Hybrid biophysical model of invasive electrical neural recordings : focus on chronic implants in the peripheral nervous system

Dans ce projet nous nous intéresserons à la création d’un nouveau modèle permettant de simuler des enregistrements extracellulaires de l’activité électrique neurale dans le système nerveux périphérique. Ce modèle fut développé pour permettre une meilleure compréhension de l’impact des différentes pr...

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Bibliographic Details
Main Author: Jehenne, Béryl
Other Authors: Sorbonne Paris Cité
Language:en
Published: 2017
Subjects:
Online Access:http://www.theses.fr/2017USPCB112
id ndltd-theses.fr-2017USPCB112
record_format oai_dc
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language en
sources NDLTD
topic Interfaces neurales
Neuro-prothèses
Implants chroniques dans le système nerveux périphérique
Électrodes invasives
Enregistrements intra-fasciculaires
Modélisation neurale
Modèles biophysique hybrides
Neural interfaces
Neuroprosthetics
Chronic implants in the PNS
Intrafascicular recordings
Invasive electrodes
Neural modeling
Hybrid biophysical models
610.28
spellingShingle Interfaces neurales
Neuro-prothèses
Implants chroniques dans le système nerveux périphérique
Électrodes invasives
Enregistrements intra-fasciculaires
Modélisation neurale
Modèles biophysique hybrides
Neural interfaces
Neuroprosthetics
Chronic implants in the PNS
Intrafascicular recordings
Invasive electrodes
Neural modeling
Hybrid biophysical models
610.28
Jehenne, Béryl
Hybrid biophysical model of invasive electrical neural recordings : focus on chronic implants in the peripheral nervous system
description Dans ce projet nous nous intéresserons à la création d’un nouveau modèle permettant de simuler des enregistrements extracellulaires de l’activité électrique neurale dans le système nerveux périphérique. Ce modèle fut développé pour permettre une meilleure compréhension de l’impact des différentes propriétés des interfaces sur la qualité des signaux recueillis. Ce projet fut en particulier conduit pour répondre au contexte actuel qui voit le développement de nombreuses applications dans le domaine des neuro-prothèses et autres interfaces neurales à but biomédical. Nos intentions étaient de fournir un nouvel outil permettant de mieux comprendre les particularités des interfaces existantes ou d’aider à leur amélioration et à la planification de futures innovations. Ce modèle est construit comme la synthèse de la compréhension actuelle des différents rouages biophysiques impactant les enregistrements. Sa structure peut être perçue comme l’assemblage de différents sous-systèmes interconnectés et représentant chacun une dimension du processus. Il s’avère particulièrement efficace pour l’analyse comparative des performances entre diffèrent types/géométries d’électrodes invasives. Dans ce document, nous nous efforcerons d’expliquer en détail la structure et les paramètres de notre modèle. Nous décrirons ensuite les différents tests que nous avons entrepris pour sa validation expérimentale, ainsi que les différentes voies d’applications que nous avons commencé à explorer. Nous finirons par décrire les améliorations qui nous sont apparues comme nécessaires ou possibles et par une discussion sur les ouvertures futures offerte à ce domaine de recherche. === Neural interfaces are becoming a newly dynamic and promising field especially thanks to the numerous applications they could have in the biomedical domain. A great deal of these applications requires a monitoring of targeted neural activity. Among the different technologies available for such recording practice, chronic electrodes implanted in the peripheral nervous system offer a good compromise on the resolution versus invasiveness technological constraint. A large array of electrodes has been developed in this intention but there is still only a limited comprehension of their recording principles and weakness. This makes difficult any targeted improvement of the electrodes and led this field to be mainly dominated by a trial and error empirical approach simultaneously costly in funds, animal lives and time. In particular, intrafascicular electrodes, while providing exiting results for stimulation, have often failed in recordings. These electrodes typically show interesting recording performance right after implantation but have rapid decline of their efficacy up to the points that they often become useless after a few weeks. Such performance proves problematic as they drastically limit the transfer of experimental results to human applications. The extent of our work has been the development of a theoretical framework for the study of implantable electrodes. Our goal here has been to construct a model that could be used as a platform to better understand implanted electrode and compare their performance and possible improvement. We focused our work on intrafascicular electrode for the peripheral nervous system. However, our procedure could easily be applied to other type of interface. During this project we first constructed a detailed model of the recording biophysical process happening at the peripheral nerve electrical interface. This model encompasses all the mechanism known to influence the quality and shape of neural activity recordings. We have then recreated within our model specific controlled experiments and by comparing the properties of the simulated recording with their experimental counterparts demonstrated the potency of our approach to produce bio-plausible signals. This validated our model as an in silico alternative to compare and test electrodes. We then further developed this model to also simulate some of the changes happening in the nerve post implantation. In particular, we found that the growth of the fibrotic scar could already explain a large part of the signal degradation happening in the first weeks. Then to demonstrate the adaptability of this model we used it to compare the performance of the main type of electrodes implanted nowadays peripherally. Finally, as the main weakness of our model relied in its relative complexity and the related long computing time, we started to analyze how this model could be simplified without losing the precision necessary for the intended applications. In conclusion, this project led to the creation of a model which in its current form can be used as an in silico platform to test and compare electrodes. This will facilitate the planning and development of future peripheral neural interface by proving both more economical and informative that current strategies. Conjointly, we opened the way to future improvement of our model, leading to more practicality.
