Dynamic causal modeling for EEG and MEG

Stefan J Kiebel, Marta I Garrido, Rosalyn Moran, Chun-Chuan Chen, Karl J Friston

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

140 Citations (Scopus)

Abstract

We present a review of dynamic causal modeling (DCM) for magneto- and electroencephalography (M/EEG) data. DCM is based on a spatiotemporal model, where the temporal component is formulated in terms of neurobiologically plausible dynamics. Following an intuitive description of the model, we discuss six recent studies, which use DCM to analyze M/EEG and local field potentials. These studies illustrate how DCM can be used to analyze evoked responses (average response in time), induced responses (average response in time-frequency), and steady-state responses (average response in frequency). Bayesian model comparison plays a critical role in these analyses, by allowing one to compare equally plausible models in terms of their model evidence. This approach might be very useful in M/EEG research; where correlations among spatial and neuronal model parameter estimates can cause uncertainty about which model best explains the data. Bayesian model comparison resolves these uncertainties in a principled and formal way. We suggest that DCM and Bayesian model comparison provides a useful way to test hypotheses about distributed processing in the brain, using electromagnetic data.

Original languageEnglish
Pages (from-to)1866-76
Number of pages11
JournalHuman Brain Mapping
Volume30
Issue number6
DOIs
Publication statusPublished - Jun 2009

Keywords

  • Algorithms
  • Bayes Theorem
  • Biofeedback, Psychology
  • Brain
  • Electroencephalography
  • Evoked Potentials
  • Feedback
  • Humans
  • Magnetoencephalography
  • Models, Neurological
  • Models, Statistical
  • Neural Networks (Computer)
  • Neurobiology
  • Parkinson Disease
  • Reproducibility of Results
  • Synaptic Transmission

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