Spinal cord stimulation (SCS) is a neuromodulation technique that applies electrical stimulation to the spinal cord to alter neural activity or processing. While SCS has historically been used as a last resort therapy for chronic pain management, novel applications and technologies have recently been developed that either increase the efficacy of treatment for chronic pain or drive neural activity to produce muscular activity/movement following a paralyzing spinal cord injury (SCI). Despite these recent innovations, there remain fundamental questions concerning the neural recruitment underlying these efficacious results. This work evaluated the neural activity and mechanisms for two novel SCS applications: closed-loop spinal cord stimulation for pain management, and ventral, high frequency spinal cord stimulation (HF-SCS) for inspiratory muscle activation following a SCI.

To evaluate neural activity, I developed computational models of SCS. Models consisted of 3 components: a finite element model (FEM) of the spinal cord to predict voltages during stimulation, biophysical neuron models, and algorithms to apply time-dependent extracellular voltages to the neuron models and simulate their response. While this cutting-edge modeling methodology could be used to predict neural activity following stimulation, it was unclear how common anatomical or technical model simplifications affected neural predictions. Therefore, the initial goal of this work was to evaluate how modeling assumptions influence neural behavior.

My initial work identified how several relevant anatomical and technical factors influence model predictions of neural activity. To evaluate these factors, I designed an FEM of a T9 thoracic spine with an implanted electrode. Then, I sequentially removed details from the model and quantified the changes in neural predictions. I identified several factors with profound (>30%) impacts on neural thresholds, including overall model impedance (for voltage-controlled stimulation), the presence of a detailed vertebral column, and dura mater conductivity. I also identified several factors that could safely be ignored in future models. This work will be invaluable as a guide for future model development.

Next, I developed a canine model to evaluate T2 ventral HF-SCS for inspiratory muscle activation. I designed and positioned two neuron models hypothesized to lead to inspiratory behavior: ventrolateral funiculus fibers (VLF) leading to diaphragm activation and inspiratory intercostal motoneurons. With this model, I predicted robust VLF and T2-T5 motoneuron recruitment within the physiologic range of stimulation. Additionally, I designed two stimulation leads that maximize inspiratory neuron recruitment. The finalized leads were evaluated via in vivo experiments, which found excellent agreement with the model. This work builds our mechanistic understanding of this novel therapy, improves its implementation, and aids in future translational efforts towards human subjects.

Finally, I developed a computational model to evaluate closed-loop stimulation for chronic pain. This work characterized the neural origins of the evoked compound action potential (ECAP), the controlling biomarker of closed-loop stimulation. I modified my modeling methodology to predict ECAPs generated during low thoracic dorsal stimulation in humans, which matched with experimental measurements. This modeling work showed that ECAP properties depend on activation of a narrow range of neuron diameters and quantified how anatomical and stimulation factors (CSF thickness, stimulation configuration, lead position, pulse width) influence ECAP morphology, timing, and neural recruitment. These results improve our mechanistic understanding of closed-loop stimulation and may lead to expanded clinical utility as well as better validation of future SCS computational models.

Date: Friday, July 9, 2021

Time: 9:00 AM EDT

Zoom: https://umich.zoom.us/j/96847307388

Chair: Dr. Scott Lempka