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.

Dietmar Cordes,Zhengshi Yang,Rajesh Nandy,Tim Curran,Muhammad F Kaleem,Xiaowei Zhuang,Virendra R Mishra,Karthik R Sreenivasan,Ryan R Walsh

doi:10.3389/fnins.2021.663403

Abstract

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.

Highlights

Related regions of the resting brain have been shown to have a high degree of temporal correlation in blood-flow fluctuations as measured by blood-oxygenation level-dependent (BOLD) fMRI signal (Biswal et al, 1995)
With the assumption that temporal profiles of spatial independent component analysis (ICA) reflect the underlying BOLD-fMRI time courses (McKeown et al, 1998), we investigated time courses associated with standard group spatial ICA brain networks and compared a decomposition of these time courses using the ShortTime Fourier Transform (STFT), the Maximal Overlap Discrete Wavelet Transform (MODWT), and Empirical Mode Decomposition (EMD) to determine if EMD has any advantages in characterizing brain networks
We investigated whether Short-Time Fourier Transform (STFT), MODWT, and EMD can find significant differences in temporal characteristics in a cohort of never-medicated early Parkinson’s Disease (PD) patients compared to normal controls (NC)

Summary

Introduction

Related regions of the resting brain have been shown to have a high degree of temporal correlation in blood-flow fluctuations as measured by blood-oxygenation level-dependent (BOLD) fMRI signal (Biswal et al, 1995). Using either seedbased or data-driven methods such as independent component analysis (ICA) or clustering methods, entire networks that fluctuate in synchrony have been found to constitute reliable and reproducible functional networks in the human resting brain (Beckmann, 2012; Lowe, 2012; Calhoun and de Lacy, 2017). These synchronous fluctuations may represent changes in local capillary blood flow secondary to fluctuations in neuronal firing rates within large distributed neural networks. The time-frequency dynamics of resting-state networks have been studied using the continuous wavelet transform and recurring patterns of connectivity determined for specific frequency values (Yaesoubi et al, 2015)

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