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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8323956/ https://www.ncbi.nlm.nih.gov/pubmed/34329328 http://dx.doi.org/10.1371/journal.pone.0254588 |
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author | Xu, Guoping Rathi, Yogesh Camprodon, Joan A. Cao, Hanqiang Ning, Lipeng |
author_facet | Xu, Guoping Rathi, Yogesh Camprodon, Joan A. Cao, Hanqiang Ning, Lipeng |
author_sort | Xu, Guoping |
collection | PubMed |
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 | Online Article Text |
id | pubmed-8323956 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-83239562021-07-31 Rapid whole-brain electric field mapping in transcranial magnetic stimulation using deep learning Xu, Guoping Rathi, Yogesh Camprodon, Joan A. Cao, Hanqiang Ning, Lipeng PLoS One Research Article 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. Public Library of Science 2021-07-30 /pmc/articles/PMC8323956/ /pubmed/34329328 http://dx.doi.org/10.1371/journal.pone.0254588 Text en © 2021 Xu et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Xu, Guoping Rathi, Yogesh Camprodon, Joan A. Cao, Hanqiang Ning, Lipeng Rapid whole-brain electric field mapping in transcranial magnetic stimulation using deep learning |
title | 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_short | 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 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8323956/ https://www.ncbi.nlm.nih.gov/pubmed/34329328 http://dx.doi.org/10.1371/journal.pone.0254588 |
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