Modeling the excitability of mammalian nerve fibers: influence of afterpotentials on the recovery cycle

Numerical simulation of neural excitation during brain tumor ablation by microsecond pulses

Replacing monopolar pulse with bipolar pulses of the same energized time can minimize unnecessary neurological side effects during irreversible electroporation (IRE). An improved neural excitation model that considers dynamic conductivity and thermal effects during brain tumor IRE ablation was proposed for the first time in this study. Nerve fiber excitation during IRE ablation by applying a monopolar pulse (100 μs) and a burst of bipolar pulses (energized time of 100 μs with both the sub-pulse length and interphase delay of 1 μs) was investigated. Our results suggest that both thermal effects and dynamic conductivity change the onset time of action potential (AP), and dynamic conductivity also changes the hyperpolarization amplitude. Considering both thermal effects and dynamic conductivity, the hyperpolarization amplitude in nerve fibers located 2 cm from the tumor center was reduced by approximately 23.8 mV and the onset time of AP was delayed by approximately 17.5 μs when a 500 V monopolar pulse was applied. Moreover, bipolar pulses decreased the excitable volume of brain tissue by approximately 68.8 % compared to monopolar pulse. Finally, bipolar pulses cause local excitation with lesser damage to surrounding healthy tissue in complete tumor ablation, demonstrating the potential benefits of bipolar pulses in brain tissue ablation.

Reverse-engineered models reveal differential membrane properties of autonomic and cutaneous unmyelinated fibers

Unmyelinated C-fibers constitute the vast majority of axons in peripheral nerves and play key roles in homeostasis and signaling pain. However, little is known about their ion channel expression, which controls their firing properties. Also, because of their small diameters (~ 1 μm), it has not been possible to characterize their membrane properties using voltage clamp. We developed a novel library of isoform-specific ion channel models to serve as the basis functions of our C-fiber models. We then developed a particle swarm optimization (PSO) framework that used the isoform-specific ion channel models to reverse engineer C-fiber membrane properties from measured autonomic and cutaneous C-fiber conduction responses. Our C-fiber models reproduced experimental conduction velocity, chronaxie, action potential duration, intracellular threshold, and paired pulse recovery cycle. The models also matched experimental activity-dependent slowing, a property not included in model optimization. We found that simple conduction responses, characterizing the action potential, were controlled by similar membrane properties in both the autonomic and cutaneous C-fiber models, but complicated conduction response, characterizing the afterpotenials, were controlled by differential membrane properties. The unmyelinated C-fiber models constitute important tools to study autonomic signaling, assess the mechanisms of pain, and design bioelectronic devices. Additionally, the novel reverse engineering approach can be applied to generate models of other neurons where voltage clamp data are not available.

Experimental validation of a PNS-optimized whole-body gradient coil

PURPOSE

Peripheral nerve stimulation (PNS) limits the usability of state-of-the-art whole-body and head-only MRI gradient coils. We used detailed electromagnetic and neurodynamic modeling to set an explicit PNS constraint during the design of a whole-body gradient coil and constructed it to compare the predicted and experimentally measured PNS thresholds to those of a matched design without PNS constraints.

METHODS

We designed, constructed, and tested two actively shielded whole-body Y-axis gradient coil winding patterns: YG1 is a conventional symmetric design without PNS-optimization, whereas YG2's design used an additional constraint on the allowable PNS threshold in the head-imaging landmark, yielding an asymmetric winding pattern. We measured PNS thresholds in 18 healthy subjects at five landmark positions (head, cardiac, abdominal, pelvic, and knee).

RESULTS

The PNS-optimized design YG2 achieved 46% higher average experimental thresholds for a head-imaging landmark than YG1 while incurring a 15% inductance penalty. For cardiac, pelvic, and knee imaging landmarks, the PNS thresholds increased between +22% and +35%. For abdominal imaging, PNS thresholds did not change significantly between YG1 and YG2 (-3.6%). The agreement between predicted and experimental PNS thresholds was within 11.4% normalized root mean square error for both coils and all landmarks. The PNS model also produced plausible predictions of the stimulation sites when compared to the sites of perception reported by the subjects.

CONCLUSION

The PNS-optimization improved the PNS thresholds for the target scan landmark as well as most other studied landmarks, potentially yielding a significant improvement in image encoding performance that can be safely used in humans.

Highly efficient modeling and optimization of neural fiber responses to electrical stimulation

Peripheral neuromodulation has emerged as a powerful modality for controlling physiological functions and treating a variety of medical conditions including chronic pain and organ dysfunction. The underlying complexity of the nonlinear responses to electrical stimulation make it challenging to design precise and effective neuromodulation protocols. Computational models have thus become indispensable in advancing our understanding and control of neural responses to electrical stimulation. However, existing approaches suffer from computational bottlenecks, rendering them unsuitable for real-time applications, large-scale parameter sweeps, or sophisticated optimization. In this work, we introduce an approach for massively parallel estimation and optimization of neural fiber responses to electrical stimulation using machine learning techniques. By leveraging advances in high-performance computing and parallel programming, we present a surrogate fiber model that generates spatiotemporal responses to a wide variety of cuff-based electrical peripheral nerve stimulation protocols. We used our surrogate fiber model to design stimulation parameters for selective stimulation of pig and human vagus nerves. Our approach yields a several-orders-of-magnitude improvement in computational efficiency while retaining generality and high predictive accuracy, demonstrating its robustness and potential to enhance the design and optimization of peripheral neuromodulation therapies.

Towards enhanced functionality of vagus neuroprostheses through in silico optimized stimulation

Bioelectronic therapies modulating the vagus nerve are promising for cardiovascular, inflammatory, and mental disorders. Clinical applications are however limited by side-effects such as breathing obstruction and headache caused by non-specific stimulation. To design selective and functional stimulation, we engineered VaStim, a realistic and efficient in-silico model. We developed a protocol to personalize VaStim in-vivo using simple muscle responses, successfully reproducing experimental observations, by combining models with trials conducted on five pigs. Through optimized algorithms, VaStim simulated the complete fiber population in minutes, including often omitted unmyelinated fibers which constitute 80% of the nerve. The model suggested that all Aα-fibers across the nerve affect laryngeal muscle, while heart rate changes were caused by B-efferents in specific fascicles. It predicted that tripolar paradigms could reduce laryngeal activity by 70% compared to typically used protocols. VaStim may serve as a model for developing neuromodulation therapies by maximizing efficacy and specificity, reducing animal experimentation.

Myelin dystrophy impairs signal transmission and working memory in a multiscale model of the aging prefrontal cortex

Normal aging leads to myelin alterations in the rhesus monkey dorsolateral prefrontal cortex (dlPFC), which are positively correlated with degree of cognitive impairment. It is hypothesized that remyelination with shorter and thinner myelin sheaths partially compensates for myelin degradation, but computational modeling has not yet explored these two phenomena together systematically. Here, we used a two-pronged modeling approach to determine how age-related myelin changes affect a core cognitive function: spatial working memory. First, we built a multicompartment pyramidal neuron model fit to monkey dlPFC empirical data, with an axon including myelinated segments having paranodes, juxtaparanodes, internodes, and tight junctions. This model was used to quantify conduction velocity (CV) changes and action potential (AP) failures after demyelination and subsequent remyelination. Next, we incorporated the single neuron results into a spiking neural network model of working memory. While complete remyelination nearly recovered axonal transmission and network function to unperturbed levels, our models predict that biologically plausible levels of myelin dystrophy, if uncompensated by other factors, can account for substantial working memory impairment with aging. The present computational study unites empirical data from ultrastructure up to behavior during normal aging, and has broader implications for many demyelinating conditions, such as multiple sclerosis or schizophrenia.

