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Scalp electroencephalography (EEG) analysis of functional connectivity (FC) shows distortions in network topology compared to source reconstruction. Metrics minimizing signal leakage and zero-lag correlations offer more reliable EEG network organization insights.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biophysics

Background:

  • Electroencephalography (EEG) is widely used to analyze functional networks via connectivity metrics.
  • Scalp-level analysis, unlike source reconstruction, has limitations in inferring interacting brain regions.
  • Ideally, network topology conclusions should be consistent across different EEG analysis approaches.

Purpose of the Study:

  • To evaluate the accuracy of scalp EEG analysis in estimating functional network topology.
  • To quantify distortions in network topology estimation using various analysis pipelines and conditions.
  • To compare scalp-level versus source-level EEG functional connectivity.

Main Methods:

  • Analysis of EEG recordings using amplitude- and phase-based functional connectivity (FC) metrics.
  • Comparison of global connectivity and network topology between scalp-level and source-level analyses.
  • Assessment of Minimum Spanning Tree (MST) leaf fraction for metrics mitigating volume conduction and signal leakage.

Main Results:

  • Strong correlation found for global connectivity between scalp and source-level EEG analyses.
  • Weak correlation observed for network topology between scalp and source-level analyses.
  • MST leaf fraction showed strongest correlation for FC metrics that limited volume conduction/signal leakage.

Conclusions:

  • Volume conduction and signal leakage significantly distort EEG network organization estimates from scalp analysis.
  • Scalp-level EEG analysis limitations hinder accurate interpretation of underlying network topology.
  • Metrics addressing zero-lag correlations may provide more reliable estimates of EEG functional network topology.