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.

由于能够更好地表征神经和精神疾病中的大脑动态和异常，大脑网络引起了越来越多的关注。近年来，深度学习取得了巨大的成功。许多人工智能算法，特别是图学习方法，已经被提出来分析大脑网络。现有图学习方法的一个重要问题是这些模型通常不容易解释。在这项研究中，我们提出了一种用于脑网络回归分析的可解释的图学习模型。我们将这个新框架应用于人类连接组计划 (HCP) 的受试者，以预测多个成人自我报告 (ASR) 分数。我们还使用其中一个 ASR 分数作为示例来演示如何使用我们的模型识别回归过程中的性别差异。