Biophysics of frequency-dependent variation in paresthesia and pain relief during spinal cord stimulation

The neurophysiological effects of spinal cord stimulation (SCS) for chronic pain are poorly understood, resulting in inefficient failure-prone programming protocols and inadequate pain relief. Nonetheless, novel stimulation patterns are regularly introduced and adopted clinically. Traditionally, paresthetic sensation is considered necessary for pain relief, although novel paradigms provide analgesia without paresthesia. However, like pain relief, the neurophysiological underpinnings of SCS-induced paresthesia are unknown. Here, we paired biophysical modeling with clinical paresthesia thresholds (of both sexes) to investigate how stimulation frequency affects the neural response to SCS relevant to paresthesia and analgesia. Specifically, we modeled the dorsal column (DC) axonal response, dorsal column nucleus (DCN) synaptic transmission, conduction failure within DC fiber collaterals, and dorsal horn network output. Importantly, we found that high-frequency stimulation reduces DC fiber activation thresholds, which in turn accurately predicts clinical paresthesia perception thresholds. Furthermore, we show that high-frequency SCS produces asynchronous DC fiber spiking and ultimately asynchronous DCN output, offering a plausible biophysical basis for why high-frequency SCS is less comfortable and produces qualitatively different sensation than low-frequency stimulation. Finally, we demonstrate that model dorsal horn network output is sensitive to SCS-inherent variations in spike timing, which could contribute to heterogeneous pain relief across patients. Importantly, we show that model DC fiber collaterals cannot reliably follow high-frequency stimulation, strongly affecting network output and typically producing anti-nociceptive effects at high frequencies. Altogether, these findings clarify how SCS affects the nervous system and provide insight into the biophysics of paresthesia generation and pain relief.Significance Statement The effects of spinal cord stimulation (SCS) on the nervous system are poorly understood, resulting in inadequate clinical success rates. Here, we use a biophysical modeling approach to investigate the neural response to SCS. We demonstrate that low- and high-frequency stimulation produce contrasting responses in the dorsal columns, brainstem, and dorsal horn. Importantly, our modeling approach was able to accurately predict clinical paresthesia thresholds as a function of frequency, as well as provide plausible biophysical explanations for frequency-dependent effects on paresthesia quality and pain relief. Overall, our results greatly enhance our understanding of the neural response to SCS, thereby offering context for interpreting clinical observations and crucial insight for development of future SCS systems.

Computational modeling of dorsal root ganglion stimulation using an Injectrode

Objective.Minimally invasive neuromodulation therapies like the Injectrode, which is composed of a tightly wound polymer-coated Platinum/Iridium microcoil, offer a low-risk approach for administering electrical stimulation to the dorsal root ganglion (DRG). This flexible electrode is aimed to conform to the DRG. The stimulation occurs through a transcutaneous electrical stimulation (TES) patch, which subsequently transmits the stimulation to the Injectrode via a subcutaneous metal collector. However, it is important to note that the effectiveness of stimulation through TES relies on the specific geometrical configurations of the Injectrode-collector-patch system. Hence, there is a need to investigate which design parameters influence the activation of targeted neural structures.Approach.We employed a hybrid computational modeling approach to analyze the impact of Injectrode system design parameters on charge delivery and neural response to stimulation. We constructed multiple finite element method models of DRG stimulation, followed by the implementation of multi-compartment models of DRG neurons. By calculating potential distribution during monopolar stimulation, we simulated neural responses using various parameters based on prior acute experiments. Additionally, we developed a canonical monopolar stimulation and full-scale model of bipolar bilateral L5 DRG stimulation, allowing us to investigate how design parameters like Injectrode size and orientation influenced neural activation thresholds.Main results.Our findings were in accordance with acute experimental measurements and indicate that the minimally invasive Injectrode system predominantly engages large-diameter afferents (Aβ-fibers). These activation thresholds were contingent upon the surface area of the Injectrode. As the charge density decreased due to increasing surface area, there was a corresponding expansion in the stimulation amplitude range before triggering any pain-related mechanoreceptor (Aδ-fibers) activity.Significance.The Injectrode demonstrates potential as a viable technology for minimally invasive stimulation of the DRG. Our findings indicate that utilizing a larger surface area Injectrode enhances the therapeutic margin, effectively distinguishing the desired Aβactivation from the undesired Aδ-fiber activation.

High-frequency amplitude-modulated sinusoidal stimulation induces desynchronized yet controllable neural firing

Regaining sensory feedback is pivotal for people living with limb amputation. Electrical stimulation of sensory fibers in peripheral nerves has been shown to restore focal percepts in the missing limb. However, conventional rectangular current pulses induce sensations often described as unnatural. This is likely due to the synchronous and periodic nature of activity evoked by these pulses. Here we introduce a fast-oscillating amplitude-modulated sinusoidal (FAMS) stimulation waveform that desynchronizes evoked neural activity. We used a computational model to show that sinusoidal waveforms evoke asynchronous and irregular firing and that firing patterns are frequency dependent. We designed the FAMS waveform to leverage both low- and high-frequency effects and found that membrane non-linearities enhance neuron-specific differences when exposed to FAMS. We implemented this waveform in a feline model of peripheral nerve stimulation and demonstrated that FAMS-evoked activity is more asynchronous than activity evoked by rectangular pulses, while being easily controllable with simple stimulation parameters. These results represent an important step towards biomimetic stimulation strategies useful for clinical applications to restore sensory feedback.

Does high-frequency stimulation of sensory axons break the causal link between pain relief and paresthesia?

Neurostimulation produces unnatural cutaneous sensations with potent analgesic effects in pain syndromes. In this issue of Neuron, Sagalajev et al.1 demonstrate that these sensations are an epiphenomenon and explain how high-frequency stimulation can provide analgesia without these unnecessary sensations.

Multiformity of extracellular microelectrode recordings from Aδ neurons in the dorsal root ganglia: a computational modeling study

Microelectrodes serve as a fundamental tool in electrophysiology research throughout the nervous system, providing a means of exploring neural function with a high resolution of neural firing information. We constructed a hybrid computational model using the finite element method and multicompartment cable models to explore factors that contribute to extracellular voltage waveforms that are produced by sensory pseudounipolar neurons, specifically smaller A-type neurons, and that are recorded by microelectrodes in dorsal root ganglia. The finite element method model included a dorsal root ganglion, surrounding tissues, and a planar microelectrode array. We built a multicompartment neuron model with multiple trajectories of the glomerular initial segment found in many A-type sensory neurons. Our model replicated both the somatic intracellular voltage profile of Aδ low-threshold mechanoreceptor neurons and the unique extracellular voltage waveform shapes that are observed in experimental settings. Results from this model indicated that tortuous glomerular initial segment geometries can introduce distinct multiphasic properties into a neuron's recorded waveform. Our model also demonstrated how recording location relative to specific microanatomical components of these neurons, and recording distance from these components, can contribute to additional changes in the multiphasic characteristics and peak-to-peak voltage amplitude of the waveform. This knowledge may provide context for research employing microelectrode recordings of pseudounipolar neurons in sensory ganglia, including functional mapping and closed-loop neuromodulation. Furthermore, our simulations gave insight into the neurophysiology of pseudounipolar neurons by demonstrating how the glomerular initial segment aids in increasing the resistance of the stem axon and mitigating rebounding somatic action potentials.NEW & NOTEWORTHY We built a computational model of sensory neurons in the dorsal root ganglia to investigate factors that influence the extracellular waveforms recorded by microelectrodes. Our model demonstrates how the unique structure of these neurons can lead to diverse and often multiphasic waveform profiles depending on the location of the recording contact relative to microanatomical neural components. Our model also provides insight into the neurophysiological function of axon glomeruli that are often present in these neurons.

