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Computational Modeling and Neuroimaging Techniques for Targeting during Deep Brain Stimulation
Accurate surgical localization of the varied targets for deep brain stimulation (DBS) is a process undergoing constant evolution, with increasingly sophisticated techniques to allow for highly precise targeting. However, despite the fastidious placement of electrodes into specific structures within...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4927621/ https://www.ncbi.nlm.nih.gov/pubmed/27445709 http://dx.doi.org/10.3389/fnana.2016.00071 |
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author | Sweet, Jennifer A. Pace, Jonathan Girgis, Fady Miller, Jonathan P. |
author_facet | Sweet, Jennifer A. Pace, Jonathan Girgis, Fady Miller, Jonathan P. |
author_sort | Sweet, Jennifer A. |
collection | PubMed |
description | Accurate surgical localization of the varied targets for deep brain stimulation (DBS) is a process undergoing constant evolution, with increasingly sophisticated techniques to allow for highly precise targeting. However, despite the fastidious placement of electrodes into specific structures within the brain, there is increasing evidence to suggest that the clinical effects of DBS are likely due to the activation of widespread neuronal networks directly and indirectly influenced by the stimulation of a given target. Selective activation of these complex and inter-connected pathways may further improve the outcomes of currently treated diseases by targeting specific fiber tracts responsible for a particular symptom in a patient-specific manner. Moreover, the delivery of such focused stimulation may aid in the discovery of new targets for electrical stimulation to treat additional neurological, psychiatric, and even cognitive disorders. As such, advancements in surgical targeting, computational modeling, engineering designs, and neuroimaging techniques play a critical role in this process. This article reviews the progress of these applications, discussing the importance of target localization for DBS, and the role of computational modeling and novel neuroimaging in improving our understanding of the pathophysiology of diseases, and thus paving the way for improved selective target localization using DBS. |
format | Online Article Text |
id | pubmed-4927621 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-49276212016-07-21 Computational Modeling and Neuroimaging Techniques for Targeting during Deep Brain Stimulation Sweet, Jennifer A. Pace, Jonathan Girgis, Fady Miller, Jonathan P. Front Neuroanat Neuroscience Accurate surgical localization of the varied targets for deep brain stimulation (DBS) is a process undergoing constant evolution, with increasingly sophisticated techniques to allow for highly precise targeting. However, despite the fastidious placement of electrodes into specific structures within the brain, there is increasing evidence to suggest that the clinical effects of DBS are likely due to the activation of widespread neuronal networks directly and indirectly influenced by the stimulation of a given target. Selective activation of these complex and inter-connected pathways may further improve the outcomes of currently treated diseases by targeting specific fiber tracts responsible for a particular symptom in a patient-specific manner. Moreover, the delivery of such focused stimulation may aid in the discovery of new targets for electrical stimulation to treat additional neurological, psychiatric, and even cognitive disorders. As such, advancements in surgical targeting, computational modeling, engineering designs, and neuroimaging techniques play a critical role in this process. This article reviews the progress of these applications, discussing the importance of target localization for DBS, and the role of computational modeling and novel neuroimaging in improving our understanding of the pathophysiology of diseases, and thus paving the way for improved selective target localization using DBS. Frontiers Media S.A. 2016-06-30 /pmc/articles/PMC4927621/ /pubmed/27445709 http://dx.doi.org/10.3389/fnana.2016.00071 Text en Copyright © 2016 Sweet, Pace, Girgis and Miller. 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 and 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 Sweet, Jennifer A. Pace, Jonathan Girgis, Fady Miller, Jonathan P. Computational Modeling and Neuroimaging Techniques for Targeting during Deep Brain Stimulation |
title | Computational Modeling and Neuroimaging Techniques for Targeting during Deep Brain Stimulation |
title_full | Computational Modeling and Neuroimaging Techniques for Targeting during Deep Brain Stimulation |
title_fullStr | Computational Modeling and Neuroimaging Techniques for Targeting during Deep Brain Stimulation |
title_full_unstemmed | Computational Modeling and Neuroimaging Techniques for Targeting during Deep Brain Stimulation |
title_short | Computational Modeling and Neuroimaging Techniques for Targeting during Deep Brain Stimulation |
title_sort | computational modeling and neuroimaging techniques for targeting during deep brain stimulation |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4927621/ https://www.ncbi.nlm.nih.gov/pubmed/27445709 http://dx.doi.org/10.3389/fnana.2016.00071 |
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