Electrophysiological signatures of resting state networks in the human brain
Электрофизиологические характеристики сетей мозга в состоянии покоя у человека
2007-08-01
SCID: 54.1/8c7ettwg
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EEG power oscillationselectroencephalographyfunctional magnetic resonance imagingindependent component analysisresting state networks
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Abstract (AI)
Functional neuroimaging and electrophysiological studies have documented a dynamic baseline of intrinsic (not stimulus- or task-evoked) brain activity during resting wakefulness. This baseline is characterized by slow (<0.1 Hz) fluctuations of functional imaging signals that are topographically organized in discrete brain networks, and by much faster (1-80 Hz) electrical oscillations. To investigate the relationship between hemodynamic and electrical oscillations, we have adopted a completely data-driven approach that combines information from simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Using independent component analysis on the fMRI data, we identified six widely distributed resting state networks. The blood oxygenation level-dependent signal fluctuations associated with each network were correlated with the EEG power variations of delta, theta, alpha, beta, and gamma rhythms. Each functional network was characterized by a specific electrophysiological signature that involved the combination of different brain rhythms. Moreover, the joint EEG/fMRI analysis afforded a finer physiological fractionation of brain networks in the resting human brain. This result supports for the first time in humans the coalescence of several brain rhythms within large-scale brain networks as suggested by biophysical studies.
Key Findings
1
A data-driven simultaneous EEG-fMRI approach identified six widely distributed resting-state networks using independent component analysis.
2
BOLD fluctuations in each resting-state network correlated with EEG power variations across delta, theta, alpha, beta, and gamma rhythms.
3
Each functional network exhibited a distinct electrophysiological signature defined by a specific combination of brain rhythms.
4
Joint EEG-fMRI analysis enabled finer physiological subdivision of resting-state networks than either modality alone.
5
The findings provide initial human evidence that multiple brain rhythms coalesce within large-scale resting-state networks, consistent with biophysical models.
Research Object
large-scale resting-state networks in the human brain during resting wakefulness
Research Subject
network-specific electrophysiological signatures and their relationship to hemodynamic fluctuations across delta, theta, alpha, beta, and gamma rhythms
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2007-08-01
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