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

Computational analysis on design and optimization of low-frequency dielectrophoretic microfluidic lab on a chip for <i>Staphylococcus aureus</i> isolation from blood.

BioImpacts : BI·2026
Same author

Improved lung nodule segmentation with a squeeze excitation dilated attention based residual UNet.

Scientific reports·2025
Same author

EEG dynamic source imaging using a regularized optimization with spatio-temporal constraints.

Medical & biological engineering & computing·2024
Same author

Epileptic source connectivity analysis based on estimating of dynamic time series of regions of interest.

Network (Bristol, England)·2019
Same author

Slow light in ultracompact photonic crystal decoder.

Applied optics·2019
Same author

Ultra-fast all-optical decoder based on nonlinear photonic crystal ring resonators.

Applied optics·2018

Related Experiment Video

Updated: Mar 21, 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

6.1K

Inferring time-varying brain connectivity graph based on a new method for link estimation.

Maryam Songhorzadeh1, Karim Ansari-Asl1, Alimorad Mahmoudi1

  • 1a Department of Electrical Engineering, Faculty of Engineering , Shahid Chamran University of Ahvaz , Ahvaz , Iran.

Network (Bristol, England)
|May 3, 2016
PubMed
Summary

This study introduces an efficient framework for estimating causal interactions between neuronal groups using graphical models and Transfer Entropy (TE). The method simplifies high-dimensional probability distributions for better brain network analysis.

Keywords:
EEG/MEG/NIRSinformation theorymodelsnetwork

More Related Videos

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.6K
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

7.7K

Related Experiment Videos

Last Updated: Mar 21, 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

6.1K
A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.6K
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

7.7K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Graph Theory

Background:

  • Causal interaction estimation is crucial for understanding brain function.
  • Graphical models represent complex neuronal networks and directional relationships.
  • Assessing interregional brain interactions requires robust statistical analysis of multivariate time series data.

Purpose of the Study:

  • To propose an efficient framework for deriving graphical models from time series data.
  • To explore interregional brain interactions using a data-driven pipeline.
  • To improve the estimation of Transfer Entropy (TE) for multivariate analysis.

Main Methods:

  • Developed an efficient framework for graphical model derivation.
  • Employed Transfer Entropy (TE) for causal interaction estimation.
  • Simplified high-dimensional conditional probability distributions in TE calculation by identifying informative subsets of variables using causal Markov properties.

Main Results:

  • The proposed method effectively estimates graph links, addressing multivariate analysis challenges.
  • Demonstrated the framework's performance on stationary processes.
  • Validated the approach using simulated data and real neurophysiological data.

Conclusions:

  • The framework provides an efficient method for causal interaction estimation in neuronal networks.
  • Simplifying TE calculations enhances the analysis of complex brain interactions.
  • The approach is applicable to both simulated and real-world neurophysiological data.