Graph convolutional networks (GCNs) have been widely used and achieved remarkable results in brain disorder classification using functional connectivity (FC) graphs derived from resting-state functional magnetic resonance imaging (rs-fMRI). In GCN-based methods, the topological structure of FC graphs dominates feature aggregation, making it crucial for learning representative features. However, many existing approaches rely on static FC (sFC) with predefined topologies, which may limit the expressive power of GCNs. Furthermore, although dynamic FC (dFC) has been explored in some studies, the associated dynamic topological variations are often underutilized in disease recognition. To address these issues, we propose a novel topology-learnable static-dynamic graph convolution network (TSD-GCN) that adaptively learns topological structures from both static and dynamic FC graphs to capture complementary information for automated brain disorder identification. Specifically, TSD-GCN is designed as a dual-branch structure to comprehensively model the topological characteristics of each sample's static and dynamic FC patterns. The static branch performs adaptive topology learning on sFC using a neural network layer to enhance the representational capacity of GCNs. The dynamic branch models topological variations of dFC by learning differential information across multiple consecutive time steps, thereby refining the dynamic topology and boosting feature expressiveness. Finally, a cross-branch collaborative block is employed to integrate holistic features from both branches for disease classification. Extensive experiments on two public datasets, ADNI and ABIDE, demonstrate that our method outperforms several state-of-the-art approaches, and the discovered discriminative FC patterns are biologically meaningful.
Wang et al. (Thu,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: