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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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2
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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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3
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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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4
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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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Micera S, Menciassi A, Cianferotti L, Gruppioni E, Lionetti V. Organ Neuroprosthetics: Connecting Transplanted and Artificial Organs with the Nervous System. Adv Healthc Mater 2024:e2302896. [PMID: 38656615 DOI: 10.1002/adhm.202302896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 04/01/2024] [Indexed: 04/26/2024]
Abstract
Implantable neural interfaces with the central and peripheral nervous systems are currently used to restore sensory, motor, and cognitive functions in disabled people with very promising results. They have also been used to modulate autonomic activities to treat diseases such as diabetes or hypertension. Here, this study proposes to extend the use of these technologies to (re-)establish the connection between new (transplanted or artificial) organs and the nervous system in order to increase the long-term efficacy and the effective biointegration of these solutions. In this perspective paper, some clinically relevant applications of this approach are briefly described. Then, the choices that neural engineers must implement about the type, implantation location, and closed-loop control algorithms to successfully realize this approach are highlighted. It is believed that these new "organ neuroprostheses" are going to become more and more valuable and very effective solutions in the years to come.
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Affiliation(s)
- Silvestro Micera
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, 56127, Italy
- Interdisciplinary Research Center Health Science, Scuola Superiore Sant'Anna, Pisa, 56127, Italy
- Bertarelli Foundation Chair in Translational Neuroengineering, Neuro-X Institute, School of Engineering, Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, 1015, Switzerland
| | - Arianna Menciassi
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, 56127, Italy
- Interdisciplinary Research Center Health Science, Scuola Superiore Sant'Anna, Pisa, 56127, Italy
| | - Luisella Cianferotti
- Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, 50121, Italy
| | | | - Vincenzo Lionetti
- Interdisciplinary Research Center Health Science, Scuola Superiore Sant'Anna, Pisa, 56127, Italy
- UOSVD Anesthesia and Resuscitation, Fondazione Toscana G. Monasterio, Pisa, 56127, Italy
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Conde SV, Sacramento JF, Zinno C, Mazzoni A, Micera S, Guarino MP. Bioelectronic modulation of carotid sinus nerve to treat type 2 diabetes: current knowledge and future perspectives. Front Neurosci 2024; 18:1378473. [PMID: 38646610 PMCID: PMC11026613 DOI: 10.3389/fnins.2024.1378473] [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: 01/29/2024] [Accepted: 03/26/2024] [Indexed: 04/23/2024] Open
Abstract
Bioelectronic medicine are an emerging class of treatments aiming to modulate body nervous activity to correct pathological conditions and restore health. Recently, it was shown that the high frequency electrical neuromodulation of the carotid sinus nerve (CSN), a small branch of the glossopharyngeal nerve that connects the carotid body (CB) to the brain, restores metabolic function in type 2 diabetes (T2D) animal models highlighting its potential as a new therapeutic modality to treat metabolic diseases in humans. In this manuscript, we review the current knowledge supporting the use of neuromodulation of the CSN to treat T2D and discuss the future perspectives for its clinical application. Firstly, we review in a concise manner the role of CB chemoreceptors and of CSN in the pathogenesis of metabolic diseases. Secondly, we describe the findings supporting the potential therapeutic use of the neuromodulation of CSN to treat T2D, as well as the feasibility and reversibility of this approach. A third section is devoted to point up the advances in the neural decoding of CSN activity, in particular in metabolic disease states, that will allow the development of closed-loop approaches to deliver personalized and adjustable treatments with minimal side effects. And finally, we discuss the findings supporting the assessment of CB activity in metabolic disease patients to screen the individuals that will benefit therapeutically from this bioelectronic approach in the future.
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Affiliation(s)
- Silvia V. Conde
- iNOVA4Health, NOVA Medical School, Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - Joana F. Sacramento
- iNOVA4Health, NOVA Medical School, Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - Ciro Zinno
- The BioRobotics Institute Scuola Superiore Sant’Anna, Pontedera, Italy
| | - Alberto Mazzoni
- The BioRobotics Institute Scuola Superiore Sant’Anna, Pontedera, Italy
| | - Silvestro Micera
- The BioRobotics Institute Scuola Superiore Sant’Anna, Pontedera, Italy
| | - Maria P. Guarino
- ciTechCare, School of Health Sciences Polytechnic of Leiria, Leiria, Portugal
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Lloyd DA, Alejandra Gonzalez-Gonzalez M, Romero-Ortega MI. AxoDetect: an automated nerve image segmentation and quantification workflow for computational nerve modeling. J Neural Eng 2024; 21:026017. [PMID: 38457836 PMCID: PMC10976901 DOI: 10.1088/1741-2552/ad31c3] [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: 10/20/2023] [Revised: 02/11/2024] [Accepted: 03/08/2024] [Indexed: 03/10/2024]
Abstract
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.
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Affiliation(s)
- David A Lloyd
- Departments of Biomedical Engineering and Biomedical Sciences, University of Houston, Houston, TX, United States of America
| | - Maria Alejandra Gonzalez-Gonzalez
- Departments of Biomedical Engineering and Biomedical Sciences, University of Houston, Houston, TX, United States of America
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX, United States of America
- Department of Pediatric Neurology, Baylor College of Medicine, Houston, TX, United States of America
| | - Mario I Romero-Ortega
- Departments of Biomedical Engineering and Biomedical Sciences, University of Houston, Houston, TX, United States of America
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ, United States of America
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Verardo C, Mele LJ, Selmi L, Palestri P. Finite-element modeling of neuromodulation via controlled delivery of potassium ions using conductive polymer-coated microelectrodes. J Neural Eng 2024; 21:026002. [PMID: 38306702 DOI: 10.1088/1741-2552/ad2581] [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: 04/25/2023] [Accepted: 02/02/2024] [Indexed: 02/04/2024]
Abstract
Objective. The controlled delivery of potassium is an interesting neuromodulation modality, being potassium ions involved in shaping neuron excitability, synaptic transmission, network synchronization, and playing a key role in pathological conditions like epilepsy and spreading depression. Despite many successful examples of pre-clinical devices able to influence the extracellular potassium concentration, computational frameworks capturing the corresponding impact on neuronal activity are still missing.Approach. We present a finite-element model describing a PEDOT:PSS-coated microelectrode (herein, simplyionic actuator) able to release potassium and thus modulate the activity of a cortical neuron in anin-vitro-like setting. The dynamics of ions in the ionic actuator, the neural membrane, and the cellular fluids are solved self-consistently.Main results. We showcase the capability of the model to describe on a physical basis the modulation of the intrinsic excitability of the cell and of the synaptic transmission following the electro-ionic stimulation produced by the actuator. We consider three case studies for the ionic actuator with different levels of selectivity to potassium: ideal selectivity, no selectivity, and selectivity achieved by embedding ionophores in the polymer.Significance. This work is the first step toward a comprehensive computational framework aimed to investigate novel neuromodulation devices targeting specific ionic species, as well as to optimize their design and performance, in terms of the induced modulation of neural activity.
