Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization
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Dynamical causal modeling (DCM) of evoked responses is a new approach to making inferences about connectivity changes in hierarchical networks measured with electro- and magnetoencephalography (EEG and MEG). In a previous paper, we illustrated this concept using a lead field that was specified with infinite prior precision. With this prior, the spatial expression of each source area, in the sensors, is fixed. In this paper, we show that using lead field parameters with finite precision enables the data to inform the network's spatial configuration and its expression at the sensors. This means that lead field and coupling parameters can be estimated simultaneously. Alternatively, one can also view DCM for evoked responses as a source reconstruction approach with temporal, physiologically informed constraints. We will illustrate this idea using, for each area, a 4-shell equivalent current dipole (ECD) model with three location and three orientation parameters. Using synthetic and real data, we show that this approach furnishes accurate and robust conditional estimates of coupling among sources and their orientations.
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Dynamic causal modeling, leveraging EEG's temporal precision, enables exploration of these mechanisms.
“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 [22]
DCMs have been implemented for a range of measurement techniques other than fMRI, including EEG, MEG, and LFPs, both in the time domain and frequency domain.
“Dynamic causal models (DCMs)1 have also been implemented for a range of measurement techniques other than fMRI, including electroencephalography (EEG), magnetoencephalography (MEG), and local field potentials (LFPs) obtained from invasive recordings in humans or animals, both in the time domain (Daunizeau et al., 2009b; David et al., 2006; Kiebel et al., 2006) and frequency domain (Chen et al., 2”
From Ten simple rules for dynamic causal modeling (2010) · cites this paper as Kiebel et al., 2006
DCM can be considered a source reconstruction approach that exploits information about coupling among sources and can be used to determine the most likely number and deployment of sources.
“In fact, DCM can be considered a source reconstruction approach that exploits information about coupling among sources and can be used to determine the most likely number and deployment of sources (Kiebel et al., 2006).”
From Ten simple rules for dynamic causal modeling (2010) · cites this paper as Kiebel et al., 2006
The GLM for fMRI data and source reconstruction methods for EEG/MEG can be thought of as special cases of DCMs.
“For example, the GLM for fMRI data and source reconstruction methods for EEG/MEG can be thought of as special cases of DCMs (for details, see Daunizeau et al., 2009b; Kiebel et al., 2006; Stephan et al., 2007a).”
From Ten simple rules for dynamic causal modeling (2010) · cites this paper as Kiebel et al., 2006
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“Nevertheless, we interpret this cautiously, as interhemi-spheric connections may be difficult to identify robustly in source-level EEG 36 , with the possibility of increasing model complexity without necessarily improving model evidence 21,22 .”
- Authors
- Kiebel, S. J., David, O., & Friston, K. J.
- Affiliations
- National Hospital for Neurology and Neurosurgery (GB)
- Corresponding author
- Kiebel, Stefan J.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2006-05
- Volume
- 30
- Issue
- 4
- Pages
- 1273-1284
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2006017444
- Cited by
- 233
- References
- 31
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 5 · 2023: 5 · 2022: 14 · 2021: 6 · 2020: 8 · 2019: 9 · 2018: 6 · 2017: 14 · 2016: 14 · 2015: 18 · 2014: 11 · 2013: 12 · 2012: 15
- FWCI
- 7.77
- Type
- journal article
- Language
- en
- Open access
- green
- inserm.hal.science
- Licence
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Wellcome Trust
- 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, evoked potentials, hierarchical neural networks, equivalent current dipole model, source orientation
- MeSH terms
- Auditory Perception, Bayes Theorem, Brain Mapping, Cerebral Cortex, Dominance, Cerebral, Electroencephalography, Electromyography, Evoked Potentials, Humans, Nerve Net, Neurons, Pattern Recognition, Visual, Neural Networks, Computer, Nonlinear Dynamics, Finite Element Analysis
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