Coherent source and connectivity analysis on simultaneously measured EEG and MEG data during isometric contraction

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Electroencephalography (EEG) and magnetoencephalography (MEG) are key non-invasive brain imaging techniques.
• Realistic head modeling is crucial for accurate source localization in EEG and MEG.
• Understanding sensorimotor network dynamics during isometric contraction (ISC) is vital for neurological research.

Purpose of the Study:

• To implement an integrated realistic head model for simultaneous EEG and MEG analysis.
• To identify coherent brain source networks and their interactions during ISC in the beta band (15-30 Hz).
• To compare effective connectivity derived from separate and combined EEG/MEG modalities.

Main Methods:

• Utilized Welch periodogram for EEG-electromyography (EMG) coherence spectrum estimation.
• Applied Dynamic Imaging of Coherent Sources (DICS) for realistic head modeling and source analysis.
• Employed renormalized partial directed coherence to determine effective connectivity.

Main Results:

• Identified a cortical and sub-cortical network including the primary sensory motor cortex (PSMC), secondary motor area (SMA), and cerebellum (C).
• Found similar sensorimotor networks across modalities, with no significant difference in signal-to-noise ratio (SNR).
• Observed significantly higher coherence values and additional effective connectivity insights when combining EEG and MEG data.

Conclusions:

• The integrated head model effectively captures brain activity during ISC using both EEG and MEG.
• Combined EEG-MEG analysis provides a more comprehensive understanding of sensorimotor network connectivity than individual modalities.
• This approach offers enhanced insights into neural interactions during motor tasks.