Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention

TL;DR

This paper introduces STAGIN, a novel graph neural network model that captures dynamic brain connectome representations using spatio-temporal attention, improving interpretability and performance over static methods.

Contribution

STAGIN is the first GNN approach to effectively incorporate dynamic functional connectivity with explainability through spatio-temporal attention mechanisms.

Findings

Outperforms existing static and dynamic FC methods on HCP datasets.

Provides neuroscientifically valid interpretability of brain connectivity.

Demonstrates the importance of temporal dynamics in brain network analysis.

Abstract

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