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Affiliation(s)
- Claudio Verardo
- Polytechnic Department of Engineering and Architecture, Università degli Studi di Udine, Udine, Italy
- BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Leandro Julian Mele
- Polytechnic Department of Engineering and Architecture, Università degli Studi di Udine, Udine, Italy
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, United States of America
| | - Luca Selmi
- Department of Engineering "Enzo Ferrari", Università degli Studi di Modena e Reggio Emilia, Modena, Italy
| | - Pierpaolo Palestri
- Polytechnic Department of Engineering and Architecture, Università degli Studi di Udine, Udine, Italy
- Department of Engineering "Enzo Ferrari", Università degli Studi di Modena e Reggio Emilia, Modena, Italy
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Couppey T, Regnacq L, Giraud R, Romain O, Bornat Y, Kölbl F. NRV: An open framework for in silico evaluation of peripheral nerve electrical stimulation strategies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.15.575628. [PMID: 38293181 PMCID: PMC10827078 DOI: 10.1101/2024.01.15.575628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
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)
| | - Louis Regnacq
- ETIS CNRS UMR 8051, CY Cergy Paris University, ENSEA
- Univ. Bordeaux, Bordeaux INP, IMS CNRS UMR 5218, Aquitaine, Talence, France
| | - Roland Giraud
- ETIS CNRS UMR 8051, CY Cergy Paris University, ENSEA
- Univ. Bordeaux, Bordeaux INP, IMS CNRS UMR 5218, Aquitaine, Talence, France
| | | | - Yannick Bornat
- Univ. Bordeaux, Bordeaux INP, IMS CNRS UMR 5218, Aquitaine, Talence, France
| | - Florian Kölbl
- ETIS CNRS UMR 8051, CY Cergy Paris University, ENSEA
- Univ. Bordeaux, Bordeaux INP, IMS CNRS UMR 5218, Aquitaine, Talence, France
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RaviChandran N, Hope J, Aw K, McDaid A. Modeling the excitation of nerve axons under transcutaneous stimulation. Comput Biol Med 2023; 165:107463. [PMID: 37699322 DOI: 10.1016/j.compbiomed.2023.107463] [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: 02/09/2023] [Revised: 08/15/2023] [Accepted: 09/04/2023] [Indexed: 09/14/2023]
Abstract
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.
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Affiliation(s)
- Narrendar RaviChandran
- Medical Devices and Technologies Group, Department of Mechanical and Mechatronics Engineering, The University of Auckland, Auckland 1010, New Zealand; Singapore Eye Research Institute, Singapore 169856, Singapore.
| | - James Hope
- Medical Devices and Technologies Group, Department of Mechanical and Mechatronics Engineering, The University of Auckland, Auckland 1010, New Zealand; Department of Mechanical Engineering, The University of Minnesota, Minneapolis, MN 55455, United States
| | - Kean Aw
- Smart Materials and Microtechnologies Group, Department of Mechanical and Mechatronics Engineering, The University of Auckland, Auckland 1010, New Zealand
| | - Andrew McDaid
- Medical Devices and Technologies Group, Department of Mechanical and Mechatronics Engineering, The University of Auckland, Auckland 1010, New Zealand
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Ciotti F, Cimolato A, Valle G, Raspopovic S. Design of an adaptable intrafascicular electrode (AIR) for selective nerve stimulation by model-based optimization. PLoS Comput Biol 2023; 19:e1011184. [PMID: 37228174 DOI: 10.1371/journal.pcbi.1011184] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 05/15/2023] [Indexed: 05/27/2023] Open
Abstract
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.
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Affiliation(s)
- Federico Ciotti
- Department of Health Sciences and Technology, Institute of Robotics and Intelligent Systems, ETH Zürich, Zürich, Switzerland
| | - Andrea Cimolato
- Department of Health Sciences and Technology, Institute of Robotics and Intelligent Systems, ETH Zürich, Zürich, Switzerland
| | - Giacomo Valle
- Department of Health Sciences and Technology, Institute of Robotics and Intelligent Systems, ETH Zürich, Zürich, Switzerland
| | - Stanisa Raspopovic
- Department of Health Sciences and Technology, Institute of Robotics and Intelligent Systems, ETH Zürich, Zürich, Switzerland
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12
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Non-rectangular neurostimulation waveforms elicit varied sensation quality and perceptive fields on the hand. Sci Rep 2023; 13:1588. [PMID: 36709376 PMCID: PMC9884304 DOI: 10.1038/s41598-023-28594-0] [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: 09/28/2022] [Accepted: 01/20/2023] [Indexed: 01/30/2023] Open
Abstract
Electrical stimulation of the nerves is known to elicit distinct sensations perceived in distal parts of the body. The stimulation is typically modulated in current with charge-balanced rectangular shapes that, although easily generated by stimulators available on the market, are not able to cover the entire range of somatosensory experiences from daily life. In this regard, we have investigated the effect of electrical neurostimulation with four non-rectangular waveforms in an experiment involving 11 healthy able-bodied subjects. Weiss curves were estimated and rheobase and chronaxie values were obtained showing increases in stimulation time required to elicit sensations for some waveforms. The localization of the sensations reported in the hand also appeared to differ between waveforms, although the total area did not vary significantly. Finally, the possibility of distinguishing different charge- and amplitude-matched stimuli was demonstrated through a two-alternative-forced-choice (2AFC) match-to-sample task, showing the ability of participants to successfully distinguish between waveforms with similar electrical characteristics but different shapes and charge transfer rates. This study provides evidence that, by using different waveforms to stimulate nerves, it is possible to affect not only the required charge to elicit sensations but also the sensation quality and its localization.
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13
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Mishra LN, Kulkarni G, Gadgil M. Modeling the Impact of the Variation in Peripheral Nerve Anatomy on Stimulation. J Pain Res 2022; 15:4097-4111. [PMID: 36605407 PMCID: PMC9809380 DOI: 10.2147/jpr.s380546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 12/06/2022] [Indexed: 12/31/2022] Open
Abstract
Introduction The peripheral nervous system has a complex anatomical structure. Stimulation of nerve fibers in the peripheral nervous system depends on the fiber diameter and myelination as well as its location within the nerve, packing fraction and fascicle distribution within the nerve bundle. This paper analyzes the impact of the variation in peripheral nervous system anatomy and the distance of the stimulating electrodes on the probability of generating an action potential. Methods A mathematical model for effective fascicle conductivity has been developed to capture the variation in the packing fraction and fiber diameter. A linear activating function is utilized to analyze the impact of this effective conductivity and fascicle distribution as an indicator of generating an action potential. Results Finite element simulations are performed for the nerve-electrode configuration to evaluate the electric field. The simulation results are used to analyze the activating function for different packing fractions and type of nerve fibers. The effect of electrode distance on activating function and the total current through a nerve bundle has also been studied. Discussion The simulation results indicate that the peripheral nerve anatomy and electrode distance have a significant effect on the action potential generation.
