Influence of Head Tissue Conductivity Uncertainties on EEG Dipole Reconstruction.

Johannes Vorwerk,Carsten H Wolters,Christopher R Butson,Ümit Aydin

doi:10.3389/fnins.2019.00531

Abstract

Reliable EEG source analysis depends on sufficiently detailed and accurate head models. In this study, we investigate how uncertainties inherent to the experimentally determined conductivity values of the different conductive compartments influence the results of EEG source analysis. In a single source scenario, the superficial and focal somatosensory P20/N20 component, we analyze the influence of varying conductivities on dipole reconstructions using a generalized polynomial chaos (gPC) approach. We find that in particular the conductivity uncertainties for skin and skull have a significant influence on the EEG inverse solution, leading to variations in source localization by several centimeters. The conductivity uncertainties for gray and white matter were found to have little influence on the source localization, but a strong influence on the strength and orientation of the reconstructed source, respectively. As the CSF conductivity is most accurately determined of all conductivities in a realistic head model, CSF conductivity uncertainties had a negligible influence on the source reconstruction. This small uncertainty is a further benefit of distinguishing the CSF in realistic volume conductor models.

Highlights

Electroencephalography (EEG) source analysis is an important tool in a variety of both clinical and scientific applications to identify the active brain areas that evoke a measured signal (Brette and Destexhe, 2012)
We investigated and compared the influence of conductivity uncertainties of the skin, skull, cerebrospinal fluid (CSF), gray matter, and white matter on EEG forward simulations and single dipole reconstructions of the somatosensory evoked potential (SEP) P20/N20 component that was measured with surface EEG
Based on the leadfield matrices that we obtained from the generalized polynomial chaos (gPC) expansions, we evaluated the effects of tissue conductivity uncertainties on EEG source analysis

Summary

Introduction

Electroencephalography (EEG) source analysis is an important tool in a variety of both clinical and scientific applications to identify the active brain areas that evoke a measured signal (Brette and Destexhe, 2012). Examples include modeling a homogenized skull compartment instead of distinguishing skull compacta and spongiosa or a homogenized brain compartment instead of distinguishing cerebrospinal fluid (CSF), gray matter, and white matter In this context, both simulation studies that focus on investigating the influence of simplifications of the modeling of a single conductive feature on EEG forward and inverse solutions, e.g., of the skull (Dannhauer et al, 2011; Montes-Restrepo et al, 2014) or of white matter anisotropy (Wolters et al, 2006; Hallez et al, 2008; Güllmar et al, 2010), and that compare the influence of several of these modeling steps on forward solutions (Haueisen et al, 1997; Ramon et al, 2004; Vorwerk et al, 2014; Azizollahi et al, 2016), source analysis of dipole sources (Acar and Makeig, 2013), or source connectivity analysis (Cho et al, 2015) have been conducted

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