Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization.
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.
Wellcome Department of Imaging Neuroscience, Functional Imaging Laboratory, 12 Queen Square, London WC1N 3BG, UK. skiebel@fil.ion.ucl.ac.uk
Stefan J
SJ
Kiebel
Olivier
O
David
Karl J
KJ
Friston
D001307:Auditory Perception / Q000502:physiology*; D001499:Bayes Theorem; D001931:Brain Mapping; D002540:Cerebral Cortex / Q000502:physiology*; D004292:Dominance, Cerebral / Q000502:physiology; D004569:Electroencephalography; D004576:Electromyography; D005071:Evoked Potentials / Q000502:physiology*; D020342:Finite Element Analysis; D006801:Humans; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D017711:Nonlinear Dynamics; D010364:Pattern Recognition, Visual / Q000502:physiology*