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Affiliation(s)
- Lakshmi Narayan Mishra
- Nalu Medical Inc., Carlsbad, CA, USA,Correspondence: Lakshmi Narayan Mishra, Nalu Medical Inc., 2320 Faraday Avenue, Suite 100, Carlsbad, CA, 92008, USA, Tel +1 760-448-2360, Email
| | | | - Mandar Gadgil
- Oneirix Engineering Laboratories Pvt. Ltd., Pune, MH, India
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14
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Donahue MJ, Ejneby MS, Jakešová M, Caravaca AS, Andersson G, Sahalianov I, Đerek V, Hult H, Olofsson PS, Głowacki ED. Wireless optoelectronic devices for vagus nerve stimulation in mice. J Neural Eng 2022; 19. [PMID: 36356313 DOI: 10.1088/1741-2552/aca1e3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 11/10/2022] [Indexed: 11/12/2022]
Abstract
Objective.Vagus nerve stimulation (VNS) is a promising approach for the treatment of a wide variety of debilitating conditions, including autoimmune diseases and intractable epilepsy. Much remains to be learned about the molecular mechanisms involved in vagus nerve regulation of organ function. Despite an abundance of well-characterized rodent models of common chronic diseases, currently available technologies are rarely suitable for the required long-term experiments in freely moving animals, particularly experimental mice. Due to challenging anatomical limitations, many relevant experiments require miniaturized, less invasive, and wireless devices for precise stimulation of the vagus nerve and other peripheral nerves of interest. Our objective is to outline possible solutions to this problem by using nongenetic light-based stimulation.Approach.We describe how to design and benchmark new microstimulation devices that are based on transcutaneous photovoltaic stimulation. The approach is to use wired multielectrode cuffs to test different stimulation patterns, and then build photovoltaic stimulators to generate the most optimal patterns. We validate stimulation through heart rate analysis.Main results.A range of different stimulation geometries are explored with large differences in performance. Two types of photovoltaic devices are fabricated to deliver stimulation: photocapacitors and photovoltaic flags. The former is simple and more compact, but has limited efficiency. The photovoltaic flag approach is more elaborate, but highly efficient. Both can be used for wireless actuation of the vagus nerve using light impulses.Significance.These approaches can enable studies in small animals that were previously challenging, such as long-termin vivostudies for mapping functional vagus nerve innervation. This new knowledge may have potential to support clinical translation of VNS for treatment of select inflammatory and neurologic diseases.
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Affiliation(s)
- Mary J Donahue
- Laboratory of Organic Electronics, Campus Norrköping, Linköping University, SE-60174 Norrköping, Sweden
| | - Malin Silverå Ejneby
- Laboratory of Organic Electronics, Campus Norrköping, Linköping University, SE-60174 Norrköping, Sweden.,Wallenberg Centre for Molecular Medicine, Linköping University, SE-58185 Linköping, Sweden
| | - Marie Jakešová
- Bioelectronics Materials and Devices Laboratory, Central European Institute of Technology, Brno University of Technology, Purkyňova 123, 61200 Brno, Czech Republic
| | - April S Caravaca
- Laboratory of Immunobiology, Center for Bioelectronic Medicine, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.,Stockholm Center for Bioelectronic Medicine, MedTechLabs, Karolinska University Hospital, Solna, Sweden
| | | | - Ihor Sahalianov
- Bioelectronics Materials and Devices Laboratory, Central European Institute of Technology, Brno University of Technology, Purkyňova 123, 61200 Brno, Czech Republic
| | - Vedran Đerek
- Department of Physics, Faculty of Science, University of Zagreb, Bijenička c. 32, 10000 Zagreb, Croatia
| | - Henrik Hult
- Stockholm Center for Bioelectronic Medicine, MedTechLabs, Karolinska University Hospital, Solna, Sweden.,Department of Mathematics, KTH, 11428 Stockholm, Sweden
| | - Peder S Olofsson
- Laboratory of Immunobiology, Center for Bioelectronic Medicine, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.,Stockholm Center for Bioelectronic Medicine, MedTechLabs, Karolinska University Hospital, Solna, Sweden.,Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Manhasset, NY, United States of America
| | - Eric Daniel Głowacki
- Laboratory of Organic Electronics, Campus Norrköping, Linköping University, SE-60174 Norrköping, Sweden.,Bioelectronics Materials and Devices Laboratory, Central European Institute of Technology, Brno University of Technology, Purkyňova 123, 61200 Brno, Czech Republic
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15
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Pitzus A, Romeni S, Vallone F, Micera S. A method to establish functional vagus nerve topography from electro-neurographic spontaneous activity. PATTERNS 2022; 3:100615. [PMID: 36419448 PMCID: PMC9676541 DOI: 10.1016/j.patter.2022.100615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 07/08/2022] [Accepted: 09/29/2022] [Indexed: 11/13/2022]
Abstract
Bioelectronic medicine is an emerging approach to treat many types of diseases via electrical stimulation of the autonomic nervous system (ANS). Because the vagus nerve (VN) is one of the most important nerves controlling several ANS functions, stimulation protocols based on knowledge of the functional organization of the VN are particularly interesting. Here, we proposed a method to localize different physiological VN functions by exploiting electro-neurographic signals recorded during spontaneous VN fibers activity. We tested our method on a realistic human cervical VN model geometry implanted via epineural or intraneural electrodes. We considered in silico ground truth scenarios of functional topography generated via different functional neural fibers activities covered by background noise. Our method accurately estimated the underlying functional VN topography by outperforming state-of-the-art methods. Our work paves the way for development of spatially selective stimulation protocols targeting multiple VN bodily functions. A functional imaging method for peripheral nerves has been proposed Our method employs spontaneous physiological signals to perform function localization Anatomical information can be included in the method to obtain more accurate results Our method is data efficient and robust when considering several kinds of noise and artifacts
Electrical stimulation of the vagus nerve modulates the activity of internal organs and has the potential to treat many pathologies. Still, high selectivity is required because altering the functioning of off-target vital organs leads to severe adverse effects. Current steering can produce selective stimulation but requires knowing the functional organization of the target structure. Here, we introduce and test in silico a functional imaging method that allows localization in a nerve section of the fibers linked to several bodily functions. It employs spontaneous electroneurographic signals recorded from the implanted stimulation electrodes and a non-invasive physiological recording related to the target bodily function. The anatomy of the target structure is not required but can be incorporated to improve localization. The results are robust when considering different sources of noise and artifacts. Our method could be employed to determine personalized neuromodulation protocols.
