Reference : Variational Bayesian inversion of the equivalent current dipole model in EEG/MEG
Scientific journals : Article
Human health sciences : Radiology, nuclear medicine & imaging
Social & behavioral sciences, psychology : Neurosciences & behavior
http://hdl.handle.net/2268/31288
Variational Bayesian inversion of the equivalent current dipole model in EEG/MEG
English
Kiebel, S. J. [> > > >]
Daunizeau, J. [> > > >]
Phillips, Christophe mailto [Université de Liège - ULg > > Centre de recherches du cyclotron >]
Friston, K. J. [> > > >]
15-Jan-2008
Neuroimage
Academic Press Inc Elsevier Science
39
2
728-741
Yes (verified by ORBi)
International
1053-8119
San Diego
[en] EEG ; MEG ; equivalent current dipole ; variational Bayes
[en] In magneto- and electroencephalography (M/EEG), spatial modelling of sensor data is necessary to make inferences about underlying brain activity. Most source reconstruction techniques belong to one of two approaches: point source models, which explain the data with a small number of equivalent current dipoles and distributed source or imaging models, which use thousands of dipoles. Much methodological research has been devoted to developing sophisticated Bayesian source imaging inversion schemes, while dipoles have received less such attention. Dipole models have their advantages; they are often appropriate summaries of evoked responses or helpful first approximations. Here, we propose a variational Bayesian algorithm that enables the fast Bayesian inversion of dipole models. The approach allows for specification of priors on all the model parameters. The posterior distributions can be used to form Bayesian confidence intervals for interesting parameters, like dipole locations. Furthermore, competing models (e.g., models with different numbers of dipoles) can be compared using their evidence or marginal likelihood. Using synthetic data, we found the scheme provides accurate dipole localizations. We illustrate the advantage of our Bayesian scheme, using a multi-subject EEG auditory study, where we compare competing models for the generation of the N100 component. (C) 2007 Elsevier Inc. All rights reserved.
http://hdl.handle.net/2268/31288
10.1016/j.neuroimage.2007.09.005
http://dx.doi.org/10.1016/j.neuroimage.2007.09.005

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