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Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention by Kim, Byung-Hoon; Ye, Jong Chul; Kim, Jae-Jin is a scholarly article available to read on EtoBox.

What is Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention about?

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 dyn

Author

Kim, Byung-Hoon; Ye, Jong Chul; Kim, Jae-Jin

Published

2021

Language

EN