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Peña E, Pelot NA, Grill WM. Computational models of compound nerve action potentials: Efficient filter-based methods to quantify effects of tissue conductivities, conduction distance, and nerve fiber parameters. PLoS Comput Biol 2024; 20:e1011833. [PMID: 38427699 PMCID: PMC10936855 DOI: 10.1371/journal.pcbi.1011833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 03/13/2024] [Accepted: 01/16/2024] [Indexed: 03/03/2024] Open
Abstract
BACKGROUND Peripheral nerve recordings can enhance the efficacy of neurostimulation therapies by providing a feedback signal to adjust stimulation settings for greater efficacy or reduced side effects. Computational models can accelerate the development of interfaces with high signal-to-noise ratio and selective recording. However, validation and tuning of model outputs against in vivo recordings remains computationally prohibitive due to the large number of fibers in a nerve. METHODS We designed and implemented highly efficient modeling methods for simulating electrically evoked compound nerve action potential (CNAP) signals. The method simulated a subset of fiber diameters present in the nerve using NEURON, interpolated action potential templates across fiber diameters, and filtered the templates with a weighting function derived from fiber-specific conduction velocity and electromagnetic reciprocity outputs of a volume conductor model. We applied the methods to simulate CNAPs from rat cervical vagus nerve. RESULTS Brute force simulation of a rat vagal CNAP with all 1,759 myelinated and 13,283 unmyelinated fibers in NEURON required 286 and 15,860 CPU hours, respectively, while filtering interpolated templates required 30 and 38 seconds on a desktop computer while maintaining accuracy. Modeled CNAP amplitude could vary by over two orders of magnitude depending on tissue conductivities and cuff opening within experimentally relevant ranges. Conduction distance and fiber diameter distribution also strongly influenced the modeled CNAP amplitude, shape, and latency. Modeled and in vivo signals had comparable shape, amplitude, and latency for myelinated fibers but not for unmyelinated fibers. CONCLUSIONS Highly efficient methods of modeling neural recordings quantified the large impact that tissue properties, conduction distance, and nerve fiber parameters have on CNAPs. These methods expand the computational accessibility of neural recording models, enable efficient model tuning for validation, and facilitate the design of novel recording interfaces for neurostimulation feedback and understanding physiological systems.
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Affiliation(s)
- Edgar Peña
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
| | - Nicole A. Pelot
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
| | - Warren M. Grill
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, United States of America
- Department of Neurobiology, Duke University School of Medicine, Durham, North Carolina, United States of America
- Department of Neurosurgery, Duke University School of Medicine, Durham, North Carolina, United States of America
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2
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Hatamie A, He X, Zhang XW, Oomen PE, Ewing AG. Advances in nano/microscale electrochemical sensors and biosensors for analysis of single vesicles, a key nanoscale organelle in cellular communication. Biosens Bioelectron 2022; 220:114899. [DOI: 10.1016/j.bios.2022.114899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/03/2022] [Accepted: 11/07/2022] [Indexed: 11/11/2022]
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3
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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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4
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Pollina L, Vallone F, Ottaviani MM, Strauss I, Carlucci L, Recchia FA, Micera S, Moccia S. A lightweight learning-based decoding algorithm for intraneural vagus nerve activity classification in pigs. J Neural Eng 2022; 19. [PMID: 35896098 DOI: 10.1088/1741-2552/ac84ab] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 07/27/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Bioelectronic medicine is an emerging field that aims at developing closed-loop neuromodulation protocols for the autonomic nervous system (ANS) to treat a wide range of disorders. When designing a closed-loop protocol for real time modulation of the ANS, the computational execution time and the memory and power demands of the decoding step are important factors to consider. In the context of cardiovascular and respiratory diseases, these requirements may partially explain why closed-loop clinical neuromodulation protocols that adapt stimulation parameters on patient's clinical characteristics are currently missing. APPROACH Here, we developed a lightweight learning-based decoder for the classification of cardiovascular and respiratory functional challenges from neural signals acquired through intraneural electrodes implanted in the cervical vagus nerve (VN) of 5 anaesthetized pigs. Our algorithm is based on signal temporal windowing, 9 handcrafted features, and Random Forest (RF) model for classification. Temporal windowing ranging from 50 ms to 1 sec, compatible in duration with cardio-respiratory dynamics, was applied to the data in order to mimic a pseudo real-time scenario. MAIN RESULTS We were able to achieve high balanced accuracy (BA) values over the whole range of temporal windowing duration. We identified 500 ms as the optimal temporal windowing duration for both BA values and computational execution time processing, achieving more than 86% for BA and a computational execution time of only ∼6.8 ms. Our algorithm outperformed in terms of balanced accuracy and computational execution time a state of the art decoding algorithm tested on the same dataset [1]. We found that RF outperformed other machine learning models such as Support Vector Machines, K-Nearest Neighbors, and Multi-Layer Perceptrons. SIGNIFICANCE Our approach could represent an important step towards the implementation of a closed-loop neuromodulation protocol relying on a single intraneural interface able to perform real-time decoding tasks and selective modulation of the VN.
