A Hierarchical Graph Learning Model for Brain Network Regression Analysis - Tang, Haoteng; Guo, Lei; Fu, Xiyao; Qu, Benjamin; Ajilore, Olusola; Wang, Yalin; Thompson, Paul M.; Huang, Heng; Leow, Alex D.; Zhan, Liang (Frontiers in Neuroscience, 2022)
Brain networks have attracted increasing attention due to the potential to better characterize brain dynamics and abnormalities in neurological and psychiatric conditions. Recent years have witnessed enormous successes in deep learning. Many AI algorithms, especially graph learning methods, have been proposed to analyze brain networks. An important issue for existing graph learning methods is that those models are not typically easy to interpret. In this study, we proposed an interpretable graph learning model for brain network regression analysis. We applied this new framework on the subjects from Human Connectome Project (HCP) for predicting multiple Adult Self-Report (ASR) scores. We also use one of the ASR scores as the example to demonstrate how to identify sex differences in the regression process using our model. In comparison with other state-of-the-art methods, our results clearly demonstrate the superiority of our new model in effectiveness, fairness, and transparency.