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

Layer-specific facial soft-tissue thickness in 1174 Chinese adults: Implications for finite-element headforms and ergonomic design.

Ergonomics·2026
Same author

Multiscale structural connectome eigenmodes constrain human brain functional dynamics.

Communications biology·2026
Same author

Association between disconnected networks and surgical outcome in drug-resistant temporal lobe epilepsy.

Seizure·2026
Same author

Independent and joint effects of ambient temperature and relative humidity on fetal distress: effect modification by air pollutants.

International journal of biometeorology·2026
Same author

Mapping the functional connectome between grey matter and white matter.

Communications biology·2026
Same author

Multi-omics characterization of the skin microbiota reveals the anti-aging roles of Stenotrophomonas maltophilia.

Microbiome·2026

Related Experiment Video

Updated: Sep 8, 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

Interictal dynamic network transitions in mesial temporal lobe epilepsy.

Rong Li1, Chijun Deng1, Xuyang Wang1

  • 1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.

Epilepsia
|June 14, 2022
PubMed
Summary

Mesial temporal lobe epilepsy (mTLE) patients show altered brain network dynamics, with unstable brain states during interictal periods. Default-mode network (DMN) dynamics can predict seizure frequency.

Keywords:
default-mode networkdynamic functional networkhippocampusmachine-learning predictive modelmesial temporal lobe epilepsy

More Related Videos

Multi-electrode Array Recordings of Human Epileptic Postoperative Cortical Tissue
13:14

Multi-electrode Array Recordings of Human Epileptic Postoperative Cortical Tissue

Published on: October 26, 2014

20.8K
Generation and On-Demand Initiation of Acute Ictal Activity in Rodent and Human Tissue
06:45

Generation and On-Demand Initiation of Acute Ictal Activity in Rodent and Human Tissue

Published on: January 19, 2019

9.0K

Related Experiment Videos

Last Updated: Sep 8, 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
Multi-electrode Array Recordings of Human Epileptic Postoperative Cortical Tissue
13:14

Multi-electrode Array Recordings of Human Epileptic Postoperative Cortical Tissue

Published on: October 26, 2014

20.8K
Generation and On-Demand Initiation of Acute Ictal Activity in Rodent and Human Tissue
06:45

Generation and On-Demand Initiation of Acute Ictal Activity in Rodent and Human Tissue

Published on: January 19, 2019

9.0K

Area of Science:

  • Neuroscience
  • Medical Imaging
  • Computational Biology

Background:

  • Mesial temporal lobe epilepsy (mTLE) is a common neurological disorder.
  • Understanding interictal brain network dynamics is crucial for predicting seizure recurrence.

Purpose of the Study:

  • To investigate dynamic brain network alterations in mTLE patients during interictal periods.
  • To develop a predictive model for seizure recurrence using brain network dynamics.

Main Methods:

  • Resting-state functional MRI (fMRI) data from 79 mTLE patients and 97 controls.
  • Dynamic functional network configuration analysis.
  • Machine learning model to predict seizure frequency based on hippocampal-default mode network (DMN) interactions.

Main Results:

  • mTLE patients exhibited increased dynamic network switching, particularly in epileptogenic regions.
  • Decreased intra-network communication and increased inter-network communication were observed in mTLE.
  • A predictive model based on hippocampal-DMN dynamics accurately correlated with actual seizure frequency.

Conclusions:

  • Interictal brain networks in mTLE are dynamically unstable.
  • DMN dynamic parameters show potential as neuroimaging markers for monitoring seizure frequency in mTLE.