HingePlace: Harnessing the neural thresholding behavior to optimize Transcranial Electrical Stimulation

TL;DR

HingePlace is a novel electrode placement algorithm for transcranial electrical stimulation that leverages neural thresholding behavior to significantly improve the focality of neural responses, outperforming traditional methods in simulations.

Contribution

The paper introduces HingePlace, a new algorithm that harnesses neural thresholding behavior, unifying and extending existing electrode placement methods for better focal stimulation.

Findings

HingePlace increases neural response focality by up to 60%.

It outperforms traditional algorithms in simulation comparisons.

The approach leverages neural thresholding properties for optimization.

Abstract

Transcranial Electrical Stimulation (tES) is a neuromodulation technique that utilizes electrodes on the scalp to stimulate target brain regions. tES has shown promise in treating many neurological conditions, such as stroke rehabilitation and chronic pain. Several electrode placement algorithms have been proposed to optimize tES-based therapies by designing multi-electrode montages that create focal neural responses. We first extend a well-known unification result by Fernandez-Corazza et al. to unify all major traditional electrode placement algorithms. We utilize this unification result to identify a common restriction among traditional electrode placement algorithms: they do not harness the thresholding behavior of neural response. Consequently, these algorithms only partially harness the properties of neural response to optimize tES, particularly increasing the focality of neural response. We propose a new electrode placement algorithm, HingePlace, that utilizes a symmetrized hinge loss to harness the thresholding behavior of neural response. We extensively compare the HingePlace algorithm with traditional electrode placement algorithms in two simulation platforms. Across both platforms, we find that HingePlace-designed montages consistently generate more focal neural responses -- by as much as 60% -- than the electrode montages designed by traditional electrode placement algorithms.