Rapid whole-brain electric field mapping in transcranial magnetic stimulation using deep learning.

Transcranial magnetic stimulation (TMS) is a non-invasive neurostimulation technique that is increasingly used in the treatment of neuropsychiatric disorders and neuroscience research. Due to the complex structure of the brain and the electrical conductivity variation across subjects, identification...

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Autores principales:	Guoping Xu, Yogesh Rathi, Joan A Camprodon, Hanqiang Cao, Lipeng Ning
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/52a9138c1b3245bcb7c4ee1fd3e65b97
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spelling	oai:doaj.org-article:52a9138c1b3245bcb7c4ee1fd3e65b972021-12-02T20:08:51ZRapid whole-brain electric field mapping in transcranial magnetic stimulation using deep learning.1932-620310.1371/journal.pone.0254588https://doaj.org/article/52a9138c1b3245bcb7c4ee1fd3e65b972021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254588https://doaj.org/toc/1932-6203Transcranial magnetic stimulation (TMS) is a non-invasive neurostimulation technique that is increasingly used in the treatment of neuropsychiatric disorders and neuroscience research. Due to the complex structure of the brain and the electrical conductivity variation across subjects, identification of subject-specific brain regions for TMS is important to improve the treatment efficacy and understand the mechanism of treatment response. Numerical computations have been used to estimate the stimulated electric field (E-field) by TMS in brain tissue. But the relative long computation time limits the application of this approach. In this paper, we propose a deep-neural-network based approach to expedite the estimation of whole-brain E-field by using a neural network architecture, named 3D-MSResUnet and multimodal imaging data. The 3D-MSResUnet network integrates the 3D U-net architecture, residual modules and a mechanism to combine multi-scale feature maps. It is trained using a large dataset with finite element method (FEM) based E-field and diffusion magnetic resonance imaging (MRI) based anisotropic volume conductivity or anatomical images. The performance of 3D-MSResUnet is evaluated using several evaluation metrics and different combinations of imaging modalities and coils. The experimental results show that the output E-field of 3D-MSResUnet provides reliable estimation of the E-field estimated by the state-of-the-art FEM method with significant reduction in prediction time to about 0.24 second. Thus, this study demonstrates that neural networks are potentially useful tools to accelerate the prediction of E-field for TMS targeting.Guoping XuYogesh RathiJoan A CamprodonHanqiang CaoLipeng NingPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254588 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Guoping Xu Yogesh Rathi Joan A Camprodon Hanqiang Cao Lipeng Ning Rapid whole-brain electric field mapping in transcranial magnetic stimulation using deep learning.
description	Transcranial magnetic stimulation (TMS) is a non-invasive neurostimulation technique that is increasingly used in the treatment of neuropsychiatric disorders and neuroscience research. Due to the complex structure of the brain and the electrical conductivity variation across subjects, identification of subject-specific brain regions for TMS is important to improve the treatment efficacy and understand the mechanism of treatment response. Numerical computations have been used to estimate the stimulated electric field (E-field) by TMS in brain tissue. But the relative long computation time limits the application of this approach. In this paper, we propose a deep-neural-network based approach to expedite the estimation of whole-brain E-field by using a neural network architecture, named 3D-MSResUnet and multimodal imaging data. The 3D-MSResUnet network integrates the 3D U-net architecture, residual modules and a mechanism to combine multi-scale feature maps. It is trained using a large dataset with finite element method (FEM) based E-field and diffusion magnetic resonance imaging (MRI) based anisotropic volume conductivity or anatomical images. The performance of 3D-MSResUnet is evaluated using several evaluation metrics and different combinations of imaging modalities and coils. The experimental results show that the output E-field of 3D-MSResUnet provides reliable estimation of the E-field estimated by the state-of-the-art FEM method with significant reduction in prediction time to about 0.24 second. Thus, this study demonstrates that neural networks are potentially useful tools to accelerate the prediction of E-field for TMS targeting.
format	article
author	Guoping Xu Yogesh Rathi Joan A Camprodon Hanqiang Cao Lipeng Ning
author_facet	Guoping Xu Yogesh Rathi Joan A Camprodon Hanqiang Cao Lipeng Ning
author_sort	Guoping Xu
title	Rapid whole-brain electric field mapping in transcranial magnetic stimulation using deep learning.
title_short	Rapid whole-brain electric field mapping in transcranial magnetic stimulation using deep learning.
title_full	Rapid whole-brain electric field mapping in transcranial magnetic stimulation using deep learning.
title_fullStr	Rapid whole-brain electric field mapping in transcranial magnetic stimulation using deep learning.
title_full_unstemmed	Rapid whole-brain electric field mapping in transcranial magnetic stimulation using deep learning.
title_sort	rapid whole-brain electric field mapping in transcranial magnetic stimulation using deep learning.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/52a9138c1b3245bcb7c4ee1fd3e65b97
work_keys_str_mv	AT guopingxu rapidwholebrainelectricfieldmappingintranscranialmagneticstimulationusingdeeplearning AT yogeshrathi rapidwholebrainelectricfieldmappingintranscranialmagneticstimulationusingdeeplearning AT joanacamprodon rapidwholebrainelectricfieldmappingintranscranialmagneticstimulationusingdeeplearning AT hanqiangcao rapidwholebrainelectricfieldmappingintranscranialmagneticstimulationusingdeeplearning AT lipengning rapidwholebrainelectricfieldmappingintranscranialmagneticstimulationusingdeeplearning
_version_	1718375123115835392

Rapid whole-brain electric field mapping in transcranial magnetic stimulation using deep learning.

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