Department of Electrical and Computer Engineering, Johns Hopkins University, USA.
Department of Biostatistics, Johns Hopkins University, USA.
Neuroimage. 2022 Nov 1;261:119519. doi: 10.1016/j.neuroimage.2022.119519. Epub 2022 Jul 26.
Recently, there has been significant interest in measuring time-varying functional connectivity (TVC) 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. However, this requires the a priori choice of type and cut-off frequencies for the bandpass filter needed to perform the analysis. Here we explore alternative approaches based on the use of various mode decomposition (MD) techniques that provide a more data driven solution to this issue. These techniques allow for the data driven decomposition of signals jointly into narrow-band components at different frequencies, thus fulfilling the requirements needed to measure PS. We explore several variants of MD, including empirical mode decomposition (EMD), bivariate EMD (BEMD), noise-assisted multivariate EMD (na-MEMD), and introduce the use of multivariate variational mode decomposition (MVMD) in the context of estimating time-varying PS. We contrast the approaches using a series of simulations and application to rs-fMRI data. Our results show that MVMD outperforms other evaluated MD approaches, and further suggests that this approach can be used as a tool to reliably investigate time-varying PS in rs-fMRI data.
最近，人们对使用静息态功能磁共振成像 (rs-fMRI) 数据测量不同脑区之间时变功能连接 (TVC) 产生了浓厚的兴趣。评估来自不同脑区的信号之间关系的一种方法是测量它们在时间上的相位同步 (PS)。然而，这需要事先选择用于执行分析的带通滤波器的类型和截止频率。在这里，我们探索了基于使用各种模态分解 (MD) 技术的替代方法，这些技术为解决这个问题提供了更具数据驱动性的解决方案。这些技术允许信号根据数据被联合分解为不同频率的窄带分量，从而满足测量 PS 的要求。我们探索了几种 MD 的变体，包括经验模态分解 (EMD)、双变量 EMD (BEMD)、噪声辅助多变量 EMD (na-MEMD)，并在估计时变 PS 的背景下引入了多变量变分模态分解 (MVMD) 的使用。我们使用一系列模拟和 rs-fMRI 数据的应用来对比这些方法。我们的结果表明 MVMD 优于其他评估的 MD 方法，并进一步表明这种方法可以用作可靠地研究 rs-fMRI 数据中时变 PS 的工具。