NRV: An open framework for in silico evaluation of peripheral nerve electrical stimulation strategies

Electrical stimulation of peripheral nerves has been used in various pathological contexts for rehabilitation purposes or to alleviate the symptoms of neuropathologies, thus improving the overall quality of life of patients. However, the development of novel therapeutic strategies is still a challenging issue requiring extensive in vivo experimental campaigns and technical development. To facilitate the design of new stimulation strategies, we provide a fully open source and self-contained software framework for the in silico evaluation of peripheral nerve electrical stimulation. Our modeling approach, developed in the popular and well-established Python language, uses an object-oriented paradigm to map the physiological and electrical context. The framework is designed to facilitate multi-scale analysis, from single fiber stimulation to whole multifascicular nerves. It also allows the simulation of complex strategies such as multiple electrode combinations and waveforms ranging from conventional biphasic pulses to more complex modulated kHz stimuli. In addition, we provide automated support for stimulation strategy optimization and handle the computational backend transparently to the user. Our framework has been extensively tested and validated with several existing results in the literature.

Simulation of FES on the forearm with muscle-specific activation resolution

Introduction

Functional electrical stimulation (FES) is an established method of supporting neurological rehabilitation. However, particularly on the forearm, it still cannot elicit selective muscle activations that form the basis of complex hand movements. Current research approaches in the context of selective muscle activation often attempt to enable targeted stimulation by increasing the number of electrodes and combining them in electrode arrays. In order to determine the best stimulation positions and settings, manual or semi-automated algorithms are used. This approach is limited due to experimental limitations. The supportive use of simulation studies is well-established, but existing simulation models are not suitable for analyses of selective muscle activation due to missing or arbitrarily arranged innervation zones.

Methods

This study introduces a new modeling method to design a person-specific digital twin that enables the prediction of muscle activations during FES on the forearm. The designed individual model consists of three parts: an anatomically based 3D volume conductor, a muscle-specific nerve fiber arrangement in various regions of interest (ROIs), and a standard nerve model. All processes were embedded in scripts or macros to enable automated changes to the model and the simulation setup.

Results

The experimental evaluation of simulated strength-duration diagrams showed good coincidence. The relative differences of the simulated amplitudes to the mean amplitude of the four experiments were in the same range as the inter-experimental differences, with mean values between 0.005 and 0.045. Based on these results, muscle-specific activation thresholds were determined and integrated into the simulation process. With this modification, simulated force-intensity curves showed good agreement with additionally measured curves.

Discussion

The results show that the model is suitable for simulating realistic muscle-specific activations. Since complex hand movements are physiologically composed of individual, selective muscle activations, it can be assumed that the model is also suitable for simulating these movements. Therefore, this study presents a new and very promising approach for developing new applications and products in the context of the rehabilitation of sensorimotor disorders.

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 the 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 the network output and typically producing antinociceptive 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.

Impact of Extracellular Current Flow on Action Potential Propagation in Myelinated Axons

Myelinated axons conduct action potentials, or spikes, in a saltatory manner. Inward current caused by a spike occurring at one node of Ranvier spreads axially to the next node, which regenerates the spike when depolarized enough for voltage-gated sodium channels to activate, and so on. The rate at which this process progresses dictates the velocity at which the spike is conducted and depends on several factors including axial resistivity and axon diameter that directly affect axial current. Here we show through computational simulations in modified double-cable axon models that conduction velocity also depends on extracellular factors whose effects can be explained by their indirect influence on axial current. Specifically, we show that a conventional double-cable model, with its outside layer connected to ground, transmits less axial current than a model whose outside layer is less absorptive. A more resistive barrier exists when an axon is packed tightly between other myelinated fibers, for example. We show that realistically resistive boundary conditions can significantly increase the velocity and energy efficiency of spike propagation, while also protecting against propagation failure. Certain factors like myelin thickness may be less important than typically thought if extracellular conditions are more resistive than normally considered. We also show how realistically resistive boundary conditions affect ephaptic interactions. Overall, these results highlight the unappreciated importance of extracellular conditions for axon function.

Influence of peripheral axon geometry and local anatomy on magnetostimulation chronaxie

Objective.Rapid switching of magnetic resonance imaging (MRI) gradient fields induces electric fields that can cause peripheral nerve stimulation (PNS) and so accurate characterization of PNS is required to maintain patient safety and comfort while maximizing MRI performance. The minimum magnetic gradient amplitude that causes stimulation, the PNS threshold, depends on intrinsic axon properties and the spatial and temporal properties of the induced electric field. The PNS strength-duration curve is widely used to characterize simulation thresholds for periodic waveforms and is parameterized by the chronaxie and rheobase. Safety limits to avoid unwanted PNS in MRI rely on a single chronaxie value to characterize the response of all nerves. However, experimental magnetostimulation peripheral nerve chronaxie values vary by an order of magnitude. Given the diverse range of chronaxies observed and the importance of this number in MRI safety models, we seek a deeper understanding of the mechanisms contributing to chronaxie variability.Approach.We use a coupled electromagnetic-neurodynamic PNS model to assess geometric sources of chronaxie variability. We study the impact of the position of the stimulating magnetic field coil relative to the body, along with the effect of local anatomical features and nerve trajectories on the driving function and the resulting chronaxie.Main results.We find realistic variation of local axon and tissue geometry can modulate a given axon's chronaxie by up to two-fold. Our results identify the temporal rate of charge redistribution as the underlying determinant of the chronaxie.Significance.This charge distribution is a function of both intrinsic axon properties and the spatial stimulus along the nerve; thus, examination of the local tissue topology, which shapes the electric fields, as well as the nerve trajectory, are critical for better understanding chronaxie variations and defining more biologically informed MRI safety guidelines.

Selective intrafascicular stimulation of myelinated and unmyelinated nerve fibers through a longitudinal electrode: A computational study

Carbon nanotube (CNT) fiber electrodes have demonstrated exceptional spatial selectivity and sustained reliability in the context of intrafascicular electrical stimulation, as evidenced through rigorous animal experimentation. A significant presence of unmyelinated C fibers, known to induce uncomfortable somatosensory experiences, exists within peripheral nerves. This presence poses a considerable challenge to the excitation of myelinated Aβ fibers, which are crucial for tactile sensation. To achieve nuanced tactile sensory feedback utilizing CNT fiber electrodes, the selective stimulation of Aβ sensory afferents emerges as a critical factor. In confronting this challenge, the present investigation sought to refine and apply a rat sciatic-nerve model leveraging the capabilities of the COMSOL-NEURON framework. This approach enables a systematic evaluation of the influence exerted by stimulation parameters and electrode geometry on the activation dynamics of both myelinated Aβ and unmyelinated C fibers. The findings advocate for the utilization of current pulses featuring a pulse width of 600 μs, alongside the deployment of CNT fibers characterized by a diminutive diameter of 10 μm, with an exclusively exposed cross-sectional area, to facilitate reduced activation current thresholds. Comparative analysis under monopolar and bipolar electrical stimulation conditions revealed proximate activation thresholds, albeit with bipolar stimulation exhibiting superior fiber selectivity relative to its monopolar counterpart. Concerning pulse waveform characteristics, the adoption of an anodic-first biphasic stimulation modality is favored, taking into account the dual criteria of activation threshold and fiber selectivity optimization. Consequently, this investigation furnishes an efficacious stimulation paradigm for the selective activation of touch-related nerve fibers, alongside provisioning a comprehensive theoretical foundation for the realization of natural tactile feedback within the domain of prosthetic hand applications.

Deep brain stimulation of symptom-specific networks in Parkinson's disease

Deep Brain Stimulation can improve tremor, bradykinesia, rigidity, and axial symptoms in patients with Parkinson's disease. Potentially, improving each symptom may require stimulation of different white matter tracts. Here, we study a large cohort of patients (N = 237 from five centers) to identify tracts associated with improvements in each of the four symptom domains. Tremor improvements were associated with stimulation of tracts connected to primary motor cortex and cerebellum. In contrast, axial symptoms are associated with stimulation of tracts connected to the supplementary motor cortex and brainstem. Bradykinesia and rigidity improvements are associated with the stimulation of tracts connected to the supplementary motor and premotor cortices, respectively. We introduce an algorithm that uses these symptom-response tracts to suggest optimal stimulation parameters for DBS based on individual patient's symptom profiles. Application of the algorithm illustrates that our symptom-tract library may bear potential in personalizing stimulation treatment based on the symptoms that are most burdensome in an individual patient.

