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Related Experiment Video

Updated: Jun 24, 2026

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

Dynamic causal modeling for EEG and MEG.

Stefan J Kiebel1, Marta I Garrido, Rosalyn Moran

  • 1The Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London, United Kingdom. skiebel@fil.ion.ucl.ac.uk

Human Brain Mapping
|April 11, 2009
PubMed
Summary
This summary is machine-generated.

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Dynamic Causal Modeling (DCM) analyzes brain activity from M/EEG data using neurobiologically plausible models. Bayesian model comparison helps select the best model for understanding brain processing.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Neuroscience

Background:

  • Dynamic Causal Modeling (DCM) is a framework for analyzing neurophysiological data.
  • Magneto- and electroencephalography (M/EEG) provide valuable insights into brain function.
  • Understanding distributed brain processing requires sophisticated analytical tools.

Purpose of the Study:

  • To review the application of Dynamic Causal Modeling (DCM) for M/EEG data.
  • To illustrate how DCM can analyze various types of neural responses.
  • To highlight the role of Bayesian model comparison in M/EEG research.

Main Methods:

  • The study reviews DCM, a spatiotemporal modeling approach.
  • It discusses six recent studies applying DCM to M/EEG and local field potential data.

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Functional Mapping with Simultaneous MEG and EEG
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Functional Mapping with Simultaneous MEG and EEG

Published on: June 14, 2010

Related Experiment Videos

Last Updated: Jun 24, 2026

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

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

Functional Mapping with Simultaneous MEG and EEG
06:04

Functional Mapping with Simultaneous MEG and EEG

Published on: June 14, 2010

  • Bayesian model comparison is employed to assess model evidence.

    • Main Results:

    • DCM can analyze evoked, induced, and steady-state responses in M/EEG data.
    • Bayesian model comparison effectively resolves uncertainties in model parameter estimates.
    • The reviewed studies demonstrate the utility of DCM in M/EEG analysis.

    Conclusions:

    • DCM combined with Bayesian model comparison offers a robust method for hypothesis testing in M/EEG research.
    • This approach facilitates the investigation of distributed brain processing using electromagnetic data.
    • DCM provides a principled way to understand complex neural dynamics.