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Dynamic causal modelling for EEG and MEG
Open accessAbstract
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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“Dynamic causal modeling (DCM), leveraging EEG’s temporal precision, enables exploration of these mechanisms21–23.”
From Deviations in effective connectivity explain different hallucination subtypes in Parkinson's disease psychosis (2026) · cites this paper as [21]
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Diaconescu, A. O., Wang, Z., & Griffiths, J. D. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.28.702100
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Ojeda, A. (2026) · Springer eBooks · doi:10.1007/978-3-032-20450-9_20
Quattrociocchi, I., Caracci, V., Rotondo, E., Colamarino, E., Pichiorri, F., Riccio, A., Febo Cincotti, A., Toppi, J., & Laura Astolfi, A. (2026) · Journal of Neural Engineering · doi:10.1088/1741-2552/ae7766
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Zhao, K., Wu, M., Zhao, J., Teng, Y., Sun, Z. X., & Sun, Y. (2026) · Frontiers in Neurology · doi:10.3389/fneur.2026.1779207
Wang, X., Li, S., Zhang, M., & Yang, C. (2026) · Brain Informatics · doi:10.1186/s40708-026-00311-3
Orzan, M., Goga, E., Trifu, D., Diaconescu, M., & Constantinescu, M. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1883915
Metadata
- 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.
- Venue
- Cognitive Neurodynamics
- Published
- 2008-04-23
- Volume
- 2
- Issue
- 2
- Pages
- 121-136
- ISSN
- 1871-4080 (ISSN-L)
- 1871-4099
- 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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