Spinal cord stimulation in chronic pain: evidence and theory for mechanisms of action.

Well-established in the field of bioelectronic medicine, Spinal Cord Stimulation (SCS) offers an implantable, non-pharmacologic treatment for patients with intractable chronic pain conditions. Chronic pain is a widely heterogenous syndrome with regard to both pathophysiology and the resultant phenotype. Despite advances in our understanding of SCS-mediated antinociception, there still exists limited evidence clarifying the pathways recruited when patterned electric pulses are applied to the epidural space. The rapid clinical implementation of novel SCS methods including burst, high frequency and dorsal root ganglion SCS has provided the clinician with multiple options to treat refractory chronic pain. While compelling evidence for safety and efficacy exists in support of these novel paradigms, our understanding of their mechanisms of action (MOA) dramatically lags behind clinical data. In this review, we reconstruct the available basic science and clinical literature that offers support for mechanisms of both paresthesia spinal cord stimulation (P-SCS) and paresthesia-free spinal cord stimulation (PF-SCS). While P-SCS has been heavily examined since its inception, PF-SCS paradigms have recently been clinically approved with the support of limited preclinical research. Thus, wide knowledge gaps exist between their clinical efficacy and MOA. To close this gap, many rich investigative avenues for both P-SCS and PF-SCS are underway, which will further open the door for paradigm optimization, adjunctive therapies and new indications for SCS. As our understanding of these mechanisms evolves, clinicians will be empowered with the possibility of improving patient care using SCS to selectively target specific pathophysiological processes in chronic pain

Enhancing selectivity of minimally invasive peripheral nerve interfaces using combined stimulation and high frequency block: from design to application

The discovery of the excitable property of nerves was a fundamental step forward in our knowledge of the nervous system and our ability to interact with it. As the injection of charge into tissue can drive its artificial activation, devices have been conceived that can serve healthcare by substituting the input or output of the peripheral nervous system when damage or disease has rendered it inaccessible or its action pathological. Applications are far-ranging and transformational as can be attested by the success of neuroprosthetics such as the cochlear implant. However, the body’s immune response to invasive implants have prevented the use of more selective interfaces, leading to therapy side-effects and off-target activation. The inherent tradeoff between the selectivity and invasiveness of neural interfaces, and the consequences thereof, is still a defining problem for the field. More recently, continued research into how nervous tissue responds to stimulation has led to the discovery of High Frequency Alternating Current (HFAC) block as a stimulation method with inhibitory effects for nerve conduction. While leveraging the structure of the peripheral nervous system, this neuromodulation technique could be a key component in efforts to improve the selectivity-invasiveness tradeoff and provide more effective neuroprosthetic therapy while retaining the safety and reliability of minimally invasive neural interfaces. This thesis describes work investigating the use of HFAC block to improve the selectivity of peripheral nerve interfaces, towards applications such as bladder control or vagus nerve stimulation where selective peripheral nerve interfaces cannot be used, and yet there is an unmet need for more selectivity from stimulation-based therapy. An overview of the underlying neuroanatomy and electrophysiology of the peripheral nervous system combined with a review of existing electrode interfaces and electrochemistry will serve to inform the problem space. Original contributions are the design of a custom multi-channel stimulator able to combine conventional and high frequency stimulation, establishing a suitable experimental platform for ex-vivo electrophysiology of the rat sciatic nerve model for HFAC block, and exploratory experiments to determine the feasibility of using HFAC block in combination with conventional stimulation to enhance the selectivity of minimally-invasive peripheral nerve interfaces.Open Acces

Computational Modeling of Spinal Cord Stimulation for Inspiratory Muscle Activation and Chronic Pain Management

