BrainGT: Multifunctional Brain Graph Transformer for Brain Disorder Diagnosis

Functional brain networks play an essential role in the diagnosis of brain disorders by enabling the identification of abnormal patterns and connections in brain activities. Previous methods often rely on whole brain functional connectivity approaches to construct these networks using Functional Magnetic Resonance Imaging (fMRI) data. However, these approaches introduce noise and overlook localized disruptions within specific brain subnetworks, leading to potential misdiagnoses. To address this challenging issue, we propose mBrainGT, a modular brain graph transformer model that focuses on modular functional connectivity (mFC) to improve the diagnosis of brain disorders. Compared to existing methods, mBrainGT constructs and analyses functional brain subnetworks individually, reflecting the inherent structure of the brain. It captures both local features within each modular network and their interactions through self-attention and cross-attention mechanisms. It also learns global interactions via adaptive fusion. We validate mBrainGT on three benchmark datasets (ADNI, PPMI, and ABIDE). The results demonstrate that mBrainGT outperforms existing methods in diagnostic accuracy, providing more robust and precise representations of the brain network essential for accurate disease detection. Our study highlights the potential of modular connectivity-based graph learning in the refinement of brain disorder diagnostics, offering a more precise and biologically relevant representation of functional brain networks.