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

Seizures: Classification01:13

Seizures: Classification

539
Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:
539

You might also read

Related Articles

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

Sort by
Same author

Impact of Intranasal Administration of Ayurveda Medicine in Apparently Healthy Individuals on Neurophysiological Variables and Functional Connectivity Using Functional Magnetic Resonance Imaging: Protocol for an Exploratory Randomized Controlled Trial.

JMIR research protocols·2026
Same author

Role of Artificial Intelligence and Machine Learning in the prediction of the pain: A scoping systematic review.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine·2022
Same author

Novel Use of a Closed Liposuction System: Treatment of an Acute Morel-Lavallée Lesion of the Upper Extremity.

Cureus·2020
Same author

Comorbid Diabetes in Inflammatory Bowel Disease Predicts Adverse Disease-Related Outcomes and Infectious Complications.

Digestive diseases and sciences·2020
Same author

Emerging manufacturers engagements in the COVID -19 vaccine research, development and supply.

Vaccine·2020
Same author

Development of CuAg/Cu<sub>2</sub>O nanoparticles on carbon nitride surface for methanol oxidation and selective conversion of carbon dioxide into formate.

Journal of colloid and interface science·2020

Related Experiment Video

Updated: Aug 22, 2025

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.5K

Localizing epileptogenic network from SEEG using non-linear correlation, mutual information and graph theory

Rohith Devisetty1, M B Amsitha1, Sasi Jyothirmai1

  • 1Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, India.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine
|November 8, 2022
PubMed
Summary

Graph theory analysis of intracranial EEG data improves the localization of the epileptogenic zone (EZ) in epilepsy surgery. This method enhances precision for resective surgery and minimally invasive ablation, aiding better patient outcomes.

Keywords:
Epileptogenic zoneSEEGbrain connectivity and patient-specific treatment decisionsgraph theoryintracranial EEG

More Related Videos

Stereo-Electro-Encephalo-Graphy SEEG With Robotic Assistance in the Presurgical Evaluation of Medical Refractory Epilepsy: A Technical Note
05:54

Stereo-Electro-Encephalo-Graphy SEEG With Robotic Assistance in the Presurgical Evaluation of Medical Refractory Epilepsy: A Technical Note

Published on: June 13, 2016

17.3K
Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization
09:57

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

Published on: September 20, 2024

2.7K

Related Experiment Videos

Last Updated: Aug 22, 2025

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.5K
Stereo-Electro-Encephalo-Graphy SEEG With Robotic Assistance in the Presurgical Evaluation of Medical Refractory Epilepsy: A Technical Note
05:54

Stereo-Electro-Encephalo-Graphy SEEG With Robotic Assistance in the Presurgical Evaluation of Medical Refractory Epilepsy: A Technical Note

Published on: June 13, 2016

17.3K
Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization
09:57

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

Published on: September 20, 2024

2.7K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Medical Technology

Background:

  • Precise localization of the epileptogenic zone (EZ) is critical for successful epilepsy surgery.
  • Visual analysis of intracranial EEG (iEEG) for EZ identification is challenging and often inaccurate.
  • Current methods struggle to accurately delineate the complex epileptogenic network.

Purpose of the Study:

  • To evaluate the efficacy of interictal connectivity and graph theory analysis on iEEG for improved EZ delineation.
  • To identify graph properties that best predict the location of the EZ.
  • To assess the potential of this computational approach in guiding epilepsy surgery.

Main Methods:

  • Retrospective analysis of iEEG data from patients with drug-refractory epilepsy.
  • Utilized graph theory metrics (Out-Degree, Out-Strength, Betweenness centrality) and computational measures (h2 nonlinear correlation, mutual information).
  • Correlated graph properties with identified EZ locations to determine predictive value.

Main Results:

  • Graph properties, specifically Out-Degree, Out-Strength, and Betweenness centrality, were strong predictors of the EZ.
  • Normalized graph properties exceeding 0.75 demonstrated high accuracy in EZ localization (87.88% sensitivity, 87.13% specificity).
  • Integration of interictal fast discharges (IFD) with connectivity and graph analysis further refined EZ localization.

Conclusions:

  • Graph theory analysis of interictal iEEG provides a more precise method for localizing the EZ compared to visual inspection.
  • This computational approach shows promise for guiding both resective surgery and minimally invasive ablation of epileptogenic hubs.
  • Prospective validation is necessary to confirm clinical utility and widespread adoption.