Whole-brain electrophysiological functional connectivity dynamics in resting-state EEG

Area of Science:

• Neuroscience
• Computational Neuroscience
• Brain Imaging

Background:

• Functional connectivity (FC) dynamics are well-studied in fMRI, but remain largely unexplored in EEG.
• Understanding dynamic FC in EEG is crucial for advancing brain activity analysis.

Purpose of the Study:

• To introduce a novel analytical framework for investigating spatiotemporal dynamics of FC (dFC) in resting-state human EEG.
• To reconstruct and characterize intrinsic connectivity networks (ICNs) from EEG data.

Main Methods:

• Independent component analysis (ICA)
• Cortical source imaging
• Sliding-window correlation analysis
• K-means clustering

Main Results:

• Major fMRI ICNs were successfully reconstructed from EEG using the proposed framework.
• Significant spatial and temporal variability were identified within these EEG ICNs.
• The framework revealed quasi-stable states within individual EEG ICNs, showing consistency with fMRI ICNs and highlighting transient communication patterns.

Conclusions:

• Rich temporal and spatial dynamics are detectable in ICNs from EEG data.
• The developed framework provides a comprehensive view of EEG ICNs, including their dynamic states.
• Future research should explore the spectral dynamics of EEG ICNs.