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.
journal_name
Front Neuroscijournal_title
Frontiers in neuroscienceauthors
Kim BH,Ye JCdoi
10.3389/fnins.2020.00630subject
Has Abstractpub_date
2020-06-30 00:00:00pages
630eissn
1662-4548issn
1662-453Xjournal_volume
14pub_type
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doi:10.3389/fnins.2019.01180
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doi:10.3389/fnins.2015.00393
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abstract::[This corrects the article DOI: 10.3389/fnins.2019.01334.]. ...
journal_title:Frontiers in neuroscience
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