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Abstract：
Resting-state functional MRI (rs-fMRI) is increasingly used to detect altered functional connectivity patterns caused by brain disorders, thereby facilitating objective quantification of brain pathology. Existing studies typically extract fMRI features using various machine/deep learning methods, but the generated imaging biomarkers are often challenging to interpret. Besides, the brain operates as a modular system with many cognitive/topological modules, where each module contains subsets of densely inter-connected regions-of-interest (ROIs) that are sparsely connected to ROIs in other modules. However, current methods cannot effectively characterize brain modularity. This paper proposes a modularity-constrained dynamic representation learning (MDRL) framework for interpretable brain disorder analysis with rs-fMRI. The MDRL consists of 3 parts: (1) dynamic graph construction, (2) modularity-constrained spatiotemporal graph neural network (MSGNN) for dynamic feature learning, and (3) prediction and biomarker detection. In particular, the MSGNN is designed to learn spatiotemporal dynamic representations of fMRI, constrained by 3 functional modules (i.e., central executive network, salience network, and default mode network). To enhance discriminative ability of learned features, we encourage the MSGNN to reconstruct network topology of input graphs. Experimental results on two public and one private datasets with a total of 1,155 subjects validate that our MDRL outperforms several state-of-the-art methods in fMRI-based brain disorder analysis. The detected fMRI biomarkers have good explainability and can be potentially used to improve clinical diagnosis.
摘要：
静息状态功能MRI（rs-fMRI）越来越多地用于检测由脑部疾病引起的功能连接模式的改变，从而促进脑病理学的客观量化。现有研究通常使用各种机器/深度学习方法提取功能磁共振成像特征，但所产生的成像生物标志物往往难以解释.此外,大脑作为具有许多认知/拓扑模块的模块化系统运行，其中每个模块包含与其他模块中的ROI稀疏连接的密集互连感兴趣区域(ROI)的子集。然而,目前的方法不能有效地表征大脑模块化。本文提出了一种模块化约束的动态表示学习（MDRL）框架，用于使用rs-fMRI进行可解释的脑部疾病分析。MDRL由三部分组成:(1)动态图构造,(2)面向动态特征学习的模块化约束时空图神经网络(MSGNN),(3)预测和生物标志物检测。特别是,MSGNN旨在学习功能磁共振成像的时空动态表示，受3个功能模块的约束(即，中央执行网络,显著性网络，和默认模式网络)。为了增强学习特征的辨别能力，我们鼓励MSGNN重建输入图的网络拓扑。在两个公共数据集和一个私有数据集（总共1,155名受试者）上的实验结果验证了我们的MDRL在基于fMRI的脑部疾病分析中优于几种最先进的方法。检测到的fMRI生物标志物具有良好的可解释性，可以潜在地用于改善临床诊断。
