Influence of anisotropic conductivity of the white matter tissue on EEG source reconstruction a FEM simulation study

The aim of this study was to quantify the influence of the inclusion of anisotropic conductivity on EEG source reconstruction. We applied high-resolution finite element modeling and performed forward and inverse simulation with over 4000 single dipoles placed around an anisotropic volume block (with an anisotropic ratio of 1:10) in a rabbit brain. We investigated three different orientation of the dipoles with respect to the anisotropy in the white matter block. We found a weak influence of the anisotropy in the forward simulation on the electric potential. The relative difference measure (RDM) between the potentials simulated with and without taking into account anisotropy was less than 0.009. The changes in magnitude (MAG) ranged from 0.944 to 1.036. Using the potentials of the forward simulation derived with the anisotropic model and performing source reconstruction by employing the isotropic model led to dipole shifts of up to 2 mm, however the mean shift over all dipoles and orientations of 0.05 mm was smaller than the grid size of the FEM model (0.6 mm). However, we found the source strength estimation to be more influenced by the anisotropy (up to 7-times magnified dipole strength)

Influence of volume conductor modeling on source reconstruction in magnetoencephalography and electroencephalography

The function and structure of the human brain is immensely complex and, at the same time, the key to understanding human behavior and many of today's prevailing diseases. In most cases, this system cannot be investigated directly, but only non-invasively from outside the head. Although several non-invasive measurement modalities are available, only magnetoencephalography (MEG) and electroencephalography (EEG) provide information with a high temporal resolution. In order to reconstruct the neuronal activity underlying measured EEG and MEG data both the forward problem (computing the electromagnetic field due to given sources) and the inverse problem (finding the best fitting sources to explain given data) have to be solved. The forward problem involves a source model and a model with the conductivities of the head. The conductivity model can be as simple as a homogeneously conducting sphere or as complex as a finite element model consisting of millions of elements, each with a different anisotropic conductivity tensor. The question is addressed how complex the employed forward model should be, and, more specifically, the influence of anisotropic volume conduction and the influence of conductivity inhomogeneities are evaluated. For this purpose high resolution finite element models of the rabbit and the human head are employed in combination with individual conductivity tensors to quantify the influence of white matter anisotropy on the solution of the forward and inverse problem in EEG and MEG. Although the current state of the art in the analysis of this influence of brain tissue anisotropy on source reconstruction does not yet allow a final conclusion, the results available indicate that the expected average source localization error due to anisotropic white matter conductivity might be within the principal accuracy limits of current inverse procedures. However, in some percent of the cases a considerably larger localization error might o- cur. In contrast, dipole orientation and dipole strength estimation are influenced significantly by anisotropy. Skull conductivity inhomogeneities such as the spongy bone structure embedded in the compact bone or surgical holes or fontanels in infants have a non-negligible effect on the EEG and MEG forward and inverse problem solution. Especially when source positions are expected to be in the vicinity of the conductivity inhomogeneity and when a large difference with respect to the skull conductivity is indicated, the modeling approach should take the inhomogeneities into account. In conclusion, models taking into account tissue anisotropy and conductivity inhomogeneities information are expected to improve source estimation procedures. Depending on the question addressed, the complexity of the forward and inverse solution approach has to be chosen