Cordes Dietmar, Kaleem Muhammad F, Yang Zhengshi, Zhuang Xiaowei, Curran Tim, Sreenivasan Karthik R, Mishra Virendra R, Nandy Rajesh, Walsh Ryan R
Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States.
University of Colorado, Boulder, CO, United States.
Front Neurosci. 2021 May 21;15:663403. doi: 10.3389/fnins.2021.663403. eCollection 2021.
Traditionally, functional networks in resting-state data were investigated with linear Fourier and wavelet-related methods to characterize their frequency content by relying on pre-specified frequency bands. In this study, Empirical Mode Decomposition (EMD), an adaptive time-frequency method, is used to investigate the naturally occurring frequency bands of resting-state data obtained by Group Independent Component Analysis. Specifically, energy-period profiles of Intrinsic Mode Functions (IMFs) obtained by EMD are created and compared for different resting-state networks. These profiles have a characteristic distribution for many resting-state networks and are related to the frequency content of each network. A comparison with the linear Short-Time Fourier Transform (STFT) and the Maximal Overlap Discrete Wavelet Transform (MODWT) shows that EMD provides a more frequency-adaptive representation of different types of resting-state networks. Clustering of resting-state networks based on the energy-period profiles leads to clusters of resting-state networks that have a monotone relationship with frequency and energy. This relationship is strongest with EMD, intermediate with MODWT, and weakest with STFT. The identification of these relationships suggests that EMD has significant advantages in characterizing brain networks compared to STFT and MODWT. In a clinical application to early Parkinson's disease (PD) vs. normal controls (NC), energy and period content were studied for several common resting-state networks. Compared to STFT and MODWT, EMD showed the largest differences in energy and period between PD and NC subjects. Using a support vector machine, EMD achieved the highest prediction accuracy in classifying NC and PD subjects among STFT, MODWT, and EMD.
传统上，静息态数据中的功能网络是通过线性傅里叶和小波相关方法进行研究的，依靠预先指定的频带来表征其频率成分。在本研究中，经验模态分解（EMD）这种自适应时频方法被用于研究通过组独立成分分析获得的静息态数据的自然出现的频带。具体而言，创建并比较了通过EMD获得的本征模态函数（IMF）的能量-周期剖面图，用于不同的静息态网络。这些剖面图对于许多静息态网络具有特征性分布，并且与每个网络的频率成分相关。与线性短时傅里叶变换（STFT）和最大重叠离散小波变换（MODWT）的比较表明，EMD为不同类型的静息态网络提供了更具频率适应性的表示。基于能量-周期剖面图对静息态网络进行聚类，会得到与频率和能量具有单调关系的静息态网络聚类。这种关系在EMD中最强，在MODWT中居中，在STFT中最弱。这些关系的确定表明，与STFT和MODWT相比，EMD在表征脑网络方面具有显著优势。在早期帕金森病（PD）与正常对照（NC）的临床应用中，研究了几个常见静息态网络的能量和周期含量。与STFT和MODWT相比，EMD在PD和NC受试者之间的能量和周期方面显示出最大差异。使用支持向量机，EMD在STFT、MODWT和EMD中对NC和PD受试者进行分类时达到了最高的预测准确率。
