Dynamic weighted hypergraph convolutional network for brain functional connectome analysis

• Published in: Medical image analysis Vol. 87; p. 102828
• Main Authors: Wang, Junqi, Li, Hailong, Qu, Gang, Cecil, Kim M., Dillman, Jonathan R., Parikh, Nehal A., He, Lili
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.07.2023
• Subjects: Brain - diagnostic imaging; Connectome - methods; Dynamic hypergraph neural network; Functional connectome; Humans; Magnetic Resonance Imaging - methods; Manifold regularization; Neural Networks, Computer; Weighted hypergraph; Weighted hypergraph; Functional connectome; Dynamic hypergraph neural network; Manifold regularization
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2023.102828

The hypergraph structure has been utilized to characterize the brain functional connectome (FC) by capturing the high order relationships among multiple brain regions of interest (ROIs) compared with a simple graph. Accordingly, hypergraph neural network (HGNN) models have emerged and provided efficient tools for hypergraph embedding learning. However, most existing HGNN models can only be applied to pre-constructed hypergraphs with a static structure during model training, which might not be a sufficient representation of the complex brain networks. In this study, we propose a dynamic weighted hypergraph convolutional network (dwHGCN) framework to consider a dynamic hypergraph with learnable hyperedge weights. Specifically, we generate hyperedges based on sparse representation and calculate the hyper similarity as node features. The hypergraph and node features are fed into a neural network model, where the hyperedge weights are updated adaptively during training. The dwHGCN facilitates the learning of brain FC features by assigning larger weights to hyperedges with higher discriminative power. The weighting strategy also improves the interpretability of the model by identifying the highly active interactions among ROIs shared by a common hyperedge. We validate the performance of the proposed model on two classification tasks with three paradigms functional magnetic resonance imaging (fMRI) data from Philadelphia Neurodevelopmental Cohort. Experimental results demonstrate the superiority of our proposed method over existing hypergraph neural networks. We believe our model can be applied to other applications in neuroimaging for its strength in representation learning and interpretation. •A hypergraph convolutional model for brain functional connectome classification.•Dynamic hypergraph estimation during training regulated by manifold regularization.•Interpretable approach to identify label-specified biomarkers.•Comparisons with competing models with three imaging paradigms.