Perspectives on Optimized Transcranial Electrical Stimulation Based on Spatial Electric Field Modeling in Humans

Abstract

Background: Transcranial electrical stimulation (tES) generates an electric field (or current density) in the brain through surface electrodes attached to the scalp. Clinical significance has been demonstrated, although with moderate and heterogeneous results partly due to a lack of control of the delivered electric currents. In the last decade, computational electric field analysis has allowed the estimation and optimization of the electric field using accurate anatomical head models. This review examines recent tES computational studies, providing a comprehensive background on the technical aspects of adopting computational electric field analysis as a standardized procedure in medical applications. Methods: Specific search strategies were designed to retrieve papers from the Web of Science database. The papers were initially screened based on the soundness of the title and abstract and then on their full contents, resulting in a total of 57 studies. Results: Recent trends were identified in individual- and population-level analysis of the electric field, including head models from non-neurotypical individuals. Advanced optimization techniques that allow a high degree of control with the required focality and direction of the electric field were also summarized. There is also growing evidence of a correlation between the computationally estimated electric field and the observed responses in real experiments. Conclusions: Computational pipelines and optimization algorithms have reached a degree of maturity that provides a rationale to improve tES experimental design and a posteriori analysis of the responses for supporting clinical studies.

Keywords: FEM; brain template; computational model; current density; electric field; neurostimulation; optimization; tACS; tDCS; tES; transcranial electrical stimulation.

Figure 1

Summary of computational analysis approaches for tES analysis. (A1) Electric field $\overrightarrow{E}(x,y,z)$ obtained from individual MRI data. (A2) Population-level which corresponds to statistical analysis (e.g., mean) of the registered electric fields $\overrightarrow{E}(f(x,y,z))$ from different individuals in the standard space where f:ℝ³→ℝ³ is the function assigning the spatial position of the electric field in the individual space to standard space. (A3) The electric field is estimated from a template model, which could be an averaged MRI of a target population. (B) Illustration of electric field estimation on the brain cortex, from left to right: MRI of the individual; volume conductor after segmentation, meshing, assigning electric properties of each tissue, and adding the electrode montage; electric field distribution on the brain cortex in the individual space; and registration of the individual electric field into a template in the standard space.

Figure 2

Generation of individual head models based on finite elements. The input images are aligned and segmented. The segmented image can be meshed to obtain the tissue surfaces, or the electrodes can be aligned to the segmented image and added to the segmentation before the meshing process takes place. The final mesh is typically composed of tetrahedral or hexahedral elements, filling the whole head volume. The electrical head model is completed by assigning electrical conductivity values to each tissue, either isotropic or anisotropic and homogeneous or inhomogeneous.

Figure 3

General tES optimization process. For an individualized head model, the forward problem is computed for every independent current injection pair to obtain the transfer matrix. The desired ROI is defined together with the optimization criteria, such as maximum focality of maximum intensity. The hardware (e.g., maximum number of electric current generators) and safety constraints (e.g., maximum current intensity per electrode) complete the specifications needed as inputs to the optimization solver. The result is a set of currents per electrode, i.e., an optimal current injection pattern. The forward problem can be computed for this pattern to obtain the electric field or current density map on the brain. From this map, the resulting intensity and focality can be computed, and these will represent a point (marked with a star for this example) in the intensity versus focality trade-off curve, also known as the Pareto front, that shows the performance of a set of optimal solutions.