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16
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Cometa A, Falasconi A, Biasizzo M, Carpaneto J, Horn A, Mazzoni A, Micera S. Clinical neuroscience and neurotechnology: An amazing symbiosis. iScience 2022; 25:105124. [PMID: 36193050 PMCID: PMC9526189 DOI: 10.1016/j.isci.2022.105124] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
In the last decades, clinical neuroscience found a novel ally in neurotechnologies, devices able to record and stimulate electrical activity in the nervous system. These technologies improved the ability to diagnose and treat neural disorders. Neurotechnologies are concurrently enabling a deeper understanding of healthy and pathological dynamics of the nervous system through stimulation and recordings during brain implants. On the other hand, clinical neurosciences are not only driving neuroengineering toward the most relevant clinical issues, but are also shaping the neurotechnologies thanks to clinical advancements. For instance, understanding the etiology of a disease informs the location of a therapeutic stimulation, but also the way stimulation patterns should be designed to be more effective/naturalistic. Here, we describe cases of fruitful integration such as Deep Brain Stimulation and cortical interfaces to highlight how this symbiosis between clinical neuroscience and neurotechnology is closer to a novel integrated framework than to a simple interdisciplinary interaction.
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Affiliation(s)
- Andrea Cometa
- The Biorobotics Institute, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
| | - Antonio Falasconi
- Friedrich Miescher Institute for Biomedical Research, 4058 Basel, Switzerland
- Biozentrum, University of Basel, 4056 Basel, Switzerland
| | - Marco Biasizzo
- The Biorobotics Institute, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
| | - Jacopo Carpaneto
- The Biorobotics Institute, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
| | - Andreas Horn
- Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
- MGH Neurosurgery & Center for Neurotechnology and Neurorecovery (CNTR) at MGH Neurology Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Department of Neurology, 10117 Berlin, Germany
| | - Alberto Mazzoni
- The Biorobotics Institute, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
| | - Silvestro Micera
- The Biorobotics Institute, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
- Translational Neural Engineering Lab, School of Engineering, École Polytechnique Fèdèrale de Lausanne, 1015 Lausanne, Switzerland
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17
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Valeriani D, Santoro F, Ienca M. The present and future of neural interfaces. Front Neurorobot 2022; 16:953968. [PMID: 36304780 PMCID: PMC9592849 DOI: 10.3389/fnbot.2022.953968] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 07/13/2022] [Indexed: 11/18/2022] Open
Abstract
The 2020's decade will likely witness an unprecedented development and deployment of neurotechnologies for human rehabilitation, personalized use, and cognitive or other enhancement. New materials and algorithms are already enabling active brain monitoring and are allowing the development of biohybrid and neuromorphic systems that can adapt to the brain. Novel brain-computer interfaces (BCIs) have been proposed to tackle a variety of enhancement and therapeutic challenges, from improving decision-making to modulating mood disorders. While these BCIs have generally been developed in an open-loop modality to optimize their internal neural decoders, this decade will increasingly witness their validation in closed-loop systems that are able to continuously adapt to the user's mental states. Therefore, a proactive ethical approach is needed to ensure that these new technological developments go hand in hand with the development of a sound ethical framework. In this perspective article, we summarize recent developments in neural interfaces, ranging from neurohybrid synapses to closed-loop BCIs, and thereby identify the most promising macro-trends in BCI research, such as simulating vs. interfacing the brain, brain recording vs. brain stimulation, and hardware vs. software technology. Particular attention is devoted to central nervous system interfaces, especially those with application in healthcare and human enhancement. Finally, we critically assess the possible futures of neural interfacing and analyze the short- and long-term implications of such neurotechnologies.
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Affiliation(s)
| | - Francesca Santoro
- Institute for Biological Information Processing - Bioelectronics, IBI-3, Forschungszentrum Juelich, Juelich, Germany
- Faculty of Electrical Engineering and Information Technology, RWTH Aachen University, Aachen, Germany
| | - Marcello Ienca
- College of Humanities, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
- *Correspondence: Marcello Ienca
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18
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Akouissi O, Lacour SP, Micera S, DeSimone A. A finite element model of the mechanical interactions between peripheral nerves and intrafascicular implants. J Neural Eng 2022; 19. [PMID: 35861557 DOI: 10.1088/1741-2552/ac7d0e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 06/29/2022] [Indexed: 11/11/2022]
Abstract
Objective.Intrafascicular peripheral nerve implants are key components in the development of bidirectional neuroprostheses such as touch-enabled bionic limbs for amputees. However, the durability of such interfaces is hindered by the immune response following the implantation. Among the causes linked to such reaction, the mechanical mismatch between host nerve and implant is thought to play a decisive role, especially in chronic settings.Approach.Here we focus on modeling mechanical stresses induced on the peripheral nerve by the implant's micromotion using finite element analysis. Through multiple parametric sweeps, we analyze the role of the implant's material, geometry (aspect-ratio and shape), and surface coating, deriving a set of parameters for the design of better-integrated implants.Main results.Our results indicate that peripheral nerve implants should be designed and manufactured with smooth edges, using materials at most three orders of magnitude stiffer than the nerve, and with innovative geometries to redistribute micromotion-associated loads to less delicate parts of the nerve such as the epineurium.Significance.Overall, our model is a useful tool for the peripheral nerve implant designer that is mindful of the importance of implant mechanics for long term applications.