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Affiliation(s)
- Leonardo Pollina
- Sant'Anna School of Advanced Studies, P.za Martiri della Liberta', 33, Pisa, 56127, ITALY
| | - Fabio Vallone
- Sant'Anna School of Advanced Studies, P.za Martiri della Liberta', 33, Pisa, 56127, ITALY
| | - Matteo M Ottaviani
- Scuola Superiore Sant'Anna, Istituto di Scienze Della Vita (ISV), P.za Martiri della Liberta', 33, Pisa, 56127, ITALY
| | - Ivo Strauss
- Scuola Superiore Sant'Anna, P.za Martiri della Libertà 33, Pisa, 56127, ITALY
| | - Lucia Carlucci
- Scuola Superiore Sant'Anna, Istituto di Scienze Della Vita (ISV), P.zza Martiri della Libertà 33, Pisa, 56127, ITALY
| | - Fabio A Recchia
- Scuola Superiore Sant'Anna, Istituto di Scienze Della Vita (ISV), P.za Martiri della Libertà 33, Pisa, 56127, ITALY
| | - Silvestro Micera
- Scuola Superiore Sant'Anna, P.za Martiri della Liberta', 33, Pisa, Toscana, 56127, ITALY
| | - Sara Moccia
- Scuola Superiore Sant'Anna, P.za Martiri della Liberta', 33, Pisa, 56127, ITALY
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5
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Sevcencu C. Single-interface bioelectronic medicines - concept, clinical applications and preclinical data. J Neural Eng 2022; 19. [PMID: 35533654 DOI: 10.1088/1741-2552/ac6e08] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 05/08/2022] [Indexed: 11/12/2022]
Abstract
Presently, large groups of patients with various diseases are either intolerant, or irresponsive to drug therapies and also intractable by surgery. For several diseases, one option which is available for such patients is the implantable neurostimulation therapy. However, lacking closed-loop control and selective stimulation capabilities, the present neurostimulation therapies are not optimal and are therefore used as only "third" therapeutic options when a disease cannot be treated by drugs or surgery. Addressing those limitations, a next generation class of closed-loop controlled and selective neurostimulators generically named bioelectronic medicines seems within reach. A sub-class of such devices is meant to monitor and treat impaired functions by intercepting, analyzing and modulating neural signals involved in the regulation of such functions using just one neural interface for those purposes. The primary objective of this review is to provide a first broad perspective on this type of single-interface devices for bioelectronic therapies. For this purpose, the concept, clinical applications and preclinical studies for further developments with such devices are here analyzed in a narrative manner.
