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Article overview
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CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis | Kaizhong Zheng
; Shujian Yu
; Badong Chen
; | Date: |
4 Jan 2023 | Abstract: | There is a recent trend to leverage the power of graph neural networks (GNNs)
for brain-network based psychiatric diagnosis, which,in turn, also motivates an
urgent need for psychiatrists to fully understand the decision behavior of the
used GNNs. However, most of the existing GNN explainers are either post-hoc in
which another interpretive model needs to be created to explain a well-trained
GNN, or do not consider the causal relationship between the extracted
explanation and the decision, such that the explanation itself contains
spurious correlations and suffers from weak faithfulness. In this work, we
propose a granger causality-inspired graph neural network (CI-GNN), a built-in
interpretable model that is able to identify the most influential subgraph
(i.e., functional connectivity within brain regions) that is causally related
to the decision (e.g., major depressive disorder patients or healthy controls),
without the training of an auxillary interpretive network. CI-GNN learns
disentangled subgraph-level representations {alpha} and {eta} that encode,
respectively, the causal and noncausal aspects of original graph under a graph
variational autoencoder framework, regularized by a conditional mutual
information (CMI) constraint. We theoretically justify the validity of the CMI
regulation in capturing the causal relationship. We also empirically evaluate
the performance of CI-GNN against three baseline GNNs and four state-of-the-art
GNN explainers on synthetic data and two large-scale brain disease datasets. We
observe that CI-GNN achieves the best performance in a wide range of metrics
and provides more reliable and concise explanations which have clinical
evidence. | Source: | arXiv, 2301.01642 | Services: | Forum | Review | PDF | Favorites |
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