Dipole estimation errors due to differences in modeling anisotropic conductivities in realistic head models for EEG source analysis

Abstract

To improve the EEG source localization in the brain, the conductivities used in the head model play a very important role. In this study, we focus on the modeling of the anisotropic conductivity of the white matter. The anisotropic conductivity profile can be derived from diffusion weighted magnetic resonance images (DW-MRI). However, deriving these anisotropic conductivities from diffusion weighted MR images of the white matter is not straightforward. In the literature, two methods can be found for calculating the conductivity from the diffusion weighted images. One method uses a fixed value for the ratio of the conductivity in different directions, while the other method uses a conductivity profile obtained from a linear scaling of the diffusion ellipsoid. We propose a model which can be used to derive the conductivity profile from the diffusion tensor images. This model is based on the variable anisotropic ratio throughout the white matter and is a combination of the linear relationship as stated in the literature, with a constraint on the magnitude of the conductivity tensor (also known as the volume constraint). This approach is stated in the paper as approach A. In our study we want to investigate dipole estimation differences due to using a more simplified model for white matter anisotropy (approach B), while the electrode potentials are derived using a head model with a more realistic approach for the white matter anisotropy (approach A). We used a realistic head model, in which the forward problem was solved using a finite difference method that can incorporate anisotropic conductivities. As error measures we considered the dipole location error and the dipole orientation error. The results show that the dipole location errors are all below 10 mm and have an average of 4 mm in gray matter regions. The dipole orientation errors ranged up to 66.4 degrees, and had a mean of, on average, 11.6 degrees in gray matter regions. In a qualitative manner, the results show that the orientation and location error is dependent on the orientation of the test dipole. The location error is larger when the orientation of the test dipole is similar to the orientation of the anisotropy, while the orientation error is larger when the orientation of the test dipole deviates from the orientation of the anisotropy. From these results, we can conclude that the modeling of white matter anisotropy plays an important role in EEG source localization. More specifically, accurate source localization will require an accurate modeling of the white matter conductivity profile in each voxel.

Influence of white matter inhomogeneous anisotropy on EEG forward computing

“…Based on this assumption we calculate the inhomogeneous anisotropy conductivity in Study I. However, in other literature 11,14 , it is found that these two transverse conductivity eigen values are not the same every where in WM due to the fiber-crossing. These eigen values produce a ratio from 1 to 10 11 .…”

“…They mentioned that the anisotropy of WM conductivity affect the EEG forward and inverse computations. Most recently, Hallez et al 14 implemented anisotropic conductivity for Volume constrained WM to analyse the source localization error. They showed that source localization is affected by WM anisotropy.…”

Measurements and models of electric fields in the in vivo human brain during transcranial electric stimulation

“…Each mesh node in the model was then associated to the nearest diffusion tensor. These diffusion tensors were then converted into conductivity tensors using one of four different algorithms used in the literature: direct mapping (Tuch et al, 2001); volume normalized (Hallez et al, 2008;Güllmar et al, 2010); volume constrained (Wolters et al, 2006;Rullmann et al, 2009); and equivalent isotropic trace (Miranda et al, 2001). Models with these anisotropic conductivities were also solved in Abaqus.…”

Transcranial Current Brain Stimulation (tCS): Models and Technologies

“…This approach was implemented to investigate the effect of white matter anisotropy on EEG source localization, first without [26] and then with a normalization of tensor ellipsoid volume [27]- [29].…”