Heterogeneous Graph-Based Multimodal Brain Network Learning

Graph neural networks (GNNs) provide powerful insights into brain neuroimaging technology from the view of graphical networks. However, most existing GNN-based models treat the brain connectome, derived from neuroimaging, as a homogeneous graph characterized by uniform node and edge types. In fact, emerging studies have reported and emphasized the significance of heterogeneity among human brain activities, especially between the two cerebral hemispheres. Thus, homogeneous-structured brain network-based graph methods are insufficient for modeling complicated cerebral activity states. To overcome this problem, we introduce a novel heterogeneous graph neural network (HeBrainGNN) for multimodal brain neuroimaging fusion learning. HeBrainGNN first conceptualizes the brain network as a heterogeneous graph with multiple types of nodes (representing the left and right hemispheres) and edges (categorizing intra- and interhemispheric interactions). We further develop a self-supervised pretraining strategy for this heterogeneous network to address the potential overfitting problem caused by the conflict between a large parameter size and a small medical data sample size. Empirical results show the superiority of the proposed model over other existing methods in brain-related disease prediction tasks. Ablation experiments show that our heterogeneous graph-based model attaches more importance to hemispheric connections that may be neglected due to their low strength by previous homogeneous graph models. Additional experiments reveal that our pretraining strategy not only addresses the challenge of limited labeled data but also significantly enhances accuracy, affirming the potential of our approach in advancing neuroimaging analysis.