A Computational Modeling Study to Investigate the Use of Epicranial Electrodes to Deliver Interferential Stimulation to Subcortical Regions

Background: Epicranial cortical stimulation (ECS) is a minimally invasive neuromodulation technique that works by passing electric current between subcutaneous electrodes positioned on the skull. ECS causes a stronger and more focused electric field in the cortex compared to transcranial electric stimulation (TES) where the electrodes are placed on the scalp. However, it is unknown if ECS can target deeper regions where the electric fields become relatively weak and broad. Recently, interferential stimulation (IF) using scalp electrodes has been proposed as a novel technique to target subcortical regions. During IF, two high, but slightly different, frequencies are applied which sum to generate a low frequency field (i.e., 10 Hz) at a target subcortical region. We hypothesized that IF using ECS electrodes would cause stronger and more focused subcortical stimulation than that using TES electrodes.Objective: Use computational modeling to determine if interferential stimulation-epicranial cortical stimulation (IF-ECS) can target subcortical regions. Then, compare the focality and field strength of IF-ECS to that of interferential Stimulation-transcranial electric stimulation (IF-TES) in the same subcortical region.Methods: A human head computational model was developed with 19 TES and 19 ECS disk electrodes positioned on a 10–20 system. After tetrahedral mesh generation the model was imported to COMSOL where the electric field distribution was calculated for each electrode separately. Then in MATLAB, subcortical targets were defined and the optimal configurations were calculated for both the TES and ECS electrodes.Results: Interferential stimulation using ECS electrodes can deliver stronger and more focused electric fields to subcortical regions than IF using TES electrodes.Conclusion: Interferential stimulation combined with ECS is a promising approach for delivering subcortical stimulation without the need for a craniotomy.

Bioelectromagnetism in Human Brain Research: New Applications, New Questions

Bioelectromagnetism has contributed some of the most commonly used techniques to human neuroscience such as magnetoencephalography (MEG), electroencephalography (EEG), transcranial magnetic stimulation (TMS), and transcranial electric stimulation (TES). The considerable differences in their technical design and practical use give rise to the impression that these are quite different techniques altogether. Here, we review, discuss and illustrate the fundamental principle of Helmholtz reciprocity that provides a common ground for all four techniques. We show that, more than 150 years after its discovery by Helmholtz in 1853, reciprocity is important to appreciate the strengths and limitations of these four classical tools in neuroscience. We build this case by explaining the concept of Helmholtz reciprocity, presenting a methodological account of this principle for all four methods and, finally, by illustrating its application in practical clinical studies.

A Method to Experimentally Estimate the Conductivity of Chronic Stroke Lesions: A Tool to Individualize Transcranial Electric Stimulation

The inconsistent response to transcranial electric stimulation in the stroke population is attributed to, among other factors, unknown effects of stroke lesion conductivity on stimulation strength at the targeted brain areas. Volume conduction models are promising tools to determine optimal stimulation settings. However, stroke lesion conductivity is often not considered in these models as a source of inter-subject variability. The goal of this study is to propose a method that combines MRI, EEG, and transcranial stimulation to estimate the conductivity of cortical stroke lesions experimentally. In this simulation study, lesion conductivity was estimated from scalp potentials during transcranial electric stimulation in 12 chronic stroke patients. To do so, first, we determined the stimulation configuration where scalp potentials are maximally affected by the lesion. Then, we calculated scalp potentials in a model with a fixed lesion conductivity and a model with a randomly assigned conductivity. To estimate the lesion conductivity, we minimized the error between the two models by varying the conductivity in the second model. Finally, to reflect realistic experimental conditions, we test the effect rotation of measurement electrode orientation and the effect of the number of electrodes used. We found that the algorithm converged to the correct lesion conductivity value when noise on the electrode positions was absent for all lesions. Conductivity estimation error was below 5% with realistic electrode coregistration errors of 0.1° for lesions larger than 50 ml. Higher lesion conductivities and lesion volumes were associated with smaller estimation errors. In conclusion, this method can experimentally estimate stroke lesion conductivity, improving the accuracy of volume conductor models of stroke patients and potentially leading to more effective transcranial electric stimulation configurations for this population.

Force measurement of electrodes on a human head model

Electroencephalography (EEG) and transcranial electric stimulation (TES) require caps for holding the respective electrodes in place. To support the optimal design of such caps, knowledge of the force-displacement curves for each electrode position is desirable. We propose a calibrated setup to traceably measure force-displacement curves which consists of a human head model, a force sensor, a linear guide, a stepper motor, and a multiplexing multimeter. Repeated measures of a textile EEG-cap and a TES-cap show significant non-linearity and hysteresis effects for the force-displacement curves. Our setup will allow for the assessment of the fit of EEG and TES-caps for various head shapes and sizes.