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:基于时间动态同步功能脑网络的精神分裂症诊断及侧化分析
Translated Abstract:
本研究揭示了动态功能连接(dFC)在静息状态下的脑功能磁共振成像(rs-fMRI)数据中,可以捕捉精神分裂症(SZ)患者的时间变化异常脑活动的机制。因此,我们采用了一种先进的动态脑网络分析模型,名为 temporal brain category graph convolutional network(Temporal-BCGCN)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,基于特征的同步时间性质,提出了一种革命性的图卷积方法,TemporalConv。最后,提出了一种基于深度学习的静息状态下脑功能磁共振成像数据的侧化异常检测工具在分类不均衡的情况下仍能有效地工作,命名为 CategoryPool。本研究验证了COBRE和UCLA数据集,并分别达到了83.62%和89.71%的平均精确度,超越了基准模型和其他最新的方法。消融结果也证明了TemporalConv相对传统的边界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,研究表明,在SZ中,左半球的较低阶知觉系统和较高阶网络区域比右半球严重功能失调,再次证实了左中上额回在SZ中的重要性。核心代码可在https://github.com/swfen/Temporal-BCGCN上获得。