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Computation of transcranial magnetic stimulation electric fields using self-supervised deep learning.

Publication ,  Journal Article
Li, H; Deng, Z-D; Oathes, D; Fan, Y
Published in: Neuroimage
December 1, 2022

Electric fields (E-fields) induced by transcranial magnetic stimulation (TMS) can be modeled using partial differential equations (PDEs). Using state-of-the-art finite-element methods (FEM), it often takes tens of seconds to solve the PDEs for computing a high-resolution E-field, hampering the wide application of the E-field modeling in practice and research. To improve the E-field modeling's computational efficiency, we developed a self-supervised deep learning (DL) method to compute precise TMS E-fields. Given a head model and the primary E-field generated by TMS coils, a DL model was built to generate a E-field by minimizing a loss function that measures how well the generated E-field fits the governing PDE. The DL model was trained in a self-supervised manner, which does not require any external supervision. We evaluated the DL model using both a simulated sphere head model and realistic head models of 125 individuals and compared the accuracy and computational speed of the DL model with a state-of-the-art FEM. In realistic head models, the DL model obtained accurate E-fields that were significantly correlated with the FEM solutions. The DL model could obtain precise E-fields within seconds for whole head models at a high spatial resolution, faster than the FEM. The DL model built for the simulated sphere head model also obtained an accurate E-field whose average difference from the analytical E-fields was 0.0054, comparable to the FEM solution. These results demonstrated that the self-supervised DL method could obtain precise E-fields comparable to the FEM solutions with improved computational speed.

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Published In

Neuroimage

DOI

EISSN

1095-9572

Publication Date

December 1, 2022

Volume

264

Start / End Page

119705

Location

United States

Related Subject Headings

  • Transcranial Magnetic Stimulation
  • Neurology & Neurosurgery
  • Humans
  • Head
  • Electromagnetic Fields
  • Deep Learning
  • 42 Health sciences
  • 32 Biomedical and clinical sciences
  • 17 Psychology and Cognitive Sciences
  • 11 Medical and Health Sciences
 

Citation

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Li, H., Deng, Z.-D., Oathes, D., & Fan, Y. (2022). Computation of transcranial magnetic stimulation electric fields using self-supervised deep learning. Neuroimage, 264, 119705. https://doi.org/10.1016/j.neuroimage.2022.119705
Li, Hongming, Zhi-De Deng, Desmond Oathes, and Yong Fan. “Computation of transcranial magnetic stimulation electric fields using self-supervised deep learning.Neuroimage 264 (December 1, 2022): 119705. https://doi.org/10.1016/j.neuroimage.2022.119705.
Li H, Deng Z-D, Oathes D, Fan Y. Computation of transcranial magnetic stimulation electric fields using self-supervised deep learning. Neuroimage. 2022 Dec 1;264:119705.
Li, Hongming, et al. “Computation of transcranial magnetic stimulation electric fields using self-supervised deep learning.Neuroimage, vol. 264, Dec. 2022, p. 119705. Pubmed, doi:10.1016/j.neuroimage.2022.119705.
Li H, Deng Z-D, Oathes D, Fan Y. Computation of transcranial magnetic stimulation electric fields using self-supervised deep learning. Neuroimage. 2022 Dec 1;264:119705.
Journal cover image

Published In

Neuroimage

DOI

EISSN

1095-9572

Publication Date

December 1, 2022

Volume

264

Start / End Page

119705

Location

United States

Related Subject Headings

  • Transcranial Magnetic Stimulation
  • Neurology & Neurosurgery
  • Humans
  • Head
  • Electromagnetic Fields
  • Deep Learning
  • 42 Health sciences
  • 32 Biomedical and clinical sciences
  • 17 Psychology and Cognitive Sciences
  • 11 Medical and Health Sciences