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Affiliation(s)
- Cristian Sevcencu
- National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, Cluj-Napoca, 400293, ROMANIA
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6
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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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7
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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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8
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Stumpp L, Smets H, Vespa S, Cury J, Doguet P, Delbeke J, Nonclercq A, El Tahry R. Vagus Nerve Electroneurogram-Based Detection of Acute Pentylenetetrazol Induced Seizures in Rats. Int J Neural Syst 2021; 31:2150024. [PMID: 34030610 DOI: 10.1142/s0129065721500246] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
On-demand stimulation improves the efficacy of vagus nerve stimulation (VNS) in refractory epilepsy. The vagus nerve is the main peripheral parasympathetic connection and seizures are known to exhibit autonomic symptoms. Therefore, we hypothesized that seizure detection is possible through vagus nerve electroneurogram (VENG) recording. We developed a metric able to measure abrupt changes in amplitude and frequency of spontaneous vagus nerve action potentials. A classifier was trained using a "leave-one-out" method on a set of 6 seizures and 3 control recordings to utilize the VENG spike feature-based metric for seizure detection. We were able to detect pentylenetetrazol (PTZ) induced acute seizures in 6/6 animals during different stages of the seizure with no false detection. The classifier detected the seizure during an early stage in 3/6 animals and at the onset of tonic clonic stage of the seizure in 3/6 animals. EMG and motion artefacts often accompany epileptic activity. We showed the "epileptic" neural signal to be independent from EMG and motion artefacts. We confirmed the existence of seizure related signals in the VENG recording and proved their applicability for seizure detection. This detection might be a promising tool to improve efficacy of VNS treatment by developing new responsive stimulation systems.
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Affiliation(s)
- Lars Stumpp
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium
| | - Hugo Smets
- BEAMS Department, Université libre de Bruxelles, Brussels, Belgium
| | - Simone Vespa
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium
| | - Joaquin Cury
- BEAMS Department, Université libre de Bruxelles, Brussels, Belgium
| | | | - Jean Delbeke
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium
| | | | - Riem El Tahry
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium.,Cliniques Universitaires Saint Luc, Center for Refractory Epilepsy, Brussels, Belgium
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9
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Cracchiolo M, Ottaviani MM, Panarese A, Strauss I, Vallone F, Mazzoni A, Micera S. Bioelectronic medicine for the autonomic nervous system: clinical applications and perspectives. J Neural Eng 2021; 18. [PMID: 33592597 DOI: 10.1088/1741-2552/abe6b9] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 02/16/2021] [Indexed: 12/11/2022]
Abstract
Bioelectronic medicine (BM) is an emerging new approach for developing novel neuromodulation therapies for pathologies that have been previously treated with pharmacological approaches. In this review, we will focus on the neuromodulation of autonomic nervous system (ANS) activity with implantable devices, a field of BM that has already demonstrated the ability to treat a variety of conditions, from inflammation to metabolic and cognitive disorders. Recent discoveries about immune responses to ANS stimulation are the laying foundation for a new field holding great potential for medical advancement and therapies and involving an increasing number of research groups around the world, with funding from international public agencies and private investors. Here, we summarize the current achievements and future perspectives for clinical applications of neural decoding and stimulation of the ANS. First, we present the main clinical results achieved so far by different BM approaches and discuss the challenges encountered in fully exploiting the potential of neuromodulatory strategies. Then, we present current preclinical studies aimed at overcoming the present limitations by looking for optimal anatomical targets, developing novel neural interface technology, and conceiving more efficient signal processing strategies. Finally, we explore the prospects for translating these advancements into clinical practice.
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Affiliation(s)
- Marina Cracchiolo
- The BioRobotics Institute and Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Matteo Maria Ottaviani
- The BioRobotics Institute and Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Alessandro Panarese
- The BioRobotics Institute and Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Ivo Strauss
- The BioRobotics Institute and Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Fabio Vallone
- The BioRobotics Institute and Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Alberto Mazzoni
- The BioRobotics Institute and Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Silvestro Micera
- The BioRobotics Institute and Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.,Bertarelli Foundation Chair in Translational NeuroEngineering, Centre for Neuroprosthetics and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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10
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Abstract
Peripheral nerve interfaces (PNIs) record and/or modulate neural activity of nerves, which are responsible for conducting sensory-motor information to and from the central nervous system, and for regulating the activity of inner organs. PNIs are used both in neuroscience research and in therapeutical applications such as precise closed-loop control of neuroprosthetic limbs, treatment of neuropathic pain and restoration of vital functions (e.g. breathing and bladder management). Implantable interfaces represent an attractive solution to directly access peripheral nerves and provide enhanced selectivity both in recording and in stimulation, compared to their non-invasive counterparts. Nevertheless, the long-term functionality of implantable PNIs is limited by tissue damage, which occurs at the implant-tissue interface, and is thus highly dependent on material properties, biocompatibility and implant design. Current research focuses on the development of mechanically compliant PNIs, which adapt to the anatomy and dynamic movements of nerves in the body thereby limiting foreign body response. In this paper, we review recent progress in the development of flexible and implantable PNIs, highlighting promising solutions related to materials selection and their associated fabrication methods, and integrated functions. We report on the variety of available interface designs (intraneural, extraneural and regenerative) and different modulation techniques (electrical, optical, chemical) emphasizing the main challenges associated with integrating such systems on compliant substrates.
