Numerical investigation of white matter anisotropic conductivity in defining current distribution under tDCS

Abstract

The study investigates the impact of white matter directional conductivity on brain current density under the influence of Transcranial direct current stimulation (tDCS). The study employed different conductivity estimation algorithms to represent conductivity distribution in the white matter (WM) of the brain. Two procedures, one mathematically driven and the second one based on the Diffusion tensor imaging (DTI) are considered. The finite element method has been applied to estimate the current density distribution across the head models. Strengths and weaknesses of these algorithms have been compared by analyzing the variation in current density magnitude and distribution patterns with respect to the isotropic case. Results indicate that anisotropy has a profound influence on the strength of current density (up to ≈50% in WM) as it causes current flow to deviate from its isotropically defined path along with diffused distribution patterns across the gray and WM. The extent of this variation is highly correlated with the degree of the anisotropy of the regions. Regions of high anisotropy and models of fixed anisotropic ratio displayed higher and wider degree of variations across the structures (topographic variations up to 48%), respectively. In contrast, models, which are correlated with the magnitude of local diffusion tensor behaved in a less exacerbated manner (≈10% topographic changes in WM). Anisotropy increased the current density strength across the cortical gyri under and between the stimulating electrodes, whereas a significant drop has been recorded in deeper regions of the GM (max % difference ≈±10). In addition, it has been observed that Equivalent isotropic trace algorithm is more suitable to incorporate directional conductivity under tDCS paradigm, than other considered approaches, as this algorithm is computationally less expensive and insensitive to the limiting factor imposed by the volume constraint.