Unification of optimal targeting methods in transcranial electrical stimulation.
- Autores
- Fernández Corazza, Mariano; Turovets, Sergei; Muravchik, Carlos Horacio
- Año de publicación
- 2020
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- One of the major questions in high-density transcranial electrical stimulation (TES) is: given a region of interest (ROI) and electric current limits for safety, how much current should be delivered by each electrode for optimal targeting of the ROI? Several solutions, apparently unrelated, have been independently proposed depending on how “optimality” is defined and on how this optimization problem is stated mathematically. The least squares (LS), weighted LS (WLS), or reciprocity-based approaches are the simplest ones and have closed-form solutions. An extended optimization problem can be stated as follows: maximize the directional intensity at the ROI, limit the electric fields at the non-ROI, and constrain total injected current and current per electrode for safety. This problem requires iterative convex or linear optimization solvers. We theoretically prove in this work that the LS, WLS and reciprocity-based closed-form solutions are specific solutions to the extended directional maximization optimization problem. Moreover, the LS/WLS and reciprocity-based solutions are the two extreme cases of the intensity-focality trade-off, emerging under variation of a unique parameter of the extended directional maximization problem, the imposed constraint to the electric fields at the non-ROI. We validate and illustrate these findings with simulations on an atlas head model. The unified approach we present here allows a better understanding of the nature of the TES optimization problem and helps in the development of advanced and more effective targeting strategies.
Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales - Materia
-
Ciencias Exactas
Transcranial electrical stimulation
Transcranial direct current stimulation
Optimal electrical stimulation
Reciprocity theoremLeast squares - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/107766
Ver los metadatos del registro completo
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Unification of optimal targeting methods in transcranial electrical stimulation.Fernández Corazza, MarianoTurovets, SergeiMuravchik, Carlos HoracioCiencias ExactasTranscranial electrical stimulationTranscranial direct current stimulationOptimal electrical stimulationReciprocity theoremLeast squaresOne of the major questions in high-density transcranial electrical stimulation (TES) is: given a region of interest (ROI) and electric current limits for safety, how much current should be delivered by each electrode for optimal targeting of the ROI? Several solutions, apparently unrelated, have been independently proposed depending on how “optimality” is defined and on how this optimization problem is stated mathematically. The least squares (LS), weighted LS (WLS), or reciprocity-based approaches are the simplest ones and have closed-form solutions. An extended optimization problem can be stated as follows: maximize the directional intensity at the ROI, limit the electric fields at the non-ROI, and constrain total injected current and current per electrode for safety. This problem requires iterative convex or linear optimization solvers. We theoretically prove in this work that the LS, WLS and reciprocity-based closed-form solutions are specific solutions to the extended directional maximization optimization problem. Moreover, the LS/WLS and reciprocity-based solutions are the two extreme cases of the intensity-focality trade-off, emerging under variation of a unique parameter of the extended directional maximization problem, the imposed constraint to the electric fields at the non-ROI. We validate and illustrate these findings with simulations on an atlas head model. The unified approach we present here allows a better understanding of the nature of the TES optimization problem and helps in the development of advanced and more effective targeting strategies.Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/107766enginfo:eu-repo/semantics/altIdentifier/url/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC7110419&blobtype=pdfinfo:eu-repo/semantics/altIdentifier/issn/1053-8119info:eu-repo/semantics/altIdentifier/pmid/31862525info:eu-repo/semantics/altIdentifier/doi/10.1016/j.neuroimage.2019.116403info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T10:56:02Zoai:sedici.unlp.edu.ar:10915/107766Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:56:02.957SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Unification of optimal targeting methods in transcranial electrical stimulation. |
title |
Unification of optimal targeting methods in transcranial electrical stimulation. |
spellingShingle |
Unification of optimal targeting methods in transcranial electrical stimulation. Fernández Corazza, Mariano Ciencias Exactas Transcranial electrical stimulation Transcranial direct current stimulation Optimal electrical stimulation Reciprocity theoremLeast squares |
title_short |
Unification of optimal targeting methods in transcranial electrical stimulation. |
title_full |
Unification of optimal targeting methods in transcranial electrical stimulation. |
title_fullStr |
Unification of optimal targeting methods in transcranial electrical stimulation. |
title_full_unstemmed |
Unification of optimal targeting methods in transcranial electrical stimulation. |
title_sort |
Unification of optimal targeting methods in transcranial electrical stimulation. |
dc.creator.none.fl_str_mv |
Fernández Corazza, Mariano Turovets, Sergei Muravchik, Carlos Horacio |
author |
Fernández Corazza, Mariano |
author_facet |
Fernández Corazza, Mariano Turovets, Sergei Muravchik, Carlos Horacio |
author_role |
author |
author2 |
Turovets, Sergei Muravchik, Carlos Horacio |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Ciencias Exactas Transcranial electrical stimulation Transcranial direct current stimulation Optimal electrical stimulation Reciprocity theoremLeast squares |
topic |
Ciencias Exactas Transcranial electrical stimulation Transcranial direct current stimulation Optimal electrical stimulation Reciprocity theoremLeast squares |
dc.description.none.fl_txt_mv |
One of the major questions in high-density transcranial electrical stimulation (TES) is: given a region of interest (ROI) and electric current limits for safety, how much current should be delivered by each electrode for optimal targeting of the ROI? Several solutions, apparently unrelated, have been independently proposed depending on how “optimality” is defined and on how this optimization problem is stated mathematically. The least squares (LS), weighted LS (WLS), or reciprocity-based approaches are the simplest ones and have closed-form solutions. An extended optimization problem can be stated as follows: maximize the directional intensity at the ROI, limit the electric fields at the non-ROI, and constrain total injected current and current per electrode for safety. This problem requires iterative convex or linear optimization solvers. We theoretically prove in this work that the LS, WLS and reciprocity-based closed-form solutions are specific solutions to the extended directional maximization optimization problem. Moreover, the LS/WLS and reciprocity-based solutions are the two extreme cases of the intensity-focality trade-off, emerging under variation of a unique parameter of the extended directional maximization problem, the imposed constraint to the electric fields at the non-ROI. We validate and illustrate these findings with simulations on an atlas head model. The unified approach we present here allows a better understanding of the nature of the TES optimization problem and helps in the development of advanced and more effective targeting strategies. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales |
description |
One of the major questions in high-density transcranial electrical stimulation (TES) is: given a region of interest (ROI) and electric current limits for safety, how much current should be delivered by each electrode for optimal targeting of the ROI? Several solutions, apparently unrelated, have been independently proposed depending on how “optimality” is defined and on how this optimization problem is stated mathematically. The least squares (LS), weighted LS (WLS), or reciprocity-based approaches are the simplest ones and have closed-form solutions. An extended optimization problem can be stated as follows: maximize the directional intensity at the ROI, limit the electric fields at the non-ROI, and constrain total injected current and current per electrode for safety. This problem requires iterative convex or linear optimization solvers. We theoretically prove in this work that the LS, WLS and reciprocity-based closed-form solutions are specific solutions to the extended directional maximization optimization problem. Moreover, the LS/WLS and reciprocity-based solutions are the two extreme cases of the intensity-focality trade-off, emerging under variation of a unique parameter of the extended directional maximization problem, the imposed constraint to the electric fields at the non-ROI. We validate and illustrate these findings with simulations on an atlas head model. The unified approach we present here allows a better understanding of the nature of the TES optimization problem and helps in the development of advanced and more effective targeting strategies. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020 |
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info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Articulo http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
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article |
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http://sedici.unlp.edu.ar/handle/10915/107766 |
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eng |
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eng |
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