Modeling the volume of tissue activated in deep brain stimulation and its clinical influence: a review

Deep brain stimulation (DBS) is a neuromodulatory therapy that has been FDA approved for the treatment of various disorders, including but not limited to, movement disorders (e.g., Parkinson's disease and essential tremor), epilepsy, and obsessive-compulsive disorder. Computational methods for estimating the volume of tissue activated (VTA), coupled with brain imaging techniques, form the basis of models that are being generated from retrospective clinical studies for predicting DBS patient outcomes. For instance, VTA models are used to generate target-and network-based probabilistic stimulation maps that play a crucial role in predicting DBS treatment outcomes. This review defines the methods for calculation of tissue activation (or modulation) including ones that use heuristic and clinically derived estimates and more computationally involved ones that rely on finite-element methods and biophysical axon models. We define model parameters and provide a comparison of commercial, open-source, and academic simulation platforms available for integrated neuroimaging and neural activation prediction. In addition, we review clinical studies that use these modeling methods as a function of disease. By describing the tissue-activation modeling methods and highlighting their application in clinical studies, we provide the neural engineering and clinical neuromodulation communities with perspectives that may influence the adoption of modeling methods for future DBS studies.

Investigation of the mechanism of action of deep brain stimulation for the treatment of Parkinson's disease

Parkinson's disease (PD) is a severe, progressive, neurological disorder. PD is not a single disease, but rather resembles a syndrome. PD includes two types of pathogenesis (i.e., classical PD and new PD). Clinically, PD patients present with a range of motor symptoms including decreased spontaneous movement, bradykinesia, muscle rigidity, changes in speech, and resting tremors. PD patients also often exhibit non-motor symptoms such as fatigue, sleep disorders, and emotional and mental health disturbances. Deep brain stimulation (DBS) performed in clinical neurosurgery has demonstrated considerable efficacy in the treatment of dyskinesia that occurs in PD patients. However, the specific neural mechanism of DBS remains unknown and is limited by several shortcomings that have hampered the popularization and development of the procedure. To address this issue, this study established a theoretical model of DBS for PD to investigate and understand the mechanism of DBS using several artificial intelligence (AI) algorithms. This model was used to investigate both classical PD and unheard-of new PD. The research described in this paper was as follows: a single neuron was used to establish a theoretical model of the basal ganglia circuit and to simulate the characteristic indicators of the potential release of the basal ganglia circuit in both normal and PD states. The state of the deep brain electrical stimulation in PD was then analyzed to identify the critical electrical stimulation index and the optimal target. We showed that the use of AI algorithms such as particle swarm optimization and other AI algorithms was beneficial for more detailed exploration and understanding of the mechanisms of DBS compared to those used in previous studies. This discovery may lead to advances in DBS technology and provide better treatment options for neurological diseases such as PD.

AxoDetect: an automated nerve image segmentation and quantification workflow for computational nerve modeling

Objective.Bioelectronic treatments targeting near-organ innervation have unprecedented clinical applications. Particularly in the spleen, the inhibition of the cholinergic inflammatory response by near-organ nerve stimulation has potential to replace pharmacological treatments in chronic and autoimmune diseases. A caveat is that the optimization of therapeutic stimulation parameters relies onin vivoexperimentation, which becomes challenging due to the small nerve diameters (2 μm), complex anatomy, and mixed axon type composition of the autonomic nerves. Effective development ofin silicomodels requires tools which allow for fast and efficient quantification of axonal composition of specific nerves. Current approaches to generate such information rely on manual image segmentation and quantification.Approach.We developed a combined image-segmentation and model-generation software called AxoDetect: a target- and format-agnostic computer vision algorithm which can segment myelin, endo/epineurium, and both myelinated and unmyelinated fibers from a nerve image without training.Main results.AxoDetect is over 10 times faster on average when compared with current automatic methods while maintaining flexibility through the use of tunable pixel threshold filters to detect different types of tissue. When compared to a distribution-based and a manually segmented model of the splenic nerve terminal branch 1, the model generated with AxoDetect had comparable threshold prediction and was able to accurately detect an increase in activation threshold caused by the addition of surrounding fat tissue to the modeled nerve.Significance.AxoDetect contributes to the acceleration of neuromodulation treatment development through faster model design and iteration without requiring training. Furthermore, the computer vision approach and tunable nature of the filters in our method allow for its use in a variety of histological applications. Our approach will impact not only the study of nerves but also the design of implantable neural interfaces to enhance bioelectronic therapeutic options.

Coupled Activation of the Hyperdirect and Cerebellothalamic Pathways with Zona Incerta Deep Brain Stimulation

BACKGROUND

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) or ventral intermediate nucleus (VIM) are established targets for the treatment of Parkinson's disease (PD) or essential tremor (ET), respectively. However, DBS of the zona incerta (ZI) can be effective for both disorders. VIM DBS is assumed to achieve its therapeutic effect via activation of the cerebellothalamic (CBT) pathway, whereas the activation of the hyperdirect (HD) pathway likely plays a role in the mechanisms of STN DBS. Interestingly, HD pathway axons also emit collaterals to the ZI and red nucleus (RN) and the CBT pathway courses nearby to the ZI.

OBJECTIVE

The aim was to examine the ability of ZI DBS to mutually activate the HD and CBT pathways in a detailed computational model of human DBS.

METHODS

We extended a previous model of the human HD pathway to incorporate axon collaterals to the ZI and RN. The anatomical framework of the model system also included representations of the CBT pathway and internal capsule (IC) fibers of passage. We then performed detailed biophysical simulations to quantify DBS activation of the HD, CBT, and IC pathways with electrodes located in either the STN or ZI.

RESULTS

STN DBS and ZI DBS both robustly activated the HD pathway. However, STN DBS was limited by IC activation at higher stimulus amplitudes. Alternatively, ZI DBS avoided IC activation while simultaneously activating the HD and CBT pathways.

CONCLUSIONS

From both neuroanatomical and biophysical perspectives, ZI DBS represents an advantageous target for coupled activation of the HD and CBT pathways. © 2024 International Parkinson and Movement Disorder Society.

Computational models of compound nerve action potentials: Efficient filter-based methods to quantify effects of tissue conductivities, conduction distance, and nerve fiber parameters

BACKGROUND

Peripheral nerve recordings can enhance the efficacy of neurostimulation therapies by providing a feedback signal to adjust stimulation settings for greater efficacy or reduced side effects. Computational models can accelerate the development of interfaces with high signal-to-noise ratio and selective recording. However, validation and tuning of model outputs against in vivo recordings remains computationally prohibitive due to the large number of fibers in a nerve.

METHODS

We designed and implemented highly efficient modeling methods for simulating electrically evoked compound nerve action potential (CNAP) signals. The method simulated a subset of fiber diameters present in the nerve using NEURON, interpolated action potential templates across fiber diameters, and filtered the templates with a weighting function derived from fiber-specific conduction velocity and electromagnetic reciprocity outputs of a volume conductor model. We applied the methods to simulate CNAPs from rat cervical vagus nerve.

RESULTS

Brute force simulation of a rat vagal CNAP with all 1,759 myelinated and 13,283 unmyelinated fibers in NEURON required 286 and 15,860 CPU hours, respectively, while filtering interpolated templates required 30 and 38 seconds on a desktop computer while maintaining accuracy. Modeled CNAP amplitude could vary by over two orders of magnitude depending on tissue conductivities and cuff opening within experimentally relevant ranges. Conduction distance and fiber diameter distribution also strongly influenced the modeled CNAP amplitude, shape, and latency. Modeled and in vivo signals had comparable shape, amplitude, and latency for myelinated fibers but not for unmyelinated fibers.

CONCLUSIONS

Highly efficient methods of modeling neural recordings quantified the large impact that tissue properties, conduction distance, and nerve fiber parameters have on CNAPs. These methods expand the computational accessibility of neural recording models, enable efficient model tuning for validation, and facilitate the design of novel recording interfaces for neurostimulation feedback and understanding physiological systems.

