OP 11. Optimized tDCS electrode configurations for five targets determined via an inverse FE modeling approach

Abstract

Introduction Transcranial direct current stimulation (tDCS) has shown potential in improving brain function in both healthy subjects and patients suffering from a wide range of neuropathologies. Unfortunately, the effects are too small and short-lived for tDCS to be used as a clinical therapy. Increasing the effect size of tDCS could possibly be achieved by better targeting the current, both in direction and amplitude. Volume conduction modeling has shown that the areas with the highest electric fields strengths do not, as is often assumed, lie beneath the electrodes (Datta et al., 2009). Attempts at optimization have been published for point and ring electrodes (Im et al., 2008; Dmochowski et al., 2011), but not for the square patches used in most labs. In order to find for these electrodes, configurations that do result in maximum stimulation at the target area, we propose an inverse modeling approach. Objectives We simulate tDCS for ∼7000 configurations and determine which of these lead to optimal electric fields, both in strength and direction, at the five most target locations in tDCS research: motor cortex (M1), dorsolateral prefrontal cortex, inferior frontal gyrus, occipital cortex and cerebellum. Methods A detailed finite element (FE) head model was made by automatic segmentation of MR images aided by manual corrections. The model contains over 4 million elements and 11 tissue types. Special attention was given to the skull, the main barrier for the tDCS current, by including the spongiosa layer and skull holes. Brain anisotropy was derived from DTI measurements. On the skin surface, a grid of 89 points was placed consisting of the standard 10–10 EEG system and extra points on the cheeks and neck. For each combination of 2 points, we placed 5×5cm electrode patches onto the model, centred on the two points, and simulated 1mA tDCS. At each target, we placed a cylindrical volume in the brain and selected the configurations leading to highest mean field strength or optimal direction in the target volume. Results For all targets, the optimized configurations did not include the commonly used configurations. Often, highest field strengths were found in configurations that are near-perpendicular to the standard configurations. Optimization based on either strength or direction of the field lead to completely different configurations. Conclusion The optimized configurations found in this study suggest that improved results of tDCS can be expected. We found different optimized configurations by looking at either strength or direction of the field. Comparing these optimized configurations experimentally will not only verify our modeling approach, but also provide valuable information on the mechanisms behind tDCS. This approach can be used to optimize stimulation for any target location.