Learnable Subdivision Graph Neural Network for Functional Brain Network Analysis and Interpretable Cognitive Disorder Diagnosis

Abstract

Different functional configurations of the brain, also named as "brain states", reflect a continuous stream of brain cognitive activities. These distinct brain states can confer heterogeneous functions to brain networks. Recent studies have revealed that extracting information from functional brain networks is beneficial for neuroscience analysis and brain disorder diagnosis. Graph neural networks (GNNs) have been demonstrated to be superior in learning network representations. However, these GNN-based methods have few concerns about the heterogeneity of brain networks, especially the heterogeneous information of brain network functions induced by intrinsic brain states. To address this issue, we propose a learnable subdivision graph neural network (LSGNN) for brain network analysis. The core idea of LSGNN is to implement a learnable subdivision method to encode brain networks into multiple latent feature subspaces corresponding to functional configurations, and realize the feature extraction of brain networks in each subspace, respectively. Furthermore, considering the complex interactions among brain states, we also employ the self-attention mechanism to acquire a comprehensive brain network representation in a joint latent space. We conduct experiments on a publicly available dataset of cognitive disorders. The results affirm that our approach can achieve outstanding performance and also instill the interpretability of the brain network functions in the latent space. Our code is available at https://github.com/haijunkenan/LSGNN.