Related Experiment Video
Updated: Jun 28, 2026
08:51
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
Published on: November 1, 2019
Dynamic causal modelling for EEG and MEG
Stefan J Kiebel1, Marta I Garrido, Rosalyn J Moran
1The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London, WC1N 3AR, UK, skiebel@fil.ion.ucl.ac.uk.
Cognitive Neurodynamics
|November 13, 2008
Summary
Dynamic Causal Modelling (DCM) now analyzes magneto/encephalography (M/EEG) data by inverting spatiotemporal models. This framework quantifies effective brain connectivity using biophysical generative models for M/EEG.
Area of Science:
- Neuroscience
- Computational Neuroscience
Background:
- Dynamic Causal Modelling (DCM) was initially developed for fMRI to analyze brain connectivity.
- The DCM framework has recently been extended to the M/EEG domain.
Purpose of the Study:
- To describe the current Dynamic Causal Modelling framework for M/EEG.
- To demonstrate the application of DCM for M/EEG through examples.
Main Methods:
- DCM for M/EEG involves inverting a full spatiotemporal model of evoked responses across multiple conditions.
- The model is based on a biophysical and neurobiological generative model for electrophysiological data.
- Parameter estimation uses a single iterative Bayesian procedure.
Main Results:
- The inversion of DCM provides conditional densities on model parameters and the model itself.
- This enables answering key questions about the underlying neural system.
Conclusions:
- DCM for M/EEG offers a comprehensive approach to quantifying effective connectivity.
- The framework integrates neuronal source dynamics with sensor-level data generation via the lead-field model.
