SEEG dipole source localization based on an empirical Bayesian approach taking into account forward model uncertainties.
Electromagnetic brain source localization consists in the inversion of a forward model based on a limited number of potential measurements. A wide range of methods has been developed to regularize this severely ill-posed problem and to reduce the solution space, imposing spatial smoothness, anatomical constraint or sparsity of the activated source map. This last criteria, based on physiological assumptions stating that in some particular events (e.g., epileptic spikes, evoked potential) few focal area of the brain are simultaneously actives, has gained more and more interest. Bayesian approaches have the ability to provide sparse solutions under adequate parametrization, and bring a convenient framework for the introduction of priors in the form of probabilistic density functions. However the quality of the forward model is rarely questioned while this parameter has undoubtedly a great influence on the solution. Its construction suffers from numerous approximation and uncertainties, even when using realistic numerical models. In addition, it often encodes a coarse sampling of the continuous solution space due to the computational burden its inversion implies. In this work we propose an empirical Bayesian approach to take into account the uncertainties of the forward model by allowing constrained variations around a prior physical model, in the particular context of SEEG measurements. We demonstrate on simulations that the method enhance the accuracy of the source time-course estimation as well as the sparsity of the resulting source map. Results on real signals prove the applicability of the method in real contexts.
Université de Lorraine, CRAN, UMR 7039, 54500 Vandœuvre-lès-Nancy, France; CNRS, CRAN, UMR, 7039, France. Electronic address: steven.le-cam@univ-lorraine.fr.
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Cam
Université de Lorraine, CRAN, UMR 7039, 54500 Vandœuvre-lès-Nancy, France; CNRS, CRAN, UMR, 7039, France.
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Ranta
Université de Lorraine, CRAN, UMR 7039, 54500 Vandœuvre-lès-Nancy, France; CNRS, CRAN, UMR, 7039, France.
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Caune
Université de Lorraine, CRAN, UMR 7039, 54500 Vandœuvre-lès-Nancy, France; CNRS, CRAN, UMR, 7039, France; Ventspils University College, 101 Inzenieruiela, LV-3601 Ventspils, Latvia.
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Korats
Université de Lorraine, CRAN, UMR 7039, 54500 Vandœuvre-lès-Nancy, France; CNRS, CRAN, UMR, 7039, France.
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Koessler
Université de Lorraine, CRAN, UMR 7039, 54500 Vandœuvre-lès-Nancy, France; CNRS, CRAN, UMR, 7039, France; CHU Nancy, Neurology Department, 54000 Nancy, France.
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Maillard
Université de Lorraine, CRAN, UMR 7039, 54500 Vandœuvre-lès-Nancy, France; CNRS, CRAN, UMR, 7039, France.
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Louis-Dorr
D001499:Bayes Theorem; D001921:Brain / Q000502:physiology* / Q000503:physiopathology; D001931:Brain Mapping / Q000379:methods*; D004558:Electric Stimulation; D004569:Electroencephalography / Q000379:methods*; D055724:Electrophysiological Phenomena; D004827:Epilepsy / Q000503:physiopathology; D005071:Evoked Potentials; D006801:Humans; D008959:Models, Neurological; D012815:Signal Processing, Computer-Assisted
Electrical source imaging; Interictal spikes; Intracerebral Electrical Stimulation (ICS); Inverse problem; Stereo-electroencephalography (SEEG); Variational Bayes