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 three SCS applications: both conventional SCS and closed-loop SCS for pain management, as well as ventral, high frequency spinal cord stimulation (HF-SCS) for inspiratory muscle activation following a SCI. I developed computational models to both predict the neural response to SCS and explore factors influencing neural activation. Models consisted of three components: a finite element model (FEM) of the spinal cord to predict the potential fields generated by stimulation, biophysical neuron models, and algorithms to apply time-dependent extracellular potentials 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 anatomical and technical factors affected neural predictions. To evaluate these factors, I designed an FEM of a T9 thoracic spine with an implanted electrode array. Then, I sequentially removed details from the model and quantified the changes in neural predictions. I identified several factors with large (>30%) effects on neural thresholds, including overall electrode impedance (for voltage-controlled stimulation), the electrode position relative to the spine, and dura mater conductivity. This work will be invaluable for basic science and clinical applications of SCS. Next, I developed a canine model to evaluate T2 ventral HF-SCS for inspiratory muscle activation after an SCI. This model infrastructure included two neuron populations 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 experimental range of stimulation. I used this model framework to optimize several design parameters related to HF-SCS for inspiration. The optimal lead design parameters were evaluated via in vivo experiments, which found excellent agreement with model predictions. This work expands 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 SCS for chronic pain management. This work characterized the neural origins of the evoked compound action potential (ECAP), the controlling biomarker of closed-loop stimulation. This modeling work showed that ECAP properties depend on activation of a narrow range of axon diameters and quantified how anatomical and stimulation factors (e.g., 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 neural recruitment during SCS. In summary, this dissertation work improves the methodology, validation, and applications of computational models of SCS. It also has direct applications to the clinical/pre-clinical implementation of SCS and may be invaluable for expanding the utility and efficacy of several treatments. The improved mechanistic understandings of neural activation described in this work may also aid in the future development of these therapies.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169928/1/hzander_1.pd

Patient-specific computational modeling for spinal cord stimulation therapy optimization

