Evaluating optimized temporal patterns of spinal cord stimulation (SCS).

Journal Article (Journal Article)

Background

Temporal patterns of stimulation represent a novel dimension for improving the efficacy of spinal cord stimulation to treat chronic neuropathic pain.

Objective

We hypothesized that nonregular temporal patterns of stimulation designed using a computational model would be superior to conventional stimulation at constant frequencies or completely random patterns of stimulation.

Methods

Using a computational model of the dorsal horn network and an optimization algorithm based on biological evolution, we designed an optimized pattern of spinal cord stimulation with comparable efficacy and increased efficiency relative to constant frequency (CF) stimulation. We evaluated the effect of different temporal patterns on individual neurons recorded in the dorsal horn of urethane-anesthetized rats.

Results

The optimized pattern and 50 Hz CF stimulation produced greater inhibition of spontaneously firing neurons recorded in vivo than random 50 Hz stimulation or a pattern designed intentionally with poor fitness. Spinal Cord Stimulation (SCS) led to significant changes in the firing patterns of recorded units, and stimulation patterns that generated significant inhibition also tended to reduce entropy and regularize the firing patterns of units, suggesting that patterns of dorsal horn neuron activity may be important for pain perception in addition to the firing rate.

Conclusions

These results demonstrate that the computational model can be used as a tool for optimizing stimulation parameters and suggest that optimized temporal patterns may increase the efficacy of spinal cord stimulation.

Full Text

Duke Authors

Cited Authors

  • Gilbert, JE; Zhang, T; Esteller, R; Grill, WM

Published Date

  • July 2022

Published In

Volume / Issue

  • 15 / 5

Start / End Page

  • 1051 - 1062

PubMed ID

  • 35918052

Electronic International Standard Serial Number (EISSN)

  • 1876-4754

International Standard Serial Number (ISSN)

  • 1935-861X

Digital Object Identifier (DOI)

  • 10.1016/j.brs.2022.07.046

Language

  • eng