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

Neural Circuits01:25

Neural Circuits

3.0K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
3.0K
Association Areas of the Cortex01:21

Association Areas of the Cortex

10.2K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
10.2K

You might also read

Related Articles

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

Sort by
Same author

Evaluating the efficacy of rimegepant as a preventive treatment for chronic and episodic migraine: a three-month longitudinal retrospective cohort study.

Frontiers in neurology·2026
Same author

A Generative AI Framework for Pharmacokinetic Clinical Study Report Authoring.

Clinical and translational science·2026
Same author

Scale-invariant brain morphometry: application to sulcal depth.

Computers in biology and medicine·2026
Same author

Conductivity Deviations as Virtual Sources in Magnetoencephalography.

Brain topography·2026
Same author

Predicting surgical outcome in drug-resistant epilepsy by combining interictal biomarkers within a machine learning framework.

Scientific reports·2026
Same author

A Digital Anatomical Atlas of the Human Cerebellum at Subfolial Resolution.

Human brain mapping·2026
Same journal

Vowel acoustic parameters in speech assessment and rehabilitation of minimally verbal and speech-motor-impaired autistic children: a narrative review.

Frontiers in human neuroscience·2026
Same journal

Toward clinical translation of TMS-EEG: an integrative review of multidimensional neurophysiological measures.

Frontiers in human neuroscience·2026
Same journal

The causal efficacy of consciousness: a neuroscientific analysis and explanation.

Frontiers in human neuroscience·2026
Same journal

Temporal-oscillatory entrainment: a multi-timescale framework for rhythmic coordination from neural to social frequencies.

Frontiers in human neuroscience·2026
Same journal

Role of AQP4 in ameliorating heat stress-induced cellular injury in a cell line model through active heat acclimation.

Frontiers in human neuroscience·2026
Same journal

Correction: Cognitive state monitoring for neuroadaptive information visualization.

Frontiers in human neuroscience·2026
See all related articles

Related Experiment Video

Updated: Apr 28, 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

5.0K

Encoding cortical dynamics in sparse features.

Sheraz Khan1, Julien Lefèvre2, Sylvain Baillet3

  • 1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School/Massachusetts Institute of Technology , Charlestown, MA , USA ; McGovern Institute, Massachusetts Institute of Technology , Cambridge, MA , USA ; Department of Neurology, Massachusetts General Hospital, Harvard Medical School , Boston, MA , USA.

Frontiers in Human Neuroscience
|June 7, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a novel automated method using Helmholtz-Hodge decomposition (HHD) to analyze complex brain activity from magnetoencephalography (MEG) and electroencephalography (EEG) data, enabling precise feature extraction and dynamic pattern identification.

Keywords:
Helmholtz–Hodge decompositionMEG source imagingepilepsymotion fieldoptical flow

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

14.3K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.6K

Related Experiment Videos

Last Updated: Apr 28, 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

5.0K
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

14.3K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.6K

Area of Science:

  • Neuroscience
  • Computational Biology
  • Medical Imaging

Background:

  • Magnetoencephalography (MEG) and electroencephalography (EEG) provide complex spatiotemporal dynamics of cortical activity.
  • Current methods for extracting features from these signals heavily rely on manual interpretation.
  • There is a significant need for automated methods to analyze high-dimensional neuroimaging data in clinical and neuroscience research.

Purpose of the Study:

  • To develop and validate an automated technique for extracting salient dynamical features from cortical signals.
  • To apply optical flow and Helmholtz-Hodge decomposition (HHD) for analyzing brain activity patterns.
  • To assess the method's efficacy in both simulated data and real-world MEG/EEG recordings.

Main Methods:

  • Utilized optical flow techniques to analyze kinematic properties of cortical surface data.
  • Extended the framework with a modified 2-Riemannian Helmholtz-Hodge decomposition (HHD) for automatic feature detection.
  • Applied the HHD model to simulated data, MEG data from a healthy individual during a somatosensory experiment, and MEG data from an epilepsy patient during sleep.

Main Results:

  • HHD accurately reproduced simulated cortical dynamics, encoding them into sparse features and representing brain activity propagation.
  • The method decoded the somatosensory N20 component into two HHD features, visualizing brain activity as a traveling source.
  • In an epilepsy patient, HHD visualized the propagation of epileptic activity around a brain lesion.

Conclusions:

  • HHD measures provide a quantitative, automated, and reproducible way to analyze cortical dynamics in healthy and diseased brains.
  • The technique facilitates the extraction of sparse features and the understanding of brain activity propagation between cortical areas.
  • This automated approach enhances the analysis of experimental and clinical MEG/EEG source imaging data.