[EN] Chronic pain disease has 35-50% of prevalence worldwide. When drugs stop working, spinal cord stimulation (SCS) therapy is a non-drug alternative treatment for several conditions of chronic pain, such as neuropathic pain. In the last 40 years, SCS computational modeling has been the key tool to analyze and understand the effect of the stimulation parameters on neural response. However, the lack of realistic models limits the model-based predictions accuracy for SCS therapy optimization concerning the stimulation parameters management and the development of clinical applications. This thesis presents three improvements in SCS modeling from cellular to organic levels: · Cellular level: a human A -beta sensory myelinated nerve fiber model is shown. The model simulates the action potential creation and propagation of human sensory fibers produced by electrical stimulation. Moreover, to consider the current losses produced at the internodal compartments, a realistic myelin model is included. · Organic level: two spinal cord volume conductor models are presented. The first one is a generalized SCS model, which is based on in vivo 3T high-resolution magnetic resonance images from the human spinal cord, solving then one of the main limitations of previous SCS models, which is the inclusion of cadaveric measurements. The second one is a 3D patient-specific SCS model, which includes the entire spinal cord geometry variation of three different vertebral levels (T8, T9, and T10) from patients undergoing SCS treatment. This novel approach is validated clinically, showing that patient-specific modeling improves model-based predictions accuracy compared to generalized SCS models. In addition to this, this thesis presents three studies related to SCS therapy by using the three computational models developed previously: - Role of stimulation frequency: it is performed using the human A-beta sensory myelinated nerve fiber model. The outcome of this study showed that frequency could have a significant influence on the reduction or increase of the neuron activity, participating thus in the selection of the targeted neural elements in SCS therapy, in tonic stimulation. - Effect of electrode polarity: using the 3D generalized SCS model, the effect of the most used and known polarities (bipolar, guarded cathode, and dual-guarded cathode) is shown. The results showed that, unlike guarded cathode, dual-guarded cathode maximized the activating area and depth in dorsal columns, also increasing the probability of activating dorsal roots fibers. - Clinical applications: the pre-implantation selection of the electrode polarity was performed with the 3D patient-specific model. The findings showed that this clinical application could determine the electrode configurations that best overlapped paresthesia coverage to the painful dermatomes of the patient before the SCS device implant. On the other hand, the effect of offset electrodes was also investigated. In this case, the results revealed that staggered offset placement canceled the left- or right-activation displacement in the dorsal columns, suggesting that offset electrodes placement should be avoided in tonic stimulation.[ES] El dolor crónico es una enfermedad que tiene una prevalencia de entre el 35% y el 50% de la población mundial. Cuando los fármacos dejan de hacer efecto, la terapia de estimulación de médula espinal (EME) es una alternativa no farmacológica que se usa para el tratamiento de diversas condiciones de dolor crónico, como el dolor neuropático. En los últimos 40 años, el modelado computacional de la EME ha sido la herramienta clave para analizar y entender el efecto de los parámetros de estimulación eléctrica en la respuesta neuronal. Sin embargo, la falta de modelos realistas limita la precisión de las predicciones de los modelos para la optimización de la terapia de EME, en referencia a la programación de los parámetros de estimulación y el desarrollo de aplicaciones clínicas. Esta tesis presenta tres mejoras en el modelado computacional de la terapia de EME, desde el nivel celular hasta el nivel orgánico: · Nivel celular: se presenta un modelo de fibra mielínica A-beta sensitiva humana. El modelo simula la creación y propagación del potencial de acción de fibras humanas sensitivas que se produce bajo el efecto de un estímulo eléctrico. Además, con el fin de considerar las pérdidas de corriente producidas en los compartimentos internodales, la mielina se modeliza de forma realista. · Nivel orgánico: se presentan dos modelos de conductor volumétrico de médula espinal. El primero se trata de un modelo de EME generalizado, el cual está basado en imágenes de resonancia magnética de 3T de alta resolución de médula espinal humana obtenidas in vivo. Esta propuesta resuelve una de las principales limitaciones presente en modelos de EME anteriores, que es la inclusión de medidas geométricas obtenidas de cadáveres. El segundo modelo es un modelo tridimensional personalizado al paciente, el cual incluye la variación de la geometría de la médula espinal en tres niveles vertebrales diferentes (T8, T9 y T10) a partir de pacientes sometidos al tratamiento de EME. Esta novedosa propuesta es validada clínicamente, mostrando además que el modelado computacional personalizado mejora la precisión de las predicciones del modelo en comparación a un modelo generalizado. Además, esta tesis presenta tres estudios relacionados con la terapia de EME usando los tres modelos desarrollados previamente: - El papel de la frecuencia de