Absence of paresthesia during high-rate spinal cord stimulation reveals importance of synchrony for sensations evoked by electrical stimulation

Electrically activating mechanoreceptive afferents inhibits pain. However, paresthesia evoked by spinal cord stimulation (SCS) at 40-60 Hz becomes uncomfortable at high pulse amplitudes, limiting SCS "dosage." Kilohertz-frequency SCS produces analgesia without paresthesia and is thought, therefore, not to activate afferent axons. We show that paresthesia is absent not because axons do not spike but because they spike asynchronously. In a pain patient, selectively increasing SCS frequency abolished paresthesia and epidurally recorded evoked compound action potentials (ECAPs). Dependence of ECAP amplitude on SCS frequency was reproduced in pigs, rats, and computer simulations and is explained by overdrive desynchronization: spikes desychronize when axons are stimulated faster than their refractory period. Unlike synchronous spikes, asynchronous spikes fail to produce paresthesia because their transmission to somatosensory cortex is blocked by feedforward inhibition. Our results demonstrate how stimulation frequency impacts synchrony based on axon properties and how synchrony impacts sensation based on circuit properties.

Effect of direct voltage induction by low-frequency security systems on neurostimulator lead

Low-frequency (LF) security systems, such as antitheft electronic article surveillance (EAS) gates emit strong magnetic fields that could potentially interfere with neurostimulator operation. Some patients reported pain and shocking sensations near EAS gates, even after they turned off their pulse generator. To investigate the direct voltage induction of EAS systems on neurostimulator leads, we evaluated voltages induced by two EAS systems (14 kHz continuous wave or 58 kHz pulsed) on a 40 cm sacral neurostimulator lead formed in a circular loop attached to a pulse generator that was turned off. The lead and neurostimulator were mounted in a saline-filled rectangular phantom placed within electromagnetic fields emitted by EAS systems. The measured voltage waveforms were applied to computational models of spinal nerve axons to predict whether these voltages may evoke action potentials. Additional in vitro testing was performed on the semicircular lead geometry, to study the effect of lead geometry on EAS induced voltages. While standard neurostimulator testing per ISO 14708-3:2017 recommends electromagnetic compatibility testing with LF magnetic fields for induction of malfunctions of the active electronic circuitry while generating intended stimulating pulses, our results show that close to the EAS antenna frames, the induced voltage on the lead could be strong enough to evoke action potentials, even with the pulse generator turned off. This work suggests that patient reports of pain and shocking sensations when near EAS systems could also be correlated with the direct EAS-induced voltage on neurostimulator lead.

Visualization of volume of tissue activated modeling in a clinical planning system for deep brain stimulation

BACKGROUND

Pathway activating models try to describe stimulation spread in deep brain stimulation (DBS). Volume of tissue activated (VTA) models are simplified model variants allowing faster and easier computation. Our study aimed to investigate, how VTA visualization can be integrated into a clinical workflow applying directional electrodes using a standard clinical DBS planning system.

METHODS

Twelve patients underwent DBS, using directional electrodes for bilateral subthalamic nucleus (STN) stimulation in Parkinson's disease. Preoperative 3T magnetic resonance imaging was used for automatic visualization of the STN outline, as well as for fiber tractography. Intraoperative computed tomography was used for automatic lead detection. The Guide XT software, closely integrated into the DBS planning software environment, was used for VTA calculation and visualization.

RESULTS

VTA visualization was possible in all cases. The percentage of VTA covering the STN volume ranged from 25% to 100% (mean: 60±25%) on the left side and from 0% to 98% (51±30%) on the right side. The mean coordinate of all VTA centers was: 12.6±1.2 mm lateral, 2.1±1.2 mm posterior, and 2.3±1.4 mm inferior in relation to the midcommissural point. Stimulation effects can be compared to the VTA visualization in relation to surrounding structures, potentially facilitating programming, which might be especially beneficial in case of suboptimal lead placement.

CONCLUSIONS

VTA visualization in a clinical planning system allows an intuitive adjustment of the stimulation parameters, supports programming, and enhances understanding of effects and side effects of DBS.

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.

Image-guided programming deep brain stimulation improves clinical outcomes in patients with Parkinson's disease

Deep brain stimulation (DBS) is an effective treatment for patients with Parkinson's disease (PD). However, some patients may not respond optimally to clinical programming adjustments. Advances in DBS technology have led to more complex and time-consuming programming. Image-guided programming (IGP) could optimize and improve programming leading to better clinical outcomes in patients for whom DBS programming is not ideal due to sub-optimal response. We conducted a prospective single-center study including 31 PD patients with subthalamic nucleus (STN) DBS and suboptimal responses refractory to clinical programming. Programming settings were adjusted according to the volumetric reconstruction of the stimulation field using commercial postoperative imaging software. Clinical outcomes were assessed at baseline and at 3-month follow-up after IGP, using motor and quality of life (QoL) scales. Additionally, between these two assessment points, follow-up visits for fine-tuning amplitude intensity and medication were conducted at weeks 2, 4, 6, and 9. After IGP, twenty-six patients (83.9%) experienced motor and QoL improvements, with 25.8% feeling much better and 38.7% feeling moderately better according to the patient global impression scale. Five patients (16.1%) had no clinical or QoL changes after IGP. The MDS-UPDRS III motor scale showed a 21.9% improvement and the DBS-IS global score improved by 41.5%. IGP optimizes STN-DBS therapy for PD patients who are experiencing suboptimal clinical outcomes. These findings support using IGP as a standard tool in clinical practice, which could save programming time and improve patients' QoL.

NRV: An open framework for in silico evaluation of peripheral nerve electrical stimulation strategies

Electrical stimulation of peripheral nerves has been used in various pathological contexts for rehabilitation purposes or to alleviate the symptoms of neuropathologies, thus improving the overall quality of life of patients. However, the development of novel therapeutic strategies is still a challenging issue requiring extensive in vivo experimental campaigns and technical development. To facilitate the design of new stimulation strategies, we provide a fully open source and self-contained software framework for the in silico evaluation of peripheral nerve electrical stimulation. Our modeling approach, developed in the popular and well-established Python language, uses an object-oriented paradigm to map the physiological and electrical context. The framework is designed to facilitate multi-scale analysis, from single fiber stimulation to whole multifascicular nerves. It also allows the simulation of complex strategies such as multiple electrode combinations and waveforms ranging from conventional biphasic pulses to more complex modulated kHz stimuli. In addition, we provide automated support for stimulation strategy optimization and handle the computational backend transparently to the user. Our framework has been extensively tested and validated with several existing results in the literature.

Towards an optimised deep brain stimulation using a large-scale computational network and realistic volume conductor model

Objective. Constructing a theoretical framework to improve deep brain stimulation (DBS) based on the neuronal spatiotemporal patterns of the stimulation-affected areas constitutes a primary target.Approach. We develop a large-scale biophysical network, paired with a realistic volume conductor model, to estimate theoretically efficacious stimulation protocols. Based on previously published anatomically defined structural connectivity, a biophysical basal ganglia-thalamo-cortical neuronal network is constructed using Hodgkin-Huxley dynamics. We define a new biomarker describing the thalamic spatiotemporal activity as a ratio of spiking vs. burst firing. The per cent activation of the different pathways is adapted in the simulation to minimise the differences of the biomarker with respect to its value under healthy conditions.Main results.This neuronal network reproduces spatiotemporal patterns that emerge in Parkinson's disease. Simulations of the fibre per cent activation for the defined biomarker propose desensitisation of pallido-thalamic synaptic efficacy, induced by high-frequency signals, as one possible crucial mechanism for DBS action. Based on this activation, we define both an optimal electrode position and stimulation protocol using pathway activation modelling.Significance. A key advantage of this research is that it combines different approaches, i.e. the spatiotemporal pattern with the electric field and axonal response modelling, to compute the optimal DBS protocol. By correlating the inherent network dynamics with the activation of white matter fibres, we obtain new insights into the DBS therapeutic action.

Defining the Boundaries of Patient Perception in Spinal Cord Stimulation Programming

OBJECTIVES

Recent developments in spinal cord stimulation (SCS) programming have initiated new modalities of imperceptible stimulation. However, the boundaries of sensory perception are not well defined. The BEnchtop NEuromodulation Following endIng of Trial study aimed to create a map of perceptual threshold responses across a broad range of SCS parameters and programming to inform subperception therapy design.

