Dynamic causal modelling for EEG and MEG

Stefan J Kiebel, Marta I Garrido, Rosalyn J Moran, Karl J Friston

Research output: Contribution to journalArticle (Academic Journal)peer-review

134 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)121-36
Number of pages16
JournalCognitive neurodynamics
Issue number2
Publication statusPublished - Jun 2008


Dive into the research topics of 'Dynamic causal modelling for EEG and MEG'. Together they form a unique fingerprint.

Cite this