Multi-Task Path-Based Heterogeneous Graph Model for Functional Brain Network Analysis and Gender-Related Diseases...

Functional brain network analysis is crucial for understanding brain operating mechanism, aging, sexual distinction and brain disorders. As a powerful neuroimaging technique, resting-state functional Magnetic ResonanceImaging(rs-fMRI) measuresbloodoxygenlevel dependent (BOLD) signals which reflect the interactions between brain regions. Existing methods representsrs fMRI based functional brain network as a fully connected graph with brain regions as nodes and functional connectivity as edges but neglects the heterogeneity of brain activities. Furthermore, current studies of functional brain network mainly focus on single tasks like age regression, gender classification and disease diagnosis, while these tasks share latent related features. To this end, we pro pose a Multi-Task Heterogeneous Path graph Network (MT HPN) for functional brain network analysis. Considering that the information of brain network mostly contains in edges, we utilize a novel Path-Based Heterogeneous Graph Convolution (PB-HGC) to fuse compact edge features of heterogeneous graph paths and generate global brain representation. Then, Path-Based Cross-Attention Block (PB CAB)is applied to exchange useful information between related tasks and emphasize the exclusive features each task respectively. PB-CAB consists of a unique cross-attention transformer designed for graph algorithm to fuse edge features and highlight crucial paths. We estimate the MT-HPN on the ADHD200 dataset and ADNI dataset for gender and disease classification. Our method shows strong capability in multi-task functional brain network analysis and gender related disease diagnosis.