Detecting Large-Scale Brain Networks Using EEG: Impact of Electrode Density, Head Modeling and Source Localization

Area of Science:

• Neuroscience
• Brain Imaging
• Signal Processing

Background:

• Resting state networks (RSNs) are fundamental to brain function.
• High-density electroencephalography (hdEEG) has emerged as a tool for detecting RSNs.
• Previous studies show similarity between EEG-derived RSNs and those from functional magnetic resonance imaging (fMRI).

Purpose of the Study:

• To investigate the technological factors influencing the detection of EEG-RSNs using spatial independent component analysis (sICA).
• To determine the impact of electrode density, head model accuracy, and source localization algorithms on RSN mapping with hdEEG.

Main Methods:

• Utilized advanced analysis workflows to estimate cortical neural signals and assess functional connectivity (FC).
• Employed spatial independent component analysis (sICA) for FC analysis.
• Systematically varied electrode density, head model accuracy, and source localization methods.

Main Results:

• High-density electrode montage is crucial for successful RSN detection by sICA.
• The choice of head modeling and source localization methods significantly affects RSN reconstruction accuracy.
• EEG-RSNs derived using sICA show high similarity to fMRI-based RSNs when optimal acquisition and analysis parameters are used.

Conclusions:

• Confirms the potential of hdEEG for mapping the human brain's functional architecture.
• Highlights the critical interplay between hdEEG acquisition technology and advanced data analysis techniques for accurate RSN mapping.
• Emphasizes the need for careful consideration of technological aspects in hdEEG analysis for reliable RSN detection.