Available evidence suggests that dynamic functional connectivity (dFC) can capture time-varying abnormalities in brain activity in 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 accuracy, 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 convolution approach of graph convolutional neural network (GCN) 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 of 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）能够捕捉静息态功能磁共振成像（rs-fMRI）数据中脑活动随时间变化的异常特征，并在揭示精神分裂症（SZ）患者异常脑活动机制方面具有天然优势。为此，本研究采用了一种名为时序脑类别图卷积网络（temporal-BCGCN）的先进动态脑网络分析模型。首先，设计了一个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，基于特征的同步时序特性，提出了一种革命性图卷积方法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测工具CategoryPool。本研究在COBRE和UCLA数据集上进行了验证，分别取得83.62%和89.71%的平均准确率，优于基线模型及其他当前最优方法。消融实验结果进一步证明了TemporalConv相比传统图卷积神经网络（GCN）边缘特征卷积方法的优势，以及CategoryPool对经典图池化方法的改进效果。有趣的是，本研究表明，SZ患者左侧半球的低级感知系统与高级网络区域功能异常程度较右侧半球更为严重，并再次证实了左侧内侧额上回在精神分裂症中的关键作用。核心代码开源地址：https://github.com/swfen/Temporal-BCGCN。