HADBN: Learnable Hierarchical Aggregated Dynamic Brain Networks for Interpretable Brain Disorder Diagnosis

The complex spatio-temporal correlations in brain networks make brain disorder diagnosis challenging. Many spatio-temporal models underutilize anatomical constraints from brain spatial topology, limiting their ability to capture spatial relationships and dynamic interactions. To address this, we propose a novel framework, Learnable Hierarchical Aggregated Dynamic Brain Networks (HADBN), which incorporates multi-level anatomical priors of brain spatial topology and jointly learns multi-scale spatial representations and time-varying connectivity patterns. Specifically, HADBN constructs spatio-temporal interaction graph neural networks (GNNs) with a three-level hierarchical brain architecture—local ROI, middle sub-network, and global network levels—guided by functional parcellation atlases. Unlike existing multi-level spatio-temporal models, HADBN introduces learnable F-matrix aggregation modules that perform data-driven pooling across spatial scales, replacing fixed anatomical aggregation. Moreover, it employs a feature-driven temporal attention mechanism that computes adaptive window weights from multi-scale features, thereby enabling principled spatio-temporal integration and cross-level information flow. Experiments show that HADBN consistently outperforms state-of-the-art methods under 10-fold cross-validation, while the learned aggregation weights offer interpretable insights into disorder-relevant brain networks and support biomarker discovery. These results establish HADBN as a robust and interpretable framework for brain disorder diagnosis.