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

Anxiety sensitivity and substance use disorders: Associations across multiple drug classes and tests of a transdiagnostic mechanism.

Behaviour research and therapy·2026
Same author

The LPP indexes baseline and treatment-related changes in anxiety sensitivity.

International journal of psychophysiology : official journal of the International Organization of Psychophysiology·2026
Same author

Positive Emotion Dysregulation in Opioid Use Disorder and Normalization by Mindfulness-Oriented Recovery Enhancement: A Secondary Analysis of a Randomized Clinical Trial.

JAMA psychiatry·2025
Same author

Posttraumatic reexperiencing and alcohol use: Mediofrontal theta as a neural mechanism for negative reinforcement.

Journal of psychopathology and clinical science·2025
Same author

Errors elicit frontoparietal theta-gamma coupling that is modulated by endogenous estradiol levels.

International journal of psychophysiology : official journal of the International Organization of Psychophysiology·2024
Same author

Evaluating the impact of water reuse educational videos on water reuse perceptions using EEG/event related potential.

Journal of environmental management·2023

Related Experiment Video

Updated: May 19, 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

A signal-processing-based approach to time-varying graph analysis for dynamic brain network identification.

Ali Yener Mutlu1, Edward Bernat, Selin Aviyente

  • 1Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA. mutluali@msu.edu

Computational and Mathematical Methods in Medicine
|August 31, 2012
PubMed
Summary

This study introduces a dynamic network summarization method to analyze evolving brain connectivity. It captures time-varying functional networks, offering a more reliable view than static brain activity snapshots.

More Related Videos

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
06:50

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

Related Experiment Videos

Last Updated: May 19, 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

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
06:50

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Computational Neuroscience

Background:

  • Analyzing human brain functional connectivity is crucial.
  • Static functional networks offer limited insight into dynamic brain communication.
  • Previous methods often provide unreliable snapshots of brain activity.

Purpose of the Study:

  • To propose a dynamic network summarization approach for analyzing time-varying functional brain connectivity.
  • To capture the evolution of connectivity patterns over time.
  • To provide a more accurate representation of brain communication.

Main Methods:

  • Identifying key event intervals by quantifying changes in connectivity patterns.
  • Summarizing activity within intervals using principal component decomposition.
  • Evaluating the method with event-related potential (ERP) data for the error-related negativity (ERN) component.

Main Results:

  • The proposed method effectively characterizes time-varying network dynamics.
  • Statistically significant connectivity patterns were identified for different intervals.
  • The dynamic nature of functional connectivity was illustrated.

Conclusions:

  • Dynamic network summarization offers a superior approach to static analysis for brain connectivity.
  • The method provides insights into the temporal evolution of cognitive control networks.
  • This approach enhances our understanding of brain function over time.