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Mattout, Jérémie, Phillips, Christophe, Penny, William D 
ORCID: https://orcid.org/0000-0001-9064-1191, Rugg, Michael D and Friston, Karl J
(2006)
MEG source localization under multiple constraints: an extended Bayesian framework.
NeuroImage, 30 (3).
pp. 753-767.
ISSN 1053-8119
Abstract
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 information 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 Bayesian inference in neuroimaging: theory. NeuroImage 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. NeuroImage 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 the best combination of priors. This enables a global strategy for multiple prior-based regularization of the MEG/EEG source reconstruction.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | bayes theorem,magnetoencephalography |
| Faculty \ School: | Faculty of Social Sciences > School of Psychology |
| UEA Research Groups: | Faculty of Social Sciences > Research Centres > Centre for Behavioural and Experimental Social Sciences |
| Depositing User: | Pure Connector |
| Date Deposited: | 22 Aug 2017 06:35 |
| Last Modified: | 05 May 2024 01:23 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/64618 |
| DOI: | 10.1016/j.neuroimage.2005.10.037 |
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