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Exploring directed functional connectivity based on electroencephalography source signals using a global cortex

Hailing Wang1, Xia Wu1, Xiaotong Wen2

  • 1College of Information Science and Technology, Beijing Normal University, Beijing, 100875, China.

Journal of Neuroscience Methods
|March 1, 2019
PubMed
Summary
This summary is machine-generated.

We developed a new method, global cortex factor-based MVAR (GCF-MVAR), to improve the analysis of directed functional connectivity in EEG source signals. GCF-MVAR offers better physiological interpretation and lower estimation error compared to existing methods.

Keywords:
Directed functional connectivityEEG sourceGlobal cortex factor-based MVAR (GCF-MVAR) modelMVAR modelWeighted PDC

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biomedical Engineering

Background:

  • Partial Directed Coherence (PDC) from Multivariate Autoregressive (MVAR) models analyzes directed functional connectivity in electroencephalography (EEG) source signals.
  • Directly fitting MVAR models to high-dimensional EEG source signals presents significant challenges.
  • The physiological plausibility of PDC results from real EEG data remains unclear.

Purpose of the Study:

  • To introduce a novel method, global cortex factor-based MVAR (GCF-MVAR), for studying directed functional connectivity using EEG source signals.
  • To address the limitations of directly fitting MVAR models to high-dimensional EEG data.
  • To enhance the physiological interpretability of connectivity measures by incorporating source spectral power.

Main Methods:

  • Proposed GCF-MVAR method utilizes low-dimensional global cortex factor signals derived from EEG sources via Principal Component Analysis (PCA).
  • Avoids direct MVAR model fitting to high-dimensional source signals.
  • Weights PDC with source spectral power (SP) to validate physiological efficacy.

Main Results:

  • GCF-MVAR demonstrated the lowest estimation error in simulations compared to FMVAR, ROI-MVAR, and MVAR.
  • Weighting PDC with source SP improved the physiological interpretation of source connectivity for both simulated and resting-state EEG data.
  • GCF-MVAR outperformed existing state-of-the-art methods.

Conclusions:

  • GCF-MVAR is a superior method for analyzing directed functional connectivity in EEG source signals.
  • The method enhances physiological interpretation and reduces estimation error.
  • GCF-MVAR is applicable to resting-state studies, task-related connectivity, and neurological disorder analysis.