Path-Based Heterogeneous Brain Transformer Network for Resting-State Functional Connectivity Analysis

Ruiyan Fang, Yu Li, Xin Zhang, Shengxian Chen, Jiale Cheng, Xiangmin Xu, Jieling Wu, Weili Lin, Li Wang, Zhengwang Wu, Gang Li

Abstract: Brain functional connectivity analysis is important for understanding brain development, aging, sexual distinction and brain disorders. Existing methods typically adopt the resting-state functional connectivity (rs-FC) measured by functional MRI as an effective tool, while they either neglect the importance of information exchange between different brain regions or the heterogeneity of brain activities. To address these issues, we propose a Path-based Heterogeneous Brain Transformer Network (PH-BTN) for analyzing rs-FC. Specifically, to integrate the path importance and heterogeneity of rs-FC for a comprehensive description of the brain, we first construct the brain functional network as a path-based heterogeneous graph using prior knowledge and gain initial edge features from rs-FC. Then, considering the constraints of graph convolution in aggregating long-distance and global information, we design a Heterogeneous Path Graph Transformer Convolution (HP-GTC) module to extract edge features by aggregating different paths’ information. Furthermore, we adopt Squeeze-and-Excitation (SE) with HP-GTC modules, which can alleviate the over-smoothing problem and enhance influential features. Finally, we apply a readout layer to generate the final graph embedding to estimate brain age and gender, and thoroughly evaluate the PH-BTN on the Baby Connectome Project (BCP) dataset. Experimental results demonstrate the superiority of PH-BTN over other state-of-the-art methods. The proposed PH-BTN offers a powerful tool to investigate and explore brain functional connectivity.

External IDs:dblp:conf/miccai/FangLZCCXWLWWL23