Article (Scientific journals)
MEG source localization under multiple constraints: An extended Bayesian framework
Mattout, J.; Phillips, Christophe; Penny, W. D. et al.
2006In NeuroImage, 30 (3), p. 753-767
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Keywords :
MEG; inverse problem; multiple constraints; empirical Bayes; restricted maximum likelihood (ReML); model selection; ROC; Multivariate Source Prelocalization (MSP)
Abstract :
[en] To use Electroencephalography (EEG) and Magnetoencephalography (MEG) as functional brain 3D imaging techniques, identifiable distributed source models are required. The reconstruction of EEG/MEG sources rests on inverting these models and is ill-posed because the solution does not depend continuously on the data and there is no unique solution in the absence of prior in formation or constraints. We have described a general framework that can account for several priors in a common inverse solution. An empirical Bayesian framework based on hierarchical linear models was proposed for the analysis of functional neuroimaging data [Friston, K., Penny, W, Phillips, C., Kiebel, S., Hinton, G., Ashburner, J., 2002. Classical and BaN inference in neuroitnaging: theory. Neurolmage 16, 465-483] and was evaluated recently in the context of EEG [Phillips, C., Mattout, J., Rugg, M.D., Maquet, P., Friston, K., 2005. An empirical Bayesian solution to the source reconstruction problem in EEG. Neurolmage 24, 997-1011]. The approach consists of estimating the expected source distribution and its conditional variance that is constrained by an empirically determined mixture of prior variance components. Estimation uses Expectation-Maximization (EM) to give the Restricted Maximum Likelihood (ReML) estimate of the variance components (in terms of hyperparameters) and the Maximum A Posteriori (MAP) estimate of the source parameters. In this paper, we extend the framework to compare different combinations of priors, using a second level of inference based on Bayesian model selection. Using Monte- Carlo simulations, ReML is first compared to a classic Weighted Minimum Norm (WMN) solution under a single constraint. Then, the ReML estimates are evaluated using various combinations of priors. Both standard criterion and ROC-based measures were used to assess localization and detection performance. The empirical Bayes approach proved useful as: (1) ReML was significantly better than WMN for single priors; (2) valid location priors improved ReML source localization; (3) invalid location priors did not significantly impair performance. Finally, we show how model selection, using the log-evidence, can be used to select file best combination of priors. This enables a global strategy for multiple prior-based regularization of the MEG/EEG source reconstruction. (c) 2005 Elsevier Inc. All rights reserved.
Disciplines :
Neurosciences & behavior
Radiology, nuclear medicine & imaging
Author, co-author :
Mattout, J.
Phillips, Christophe  ;  Université de Liège - ULiège > Centre de recherches du cyclotron
Penny, W. D.
Rugg, M. D.
Friston, K. J.
Language :
English
Title :
MEG source localization under multiple constraints: An extended Bayesian framework
Publication date :
15 April 2006
Journal title :
NeuroImage
ISSN :
1053-8119
eISSN :
1095-9572
Publisher :
Academic Press Inc Elsevier Science, San Diego, United States - California
Volume :
30
Issue :
3
Pages :
753-767
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 19 April 2010

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