Computational Study of the Effect of Electrode Polarity on Neural Activation Related to Paresthesia Coverage in Spinal Cord Stimulation Therapy

Neurostimulation in the patient with chronic pain: forecasting the future with data from the present - data-driven analysis or just dreams?

Chronic pain involves a structured and individualized development of neurophysiological and biological responses. The final expression in each patient correlates with diverse expressions of mediators and activations of different transmission and modulation pathways, as well as alterations in the structure and function of the brain, all of which develop according to the pain phenotype. Still today, the selection process for the ideal candidate for spinal cord stimulation (SCS) is based on results from test and functional variables analysis as well as pain evaluation. In addition to the difficulties in the initial selection of patients and the predictive analysis of the test phase, which undoubtedly impact on the results in the middle and long term, the rate of explants is one of the most important concerns, in the analysis of suitability of implanted candidates. A potential for useful integration of genome analysis and lymphocyte expression in the daily practice of neurostimulation, for pain management is presented. Structural and functional quantitative information provided by imaging biomarkers will allow establishing a clinical decision support system that improve the effectiveness of the SCS implantation, optimizing human, economic and psychological resources. A correct programming of the neurostimulator, as well as other factors associated with the choice of leads and their position in the epidural space, are the critical factors for the effectiveness of the therapy. Using a model of SCS based on mathematical methods and computational simulation, the effect of different factors of influence on clinical practice studied, as several configurations of electrodes, position of these, and programming of polarities, in order to draw conclusions of clinical utility in neuroestimulation therapy.

Methods and system for recording human physiological signals from implantable leads during spinal cord stimulation

Objectives

This article presents a method-including hardware configuration, sampling rate, filtering settings, and other data analysis techniques-to measure evoked compound action potentials (ECAPs) during spinal cord stimulation (SCS) in humans with externalized percutaneous electrodes. The goal is to provide a robust and standardized protocol for measuring ECAPs on the non-stimulation contacts and to demonstrate how measured signals depend on hardware and processing decisions.

Methods

Two participants were implanted with percutaneous leads for the treatment of chronic pain with externalized leads during a trial period for stimulation and recording. The leads were connected to a Neuralynx ATLAS system allowing us to simultaneously stimulate and record through selected electrodes. We examined different hardware settings, such as online filters and sampling rate, as well as processing techniques, such as stimulation artifact removal and offline filters, and measured the effects on the ECAPs metrics: the first negative peak (N1) time and peak-valley amplitude.

Results

For accurate measurements of ECAPs, the hardware sampling rate should be least at 8 kHz and should use a high pass filter with a low cutoff frequency, such as 0.1 Hz, to eliminate baseline drift and saturation (railing). Stimulation artifact removal can use a double exponential or a second-order polynomial. The polynomial fit is 6.4 times faster on average in computation time than the double exponential, while the resulting ECAPs' N1 time and peak-valley amplitude are similar between the two. If the baseline raw measurement drifts with stimulation, a median filter with a 100-ms window or a high pass filter with an 80-Hz cutoff frequency preserves the ECAPs.

Conclusions

This work is the first comprehensive analysis of hardware and processing variations on the observed ECAPs from SCS leads. It sets recommendations to properly record and process ECAPs from the non-stimulation contacts on the implantable leads.

3D patient-specific spinal cord computational model for SCS management: potential clinical applications

BACKGROUND AND OBJECTIVE

Although Spinal Cord Stimulation (SCS) is an established therapy for treating neuropathic chronic pain, in tonic stimulation, postural changes, electrode migration or badly-positioned electrodes can produce annoying stimulation (intercostal neuralgia) in about 35% of the patients. SCS models are used to study the effect of electrical stimulation to better manage the stimulation parameters and electrode position. The goal of this work was to develop a realistic 3D patient-specific spinal cord model from a real patient and develop a future clinical application that would help physicians to optimize paresthesia coverage in SCS therapy.

