TY - JOUR
T1 - Energy-Period Profiles of Brain Networks in Group fMRI Resting-State Data
T2 - A Comparison of Empirical Mode Decomposition With the Short-Time Fourier Transform and the Discrete Wavelet Transform
AU - Cordes, Dietmar
AU - Kaleem, Muhammad F.
AU - Yang, Zhengshi
AU - Zhuang, Xiaowei
AU - Curran, Tim
AU - Sreenivasan, Karthik R.
AU - Mishra, Virendra R.
AU - Nandy, Rajesh
AU - Walsh, Ryan R.
N1 - Funding Information:
The research was supported by the National Institute of Health (grant numbers RF1AG071566, R01NS117547, and COBRE P20GM109025). Other funding partners include a consortium of industry players, non-profit organizations and private individuals: AbbVie, Avid Radiopharmaceuticals, Biogen Idec, Bristol-Meyers Squibb, Covance, GE Healthcare, Genentech, GlaxoSmithKline, Eli Lilly and Company, Lundbeck, Merck, Meso Scale Discovery, Pfizer Inc., Piramal Imaging, Roche CNS group, Servier, UCB and Golub Capital.
Funding Information:
Funding. The research was supported by the National Institute of Health (grant numbers RF1AG071566, R01NS117547, and COBRE P20GM109025). Other funding partners include a consortium of industry players, non-profit organizations and private individuals: AbbVie, Avid Radiopharmaceuticals, Biogen Idec, Bristol-Meyers Squibb, Covance, GE Healthcare, Genentech, GlaxoSmithKline, Eli Lilly and Company, Lundbeck, Merck, Meso Scale Discovery, Pfizer Inc., Piramal Imaging, Roche CNS group, Servier, UCB and Golub Capital.
Publisher Copyright:
© Copyright © 2021 Cordes, Kaleem, Yang, Zhuang, Curran, Sreenivasan, Mishra, Nandy and Walsh.
PY - 2021/5/21
Y1 - 2021/5/21
N2 - 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.
AB - 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.
KW - EMD
KW - IMF
KW - empirical mode decomposition
KW - energy-period
KW - functional connectivity
KW - group ICA
KW - intrinsic mode function
KW - resting-state fMRI
UR - http://www.scopus.com/inward/record.url?scp=85107346781&partnerID=8YFLogxK
U2 - 10.3389/fnins.2021.663403
DO - 10.3389/fnins.2021.663403
M3 - Article
AN - SCOPUS:85107346781
VL - 15
JO - Frontiers in Neuroscience
JF - Frontiers in Neuroscience
SN - 1662-4548
M1 - 663403
ER -