MATERIALS AND METHODS

This multicenter study was conducted at seven US sites. A total of 43 patients with low back and/or leg pain who completed a percutaneous commercial SCS trial were enrolled. Test stimulation was delivered through trial leads for approximately 90 minutes before removal. SCS parameters, including amplitude, frequency, pulse width (PW), electrode configuration, cycling, and multifrequency stimulation were varied during testing. Paresthesia threshold (PT), comfort level (CL), perceptual coverage area, and paresthesia quality (through patient selection of keywords) were collected. Differences were evaluated with analysis of variance followed by post hoc multiple comparisons using t-tests with Bonferroni correction.

RESULTS

PT was primarily determined by PW and was insensitive to frequency for constant frequency stimulation (range: 20 Hz-10 kHz; F(1284) = 69.58, p < 0.0001). For all tests, CL was approximately 25% higher than PT. The dominant variable that influenced paresthesia quality was frequency. Sensations described as comfortable and tingling were most common for frequencies between 60 Hz and 2.4 kHz; unpleasant sensations were generally more common outside this range. Increasing distance between active electrodes from 7 mm to 14 mm, or cycling the SCS waveform at 1 Hz, decreased PT (p < 0.0001). Finally, PT for a low-frequency stimulus (ie, 60 Hz) was unaffected by mixing with a sub-PT high-frequency stimulus.

CONCLUSIONS

In contrast to previous work investigating narrower ranges, PW primarily influenced PT, independently of frequency. Paresthesia quality was primarily influenced by pulse frequency. These findings advance our understanding of SCS therapy and may be used to improve future novel neuromodulation paradigms.

Towards a more accurate quasi-static approximation of the electric potential for neurostimulation with kilohertz-frequency sources. J Neural Eng 2023;20:10.1088/1741-2552/ad1612. [PMID: 38100821 PMCID: PMC10822676 DOI: 10.1088/1741-2552/ad1612]

Objective.Our goal was to determine the conditions for which a more precise calculation of the electric potential than the quasi-static approximation may be needed in models of electrical neurostimulation, particularly for signals with kilohertz-frequency components.Approach.We conducted a comprehensive quantitative study of the differences in nerve fiber activation and conduction block when using the quasi-static and Helmholtz approximations for the electric potential in a model of electrical neurostimulation.Main results.We first show that the potentials generated by sources of unbalanced pulses exhibit different transients as compared to those of charge-balanced pulses, and this is disregarded by the quasi-static assumption. Secondly, relative errors for current-distance curves were below 3%, while for strength-duration curves these ranged between 1%-17%, but could be improved to less than 3% across the range of pulse duration by providing a corrected quasi-static conductivity. Third, we extended our analysis to trains of pulses and reported a 'congruence area' below 700 Hz, where the fidelity of fiber responses is maximal for supra-threshold stimulation. Further examination of waveforms and polarities revealed similar fidelities in the congruence area, but significant differences were observed beyond this area. However, the spike-train distance revealed differences in activation patterns when comparing the response generated by each model. Finally, in simulations of conduction-block, we found that block thresholds exhibited errors above 20% for repetition rates above 10 kHz. Yet, employing a corrected value of the conductivity improved the agreement between models, with errors no greater than 8%.Significance.Our results emphasize that the quasi-static approximation cannot be naively extended to electrical stimulation with high-frequency components, and notable differences can be observed in activation patterns. As well, we introduce a methodology to obtain more precise model responses using the quasi-static approach, retaining its simplicity, which can be a valuable resource in computational neuroengineering.

Enhancing the selective electrical activation of human vagal nerve fibers: a comparative computational modeling study with validation in a rat sciatic model

Objective.Vagus nerve stimulation (VNS) is an emerging treatment option for a myriad of medical disorders, where the method of delivering electrical pulses can vary depending on the clinical indication. In this study, we investigated the relative effectiveness of electrically activating the cervical vagus nerve among three different approaches: nerve cuff electrode stimulation (NCES), transcutaneous electrical nerve stimulation (TENS), and enhanced TENS (eTENS). The objectives were to characterize factors that influenced nerve activation and to compare the nerve recruitment properties as a function of nerve fiber diameter.Methods.The Finite Element Model, based on data from the Visible Human Project, was implemented in COMSOL. The three simulation types were compared under a range of vertical and horizontal displacements relative to the location of the vagus nerve. Monopolar anodic stimulation was examined, along with latency and activation of different fiber sizes. Nerve activation was determined via the activating function and McIntyre-Richardson-Grill models, and activation thresholds were validated in anin-vivorodent model.Results.While NCES produced the lowest activation thresholds, eTENS generally performed superior to TENS under the range of conditions and fiber diameters, producing activation thresholds up to three times lower than TENS. eTENS also preserved its enhancement when surface electrodes were displaced away from the nerve. Anodic stimulation revealed an inhibitory region that removed eTENS benefits. eTENS also outperformed TENS by up to four times when targeting smaller diameter nerve fibers, scaling similar to a cuff electrode. In latency and activation of smaller diameter nerve fibers, eTENS results resembled those of NCES more than a TENS electrode. Activation threshold ratios were consistent inin-vivovalidation.Significance.Our findings expand upon previously identified mechanisms for eTENS and further demonstrate how eTENS emulates a nerve cuff electrode to achieve lower activation thresholds. This work further characterizes considerations required for VNS under the three stimulation methods.

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.

Modeling the excitation of nerve axons under transcutaneous stimulation

Computational models enable a safe and convenient way to study the excitation of nerve fibers under external stimulation. Contemporary models calculate the electric field distribution from transcutaneous stimulation and the resulting neuronal response separately. This study uses finite element methods to develop a multi-scale model that couples electric fields within macroscopic tissue layers and microscopic nerve fibers in a single-stage computational framework. The model included a triaxial myelinated nerve fiber bundle embedded within a volume conductor of tissue layers to represent the median nerve innervating the forearm muscles. The model captured the excitability of nerve fibers under transcutaneous stimulation and their nerve-tissue interactions to a transient external stimulus. The determinants of the strength-duration curve, rheobase, and chronaxie for the proposed model had close correlations with in-vivo experimentation on human participants. Additionally, the excitability indices for the triaxial myelinated nerve fiber implemented using the finite element method agreed well with experimental data from the literature. The validity of the proposed model encourages its use for applications involving transcutaneous stimulation. Capable of capturing field distribution across realistic morphologies, the model can serve as a testbed to improve stimulation protocols and electrode designs with subject-level specificity.

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.

Myelin dystrophy in the aging prefrontal cortex leads to impaired signal transmission and working memory decline: a multiscale computational study

Normal aging leads to myelin alternations in the rhesus monkey dorsolateral prefrontal cortex (dlPFC), which are often correlated with cognitive impairment. It is hypothesized that remyelination with shorter and thinner myelin sheaths partially compensates for myelin degradation, but computational modeling has not yet explored these two phenomena together systematically. Here, we used a two-pronged modeling approach to determine how age-related myelin changes affect a core cognitive function: spatial working memory. First we built a multicompartment pyramidal neuron model fit to monkey dlPFC data, with axon including myelinated segments having paranodes, juxtaparanodes, internodes, and tight junctions, to quantify conduction velocity (CV) changes and action potential (AP) failures after demyelination and subsequent remyelination in a population of neurons. Lasso regression identified distinctive parameter sets likely to modulate an axon's susceptibility to CV changes following demyelination versus remyelination. Next we incorporated the single neuron results into a spiking neural network model of working memory. While complete remyelination nearly recovered axonal transmission and network function to unperturbed levels, our models predict that biologically plausible levels of myelin dystrophy, if uncompensated by other factors, can account for substantial working memory impairment with aging. The present computational study unites empirical data from electron microscopy up to behavior on aging, and has broader implications for many demyelinating conditions, such as multiple sclerosis or schizophrenia.

