Energy-Period Profiles of Brain Networks in Group fMRI Resting-State Data: A Comparison of Empirical Mode Decomposition With the Short-Time Fourier Transform and the Discrete Wavelet Transform.
Energy-Period Profiles of Brain Networks in Group fMRI Resting-State Data: A Comparison of Empirical Mode Decomposition With the Short-Time Fourier Transform and the Discrete Wavelet Transform.
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DOI：
10.3389/fnins.2021.663403
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发表时间：
2021
影响因子：
4.3
通讯作者：
Walsh RR
中科院分区：
文献类型：
--
作者：
Cordes D;Kaleem MF;Yang Z;Zhuang X;Curran T;Sreenivasan KR;Mishra VR;Nandy R;Walsh RR
传统上，利用线性傅立叶和小波相关方法研究静态数据中的函数网络，以依赖于预先指定的频带来表征其频率成分。在这项研究中，经验模式分解(EMD)，一种自适应的时频方法，被用来研究由组独立分量分析获得的静息数据的自然出现的频带。具体地，建立了EMD获得的本征模式函数(IMF)的能量周期分布，并比较了不同休眠状态网络的能量周期分布。这些分布具有许多休眠状态网络的特征分布，并且与每个网络的频率内容相关。与线性短时傅立叶变换(STFT)和最大重叠离散小波变换(MODWT)的比较表明，EMD对不同类型的静态网络具有更强的频率适应性。基于能量周期分布的休眠状态网络的聚簇导致与频率和能量具有单调关系的休眠状态网络的聚簇。这种关系在EMD中最强，在MODWT中次之，在STFT中最弱。这些关系的识别表明，与短时傅里叶变换和多维离散余弦变换相比，EMD在表征大脑网络方面具有显著优势。在对早期帕金森病(PD)和正常对照组(NC)的临床应用中，研究了几种常见的静息状态网络的能量和周期内容。与短时短时傅里叶变换和短时短时傅里叶变换相比，EMD在能量和周期上的差异在PD和NC之间最大。使用支持向量机，EMD在短时傅立叶变换、MODWT和EMD之间的NC和PD分类中获得了最高的预测精度。
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.
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通讯作者：
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