Model-based optimization of spinal cord stimulation electrodes
An estimated 100 million Americans suffer from chronic pain. Neurostimulation has emerged as a cost-effective therapy for managing chronic pain and reducing the use of opioid medications. It involves delivering electrical impulses via surface electrodes or an implanted device to the peripheral nerves or spinal cord. However, current neurostimulators, prone to failure due to an incomplete knowledge of the neural circuits and systems underlying chronic pain and the interaction of spinal cord stimulation. The efficacy of spinal cord stimulation depends on the geometry, polarity, and location of the stimulation electrodes. However, lead design, lead placement, and selection of stimulation parameters has been largely a trial and error process. Therefore, there is a need for better understanding of the interactions between spinal cord stimulation and pain pathways in the nervous system to improve efficacy of neurostimulation devices.
Researchers at Duke have developed a method for improving spinal cord stimulation device design for the treatment of chronic pain. A model-based optimization provides a patient-specific electro-anatomical model and optimal spinal cord stimulation electrode configuration. The configuration is based on the determined differences in activation thresholds between the target neural elements and non-target neural elements. The method enhances the selectivity of spinal cord stimulation.
- Optimized intradural placement of electrodes can substantially increase the battery lives of spinal cord stimulation devices
- Model-based optimization of spinal cord stimulation electrode design offers more selective stimulation