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.

A Definition of Neuromodulation and Classification of Implantable Electrical Modulation for Chronic Pain

OBJECTIVES

Neuromodulation therapies use a variety of treatment modalities (eg, electrical stimulation) to treat chronic pain. These therapies have experienced rapid growth that has coincided with escalating confusion regarding the nomenclature surrounding these neuromodulation technologies. Furthermore, studies are often published without a complete description of the effective stimulation dose, making it impossible to replicate the findings. To improve clinical care and facilitate dissemination among the public, payors, research groups, and regulatory bodies, there is a clear need for a standardization of terms.

APPROACH

We formed an international group of authors comprising basic scientists, anesthesiologists, neurosurgeons, and engineers with expertise in neuromodulation. Because the field of neuromodulation is extensive, we chose to focus on creating a taxonomy and standardized definitions for implantable electrical modulation of chronic pain.

RESULTS

We first present a consensus definition of neuromodulation. We then describe a classification scheme based on the 1) intended use (the site of modulation and its indications) and 2) physical properties (waveforms and dose) of a neuromodulation therapy.

CONCLUSIONS

This framework will help guide future high-quality studies of implantable neuromodulatory treatments and improve reporting of their findings. Standardization with this classification scheme and clear definitions will help physicians, researchers, payors, and patients better understand the applications of implantable electrical modulation for pain and guide informed treatment decisions.

Conversion of a medical implant into a versatile computer-brain interface

BACKGROUND

Information transmission into the human nervous system is the basis for a variety of prosthetic applications. Spinal cord stimulation (SCS) systems are widely available, have a well documented safety record, can be implanted minimally invasively, and are known to stimulate afferent pathways. Nonetheless, SCS devices are not yet used for computer-brain-interfacing applications.

OBJECTIVE

Here we aimed to establish computer-to-brain communication via medical SCS implants in a group of 20 individuals who had been operated for the treatment of chronic neuropathic pain.

METHODS

In the initial phase, we conducted interface calibration with the aim of determining personalized stimulation settings that yielded distinct and reproducible sensations. These settings were subsequently utilized to generate inputs for a range of behavioral tasks. We evaluated the required calibration time, task training duration, and the subsequent performance in each task.

RESULTS

We could establish a stable spinal computer-brain interface in 18 of the 20 participants. Each of the 18 then performed one or more of the following tasks: A rhythm-discrimination task (n = 13), a Morse-decoding task (n = 3), and/or two different balance/body-posture tasks (n = 18; n = 5). The median calibration time was 79 min. The median training time for learning to use the interface in a subsequent task was 1:40 min. In each task, every participant demonstrated successful performance, surpassing chance levels.

CONCLUSION

The results constitute the first proof-of-concept of a general purpose computer-brain interface paradigm that could be deployed on present-day medical SCS platforms.

Model-based analysis of subthreshold mechanisms of spinal cord stimulation for pain

Objective.Spinal cord stimulation (SCS) is a common treatment for chronic pain. For decades, SCS maximized overlap between stimulation-induced paresthesias and the patient's painful areas. Recently developed SCS paradigms relieve pain at sub-perceptible amplitudes, yet little is known about the neural response to these new waveforms or their analgesic mechanisms of action. Therefore, in this study, we investigated the neural response to multiple forms of paresthesia-free SCS.Approach.We used computational modeling to investigate the neurophysiological effects and the plausibility of commonly proposed mechanisms of three paresthesia-free SCS paradigms: burst, 1 kHz, and 10 kHz SCS. Specifically, in C- and Aβ-fibers, we investigated the effects of different SCS waveforms on spike timing and activation thresholds, as well as how stochastic ion channel gating affects the response of dorsal column axons. Finally, we characterized membrane polarization of superficial dorsal horn neurons.Main results.We found that none of the SCS waveforms activate nor modulate spike timing in C-fibers. Spike timing was modulated in Aβ-fibers only at suprathreshold amplitudes. Ion channel stochasticity had little effect on Aβ-fiber activation thresholds but produced heterogeneous spike timings at suprathreshold amplitudes. Finally, local cells were preferentially polarized in their axon terminals, and the magnitude of this polarization was dependent on cellular morphology and position relative to the stimulation electrodes.Significance.Overall, the mechanisms of action of subparesthetic SCS remain unclear. Our results suggest that no SCS waveforms directly activate C-fibers, and modulation of spike timing is unlikely at subthreshold amplitudes. We conclude that potential subthreshold neuromodulatory effects of SCS on local cells are likely to be presynaptic in nature, as axons are preferentially depolarized during SCS.

An optimization framework for targeted spinal cord stimulation

Objective. Spinal cord stimulation (SCS) is a common neurostimulation therapy to manage chronic pain. Technological advances have produced new neurostimulation systems with expanded capabilities in an attempt to improve the clinical outcomes associated with SCS. However, these expanded capabilities have dramatically increased the number of possible stimulation parameters and made it intractable to efficiently explore this large parameter space within the context of standard clinical programming procedures. Therefore, in this study, we developed an optimization approach to define the optimal current amplitudes or fractions across individual contacts in an SCS electrode array(s).Approach. We developed an analytic method using the Lagrange multiplier method along with smoothing approximations. To test our optimization framework, we used a hybrid computational modeling approach that consisted of a finite element method model and multi-compartment models of axons and cells within the spinal cord. Moreover, we extended our approach to multi-objective optimization to explore the trade-off between activating regions of interest (ROIs) and regions of avoidance (ROAs).Main results. For simple ROIs, our framework suggested optimized configurations that resembled simple bipolar configurations. However, when we considered multi-objective optimization, our framework suggested nontrivial stimulation configurations that could be selected from Pareto fronts to target multiple ROIs or avoid ROAs.Significance. We developed an optimization framework for targeted SCS. Our method is analytic, which allows for the fast calculation of optimal solutions. For the first time, we provided a multi-objective approach for selective SCS. Through this approach, we were able to show that novel configurations can provide neural recruitment profiles that are not possible with conventional stimulation configurations (e.g. bipolar stimulation). Most importantly, once integrated with computational models that account for sources of interpatient variability (e.g. anatomy, electrode placement), our optimization framework can be utilized to provide stimulation settings tailored to the needs of individual patients.

