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

Long-Term Variability in Visual Processing versus Perceptual Stability.

eNeuro·2026
Same author

Modelling discrete states and long-term dynamics in functional brain networks.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Modelling variability in functional brain networks using embeddings.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Effects of Age on Resting-State Cortical Networks.

Human brain mapping·2026
Same author

Normative modeling of brain function abnormalities in complex pathology requires a whole-brain approach.

Progress in neurobiology·2026
Same author

The role of age in the relationship between brain structure and cognition: moderator or confound?

Cerebral cortex (New York, N.Y. : 1991)·2026

Related Experiment Video

Updated: Apr 7, 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

6.2K

Canonical Hidden Markov Model Networks for studying M/EEG.

Chetan Gohil1, Rukuang Huang1, Cameron Higgins1

  • 1Oxford Centre for Human Brain Activity, Oxford Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, United Kingdom.

Imaging Neuroscience (Cambridge, Mass.)
|April 6, 2026
PubMed
Summary

This study introduces a canonical Hidden Markov Model (HMM) to analyze brain networks from magneto/encephalography (M/EEG) data. This approach provides a common reference for comparing diverse M/EEG datasets, reducing computational costs and enhancing reproducibility.

Keywords:
EEGHidden Markov ModelMEGdynamic functional connectivityelectrophysiologyfunctional networksmachine learningneuronal oscillations

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

15.4K
Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
10:22

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Published on: December 6, 2016

21.4K

Related Experiment Videos

Last Updated: Apr 7, 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

6.2K
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

15.4K
Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
10:22

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Published on: December 6, 2016

21.4K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Brain Network Analysis

Background:

  • Magneto/encephalography (M/EEG) reveals dynamic brain networks crucial for understanding brain function.
  • Hidden Markov Models (HMMs) are effective for inferring reproducible brain networks from M/EEG data.
  • Current HMM studies often use small, isolated datasets, limiting generalizability and increasing computational burden.

Purpose of the Study:

  • To develop and validate a 'canonical HMM' as a standardized reference for analyzing M/EEG brain networks.
  • To enable efficient comparison of brain network dynamics across diverse M/EEG datasets.
  • To provide an open-access resource of canonical brain networks for the research community.

Main Methods:

  • Trained Hidden Markov Models (HMMs) with varying states (4-16) on a large dataset of 1849 MEG recordings (N=621).
  • Developed canonical HMMs applicable in both parcellated source space and sensor space.
  • Demonstrated the application of the canonical HMM using independent M/EEG datasets.

Main Results:

  • The canonical HMM successfully describes brain activity across diverse M/EEG datasets using a shared set of brain networks.
  • The approach was validated on boutique MEG and EEG datasets, showing its utility in comparing individuals and studies.
  • Canonical HMMs were made publicly available, facilitating broader research applications.

Conclusions:

  • A canonical HMM provides a computationally efficient and reproducible framework for analyzing dynamic brain networks across M/EEG studies.
  • This standardized approach allows for robust comparison of brain network states within and between datasets.
  • The open-access canonical HMM resource promotes wider adoption and advancement in M/EEG-based brain network research.