Recent applications of pattern recognition techniques on brain connectome classification using functional connectivity (FC) neglect the non-Euclidean topology and causal dynamics of brain connectivity across time. In this paper, a deep probabilistic spatiotemporal framework developed based on variational Bayes (DSVB) is proposed to learn time-varying topological structures in dynamic brain FC networks for autism spectrum disorder (ASD) identification. The proposed framework incorporates a spatial-aware recurrent neural network to capture rich spatiotemporal patterns across dynamic FC networks, followed by a fully-connected neural network to exploit these learned patterns for subject-level classification. To overcome model overfitting on limited training datasets, an adversarial training strategy is introduced to learn graph embedding models that generalize well to unseen brain networks. Evaluation on the ABIDE resting-state functional magnetic resonance imaging dataset shows that our proposed framework significantly outperformed state-of-the-art methods in identifying ASD. Dynamic FC analyses with DSVB learned embeddings reveal apparent group difference between ASD and healthy controls in network profiles and switching dynamics of brain states.

翻译：近期在基于功能连接（FC）的脑连接组分类中应用模式识别技术的研究，忽略了大脑连接在时间维度上的非欧几里得拓扑结构及因果动力学。本文提出了一种基于变分贝叶斯的深度概率时空框架（DSVB），用于学习自闭症谱系障碍（ASD）识别中动态脑FC网络的时变拓扑结构。该框架通过融入空间感知的递归神经网络捕获跨动态FC网络的丰富时空模式，再利用全连接神经网络挖掘这些学习到的模式进行受试者级分类。为克服有限训练数据集上的过拟合问题，引入对抗训练策略以学习能良好泛化至未知脑网络的图嵌入模型。在ABIDE静息态功能磁共振成像数据集上的评估表明，本框架在ASD识别中显著优于现有最优方法。利用DSVB学习嵌入进行的动态FC分析显示，ASD患者与健康对照组在网络特征及脑状态切换动力学方面存在显著组间差异。