Opportunities and methodological challenges in EEG and MEG resting state functional brain network research

Area of Science:

• Neuroscience
• Computational Neuroscience
• Biophysics

Background:

• Resting state electroencephalogram (EEG) and magnetoencephalogram (MEG) are increasingly utilized for studying brain functional connectivity and network topology.
• The proliferation of diverse analysis approaches complicates cross-study comparisons.
• Methodological standardization is crucial for reliable interpretation of functional network studies.

Purpose of the Study:

• To provide a comprehensive overview of methodological considerations in resting state EEG and MEG data analysis.
• To highlight opportunities and pitfalls in functional connectivity and network analysis.
• To guide researchers towards best practices for improved data interpretation.

Main Methods:

• Review and summarization of current common practices in resting state EEG/MEG data processing and analysis.
• Discussion of advantages and disadvantages of various methodological choices.
• Identification of potential pitfalls and practical tips for researchers.

Main Results:

• Methodological choices significantly impact the construction and interpretation of functional brain networks.
• Common practices in resting state EEG/MEG analysis have inherent limitations.
• Standardized approaches can enhance the accuracy and comparability of network studies.

Conclusions:

• Adherence to best practices and avoidance of common pitfalls are essential for robust functional connectivity and network analysis.
• Improved methodological rigor will facilitate more accurate interpretation and comparison of resting state EEG/MEG studies.
• Future research should focus on refining and validating analysis pipelines for resting state neuroimaging data.