  Functional connectivity (FC) between regions of the brain can be assessed by
the degree of temporal correlation measured with functional neuroimaging
modalities. Based on the fact that these connectivities build a network,
graph-based approaches for analyzing the brain connectome have provided
insights into the functions of the human brain. The development of graph neural
networks (GNNs) capable of learning representation from graph structured data
has led to increased interest in learning the graph representation of the brain
connectome. Although recent attempts to apply GNN to the FC network have shown
promising results, there is still a common limitation that they usually do not
incorporate the dynamic characteristics of the FC network which fluctuates over
time. In addition, a few studies that have attempted to use dynamic FC as an
input for the GNN reported a reduction in performance compared to static FC
methods, and did not provide temporal explainability. Here, we propose STAGIN,
a method for learning dynamic graph representation of the brain connectome with
spatio-temporal attention. Specifically, a temporal sequence of brain graphs is
input to the STAGIN to obtain the dynamic graph representation, while novel
READOUT functions and the Transformer encoder provide spatial and temporal
explainability with attention, respectively. Experiments on the HCP-Rest and
the HCP-Task datasets demonstrate exceptional performance of our proposed
method. Analysis of the spatio-temporal attention also provide concurrent
interpretation with the neuroscientific knowledge, which further validates our
method. Code is available at https://github.com/egyptdj/stagin