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Affiliation(s)
- Valentina Paggi
- Bertarelli Foundation Chair in Neuroprosthetic Technology, Laboratory for Soft Bioelectronic Interfaces, Institute of Microengineering, Institute of Bioengineering, Centre for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland. Equally contributing authors
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11
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Stumpp L, Smets H, Vespa S, Cury J, Doguet P, Delbeke J, Hermans E, Sevcencu C, Nielsen TN, Nonclercq A, Tahry RE. Recording of spontaneous vagus nerve activity during Pentylenetetrazol-induced seizures in rats. J Neurosci Methods 2020; 343:108832. [DOI: 10.1016/j.jneumeth.2020.108832] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 06/08/2020] [Accepted: 06/24/2020] [Indexed: 01/23/2023]
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12
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Zanos TP. Recording and Decoding of Vagal Neural Signals Related to Changes in Physiological Parameters and Biomarkers of Disease. Cold Spring Harb Perspect Med 2019; 9:a034157. [PMID: 30670469 PMCID: PMC6886457 DOI: 10.1101/cshperspect.a034157] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Our bodies have built-in neural reflexes that continuously monitor organ function and maintain physiological homeostasis. Whereas the field of bioelectronic medicine has mainly focused on the stimulation of neural circuits to treat various conditions, recent studies have started to investigate the possibility of leveraging the sensory arm of these reflexes to diagnose disease states. To accomplish this, neural signals emanating from the body's built-in biosensors and propagating through peripheral nerves must be recorded and decoded to identify the presence or levels of relevant biomarkers of disease. The process of acquiring these signals poses several technical challenges related to the neural interfaces, surgical techniques, and data-processing framework needed to record and analyze them. However, these challenges can be addressed with a rigorous experimental approach and new advances in implantable electrodes, signal processing, and machine learning methods. Outlined in this review are studies decoding vagus nerve activity as it related to inflammatory, metabolic, and cardiopulmonary biomarkers. Successfully decoding peripheral nerve activity related to disease states will not only enable the development of real-time diagnostic devices, but also help advancing truly closed-loop neuromodulation technologies.
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Affiliation(s)
- Theodoros P Zanos
- Center for Bioelectronic Medicine, The Feinstein Institute for Medical Research, Donald & Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, New York 11030
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13
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Cracchiolo M, Sacramento JF, Mazzoni A, Panarese A, Carpaneto J, Conde SV, Micera S. Decoding Neural Metabolic Markers From the Carotid Sinus Nerve in a Type 2 Diabetes Model. IEEE Trans Neural Syst Rehabil Eng 2019; 27:2034-2043. [DOI: 10.1109/tnsre.2019.2942398] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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14
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Ganzer PD, Sharma G. Opportunities and challenges for developing closed-loop bioelectronic medicines. Neural Regen Res 2019; 14:46-50. [PMID: 30531069 PMCID: PMC6262994 DOI: 10.4103/1673-5374.243697] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
The peripheral nervous system plays a major role in the maintenance of our physiology. Several peripheral nerves intimately regulate the state of the brain, spinal cord, and visceral systems. A new class of therapeutics, called bioelectronic medicines, are being developed to precisely regulate physiology and treat dysfunction using peripheral nerve stimulation. In this review, we first discuss new work using closed-loop bioelectronic medicine to treat upper limb paralysis. In contrast to open-loop bioelectronic medicines, closed-loop approaches trigger ‘on demand’ peripheral nerve stimulation due to a change in function (e.g., during an upper limb movement or a change in cardiopulmonary state). We also outline our perspective on timing rules for closed-loop bioelectronic stimulation, interface features for non-invasively stimulating peripheral nerves, and machine learning algorithms to recognize disease events for closed-loop stimulation control. Although there will be several challenges for this emerging field, we look forward to future bioelectronic medicines that can autonomously sense changes in the body, to provide closed-loop peripheral nerve stimulation and treat disease.
