Abstract
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.
Highlights
This article concerns computational source localization methods for the activity of the brain in electro- and magnetoencephalography (EEG and MEG) [1,2,3]
The source localization experiments were performed while using the Zeffiro interface (ZI) software pipeline [38], which couples the finite element method (FEM) forward model with conditionally Gaussian hierarchical Bayesian model (CG-HBM)
Based on our findings for three subjects, we suggest that, by choosing an optimization-based shape parameter value β = 3 and a PM-signal-to-noise ratio (SNR) of 0–30 dB, with the exact value being determined by the modeling accuracy assumed, the cortical generator of the P20/N20 component can be localized in the Brodmann area 3b with both simulated and measured data
Summary
This article concerns computational source localization methods for the activity of the brain in electro- and magnetoencephalography (EEG and MEG) [1,2,3]. We focused on developing the conditionally Gaussian hierarchical Bayesian model (CG-HBM) [11], which, based on numerical simulations [4,5,6,7,8], has been suggested as a potential approach to reconstructing networks of focal sources with variable depth. In CG-HBM, the prior has a hierarchical structure; the variance of a Gaussian conditional prior is steered by a heavy-tailed hyperprior. This allows the primary current density to have a considerably greater focal amplitude when compared to the background fluctuations than what is otherwise possible with a Gaussian prior. Focal reconstructions are needed, e.g., in epileptic focus localization and in the analysis of epileptic networks during seizures for adults and paediatrics [13,14,15]
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