Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization. - Inserm - Institut national de la santé et de la recherche médicale Access content directly
Journal Articles NeuroImage Year : 2006

Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization.

Abstract

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.
Fichier principal
Vignette du fichier
DCM_ERP_lead_field.pdf (173.34 Ko) Télécharger le fichier
DCM_ERP_figures.pdf (1.34 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Origin : Files produced by the author(s)
Loading...

Dates and versions

inserm-00388972 , version 1 (04-06-2009)

Identifiers

Cite

Stefan J. Kiebel, Olivier David, Karl J. Friston. Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization.. NeuroImage, 2006, 30 (4), pp.1273-84. ⟨10.1016/j.neuroimage.2005.12.055⟩. ⟨inserm-00388972⟩

Collections

INSERM U836
222 View
893 Download

Altmetric

Share

Gmail Facebook X LinkedIn More