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

Measurement of the lifetime difference between Bs mass eigenstates.

Physical review letters·2005
Same author

Destruction of organic pollutants in reusable wastewater using advanced oxidation technology.

Chemosphere·2005
Same author

[A high performance liquid chromatographic method for the determination of teniposide in brain tissue using electro-chemical detection].

Se pu = Chinese journal of chromatography·2005
Same author

Correlating gene expression with chemical scaffolds of cytotoxic agents: ellipticines as substrates and inhibitors of MDR1.

The pharmacogenomics journal·2005
Same author

[Effects of cytokines on multidrug-resistance in K562/A02 cells].

Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi·2005
Same author

Analysis of the neuroligin 3 and 4 genes in autism and other neuropsychiatric patients.

Molecular psychiatry·2004

Related Experiment Video

Updated: Jun 6, 2026

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

Detecting causal interdependence in simulated neural signals based on pairwise and multivariate analysis.

C Yang1, R Le Bouquin Jeannes, G Faucon

  • 1INSERM, U642, Rennes, F-35000, France.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 25, 2010
PubMed
Summary

This study analyzes electroencephalogram (EEG) signals during epileptic seizures to understand brain activity. It uses Granger causality to reveal how different brain regions influence each other during fast onset activity (FOA).

More Related Videos

New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies
05:59

New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies

Published on: October 6, 2023

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Related Experiment Videos

Last Updated: Jun 6, 2026

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

New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies
05:59

New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies

Published on: October 6, 2023

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Area of Science:

  • Neuroscience
  • Computational Biology
  • Epilepsy Research

Background:

  • Drug-resistant epilepsy presents complex seizure dynamics.
  • Understanding brain region interactions during seizures is crucial.
  • Fast onset activity (FOA) is a key seizure phase.

Purpose of the Study:

  • To analyze electroencephalogram (EEG) signals during seizures in patients with drug-resistant epilepsy.
  • To investigate the involvement and directional influence of cerebral structures during FOA.
  • To assess the utility of Granger causality for characterizing information flow in neuronal networks.

Main Methods:

  • Recording EEG signals with depth electrodes during seizures.
  • Utilizing a physiology-based model of coupled neuronal populations.
  • Applying a linear Granger causality measure to multivariate signals.

Main Results:

  • The study demonstrates the relevance of Granger causality for detecting causal relationships in coupled neuronal signals.
  • Statistical analysis supports the index's ability to characterize information flow direction.
  • The findings are relevant for understanding inter-regional brain communication during epileptic seizures.

Conclusions:

  • Linear Granger causality is a valuable tool for analyzing directed information flow between neuronal populations.
  • This method can help elucidate the dynamic involvement of cerebral structures during epileptic seizure onset.
  • Further research can explore complex causality measures for more intricate network analyses.