Computational modeling of dorsal root ganglion stimulation using an Injectrode

Objective

Minimally invasive neuromodulation therapies like the Injectrode, which is composed of a tightly wound polymer-coated platinum/iridium microcoil, offer a low-risk approach for administering electrical stimulation to the dorsal root ganglion (DRG). This flexible electrode is aimed to conform to the DRG. The stimulation occurs through a transcutaneous electrical stimulation (TES) patch, which subsequently transmits the stimulation to the Injectrode via a subcutaneous metal collector. However, effectiveness of stimulation relies on the specific geometrical configurations of the Injectrode-collector-patch system. Hence, there is a need to investigate which design parameters influence the activation of targeted neural structures.

Approach

We employed a hybrid computational modeling approach to analyze the impact of the Injectrode system design parameters on charge delivery and the neural response to stimulation. We constructed multiple finite element method models of DRG stimulation and multi-compartment models of DRG neurons. We simulated the neural responses using parameters based on prior acute preclinical experiments. Additionally, we developed multiple human-scale computational models of DRG stimulation to investigate how design parameters like Injectrode size and orientation influenced neural activation thresholds.

Main results

Our findings were in accordance with acute experimental measurements and indicated that the Injectrode system predominantly engages large-diameter afferents (Aβ-fibers). These activation thresholds were contingent upon the surface area of the Injectrode. As the charge density decreased due to increasing surface area, there was a corresponding expansion in the stimulation amplitude range before triggering any pain-related mechanoreceptor (Aδ-fibers) activity.

Significance

The Injectrode demonstrates potential as a viable technology for minimally invasive stimulation of the DRG. Our findings indicate that utilizing a larger surface area Injectrode enhances the therapeutic margin, effectively distinguishing the desired Aβ activation from the undesired Aδ-fiber activation.

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.

A systematic review of computational models for the design of spinal cord stimulation therapies: from neural circuits to patient-specific simulations

Seventy years ago, Hodgkin and Huxley published the first mathematical model to describe action potential generation, laying the foundation for modern computational neuroscience. Since then, the field has evolved enormously, with studies spanning from basic neuroscience to clinical applications for neuromodulation. Computer models of neuromodulation have evolved in complexity and personalization, advancing clinical practice and novel neurostimulation therapies, such as spinal cord stimulation. Spinal cord stimulation is a therapy widely used to treat chronic pain, with rapidly expanding indications, such as restoring motor function. In general, simulations contributed dramatically to improve lead designs, stimulation configurations, waveform parameters and programming procedures and provided insight into potential mechanisms of action of electrical stimulation. Although the implementation of neural models are relentlessly increasing in number and complexity, it is reasonable to ask whether this observed increase in complexity is necessary for improved accuracy and, ultimately, for clinical efficacy. With this aim, we performed a systematic literature review and a qualitative meta-synthesis of the evolution of computational models, with a focus on complexity, personalization and the use of medical imaging to capture realistic anatomy. Our review showed that increased model complexity and personalization improved both mechanistic and translational studies. More specifically, the use of medical imaging enabled the development of patient-specific models that can help to transform clinical practice in spinal cord stimulation. Finally, we combined our results to provide clear guidelines for standardization and expansion of computational models for spinal cord stimulation.

Neurotechnology for Pain

Neurotechnologies for treating pain rely on electrical stimulation of the central or peripheral nervous system to disrupt or block pain signaling and have been commercialized to treat a variety of pain conditions. While their adoption is accelerating, neurotechnologies are still frequently viewed as a last resort, after many other treatment options have been explored. We review the pain conditions commonly treated with electrical stimulation, as well as the specific neurotechnologies used for treating those conditions. We identify barriers to adoption, including a limited understanding of mechanisms of action, inconsistent efficacy across patients, and challenges related to selectivity of stimulation and off-target side effects. We describe design improvements that have recently been implemented, as well as some cutting-edge technologies that may address the limitations of existing neurotechnologies. Addressing these challenges will accelerate adoption and change neurotechnologies from last-line to first-line treatments for people living with chronic pain.

Modeling extracellular stimulation of retinal ganglion cells: theoretical and practical aspects

Objective.Retinal prostheses use electric current to activate inner retinal neurons, providing artificial vision for blind people. Epiretinal stimulation primarily targets retinal ganglion cells (RGCs), which can be modeled with cable equations. Computational models provide a tool to investigate the mechanisms of retinal activation, and improve stimulation paradigms. However, documentation of RGC model structure and parameters is limited, and model implementation can influence model predictions.Approach.We created a functional guide for building a mammalian RGC multi-compartment cable model and applying extracellular stimuli. Next, we investigated how the neuron's three-dimensional shape will influence model predictions. Finally, we tested several strategies to maximize computational efficiency.Main results.We conducted sensitivity analyses to examine how dendrite representation, axon trajectory, and axon diameter influence membrane dynamics and corresponding activation thresholds. We optimized the spatial and temporal discretization of our multi-compartment cable model. We also implemented several simplified threshold prediction theories based on activating function, but these did not match the prediction accuracy achieved by the cable equations.Significance.Through this work, we provide practical guidance for modeling the extracellular stimulation of RGCs to produce reliable and meaningful predictions. Robust computational models lay the groundwork for improving the performance of retinal prostheses.

Molecular Determinants of Mechanical Itch Sensitization in Chronic Itch

Chronic itch is associated with sensitization of the somatosensory nervous system. Recent studies have identified the neural circuits transmitting acute itch; however, the mechanisms by which itch transforms into a pathological state remain largely unknown. We have previously shown that Aβ low-threshold mechanoreceptors, together with spinal urocortin 3-positive (Ucn3+) excitatory interneurons and neuropeptide Y-positive (NPY+) inhibitory interneurons, form a microcircuit that transmits and gates acute mechanical itch. Here, using whole-cell patch-clamp recordings, we observed increased excitability in spinal Ucn3+ neurons under chronic itch conditions. In contrast to Ucn3+ neurons, the excitability of spinal NPY+ neurons was largely reduced under chronic itch conditions. To explore the molecular mechanisms underlying sensitization of this microcircuit, we examined the mRNA expression levels of voltage-gated ion channels in recorded spinal Ucn3+ and NPY+ neurons by single-cell quantitative real-time PCR (qRT-PCR). We found that the expression levels of Nav1.6 and Cav2.3 channels were increased in spinal Ucn3+ neurons in chronic itch mice, while the expression level of SK3 channels was decreased. By contrast, the expression levels of Nav1.6 and BK channels were decreased in spinal NPY+ neurons in chronic itch mice. To determine the contribution of different ion channels in chronic itch sensitization, we then used a Markov Chain Monte Carlo method to parameterize a large number of biophysically distinct multicompartment models of Ucn3+ and NPY+ neurons. These models included explicit representations of the ion channels that we found to be up- or down-regulated under chronic itch conditions. Our models demonstrated that changes in Nav1.6 conductance are predominantly responsible for the changes in excitability of both Ucn3+ and NPY+ neurons during chronic itch pathogenesis. Furthermore, when simulating microcircuits of our Ucn3+ and NPY+ models, we found that reduced Nav1.6 conductance in NPY+ models played a major role in opening the itch gate under chronic itch conditions. However, changing SK, BK, or R-type calcium channel conductance had negligible effects on the sensitization of this circuit. Therefore, our results suggest that Nav1.6 channels may play an essential role in mechanical itch sensitization. The findings presented here may open a new avenue for developing pharmaceutical strategies to treat chronic itch.