estimulación: se realiza mediante el uso del modelo de fibra mielínica A -beta sensitiva humana. Los resultados de este estudio muestran que la frecuencia podría tener una influencia significante en la reducción o aumento de la actividad de la neurona, participando de este modo en la selección de los elementos neurales objetivo en la terapia de EME, en estimulación tónica. - Efecto de la polaridad del electrodo: usando el modelo 3D generalizado de EME, se muestra el efecto de las polaridades más conocidas y usadas: bipolar, cátodo guardado y doble-cátodo guardado. Los resultados muestran que, a diferencia del cátodo guardado, la polaridad de doble-cátodo guardado maximiza el área y profundidad de activación en los cordones posteriores, aumentando también la probabilidad de activar las fibras de las raíces dorsales. - Aplicaciones clínicas: usando el modelo 3D personalizado al paciente, se ha realizado la selección pre-implante de la polaridad del electrodo. Los resultados muestran que esta aplicación clínica podría determinar las configuraciones de electrodos que mejor solapan la cobertura de parestesia con los dermatomas dolorosos del paciente antes del implante del dispositivo de EME. Por otro lado, también se ha estudiado el efecto de la posición escalonada de los electrodos en el paciente. En este caso, los resultados revelan que el posicionamiento escalonado cancela el desplazamiento izquierdo o derecho de la activación neuronal en los cordones posteriores, sugiriendo así que el posicionamiento escalonado debería evitarse cuando se aplica la estimu[CAT] El dolor crònic es una enfermetat amb una prevalència d'entre el 35% i el 50% de la població mundial. Quan els fàrmacs deixen de fer efecte, la teràpia d'estimulació de mèdul·la espinal (EME) és una alternativa no farmacològica que s'usa per al tractament de diverses condicions de dolor crònic, com el dolor neuropàtic. En els últims 40 anys, el modelatge computacional de l'EME ha sigut la ferramenta clau per a analitzar i entendre l'efecte dels paràmetres d'estimulació elèctrica en la resposta neuronal. No obstant això, la falta de models realistes limita la precisió de les prediccions dels models per a l'optimizació de la teràpia d'EME, en referència a la programació dels paràmetres d'estimulació i el desenvolupament d'aplicacions clíniques. Esta tesi presenta tres millores en el modelatge computacional de la teràpia d'EME, des del nivell cel·lular fins al nivell orgànic: · Nivell cel·lular: es presenta un model de fibra mielínica A-beta sensitiva humana. El model simula la creació i propagació del potencial d'acció de fibres humanes sensitives que es produeix baix l'efecte d'un estímul elèctric. A més a més, amb la finalitat de considerar les pèrdues de corrent produïdes als compartiments internodals, la mielina es modela de forma realista. · Nivell orgànic: es presenten dos models de conductor volumètric de mèdul·la espinal. El primer es tracta d'un model d'EME generalitzat, el qual es basa en imatges de ressonància magnètica de 3T d'alta resolució de mèdul·la espinal humana obtingudes in vivo. Esta proposta resol una de les principals limitacions present en models d'EME anteriors, que és la inclusió de mesures geomètriques obtingudes de cadàvers. El segon model és un model tridimensional personalitzat al pacient, el qual inclou la variació de la geometria de la mèdul·la espinal en tres nivells vertebrals diferentes (T8, T9 i T10) a partir de pacients sotmesos al tractament d'EME. Aquesta innovadora proposta és validada clínicament, demostrant també que el modelatge computacional personalitzat millora la precisió de les prediccions del model en comparació a un model generalitzat. A més, esta tesi presenta tres estudis relacionats amb la teràpia d'EME utilitzant els tres models desenvolupats prèviament: - El paper de la freqüència d'estimulació: es realitza mitjançant l'ús del model de fibra mielínica A-beta sensitiva humana. Els resultats d'este estudi mostren que la freqüència podria tindre una influència significant en la reducció o augment de l'activitat de la neurona, participant així en la selecció dels elements neurals objectiu en la teràpia d'EME, en estimulació tònica. - Efecte de la polaritat de l'elèctrode: usant el model 3D generalitzat d'EME, es mostra l'efecte de les polaritats més conegudes i utilitzades: bipolar, càtode guardat i doble-càtode guardat. Els resultats mostren que, a diferència del càtode guardat, la polaritat de doble-càtode guardat maximitza l'àrea i profunditat d'activació en els cordons posteriors, augmentant també la probabilitat d'activar les fibres de les arrels dorsals. - Aplicacions clíniques: usant el model 3D personalitzat al pacient, s'ha realitzat la selecció pre-implant de la polaritat de l'elèctrode. Els resultats mostren que esta aplicació clínica podria determinar les configuracions d'elèctrodes que millor solapen la cobertura de parestèsia amb els dermatomes dolorosos del pacient abans de l'implant del dispositiu d'EME. D'altra banda, també s'ha estudiat l'efecte de la posició esglaonada dels elèctrodes en el pacient. En este cas, els resultats revelen que el posicionament esglaonat cancel·la el desplaçament esquerre o dret de l'activació neuronal en els cordons posteriors, sugerint així que el posicionament esglaonat deuria evitar-se quan s'aplica l'estimulació tònica.Solanes Galbis, C. (2021). Patient-specific computational modeling for spinal cord stimulation therapy optimization [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/176007TESI

