Improving Lesion Conductivity Estimate by Means of EEG Source Localization Sensitivity to Model Parameter

Abstract

EEG-based source localization techniques use scalp-potential data to estimate the location of underlying neural activity. EEG source location reconstruction requires the assumption of a source model and the assumption of a conductive head model. Brain lesions can present conductivity values that are dramatically different from those of surrounding normal tissues and have to be included in head models for accurate neural source reconstruction. It is therefore necessary to analyze subjects' anatomic images (using MRI or computed tomography) to identify lesion type and to assign the appropriate conductivity value. Source localization accuracy may be influenced by uncertainties in tissue conductivity assignment during head model construction. The authors present a sensitivity study quantifying the effect of uncertainty in brain lesion conductivity assignment on EEG dipole source localization. They adopted an eccentric-spheres head model in which an eccentric bubble approximated the effects of actual brain lesions. After simulating EEG signal measurement in 64 different pathologic situations, an inverse dipole fitting procedure was carried out, assuming an incorrect lesion conductivity assignment ranging from a half to twice the real value. Incorrect lesion conductivity assignment led to markedly wrong source reconstruction for highly conductive lesions like liquid-filled ones (localization errors as much as 1.7 cm). Conversely, low sensitivity to uncertainties in conductivity assignment was found for lesions with low conductivity like calcified tumors. The authors propose a method based on residual error analysis to improve the lesion conductivity estimate. This procedure can identify lesion tissue conductivity with only a few percent error and guarantees source localization errors less than 5 mm.

EEG source localization sensitivity due to brain lesions modeling errors

“…In previous studies [7], [10], where we analyzed separately the localization error due to lesion conductivity uncertainty and that due to lesion dimension estimate error, we found errors of 17 and 19 mm respectively, meanwhile in the present work those errors were reduced of about 50% (8.2 and 10.4 mm respectively). The error reduction is due to a different sampling of the scalp potentials (128 vs. 64 electrodes covering a larger scalp area).…”

“…An error on lesion conductivity assignment may lead to a location of the reconstructed source far away from the "real source". Thus, we must account for errors in lesion conductivity assignment [7].…”

“…An error on lesion conductivity assignment may lead to a location of the reconstructed source far away from the "real source". Thus, we must account for errors in lesion conductivity assignment [7].With the availability of magnetic resonance imaging (MRI) and computed tomography (CT) it is now possible to detect the presence of lesions in the brain. Both these imaging techniques derive information about lesion morphology from the volumetric distribution of some tissue properties (e.g.…”

Interaction between noise and lesion modeling errors on EEG source localization accuracy

“…We selected the five conditions presented here on the base of the previous study [3] in which we proposed and quantified the efficacy of the reduction method (LIT) of localization errors due to LCA.…”

“…In a previous work [3] we performed a sensitivity study of the effects of lesion conductivity mispecification on the EEG dipole localization accuracy in presence of brain lesions. We found that Lesion Conductivity Assignment (LCA) errors determine source localization errors.…”