Modularity-Constrained Dynamic Representation Learning for Interpretable Brain Disorder Analysis with Functional MRI.

Qianqian Wang, Mengqi Wu, Yuqi Fang, Wei Wang, Lishan Qiao, Mingxia Liu
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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.

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利用功能性核磁共振成像进行可解释的大脑障碍分析的模块化约束动态表征学习
静息态功能磁共振成像(rs-fMRI)越来越多地用于检测脑部疾病引起的功能连接模式改变,从而促进脑部病理的客观量化。现有研究通常使用各种机器/深度学习方法提取 fMRI 特征,但生成的成像生物标志物往往难以解释。此外,大脑是一个模块化系统,有许多认知/拓扑模块,每个模块都包含密集相互连接的兴趣区(ROI)子集,这些兴趣区与其他模块的 ROI 呈稀疏连接。然而,目前的方法无法有效描述大脑模块化的特征。本文提出了一种模块化约束动态表征学习(MDRL)框架,用于利用 rs-fMRI 进行可解释的大脑失调分析。MDRL 包括三个部分:(1)动态图构建;(2)用于动态特征学习的模块化约束时空图神经网络(MSGNN);(3)预测和生物标记检测。其中,MSGNN 的设计目的是在 3 个功能模块(即中央执行网络、显著性网络和默认模式网络)的约束下学习 fMRI 的时空动态表征。为了提高所学特征的判别能力,我们鼓励 MSGNN 重构输入图的网络拓扑结构。在两个公共数据集和一个私人数据集(共 1155 名受试者)上的实验结果验证了我们的 MDRL 在基于 fMRI 的脑失调分析中优于几种最先进的方法。检测到的 fMRI 生物标记物具有良好的可解释性,可用于改善临床诊断。
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