Deriving frequency-dependent spatial patterns in MEG-derived resting state sensorimotor network: A novel multiband ICA technique.
Hum Brain Mapp
; 38(2): 779-791, 2017 02.
Article
in En
| MEDLINE
| ID: mdl-27770478
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 38779-791, 2017. © 2016 Wiley Periodicals, Inc.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Rest
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Brain
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Brain Mapping
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Magnetoencephalography
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Brain Waves
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Nerve Net
Type of study:
Etiology_studies
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Incidence_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limits:
Adult
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Female
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Humans
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Male
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Middle aged
Language:
En
Journal:
Hum Brain Mapp
Journal subject:
CEREBRO
Year:
2017
Document type:
Article