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Baldwin A, States G, Pikov V, Gunalan P, Elyahoodayan S, Kilgore K, Meng E. Recent advances in facilitating the translation of bioelectronic medicine therapies. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2025; 33:100575. [PMID: 39896232 PMCID: PMC11781353 DOI: 10.1016/j.cobme.2024.100575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2025]
Abstract
Bioelectronic medicine is a growing field which involves directly interfacing with the vagus, sacral, enteric, and other autonomic nerves to treat conditions. Therapies based on bioelectronic medicine could address previously intractable diseases and provide an alternative to pharmaceuticals. However, translating a bioelectronic medicine therapy to the clinic requires overcoming several challenges, including titrating stimulation parameters to an individual's physiology, selectively stimulating target nerves without inducing off-target activation or block, and improving accessibility to clinically approved devices. This review describes recent progress towards solving these problems, including advances in mapping and characterizing the human autonomic nervous system, new sensor technology and signal processing techniques to enable closed-loop therapies, new methods for selectively stimulating autonomic nerves without inducing off-target effects, and efforts to develop open-source implantable devices. Recent commercial successes in bringing bioelectronic medicine therapies to the clinic are highlighted showing how addressing these challenges can lead to novel therapies.
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Affiliation(s)
- Alex Baldwin
- Alfred E. Mann Department of Biomedical Engineering,
University of Southern California, USA
| | - Gregory States
- Department of Physical Medicine & Rehabilitation, Case
Western Reserve University and The MetroHealth System, Cleveland, OH, USA
| | | | - Pallavi Gunalan
- Alfred E. Mann Department of Biomedical Engineering,
University of Southern California, USA
| | - Sahar Elyahoodayan
- Alfred E. Mann Department of Biomedical Engineering,
University of Southern California, USA
| | - Kevin Kilgore
- Department of Physical Medicine & Rehabilitation, Case
Western Reserve University and The MetroHealth System, Cleveland, OH, USA
| | - Ellis Meng
- Alfred E. Mann Department of Biomedical Engineering,
University of Southern California, USA
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Butler AG, Bassi JK, Connelly AA, Melo MR, Allen AM, McDougall SJ. Vagal nerve stimulation dynamically alters anxiety-like behavior in rats. Brain Stimul 2025:S1935-861X(25)00020-8. [PMID: 39892503 DOI: 10.1016/j.brs.2025.01.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2024] [Revised: 01/15/2025] [Accepted: 01/22/2025] [Indexed: 02/03/2025] Open
Abstract
BACKGROUND Electrical vagal nerve stimulation (VNS), at currents designed to target sensory, interoceptive neurons, decreases anxiety-like behavior. OBJECTIVE/HYPOTHESIS We hypothesized that different VNS current intensities would differentially alter anxiety-like behavior through the activation of distinct brainstem circuits. METHODS Electrodes were implanted to stimulate the left vagus nerve and to record diaphragm muscle and electrocardiogram activity. The VNS current required to elicit the A-fiber-mediated Hering-Breuer Reflex (HBR) was determined for each animal. Based on this threshold, animals received either sham stimulation or VNS at 1.5 (mid-intensity VNS) or 3 (higher-intensity VNS) times the threshold for HBR activation. Anxiety-like behavior was assessed using the elevated plus maze, open field test, and novelty-suppressed feeding test. Additionally, a place preference assay determined whether VNS is rewarding or aversive. Finally, a c-Fos assay was performed to evaluate VNS-driven neuronal activation within the brainstem. RESULTS Mid-intensity VNS reduced anxiety-like behavior in the elevated plus maze and open field test. Higher-intensity VNS was aversive during the place preference assay, confounding anxiety measures. Both intensities increased overall c-Fos expression in neurons within the nucleus of the solitary tract, but mid-intensity VNS specifically increased c-Fos expression in noradrenergic neurons within the nucleus of the solitary tract while decreasing it in the locus coeruleus. In contrast, higher-intensity VNS had no effect on c-Fos expression in noradrenergic neurons of either the nucleus of the solitary tract or locus coeruleus. CONCLUSION Delivery of VNS induced reproducible, current intensity-dependent, effects on anxiety-like and aversive behavior in rats.
