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 Experiment Videos

A general linear model for MEG beamformer imaging.

Matthew J Brookes1, Andrew M Gibson, Stephen D Hall

  • 1Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK.

Neuroimage
|November 6, 2004
PubMed
Summary
This summary is machine-generated.

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

Neurometabolites and Antipsychotic Response in Psychosis: A Mega-Analysis.

JAMA psychiatry·2026
Same author

The functional relevance of a short assessment of formal thought disorder in psychosis.

The British journal of psychiatry : the journal of mental science·2026
Same author

Increasing participation of people with thought disorder in clinical research.

European psychiatry : the journal of the Association of European Psychiatrists·2026
Same author

EEG-Pype: An accessible MNE-Python pipeline with graphical user interface for preprocessing and analysis of resting-state electroencephalography data.

PLoS computational biology·2026
Same author

Exploring the association between mental imagery, sensory sensitivity, and autistic traits in autistic and non-autistic adults.

Scientific reports·2026
Same author

Sensory sensitivity and visual discomfort are not associated with altered gamma oscillations: A test of the excitation-inhibition hypothesis.

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

Decoding neuronal criticality firing patterns for large brain based EEG models.

NeuroImage·2026
Same journal

Segmentation of the parasagittal dura mater on multi-center 3D-FLAIR MRI.

NeuroImage·2026
Same journal

Spatial frequency channels implement a mental ruler in spatial vision.

NeuroImage·2026
Same journal

Exploring the Link Between Intravoxel Incoherent Motion Measured Brain Diffusivity During Wakefulness and Sleep Macrostructure in the Elderly.

NeuroImage·2026
Same journal

Closed-loop adaptation of transcranial magnetic stimulation intensity with electroencephalography feedback.

NeuroImage·2026
Same journal

Volumetric postmortem MRI of the medial temporal lobe in Alzheimer's disease and related disorders: methodological advances and implications for in vivo biomarker development.

NeuroImage·2026
See all related articles

A new general linear model (GLM) beamformer method accurately localizes neuronal activity in magnetoencephalography (MEG) data. This approach enhances the analysis of changing brain activity patterns across all frequencies.

Area of Science:

  • Neuroscience
  • Biophysics
  • Signal Processing

Background:

  • Magnetoencephalography (MEG) is a non-invasive technique to measure brain activity.
  • Accurate source localization of neuronal activity is crucial for understanding brain function.
  • Existing beamformer methods have limitations in analyzing complex or time-varying activity patterns.

Purpose of the Study:

  • To introduce a novel General Linear Model (GLM) beamformer for MEG data processing.
  • To enhance the localization accuracy of neuronal activity, including sustained and oscillatory fields.
  • To integrate the statistical power of GLM into MEG source analysis.

Main Methods:

  • A nonlinear beamformer was employed to obtain time courses of neuronal activation.
  • A Hilbert transform was used to derive the envelope of oscillatory activity.

Related Experiment Videos

  • The General Linear Model (GLM) was applied to the enveloped data to generate T statistic images.

    • Main Results:

    • The new GLM beamformer method demonstrated accurate source localization in simulations involving sustained and 20 Hz activity.
    • The method successfully localized gamma activity to the temporal and frontal lobes in a scintillating scotoma case.
    • Validation through simulation and real-world data confirmed the method's precision.

    Conclusions:

    • The developed GLM beamformer offers an advanced tool for MEG data analysis.
    • This method improves the localization of dynamic neuronal activity across various frequency bands, including DC fields.
    • The approach is expected to be valuable for a wide range of MEG research applications.