Modulating and Monitoring Autonomic Nerves for Glycemic Control

Diabetic patients suffer from a long-term condition that results in high blood glucose levels (hyperglycemia). Many medications for diabetes lose their glycemic control effectiveness over time and patient compliance to these medications is a major challenge. Glycemic control is a vital continuous process and is innately regulated by the endocrine and autonomic nervous systems. There is an opportunity for developing an implantable and automated treatment for diabetic patients by accurately detecting and altering neural activity in autonomic nerves. Renal nerves provide neural control for glucose reabsorption in the kidneys, and the vagus nerve conveys important glucose regulation signals to and from the liver and pancreas. This dissertation investigated stimulation of renal nerves for glycemic control, assembled an implantation procedure for neural interface arrays designed for autonomic nerves, and recorded physiological action potential signals in the vagus nerve. In a first study, stimulation of renal nerves in anesthetized, normal rats at kilohertz frequency (33 kHz) showed a notable average increase in urine glucose excretion (+24.5%). In contrast, low frequency (5 Hz) stimulation of renal nerves showed a substantial decrease in urine glucose excretion (−40.4%). However, these responses may be associated with urine flow rate. In a second study, kilohertz frequency stimulation (50 kHz) of renal nerves in anesthetized, diabetic rats showed a significant average decrease (-168.4%) in blood glucose concentration rate, and an increase (+18.9%) in the overall average area under the curve for urine glucose concentration, with respect to values before stimulation. In a third study, an innovative procedure was assembled for the chronic implantation of novel intraneural MIcroneedle Nerve Arrays (MINAs) in rat vagus nerves. Two array attachment approaches (fibrin sealant and rose-bengal bonding) were investigated to secure non-wired MINAs in nerves. The fibrin sealant approach was unsuccessful in securing the MINA-nerve interface for 4- and 8-week implant durations. The rose-bengal coated MINAs were in close proximity to axons (≤ 50 μm) in 75% of 1-week and 14% of 6-week implants with no significant harm to the implanted nerves or the overall health of the rats. In a fourth study, physiological neural activity in the vagus nerve of anesthetized rats was recorded using Carbon Fiber Microelectrode Arrays (CFMAs). Neural activity was observed on 51% of inserted functional carbon fibers, and 1-2 neural clusters were sorted on each carbon fiber with activity. The mean peak-to-peak amplitudes of the sorted clusters were 15.1-91.7 µV with SNR of 2.0-7.0. Conducting signals were detected in the afferent direction (0.7-1.0 m/sec conduction velocities) and efferent direction (0.7-8.8 m/sec). These conduction velocities are within the conduction velocity range of unmyelinated and myelinated vagus fibers. Furthermore, changes in vagal nerve activity were monitored in breathing and blood glucose modulated conditions. This dissertation, to our knowledge, was the first to demonstrate glucose regulation benefits by stimulation of renal nerves, chronically implant intraneural arrays in rat vagus nerves, and record physiological action potential in vagus nerves using multi-channel intraneural electrodes. Future work is needed to evaluate the long-term glucose regulation benefits of stimulation of renal nerves, and assess the tissue reactivity and recording integrity of implanted intraneural electrodes in autonomic nerves. This work supports the potential development of an alternative implantable treatment modality for diabetic patients by modulating and monitoring neural activity in autonomic nerves.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155245/1/ajiman_1.pd

Functional impairment following axonal injury

Following trauma or other neurological disorders, a series of events happen that cause axonal dysfunction or ultimately lead to axonal death. Computational modeling of the nervous system facilitates systematic study of the effects of each injury parameter on the output. The overall goal of this research was to develop a new method of simulating axon damage in a biophysical model and quantify the effects of structural damage on signal conduction. To achieve this, three objectives were addressed 1) quantify the effects of normal morphological variation and demyelination on axonal conduction characteristics, 2) develop a new computationally efficient method for modeling damage in axons, and 3) characterize the structure changes observed in human axons and quantify the relationship between these observed changes and axonal function. Biophysical computational models developed in NEURON were employed to characterize morphological changes in damaged axons and study the effects of some of the most common axonal injuries such as myelin damage and spheroid formation on signal propagation in axons with different calibers. To facilitate efficient computational simulation, a new approach for increasing geometrical resolution in NEURON was developed and assessed. To investigate the effects of axonal swelling on action potential conduction in myelinated axons, the morphological properties of axonal spheroids were characterized by analyzing a series of confocal images captured from post-mortem human brain samples of patients with MS and infarction. Our results indicate that subtle abnormalities in nodal, paranodal and juxtaparanodal regions may have sizable effects on action potential amplitude and velocity and more targeted treatments need to be developed that focus on these regions. In addition, the results of our histopathological and computational studies suggest that axons with different diameters may respond differently to injuries and diseases. Therefore, it is important to perform experimental injury models across a wide range of axons to get a more comprehensive understanding of the relationship between axonal morphological features, injury parameters and functional responses. We expect this research to lay the quantitative foundation for finding new potential functional markers of white matter tissue damage and provide further insights into how myelin damage and axonal spheroids may affect function