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Affiliation(s)
- A G Butler
- Florey Institute of Neuroscience and Mental Health and University of Melbourne, Parkville, Victoria, Australia; Department of Anatomy and Physiology, University of Melbourne, Parkville, Victoria, Australia
| | - J K Bassi
- Department of Anatomy and Physiology, University of Melbourne, Parkville, Victoria, Australia
| | - A A Connelly
- Department of Anatomy and Physiology, University of Melbourne, Parkville, Victoria, Australia
| | - M R Melo
- Department of Anatomy and Physiology, University of Melbourne, Parkville, Victoria, Australia
| | - A M Allen
- Florey Institute of Neuroscience and Mental Health and University of Melbourne, Parkville, Victoria, Australia; Department of Anatomy and Physiology, University of Melbourne, Parkville, Victoria, Australia.
| | - S J McDougall
- Florey Institute of Neuroscience and Mental Health and University of Melbourne, Parkville, Victoria, Australia.
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Grill WM, Pelot NA. Computational modeling of autonomic nerve stimulation: Vagus et al. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2024; 32:100557. [PMID: 39650310 PMCID: PMC11619812 DOI: 10.1016/j.cobme.2024.100557] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2024]
Abstract
Computational models of electrical stimulation, block and recording of autonomic nerves enable analysis of mechanisms of action underlying neural responses and design of optimized stimulation parameters. We reviewed advances in computational modeling of autonomic nerve stimulation, block, and recording over the past five years, with a focus on vagus nerve stimulation, including both implanted and less invasive approaches. Few models achieved quantitative validation, but integrated computational pipelines increase the reproducibility, reusability, and accessibility of computational modeling. Model-based optimization enabled design of electrode geometries and stimulation parameters for selective activation (across fiber locations or types). Growing efforts link models of neural activity to downstream physiological responses to represent more directly the therapeutic effects and side effects of stimulation. Thus, computational modeling is an increasingly important tool for analysis and design of bioelectronic therapies.
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Hussain MA, Grill WM, Pelot NA. Highly efficient modeling and optimization of neural fiber responses to electrical stimulation. Nat Commun 2024; 15:7597. [PMID: 39217179 PMCID: PMC11365978 DOI: 10.1038/s41467-024-51709-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 08/13/2024] [Indexed: 09/04/2024] Open
Abstract
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.
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Affiliation(s)
- Minhaj A Hussain
- Department of Biomedical Engineering, Duke University, Durham, NC, 27708, USA
| | - Warren M Grill
- Department of Biomedical Engineering, Duke University, Durham, NC, 27708, USA
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, 27708, USA
- Department of Neurobiology, Duke University, Durham, NC, 27708, USA
- Department of Neurosurgery, Duke University, Durham, NC, 27708, USA
| | - Nicole A Pelot
- Department of Biomedical Engineering, Duke University, Durham, NC, 27708, USA.
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Ciotti F, John R, Katic Secerovic N, Gozzi N, Cimolato A, Jayaprakash N, Song W, Toth V, Zanos T, Zanos S, Raspopovic S. Towards enhanced functionality of vagus neuroprostheses through in silico optimized stimulation. Nat Commun 2024; 15:6119. [PMID: 39033186 PMCID: PMC11271449 DOI: 10.1038/s41467-024-50523-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 07/10/2024] [Indexed: 07/23/2024] Open
Abstract
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.
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Affiliation(s)
- Federico Ciotti
- Laboratory for Neuroengineering, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, Zürich, Switzerland
| | - Robert John
- Laboratory for Neuroengineering, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, Zürich, Switzerland
| | - Natalija Katic Secerovic
- Laboratory for Neuroengineering, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, Zürich, Switzerland
- The Mihajlo Pupin Institute, University of Belgrade, Belgrade, Serbia
| | - Noemi Gozzi
- Laboratory for Neuroengineering, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, Zürich, Switzerland
| | - Andrea Cimolato
- Laboratory for Neuroengineering, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, Zürich, Switzerland
| | - Naveen Jayaprakash
- Northwell Health, New Hyde Park, NY, USA
- Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Weiguo Song
- Northwell Health, New Hyde Park, NY, USA
- Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Viktor Toth
- Northwell Health, New Hyde Park, NY, USA
- Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Theodoros Zanos
- Northwell Health, New Hyde Park, NY, USA
- Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Elmezzi Graduate School of Molecular Medicine, Manhasset, NY, USA
| | - Stavros Zanos
- Northwell Health, New Hyde Park, NY, USA
- Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Elmezzi Graduate School of Molecular Medicine, Manhasset, NY, USA
| | - Stanisa Raspopovic
- Laboratory for Neuroengineering, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, Zürich, Switzerland.
