Patient‐Specific Analysis of Neural Activation During Spinal Cord Stimulation for Pain. Issue 5 (28th August 2019)

• Record Type: Journal Article
• Authors: Lempka, Scott F.; Zander, Hans J.; Anaya, Carlos J.; Wyant, Alexandria; Ozinga, John G.; Machado, Andre G.
• Is Part Of: Neuromodulaton. Volume 23:Issue 5(2020)
• Page Start: 572
• Page End: 581
• Publication Date: 2019-08-28
• Subjects: Chronic pain -- computer simulation -- failed back surgery syndrome -- spinal cord stimulation; Central nervous system -- Physiology -- Periodicals; Central nervous system -- Diseases -- Periodicals; 616.8
• DOI: 10.1111/ner.13037
• Languages: English
• ISSNs: 1094-7159

Abstract

Abstract : Objective: Despite the widespread use of spinal cord stimulation (SCS) for chronic pain management, its neuromodulatory effects remain poorly understood. Computational models provide a valuable tool to study SCS and its effects on axonal pathways within the spinal cord. However, these models must include sufficient detail to correlate model predictions with clinical effects, including patient‐specific data. Therefore, the goal of this study was to investigate axonal activation at clinically relevant SCS parameters using a computer model that incorporated patient‐specific anatomy and electrode locations. Methods: We developed a patient‐specific computer model for a patient undergoing SCS to treat chronic pain. This computer model consisted of two main components: 1) finite element model of the extracellular voltages generated by SCS and 2) multicompartment cable models of axons in the spinal cord. To determine the potential significance of a patient‐specific approach, we also performed simulations with standard canonical models of SCS. We used the computer models to estimate axonal activation at clinically measured sensory, comfort, and discomfort thresholds. Results: The patient‐specific and canonical models predicted significantly different axonal activation. Relative to the canonical models, the patient‐specific model predicted sensory threshold estimates that were more consistent with the corresponding clinical measurements. These results suggest that it is important to account for sources of interpatient variability (e.g., anatomy, electrode locations) in model‐based analysis of SCS. Conclusions: This study demonstrates the potential for patient‐specific computer models to quantitatively describe the axonal response to SCS and to address scientific questions related to clinical SCS.