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.

Dorsal Root Ganglion Stimulation for Chronic Pain: Hypothesized Mechanisms of Action

Dorsal root ganglion stimulation (DRGS) is a neuromodulation therapy for chronic pain that is refractory to conventional medical management. Currently, the mechanisms of action of DRGS-induced pain relief are unknown, precluding both our understanding of why DRGS fails to provide pain relief to some patients and the design of neurostimulation technologies that directly target these mechanisms to maximize pain relief in all patients. Due to the heterogeneity of sensory neurons in the dorsal root ganglion (DRG), the analgesic mechanisms could be attributed to the modulation of one or many cell types within the DRG and the numerous brain regions that process sensory information. Here, we summarize the leading hypotheses of the mechanisms of DRGS-induced analgesia, and propose areas of future study that will be vital to improving the clinical implementation of DRGS. PERSPECTIVE: This article synthesizes the evidence supporting the current hypotheses of the mechanisms of action of DRGS for chronic pain and suggests avenues for future interdisciplinary research which will be critical to fully elucidate the analgesic mechanisms of the therapy.

Augmented Transcutaneous Stimulation Using an Injectable Electrode: A Computational Study

Minimally invasive neuromodulation technologies seek to marry the neural selectivity of implantable devices with the low-cost and non-invasive nature of transcutaneous electrical stimulation (TES). The Injectrode^® is a needle-delivered electrode that is injected onto neural structures under image guidance. Power is then transcutaneously delivered to the Injectrode using surface electrodes. The Injectrode serves as a low-impedance conduit to guide current to the deep on-target nerve, reducing activation thresholds by an order of magnitude compared to using only surface stimulation electrodes. To minimize off-target recruitment of cutaneous fibers, the energy transfer efficiency from the surface electrodes to the Injectrode must be optimized. TES energy is transferred to the Injectrode through both capacitive and resistive mechanisms. Electrostatic finite element models generally used in TES research consider only the resistive means of energy transfer by defining tissue conductivities. Here, we present an electroquasistatic model, taking into consideration both the conductivity and permittivity of tissue, to understand transcutaneous power delivery to the Injectrode. The model was validated with measurements taken from (n = 4) swine cadavers. We used the validated model to investigate system and anatomic parameters that influence the coupling efficiency of the Injectrode energy delivery system. Our work suggests the relevance of electroquasistatic models to account for capacitive charge transfer mechanisms when studying TES, particularly when high-frequency voltage components are present, such as those used for voltage-controlled pulses and sinusoidal nerve blocks.

Dorsal root ganglion stimulation produces differential effects on action potential propagation across a population of biophysically distinct C-neurons

Dorsal root ganglion stimulation (DRGS) is a neurostimulation therapy used to manage chronic pain that does not respond to conventional therapies. Unfortunately, not all patients receive sufficient pain relief from DRGS, leaving them with few other treatment options. Presently, our understanding of the mechanisms of action of DRGS is incomplete, preventing us from determining why some patients do not receive analgesia from the therapy. One hypothesis suggests that DRGS augments the filtering of action potentials (APs) at the T-junction of nociceptive C-neurons. To test this hypothesis, we utilized a computational modeling approach in which we developed a population of one thousand biophysically distinct C-neuron models which each produced electrophysiological characteristics (e.g., AP height, AP duration) reported in previous experimental studies. We used this population of model C-neurons to study how morphological and electrophysiological characteristics affected the propagation of APs through the T-junction. We found that trains of APs can propagate through the T-junction in the orthodromic direction at a higher frequency than in the antidromic direction due to the decrease in axonal diameter from the peripheral to spinal axon. Including slow outward conductances in the axonal compartments near the T-junction reduced following frequencies to ranges measured experimentally. We next used the population of C-neuron models to investigate how DRGS affected the orthodromic propagation of APs through the T-junction. Our data suggest that suprathreshold DRGS augmented the filtering of APs at the T-junction of some model C-neurons while increasing the activity of other model C-neurons. However, the stimulus pulse amplitudes required to induce activity in C-neurons (i.e., several mA) fell outside the range of stimulation pulse amplitudes used clinically (i.e., typically ≤1 mA). Furthermore, our data suggest that somatic GABA currents activated directly or indirectly by the DRGS pulse may produce diverse effects on orthodromic AP propagation in C-neurons. These data suggest DRGS may produce differential effects across a population of C-neurons and indicate that understanding how inherent biological variability affects a neuron's response to therapeutic electrical stimulation may be helpful in understanding its mechanisms of action.

Stimulation of the dorsal root ganglion using an Injectrode®

Objective. The goal of this work was to compare afferent fiber recruitment by dorsal root ganglion (DRG) stimulation using an injectable polymer electrode (Injectrode^®) and a more traditional cylindrical metal electrode.Approach. We exposed the L6 and L7 DRG in four cats via a partial laminectomy or burr hole. We stimulated the DRG using an Injectrode or a stainless steel (SS) electrode using biphasic pulses at three different pulse widths (80, 150, 300μs) and pulse amplitudes spanning the range used for clinical DRG stimulation. We recorded antidromic evoked compound action potentials (ECAPs) in the sciatic, tibial, and common peroneal nerves using nerve cuffs. We calculated the conduction velocity of the ECAPs and determined the charge-thresholds and recruitment rates for ECAPs from Aα, Aβ, and Aδfibers. We also performed electrochemical impedance spectroscopy measurements for both electrode types.Main results. The ECAP thresholds for the Injectrode did not differ from the SS electrode across all primary afferents (Aα, Aβ, Aδ) and pulse widths; charge-thresholds increased with wider pulse widths. Thresholds for generating ECAPs from Aβfibers were 100.0 ± 32.3 nC using the SS electrode, and 90.9 ± 42.9 nC using the Injectrode. The ECAP thresholds from the Injectrode were consistent over several hours of stimulation. The rate of recruitment was similar between the Injectrodes and SS electrode and decreased with wider pulse widths.Significance. The Injectrode can effectively excite primary afferents when used for DRG stimulation within the range of parameters used for clinical DRG stimulation. The Injectrode can be implanted through minimally invasive techniques while achieving similar neural activation to conventional electrodes, making it an excellent candidate for future DRG stimulation and neuroprosthetic applications.

