论文标题
fMRI数据的基于模式分解的时变相同步
Mode decomposition-based time-varying phase synchronization for fMRI Data
论文作者
论文摘要
最近，使用静止状态功能磁共振成像（RS-FMRI）数据来测量不同大脑区域之间的时变功能连通性（TVC）。评估来自不同大脑区域信号之间关系的一种方法是在时间上测量其相位同步（PS）。但是，这需要进行分析所需的带通滤波器的类型和截止频率的选择\ textIt {先验{先验}。在这里，我们根据使用各种模式分解（MD）技术来探索替代方法。这些技术允许在不同频率下共同将信号分解为窄带组件，从而满足测量PS所需的要求。我们探索了MD的几种变体，包括经验模式分解（EMD），双变量EMD（BEMD），噪声辅助的多元EMD（NA-MEMD），并在估计时间变化的PS的上下文中介绍了多变量变异模式分解（MVMD）的使用。我们使用一系列模拟和应用程序与RS-FMRI数据进行对比。我们的结果表明，MVMD的表现优于其他评估的MD方法，进一步表明该方法可以用作可靠研究RS-FMRI数据中时间变化的PS的工具。
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 \textit{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 circumvent 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.