Modulating individual axons and axonal populations in the peripheral nerve using transverse intrafascicular multichannel electrodes

Objective.A transverse intrafascicular multichannel electrode (TIME) may offer advantages over more conventional cuff electrodes including higher spatial selectivity and reduced stimulation charge requirements. However, the performance of TIME, especially in the context of non-conventional stimulation waveforms, remains relatively unexplored. As part of our overarching goal of investigating stimulation efficacy of TIME, we developed a computational toolkit that automates the creation and usage ofin siliconerve models with TIME setup, which solves nerve responses using cable equations and computes extracellular potentials using finite element method.Approach.We began by implementing a flexible and scalable Python/MATLAB-based toolkit for automatically creating models of nerve stimulation in the hybrid NEURON/COMSOL ecosystems. We then developed a sciatic nerve model containing 14 fascicles with 1,170 myelinated (A-type, 30%) and unmyelinated (C-type, 70%) fibers to study fiber responses over a variety of TIME arrangements (monopolar and hexapolar) and stimulation waveforms (kilohertz stimulation and cathodic ramp modulation).Main results.Our toolkit obviates the conventional need to re-create the same nerve in two disparate modeling environments and automates bi-directional transfer of results. Our population-based simulations suggested that kilohertz stimuli provide selective activation of targeted C fibers near the stimulating electrodes but also tended to activate non-targeted A fibers further away. However, C fiber selectivity can be enhanced by hexapolar TIME arrangements that confined the spatial extent of electrical stimuli. Improved upon prior findings, we devised a high-frequency waveform that incorporates cathodic DC ramp to completely remove undesirable onset responses.Conclusion.Our toolkit allows agile, iterative design cycles involving the nerve and TIME, while minimizing the potential operator errors during complex simulation. The nerve model created by our toolkit allowed us to study and optimize the design of next-generation intrafascicular implants for improved spatial and fiber-type selectivity.

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

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

Model Analysis of Post-Stimulation Block of a Myelinated Axon by Direct Current

OBJECTIVE

To determine the role of ion concentrations and ion pump activity in conduction block of myelinated axon induced by a long-duration direct current (DC).

METHODS

A new axonal conduction model for myelinated axons based on the classical Frankenhaeuser-Huxley (FH) equations is developed that includes ion pump activity and allows the intracellular and extracellular Na+ and K+ concentrations to change with axonal activity.

RESULTS

Action potential generation, propagation, and acute DC block occurring within a short period (milliseconds) that do not significantly change the ion concentrations or trigger ion pump activity are successfully simulated by the new model in a similar way as the classical FH model. Different from the classical model, the new model also successfully simulates the post-stimulation block phenomenon, i.e., the axonal conduction block occurring after terminating a long-duration (30 seconds) DC stimulation as observed recently in animal studies. The model reveals a significant K+ accumulation outside the axonal node as the possible mechanism underlying the post-DC block that is slowly reversed by ion pump activity during the post-stimulation period.

CONCLUSION

Changes in ion concentrations and ion pump activity play an important role in post-stimulation block induced by long-duration DC stimulation.

SIGNIFICANCE

Long-duration stimulation is used clinically for many neuromodulation therapies, but the effects on axonal conduction/block are poorly understood. This new model will be useful for better understanding of the mechanisms underlying long-duration stimulation that changes ion concentrations and triggers ion pump activity.

Influence of charges deposited on membranes of human hyperdirect pathway axons on depolarization during subthalamic deep brain stimulation

Objective.The motor hyperdirect pathway (HDP) is a key target in the treatment of Parkinson's disease with deep brain stimulation (DBS). Biophysical models of HDP DBS have been used to explore the mechanisms of stimulation. Built upon finite element method volume conductor solutions, such models are limited by a resolution mismatch, where the volume conductor is modeled at the macro scale, while the neural elements are at the micro scale. New techniques are needed to better integrate volume conductor models with neuron models.Approach.We simulated subthalamic DBS of the human HDP using finely meshed axon models to calculate surface charge deposition on insulting membranes of nonmyelinated axons. We converted the corresponding double layer extracellular problem to a single layer problem and applied the well-conditioned charge-based boundary element fast multipole method (BEM-FMM) with unconstrained numerical spatial resolution. Commonly used simplified estimations of membrane depolarization were compared with more realistic solutions.Main result.Neither centerline potential nor estimates of axon recruitment were impacted by the estimation method used except at axon bifurcations and hemispherical terminations. Local estimates of axon polarization were often much higher at bifurcations and terminations than at any other place along the axon and terminal arbor. Local average estimates of terminal electric field are higher by 10%-20%.Significance. Biophysical models of action potential initiation in the HDP suggest that axon terminations are often the lowest threshold elements for activation. The results of this study reinforce that hypothesis and suggest that this phenomenon is even more pronounced than previously realized.

Modulating functionally-distinct vagus nerve fibers using microelectrodes and kilohertz frequency electrical stimulation

Modulation of functionally distinct nerve fibers with bioelectronic devices provides a therapeutic opportunity for various diseases. In this study, we began by developing a computational model including four major subtypes of myelinated fibers and one unmyelinated fiber. Second, we used an intrafascicular electrode to perform kHz-frequency electric stimulation to preferentially modulate a population of fibers. Our model suggests that fiber physical properties and electrode-to-fascicle distance severely impacts stimulus-response relationships. Large diameter fibers (Aα- and Aβ-) were only minimally influenced by the fascicle size and electrode location, while smaller diameter fibers (Aδ-, B- and C-) indicated a stronger dependency.Clinical Relevance- Our findings support the possibility of selectively modulating functionally-distinct nerve fibers using electrical stimulation in a small, localized region. Our model provides an effective tool to design next-generation implantable devices and therapeutic stimulation strategies toward minimizing off-target effects.

Validated computational models predict vagus nerve stimulation thresholds in preclinical animals and humans

Objective.We demonstrated how automated simulations to characterize electrical nerve thresholds, a recently published open-source software for modeling stimulation of peripheral nerves, can be applied to simulate accurately nerve responses to electrical stimulation.Approach.We simulated vagus nerve stimulation (VNS) for humans, pigs, and rats. We informed our models using histology from sample-specific or representative nerves, device design features (i.e. cuff, waveform), published material and tissue conductivities, and realistic fiber models.Main results.Despite large differences in nerve size, cuff geometry, and stimulation waveform, the models predicted accurate activation thresholds across species and myelinated fiber types. However, our C fiber model thresholds overestimated thresholds across pulse widths, suggesting that improved models of unmyelinated nerve fibers are needed. Our models of human VNS yielded accurate thresholds to activate laryngeal motor fibers and captured the inter-individual variability for both acute and chronic implants. For B fibers, our small-diameter fiber model underestimated threshold and saturation for pulse widths >0.25 ms. Our models of pig VNS consistently captured the range ofin vivothresholds across all measured nerve and physiological responses (i.e. heart rate, Aδ/B fibers, Aγfibers, electromyography, and Aαfibers). In rats, our smallest diameter myelinated fibers accurately predicted fast fiber thresholds across short and intermediate pulse widths; slow unmyelinated fiber thresholds overestimated thresholds across shorter pulse widths, but there was overlap for pulse widths >0.3 ms.Significance.We elevated standards for models of peripheral nerve stimulation in populations of models across species, which enabled us to model accurately nerve responses, demonstrate that individual-specific differences in nerve morphology produce variability in neural and physiological responses, and predict mechanisms of VNS therapeutic and side effects.

Optimized monophasic pulses with equivalent electric field for rapid-rate transcranial magnetic stimulation

Objective.Transcranial magnetic stimulation (TMS) with monophasic pulses achieves greater changes in neuronal excitability but requires higher energy and generates more coil heating than TMS with biphasic pulses, and this limits the use of monophasic pulses in rapid-rate protocols. We sought to design a stimulation waveform that retains the characteristics of monophasic TMS but significantly reduces coil heating, thereby enabling higher pulse rates and increased neuromodulation effectiveness.Approach.A two-step optimization method was developed that uses the temporal relationship between the electric field (E-field) and coil current waveforms. The model-free optimization step reduced the ohmic losses of the coil current and constrained the error of the E-field waveform compared to a template monophasic pulse, with pulse duration as a second constraint. The second, amplitude adjustment step scaled the candidate waveforms based on simulated neural activation to account for differences in stimulation thresholds. The optimized waveforms were implemented to validate the changes in coil heating.Main results.Depending on the pulse duration and E-field matching constraints, the optimized waveforms produced 12%-75% less heating than the original monophasic pulse. The reduction in coil heating was robust across a range of neural models. The changes in the measured ohmic losses of the optimized pulses compared to the original pulse agreed with numeric predictions.Significance.The first step of the optimization approach was independent of any potentially inaccurate or incorrect model and exhibited robust performance by avoiding the highly nonlinear behavior of neural responses, whereas neural simulations were only run once for amplitude scaling in the second step. This significantly reduced computational cost compared to iterative methods using large populations of candidate solutions and more importantly reduced the sensitivity to the choice of neural model. The reduced coil heating and power losses of the optimized pulses can enable rapid-rate monophasic TMS protocols.

