Dynamic causal modelling for EEG and MEG

Kiebel, Stefan J., Garrido, Marta I., Moran, Rosalyn J. and Friston, Karl J. (2008) Dynamic causal modelling for EEG and MEG. Cognitive Neurodynamics, 2 2: 121-136. doi:10.1007/s11571-008-9038-0


Author Kiebel, Stefan J.
Garrido, Marta I.
Moran, Rosalyn J.
Friston, Karl J.
Title Dynamic causal modelling for EEG and MEG
Journal name Cognitive Neurodynamics   Check publisher's open access policy
ISSN 1871-4080
1871-4099
Publication date 2008-06-01
Year available 2008
Sub-type Article (original research)
DOI 10.1007/s11571-008-9038-0
Open Access Status Not yet assessed
Volume 2
Issue 2
Start page 121
End page 136
Total pages 16
Place of publication Dordrecht, The Netherlands
Publisher Springer Netherlands
Language eng
Formatted 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. 
Keyword Magnetoencephalography
Electroencephalography
Dynamic system
Connectivity
Bayesian
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID 088130
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Queensland Brain Institute Publications
 
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