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Significance of Biological Membranes for Accurate Computational Dosimetry of Low Frequency Electric Fields

Computational dosimetry has become the main tool for estimating induced electric fields within brain tissues in transcranial direct current stimulation (tDCS) which is recently attracting the attention of researches for motor function disturbances such as Parkinson’s disease. This paper investigates...

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Detalles Bibliográficos
Autores principales: Marzban, S., Saviz, M., Towhidkhah, F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Sciendo 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7852019/
https://www.ncbi.nlm.nih.gov/pubmed/33584920
http://dx.doi.org/10.2478/joeb-2018-0009
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author Marzban, S.
Saviz, M.
Towhidkhah, F.
author_facet Marzban, S.
Saviz, M.
Towhidkhah, F.
author_sort Marzban, S.
collection PubMed
description Computational dosimetry has become the main tool for estimating induced electric fields within brain tissues in transcranial direct current stimulation (tDCS) which is recently attracting the attention of researches for motor function disturbances such as Parkinson’s disease. This paper investigates the effect of including or excluding the very thin meninges in computing tDCS electric fields using CST software. For this purpose, two models of the brain with and without meninges were used to induce electric field with two DC current electrodes (2 mA) in regions of the model referring to M1 and Prefrontal Cortex (FP2) similar to tDCS. Considering meninges, the results have shown differences in the induced field showing that there might be problems with conventional models in which meninges are not taken into account.
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spelling pubmed-78520192021-02-11 Significance of Biological Membranes for Accurate Computational Dosimetry of Low Frequency Electric Fields Marzban, S. Saviz, M. Towhidkhah, F. J Electr Bioimpedance Research Articles Computational dosimetry has become the main tool for estimating induced electric fields within brain tissues in transcranial direct current stimulation (tDCS) which is recently attracting the attention of researches for motor function disturbances such as Parkinson’s disease. This paper investigates the effect of including or excluding the very thin meninges in computing tDCS electric fields using CST software. For this purpose, two models of the brain with and without meninges were used to induce electric field with two DC current electrodes (2 mA) in regions of the model referring to M1 and Prefrontal Cortex (FP2) similar to tDCS. Considering meninges, the results have shown differences in the induced field showing that there might be problems with conventional models in which meninges are not taken into account. Sciendo 2018-12-19 /pmc/articles/PMC7852019/ /pubmed/33584920 http://dx.doi.org/10.2478/joeb-2018-0009 Text en © 2018 S. Marzban, M. Saviz, F. Towhidkhah published by Sciendo http://creativecommons.org/licenses/by-nc-nd/3.0 This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
spellingShingle Research Articles
Marzban, S.
Saviz, M.
Towhidkhah, F.
Significance of Biological Membranes for Accurate Computational Dosimetry of Low Frequency Electric Fields
title Significance of Biological Membranes for Accurate Computational Dosimetry of Low Frequency Electric Fields
title_full Significance of Biological Membranes for Accurate Computational Dosimetry of Low Frequency Electric Fields
title_fullStr Significance of Biological Membranes for Accurate Computational Dosimetry of Low Frequency Electric Fields
title_full_unstemmed Significance of Biological Membranes for Accurate Computational Dosimetry of Low Frequency Electric Fields
title_short Significance of Biological Membranes for Accurate Computational Dosimetry of Low Frequency Electric Fields
title_sort significance of biological membranes for accurate computational dosimetry of low frequency electric fields
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7852019/
https://www.ncbi.nlm.nih.gov/pubmed/33584920
http://dx.doi.org/10.2478/joeb-2018-0009
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