Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Xu, Guoping, Rathi, Yogesh, Camprodon, Joan A., Cao, Hanqiang, Ning, Lipeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
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
_version_ 1783731340507611136
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
work_keys_str_mv AT xuguoping rapidwholebrainelectricfieldmappingintranscranialmagneticstimulationusingdeeplearning
AT rathiyogesh rapidwholebrainelectricfieldmappingintranscranialmagneticstimulationusingdeeplearning
AT camprodonjoana rapidwholebrainelectricfieldmappingintranscranialmagneticstimulationusingdeeplearning
AT caohanqiang rapidwholebrainelectricfieldmappingintranscranialmagneticstimulationusingdeeplearning
AT ninglipeng rapidwholebrainelectricfieldmappingintranscranialmagneticstimulationusingdeeplearning