High Frequency Stimulation of the Pelvic Nerve Inhibits Urinary Voiding in Anesthetized Rats

Urge Urinary Incontinence: “a sudden and uncontrollable desire to void which is impossible to defer” is extremely common and considered the most bothersome of lower urinary tract conditions. Current treatments rely on pharmacological, neuromodulatory, and neurotoxicological approaches to manage the disorder, by reducing the excitability of the bladder muscle. However, some patients remain refractory to treatment. An alternative approach would be to temporarily suppress activity of the micturition control circuitry at the time of need i.e., urgency. In this study we investigated, in a rat model, the utility of high frequency pelvic nerve stimulation to produce a rapid onset, reversible suppression of voiding. In urethane-anesthetized rats periodic voiding was induced by continuous infusion of saline into the bladder whilst recording bladder pressure and electrical activity from the external urethral sphincter (EUS). High frequency (1–3 kHz), sinusoidal pelvic nerve stimulation initiated at the onset of the sharp rise in bladder pressure signaling an imminent void aborted the detrusor contraction. Urine output was suppressed and tone in the EUS increased. Stimulating the right or left nerve was equally effective. The effect was rapid in onset, reversible, and reproducible and evoked only minimal “off target” side effects on blood pressure, heart rate, respiration, uterine pressure, or rectal pressure. Transient contraction of abdominal wall was observed in some animals. Stimulation applied during the filling phase evoked a small, transient rise in bladder pressure and increased tonic activity in the EUS, but no urine output. Suppression of micturition persisted after section of the contralateral pelvic nerve or after ligation of the nerve distal to the electrode cuff on the ipsilateral side. We conclude that high frequency pelvic nerve stimulation initiated at the onset of an imminent void provides a potential means to control urinary continence

Model of Impedance Changes in Unmyelinated Nerve Fibres

OBJECTIVE: Currently there is no imaging method which is able to distinguish the functional activity inside nerves. Such a method would be essential for understanding peripheral nerve physiology and would allow precise neuromodulation of organs these nerves supply. Electrical Impedance Tomography (EIT) is a method which produces images of electrical impedance change (dZ) of an object by injecting alternating current and recording surface voltages. It has been shown to be able to image fast activity in the brain and large peripheral nerves. To image inside small autonomic nerves, mostly containing unmyelinated fibres, it is necessary to maximise SNR and optimize the EIT parameters. An accurate model of the nerve is required to identify these optimal parameters as well as to validate data obtained in the experiments. METHODS: In this study, we developed two 3D models of unmyelinated fibres: Hodgkin-Huxley (HH) squid giant axon (single and multiple) and mammalian C-nociceptor. A coupling feedback system was incorporated into the models to simulate direct (DC) and alternating current (AC) application and simultaneously record external field during action potential propagation. RESULTS: Parameters of the developed models were varied to study their influence on the recorded impedance changes; the optimal parameters were identified. The negative dZ was found to monotonically decrease with frequency for both HH and C fibre models, in accordance with the experimental data. CONCLUSION AND SIGNIFICANCE: The accurate realistic model of unmyelinated nerve allows optimisation of EIT parameters and matches literature and experimental results