Honari Hamed, Choe Ann S, Lindquist Martin A
Department of Electrical and Computer Engineering, Johns Hopkins University, USA.
F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, USA; International Center for Spinal Cord Injury, Kennedy Krieger Institute, USA; Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine, USA.
Neuroimage. 2021 Mar;228:117704. doi: 10.1016/j.neuroimage.2020.117704. Epub 2020 Dec 30.
In recent years there has been growing interest in measuring time-varying functional connectivity between different brain regions using resting-state functional magnetic resonance imaging (rs-fMRI) data. One way to assess the relationship between signals from different brain regions is to measure their phase synchronization (PS) across time. There are several ways to perform such analyses, and we compare methods that utilize a PS metric together with a sliding window, referred to here as windowed phase synchronization (WPS), with those that directly measure the instantaneous phase synchronization (IPS). In particular, IPS has recently gained popularity as it offers single time-point resolution of time-resolved fMRI connectivity. In this paper, we discuss the underlying assumptions required for performing PS analyses and emphasize the importance of band-pass filtering the data to obtain valid results. Further, we contrast this approach with the use of Empirical Mode Decomposition (EMD) to achieve similar goals. We review various methods for evaluating PS and introduce a new approach within the IPS framework denoted the cosine of the relative phase (CRP). We contrast methods through a series of simulations and application to rs-fMRI data. Our results indicate that CRP outperforms other tested methods and overcomes issues related to undetected temporal transitions from positive to negative associations common in IPS analysis. Further, in contrast to phase coherence, CRP unfolds the distribution of PS measures, which benefits subsequent clustering of PS matrices into recurring brain states.
近年来,人们越来越关注使用静息态功能磁共振成像(rs-fMRI)数据来测量不同脑区之间随时间变化的功能连接。评估不同脑区信号之间关系的一种方法是测量它们随时间的相位同步(PS)。有几种方法可以进行此类分析,我们将使用PS度量并结合滑动窗口的方法(这里称为窗口化相位同步,WPS)与直接测量瞬时相位同步(IPS)的方法进行比较。特别是,IPS最近越来越受欢迎,因为它提供了时间分辨功能磁共振成像连接的单时间点分辨率。在本文中,我们讨论了进行PS分析所需的基本假设,并强调对数据进行带通滤波以获得有效结果的重要性。此外,我们将这种方法与使用经验模态分解(EMD)来实现类似目标的方法进行对比。我们回顾了评估PS的各种方法,并在IPS框架内引入了一种新方法,称为相对相位余弦(CRP)。我们通过一系列模拟和对rs-fMRI数据的应用来对比各种方法。我们的结果表明,CRP优于其他测试方法,并克服了与IPS分析中常见的未检测到的从正相关到负相关的时间转换相关的问题。此外,与相位相干性不同,CRP展开了PS测量的分布,这有利于随后将PS矩阵聚类为反复出现的脑状态。