  Recent applications of pattern recognition techniques on brain connectome
classification using functional connectivity (FC) are shifting towards
acknowledging the non-Euclidean topology and causal dynamics of brain
connectivity across time. In this paper, a deep spatiotemporal variational
Bayes (DSVB) framework is proposed to learn time-varying topological structures
in dynamic FC networks for identifying autism spectrum disorder (ASD) in human
participants. The framework incorporates a spatial-aware recurrent neural
network with an attention-based message passing scheme to capture rich
spatiotemporal patterns across dynamic FC networks. 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 substantially outperforms
state-of-the-art methods in identifying patients with ASD. Dynamic FC analyses
with DSVB-learned embeddings reveal apparent group differences between ASD and
healthy controls in brain network connectivity patterns and switching dynamics
of brain states.
