Joint Dynamic Brain Network Estimation and Graph Representation Learning for the Recognition of Neurological Disorders

Recently, Graph Neural Networks (GNNs) have shown significant improvements in the recognition of neurological disorders by incorporating brain networks/graphs. However, most existing approaches have three main limitations. First, these methodologies rely on precomputed brain networks as input, typically derived from statistical metrics (e.g., Pearson correlation), which are inherently not learnable. Second, methods often assume that the magnitude of the brain interactions remains constant across the whole scan duration. Third, representations produced by models often lack interpretability and robustness when applied across brain disorders. To address these limitations, we propose a novel model called the Effective Brain Inference Graph Neural Network (EBIGNN), which infers dynamic Effective Connectivity (dEC) to characterize brain networks trained with direct feedback from downstream tasks within a unified end-to-end framework. EBIGNN is highly flexible in learning the most relevant graph structures customized to the specific underlying brain condition. The proposed model offers strong interpretability, providing valuable insights into the temporal evolution and altered connectivity patterns essential for understanding brain disorders. The model is validated on three publicly available datasets, demonstrating superior performance compared to other state-of-the-art methods. Moreover, the findings are consistent with previous neuroimaging-derived evidence of biomarkers, underscoring the model's robustness in clinical settings.