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
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/107766

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spelling 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/altIdentifier/pmid/31862525
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.neuroimage.2019.116403
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Creative Commons Attribution 4.0 International (CC BY 4.0)
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