In recent years, functional magnetic resonance imaging has emerged as a powerful tool for investigating the human brain's functional connectivity networks. Related studies demonstrate that functional connectivity networks in the human brain can help to improve the efficiency of diagnosing neurological disorders. However, there still exist two challenges that limit the progress of functional neuroimaging. Firstly, there exists an abundance of noise and redundant information in functional connectivity data, resulting in poor performance. Secondly, existing brain network models have tended to prioritize either classification performance or the interpretation of neuroscience findings behind the learned models. To deal with these challenges, this paper proposes a novel brain graph learning framework called Template-induced Brain Graph Learning (TiBGL), which has both discriminative and interpretable abilities. Motivated by the related medical findings on functional connectivites, TiBGL proposes template-induced brain graph learning to extract template brain graphs for all groups. The template graph can be regarded as an augmentation process on brain networks that removes noise information and highlights important connectivity patterns. To simultaneously support the tasks of discrimination and interpretation, TiBGL further develops template-induced convolutional neural network and template-induced brain interpretation analysis. Especially, the former fuses rich information from brain graphs and template brain graphs for brain disorder tasks, and the latter can provide insightful connectivity patterns related to brain disorders based on template brain graphs. Experimental results on three real-world datasets show that the proposed TiBGL can achieve superior performance compared with nine state-of-the-art methods and keep coherent with neuroscience findings in recent literatures.

翻译：近年来，功能磁共振成像已成为研究人脑功能连接网络的有力工具。相关研究表明，人脑功能连接网络有助于提高神经系统疾病诊断的效率。然而，目前仍存在两个挑战限制了功能神经影像学的发展。首先，功能连接数据中存在大量噪声和冗余信息，导致性能不佳。其次，现有脑网络模型往往优先考虑分类性能或对所学模型背后的神经科学发现进行解释。为应对这些挑战，本文提出了一种名为模板诱导的脑图学习（TiBGL）的新型脑图学习框架，该框架兼具判别性和可解释性。受功能连接相关医学发现的启发，TiBGL 提出模板诱导的脑图学习，为所有组提取模板脑图。模板图可视为对脑网络的增强过程，能够去除噪声信息并突出重要连接模式。为同时支持判别和解释任务，TiBGL 进一步发展了模板诱导卷积神经网络和模板诱导脑解释分析。其中，前者融合脑图和模板脑图中的丰富信息用于脑疾病分类任务，后者基于模板脑图可提供与脑疾病相关的洞察性连接模式。在三个真实世界数据集上的实验结果表明，所提出的 TiBGL 相比九种最先进方法能够实现优越的性能，并与近期文献中的神经科学发现保持一致。