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

Brain Waves01:23

Brain Waves

3.0K
Brain waves are electrical signals generated by the neurons in the brain, which are regularly monitored to measure mental activities. Brain waves and their frequency ranges can be measured using an electroencephalogram or EEG. There are four main types of brain waves, each with distinct characteristics:
3.0K

You might also read

Related Articles

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

Sort by
Same author

Detection of time reversibility in time series by ordinal patterns analysis.

Chaos (Woodbury, N.Y.)·2019
Same author

Bandt-Pompe symbolization dynamics for time series with tied values: A data-driven approach.

Chaos (Woodbury, N.Y.)·2018
Same author

An analysis of high-frequency cryptocurrencies prices dynamics using permutation-information-theory quantifiers.

Chaos (Woodbury, N.Y.)·2018
Same author

Analysis of ischaemic crisis using the informational causal entropy-complexity plane.

Chaos (Woodbury, N.Y.)·2018
Same author

Rhythmic activities of the brain: Quantifying the high complexity of beta and gamma oscillations during visuomotor tasks.

Chaos (Woodbury, N.Y.)·2018
Same author

Nonlinear dynamics of river runoff elucidated by horizontal visibility graphs.

Chaos (Woodbury, N.Y.)·2018

Related Experiment Video

Updated: Nov 23, 2025

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

10.5K

Dynamics in cortical activity revealed by resting-state MEG rhythms.

J Mendoza-Ruiz1, C E Alonso-Malaver1, M Valderrama2

  • 1Department of Statistics, Universidad Nacional de Colombia, Cr 45 #26-85, Bogotá, Colombia.

Chaos (Woodbury, N.Y.)
|December 31, 2020
PubMed
Summary

This study reveals an order relation between brain activity

More Related Videos

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.9K
Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

12.0K

Related Experiment Videos

Last Updated: Nov 23, 2025

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

10.5K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.9K
Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

12.0K

Area of Science:

  • Neuroscience
  • Information Theory
  • Network Science

Background:

  • The brain functions as a complex biophysical system with information flowing through neuronal structures.
  • Understanding brain dynamics at rest is crucial for grasping fundamental brain function and identifying pathologies.
  • Resting-state brain activity dynamics are complex and require advanced analytical methods.

Purpose of the Study:

  • To investigate the spatiotemporal dynamics of cortical fluctuations in healthy subjects during resting-state.
  • To explore the relationship between entropy and complexity in brain activity across different frequency bands and scales.
  • To characterize the role of the posterior cortex in brain dynamics and network structure during rest.

Main Methods:

  • Utilized magnetoencephalography (MEG) signals to analyze brain activity.
  • Applied information theory concepts, specifically entropy-complexity, to quantify signal dynamics.
  • Constructed cortical connectivity networks to study topology and dynamics.

Main Results:

  • Identified an order relation between entropy and complexity across various frequency bands and temporal scales.
  • Discovered that the posterior cortex exhibits strong dynamics and high clustering in the alpha (α) band.
  • Observed that the posterior cortex plays a significant role in both the dynamics and structure of resting-state brain activity.

Conclusions:

  • The findings suggest an emergent phenomenon in brain dynamics, characterized by an order relation between entropy and complexity, which is band-specific.
  • The posterior cortex emerges as a critical region with dual functional importance during resting-state.
  • This study pioneers the integration of information theory and network science with MEG to elucidate resting-state brain dynamics.