One paper has been accepted by PR 🎉
Title: Exploring Dynamic Interpretable Brain Networks via Hierarchical Graph Transformer
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.