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

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

8.8K
Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
8.8K

You might also read

Related Articles

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

Sort by
Same author

Shared spatial and temporal principles govern connectome dynamics across timescales.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Animal acoustic communication has a conserved optimal rhythm within the neural delta range.

PLoS biology·2026
Same author

Forecasting Perception Before It Happens: Context-Specific Connectivity Patterns Predict Perceptual Outcomes.

bioRxiv : the preprint server for biology·2026
Same author

Languages evolve ergodically: Clarifications and responses.

Physics of life reviews·2026
Same author

A case-control neuroimaging investigation of chronic Zika virus-infected adults.

Frontiers in human neuroscience·2026
Same author

Aligning statistical models with inference goals in the neuroscience of language: A dual-dependency taxonomy.

Imaging neuroscience (Cambridge, Mass.)·2026

Related Experiment Video

Updated: Dec 20, 2025

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

12.1K

Concurrent EEG- and fMRI-derived functional connectomes exhibit linked dynamics.

Jonathan Wirsich1, Anne-Lise Giraud2, Sepideh Sadaghiani1

  • 1Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA; Department of Psychology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.

Neuroimage
|June 2, 2020
PubMed
Summary

Human brain connectivity measured by fMRI and EEG shows synchronized infraslow dynamics. This reveals that fast brain oscillations reconfigure slowly across the whole brain, linking functional Magnetic Resonance Imaging (fMRI) and electroencephalography (EEG) signals.

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

7.6K
Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.4K

Related Experiment Videos

Last Updated: Dec 20, 2025

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

12.1K
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

7.6K
Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.4K

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Biophysics

Background:

  • Long-range functional connectivity is a key focus in human functional Magnetic Resonance Imaging (fMRI).
  • The relationship between whole-brain dynamics in fMRI and electrophysiological connectivity is not well understood.
  • fMRI connectivity shows infraslow (<0.1Hz) dynamics, while electrophysiological connectivity relies on fast oscillations (~1-100Hz).

Purpose of the Study:

  • To investigate if fast oscillation-based coupling in electrophysiology varies at infraslow speeds, mirroring fMRI dynamics.
  • To determine if this cross-modal association spatially varies across the connectome and differs between electrophysiological frequency bands.
  • To assess the reliability of fMRI and EEG for measuring neural connectivity dynamics.

Main Methods:

  • Utilized two concurrent resting-state electroencephalography (EEG)-fMRI datasets.
  • Analyzed oscillation-based coherence in delta, theta, alpha, beta, and gamma EEG bands.
  • Examined the temporal reconfiguration of EEG connectivity in tandem with fMRI connectivity dynamics.

Main Results:

  • Oscillation-based coherence in all canonical EEG bands reconfigured at infraslow speeds, synchronized with fMRI connectivity changes.
  • The cross-modal association of connectivity dynamics was widespread across the entire connectome, irrespective of EEG frequency band.
  • Frequency-specific differences were observed: slower bands showed stronger associations in visual-somatomotor connections, while faster bands were stronger in Default Mode Network connections.

Conclusions:

  • Neural connectivity dynamics are reliably measurable by both fMRI and EEG, despite their respective limitations.
  • Electrophysiological connectivity, across all oscillation bands, reconfigures slowly and in a distributed manner throughout the whole-brain connectome.
  • Findings challenge traditional views of localized oscillation power and suggest a unified, dynamic framework for brain connectivity.