Neuromodulation using ultra low frequency current waveform reversibly blocks axonal conduction and chronic pain

Chronic pain remains a leading cause of disability worldwide, and there is still a clinical reliance on opioids despite the medical side effects associated with their use and societal impacts associated with their abuse. An alternative approach is the use of electrical neuromodulation to produce analgesia. Direct current can block action potential propagation but leads to tissue damage if maintained. We have developed a form of ultra low frequency (ULF) biphasic current and studied its effects. In anesthetized rats, this waveform produced a rapidly developing and completely reversible conduction block in >85% of spinal sensory nerve fibers excited by peripheral stimulation. Sustained ULF currents at lower amplitudes led to a slower onset but reversible conduction block. Similar changes were seen in an animal model of neuropathic pain, where ULF waveforms blocked sensory neuron ectopic activity, known to be an important driver of clinical neuropathic pain. Using a computational model, we showed that prolonged ULF currents could induce accumulation of extracellular potassium, accounting for the slowly developing block observed in rats. Last, we tested the analgesic effects of epidural ULF currents in 20 subjects with chronic leg and back pain. Pain ratings improved by 90% after 2 weeks. One week after explanting the electrodes, pain ratings reverted to 72% of pretreatment screening value. We conclude that epidural spinal ULF neuromodulation represents a promising therapy for treating chronic pain.

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.

Quantitative Sensory Testing of Spinal Cord and Dorsal Root Ganglion Stimulation in Chronic Pain Patients

BACKGROUND/OBJECTIVES

The physiological mechanisms underlying the pain-modulatory effects of clinical neurostimulation therapies, such as spinal cord stimulation (SCS) and dorsal root ganglion stimulation (DRGS), are only partially understood. In this pilot prospective study, we used patient-reported outcomes (PROs) and quantitative sensory testing (QST) to investigate the physiological effects and possible mechanisms of action of SCS and DRGS therapies.

MATERIALS AND METHODS

We tested 16 chronic pain patients selected for SCS and DRGS therapy, before and after treatment. PROs included pain intensity, pain-related symptoms (e.g., pain interference, pain coping, sleep interference) and disability, and general health status. QST included assessments of vibration detection theshold (VDT), pressure pain threshold (PPT) and tolerance (PPToL), temporal summation (TS), and conditioned pain modulation (CPM), at the most painful site.

RESULTS

Following treatment, all participants reported significant improvements in PROs (e.g., reduced pain intensity [p < 0.001], pain-related functional impairment [or pain interference] and disability [p = 0.001 for both]; better pain coping [p = 0.03], sleep [p = 0.002]), and overall health [p = 0.005]). QST showed a significant treatment-induced increase in PPT (p = 0.002) and PPToL (p = 0.011), and a significant reduction in TS (p = 0.033) at the most painful site, but showed no effects on VDT and CPM. We detected possible associations between a few QST measures and a few PROs. Notably, higher TS was associated with increased pain interference scores at pre-treatment (r = 0.772, p = 0.009), and a reduction in TS was associated with the reduction in pain interference (r = 0.669, p = 0.034) and pain disability (r = 0.690, p = 0.027) scores with treatment.

CONCLUSIONS

Our preliminary findings suggest significant clinical and therapeutic benefits associated with SCS and DRGS therapies, and the possible ability of these therapies to modulate pain processing within the central nervous system. Replication of our pilot findings in future, larger studies is necessary to characterize the physiological mechanisms of SCS and DRGS therapies.

Biophysics of Temporal Interference Stimulation

Temporal interference (TI) is a non-invasive neurostimulation technique that utilizes high-frequency external electric fields to stimulate deep neuronal structures without affecting superficial, off-target structures. TI represents a potential breakthrough for treating conditions, such as Parkinson's disease and chronic pain. However, early clinical work on TI stimulation was met with mixed outcomes challenging its fundamental mechanisms and applications. Here, we apply established physics to study the mechanisms of TI with the goal of optimizing it for clinical use. We argue that TI stimulation cannot work via passive membrane filtering, as previously hypothesized. Instead, TI stimulation requires an ion-channel mediated signal rectification process. Unfortunately, this mechanism is also responsible for high-frequency conduction block in off-target tissues, thus challenging clinical applications of TI. In consequence, we propose a set of experimental controls that should be performed in future experiments to refine our understanding and practice of TI stimulation. A record of this paper's transparent peer review process is included in the Supplemental Information.

Distinct perceptive pathways selected with tonic and bursting patterns of thalamic stimulation

BACKGROUND

Novel patterns of electrical stimulation of the brain and spinal cord hold tremendous promise to improve neuromodulation therapies for diverse disorders, including tremor and pain. To date, there are limited numbers of experimental studies in human subjects to help explain how stimulation patterns impact the clinical response, especially with deep brain stimulation. We propose using novel stimulation patterns during electrical stimulation of somatosensory thalamus in awake deep brain stimulation surgeries and hypothesize that stimulation patterns will influence the sensory percept without moving the electrode.

METHODS

In this study of 15 fully awake patients, the threshold of perception as well as perceptual characteristics were compared for tonic (trains of regularly-repeated pulses) and bursting stimulation patterns.

RESULTS

In a majority of subjects, tonic and burst percepts were located in separate, non-overlapping body regions (i.e., face vs. hand) without moving the stimulating electrode (p < 0.001; binomial test). The qualitative features of burst percepts also differed from those of tonic-evoked percepts as burst patterns were less likely to evoke percepts described as tingling (p = 0.013; Fisher's exact test).

CONCLUSIONS

Because somatosensory thalamus is somatotopically organized, percept location can be related to anatomic thalamocortical pathways. Thus, stimulation pattern may provide a mechanism to select for different thalamocortical pathways. This added control could lead to improvements in neuromodulation - such as improved efficacy and side effect attenuation - and may also improve localization for sensory prostheses.

Functional Magnetic Resonance Imaging Correlates of Ventral Striatal Deep Brain Stimulation for Poststroke Pain

OBJECTIVE

Deep brain stimulation (DBS) for pain has largely been implemented in an uncontrolled manner to target the somatosensory component of pain, with research leading to mixed results. We have previously shown that patients with poststroke pain syndrome who were treated with DBS targeting the ventral striatum/anterior limb of the internal capsule (VS/ALIC) demonstrated a significant improvement in measures related to the affective sphere of pain. In this study, we sought to determine how DBS targeting the VS/ALIC modifies brain activation in response to pain.

MATERIALS AND METHODS

Five patients with poststroke pain syndrome who were blinded to DBS status (ON/OFF) and six age- and sex-matched healthy controls underwent functional magnetic resonance imaging (fMRI) measuring blood oxygen level-dependent activation in a block design. In this design, each participant received heat stimuli to the affected or unaffected wrist area. Statistical comparisons were performed using fMRI z-maps.

RESULTS

In response to pain, patients in the DBS OFF state showed significant activation (p < 0.001) in the same regions as healthy controls (thalamus, insula, and operculum) and in additional regions (orbitofrontal and superior convexity cortical areas). DBS significantly reduced activation of these additional regions and introduced foci of significant inhibitory activation (p < 0.001) in the hippocampi when painful stimulation was applied to the affected side.

CONCLUSIONS

These findings suggest that DBS of the VS/ALIC modulates affective neural networks.