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.
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Couppey T, Regnacq L, Giraud R, Romain O, Bornat Y, Kolbl F. NRV: An open framework for in silico evaluation of peripheral nerve electrical stimulation strategies. PLoS Comput Biol 2024; 20:e1011826. [PMID: 38995970 PMCID: PMC11268605 DOI: 10.1371/journal.pcbi.1011826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 07/24/2024] [Accepted: 06/20/2024] [Indexed: 07/14/2024] Open
Abstract
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.
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Affiliation(s)
- Thomas Couppey
- Laboratoire ETIS, Cergy Paris Université, ENSEA, CNRS UMR 8051, Cergy, France
| | - Louis Regnacq
- Laboratoire ETIS, Cergy Paris Université, ENSEA, CNRS UMR 8051, Cergy, France
- Univ. Bordeaux, CNRS, Bordeaux INP, IMS, UMR 5218, Talence, France
| | - Roland Giraud
- Laboratoire ETIS, Cergy Paris Université, ENSEA, CNRS UMR 8051, Cergy, France
- Univ. Bordeaux, CNRS, Bordeaux INP, IMS, UMR 5218, Talence, France
| | - Olivier Romain
- Laboratoire ETIS, Cergy Paris Université, ENSEA, CNRS UMR 8051, Cergy, France
| | - Yannick Bornat
- Univ. Bordeaux, CNRS, Bordeaux INP, IMS, UMR 5218, Talence, France
| | - Florian Kolbl
- Laboratoire ETIS, Cergy Paris Université, ENSEA, CNRS UMR 8051, Cergy, France
- Univ. Bordeaux, CNRS, Bordeaux INP, IMS, UMR 5218, Talence, France
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Wang X, Zhang Y, Guo T, Wu S, Zhong J, Cheng C, Sui X. Selective intrafascicular stimulation of myelinated and unmyelinated nerve fibers through a longitudinal electrode: A computational study. Comput Biol Med 2024; 176:108556. [PMID: 38733726 DOI: 10.1016/j.compbiomed.2024.108556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 05/05/2024] [Indexed: 05/13/2024]
Abstract
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.
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Affiliation(s)
- Xintong Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yapeng Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Tianruo Guo
- Graduate School of Biomedical Engineering, The University of New South Wales, Sydney, NSW, 2052, Australia
| | - Shuhui Wu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Junwen Zhong
- Department of Electromechanical Engineering, University of Macau, Macau SAR, 999078, China
| | - Chengkung Cheng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Med-X Research Institute, Shanghai Jiao Tong University, Engineering Research Center of Digital Medicine, Ministry of Education, Shanghai, China
| | - Xiaohong Sui
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
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8
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Peña E, Pelot NA, Grill WM. Computational models of compound nerve action potentials: Efficient filter-based methods to quantify effects of tissue conductivities, conduction distance, and nerve fiber parameters. PLoS Comput Biol 2024; 20:e1011833. [PMID: 38427699 PMCID: PMC10936855 DOI: 10.1371/journal.pcbi.1011833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 03/13/2024] [Accepted: 01/16/2024] [Indexed: 03/03/2024] Open
Abstract
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.
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Affiliation(s)
- Edgar Peña
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
| | - Nicole A. Pelot
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
| | - Warren M. Grill
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, United States of America
- Department of Neurobiology, Duke University School of Medicine, Durham, North Carolina, United States of America
- Department of Neurosurgery, Duke University School of Medicine, Durham, North Carolina, United States of America
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9
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B C Girard C, Song D. Adaptive octree meshes for simulation of extracellular electrophysiology. J Neural Eng 2023; 20:056028. [PMID: 37722378 DOI: 10.1088/1741-2552/acfabf] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 09/18/2023] [Indexed: 09/20/2023]
Abstract
Objective.The interaction between neural tissues and artificial electrodes is crucial for understanding and advancing neuroscientific research and therapeutic applications. However, accurately modeling this space around the neurons rapidly increases the computational complexity of neural simulations.Approach.This study demonstrates a dynamically adaptive simulation method that greatly accelerates computation by adjusting spatial resolution of the simulation as needed. Use of an octree structure for the mesh, in combination with the admittance method for discretizing conductivity, provides both accurate approximation and ease of modification on-the-fly.Main results.In tests of both local field potential estimation and multi-electrode stimulation, dynamically adapted meshes achieve accuracy comparable to high-resolution static meshes in an order of magnitude less time.Significance.The proposed simulation pipeline improves model scalability, allowing greater detail with fewer computational resources. The implementation is available as an open-source Python module, providing flexibility and ease of reuse for the broader research community.
