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

Implicit learning across varying temporal scales in individuals with and without mood instability.

Journal of affective disorders·2025
Same author

Palbociclib with adjuvant endocrine therapy in early breast cancer: 5-year follow-up analysis of the global multicenter, open-label, randomized phase III PALLAS trial (ABCSG-42/AFT-05/PrE0109/BIG-14-13).

Annals of oncology : official journal of the European Society for Medical Oncology·2025
Same author

CTRIAL-IE (ICORG) 15-34: The impact of the 21 gene breast recurrence score® assay on chemotherapy prescribing in oestrogen receptor positive, lymph node positive early stage breast cancer in Ireland.

Irish journal of medical science·2025
Same author

Irish national real-world analysis of the clinical and economic impact of 21-gene oncotype DX® testing in early-stage, 1-3 lymph node-positive, oestrogen receptor-positive, HER2-negative, breast cancer.

Breast cancer research and treatment·2024
Same author

Illness representations of people with later-onset functional seizures.

Epilepsy & behavior : E&B·2024
Same author

Bespoke magnetic field design for a magnetically shielded cold atom interferometer.

Scientific reports·2022

Related Experiment Video

Updated: May 22, 2026

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography
09:25

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography

Published on: July 26, 2019

Inferring task-related networks using independent component analysis in magnetoencephalography.

H Luckhoo1, J R Hale, M G Stokes

  • 1Oxford Centre for Human Brain Activity, University of Oxford, Warneford Hospital, Oxford, UK. henry.luckhoo@trinity.ox.ac.uk

Neuroimage
|May 10, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for analyzing magnetoencephalography (MEG) data to identify brain networks during tasks. The optimized method enhances the detection of functional connectivity and task-related activity, improving upon existing techniques.

More Related Videos

How to Detect Amygdala Activity with Magnetoencephalography using Source Imaging
10:48

How to Detect Amygdala Activity with Magnetoencephalography using Source Imaging

Published on: June 3, 2013

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

Related Experiment Videos

Last Updated: May 22, 2026

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography
09:25

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography

Published on: July 26, 2019

How to Detect Amygdala Activity with Magnetoencephalography using Source Imaging
10:48

How to Detect Amygdala Activity with Magnetoencephalography using Source Imaging

Published on: June 3, 2013

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

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Biophysics

Background:

  • Magnetoencephalography (MEG) is a neuroimaging technique used to measure magnetic fields produced by electrical activity in the brain.
  • Existing methods for analyzing resting-state MEG data, such as combining beamforming, Hilbert transform, and temporal independent component analysis (ICA), can extract resting-state networks similar to those found in fMRI.
  • However, analyzing task-positive MEG data to identify task-related networks requires novel analytical frameworks.

Purpose of the Study:

  • To develop and validate a novel framework for analyzing task-positive magnetoencephalography (MEG) data to identify task-related functional networks.
  • To systematically investigate the optimization of time-frequency windows for accurate functional connectivity measurements in MEG data.
  • To combine Independent Component Analysis (ICA) with the General Linear Model (GLM) to enhance the analysis of task-related brain activity.

Main Methods:

  • Systematic investigation of time-frequency windows for connectivity measurement by contrasting functional connectivity scores with artefactual scores due to spatial leakage.
  • Estimation of functional connectivity via correlations in the oscillatory envelope within the 8-20 Hz frequency range, temporally down-sampled with 1-4s windows.
  • Integration of ICA with the GLM to incorporate task structure information and overcome limitations of independent analyses.

Main Results:

  • Functional connectivity is best estimated using correlations in the oscillatory envelope (8-20 Hz) with 1-4s temporal down-sampling windows, for both resting-state and task data.
  • The combined ICA-GLM approach effectively separates task-relevant components from noise and corrects for multiple comparisons.
  • The novel framework successfully elucidated functional networks during a 2-back working memory task, revealing localized activity in the hippocampus, which is difficult to detect with standard methods.

Conclusions:

  • The developed framework provides a powerful tool for analyzing task-positive MEG data and identifying task-related functional networks.
  • Optimized time-frequency window selection and the integration of ICA with GLM significantly improve the analysis of MEG connectivity and task-related activity.
  • This approach offers enhanced sensitivity for detecting localized brain activity, particularly in regions like the hippocampus, which are challenging for conventional methods.