Effect of Inverse Solutions, Connectivity Measures, and Node Sizes on EEG Source Network: A Simultaneous EEG Study.

Area of Science:

• Neuroscience
• Biophysics
• Computational Neuroscience

Background:

• Brain network analysis offers insights into brain function and dysfunction.
• Reconstructing brain networks in source space is crucial for applications like non-invasive neuromodulation.
• Estimating source activity from scalp EEG is complex due to the ill-posed nature of the problem and volume conduction.

Purpose of the Study:

• To investigate the impact of inverse solutions, connectivity measures, and node sizes on reconstructing EEG source networks.
• To compare the performance of various methods for source activity and network reconstruction.
• To establish a reliable method for source space network reconstruction from scalp EEG.

Main Methods:

• Simultaneous scalp EEG and stereo-EEG recordings were used.
• Multiple inverse solutions (sLORETA, wMNE, LCMV beamforming) and connectivity measures (wPLI, DTF, PDC, EEC, PCC, AEC) were evaluated.
• Numerical simulations were performed for comparative analysis.

Main Results:

• The weighted phase-lag index (wPLI) demonstrated significantly better performance in reconstructing source space networks compared to five other connectivity measures.
• No significant differences were observed between the evaluated inverse solutions (sLORETA, wMNE, LCMV) regarding reconstructed source networks.
• Source networks derived from signal phases showed better congruence with intracranial activities than those based on waveform properties or causality.

Conclusions:

• The weighted phase-lag index (wPLI) is a highly effective measure for reconstructing brain networks in source space from scalp EEG.
• Signal phase-based network reconstruction provides a more accurate representation of intracranial brain activity.
• This research provides a foundational framework for source space network reconstruction, particularly beneficial for future neuromodulation research.