Dependence of cortical neuronal strength-duration properties on TMS pulse shape

OBJECTIVE

The aim of present study was to explore the effects of different combinations of transcranial magnetic stimulation (TMS) pulse width and pulse shape on cortical strength-duration time constant (SDTC) and rheobase measurements.

METHODS

Resting motor thresholds (RMT) at pulse widths (PW) of 30, 45, 60, 90 and 120 µs and M-ratios of 0.2, 0.1 and 0.025 were determined using figure-of-eight coil with initial posterior-to-anterior induced current. The M-ratio indicates the relative phases of the induced current with lower values signifying a more unidirectional stimulus. Strength-duration time constant (SDTC) and rheobase were estimated for each M-ratio and various PW combinations. Simulations of biophysically realistic cortical neuron models assessed underlying neuronal populations and physiological mechanisms mediating pulse shape effects on strength-duration properties.

RESULTS

The M-ratio exerted significant effect on SDTC (F(2,44) = 4.386, P = 0.021), which was longer for M-ratio of 0.2 (243.4 ± 61.2 µs) compared to 0.025 (186.7 ± 52.5 µs, P = 0.034). Rheobase was significantly smaller when assessed with M-ratio 0.2 compared to 0.025 (P = 0.026). SDTC and rheobase values were most consistent with pulse width sets of 30/45/60/90/120 µs, 30/60/90/120 µs, and 30/60/120 µs. Simulation studies indicated that isolated pyramidal neurons in layers 2/3, 5, and large basket-cells in layer 4 exhibited SDTCs comparable to experimental results. Further, simulation studies indicated that reducing transient Na+ channel conductance increased SDTC with larger increases for higher M-ratios.

CONCLUSIONS

Cortical strength-duration curve properties vary with pulse shape, and the modulating effect of the hyperpolarising pulse phase on cortical axonal transient Na+ conductances could account for these changes, although a shift in the recruited neuronal populations may contribute as well.

SIGNIFICANCE

The dependence of the cortical strength-duration curve properties on the TMS pulse shape and pulse width selection underscores the need for consistent measurement methods across studies and the potential to extract information about pathophysiological processes.

Design of an adaptable intrafascicular electrode (AIR) for selective nerve stimulation by model-based optimization

Peripheral nerve stimulation is being investigated as a therapeutic tool in several clinical scenarios. However, the adopted devices have restricted ability to obtain desired outcomes with tolerable off-target effects. Recent promising solutions are not yet employed in clinical practice due to complex required surgeries, lack of long-term stability, and implant invasiveness. Here, we aimed to design a neural interface to address these issues, specifically dimensioned for pudendal and sacral nerves to potentially target sexual, bladder, or bowel dysfunctions. We designed the adaptable intrafascicular radial electrode (AIR) through realistic computational models. They account for detailed human anatomy, inhomogeneous anisotropic conductance, following the trajectories of axons along curving and branching fascicles, and detailed biophysics of axons. The model was validated against available experimental data. Thanks to computationally efficient geometry-based selectivity estimations we informed the electrode design, optimizing its dimensions to obtain the highest selectivity while maintaining low invasiveness. We then compared the AIR with state-of-the-art electrodes, namely InterStim leads, multipolar cuffs and transversal intrafascicular multichannel electrodes (TIME). AIR, comprising a flexible substrate, surface active sites, and radially inserted intrafascicular needles, is designed to be implanted in a few standard steps, potentially enabling fast implants. It holds potential for repeatable stimulation outcomes thanks to its radial structural symmetry. When compared in-silico, AIR consistently outperformed cuff electrodes and InterStim leads in terms of recruitment threshold and stimulation selectivity. AIR performed similarly or better than a TIME, with quantified less invasiveness. Finally, we showed how AIR can adapt to different nerve sizes and varying shapes while maintaining high selectivity. The AIR electrode shows the potential to fill a clinical need for an effective peripheral nerve interface. Its high predicted performance in all the identified requirements was enabled by a model-based approach, readily applicable for the optimization of electrode parameters in any peripheral nerve stimulation scenario.

Peripheral nerve stimulation informed design of a high-performance asymmetric head gradient coil

PURPOSE

Peripheral nerve stimulation (PNS) limits the image encoding performance of both body gradient coils and the latest generation of head gradients. We analyze a variety of head gradient design aspects using a detailed PNS model to guide the design process of a new high-performance asymmetric head gradient to raise PNS thresholds and maximize the usable image-encoding performance.

METHODS

A novel three-layer coil design underwent PNS optimization involving PNS predictions of a series of candidate designs. The PNS-informed design process sought to maximize the usable parameter space of a coil with <10% nonlinearity in a 22 cm region of linearity, a relatively large inner diameter (44 cm), maximum gradient amplitude of 200 mT/m, and a high slew rate of 900 T/m/s. PNS modeling allowed identification and iterative adjustment of coil features with beneficial impact on PNS such as the number of winding layers, shoulder accommodation strategy, and level of asymmetry. PNS predictions for the final design were compared to measured thresholds in a constructed prototype.

RESULTS

The final head gradient achieved up to 2-fold higher PNS thresholds than the initial design without PNS optimization and compared to existing head gradients with similar design characteristics. The inclusion of a third intermediate winding layer provided the additional degrees of freedom necessary to improve PNS thresholds without significant sacrifices to the other design metrics.

CONCLUSION

Augmenting the design phase of a new high-performance head gradient coil by PNS modeling dramatically improved the usable image-encoding performance by raising PNS thresholds.

Fibers in smaller fascicles have lower activation thresholds with cuff electrodes due to thinner perineurium and smaller cross-sectional area

Objective. In nerve stimulation therapies, fibers in larger fascicles generally have higher activation thresholds, but the mechanisms are not well understood. We implemented and analyzed computational models to uncover the effects of morphological parameters on activation thresholds.Approach. We implemented finite element models of human vagus nerve stimulation to quantify the effects of morphological parameters on thresholds in realistic nerves. We also implemented simplified models to isolate effects of perineurium thickness, endoneurium diameter, fiber diameter, and fascicle location on current density, potential distributions (Ve), and activation thresholds across cuff geometries and stimulation waveforms. UsingVefrom each finite element model, we simulated activation thresholds in biophysical cable models of mammalian axons.Main results. Perineurium thickness increases with fascicle diameter, and both thicker perineurium and larger endoneurial diameter contributed to higher activation thresholds via lower peak and broader longitudinal potentials. Thicker perineurium caused less current to enter the fascicle transversely, decreasing peakVe. Thicker perineurium also inhibited current from leaving the fascicle, causing more constant longitudinal current density, broadeningVe. With increasing endoneurial diameter, intrafascicular volume increased faster than surface area, thereby decreasing intrafascicular current density and peakVe. Additionally, larger fascicles have greater cross-sectional area, thereby facilitating longitudinal intrafascicular current flow and broadeningVe. A large neighboring fascicle could increase activation thresholds, and for a given fascicle, fiber diameter had the greatest effect on thresholds, followed by fascicle diameter, and lastly, fascicle location within the epineurium. The circumneural cuff elicited robust activation across the nerve, whereas a bipolar transverse cuff with small contacts delivering a pseudo-monophasic waveform enabled more selective activation across fiber diameters and locations.Significance. Our computational studies provide mechanistic understanding of neural responses across relevant morphological parameters of peripheral nerves, thereby informing rational design of effective therapies.

