Postural Changes in Spinal Cord Stimulation Thresholds: Current and Voltage Sources.

OBJECTIVE: Spinal cord stimulation (SCS) thresholds are known to change with body position; however, these changes have not been fully characterized for both "constant-voltage" and "constant-current" pulse generators. This study aimed to evaluate and quantify changes in psychophysical thresholds resulting from postural changes that may affect both conventional paresthesia-based SCS and novel paresthesia-free SCS technologies. MATERIALS AND METHODS: We measured perceptual, usage, and discomfort thresholds in four body positions (prone, supine, sitting, standing) in 149 consecutive patients, with temporary lower thoracic percutaneous epidural electrodes placed for treating persistent low back and leg pain. We trialed 119 patients with constant-voltage stimulators and 30 patients with constant-current stimulators. RESULTS: Moving from supine to the sitting, standing, or prone positions caused all three thresholds (perceptual, usage, and discomfort) to increase by 22% to 34% for constant-voltage stimulators and by 44% to 82% for constant-current stimulators. Changing from a seated to a supine position caused stimulation to exceed discomfort threshold significantly more often for constant-current (87%) than for constant-voltage (63%) stimulators (p = 0.01). CONCLUSIONS: Posture-induced changes in SCS thresholds occurred consistently as patients moved from lying (supine or prone) to upright (standing or sitting) positions. These changes were more pronounced for constant-current than for constant-voltage pulse generators and more often led to stimulation-evoked discomfort. These observations are consistent with postural changes in spinal cord position measured in imaging studies, and with computer model predictions of neural recruitment for these different spinal cord positions. These observations have implications for the design, implantation, and clinical application of spinal cord stimulators, not only for conventional paresthesia-based SCS but also for paresthesia-free SCS.

Evoked compound action potentials during spinal cord stimulation: effects of posture and pulse width on signal features and neural activation within the spinal cord.

Objective.Evoked compound action potential (ECAP) recordings have emerged as a quantitative measure of the neural response during spinal cord stimulation (SCS) to treat pain. However, utilization of ECAP recordings to optimize stimulation efficacy requires an understanding of the factors influencing these recordings and their relationship to the underlying neural activation.Approach.We acquired a library of ECAP recordings from 56 patients over a wide assortment of postures and stimulation parameters, and then processed these signals to quantify several aspects of these recordings (e.g., ECAP threshold (ET), amplitude, latency, growth rate). We compared our experimental findings against a computational model that examined the effect of variable distances between the spinal cord and the SCS electrodes.Main results.Postural shifts strongly influenced the experimental ECAP recordings, with a 65.7% lower ET and 178.5% higher growth rate when supine versus seated. The computational model exhibited similar trends, with a 71.9% lower ET and 231.5% higher growth rate for a 2.0 mm cerebrospinal fluid (CSF) layer (representing a supine posture) versus a 4.4 mm CSF layer (representing a prone posture). Furthermore, the computational model demonstrated that constant ECAP amplitudes may not equate to a constant degree of neural activation.Significance.These results demonstrate large variability across all ECAP metrics and the inability of a constant ECAP amplitude to provide constant neural activation. These results are critical to improve the delivery, efficacy, and robustness of clinical SCS technologies utilizing these ECAP recordings to provide closed-loop stimulation.

Model-Based Optimization of Spinal Cord Stimulation for Inspiratory Muscle Activation.

OBJECTIVE: High-frequency spinal cord stimulation (HF-SCS) is a potential method to provide natural and effective inspiratory muscle pacing in patients with ventilator-dependent spinal cord injuries. Experimental data have demonstrated that HF-SCS elicits physiological activation of the diaphragm and inspiratory intercostal muscles via spinal cord pathways. However, the activation thresholds, extent of activation, and optimal electrode configurations (i.e., lead separation, contact spacing, and contact length) to activate these neural elements remain unknown. Therefore, the goal of this study was to use a computational modeling approach to investigate the direct effects of HF-SCS on the spinal cord and to optimize electrode design and stimulation parameters. MATERIALS AND METHODS: We developed a computer model of HF-SCS that consisted of two main components: 1) finite element models of the electric field generated during HF-SCS, and 2) multicompartment cable models of axons and motoneurons within the spinal cord. We systematically evaluated the neural recruitment during HF-SCS for several unique electrode designs and stimulation configurations to optimize activation of these neural elements. We then evaluated our predictions by testing two of these lead designs with in vivo canine experiments. RESULTS: Our model results suggested that within physiological stimulation amplitudes, HF-SCS activates both axons in the ventrolateral funiculi (VLF) and inspiratory intercostal motoneurons. We used our model to predict a lead design to maximize HF-SCS activation of these neural targets. We evaluated this lead design via in vivo experiments, and our computational model predictions demonstrated excellent agreement with our experimental testing. CONCLUSIONS: Our computational modeling and experimental results support the potential advantages of a lead design with longer contacts and larger edge-to-edge contact spacing to maximize inspiratory muscle activation during HF-SCS at the T2 spinal level. While these results need to be further validated in future studies, we believe that the results of this study will help improve the efficacy of HF-SCS technologies for inspiratory muscle pacing.

Neural Recruitment During Conventional, Burst, and 10-kHz Spinal Cord Stimulation for Pain.

