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OSS-DBS: Open-source simulation platform for deep brain stimulation with a comprehensive automated modeling
In this study, we propose a new open-source simulation platform that comprises computer-aided design and computer-aided engineering tools for highly automated evaluation of electric field distribution and neural activation during Deep Brain Stimulation (DBS). It will be shown how a Volume Conductor...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7384674/ https://www.ncbi.nlm.nih.gov/pubmed/32628719 http://dx.doi.org/10.1371/journal.pcbi.1008023 |
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author | Butenko, Konstantin Bahls, Christian Schröder, Max Köhling, Rüdiger van Rienen, Ursula |
author_facet | Butenko, Konstantin Bahls, Christian Schröder, Max Köhling, Rüdiger van Rienen, Ursula |
author_sort | Butenko, Konstantin |
collection | PubMed |
description | In this study, we propose a new open-source simulation platform that comprises computer-aided design and computer-aided engineering tools for highly automated evaluation of electric field distribution and neural activation during Deep Brain Stimulation (DBS). It will be shown how a Volume Conductor Model (VCM) is constructed and examined using Python-controlled algorithms for generation, discretization and adaptive mesh refinement of the computational domain, as well as for incorporation of heterogeneous and anisotropic properties of the tissue and allocation of neuron models. The utilization of the platform is facilitated by a collection of predefined input setups and quick visualization routines. The accuracy of a VCM, created and optimized by the platform, was estimated by comparison with a commercial software. The results demonstrate no significant deviation between the models in the electric potential distribution. A qualitative estimation of different physics for the VCM shows an agreement with previous computational studies. The proposed computational platform is suitable for an accurate estimation of electric fields during DBS in scientific modeling studies. In future, we intend to acquire SDA and EMA approval. Successful incorporation of open-source software, controlled by in-house developed algorithms, provides a highly automated solution. The platform allows for optimization and uncertainty quantification (UQ) studies, while employment of the open-source software facilitates accessibility and reproducibility of simulations. |
format | Online Article Text |
id | pubmed-7384674 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-73846742020-08-05 OSS-DBS: Open-source simulation platform for deep brain stimulation with a comprehensive automated modeling Butenko, Konstantin Bahls, Christian Schröder, Max Köhling, Rüdiger van Rienen, Ursula PLoS Comput Biol Research Article In this study, we propose a new open-source simulation platform that comprises computer-aided design and computer-aided engineering tools for highly automated evaluation of electric field distribution and neural activation during Deep Brain Stimulation (DBS). It will be shown how a Volume Conductor Model (VCM) is constructed and examined using Python-controlled algorithms for generation, discretization and adaptive mesh refinement of the computational domain, as well as for incorporation of heterogeneous and anisotropic properties of the tissue and allocation of neuron models. The utilization of the platform is facilitated by a collection of predefined input setups and quick visualization routines. The accuracy of a VCM, created and optimized by the platform, was estimated by comparison with a commercial software. The results demonstrate no significant deviation between the models in the electric potential distribution. A qualitative estimation of different physics for the VCM shows an agreement with previous computational studies. The proposed computational platform is suitable for an accurate estimation of electric fields during DBS in scientific modeling studies. In future, we intend to acquire SDA and EMA approval. Successful incorporation of open-source software, controlled by in-house developed algorithms, provides a highly automated solution. The platform allows for optimization and uncertainty quantification (UQ) studies, while employment of the open-source software facilitates accessibility and reproducibility of simulations. Public Library of Science 2020-07-06 /pmc/articles/PMC7384674/ /pubmed/32628719 http://dx.doi.org/10.1371/journal.pcbi.1008023 Text en © 2020 Butenko et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Butenko, Konstantin Bahls, Christian Schröder, Max Köhling, Rüdiger van Rienen, Ursula OSS-DBS: Open-source simulation platform for deep brain stimulation with a comprehensive automated modeling |
title | OSS-DBS: Open-source simulation platform for deep brain stimulation with a comprehensive automated modeling |
title_full | OSS-DBS: Open-source simulation platform for deep brain stimulation with a comprehensive automated modeling |
title_fullStr | OSS-DBS: Open-source simulation platform for deep brain stimulation with a comprehensive automated modeling |
title_full_unstemmed | OSS-DBS: Open-source simulation platform for deep brain stimulation with a comprehensive automated modeling |
title_short | OSS-DBS: Open-source simulation platform for deep brain stimulation with a comprehensive automated modeling |
title_sort | oss-dbs: open-source simulation platform for deep brain stimulation with a comprehensive automated modeling |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7384674/ https://www.ncbi.nlm.nih.gov/pubmed/32628719 http://dx.doi.org/10.1371/journal.pcbi.1008023 |
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