Cited by 211Functional Brain Connectivity StudiesNeural dynamics and brain functionEEG and Brain-Computer Interfaces

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Dynamic causal modelling for EEG and MEG

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Kiebel, Stefan J., Garrido, Marta I., Moran, Rosalyn J., Friston, Karl J.·2008·Cognitive Neurodynamics

DOI 10.1007/s11571-008-9038-0PMC PMC2427062

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Abstract

Dynamic Causal Modelling (DCM) is an approach first introduced for the analysis of functional magnetic resonance imaging (fMRI) to quantify effective connectivity between brain areas. Recently, this framework has been extended and established in the magneto/encephalography (M/EEG) domain. DCM for M/EEG entails the inversion a full spatiotemporal model of evoked responses, over multiple conditions. This model rests on a biophysical and neurobiological generative model for electrophysiological data. A generative model is a prescription of how data are generated. The inversion of a DCM provides conditional densities on the model parameters and, indeed on the model itself. These densities enable one to answer key questions about the underlying system. A DCM comprises two parts; one part describes the dynamics within and among neuronal sources, and the second describes how source dynamics generate data in the sensors, using the lead-field. The parameters of this spatiotemporal model are estimated using a single (iterative) Bayesian procedure. In this paper, we will motivate and describe the current DCM framework. Two examples show how the approach can be applied to M/EEG experiments.

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Authors: Kiebel, S. J., Garrido, M. I., Moran, R. J., & Friston, K. J.
Affiliations: Wellcome Centre for Human Neuroimaging (GB)
Corresponding author: Kiebel, Stefan J.
ORCID: Kiebel, Stefan J.
Garrido, Marta I.
Moran, Rosalyn J.
Friston, Karl J.
Venue: Cognitive Neurodynamics
Publisher: Springer Science and Business Media LLC
Published: 2008-04-23
Volume: 2
Issue: 2
Pages: 121-136
ISSN: 1871-4080 (ISSN-L)
1871-4099
DOI: 10.1007/s11571-008-9038-0
PMC: PMC2427062
OpenAlex: W2124995442
Cited by: 211
References: 86
Citations by year: 2026: 13 · 2025: 14 · 2024: 12 · 2023: 18 · 2022: 24 · 2021: 17 · 2020: 23 · 2019: 12 · 2018: 11 · 2017: 9 · 2016: 6 · 2015: 8 · 2014: 4 · 2013: 13 · 2012: 7
FWCI: 3.28
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc
Locations: Cognitive Neurodynamics (publishedVersion, cc-by-nc)
Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
PubMed Central (publishedVersion, cc-by-nc)
PubMed (publishedVersion)
Minerva Access (University of Melbourne) (submittedVersion, cc-by-nc)
UCL Discovery (University College London) (submittedVersion)
Max Planck Digital Library (submittedVersion, other-oa)
Max Planck Digital Library (submittedVersion)
MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
MPG.PuRe (Max Planck Society) (submittedVersion)
Explore Bristol Research (submittedVersion)
Bristol Research (University of Bristol) (submittedVersion)
Funders: Wellcome Trust (088130)
Irish Research Council
Fundação para a Ciência e a Tecnologia
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Keywords: dynamic causal modeling, M/EEG, effective connectivity, generative models, Bayesian parameter estimation, evoked potentials, lead field

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Dynamic causal modelling for EEG and MEG

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