现有证据表明，动态功能连接（dFC）能够捕捉静息态脑功能磁共振成像（rs-fMRI）数据中脑活动的时间变异异常，在揭示精神分裂症（SZ）患者脑活动异常机制方面具有天然优势。为此，采用了一种名为时间脑类别图卷积网络（Temporal-BCGCN）的先进动态脑网络分析模型。首先，设计了独特的动态脑网络分析模块DSF-BrainNet，用于构建动态同步特征。随后，基于特征的同步时间特性，提出了一种革命性的图卷积方法TemporalConv。最后，提出了首个基于rs-fMRI数据的深度学习模块化异常半球偏侧化检验工具，命名为CategoryPool。本研究在COBRE和UCLA数据集上进行了验证，分别达到了83.62%和89.71%的平均准确率，优于基线模型及其他最先进方法。消融实验结果也证明了TemporalConv相对于传统边特征图卷积方法的优势，以及CategoryPool对经典图池化方法的改进。有趣的是，本研究表明，在SZ患者中，左半球的低阶感知系统和高阶网络区域功能失调比右半球更为严重，并再次证实了左侧内侧额上回在SZ中的重要性。我们的核心代码可在https://github.com/swfen/Temporal-BCGCN获取。