NDLI: Parametrizing the Conditionally Gaussian Prior Model for Source Localization with Reference to the P20/N20 component of Median Nerve SEP/SEF

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Parametrizing the Conditionally Gaussian Prior Model for Source Localization with Reference to the P20/N20 component of Median Nerve SEP/SEF

Content Provider	MDPI
Author	Rezaei, Atena Antonakakis, Marios Piastra, Mariacarla Wolters, Carsten H. Pursiainen, Sampsa
Copyright Year	2020
Description	In this article, we focused on developing the conditionally Gaussian hierarchical Bayesian model (CG-HBM), which forms a superclass of several inversion methods for source localization of brain activity using somatosensory evoked potential (SEP) and field (SEF) measurements. The goal of this proof-of-concept study was to improve the applicability of the CG-HBM as a superclass by proposing a robust approach for the parametrization of focal source scenarios. We aimed at a parametrization that is invariant with respect to altering the noise level and the source space size. The posterior difference between the gamma and inverse gamma hyperprior was minimized by optimizing the shape parameter, while a suitable range for the scale parameter can be obtained via the prior-over-measurement signal-to-noise ratio, which we introduce as a new concept in this study. In the source localization experiments, the primary generator of the P20/N20 component was detected in the Brodmann area 3b using the CG-HBM approach and a parameter range derived from the existing knowledge of the Tikhonov-regularized minimum norm estimate, i.e., the classical Gaussian prior model. Moreover, it seems that the detection of deep thalamic activity simultaneously with the P20/N20 component with the gamma hyperprior can be enhanced while using a close-to-optimal shape parameter value.
Starting Page	934
e-ISSN	20763425
DOI	10.3390/brainsci10120934
Journal	Brain Sciences
Issue Number	12
Volume Number	10
Language	English
Publisher	MDPI
Publisher Date	2020-12-03
Access Restriction	Open
Subject Keyword	Brain Sciences Statistics and Probability Electroencephalography (eeg) Magnetoencephalography (meg) Somatosensory Evoked Potentials Somatosensory Evoked Fields P20/n20 Component Hierarchical Bayesian Model Parametrization Deep Activity
Content Type	Text
Resource Type	Article

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