Stimulation-induced changes at the electrode–tissue interface and their influence on deep brain stimulation

Abstract

Abstract Objective. During deep brain stimulation (DBS) the electrode–tissue interface (ETI) forms a critical path between device and brain tissue. Although changes in the electrical double layer (EDL) and glial scar can impact stimulation efficacy, the effects of chronic DBS on the ETI have not yet been established. Approach. In this study, we characterised the ETI surrounding chronically implanted DBS electrodes in rats and compared the impedance and histological properties at the electrode interface in animals that received daily stimulation and in those where no stimulation was applied, up to 8 weeks post-surgery. A computational model was developed based on the experimental data, which allowed the dispersive electrical properties of the surrounding encapsulation tissue to be estimated. The model was then used to study the effect of stimulation-induced changes in the ETI on the electric field and neural activation during voltage- and current-controlled stimulation. Main results. Incorporating the observed changes in simulations in silico, we estimated the frequency-dependent dielectric properties of the EDL and surrounding encapsulation tissue. Through simulations we show how stimulation-induced changes in the properties of the ETI influence the electric field and alter neural activation during voltage-controlled stimulation. A substantial increase in the number of stimulated collaterals, and their distance from the electrode, was observed during voltage-controlled stimulation with stimulated ETI properties. In vitro examination of stimulated electrodes confirmed that high frequency stimulation leads to desorption of proteins at the electrode interface, with a concomitant reduction in impedance. Significance. The demonstration of stimulation-induced changes in the ETI has important implications for future DBS systems including closed-loop systems where the applied stimulation may change over time. Understanding these changes is particularly important for systems incorporating simultaneous stimulation and sensing, which interact dynamically with brain networks.