The Effect of Clinically Controllable Factors on Neural Activation During Dorsal Root Ganglion Stimulation

OBJECTIVE

Dorsal root ganglion stimulation (DRGS) is an effective therapy for chronic pain, though its mechanisms of action are unknown. Currently, we do not understand how clinically controllable parameters (e.g., electrode position, stimulus pulse width) affect the direct neural response to DRGS. Therefore, the goal of this study was to utilize a computational modeling approach to characterize how varying clinically controllable parameters changed neural activation profiles during DRGS.

MATERIALS AND METHODS

We coupled a finite element model of a human L5 DRG to multicompartment models of primary sensory neurons (i.e., Aα-, Aβ-, Aδ-, and C-neurons). We calculated the stimulation amplitudes necessary to elicit one or more action potentials in each neuron, and examined how neural activation profiles were affected by varying clinically controllable parameters.

RESULTS

In general, DRGS predominantly activated large myelinated Aα- and Aβ-neurons. Shifting the electrode more than 2 mm away from the ganglion abolished most DRGS-induced neural activation. Increasing the stimulus pulse width to 500 μs or greater increased the number of activated Aδ-neurons, while shorter pulse widths typically only activated Aα- and Aβ-neurons. Placing a cathode near a nerve root, or an anode near the ganglion body, maximized Aβ-mechanoreceptor activation. Guarded active contact configurations did not activate more Aβ-mechanoreceptors than conventional bipolar configurations.

CONCLUSIONS

Our results suggest that DRGS applied with stimulation parameters within typical clinical ranges predominantly activates Aβ-mechanoreceptors. In general, varying clinically controllable parameters affects the number of Aβ-mechanoreceptors activated, although longer pulse widths can increase Aδ-neuron activation. Our data support several Neuromodulation Appropriateness Consensus Committee guidelines on the clinical implementation of DRGS.

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 computational outlook on neurostimulation

Efficient identification of effective neurostimulation strategies is critical due to the growing number of clinical applications and the increasing complexity of the corresponding technology. In consequence, investigators are encouraged to accelerate translational research of neurostimulation technologies and move quickly to clinical applications. However, this process is hampered by rigorous, but necessary, regulations and lack of a mechanistic understanding of the interactions between electric fields and neural circuits. Here we discuss how computational models have influenced the field of neurostimulation for pain and movement recovery, deep brain stimulation, and even device regulations. Finally, we propose our vision on how computational models will be key to accelerate clinical developments through mechanistic understanding.

Spatial models of cell distribution in human lumbar dorsal root ganglia

Dorsal root ganglia (DRG), which contain the somata of primary sensory neurons, have increasingly been considered as novel targets for clinical neural interfaces, both for neuroprosthetic and pain applications. Effective use of either neural recording or stimulation technologies requires an appropriate spatial position relative to the target neural element, whether axon or cell body. However, the internal three-dimensional spatial organization of human DRG neural fibers and somata has not been quantitatively described. In this study, we analyzed 202 cross-sectional images across the length of 31 human L4 and L5 DRG from 10 donors. We used a custom semi-automated graphical user interface to identify the locations of neural elements in the images and normalize the output to a consistent spatial reference for direct comparison by spinal level. By applying a recursive partitioning algorithm, we found that the highest density of cell bodies at both spinal levels could be found in the inner 85% of DRG length, the outer-most 25-30% radially, and the dorsal-most 69-76%. While axonal density was fairly homogeneous across the DRG length, there was a distinct low density region in the outer 7-11% radially. These findings are consistent with previous qualitative reports of neural distribution in DRG. The quantitative measurements we provide will enable improved targeting of future neural interface technologies and DRG-focused pharmaceutical therapies, and provide a rigorous anatomical description of the bridge between the central and peripheral nervous systems.

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.

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.

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.

Intradural Spinal Cord Stimulation: Performance Modeling of a New Modality

Introduction: Intradural spinal cord stimulation (SCS) may offer significant therapeutic benefits for those with intractable axial and extremity pain, visceral pain, spasticity, autonomic dysfunction and related disorders. A novel intradural electrical stimulation device, limited by the boundaries of the thecal sac, CSF and spinal cord was developed to test this hypothesis. In order to optimize device function, we have explored finite element modeling (FEM).

Methods: COMSOL^®Multiphysics Electrical Currents was used to solve for fields and currents over a geometric model of a spinal cord segment. Cathodic and anodic currents are applied to the center and tips of the T-cross component of the electrode array to shape the stimulation field and constrain charge-balanced cathodic pulses to the target area.

Results: Currents from the electrode sites can move the effective stimulation zone horizontally across the cord by a linear step method, which can be diversified considerably to gain greater depth of penetration relative to standard epidural SCS. It is also possible to prevent spread of the target area with no off-target action potential.

Conclusion: Finite element modeling of a T-shaped intradural spinal cord stimulator predicts significant gains in field depth and current shaping that are beyond the reach of epidural stimulators. Future studies with in vivo models will investigate how this approach should first be tested in humans.

Dorsal root ganglion stimulation for chronic pain modulates Aβ-fiber activity but not C-fiber activity: A computational modeling study

OBJECTIVE

The goal of this project was to use computational models to investigate which types of primary sensory neurons are modulated by dorsal root ganglion stimulation (DRGS) to provide pain relief.

METHODS

We modeled DRGS by coupling an anatomical finite element model of a human L5 dorsal root ganglion to biophysical models of primary sensory neurons. We calculated the stimulation amplitude needed to elicit an action potential in each neuron, and examined how DRGS affected sensory neuron activity.

RESULTS

We showed that within clinical ranges of stimulation parameters, DRGS drives the activity of large myelinated Aβ-fibers but does not directly activate small nonmyelinated C-fibers. We also showed that the position of the active and return electrodes and the polarity of the stimulus pulse influence neural activation.

CONCLUSIONS

Our results indicate that DRGS may provide pain relief by activating pain-gating mechanisms in the dorsal horn via repeated activation of large myelinated afferents.

SIGNIFICANCE

Understanding the mechanisms of action of DRGS-induced pain relief may lead to innovations in stimulation technologies that improve patient outcomes.

Innovations in spinal cord stimulation for pain

Spinal cord stimulation (SCS) is a neuromodulation therapy used to treat medically refractory chronic pain. In SCS, an implanted pulse generator produces electrical signals that are conveyed through electrode arrays located in the region of the spinal cord. The goal of SCS is to modulate neural signaling through spinal and supraspinal mechanisms to reduce pain. Although available for decades, SCS still enjoys only limited clinical success, limited quality-of-life improvement, and limited long-term efficacy. To improve SCS outcomes, advances in lead design, stimulator features, and waveform paradigms have been recently introduced. While it is an exciting time for the neuromodulation field, empirical SCS advances have surpassed scientific understanding of SCS mechanisms of action. We still do not know why SCS works in some patients but not in others. We also lack information-rich biomarkers of pain and pain relief through which to optimize SCS programming. To optimize both system designs and clinical implementations of SCS, it is critical that we address these scientific and mechanistic knowledge gaps.

