Biomedical Engineering Department, Tulane University, New Orleans, LA, United States; Tianjin University, School of Precision Instruments and Optoelectronics Engineering, Tianjin, China.
Biomedical Engineering Department, Tulane University, New Orleans, LA, United States.
Neuroimage. 2020 Nov 1;221:117190. doi: 10.1016/j.neuroimage.2020.117190. Epub 2020 Jul 22.
Recently, functional network connectivity (FNC) has been extended from static to dynamic analysis to explore the time-varying functional organization of brain networks. Nowadays, a majority of dynamic FNC (dFNC) analysis frameworks identified recurring FNC patterns with linear correlations based on the amplitude of fMRI time series. However, the brain is a complex dynamical system and phase synchronization provides more informative measures. This paper proposes a novel framework for the prediction/classification of behaviors and cognitions based on the dFNCs derived from phase locking value. When applying to the analysis of fMRI data from Human Connectome Project (HCP), four dFNC states are identified for the study of sleep quality. State 1 exhibits most intense phase synchronization across the whole brain. States 2 and 3 have low and weak connections, respectively. State 4 exhibits strong phase synchronization in intra and inter-connections of somatomotor, visual and cognitive control networks. Through the two-sample t-test, we reveal that for the group with bad sleep quality, state 4 shows decreased phase synchronization within and between networks such as subcortical, auditory, somatomotor and visual, but increased phase synchronization within cognitive control network, and between this network and somatomotor/visual/default-mode/cerebellar networks. The networks with increased phase synchronization in state 4 behave oppositely in state 2. Group differences are absent in state 3, and weak in state 1. We establish a prediction model by linear regression of FNC against sleep quality, and adopt a support vector machine approach for the classification. We compare the performance between conventional FNC and PLV-based dFNC with cross-validation. Results show that the PLV-based dFNC significantly outperforms the conventional FNC in terms of both predictive power and classification accuracy. We also observe that combining static and dynamic features does not significantly improve the classification over using dFNC features alone. Overall, the proposed approach provides a novel means to assess dFNC, which can be used as brain fingerprints to facilitate prediction and classification.
最近，功能网络连通性（FNC）已从静态分析扩展到动态分析，以探索脑网络的时变功能组织。如今，大多数动态 FNC（dFNC）分析框架基于 fMRI 时间序列的幅度，通过线性相关来识别重复出现的 FNC 模式。然而，大脑是一个复杂的动力系统，相位同步提供了更有信息量的测量方法。本文提出了一种基于相位锁定值的 dFNC 预测/分类的新框架。当应用于人类连接组计划（HCP）的 fMRI 数据分析时，我们从相位锁定值衍生的 dFNC 中识别出四个状态来研究睡眠质量。状态 1 表现出整个大脑最强烈的相位同步。状态 2 和状态 3 分别具有低强度和弱连接。状态 4 表现出躯体运动、视觉和认知控制网络的内、外连接具有强烈的相位同步。通过两样本 t 检验，我们发现对于睡眠质量差的组，状态 4 表现出网络内和网络间（如皮质下、听觉、躯体运动和视觉）的相位同步降低，但认知控制网络内和网络间（躯体运动/视觉/默认模式/小脑网络）的相位同步增加。在状态 4 中相位同步增加的网络在状态 2 中表现相反。在状态 3 中没有组间差异，在状态 1 中差异较弱。我们通过线性回归将 FNC 与睡眠质量进行预测模型建立，并采用支持向量机方法进行分类。我们通过交叉验证比较了传统 FNC 和基于 PLV 的 dFNC 的性能。结果表明，基于 PLV 的 dFNC 在预测能力和分类准确性方面都明显优于传统 FNC。我们还观察到，与单独使用 dFNC 特征相比，结合静态和动态特征并没有显著提高分类效果。总的来说，所提出的方法为评估 dFNC 提供了一种新的手段，可以作为脑指纹来促进预测和分类。