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Affiliation(s)
- Outman Akouissi
- Bertarelli Foundation Chair in Neuroprosthetic Technology, Laboratory for Soft Bioelectronic Interfaces, Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, 1202, Switzerland.,Bertarelli Foundation Chair in Translational Neuroengineering, Translational Neural Engineering Laboratory, Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, 1202, Switzerland
| | - Stéphanie P Lacour
- Bertarelli Foundation Chair in Neuroprosthetic Technology, Laboratory for Soft Bioelectronic Interfaces, Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, 1202, Switzerland
| | - Silvestro Micera
- Bertarelli Foundation Chair in Translational Neuroengineering, Translational Neural Engineering Laboratory, Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, 1202, Switzerland.,The Biorobotics Institute and Department of Excellence in Robotics & AI, Health Science Interdisciplinary Center, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Antonio DeSimone
- The Biorobotics Institute and Department of Excellence in Robotics & AI, Health Science Interdisciplinary Center, Scuola Superiore Sant'Anna, Pisa, Italy.,SISSA-International School for Advanced Studies, 34136 Trieste, Italy
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19
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A simple model considering spiking probability during extracellular axon stimulation. PLoS One 2022; 17:e0264735. [PMID: 35446861 PMCID: PMC9022861 DOI: 10.1371/journal.pone.0264735] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 02/14/2022] [Indexed: 11/20/2022] Open
Abstract
The spiking probability of an electrically stimulated axon as a function of stimulus amplitude increases in a sigmoidal dependency from 0 to 1. However, most computer simulation studies for neuroprosthetic applications calculate thresholds for neural targets with a deterministic model and by reducing the sigmoid curve to a step function, they miss an important information about the control signal, namely how the spiking efficiency increases with stimulus intensity. Here, this spiking efficiency is taken into account in a compartment model of the Hodgkin Huxley type where a noise current is added in every compartment with an active membrane. A key parameter of the model is a common factor knoise which defines the ion current fluctuations across the cell membrane for every compartment by its maximum sodium ion conductance. In the standard model Gaussian signals are changed every 2.5 μs as a compromise of accuracy and computational costs. Additionally, a formula for other noise transmission times is presented and numerically tested. Spiking probability as a function of stimulus intensity can be approximated by the cumulative distribution function of the normal distribution with RS = σ/μ. Relative spread RS, introduced by Verveen, is a measure for the spread (normalized by the threshold intensity μ), that decreases inversely with axon diameter. Dynamic range, a related measure used in neuroprosthetic studies, defines the intensity range between 10% and 90% spiking probability. We show that (i) the dynamic range normalized by threshold is 2.56 times RS, (ii) RS increases with electrode—axon distance and (iii) we present knoise values for myelinated and unmyelinated axon models in agreement with recoded RS data. The presented method is applicable for other membrane models and can be extended to whole neurons that are described by multi-compartment models.
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20
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Ottaviani MM, Vallone F, Micera S, Recchia FA. Closed-Loop Vagus Nerve Stimulation for the Treatment of Cardiovascular Diseases: State of the Art and Future Directions. Front Cardiovasc Med 2022; 9:866957. [PMID: 35463766 PMCID: PMC9021417 DOI: 10.3389/fcvm.2022.866957] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 03/14/2022] [Indexed: 01/07/2023] Open
Abstract
The autonomic nervous system exerts a fine beat-to-beat regulation of cardiovascular functions and is consequently involved in the onset and progression of many cardiovascular diseases (CVDs). Selective neuromodulation of the brain-heart axis with advanced neurotechnologies is an emerging approach to corroborate CVDs treatment when classical pharmacological agents show limited effectiveness. The vagus nerve is a major component of the cardiac neuroaxis, and vagus nerve stimulation (VNS) is a promising application to restore autonomic function under various pathological conditions. VNS has led to encouraging results in animal models of CVDs, but its translation to clinical practice has not been equally successful, calling for more investigation to optimize this technique. Herein we reviewed the state of the art of VNS for CVDs and discuss avenues for therapeutic optimization. Firstly, we provided a succinct description of cardiac vagal innervation anatomy and physiology and principles of VNS. Then, we examined the main clinical applications of VNS in CVDs and the related open challenges. Finally, we presented preclinical studies that aim at overcoming VNS limitations through optimization of anatomical targets, development of novel neural interface technologies, and design of efficient VNS closed-loop protocols.
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Affiliation(s)
- Matteo Maria Ottaviani
- Institute of Life Sciences, Scuola Superiore Sant’Anna, Pisa, Italy
- Department of Excellence in Robotics and Artificial Intelligence, The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Fabio Vallone
- Department of Excellence in Robotics and Artificial Intelligence, The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Silvestro Micera
- Department of Excellence in Robotics and Artificial Intelligence, The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy
- Bertarelli Foundation Chair in Translational Neural Engineering, Center for Neuroprosthetics, Institute of Bioengineering, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Fabio A. Recchia
- Institute of Life Sciences, Scuola Superiore Sant’Anna, Pisa, Italy
- Fondazione Toscana Gabriele Monasterio, Pisa, Italy
- Department of Physiology, Cardiovascular Research Center, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, United States
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21
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Fellner A, Heshmat A, Werginz P, Rattay F. A finite element method framework to model extracellular neural stimulation. J Neural Eng 2022; 19. [PMID: 35320783 DOI: 10.1088/1741-2552/ac6060] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 03/23/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Increasing complexity in extracellular stimulation experiments and neural implant design also requires realistic computer simulations capable of modeling the neural activity of nerve cells under the influence of an electrical stimulus. Classical model approaches are often based on simplifications, are not able to correctly calculate the electric field generated by complex electrode designs, and do not consider electrical effects of the cell on its surrounding. A more accurate approach is the finite element method (FEM), which provides necessary techniques to solve the Poisson equation for complex geometries under consideration of electrical tissue properties. Especially in situations where neurons experience large and non-symmetric extracellular potential gradients, a FEM solution that implements the cell membrane model can improve the computer simulation results. To investigate the response of neurons in an electric field generated by complex electrode designs, a FEM framework for extracellular stimulation was developed in COMSOL. APPROACH Methods to implement morphologically- and biophysically-detailed neurons including active Hodgkin-Huxley (HH) cell membrane dynamics as well as the stimulation setup are described in detail. Covered methods are (i) development of cell and electrode geometries including meshing strategies, (ii) assignment of physics for the conducting spaces and the realization of active electrodes, (iii) implementation of the HH model, and (iv) coupling of the physics to get a fully described model. MAIN RESULTS Several implementation examples are briefly presented: (i) a full FEM implementation of a HH model cell stimulated with a honeycomb electrode, (ii) the electric field of a cochlear electrode placed inside the cochlea, and (iii) a proof of concept implementation of a detailed double-cable cell membrane model for myelinated nerve fibers. SIGNIFICANCE The presented concepts and methods provide basic and advanced techniques to realize a full FEM framework for innovative studies of neural excitation in response to extracellular stimulation.
