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Updated: May 27, 2026
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Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
Published on: March 21, 2019
Frequency-dependent functional connectivity within resting-state networks: an atlas-based MEG beamformer solution.
Arjan Hillebrand1, Gareth R Barnes, Johannes L Bosboom
1Department of Clinical Neurophysiology and Magnetoencephalography Center, VU University Medical Center, Amsterdam, The Netherlands. a.hillebrand@vumc.nl
Neuroimage
|November 30, 2011
Summary
This study introduces a new framework for analyzing brain connectivity using Magnetoencephalography (MEG). The Phase Lag Index (PLI) method reliably identifies frequency-specific resting-state networks, overcoming limitations of previous techniques.
Area of Science:
- Neuroscience
- Cognitive Neuroscience
- Brain Imaging
Background:
- Cognitive functions rely on coordinated activity across spatially distributed brain regions.
- Magnetoencephalography (MEG) offers high temporal resolution for studying brain interactions.
- Direct MEG analysis can yield spurious functional connectivity estimates due to volume conduction and field spread.
Purpose of the Study:
- To develop a reliable analysis framework for determining functional connectivity from MEG data.
- To address biases from volume conduction and source reconstruction in connectivity estimates.
- To characterize frequency-dependent resting-state functional connectivity networks in the human brain.
Main Methods:
- Utilized an atlas-based region of interest (ROI) approach in anatomical space for comprehensive brain coverage.
- Employed the Phase Lag Index (PLI) as a functional connectivity estimator insensitive to volume conduction and field spread.
- Applied the framework to eyes-closed resting-state MEG data from thirteen healthy participants.
Main Results:
- Demonstrated that phase coherence-based connectivity estimates are biased, while PLI-based estimates are not.
- Identified significant mean functional connectivity in alpha, beta, and gamma frequency bands.
- Observed distinct frequency-dependent patterns of resting-state connectivity, with alpha and beta bands localized to posterior and sensorimotor areas, respectively.
Conclusions:
- The proposed framework reliably characterizes resting-state brain dynamics and functional connectivity.
- PLI effectively mitigates biases in MEG-based connectivity analysis.
- Resting-state functional networks exhibit frequency-dependent organization across the cortex.
