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

Flexible Gravitational-Wave Parameter Estimation with Transformers.

Physical review letters·2026
Same author

A Conversational Brain-Artificial Intelligence Interface.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

A critical perspective on finite sample conformal prediction theory in medical applications.

Artificial intelligence in medicine·2026
Same author

Conscious Self-Regulation and Psychological Well-Being in Students Experiencing Stress: A Cross-Sectional Study.

Consortium psychiatricum·2026
Same author

Imagining and building wise machines: the centrality of AI metacognition.

Trends in cognitive sciences·2026
Same author

Latent Causal Diffusions for Single-Cell Perturbation Modeling.

ArXiv·2026

Related Experiment Video

Updated: Mar 27, 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

Identification of the Default Mode Network with electroencephalography.

Tatiana Fomina, Matthias Hohmann, Bernhard Scholkopf

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 7, 2016
    PubMed
    Summary

    Electroencephalography (EEG) can now identify the Default Mode Network (DMN), a brain network linked to consciousness and disorders. This offers a portable, safe alternative to fMRI and PET for studying patient groups.

    More Related Videos

    Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
    06:37

    Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

    Published on: July 14, 2023

    1.4K
    Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI
    11:00

    Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI

    Published on: March 19, 2021

    5.2K

    Related Experiment Videos

    Last Updated: Mar 27, 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
    Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
    06:37

    Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

    Published on: July 14, 2023

    1.4K
    Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI
    11:00

    Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI

    Published on: March 19, 2021

    5.2K

    Area of Science:

    • Neuroscience
    • Brain Imaging
    • Cognitive Neuroscience

    Background:

    • The Default Mode Network (DMN) is crucial for consciousness and implicated in neuropsychiatric disorders.
    • Current DMN identification methods like fMRI and PET have limitations for certain patient populations.

    Purpose of the Study:

    • To demonstrate that electroencephalography (EEG) can effectively identify the DMN.
    • To offer a more accessible method for studying DMN alterations in diverse patient groups.

    Main Methods:

    • Utilized electroencephalography (EEG) to measure brain activity.
    • Instructed participants to alternate between self-referential memory recall and focused breathing.
    • Analyzed spectral band power modulation in the θ- and α-bands (4-16 Hz).

    Main Results:

    • A distinct spatial pattern of spectral power modulation was observed.
    • This EEG-identified pattern aligns with DMN patterns previously seen with fMRI and PET.
    • EEG provides a viable method for DMN characterization.

    Conclusions:

    • EEG can successfully identify the Default Mode Network (DMN).
    • This breakthrough allows for DMN studies in patient groups previously inaccessible to fMRI or PET.
    • EEG's portability, low cost, and safety enhance DMN research accessibility.