Multiscale Graph Transformer for Brain Disorder Diagnosis

Ahsan Shehzad, Dongyu Zhang, Shuo Yu, Shagufta Abid, Dinesh Kant Kumar, Feng Xia

Abstract: Multiscale brain networks are crucial for diagnosing brain disorders by revealing the hierarchical organization of brain function and connectivity. However, previous methods that explored multi-atlas approaches to model these networks often failed to represent this organization across multiple spatial and temporal scales, leading to limited representations and potentially inaccurate diagnoses. To address this issue, we propose the Multiscale Brain Graph Transformer (BrainMGT), which captures the hierarchical organization of brain connectivity at various spatial and temporal scales to improve the diagnosis of brain disorders. BrainMGT constructs multiscale brain networks that model spatial hierarchies, i.e., microscale, mesoscale, and macroscale, while preserving their modular structure. It also incorporates multiple temporal scales, i.e., fast, intermediate, and slow, for estimating connectivity, rather than relying on a single temporal scale from blood-oxygen-level-dependent (BOLD) signals. Using self-attention and cross-attention mechanisms, BrainMGT extracts and integrates features both within and between these scales, generating fine-coarse feature representations that improve diagnostic precision. We validated BrainMGT on three real-world functional magnetic resonance imaging (fMRI) datasets, and the results show that BrainMGT outperformed existing methods in diagnosing neurological disorders.

External IDs:dblp:journals/tce/ShehzadZYAKX25