author2 Sorbonne Paris Cité
author_facet Sorbonne Paris Cité
Jehenne, Béryl
author Jehenne, Béryl
author_sort Jehenne, Béryl
title Hybrid biophysical model of invasive electrical neural recordings : focus on chronic implants in the peripheral nervous system
title_short Hybrid biophysical model of invasive electrical neural recordings : focus on chronic implants in the peripheral nervous system
title_full Hybrid biophysical model of invasive electrical neural recordings : focus on chronic implants in the peripheral nervous system
title_fullStr Hybrid biophysical model of invasive electrical neural recordings : focus on chronic implants in the peripheral nervous system
title_full_unstemmed Hybrid biophysical model of invasive electrical neural recordings : focus on chronic implants in the peripheral nervous system
title_sort hybrid biophysical model of invasive electrical neural recordings : focus on chronic implants in the peripheral nervous system
publishDate 2017
url http://www.theses.fr/2017USPCB112
work_keys_str_mv AT jehenneberyl hybridbiophysicalmodelofinvasiveelectricalneuralrecordingsfocusonchronicimplantsintheperipheralnervoussystem
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spelling ndltd-theses.fr-2017USPCB1122019-12-21T03:31:27Z Hybrid biophysical model of invasive electrical neural recordings : focus on chronic implants in the peripheral nervous system Interfaces neurales Neuro-prothèses Implants chroniques dans le système nerveux périphérique Électrodes invasives Enregistrements intra-fasciculaires Modélisation neurale Modèles biophysique hybrides Neural interfaces Neuroprosthetics Chronic implants in the PNS Intrafascicular recordings Invasive electrodes Neural modeling Hybrid biophysical models 610.28 Dans ce projet nous nous intéresserons à la création d’un nouveau modèle permettant de simuler des enregistrements extracellulaires de l’activité électrique neurale dans le système nerveux périphérique. Ce modèle fut développé pour permettre une meilleure compréhension de l’impact des différentes propriétés des interfaces sur la qualité des signaux recueillis. Ce projet fut en particulier conduit pour répondre au contexte actuel qui voit le développement de nombreuses applications dans le domaine des neuro-prothèses et autres interfaces neurales à but biomédical. Nos intentions étaient de fournir un nouvel outil permettant de mieux comprendre les particularités des interfaces existantes ou d’aider à leur amélioration et à la planification de futures innovations. Ce modèle est construit comme la synthèse de la compréhension actuelle des différents rouages biophysiques impactant les enregistrements. Sa structure peut être perçue comme l’assemblage de différents sous-systèmes interconnectés et représentant chacun une dimension du processus. Il s’avère particulièrement efficace pour l’analyse comparative des performances entre diffèrent types/géométries d’électrodes invasives. Dans ce document, nous nous efforcerons d’expliquer en détail la structure et les paramètres de notre modèle. Nous décrirons ensuite les différents tests que nous avons entrepris pour sa validation expérimentale, ainsi que les différentes voies d’applications que nous avons commencé à explorer. Nous finirons par décrire les améliorations qui nous sont apparues comme nécessaires ou possibles et par une discussion sur les ouvertures futures offerte à ce domaine de recherche. Neural interfaces are becoming a newly dynamic and promising field especially thanks to the numerous applications they could have in the biomedical domain. A great deal of these applications requires a monitoring of targeted neural activity. Among the different technologies available for such recording practice, chronic electrodes implanted in the peripheral nervous system offer a good compromise on the resolution versus invasiveness technological constraint. A large array of electrodes has been developed in this intention but there is still only a limited comprehension of their recording principles and weakness. This makes difficult any targeted improvement of the electrodes and led this field to be mainly dominated by a trial and error empirical approach simultaneously costly in funds, animal lives and time. In particular, intrafascicular electrodes, while providing exiting results for stimulation, have often failed in recordings. These electrodes typically show interesting recording performance right after implantation but have rapid decline of their efficacy up to the points that they often become useless after a few weeks. Such performance proves problematic as they drastically limit the transfer of experimental results to human applications. The extent of our work has been the development of a theoretical framework for the study of implantable electrodes. Our goal here has been to construct a model that could be used as a platform to better understand implanted electrode and compare their performance and possible improvement. We focused our work on intrafascicular electrode for the peripheral nervous system. However, our procedure could easily be applied to other type of interface. During this project we first constructed a detailed model of the recording biophysical process happening at the peripheral nerve electrical interface. This model encompasses all the mechanism known to influence the quality and shape of neural activity recordings. We have then recreated within our model specific controlled experiments and by comparing the properties of the simulated recording with their experimental counterparts demonstrated the potency of our approach to produce bio-plausible signals. This validated our model as an in silico alternative to compare and test electrodes. We then further developed this model to also simulate some of the changes happening in the nerve post implantation. In particular, we found that the growth of the fibrotic scar could already explain a large part of the signal degradation happening in the first weeks. Then to demonstrate the adaptability of this model we used it to compare the performance of the main type of electrodes implanted nowadays peripherally. Finally, as the main weakness of our model relied in its relative complexity and the related long computing time, we started to analyze how this model could be simplified without losing the precision necessary for the intended applications. In conclusion, this project led to the creation of a model which in its current form can be used as an in silico platform to test and compare electrodes. This will facilitate the planning and development of future peripheral neural interface by proving both more economical and informative that current strategies. Conjointly, we opened the way to future improvement of our model, leading to more practicality. Electronic Thesis or Dissertation Text en http://www.theses.fr/2017USPCB112 Jehenne, Béryl 2017-11-21 Sorbonne Paris Cité Arleo, Angelo