Customized Relationship Graph Neural Network for Brain Disorder Identification

Zhengwang Xia, Huan Wang, Tao Zhou, Zhuqing Jiao, Jianfeng Lu

Abstract: The connectivity structure of brain networks/graphs provides insights into the segregation and integration patterns among diverse brain regions. Numerous studies have demonstrated that specific brain disorders are associated with abnormal connectivity patterns within distinct regions. Consequently, several Graph Neural Network (GNN) models have been developed to automatically identify irregular integration patterns in brain graphs. However, the inputs for these GNN-based models, namely brain networks/graphs, are typically constructed using statistical-specific metrics and cannot be trained. This limitation might render them ineffective for downstream tasks, potentially leading to suboptimal outcomes. To address this issue, we propose a Customized Relationship Graph Neural Network (CRGNN) that can bridge the gap between the graph structure and the downstream task. The proposed method can dynamically learn the optimal brain networks/graphs for each task. Specifically, we design a block that contains multiple parameterized gates to preserve causal relationships among different brain regions. In addition, we devise a novel node aggregation rule and an appropriate constraint to improve the robustness of the model. The proposed method is evaluated on two publicly available datasets, demonstrating superior performance compared to existing methods. The implementation code is available at https://github.com/NJUSTxiazw/CRGNN.