Parameter-Dependent Effects of Spinal Cord Stimulation on Neural Activation and Evoked Compound Action Potentials

Abstract

ObjectiveSpinal evoked compound action potentials (ECAPs) provide a quantitative measure of the neural response during spinal cord stimulation (SCS) and can be leveraged in closed-loop applications to control dose in response to spinal cord movement. However, interpretation of ECAPs recorded in vivo is limited by susceptibility to noise, inter-subject variability, and other confounding factors. As SCS systems evolve and the clinical and research applications of ECAPs expand, it is critical to understand how physiological and technical factors influence ECAP generation and morphology. ApproachWe used a computational modeling framework to systematically investigate the influence of anatomical (e.g., dorsal cerebrospinal fluid (dCSF) thickness), stimulation (e.g., pulse width, waveform shape, stimulation configuration, stimulation frequency), and recording configurations on the neural responses and ECAPs generated during SCS. We employed a hybrid computational modeling approach, coupling finite element method models with multicompartment axon models to simulate neural responses to SCS. Using these models, we characterized the spatiotemporal dynamics of neural recruitment and the resulting ECAP waveforms. Main resultsNeural responses and model ECAPs were strongly influenced by factors, such as dCSF thickness, pulse width, and stimulation waveform shape. Stimulation parameters introduced trade-offs between axonal recruitment thresholds, neural activation selectivity, and ECAP timing and morphology. Notably, similar ECAP amplitudes could obscure differences in the underlying neural recruitment. Complex ECAP morphologies also emerged in response to distinct stimulation paradigms, reflecting changes in the spatiotemporal properties of axonal activation. Additionally, we demonstrate that the selection of recording electrodes can be optimized to enhance recorded ECAP amplitudes. SignificanceOur findings provide a theoretical framework to advance our mechanistic understanding of SCS-induced ECAPs and offer insights into optimization strategies to improve closed-loop SCS therapies.