Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

High-resolution whole-brain magnetic resonance spectroscopic imaging in youth at risk for psychosis.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Seizure relapse in new onset epilepsy: It is not always drug resistance.

Epilepsia·2026
Same author

The microstructure-weighted human connectome: network properties and structure-function correlations across spatial scales.

bioRxiv : the preprint server for biology·2026
Same author

Myo-inositol concentration in the medial prefrontal cortex is associated with changes in brain white matter microstructure in early psychosis.

Translational psychiatry·2026
Same author

Altered structural connectivity in epilepsy with myoclonic atonic seizures.

Epilepsy research·2026
Same author

The effect of human ratings reliability on machine learning model performance: a case study in infant pain assessment.

Scientific reports·2026
Same journal

Cortical similarity networks in the rat brain: Postnatal development and sensitivity to early life stress.

Network neuroscience (Cambridge, Mass.)·2026
Same journal

Increased sensitivity in identifying language-related functional connectivity using jackknife resampling analyses.

Network neuroscience (Cambridge, Mass.)·2026
Same journal

Phase-dependent stimulation response is shaped by the brain's dynamic functional connectivity.

Network neuroscience (Cambridge, Mass.)·2026
Same journal

Restoring oscillatory dynamics in Alzheimer's disease: A laminar whole-brain model of serotonergic psychedelic effects.

Network neuroscience (Cambridge, Mass.)·2026
Same journal

Distributed cortical network dynamics of binocular convergent eye movements in humans.

Network neuroscience (Cambridge, Mass.)·2026
Same journal

High-resolution Bayesian Virtual Epileptic Patient using neural field models.

Network neuroscience (Cambridge, Mass.)·2026
See all related articles

Related Experiment Video

Updated: Dec 10, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.9K

Using structural connectivity to augment community structure in EEG functional connectivity.

Katharina Glomb1, Emeline Mullier1, Margherita Carboni2

  • 1Connectomics Lab, Department of Radiology, University Hospital of Lausanne and University of Lausanne, Lausanne (CHUV-UNIL), Vaud, Switzerland.

Network Neuroscience (Cambridge, Mass.)
|September 5, 2020
PubMed
Summary
This summary is machine-generated.

This study reveals how to improve electroencephalography (EEG) functional connectivity (FC) analysis by using structural connectivity (SC) to reduce noise. Smoothing EEG signals with SC data enhances genuine brain network dynamics, making EEG a more reliable tool for network neuroscience.

Keywords:
Brain connectivityEEGResting stateStructure-function-relationship

More Related Videos

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.4K
Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

26.8K

Related Experiment Videos

Last Updated: Dec 10, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.9K
Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.4K
Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

26.8K

Area of Science:

  • Neuroscience
  • Brain Network Analysis
  • Signal Processing

Background:

  • Electroencephalography (EEG) allows for the study of fast brain network dynamics.
  • Volume conduction in EEG complicates the accurate measurement of functional connectivity (FC).
  • Distinguishing genuine FC from spurious connections caused by volume conduction remains a challenge.

Purpose of the Study:

  • To investigate the relationship between white matter structural connectivity (SC) and large-scale network structure in EEG-FC.
  • To develop a method for enhancing genuine FC in EEG data by leveraging SC information.
  • To improve the accuracy of EEG-based brain network analysis.

Main Methods:

  • EEG data was analyzed in source space, considering whole-cortex signals.
  • Structural connectivity (SC) data from white matter was used to inform EEG signal processing.
  • A graph-based smoothing technique was applied to EEG signals using SC information.
  • Functional connectivity (FC) derived from EEG was compared with SC and fMRI-FC.

Main Results:

  • SC predicts EEG-FC beyond Euclidean distance, supporting SC's role in mediating genuine FC.
  • Smoothing EEG signals with SC increased FC between structurally connected regions.
  • The proposed smoothing method enhanced the resemblance between EEG-FC and volume-conduction-free fMRI-FC.
  • Improvements were observed in overall correlation and community structure analysis.

Conclusions:

  • Leveraging white matter structural connectivity can significantly improve the accuracy of EEG functional connectivity analysis.
  • The developed method effectively boosts genuine, SC-mediated FC in EEG data.
  • This approach offers a promising solution for overcoming volume conduction limitations in EEG network neuroscience.