Developing a new diagnostic models based on the underlying biological mechanisms rather than subjective symptoms for psychiatric disorders is an emerging consensus. Recently, machine learning-based classifiers using functional connectivity (FC) for psychiatric disorders and healthy controls are developed to identify brain markers. However, existing machine learning-based diagnostic models are prone to over-fitting (due to insufficient training samples) and perform poorly in new test environment. Furthermore, it is difficult to obtain explainable and reliable brain biomarkers elucidating the underlying diagnostic decisions. These issues hinder their possible clinical applications. In this work, we propose BrainIB, a new graph neural network (GNN) framework to analyze functional magnetic resonance images (fMRI), by leveraging the famed Information Bottleneck (IB) principle. BrainIB is able to identify the most informative edges in the brain (i.e., subgraph) and generalizes well to unseen data. We evaluate the performance of BrainIB against 3 baselines and 7 state-of-the-art brain network classification methods on three psychiatric datasets and observe that our BrainIB always achieves the highest diagnosis accuracy. It also discovers the subgraph biomarkers which are consistent to clinical and neuroimaging findings. The source code and implementation details of BrainIB are freely available at GitHub repository (https://github.com/SJYuCNEL/brain-and-Information-Bottleneck/).

翻译：基于内在生物学机制而非主观症状开发精神疾病的新型诊断模型已成为新兴共识。近期，研究者利用功能连接性构建基于机器学习的分类器，以区分精神疾病患者与健康对照者并识别脑标记物。然而，现有基于机器学习的诊断模型易因训练样本不足而过度拟合，在新测试环境中表现欠佳。此外，难以获得可解释且可靠的脑生物标记物以阐明诊断决策的内在依据，这些问题阻碍了其潜在的临床应用。本研究提出BrainIB——一种基于著名信息瓶颈原理的图神经网络框架，用于分析功能磁共振成像数据。BrainIB能够识别大脑中最具信息量的连接边（即子图），并在未见数据上表现出良好的泛化能力。我们在三个精神疾病数据集上，将BrainIB与3个基线方法和7个前沿脑网络分类方法进行性能对比，结果表明BrainIB始终获得最高的诊断准确率。同时，其发现的子图生物标记物与临床及神经影像学研究成果具有一致性。BrainIB的源代码与实现细节已发布于GitHub仓库（https://github.com/SJYuCNEL/brain-and-Information-Bottleneck/）。