Heterogeneous Graph Convolutional Neural Network via Hodge-Laplacian for Brain Functional Data
This work addresses the problem of interpreting brain functional connectivity for cognitive prediction, representing an incremental advance in graph neural network methods for neuroscience applications.
The study tackled the challenge of analyzing complex brain fMRI data by proposing a heterogeneous graph convolutional neural network (HGCNN) using Hodge-Laplacian operators, which outperformed state-of-the-art graph neural networks in predicting general intelligence from fMRI data of 7,693 adolescents.
This study proposes a novel heterogeneous graph convolutional neural network (HGCNN) to handle complex brain fMRI data at regional and across-region levels. We introduce a generic formulation of spectral filters on heterogeneous graphs by introducing the $k-th$ Hodge-Laplacian (HL) operator. In particular, we propose Laguerre polynomial approximations of HL spectral filters and prove that their spatial localization on graphs is related to the polynomial order. Furthermore, based on the bijection property of boundary operators on simplex graphs, we introduce a generic topological graph pooling (TGPool) method that can be used at any dimensional simplices. This study designs HL-node, HL-edge, and HL-HGCNN neural networks to learn signal representation at a graph node, edge levels, and both, respectively. Our experiments employ fMRI from the Adolescent Brain Cognitive Development (ABCD; n=7693) to predict general intelligence. Our results demonstrate the advantage of the HL-edge network over the HL-node network when functional brain connectivity is considered as features. The HL-HGCNN outperforms the state-of-the-art graph neural networks (GNNs) approaches, such as GAT, BrainGNN, dGCN, BrainNetCNN, and Hypergraph NN. The functional connectivity features learned from the HL-HGCNN are meaningful in interpreting neural circuits related to general intelligence.