Towards Graph Neural Networks with Domain-Generalizable Explainability for fMRI-Based Brain Disorder Diagnosis

Graph neural networks (GNNs) represent a cutting-edge methodology in diagnosing brain disorders via fMRI data. Explainability and generalizability are two critical issues of GNNs for fMRI-based diagnoses, considering the high complexity of functional brain networks and the strong variations in fMRI data across different clinical centers. Although there have been many studies on GNNs’ explainability and generalizability, yet few have addressed both aspects simultaneously. In this paper, we unify these two issues and revisit the domain generalization (DG) of fMRI-based diagnoses from the view of explainability. That is, we aim to learn domain-generalizable explanation factors to enhance center-agnostic graph representation learning and therefore brain disorder diagnoses. To this end, a specialized meta-learning framework coupled with explainability-generalizable (XG) regularizations is designed to learn diagnostic GNN models (termed XG-GNN) from fMRI BOLD signals. Our XG-GNN features the ability to build nonlinear functional networks in a task-oriented fashion. More importantly, the group-wise differences of such learned individual networks can be stably captured and maintained to unseen fMRI centers to jointly boost the DG of diagnostic explainability and accuracy. Experimental results on the ABIDE dataset demonstrate the effectiveness of our XG-GNN. The source code will be released on  https://github.com/ladderlab-xjtu/XG-GNN .