METHODS

We developed two 3D patient-specific models from a high-resolution MRI of two patients undergoing SCS treatment. The model consisted of a finite element model of the spinal cord and a sensory myelinated nerve fiber model. The same simulations were performed with a generalized spinal cord model and we compared the results with the clinical data to evaluate the advantages of a patient-specific model. To identify the geometrical parameters that most influence the stimulation predictions, a sensitivity analysis was conducted. We used the patient-specific model to perform a clinical application involving the pre-implantation selection of electrode polarity and study the effect of electrode offset.

RESULTS

The patient-specific model correlated better with clinical data than the generalized model. Electrode-dura mater distance, dorsal CSF thickness, and CSF diameter are the geometrical parameters that caused significant changes in the stimulation predictions. Electrode polarity could be planned and optimized to stimulate the patient's painful dermatomes. The addition of offset in parallel electrodes would not have been beneficial for one of the patients of this study because they reduce neural activation displacement.

CONCLUSIONS

This is the first study to relate the activation area model prediction in dorsal columns with the clinical effect on paresthesia coverage. The outcomes show that 3D patient-specific models would help physicians to choose the best stimulation parameters to optimize neural activation and SCS therapy in tonic stimulation.

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.

Spinal cord stimulation programming: a crash course

The aim of this comprehensive review is to provide an instructional guide for providers regarding the parameters and programming of spinal cord stimulation (SCS) devices. Knowing these fundamentals will aid in providing superior pain relief to patients. SCS has four programmable parameters: contact (electrode) selection, amplitude, pulse width, and frequency. Each parameter needs to be accounted for when assessing which program works for which patient. Traditional open-loop systems allow for different "programs," or combinations of these four parameters, to be pre-set by the provider and medical device representative. These allow for flexibility in the type of stimulation delivered to the patient depending on activity. Patients are also given control over programs and changing the amplitudes of these programs. However, some open-loop systems place the burden of toggling between programs to manage pain control on patients, though this tends to be less in subparesthesia programs. Newer closed-loop systems make it possible for stimulation settings to automatically adjust in response to accelerometry and evoked compound action potential feedback, and therefore have the potential to streamline the patient experience. This article provides practitioners with the basic knowledge of SCS parameters and programming systems. Understanding their use is essential to providing optimal pain relief to patients.

The Quasi-uniform assumption for Spinal Cord Stimulation translational research

BACKGROUND

Quasi-uniform assumption is a general theory that postulates local electric field predicts neuronal activation. Computational current flow model of spinal cord stimulation (SCS) of humans and animal models inform how the quasi-uniform assumption can support scaling neuromodulation dose between humans and translational animal.

NEW METHOD

Here we developed finite element models of cat and rat SCS, and brain slice, alongside SCS models. Boundary conditions related to species specific electrode dimensions applied, and electric fields per unit current (mA) predicted.

RESULTS

Clinically and across animal, electric fields change abruptly over small distance compared to the neuronal morphology, such that each neuron is exposed to multiple electric fields. Per unit current, electric fields generally decrease with body mass, but not necessarily and proportionally across tissues. Peak electric field in dorsal column rat and cat were ∼17x and ∼1x of clinical values, for scaled electrodes and equal current. Within the spinal cord, the electric field for rat, cat, and human decreased to 50% of peak value caudo-rostrally (C5-C6) at 0.48 mm, 3.2 mm, and 8 mm, and mediolaterally at 0.14 mm, 2.3 mm, and 3.1 mm. Because these space constants are different, electric field across species cannot be matched without selecting a region of interest (ROI).

COMPARISON WITH EXISTING METHOD

This is the first computational model to support scaling neuromodulation dose between humans and translational animal.

CONCLUSIONS

Inter-species reproduction of the electric field profile across the entire surface of neuron populations is intractable. Approximating quasi-uniform electric field in a ROI is a rational step to translational scaling.