Mechanisms Underlying Poststimulation Block Induced by High-Frequency Biphasic Stimulation

OBJECTIVE

To reveal the possible mechanisms underlying poststimulation block induced by high-frequency biphasic stimulation (HFBS).

MATERIALS AND METHODS

A new axonal conduction model is developed for unmyelinated axons. This new model is different from the classical axonal conduction model by including both ion concentrations and membrane ion pumps to allow analysis of axonal responses to long-duration stimulation. Using the new model, the post-HFBS block phenomenon reported in animal studies is simulated and analyzed for a wide range of stimulation frequencies (100 Hz-10 kHz).

RESULTS

HFBS can significantly change the Na+ and K+ concentrations inside and outside the axon to produce a post-HFBS block of either short-duration (<500 msec) or long-duration (>3 sec) depending on the duration of HFBS. The short-duration block is due to the fast recovery of the Na+ and K+ concentrations outside the axon in periaxonal space by diffusion of ions into and from the large extracellular space, while the long-duration block is due to the slow restoration of the normal Na+ concentration inside the axon by membrane ion pumps. The 100 Hz HFBS requires the minimal electrical energy to achieve the post-HFBS block, while the 10 kHz stimulation is the least effective frequency requiring high intensity and long duration to achieve the block.

CONCLUSION

This study reveals two possible ionic mechanisms underlying post-HFBS block of axonal conduction. Understanding these mechanisms is important for improving clinical applications of HFBS block and for developing new nerve block methods employing HFBS.

Symbiotic electroneural and musculoskeletal framework to encode proprioception via neurostimulation: ProprioStim

Peripheral nerve stimulation in amputees achieved the restoration of touch, but not proprioception, which is critical in locomotion. A plausible reason is the lack of means to artificially replicate the complex activity of proprioceptors. To uncover this, we coupled neuromuscular models from ten subjects and nerve histologies from two implanted amputees to develop ProprioStim: a framework to encode proprioception by electrical evoking neural activity in close agreement with natural proprioceptive activity. We demonstrated its feasibility through non-invasive stimulation on seven healthy subjects comparing it with standard linear charge encoding. Results showed that ProprioStim multichannel stimulation was felt more natural, and hold promises for increasing accuracy in knee angle tracking, especially in future implantable solutions. Additionally, we quantified the importance of realistic 3D-nerve models against extruded models previously adopted for further design and validation of novel neurostimulation encoding strategies. ProprioStim provides clear guidelines for the development of neurostimulation policies restoring natural proprioception.

Asynchronous axonal firing patterns evoked via continuous subthreshold kilohertz stimulation

Objective.Transcutaneous electrical stimulation of peripheral nerves is a common technique to assist or rehabilitate impaired muscle activation. However, conventional stimulation paradigms activate nerve fibers synchronously with action potentials time-locked with stimulation pulses. Such synchronous activation limits fine control of muscle force due to synchronized force twitches. Accordingly, we developed a subthreshold high-frequency stimulation waveform with the goal of activating axons asynchronously.Approach.We evaluated our waveform experimentally and through model simulations. During the experiment, we delivered continuous subthreshold pulses at frequencies of 16.67, 12.5, or 10 kHz transcutaneously to the median and ulnar nerves. We obtained high-density electromyographic (EMG) signals and fingertip forces to quantify the axonal activation patterns. We used a conventional 30 Hz stimulation waveform and the associated voluntary muscle activation for comparison. We modeled stimulation of biophysically realistic myelinated mammalian axons using a simplified volume conductor model to solve for extracellular electric potentials. We compared the firing properties under kHz and conventional 30 Hz stimulation.Main results.EMG activity evoked by kHz stimulation showed high entropy values similar to voluntary EMG activity, indicating asynchronous axon firing activity. In contrast, we observed low entropy values in EMG evoked by conventional 30 Hz stimulation. The muscle forces evoked by kHz stimulation also showed more stable force profiles across repeated trials compared with 30 Hz stimulation. Our simulation results provide direct evidence of asynchronous firing patterns across a population of axons in response to kHz frequency stimulation, while 30 Hz stimulation elicited synchronized time-locked responses across the population.Significance.We demonstrate that the continuous subthreshold high-frequency stimulation waveform can elicit asynchronous axon firing patterns, which can lead to finer control of muscle forces.

Lead-DBS v3.0: Mapping deep brain stimulation effects to local anatomy and global networks

Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics.

A study of flex miniaturized coils for focal nerve magnetic stimulation

BACKGROUND

Peripheral magnetic stimulation (PMS) is emerging as a complement to standard electrical stimulation (ES) of the peripheral nervous system (PNS). PMS may stimulate sensory and motor nerve fibers without the discomfort associated with the ES used for standard nerve conduction studies. The PMS coils are the same ones used in transcranial magnetic stimulation (TMS) and lack focality and selectiveness in the stimulation.

PURPOSE

This study presents a novel coil for PMS, developed using Flexible technologies, and characterized by reduced dimensions for a precise and controlled targeting of peripheral nerves.

METHODS

We performed hybrid electromagnetic (EM) and electrophysiological simulations to study the EM exposure induced by a novel miniaturized coil (or mcoil) in and around the radial nerve of the neuro-functionalized virtual human body model Yoon-Sun, and to estimate the current threshold to induce magnetic stimulation (MS) of the radial nerve. Eleven healthy subjects were studied with the mcoil, which consisted of two 15 mm diameter coils in a figure-of-eight configuration, each with a hundred turns of a 25 μm copper-clad four-layer foil. Sensory nerve action potentials (SNAPs) were measured in each subject using two electrodes and compared with those obtained from standard ES. The SNAPs conduction velocities were estimated as a performance metric.

RESULTS

The induced electric field was estimated numerically to peak at a maximum intensity of 39 V/m underneath the mcoil fed by 70 A currents. In such conditions, the electrophysiological simulations suggested that the mcoil elicits SNAPs originating at 7 mm from the center of the mcoil. Furthermore, the numerically estimated latencies and waveforms agreed with those obtained during the PMS experiments on healthy subjects, confirming the ability of the mcoil to stimulate the radial nerve sensory fibers.

CONCLUSION

Hybrid EM-electrophysiological simulations assisted the development of a miniaturized coil with a small diameter and a high number of turns using flexible electronics. The numerical dosimetric analysis predicted the threshold current amplitudes required for a suprathreshold peripheral nerve sensory stimulation, which was experimentally confirmed. The developed and now validated computational pipeline will be used to improve the performances (e.g., focality and minimal currents) of new generations of mcoil designs.

Measuring and modeling the effects of vagus nerve stimulation on heart rate and laryngeal muscles

BACKGROUND

Reduced heart rate (HR) during vagus nerve stimulation (VNS) is associated with therapy for heart failure, but stimulation frequency and amplitude are limited by patient tolerance. An understanding of physiological responses to parameter adjustments would allow differential control of therapeutic and side effects. To investigate selective modulation of the physiological responses to VNS, we quantified the effects and interactions of parameter selection on two physiological outcomes: one related to therapy (reduced HR) and one related to side effects (laryngeal muscle EMG).

METHODS

We applied a broad range of stimulation parameters (mean pulse rates (MPR), intra-burst frequencies, and amplitudes) to the vagus nerve of anesthetized mice. We leveraged the in vivo recordings to parameterize and validate computational models of HR and laryngeal muscle activity across amplitudes and temporal patterns of VNS. We constructed a finite element model of excitation of fibers within the mouse cervical vagus nerve.

RESULTS

HR decreased with increased amplitude, increased MPR, and decreased intra-burst frequency. EMG increased with increased MPR. Preferential HR effects over laryngeal EMG effects required combined adjustments of amplitude and MPR. The model of HR responses highlighted contributions of ganglionic filtering to VNS-evoked changes in HR at high stimulation frequencies. Overlap in activation thresholds between small and large modeled fibers was consistent with the overlap in dynamic ranges of related physiological measures (HR and EMG).

CONCLUSION

The present study provides insights into physiological responses to VNS required for informed parameter adjustment to modulate selectively therapeutic effects and side effects.