Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysis

TL;DR

This paper introduces a framework using Graph Isomorphism Networks (GIN) to analyze rs-fMRI data, providing neuroscientifically interpretable visualizations of brain regions relevant to sex classification.

Contribution

It reveals that GIN acts as a dual of CNN in graph space, enabling the use of CNN saliency techniques for explainability in brain graph analysis.

Findings

Saliency maps align with known sex-related brain differences

GIN effectively classifies sex based on brain connectivity

Framework enhances interpretability of GNNs in neuroimaging

Abstract

Graph neural networks (GNN) rely on graph operations that include neural network training for various graph related tasks. Recently, several attempts have been made to apply the GNNs to functional magnetic resonance image (fMRI) data. Despite recent progresses, a common limitation is its difficulty to explain the classification results in a neuroscientifically explainable way. Here, we develop a framework for analyzing the fMRI data using the Graph Isomorphism Network (GIN), which was recently proposed as a powerful GNN for graph classification. One of the important contributions of this paper is the observation that the GIN is a dual representation of convolutional neural network (CNN) in the graph space where the shift operation is defined using the adjacency matrix. This understanding enables us to exploit CNN-based saliency map techniques for the GNN, which we tailor to the proposed GIN with one-hot encoding, to visualize the important regions of the brain. We validate our proposed framework using large-scale resting-state fMRI (rs-fMRI) data for classifying the sex of the subject based on the graph structure of the brain. The experiment was consistent with our expectation such that the obtained saliency map show high correspondence with previous neuroimaging evidences related to sex differences.