Objective Measures to Characterize the Physiological Effects of Spinal Cord Stimulation in Neuropathic Pain: A Literature Review

OBJECTIVE

The physiological mechanisms behind the therapeutic effects of spinal cord stimulation (SCS) are only partially understood. Our aim was to perform a literature review of studies that used objective measures to characterize mechanisms of action of SCS in neuropathic pain patients.

MATERIALS AND METHODS

We searched the PubMed data base to identify clinical studies that used objective measures to assess the effects of SCS in neuropathic pain. We extracted the study factors (e.g., type of measure, diagnoses, painful area[s], and SCS parameters) and outcomes from the included studies.

RESULTS

We included 67 studies. Of these, 24 studies used neurophysiological measures, 14 studies used functional neuroimaging techniques, three studies used a combination of neurophysiological and functional neuroimaging techniques, 14 studies used quantitative sensory testing, and 12 studies used proteomic, vascular, and/or pedometric measures. Our findings suggest that SCS largely inhibits somatosensory processing and/or spinal nociceptive activity. Our findings also suggest that SCS modulates activity across specific regions of the central nervous system that play a prominent role in the sensory and emotional functions of pain.

CONCLUSIONS

SCS appears to modulate pain via spinal and/or supraspinal mechanisms of action (e.g., pain gating, descending pain inhibition). However, to better understand the mechanisms of action of SCS, we believe that it is necessary to carry out systematic, controlled, and well-powered studies using objective patient measures. To optimize the clinical effectiveness of SCS for neuropathic pain, we also believe that it is necessary to develop and implement patient-specific approaches.

Biophysical basis of subthalamic local field potentials recorded from deep brain stimulation electrodes

Clinical deep brain stimulation (DBS) technology is evolving to enable chronic recording of local field potentials (LFPs) that represent electrophysiological biomarkers of the underlying disease state. However, little is known about the biophysical basis of LFPs, or how the patient's unique brain anatomy and electrode placement impact the recordings. Therefore, we developed a patient-specific computational framework to analyze LFP recordings within a clinical DBS context. We selected a subject with Parkinson's disease implanted with a Medtronic Activa PC+S DBS system and reconstructed their subthalamic nucleus (STN) and DBS electrode location using medical imaging data. The patient-specific STN volume was populated with 235,280 multicompartment STN neuron models, providing a neuron density consistent with histological measurements. Each neuron received time-varying synaptic inputs and generated transmembrane currents that gave rise to the LFP signal recorded at DBS electrode contacts residing in a finite element volume conductor model. We then used the model to study the role of synchronous beta-band inputs to the STN neurons on the recorded power spectrum. Three bipolar pairs of simultaneous clinical LFP recordings were used in combination with an optimization algorithm to customize the neural activity parameters in the model to the patient. The optimized model predicted a 2.4-mm radius of beta-synchronous neurons located in the dorsolateral STN. These theoretical results enable biophysical dissection of the LFP signal at the cellular level with direct comparison to the clinical recordings, and the model system provides a scientific platform to help guide the design of DBS technology focused on the use of subthalamic beta activity in closed-loop algorithms. NEW & NOTEWORTHY The analysis of deep brain stimulation of local field potential (LFP) data is rapidly expanding from scientific curiosity to the basis for clinical biomarkers capable of improving the therapeutic efficacy of stimulation. With this growing clinical importance comes a growing need to understand the underlying electrophysiological fundamentals of the signals and the factors contributing to their modulation. Our model reconstructs the clinical LFP from first principles and highlights the importance of patient-specific factors in dictating the signals recorded.

Quantitative simulation of extracellular single unit recording from the surface of cortex

OBJECTIVE

Neural recording is important for a wide variety of clinical applications. Until recently, recording from the surface of the brain, even when using micro-electrocorticography (μECoG) arrays, was not thought to enable recording from individual neurons. Recent results suggest that when the surface electrode contact size is sufficiently small, it may be possible to record single neurons from the brain's surface. In this study, we use computational techniques to investigate the ability of surface electrodes to record the activity of single neurons.

APPROACH

The computational model included the rat head, μECoG electrode, two existing multi-compartmental neuron models, and a novel multi-compartmental neuron model derived from patch clamp experiments in layer 1 of the cortex.

MAIN RESULTS

Using these models, we reproduced single neuron recordings from μECoG arrays, and elucidated their possible source. The model resembles the experimental data when spikes originate from layer 1 neurons that are less than 60 μm from the cortical surface. We further used the model to explore the design space for surface electrodes. Although this model does not include biological or thermal noise, the results indicate the electrode contact area should be 100 μm² or smaller to maintain a detectable waveform amplitude. Furthermore, the model shows the width of lateral insulation could be reduced, which may reduce scar formation, while retaining 95% of signal amplitude.

SIGNIFICANCE

Overall, the model suggests single-unit surface recording is limited to neurons in layer 1 and further improvement in electrode design is needed.

Deep brain stimulation of the ventral striatal area for poststroke pain syndrome: a magnetoencephalography study

Poststroke pain syndrome (PSPS) is an often intractable disorder characterized by hemiparesis associated with unrelenting chronic pain. Although traditional analgesics have largely failed, integrative approaches targeting affective-cognitive spheres have started to show promise. Recently, we demonstrated that deep brain stimulation (DBS) of the ventral striatal area significantly improved the affective sphere of pain in patients with PSPS. In the present study, we examined whether electrophysiological correlates of pain anticipation were modulated by DBS that could serve as signatures of treatment effects. We recorded event-related fields (ERFs) of pain anticipation using magnetoencephalography (MEG) in 10 patients with PSPS preoperatively and postoperatively in DBS OFF and ON states. Simple visual cues evoked anticipation as patients awaited a painful (PS) or nonpainful stimulus (NPS) to the nonaffected or affected extremity. Preoperatively, ERFs showed no difference between PS and NPS anticipation to the affected extremity, possibly due to loss of salience in a network saturated by pain experience. DBS significantly modulated the early N1, consistent with improvements in affective networks involving restoration of salience and discrimination capacity. Additionally, DBS suppressed the posterior P2 (aberrant anticipatory anxiety) while enhancing the anterior N1 (cognitive and emotional regulation) in responders. DBS-induced changes in ERFs could potentially serve as signatures for clinical outcomes. NEW & NOTEWORTHY We examined the electrophysiological correlates of pain affect in poststroke pain patients who underwent deep brain stimulation (DBS) targeting the ventral striatal area under a randomized, controlled trial. DBS significantly modulated early event-related components, particularly N1 and P2, measured with magnetoencephalography during a pain anticipatory task, compared with baseline and the DBS-OFF condition, pointing to possible mechanisms of action. DBS-induced changes in event-related fields could potentially serve as biomarkers for clinical outcomes.

Characterization of the stimulus waveforms generated by implantable pulse generators for deep brain stimulation

OBJECTIVE

To determine the circuit elements required to theoretically describe the stimulus waveforms generated by an implantable pulse generator (IPG) during clinical deep brain stimulation (DBS).

