Spiking-Temporal Directed Hierarchical Propagation Network for Brain Disorders Diagnosis

The brain organizes its functions hierarchically. Clarifying how local activity in brain disorders is transformed and propagated across functional hierarchies is essential for pursuing more precise neurobiological markers. Existing diagnosis methods typically construct graphs from inter-regional correlations or aggregate regions into higher-level representations, but they often decouple connectivity from ongoing regional dynamics and rely on undirected interactions, leaving the pathway from local temporal activity to system-level information flow insufficiently modeled. Thus, we propose Spiking-Temporal Directed Hierarchical Propagation Network (ST-DHPNet), a biologically inspired framework for multi-level brain network modeling for brain disorder diagnosis. ST-DHPNet first introduces a Spiking Temporal Brain Encoder that decomposes regional BOLD signals and processes consecutive observations through a stateful spiking encoder, producing activity-sensitive regional representations. It then employs an activity-guided spatial encoder that conditions functional-connectivity attention on regional activity states. Finally, a directional hierarchical propagation module aggregates regions into functional subnetworks using anatomical priors and performs effective-connectivity-guided propagation within and between subnetworks. This design explicitly couples regional temporal dynamics, activity-dependent spatial interactions, and directed subnetwork propagation. Experiments on three brain disorder datasets demonstrate performance improvements over other state-of-the-art methods in brain disorder diagnosis. ST-DHPNet reveals candidate biomarkers at three complementary levels: regional latent spike representations, inter-regional connectivity, and subnetwork coordination, providing an interpretable computational framework for brain disorder classification.