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Affiliation(s)
- Patrick D Ganzer
- Medical Devices and Neuromodulation, Battelle Memorial Institute, Columbus, OH, USA
| | - Gaurav Sharma
- Medical Devices and Neuromodulation, Battelle Memorial Institute, Columbus, OH, USA
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González-González MA, Kanneganti A, Joshi-Imre A, Hernandez-Reynoso AG, Bendale G, Modi R, Ecker M, Khurram A, Cogan SF, Voit WE, Romero-Ortega MI. Thin Film Multi-Electrode Softening Cuffs for Selective Neuromodulation. Sci Rep 2018; 8:16390. [PMID: 30401906 PMCID: PMC6219541 DOI: 10.1038/s41598-018-34566-6] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Accepted: 10/18/2018] [Indexed: 01/21/2023] Open
Abstract
Silicone nerve cuff electrodes are commonly implanted on relatively large and accessible somatic nerves as peripheral neural interfaces. While these cuff electrodes are soft (1–50 MPa), their self-closing mechanism requires of thick walls (200–600 µm), which in turn contribute to fibrotic tissue growth around and inside the device, compromising the neural interface. We report the use of thiol-ene/acrylate shape memory polymer (SMP) for the fabrication of thin film multi-electrode softening cuffs (MSC). We fabricated multi-size MSC with eight titanium nitride (TiN) electrodes ranging from 1.35 to 13.95 × 10−4 cm2 (1–3 kΩ) and eight smaller gold (Au) electrodes (3.3 × 10−5 cm2; 750 kΩ), that soften at physiological conditions to a modulus of 550 MPa. While the SMP material is not as soft as silicone, the flexural forces of the SMP cuff are about 70–700 times lower in the MSC devices due to the 30 μm thick film compared to the 600 μm thick walls of the silicone cuffs. We demonstrated the efficacy of the MSC to record neural signals from rat sciatic and pelvic nerves (1000 µm and 200 µm diameter, respectively), and the selective fascicular stimulation by current steering. When implanted side-by-side and histologically compared 30 days thereafter, the MSC devices showed significantly less inflammation, indicated by a 70–80% reduction in ED1 positive macrophages, and 54–56% less fibrotic vimentin immunoreactivity. Together, the data supports the use of MSC as compliant and adaptable technology for the interfacing of somatic and autonomic peripheral nerves.
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Affiliation(s)
- María A González-González
- Department of Bioengineering, University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX, 75080, USA
| | - Aswini Kanneganti
- Department of Bioengineering, University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX, 75080, USA
| | - Alexandra Joshi-Imre
- Department of Material Science and Engineering, University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX, 75080, USA
| | - Ana G Hernandez-Reynoso
- Department of Bioengineering, University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX, 75080, USA
| | - Geetanjali Bendale
- Department of Bioengineering, University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX, 75080, USA
| | - Romil Modi
- Department of Material Science and Engineering, University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX, 75080, USA
| | - Melanie Ecker
- Department of Material Science and Engineering, University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX, 75080, USA
| | - Ali Khurram
- Department of Material Science and Engineering, University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX, 75080, USA
| | - Stuart F Cogan
- Department of Bioengineering, University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX, 75080, USA
| | - Walter E Voit
- Department of Material Science and Engineering, University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX, 75080, USA
| | - Mario I Romero-Ortega
- Department of Bioengineering, University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX, 75080, USA.
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16
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Sevcencu C, Nielsen TN, Struijk JJ. An Intraneural Electrode for Bioelectronic Medicines for Treatment of Hypertension. Neuromodulation 2018; 21:777-786. [DOI: 10.1111/ner.12758] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Revised: 10/23/2017] [Accepted: 01/04/2018] [Indexed: 01/18/2023]
Affiliation(s)
- Cristian Sevcencu
- Department of Health Science and Technology, Center for Sensory-Motor Interaction (SMI); Aalborg University; Aalborg Denmark
| | - Thomas N. Nielsen
- Department of Health Science and Technology, Center for Sensory-Motor Interaction (SMI); Aalborg University; Aalborg Denmark
| | - Johannes J. Struijk
- Department of Health Science and Technology, Medical Informatics; Aalborg University; Aalborg Denmark
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