METHODS

We experimentally interrogated the Medtronic Activa PC DBS IPG and defined an equivalent circuit model that accurately captured the output of the IPG. We then compared the detailed circuit model of the clinical stimulus waveforms to simplified representations commonly used in computational models of DBS. We quantified the errors associated with these simplifications using theoretical activation thresholds of myelinated axons in response to DBS.

RESULTS

We found that the detailed IPG model generated substantial differences in activation thresholds compared to simplified models. These differences were largest for bipolar stimulation with long pulse widths. Average errors were ∼3 to 24% for voltage-controlled stimulation and ∼2 to 11% for current-controlled stimulation.

CONCLUSIONS

Our results demonstrate the importance of including basic circuit elements (e.g. blocking capacitors, lead wire resistance, electrode capacitance) in model analysis of DBS.

SIGNIFICANCE

Computational models of DBS are now commonly used in academic research, industrial technology development, and in the selection of clinical stimulation parameters. Incorporating a realistic representation of the IPG output is necessary to improve the accuracy and utility of these clinical and scientific tools.

Evolving Applications, Technological Challenges and Future Opportunities in Neuromodulation: Proceedings of the Fifth Annual Deep Brain Stimulation Think Tank

The annual Deep Brain Stimulation (DBS) Think Tank provides a focal opportunity for a multidisciplinary ensemble of experts in the field of neuromodulation to discuss advancements and forthcoming opportunities and challenges in the field. The proceedings of the fifth Think Tank summarize progress in neuromodulation neurotechnology and techniques for the treatment of a range of neuropsychiatric conditions including Parkinson's disease, dystonia, essential tremor, Tourette syndrome, obsessive compulsive disorder, epilepsy and cognitive, and motor disorders. Each section of this overview of the meeting provides insight to the critical elements of discussion, current challenges, and identified future directions of scientific and technological development and application. The report addresses key issues in developing, and emphasizes major innovations that have occurred during the past year. Specifically, this year's meeting focused on technical developments in DBS, design considerations for DBS electrodes, improved sensors, neuronal signal processing, advancements in development and uses of responsive DBS (closed-loop systems), updates on National Institutes of Health and DARPA DBS programs of the BRAIN initiative, and neuroethical and policy issues arising in and from DBS research and applications in practice.

Creating and parameterizing patient-specific deep brain stimulation pathway-activation models using the hyperdirect pathway as an example

BACKGROUND

Deep brain stimulation (DBS) is an established clinical therapy and computational models have played an important role in advancing the technology. Patient-specific DBS models are now common tools in both academic and industrial research, as well as clinical software systems. However, the exact methodology for creating patient-specific DBS models can vary substantially and important technical details are often missing from published reports.

OBJECTIVE

Provide a detailed description of the assembly workflow and parameterization of a patient-specific DBS pathway-activation model (PAM) and predict the response of the hyperdirect pathway to clinical stimulation.

METHODS

Integration of multiple software tools (e.g. COMSOL, MATLAB, FSL, NEURON, Python) enables the creation and visualization of a DBS PAM. An example DBS PAM was developed using 7T magnetic resonance imaging data from a single unilaterally implanted patient with Parkinson's disease (PD). This detailed description implements our best computational practices and most elaborate parameterization steps, as defined from over a decade of technical evolution.

RESULTS

Pathway recruitment curves and strength-duration relationships highlight the non-linear response of axons to changes in the DBS parameter settings.

CONCLUSION

Parameterization of patient-specific DBS models can be highly detailed and constrained, thereby providing confidence in the simulation predictions, but at the expense of time demanding technical implementation steps. DBS PAMs represent new tools for investigating possible correlations between brain pathway activation patterns and clinical symptom modulation.

Pain anticipatory phenomena in patients with central poststroke pain: a magnetoencephalography study

Central poststroke pain (CPSP) is characterized by hemianesthesia associated with unrelenting chronic pain. The final pain experience stems from interactions between sensory, affective, and cognitive components of chronic pain. Hence, managing CPSP will require integrated approaches aimed not only at the sensory but also the affective-cognitive spheres. A better understanding of the brain's processing of pain anticipation is critical for the development of novel therapeutic approaches that target affective-cognitive networks and alleviate pain-related disability. We used magnetoencephalography (MEG) to characterize the neural substrates of pain anticipation in patients suffering from intractable CPSP. Simple visual cues evoked anticipation while patients awaited impending painful (PS), nonpainful (NPS), or no stimulus (NOS) to their nonaffected and affected extremities. MEG responses were studied at gradiometer level using event-related fields analysis and time-frequency oscillatory analysis upon source localization. On the nonaffected side, significantly greater responses were recorded during PS. PS (vs. NPS and NOS) exhibited significant parietal and frontal cortical activations in the beta and gamma bands, respectively, whereas NPS (vs. NOS) displayed greater activation in the orbitofrontal cortex. On the affected extremity, PS (vs. NPS) did not show significantly greater responses. These data suggest that anticipatory phenomena can modulate neural activity when painful stimuli are applied to the nonaffected extremity but not the affected extremity in CPSP patients. This dichotomy may stem from the chronic effects of pain on neural networks leading to habituation or saturation. Future clinically effective therapies will likely be associated with partial normalization of the neurophysiological correlates of pain anticipation.

Data-driven model comparing the effects of glial scarring and interface interactions on chronic neural recordings in non-human primates

OBJECTIVE

We characterized electrode stability over twelve weeks of impedance and neural recording data from four chronically-implanted Utah arrays in two rhesus macaques, and investigated the effects of glial scarring and interface interactions at the electrode recording site on signal quality using a computational model.

APPROACH

A finite-element model of a Utah array microelectrode in neural tissue was coupled with a multi-compartmental model of a neuron to quantify the effects of encapsulation thickness, encapsulation resistivity, and interface resistivity on electrode impedance and waveform amplitude. The coupled model was then reconciled with the in vivo data. Histology was obtained seventeen weeks post-implantation to measure gliosis.

MAIN RESULTS

From week 1-3, mean impedance and amplitude increased at rates of 115.8 kΩ/week and 23.1 μV/week, respectively. This initial ramp up in impedance and amplitude was observed across all arrays, and is consistent with biofouling (increasing interface resistivity) and edema clearing (increasing tissue resistivity), respectively, in the model. Beyond week 3, the trends leveled out. Histology showed that thin scars formed around the electrodes. In the model, scarring could not match the in vivo data. However, a thin interface layer at the electrode tip could. Despite having a large effect on impedance, interface resistivity did not have a noticeable effect on amplitude.

SIGNIFICANCE

This study suggests that scarring does not cause an electrical problem with regard to signal quality since it does not appear to be the main contributor to increasing impedance or significantly affect amplitude unless it displaces neurons. This, in turn, suggests that neural signals can be obtained reliably despite scarring as long as the recording site has sufficiently low impedance after accumulating a thin layer of biofouling. Therefore, advancements in microelectrode technology may be expedited by focusing on improvements to the recording site-tissue interface rather than elimination of the glial scar.