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Affiliation(s)
- Andreas Fellner
- Institute of Analysis and Scientific Computing, Vienna University of Technology, Wiedner Hauptstraße 8-10, Vienna, Vienna, 1040, AUSTRIA
| | - Amirreza Heshmat
- Department of Otorhinolaryngology, Innsbruck Medical University, Anichstrasse 35, Innsbruck, 6020, AUSTRIA
| | - Paul Werginz
- Institute of Analysis and Scientific Computing, Vienna University of Technology, Wiedner Hauptstraße 8-10, Vienna, Vienna, 1040, AUSTRIA
| | - Frank Rattay
- Institute of Analysis and Scientific Computing, Vienna University of Technology, Wiedner Hauptstraße 8-10, Vienna, Vienna, 1040, AUSTRIA
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22
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Valle G, Iberite F, Strauss I, D'Anna E, Granata G, Di Iorio R, Stieglitz T, Raspopovic S, Petrini FM, Rossini PM, Micera S. A Psychometric Platform to Collect Somatosensory Sensations for Neuroprosthetic Use. FRONTIERS IN MEDICAL TECHNOLOGY 2022; 3:619280. [PMID: 35047903 PMCID: PMC8757828 DOI: 10.3389/fmedt.2021.619280] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 02/08/2021] [Indexed: 11/13/2022] Open
Abstract
Somatosensory neuroprostheses exploit invasive and non-invasive feedback technologies to restore sensorimotor functions lost to disease or trauma. These devices use electrical stimulation to communicate sensory information to the brain. A sensation characterization procedure is thus necessary to determine the appropriate stimulation parameters and to establish a clear personalized map of the sensations that can be restored. Several questionnaires have been described in the literature to collect the quality, type, location, and intensity of the evoked sensations, but there is still no standard psychometric platform. Here, we propose a new psychometric system containing previously validated questionnaires on evoked sensations, which can be applied to any kind of somatosensory neuroprosthesis. The platform collects stimulation parameters used to elicit sensations and records subjects' percepts in terms of sensation location, type, quality, perceptual threshold, and intensity. It further collects data using standardized assessment questionnaires and scales, performs measurements over time, and collects phantom limb pain syndrome data. The psychometric platform is user-friendly and provides clinicians with all the information needed to assess the sensory feedback. The psychometric platform was validated with three trans-radial amputees. The platform was used to assess intraneural sensory feedback provided through implanted peripheral nerve interfaces. The proposed platform could act as a new standardized assessment toolbox to homogenize the reporting of results obtained with different technologies in the field of somatosensory neuroprosthetics.
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Affiliation(s)
- Giacomo Valle
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.,Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, Zurich, Switzerland
| | | | - Ivo Strauss
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Edoardo D'Anna
- Bertarelli Foundation Chair in Translational Neuroengineering, Centre for Neuroprosthetics, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Institute of Bioengineering, Lausanne, Switzerland
| | - Giuseppe Granata
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS)-Policlinic A. Gemelli Foundation, Institute of Neurology, Catholic University of the Sacred Heart, Rome, Italy
| | - Riccardo Di Iorio
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS)-Policlinic A. Gemelli Foundation, Institute of Neurology, Catholic University of the Sacred Heart, Rome, Italy
| | - Thomas Stieglitz
- Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering-IMTEK, Bernstein Center, BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, Germany
| | - Stanisa Raspopovic
- Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, Zurich, Switzerland
| | - Francesco M Petrini
- Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, Zurich, Switzerland
| | - Paolo M Rossini
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS)-Policlinic A. Gemelli Foundation, Institute of Neurology, Catholic University of the Sacred Heart, Rome, Italy
| | - Silvestro Micera
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.,Bertarelli Foundation Chair in Translational Neuroengineering, Centre for Neuroprosthetics, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Institute of Bioengineering, Lausanne, Switzerland
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23
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Eiber CD, Payne SC, Biscola NP, Havton LA, Keast JR, Osborne PB, Fallon JB. Computational modelling of nerve stimulation and recording with peripheral visceral neural interfaces. J Neural Eng 2021; 18. [PMID: 34740201 DOI: 10.1088/1741-2552/ac36e2] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 11/05/2021] [Indexed: 12/30/2022]
Abstract
Objective.Neuromodulation of visceral nerves is being intensively studied for treating a wide range of conditions, but effective translation requires increasing the efficacy and predictability of neural interface performance. Here we use computational models of rat visceral nerve to predict how neuroanatomical variability could affect both electrical stimulation and recording with an experimental planar neural interface.Approach.We developed a hybrid computational pipeline,VisceralNerveEnsembleRecording andStimulation (ViNERS), to couple finite-element modelling of extracellular electrical fields with biophysical simulations of individual axons. Anatomical properties of fascicles and axons in rat pelvic and vagus nerves were measured or obtained from public datasets. To validate ViNERS, we simulated pelvic nerve stimulation and recording with an experimental four-electrode planar array.Main results.Axon diameters measured from pelvic nerve were used to model a population of myelinated and unmyelinated axons and simulate recordings of electrically evoked single-unit field potentials (SUFPs). Across visceral nerve fascicles of increasing size, our simulations predicted an increase in stimulation threshold and a decrease in SUFP amplitude. Simulated threshold changes were dominated by changes in perineurium thickness, which correlates with fascicle diameter. We also demonstrated that ViNERS could simulate recordings of electrically-evoked compound action potentials (ECAPs) that were qualitatively similar to pelvic nerve recording made with the array used for simulation.Significance.We introduce ViNERS as a new open-source computational tool for modelling large-scale stimulation and recording from visceral nerves. ViNERS predicts how neuroanatomical variation in rat pelvic nerve affects stimulation and recording with an experimental planar electrode array. We show ViNERS can simulate ECAPS that capture features of our recordings, but our results suggest the underlying NEURON models need to be further refined and specifically adapted to accurately simulate visceral nerve axons.
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Affiliation(s)
- Calvin D Eiber
- Department of Anatomy and Physiology, The University of Melbourne, Victoria, Australia
| | - Sophie C Payne
- Bionics Institute, East Melbourne, Victoria, Australia.,Medical Bionics Department, The University of Melbourne, Victoria, Australia
| | - Natalia P Biscola
- Icahn School of Medicine at Mount Sinai, New York, United States of America
| | - Leif A Havton
- Icahn School of Medicine at Mount Sinai, New York, United States of America
| | - Janet R Keast
- Department of Anatomy and Physiology, The University of Melbourne, Victoria, Australia
| | - Peregrine B Osborne
- Department of Anatomy and Physiology, The University of Melbourne, Victoria, Australia
| | - James B Fallon
- Bionics Institute, East Melbourne, Victoria, Australia.,Medical Bionics Department, The University of Melbourne, Victoria, Australia
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24
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Lei IM, Jiang C, Lei CL, de Rijk SR, Tam YC, Swords C, Sutcliffe MPF, Malliaras GG, Bance M, Huang YYS. 3D printed biomimetic cochleae and machine learning co-modelling provides clinical informatics for cochlear implant patients. Nat Commun 2021; 12:6260. [PMID: 34716306 PMCID: PMC8556326 DOI: 10.1038/s41467-021-26491-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 10/06/2021] [Indexed: 02/07/2023] Open
Abstract
Cochlear implants restore hearing in patients with severe to profound deafness by delivering electrical stimuli inside the cochlea. Understanding stimulus current spread, and how it correlates to patient-dependent factors, is hampered by the poor accessibility of the inner ear and by the lack of clinically-relevant in vitro, in vivo or in silico models. Here, we present 3D printing-neural network co-modelling for interpreting electric field imaging profiles of cochlear implant patients. With tuneable electro-anatomy, the 3D printed cochleae can replicate clinical scenarios of electric field imaging profiles at the off-stimuli positions. The co-modelling framework demonstrated autonomous and robust predictions of patient profiles or cochlear geometry, unfolded the electro-anatomical factors causing current spread, assisted on-demand printing for implant testing, and inferred patients' in vivo cochlear tissue resistivity (estimated mean = 6.6 kΩcm). We anticipate our framework will facilitate physical modelling and digital twin innovations for neuromodulation implants.
