Deriving frequency‐dependent spatial patterns in MEG‐derived resting state sensorimotor network: A novel multiband ICA technique. Issue 2 (22nd October 2016)

• Record Type: Journal Article
• Authors: Nugent, Allison C.; Luber, Bruce; Carver, Frederick W; Robinson, Stephen E.; Coppola, Richard; Zarate, Carlos A.
• Is Part Of: Human brain mapping. Volume 38:Issue 2(2017)
• Page Start: 779
• Page End: 791
• Publication Date: 2016-10-22
• Subjects: magnetoencephalography -- resting‐state -- oscillations -- independent components analysis -- synthetic aperture magnetometry -- connectivity -- network; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.23417
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: Recently, independent components analysis (ICA) of resting state magnetoencephalography (MEG) recordings has revealed resting state networks (RSNs) that exhibit fluctuations of band‐limited power envelopes. Most of the work in this area has concentrated on networks derived from the power envelope of beta bandpass‐filtered data. Although research has demonstrated that most networks show maximal correlation in the beta band, little is known about how spatial patterns of correlations may differ across frequencies. This study analyzed MEG data from 18 healthy subjects to determine if the spatial patterns of RSNs differed between delta, theta, alpha, beta, gamma, and high gamma frequency bands. To validate our method, we focused on the sensorimotor network, which is well‐characterized and robust in both MEG and functional magnetic resonance imaging (fMRI) resting state data. Synthetic aperture magnetometry (SAM) was used to project signals into anatomical source space separately in each band before a group temporal ICA was performed over all subjects and bands. This method preserved the inherent correlation structure of the data and reflected connectivity derived from single‐band ICA, but also allowed identification of spatial spectral modes that are consistent across subjects. The implications of these results on our understanding of sensorimotor function are discussed, as are the potential applications of this technique. Hum Brain Mapp 38:779–791, 2017 . ©2016 Wiley Periodicals, Inc.