Tissue and electrode capacitance reduce neural activation volumes during deep brain stimulation

Butson, Christopher R.; McIntyre, Cameron C.

doi:10.1016/j.clinph.2005.06.023

Cited by 286 publications

(368 citation statements)

References 38 publications

(43 reference statements)

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“…Variable density FEM meshes were used to maximize solution accuracy; mesh density was highest where the electric field gradient was largest. The voltage within the volume was determined using a Fourier FEM solver which solves the Poisson equation in time and space simultaneously (Butson and McIntyre, 2005). The purpose of this solver was to combine the actual DBS waveform and the capacitance of the electrode-tissue interface into the bioelectric field model.…”

Section: Deep Brain Stimulation Finite Element Modelmentioning

confidence: 99%

“…1 shows an equivalent circuit diagram of the model DBS system. To enable a more accurate comparison with clinical DBS impedance measurements, an 80 Ω wire resistance and the capacitance of the electrode contact were present in the model (Butson and McIntyre, 2005;Holsheimer et al, 2000). All model impedances were calculated at the onset of the cathodic phase of the stimulation pulse.…”

Section: Impedance Modelmentioning

confidence: 99%

“…Field-axon simulations were conducted using Fourier FEM DBS electrode models coupled to 5.7 μm diameter myelinated axon models (Butson and McIntyre, 2005;McIntyre et al, 2002). A collection of 119 model axons were distributed in a 17×7 matrix oriented perpendicular to the electrode shaft.…”

Section: Estimation Of the Volume Of Tissue Activatedmentioning

confidence: 99%

“…In previous work we have provided quantitative predictions of the volume of tissue activated (VTA) during DBS (Butson and McIntyre, 2005;McIntyre et al, 2004a,c); however, these studies ignored the impact of impedance variability. In this study we explored the sensitivity of electrode impedance to changes in the properties of electrode(s) and tissue medium.…”

Section: Introductionmentioning

confidence: 99%

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Sources and effects of electrode impedance during deep brain stimulation

Butson

Maks

McIntyre

2006

Clinical Neurophysiology

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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.

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Section: Deep Brain Stimulation Finite Element Modelmentioning

confidence: 99%

Section: Impedance Modelmentioning

confidence: 99%

Section: Estimation Of the Volume Of Tissue Activatedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sources and effects of electrode impedance during deep brain stimulation

Butson

Maks

McIntyre

2006

Clinical Neurophysiology

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313

278

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“…Recent efforts include the development of the activation function for excitation of nerve fibers developed by Rattay (Rattay 1986;Rattay 1988;Rattay 1989), multicompartment fiber models (Warman et al 1992;Richardson et al 2000;Moffitt et al 2004), and several analyses of deep brain stimulation electrodes Butson and McIntyre 2005). Of relevance to this work, simulations of central nervous system neurons in the context of the cortex as well as the spinal cord have also been performed Grill 1999, McIntyre andGrill 2002), and indicate along with experimental studies that the primary location of action potential (AP) generation induced by applied electric fields is at the axon initial segment Bullier 1998a, Nowak andBullier 1998b).…”

Section: Introductionmentioning

confidence: 99%

Studies of stimulus parameters for seizure disruption using neural network simulations

et al. 2007

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A large scale neural network simulation with realistic cortical architecture has been undertaken to investigate the effects of external electrical stimulation on the propagation and evolution of ongoing seizure activity. This is an effort to explore the parameter space of stimulation variables to uncover promising avenues of research for this therapeutic modality. The model consists of an approximately 800 μm × 800 μm region of simulated cortex, and includes seven neuron classes organized by cortical layer, inhibitory or excitatory properties, and electrophysiological characteristics. The cell dynamics are governed by a modified version of the Hodgkin-Huxley equations in single compartment format. Axonal connections are patterned after histological data and published models of local cortical wiring. Stimulation induced action potentials take place at the axon initial segments, according to threshold requirements on the applied electric field distribution. Stimulation induced action potentials in horizontal axonal branches are also separately simulated. The calculations are performed on a 16 node distributed 32-bit processor system. Clear differences in seizure evolution are presented for stimulated versus the undisturbed rhythmic activity. Data is provided for frequency dependent stimulation effects demonstrating a plateau effect of stimulation efficacy as the applied frequency is increased from 60 Hz to 200 Hz. Timing of the stimulation with respect to the underlying rhythmic activity demonstrates a phase dependent sensitivity. Electrode height and position effects are also presented. Using a dipole stimulation electrode arrangement, clear orientation effects of the dipole with respect to the model connectivity is also demonstrated. A sensitivity analysis of these results as a function of the stimulation threshold is also provided.

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