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Affiliation(s)
- Iek Man Lei
- Department of Engineering, University of Cambridge, Cambridge, United Kingdom.,The Nanoscience Centre, University of Cambridge, Cambridge, United Kingdom
| | - Chen Jiang
- Department of Engineering, University of Cambridge, Cambridge, United Kingdom.,Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom.,Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China
| | - Chon Lok Lei
- Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macau.,Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Simone Rosalie de Rijk
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Yu Chuen Tam
- Emmeline Centre for Hearing Implants, Addenbrookes Hospital, Cambridge, United Kingdom
| | - Chloe Swords
- Department of Physiology, Development and Neurosciences, Cambridge, United Kingdom
| | | | - George G Malliaras
- Department of Engineering, University of Cambridge, Cambridge, United Kingdom
| | - Manohar Bance
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom.
| | - Yan Yan Shery Huang
- Department of Engineering, University of Cambridge, Cambridge, United Kingdom. .,The Nanoscience Centre, University of Cambridge, Cambridge, United Kingdom.
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25
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Badi M, Wurth S, Scarpato I, Roussinova E, Losanno E, Bogaard A, Delacombaz M, Borgognon S, C Vanc Ara P, Fallegger F, Su DK, Schmidlin E, Courtine G, Bloch J, Lacour SP, Stieglitz T, Rouiller EM, Capogrosso M, Micera S. Intrafascicular peripheral nerve stimulation produces fine functional hand movements in primates. Sci Transl Med 2021; 13:eabg6463. [PMID: 34705521 DOI: 10.1126/scitranslmed.abg6463] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- Marion Badi
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics, and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Sophie Wurth
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics, and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Ilaria Scarpato
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics, and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Evgenia Roussinova
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics, and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Elena Losanno
- Biorobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56025 Pisa, Italy
| | - Andrew Bogaard
- Department of Neuroscience and Movement Sciences, Platform of Translational Neurosciences, Section of Medicine, Faculty of Sciences and Medicine, University of Fribourg, 1700 Fribourg, Switzerland
| | - Maude Delacombaz
- Department of Neuroscience and Movement Sciences, Platform of Translational Neurosciences, Section of Medicine, Faculty of Sciences and Medicine, University of Fribourg, 1700 Fribourg, Switzerland
| | - Simon Borgognon
- Department of Neuroscience and Movement Sciences, Platform of Translational Neurosciences, Section of Medicine, Faculty of Sciences and Medicine, University of Fribourg, 1700 Fribourg, Switzerland.,Center for Neuroprosthetics and Brain Mind Institute, School of Life Sciences, EPFL, 1015 Lausanne, Switzerland
| | - Paul C Vanc Ara
- Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering-IMTEK, Bernstein Center Freiburg, and BrainLinks-BrainTools Center, University of Freiburg, 79110 Freiburg, Germany
| | - Florian Fallegger
- Bertarelli Foundation Chair in Neuroprosthetic Technology, Laboratory for Soft Bioelectronics Interface, Institute of Microengineering, Institute of Bioengineering, Centre for Neuroprosthetics, 1202 Geneva, Switzerland
| | - David K Su
- Neurological Surgery, Harborview Medical Center, Seattle, WA 98104, USA
| | - Eric Schmidlin
- Department of Neuroscience and Movement Sciences, Platform of Translational Neurosciences, Section of Medicine, Faculty of Sciences and Medicine, University of Fribourg, 1700 Fribourg, Switzerland
| | - Grégoire Courtine
- Center for Neuroprosthetics and Brain Mind Institute, School of Life Sciences, EPFL, 1015 Lausanne, Switzerland.,Defitech Center for Interventional Neurotherapies (NeuroRestore), EPFL, University Hospital of Lausanne (CHUV), and University of Lausanne (UNIL), 1015 Lausanne, Switzerland
| | - Jocelyne Bloch
- Defitech Center for Interventional Neurotherapies (NeuroRestore), EPFL, University Hospital of Lausanne (CHUV), and University of Lausanne (UNIL), 1015 Lausanne, Switzerland
| | - Stéphanie P Lacour
- Bertarelli Foundation Chair in Neuroprosthetic Technology, Laboratory for Soft Bioelectronics Interface, Institute of Microengineering, Institute of Bioengineering, Centre for Neuroprosthetics, 1202 Geneva, Switzerland
| | - Thomas Stieglitz
- Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering-IMTEK, Bernstein Center Freiburg, and BrainLinks-BrainTools Center, University of Freiburg, 79110 Freiburg, Germany
| | - Eric M Rouiller
- Department of Neuroscience and Movement Sciences, Platform of Translational Neurosciences, Section of Medicine, Faculty of Sciences and Medicine, University of Fribourg, 1700 Fribourg, Switzerland
| | - Marco Capogrosso
- Department of Neuroscience and Movement Sciences, Platform of Translational Neurosciences, Section of Medicine, Faculty of Sciences and Medicine, University of Fribourg, 1700 Fribourg, Switzerland.,Department of Neurological Surgery, Rehabilitation and Neural Engineering Laboratories, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Silvestro Micera
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics, and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.,Biorobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56025 Pisa, Italy
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26
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ASCENT (Automated Simulations to Characterize Electrical Nerve Thresholds): A pipeline for sample-specific computational modeling of electrical stimulation of peripheral nerves. PLoS Comput Biol 2021; 17:e1009285. [PMID: 34492004 PMCID: PMC8423288 DOI: 10.1371/journal.pcbi.1009285] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 07/18/2021] [Indexed: 12/04/2022] Open
Abstract
Electrical stimulation and block of peripheral nerves hold great promise for treatment of a range of disease and disorders, but promising results from preclinical studies often fail to translate to successful clinical therapies. Differences in neural anatomy across species require different electrodes and stimulation parameters to achieve equivalent nerve responses, and accounting for the consequences of these factors is difficult. We describe the implementation, validation, and application of a standardized, modular, and scalable computational modeling pipeline for biophysical simulations of electrical activation and block of nerve fibers within peripheral nerves. The ASCENT (Automated Simulations to Characterize Electrical Nerve Thresholds) pipeline provides a suite of built-in capabilities for user control over the entire workflow, including libraries for parts to assemble electrodes, electrical properties of biological materials, previously published fiber models, and common stimulation waveforms. We validated the accuracy of ASCENT calculations, verified usability in beta release, and provide several compelling examples of ASCENT-implemented models. ASCENT will enable the reproducibility of simulation data, and it will be used as a component of integrated simulations with other models (e.g., organ system models), to interpret experimental results, and to design experimental and clinical interventions for the advancement of peripheral nerve stimulation therapies. Despite promising results from preclinical studies, novel therapies using electrical stimulation of peripheral nerves often fail to produce successful clinical outcomes due to differences in neural anatomy across species. These differences often require different electrodes to interface with the nerves and/or different stimulation parameters to achieve equivalent nerve responses. Further, differences in nerve anatomy across a population contribute to differences in nerve responses to stimulation. These inter-species and inter-individual differences can be studied using computational modeling of individual-specific peripheral nerve morphology and biophysical properties. To accelerate the process of computational modeling of individual nerve anatomy, we developed ASCENT, a software platform for simulating the responses of sample-specific nerves to electrical stimulation with custom electrodes and stimulation parameters. ASCENT automates the complex, multi-step process required to build computational models of preclinical and clinical studies and to design novel stimulation protocols using biophysically realistic simulations. The ASCENT pipeline will be used to develop technologies that increase the selectivity and efficiency of stimulation and to accelerate the translation of novel peripheral nerve stimulation therapies to the clinic.
