Hierarchical sparse spatiotemporal graph neural network for brain graph classification

Brain graph classification from resting-state fMRI (rs-fMRI) can support the identification of neurological conditions and inform personalized analysis. Here, we present a hierarchical sparse spatiotemporal graph neural network (STGNN)-GLNSTGNN-to address sparse feature selection in spatiotemporal brain graph classification. We evaluated GLNSTGNN on two rs-fMRI datasets comprising 1,956 participants with 200 regions of interest (ROIs) and 12 subnetworks after standardized preprocessing. GLNSTGNN applies GroupLassoNet-based hierarchical sparsity to select informative features, while combining spatial graph convolution on a fixed functional connectivity adjacency with temporal convolution on time-varying BOLD signals to capture spatial dependencies and temporal dynamics. Across multiple baselines, GLNSTGNN showed improved discriminative performance and consistent ROI selection, supporting interpretable subnetwork-level patterns. These results suggest that integrating hierarchical sparsity with spatiotemporal graph learning can provide a practical framework for robust and interpretable brain graph classification.