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Related Experiment Video

Updated: Aug 27, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Assessing HD-EEG functional connectivity states using a human brain computational model.

Judie Tabbal^1,2, Aya Kabbara^3,2, Maxime Yochum⁴

¹Institute of Clinical Neurosciences of Rennes (INCR), Rennes, France.

Journal of Neural Engineering

|September 27, 2022

Summary

This summary is machine-generated.

This study introduces a computational framework for optimizing electroencephalography/magnetoencephalography (EEG/MEG) network analysis. It quantifies pipeline steps, identifying weighted minimum norm estimate/Phase Locking Value and ICA as effective for dynamic brain network states.

Keywords:

brain networks states dynamic functional connectivity electroencephalography neural mass models

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Last Updated: Aug 27, 2025

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Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

Neuroscience
Computational Neuroscience
Biophysics

Background:

Electro/Magnetoencephalography (EEG/MEG) source-space network analysis tracks fast brain dynamics.
Evaluating EEG/MEG pipeline steps lacks realistic, controlled data.

Purpose of the Study:

Quantitatively assess advantages and limitations of EEG/MEG network analysis techniques.
Introduce a framework to optimize the entire EEG/MEG source connectivity pipeline.

Main Methods:

Utilized a human brain computational model with physiologically based circuits and Diffusion Tensor Imaging to generate EEG data.
Simulated gamma-band oscillations for a virtual picture-naming task.
Evaluated inverse models, functional connectivity measures, and dimensionality reduction for brain network states (BNS).

Main Results:

Significant variability found among decomposition techniques in spatial and temporal accuracy.
Outlined spatial precision, temporal sensitivity, and global accuracy of extracted BNS.
Weighted minimum norm estimate/Phase Locking Value and ICA showed good performance for functional networks and dynamic BNS.

Conclusions:

Brain models can further evaluate EEG/MEG source-space network analysis steps.
Reduces empirical selection of pipeline methods due to their considerable impact on results.
Framework aids in optimizing EEG/MEG source connectivity analysis.