One paper has been accepted by PR 🎉
Title: Exploring Dynamic Interpretable Brain Networks via Hierarchical Graph Transformer
Our paper on dynamic interpretable brain networks has been accepted by Pattern Recognition (Hu‡ et al., 2026).
Dynamic brain network analysis is important for understanding neurological disorders, but existing methods often struggle to jointly model time-varying functional connectivity and hierarchical brain organization. This work proposes a hierarchical graph transformer framework for dynamic interpretable brain networks, learning adaptive brain network representations that bridge ROI-level dynamics and subnetwork-level coordination. The method is validated on multiple neurological disorder diagnosis datasets.
