Beamformer source analysis and connectivity on concurrent EEG and MEG data during voluntary movements.

Area of Science:

• Neuroscience
• Biophysics
• Signal Processing

Background:

• Electroencephalography (EEG) and magnetoencephalography (MEG) offer millisecond temporal resolution for neuronal dynamics.
• Source analysis methods are applied to EEG and MEG individually, but combined modality comparisons are rare.
• Understanding brain network interactions during voluntary movements requires robust source localization.

Purpose of the Study:

• To compare the efficacy of EEG, MEG, and combined EEG+MEG for analyzing coherent neural sources during voluntary movements.
• To investigate the advantages of combined modalities in source analysis and functional connectivity.
• To assess the signal-to-noise ratio (SNR) and network detection capabilities of each modality.

Main Methods:

• Simultaneous recording of EEG and MEG data from 15 healthy subjects during a finger tapping task.
• Application of Dynamic Imaging of Coherent Sources (DICS) beamformer approach for source localization.
• Source signal reconstruction and Renormalized Partial Directed Coherence (RPDC) analysis for functional connectivity.

Main Results:

• MEG and combined EEG+MEG successfully identified cortical and sub-cortical coherent sources at the finger tapping frequency (2-4 Hz).
• EEG alone failed to detect sub-cortical sources and exhibited a significantly lower SNR compared to MEG and combined data.
• The combined EEG+MEG approach revealed greater functional connectivity with more active connections than either modality alone.

Conclusions:

• MEG is superior for detecting deep coherent neural sources compared to EEG.
• Higher SNR is crucial for accurate source analysis, outweighing dipole orientation sensitivity and volume conduction effects in EEG.
• Combining EEG and MEG provides a more comprehensive analysis of brain network dynamics and functional connectivity during motor tasks.