Abstract

We focus on electro-/magnetoencephalography imaging of the neural activity and, in particular, finding a robust estimate for the primary current distribution via the hierarchical Bayesian model (HBM). Our aim is to develop a reasonably fast maximum a posteriori (MAP) estimation technique which would be applicable for both superficial and deep areas without specific a priori knowledge of the number or location of the activity. To enable source distinguishability for any depth, we introduce a randomized multiresolution scanning (RAMUS) approach in which the MAP estimate of the brain activity is varied during the reconstruction process. RAMUS aims to provide a robust and accurate imaging outcome for the whole brain, while maintaining the computational cost on an appropriate level. The inverse gamma (IG) distribution is applied as the primary hyperprior in order to achieve an optimal performance for the deep part of the brain. In this proof-of-the-concept study, we consider the detection of simultaneous thalamic and somatosensory activity via numerically simulated data modeling the 14-20 ms post-stimulus somatosensory evoked potential and field response to electrical wrist stimulation. Both a spherical and realistic model are utilized to analyze the source reconstruction discrepancies. In the numerically examined case, RAMUS was observed to enhance the visibility of deep components and also marginalizing the random effects of the discretization and optimization without a remarkable computation cost. A robust and accurate MAP estimate for the primary current density was obtained in both superficial and deep parts of the brain.

Highlights

  • This study concentrates on electro-/magnetoencephalography (E/MEG) imaging of the brain activity (He et al 2018)

  • The results suggest that a randomized set of decompositions (Mallat 1989; Clark et al 1995) is essential to marginalize out the possible modeling errors due to projecting the source space into different resolution levels which, again, is necessary in order to achieve the depth-invariance of the final maximum a posteriori (MAP) estimate

  • We aim to reduce the effect of the discretization via the following two strategies motivating the introduction of the randomized multiresolution scanning (RAMUS) approach: (1) The reduction of the source space is essential to improve the ability of a solver to recover focal sources both in deep and superficial locations

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Summary

Introduction

This study concentrates on electro-/magnetoencephalography (E/MEG) imaging of the brain activity (He et al 2018). In particular, to develop a fast maximum a posteriori (MAP) estimation technique which would be applicable for both superficial. We introduce a randomized multiresolution scanning (RAMUS) method in which the MAP estimate of the brain activity is refined gradually in the reconstruction procedure. RAMUS aims at reducing the random effects of the numerical discretization on the final estimate. It processes the well and ill-conditioned parts of the source space separately which has been suggested for ill-posed problems, e.g., in (Pursiainen 2008; Liu et al 1995; Piana and Bertero 1997a). A multiresolution decomposition provides an approximative split between detectable and undetectable parts for different

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