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27
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Shokur S, Mazzoni A, Schiavone G, Weber DJ, Micera S. A modular strategy for next-generation upper-limb sensory-motor neuroprostheses. MED 2021; 2:912-937. [DOI: 10.1016/j.medj.2021.05.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 04/28/2021] [Accepted: 05/10/2021] [Indexed: 02/06/2023]
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28
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Romeni S, Zoccolan D, Micera S. A machine learning framework to optimize optic nerve electrical stimulation for vision restoration. PATTERNS (NEW YORK, N.Y.) 2021; 2:100286. [PMID: 34286301 PMCID: PMC8276026 DOI: 10.1016/j.patter.2021.100286] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 03/05/2021] [Accepted: 05/17/2021] [Indexed: 11/25/2022]
Abstract
Optic nerve electrical stimulation is a promising technique to restore vision in blind subjects. Machine learning methods can be used to select effective stimulation protocols, but they require a model of the stimulated system to generate enough training data. Here, we use a convolutional neural network (CNN) as a model of the ventral visual stream. A genetic algorithm drives the activation of the units in a layer of the CNN representing a cortical region toward a desired pattern, by refining the activation imposed at a layer representing the optic nerve. To simulate the pattern of activation elicited by the sites of an electrode array, a simple point-source model was introduced and its optimization process was investigated for static and dynamic scenes. Psychophysical data confirm that our stimulation evolution framework produces results compatible with natural vision. Machine learning approaches could become a very powerful tool to optimize and personalize neuroprosthetic systems.
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Affiliation(s)
- Simone Romeni
- Bertarelli Foundation Chair in Translational NeuroEngineering, Center for Neuroprosthetics and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Davide Zoccolan
- Visual Neuroscience Lab, International School for Advanced Studies (SISSA), Trieste, Italy
| | - Silvestro Micera
- Bertarelli Foundation Chair in Translational NeuroEngineering, Center for Neuroprosthetics and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- The Biorobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, Pontedera, Italy
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29
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Vallone F, Ottaviani MM, Dedola F, Cutrone A, Romeni S, Panarese AM, Bernini F, Cracchiolo M, Strauss I, Gabisonia K, Gorgodze N, Mazzoni A, Recchia FA, Micera S. Simultaneous decoding of cardiovascular and respiratory functional changes from pig intraneural vagus nerve signals. J Neural Eng 2021; 18. [PMID: 34153949 DOI: 10.1088/1741-2552/ac0d42] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 06/21/2021] [Indexed: 12/15/2022]
Abstract
Objective. Bioelectronic medicine is opening new perspectives for the treatment of some major chronic diseases through the physical modulation of autonomic nervous system activity. Being the main peripheral route for electrical signals between central nervous system and visceral organs, the vagus nerve (VN) is one of the most promising targets. Closed-loop VN stimulation (VNS) would be crucial to increase effectiveness of this approach. Therefore, the extrapolation of useful physiological information from VN electrical activity would represent an invaluable source for single-target applications. Here, we present an advanced decoding algorithm novel to VN studies and properly detecting different functional changes from VN signals.Approach. VN signals were recorded using intraneural electrodes in anaesthetized pigs during cardiovascular and respiratory challenges mimicking increases in arterial blood pressure, tidal volume and respiratory rate. We developed a decoding algorithm that combines discrete wavelet transformation, principal component analysis, and ensemble learning made of classification trees.Main results. The new decoding algorithm robustly achieved high accuracy levels in identifying different functional changes and discriminating among them. Interestingly our findings suggest that electrodes positioning plays an important role on decoding performances. We also introduced a new index for the characterization of recording and decoding performance of neural interfaces. Finally, by combining an anatomically validated hybrid neural model and discrimination analysis, we provided new evidence suggesting a functional topographical organization of VN fascicles.Significance. This study represents an important step towards the comprehension of VN signaling, paving the way for the development of effective closed-loop VNS systems.
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Affiliation(s)
- Fabio Vallone
- The BioRobotics Institute and Department of Excellence in Robotics and Artificial Intelligence, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Matteo Maria Ottaviani
- The BioRobotics Institute and Department of Excellence in Robotics and Artificial Intelligence, Scuola Superiore Sant'Anna, Pisa, Italy.,Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Francesca Dedola
- The BioRobotics Institute and Department of Excellence in Robotics and Artificial Intelligence, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Annarita Cutrone
- The BioRobotics Institute and Department of Excellence in Robotics and Artificial Intelligence, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Simone Romeni
- Bertarelli Foundation Chair in Translational Neural Engineering, Center for Neuroprosthetics and Institute of Bioengineering, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Adele Macrí Panarese
- The BioRobotics Institute and Department of Excellence in Robotics and Artificial Intelligence, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Fabio Bernini
- Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Marina Cracchiolo
- The BioRobotics Institute and Department of Excellence in Robotics and Artificial Intelligence, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Ivo Strauss
- The BioRobotics Institute and Department of Excellence in Robotics and Artificial Intelligence, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Khatia Gabisonia
- Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, Italy.,Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | - Nikoloz Gorgodze
- Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, Italy.,Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | - Alberto Mazzoni
- The BioRobotics Institute and Department of Excellence in Robotics and Artificial Intelligence, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Fabio A Recchia
- Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, Italy.,Fondazione Toscana Gabriele Monasterio, Pisa, Italy.,Department of Physiology, Cardiovascular Research Center, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, United States of America
| | - Silvestro Micera
- The BioRobotics Institute and Department of Excellence in Robotics and Artificial Intelligence, Scuola Superiore Sant'Anna, Pisa, Italy.,Bertarelli Foundation Chair in Translational Neural Engineering, Center for Neuroprosthetics and Institute of Bioengineering, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
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