  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.
