Interpretable Graph Convolutional Network for Alzheimer's Disease Diagnosis using Multi-Modal Imaging Genetics.

Integrating brain images and genetic data provides a great opportunity to discover potential biomarkers for neurological disorder diagnosis. However, learning genetic information and brain network dysfunction remains a challenging task. In this paper, we propose an interpretable multi-modal imaging and genetic graph convolution network (GCN) for Alzheimer’s disease diagnosis. Our genetic network uses hierarchical GCN to mimic a gene ontology-based graph of biological processes and learn the information flow in this graph. In parallel, our imaging network uses a sparse interpretable GCN with node and edge importance probabilities to learn the brain network from multi-modal images. After multi-modal fusion, the final representation guided by a cluster-based consistency constraint is used to predict the disease-related clinical measures. We evaluate our method on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Our result shows that our imaging-genetics framework achieves superior prediction performance compared to all state-of-the-art methods. The interpretation demonstrated that the salient SNPs, and salient regions interpreted by important probabilities were significantly correlated with AD-related clinical symptoms, and considerably important for developing novel biomarkers. The code is available at https://github.com/Houliang-Zhou/IG-GCN.