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Multi-Scale Computational Models for Electrical Brain Stimulation

Electrical brain stimulation (EBS) is an appealing method to treat neurological disorders. To achieve optimal stimulation effects and a better understanding of the underlying brain mechanisms, neuroscientists have proposed computational modeling studies for a decade. Recently, multi-scale models tha...

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Detalles Bibliográficos
Autores principales: Seo, Hyeon, Jun, Sung C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5662877/
https://www.ncbi.nlm.nih.gov/pubmed/29123476
http://dx.doi.org/10.3389/fnhum.2017.00515
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author Seo, Hyeon
Jun, Sung C.
author_facet Seo, Hyeon
Jun, Sung C.
author_sort Seo, Hyeon
collection PubMed
description Electrical brain stimulation (EBS) is an appealing method to treat neurological disorders. To achieve optimal stimulation effects and a better understanding of the underlying brain mechanisms, neuroscientists have proposed computational modeling studies for a decade. Recently, multi-scale models that combine a volume conductor head model and multi-compartmental models of cortical neurons have been developed to predict stimulation effects on the macroscopic and microscopic levels more precisely. As the need for better computational models continues to increase, we overview here recent multi-scale modeling studies; we focused on approaches that coupled a simplified or high-resolution volume conductor head model and multi-compartmental models of cortical neurons, and constructed realistic fiber models using diffusion tensor imaging (DTI). Further implications for achieving better precision in estimating cellular responses are discussed.
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spelling pubmed-56628772017-11-09 Multi-Scale Computational Models for Electrical Brain Stimulation Seo, Hyeon Jun, Sung C. Front Hum Neurosci Neuroscience Electrical brain stimulation (EBS) is an appealing method to treat neurological disorders. To achieve optimal stimulation effects and a better understanding of the underlying brain mechanisms, neuroscientists have proposed computational modeling studies for a decade. Recently, multi-scale models that combine a volume conductor head model and multi-compartmental models of cortical neurons have been developed to predict stimulation effects on the macroscopic and microscopic levels more precisely. As the need for better computational models continues to increase, we overview here recent multi-scale modeling studies; we focused on approaches that coupled a simplified or high-resolution volume conductor head model and multi-compartmental models of cortical neurons, and constructed realistic fiber models using diffusion tensor imaging (DTI). Further implications for achieving better precision in estimating cellular responses are discussed. Frontiers Media S.A. 2017-10-26 /pmc/articles/PMC5662877/ /pubmed/29123476 http://dx.doi.org/10.3389/fnhum.2017.00515 Text en Copyright © 2017 Seo and Jun. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Seo, Hyeon
Jun, Sung C.
Multi-Scale Computational Models for Electrical Brain Stimulation
title Multi-Scale Computational Models for Electrical Brain Stimulation
title_full Multi-Scale Computational Models for Electrical Brain Stimulation
title_fullStr Multi-Scale Computational Models for Electrical Brain Stimulation
title_full_unstemmed Multi-Scale Computational Models for Electrical Brain Stimulation
title_short Multi-Scale Computational Models for Electrical Brain Stimulation
title_sort multi-scale computational models for electrical brain stimulation
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5662877/
https://www.ncbi.nlm.nih.gov/pubmed/29123476
http://dx.doi.org/10.3389/fnhum.2017.00515
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