BibTeX
@MISC{Kiebel09nonlineardynamics,
author = {Stefan J. Kiebel and Olivier David and Karl J. Friston},
title = {Nonlinear dynamics},
year = {2009}
}
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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 inserm-00388972, version 1- 4 Jun 2009 three orientation parameters. Using synthetic and real data we show that this approach furnishes accurate and robust conditional estimates of coupling among source and their orientation. 2 I
Keyphrases
nonlinear dynamic evoked response hierarchical network robust conditional estimate dynamical causal modeling previous paper connectivity change 4-shell equivalent current dipole finite precision source area lead-field parameter new approach spatial expression infinite prior precision orientation parameter real data physiologically-informed constraint network spatial configuration source reconstruction approach
