Sources and effects of electrode impedance during deep brain stimulation

Abstract

Objective-Clinical impedance measurements for deep brain stimulation (DBS) electrodes in human patients are normally in the range 500-1500 Ω. DBS devices utilize voltage-controlled stimulation; therefore, the current delivered to the tissue is inversely proportional to the impedance. The goals of this study were to evaluate the effects of various electrical properties of the tissue medium and electrode-tissue interface on the impedance and to determine the impact of clinically relevant impedance variability on the volume of tissue activated (VTA) during DBS.Methods-Axisymmetric finite-element models (FEM) of the DBS system were constructed with explicit representation of encapsulation layers around the electrode and implanted pulse generator. Impedance was calculated by dividing the stimulation voltage by the integrated current density along the active electrode contact. The models utilized a Fourier FEM solver that accounted for the capacitive components of the electrode-tissue interface during voltagecontrolled stimulation. The resulting time-and space-dependent voltage waveforms generated in the tissue medium were superimposed onto cable model axons to calculate the VTA.Results-The primary determinants of electrode impedance were the thickness and conductivity of the encapsulation layer around the electrode contact and the conductivity of the bulk tissue medium. The difference in the VTA between our low (790 Ω) and high (1244 Ω) impedance models with typical DBS settings (−3 V, 90 μs, 130 Hz pulse train) was 121 mm 3 , representing a 52% volume reduction.Conclusions-Electrode impedance has a substantial effect on the VTA and accurate representation of electrode impedance should be an explicit component of computational models of voltage-controlled DBS.Significance-Impedance is often used to identify broken leads (for values >2000 Ω) or short circuits in the hardware (for values <50 Ω); however, clinical impedance values also represent an important parameter in defining the spread of stimulation during DBS.

Method for patient-specific finite element modeling and simulation of deep brain stimulation

“…The distribution of the electric potential in the vicinity of a DBS electrode has previously been modelled using isotropic homogenous tissue [8][9][10][11][12][13][14][15][16]. More recently, Butson et al [17] presented a three dimensional (3D) patient-specific method to predict the volume of tissue activated by DBS based on diffusion tensor images (DTI).…”

Differences among implanted pulse generator waveforms cause variations in the neural response to deep brain stimulation

“…Neural activation thresholds were calculated using a previously published field-neuron simulation model (Butson et al, 2006a) (Figure 1C). Briefly, a finite element electric field model was used to calculate the voltage distribution surrounding the DBS electrode as a function of stimulation parameters.…”

“…The model included the capacitance of the electrode-tissue interface, which causes a voltage decay in the plateau phase of the stimulus waveform (Butson and McIntyre, 2005). The model also included explicit representation of the encapsulation tissue that surrounds the electrode, which is correlated with electrode impedance (Butson et al, 2006a). Both the electrode capacitance and the encapsulation layer cause increased threshold voltages.…”

“…The model system is based on the concept that the primary neural targets of DBS are myelinated axons (McIntyre et al, 2004a;McIntyre et al, 2004b;Miocinovic et al, 2006). Hence, the time-and space-dependent voltage distribution generated in the tissue medium was interpolated onto multi-compartment cable-model axons to determine their activation thresholds (Butson et al, 2006a) (Figure 1C). This general modeling approach has been experimentally validated by examining stimulation side effects due to spillover into oculomotor nerve (Butson et al, 2006b) and corticospinal tract (Miocinovic et al, 2006;Butson et al, 2007).…”

Intracranial neuronal ensemble recordings and analysis in epilepsy

“…The main factors influencing SUA recordings are the diameter and the impedance of the electrode. The relationship between the size of the electrode surface and the impedance is inversely proportional, with electrodes with larger area exhibiting lower impedance (Butson et al, 2006;Ludwig et al, 2006). Prasad et al found that the ideal resistance for SUA detection is between 40-150kΩ (Prasad and Sanchez, 2012).…”