Spinal cord stimulation (SCS) is a popular neurostimulation therapy for severe chronic pain. To improve stimulation efficacy, multiple modes are now used clinically, including conventional, burst, and 10-kHz SCS. Clinical observations have produced speculation that these modes target different neural elements and/or work via distinct mechanisms of action. However, in humans, these hypotheses cannot be conclusively answered via experimental methods. Therefore, we utilized computational modeling to assess the response of primary afferents, interneurons, and projection neurons to conventional, burst, and 10-kHz SCS. We found that local cell thresholds were always higher than afferent thresholds, arguing against direct recruitment of these local cells. Furthermore, although we observed relative threshold differences between conventional, burst, and 10-kHz SCS, the recruitment order was the same. Finally, contrary to previous reports, axon collateralization produced complex changes in activation thresholds of primary afferents. These results motivate future work to contextualize clinical observations across SCS paradigms. PERSPECTIVE: This article presents the first computational modeling study to investigate neural recruitment during conventional, burst, and 10-kilohertz spinal cord stimulation for chronic pain within a single modeling framework. The results provide insight into these treatments' unknown mechanisms of action and offer context to interpreting clinical observations.

Anatomical and technical factors affecting the neural response to epidural spinal cord stimulation.

OBJECTIVE: Spinal cord stimulation (SCS) is a common neurostimulation therapy to treat chronic pain. Computational models represent a valuable tool to study the potential mechanisms of action of SCS and to optimize the design and implementation of SCS technologies. However, it is imperative that these computational models include the appropriate level of detail to accurately predict the neural response to SCS and to correlate model predictions with clinical outcomes. Therefore, the goal of this study was to investigate several anatomic and technical factors that may affect model-based predictions of neural activation during thoracic SCS. APPROACH: We developed computational models that consisted of detailed finite element models of the lower thoracic spinal cord, surrounding tissues, and implanted SCS electrode arrays. We positioned multicompartment models of sensory axons within the spinal cord to calculate the activation threshold for each sensory axon. We then investigated how activation thresholds changed as a function of several anatomical variables (e.g. spine geometry, dorsal rootlet anatomy), stimulation type (i.e. voltage-controlled vs. current-controlled), electrode impedance, lead position, lead type, and electrical properties of surrounding tissues (e.g. dura conductivity, frequency-dependent conductivity). MAIN RESULTS: Several anatomic and modeling factors produced significant percent differences or errors in activation thresholds. Rostrocaudal positioning of the cathode with respect to the vertebrae had a large effect (up to 32%) on activation thresholds. Variability in electrode impedance produced significant changes in activation thresholds for voltage-controlled stimulation (38% to 51%), but had little effect on activation thresholds for current-controlled stimulation (less than 13%). Changing the dura conductivity also produced significant differences in activation thresholds. SIGNIFICANCE: This study demonstrates several anatomic and technical factors that can affect the neural response to SCS. These factors should be considered in clinical implementation and in future computational modeling studies of thoracic SCS.

A Patient-Specific Computational Framework for the Argus II Implant.

GOAL: Retinal prosthesis performance is limited by the variability of elicited phosphenes. The stimulating electrode's position with respect to retinal ganglion cells (RGCs) affects both perceptual threshold and phosphene shape. We created a modeling framework incorporating patient-specific anatomy and electrode location to investigate RGC activation and predict inter-electrode differences for one Argus II user. METHODS: We used ocular imaging to build a three-dimensional finite element model characterizing retinal morphology and implant placement. To predict the neural response to stimulation, we coupled electric fields with multi-compartment cable models of RGCs. We evaluated our model predictions by comparing them to patient-reported perceptual threshold measurements. RESULTS: Our model was validated by the ability to replicate clinical impedance and threshold values, along with known neurophysiological trends. Inter-electrode threshold differences in silico correlated with in vivo results. CONCLUSIONS: We developed a patient-specific retinal stimulation framework to quantitatively predict RGC activation and better explain phosphene variations.

Evoked Potentials Recorded From the Spinal Cord During Neurostimulation for Pain: A Computational Modeling Study.

OBJECTIVES: Spinal cord stimulation (SCS) for pain is typically implemented in an open-loop manner using parameters that remain largely unchanged. To improve the overall efficacy and consistency of SCS, one closed-loop approach proposes to use evoked compound action potentials (ECAPs) recorded from the SCS lead(s) as a feedback control signal to guide parameter selection. The goal of this study was to use a computational modeling approach to investigate the source of these ECAP recordings and technical and physiological factors that affect their composition. METHODS: We developed a computational model that coupled a finite element model of lower thoracic SCS with multicompartment models of sensory axons within the spinal cord. We used a reciprocity-based approach to calculate SCS-induced ECAPs recorded from the SCS lead. RESULTS: Our model ECAPs contained a triphasic, P1, N1, P2 morphology. The model P2-N1 amplitudes and conduction velocities agreed with previous experimental data from human subjects. Model results suggested that the ECAPs are dominated by the activation of axons with diameters 8.7-10.0 µm located in the dorsal aspect of the spinal cord. We also observed changes in the ECAP amplitude and shape due to the electrode location relative to the vertebrae and spinal cord. CONCLUSION: Our modeling results suggest that clinically effective SCS relies on the activation of numerous axons within a narrow fiber diameter range and that several factors affect the composition of the ECAP recordings. These results can improve how we interpret and implement these recordings in a potential closed-loop approach to SCS.

Patient-Specific Analysis of Neural Activation During Spinal Cord Stimulation for Pain.

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.