Investigating Potential Mechanisms of Subperception Spinal Cord Stimulation

Spinal cord stimulation (SCS) is a surgically implanted therapy for chronic pain that delivers electrical stimulation to the spinal cord. Recently, SCS paradigms have rapidly expanded to include more frequencies, amplitudes, and indications, but the therapeutic success of SCS remains stagnant(Titus et al., 2020). Many of the new SCS paradigms treat pain by applying stimulation at amplitudes less than the amount required for patients to perceive the treatments. However, there is little information regarding how these “subperception” SCS therapies effect analgesia. The work in this dissertation uses computational modeling and in vivo neural recordings to investigate and explore the potential analgesic mechanisms of subperception SCS.The combination of computational modeling and preclinical experimental data displayed in this dissertation provides many insights into the mechanisms of subperception SCS. A tool was built to design temporal patterns of SCS which predicted features of effective SCS patterns observed during in vivo single-unit recordings. A model was built to explain dorsal column responses to 10 kHz SCS, and this model accurately predicted in vivo single-unit recordings of dorsal column responses to low-amplitude, low-frequency SCS. This model was used to build a population of responses which was applied to a network model of SCS, and this modeling combination predicted in vivo multi-unit recordings of responses to a novel subperception SCS paradigm. Finally, models were developed which predicted the direct response of dorsal horn neurons to SCS. Feeding these responses into a network model of dorsal horn circuitry yielded similar changes in neural activity to measurements of neural activity obtained from post-mortem tissue of animals which had undergone SCS. Overall, this thesis work improved understanding of the mechanisms of action underlying multiple subperception SCS paradigms, provided models to predict and explain responses of neural elements to novel SCS paradigms, and developed a tool for designing new, effective patterns of SCS.