HiBiGNN: Hierarchical Bilateral Graph Neural Network for fMRI Analysis - Fan, Zhengyuan; Ji, Hongfei; Li, Jie; Zhuang, Jie; Qian, Qian (Computing and Informatics, 2025)
Graph Neural Networks (GNNs) have shown great promise in functional Magnetic Resonance Imaging (fMRI) analysis due to their ability to capture complex interactions between brain regions. However, existing models often overlook the brain's physiological structure and fail to leverage hierarchical information from brain atlases. In this paper, we propose Hierarchical Bilateral Graph Neural Network (HiBiGNN), a generic architecture that integrates hierarchical information from brain atlases and incorporates the bilateral structure of the brain, with the ability to be instantiated with various existing GNNs as its foundation. HiBiGNN processes a special heterogeneous graph structure, called the Hierarchical Bilateral Graph (HiBiG), which combines multi-level brain graphs derived from functional regions defined by multi-level brain atlases and divides each brain graph into left and right subgraphs, thereby modeling multiple types of nodes and relations. During feature extraction, HiBiGNN performs deep fusion of features from different types of nodes using a unique convolution operation (HiBiG-Conv) and generates graph-level representations via a specialized readout operation (HiBiG-Readout) for graph classification tasks. To assess the effectiveness of HiBiGNN, we conducted extensive experiments on a graph classification task using an fMRI dataset we collected from a response inhibition task, testing multiple HiBiGNN instances with different base GNN models. The results show that our HiBiGNN instances outperforms several generic GNN models as well as those specifically designed for fMRI analysis, demonstrating the significant potential of HiBiGNN for future applications.