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Affiliation(s)
- Christopher B C Girard
- Fowler School of Engineering, Chapman University, Orange, CA, United States of America
- Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Dong Song
- Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, United States of America
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10
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Musselman ED, Pelot NA, Grill WM. Validated computational models predict vagus nerve stimulation thresholds in preclinical animals and humans. J Neural Eng 2023; 20: 10.1088/1741-2552/acda64. [PMID: 37257454 PMCID: PMC10324064 DOI: 10.1088/1741-2552/acda64] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 05/31/2023] [Indexed: 06/02/2023]
Abstract
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.
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Affiliation(s)
- Eric D Musselman
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
| | - Nicole A Pelot
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
| | - Warren M Grill
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States of America
- Department of Neurobiology, Duke University, Durham, NC, United States of America
- Department of Neurosurgery, Duke University, Durham, NC, United States of America
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11
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Buyukcelik ON, Lapierre-Landry M, Kolluru C, Upadhye AR, Marshall DP, Pelot NA, Ludwig KA, Gustafson KJ, Wilson DL, Jenkins MW, Shoffstall AJ. Deep-learning segmentation of fascicles from microCT of the human vagus nerve. Front Neurosci 2023; 17:1169187. [PMID: 37332862 PMCID: PMC10275336 DOI: 10.3389/fnins.2023.1169187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 04/12/2023] [Indexed: 06/20/2023] Open
Abstract
Introduction MicroCT of the three-dimensional fascicular organization of the human vagus nerve provides essential data to inform basic anatomy as well as the development and optimization of neuromodulation therapies. To process the images into usable formats for subsequent analysis and computational modeling, the fascicles must be segmented. Prior segmentations were completed manually due to the complex nature of the images, including variable contrast between tissue types and staining artifacts. Methods Here, we developed a U-Net convolutional neural network (CNN) to automate segmentation of fascicles in microCT of human vagus nerve. Results The U-Net segmentation of ~500 images spanning one cervical vagus nerve was completed in 24 s, versus ~40 h for manual segmentation, i.e., nearly four orders of magnitude faster. The automated segmentations had a Dice coefficient of 0.87, a measure of pixel-wise accuracy, thus suggesting a rapid and accurate segmentation. While Dice coefficients are a commonly used metric to assess segmentation performance, we also adapted a metric to assess fascicle-wise detection accuracy, which showed that our network accurately detects the majority of fascicles, but may under-detect smaller fascicles. Discussion This network and the associated performance metrics set a benchmark, using a standard U-Net CNN, for the application of deep-learning algorithms to segment fascicles from microCT images. The process may be further optimized by refining tissue staining methods, modifying network architecture, and expanding the ground-truth training data. The resulting three-dimensional segmentations of the human vagus nerve will provide unprecedented accuracy to define nerve morphology in computational models for the analysis and design of neuromodulation therapies.
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Affiliation(s)
- Ozge N. Buyukcelik
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
- Advanced Platform Technologies Center, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, United States
| | - Maryse Lapierre-Landry
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Chaitanya Kolluru
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Aniruddha R. Upadhye
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
- Advanced Platform Technologies Center, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, United States
| | - Daniel P. Marshall
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Nicole A. Pelot
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Kip A. Ludwig
- Department of Biomedical Engineering, University of Wisconsin Madison, Madison, WI, United States
- Department of Neurological Surgery, University of Wisconsin Madison, Madison, WI, United States
- Wisconsin Institute for Translational Neuroengineering, Madison, WI, United States
| | - Kenneth J. Gustafson
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
- Functional Electrical Stimulation Center, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, United States
| | - David L. Wilson
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Michael W. Jenkins
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
- Department of Pediatrics, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Andrew J. Shoffstall
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
- Advanced Platform Technologies Center, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, United States
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