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：最近运用功能连通(FC)对大脑连接器分类应用模式识别技术,忽略了非欧元的表层学和大脑连通性的长期因果动态。在本文件中,提议根据变异贝氏体(DSVB)开发的深度概率表球时空框架,以学习动态大脑FC自闭症谱系障碍识别(ASD)网络中时间变化的表层结构。拟议框架包含一个有空间觉的经常性神经网络,以捕捉动态FC网络中丰富的波段时空模式,随后是一个完全连接的神经网络,以利用这些学习的模型进行主题分类。为了克服在有限的培训数据集上过度应用模型,引入了一种对抗性培训战略,以学习能够向看不见的脑网络通俗化的图形嵌入模型。对ABIDE休息状态功能磁共振成成成像数据集的评价显示,我们拟议的框架在确定ASDD方面大大超越了状态的电磁振动方法。动态FC分析与DSVB学会的嵌入显示ASD和健康控制在网络剖面和大脑状态上明显存在群体差异。