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Paper Abstract and Keywords
Presentation 2025-03-20 11:26
[Short Paper] Pial Surface based Electric field regression with Neural Networks for Transcranial Magnetical Stimulation
Toyohiro Maki, Tatsuya Yokota, Akimasa Hirata, Hontani Hidekata (Nitech) MI2024-82
Abstract (in Japanese) (See Japanese page) 
(in English) Transcranial Magnetic Stimulation (TMS) is a non-invasive neuromodulation technique that induces an electric field in the brain using a coil placed on the scalp.Accurately determining the stimulated region requires precise computation of the induced electric field, which is typically obtained through electromagnetic simulations based on head MRI data.These simulations involve two key steps: constructing a Volume Conductor Model (VCM) that represents the spatial distribution of conductivity derived from MRI images, and computing the electric field from the VCM. However, both steps are computationally intensive and cannot be performed in real time. To address this challenge, recent studies have explored the use of neural networks for real-time electric field estimation. Convolutional neural networks (CNNs) have been proposed to learn a mapping from head MRI images to voxel-wise electric field distributions through supervised learning. In TMS, the electric field in the gray matter where neurons are primarily located is of particular importance. In this study, we propose a novel approach using Graph Neural Networks (GNNs) to compute the induced electric field on a mesh representation. Our method offers two key advantages over conventional approaches: improved computational efficiency and the ability to estimate electric fields along the gray matter.
Keyword (in Japanese) (See Japanese page) 
(in English) Transcranial Magnetic Stimulation / Deep learning / Electric field estimation / / / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 448, MI2024-82, pp. 126-127, March 2025.
Paper # MI2024-82 
Date of Issue 2025-03-12 (MI) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF MI2024-82

Conference Information
Committee MI  
Conference Date 2025-03-19 - 2025-03-20 
Place (in Japanese) (See Japanese page) 
Place (in English) Kagawa International Conference Hall 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Imaging, etc. 
Paper Information
Registration To MI 
Conference Code 2025-03-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Pial Surface based Electric field regression with Neural Networks for Transcranial Magnetical Stimulation 
Sub Title (in English)  
Keyword(1) Transcranial Magnetic Stimulation  
Keyword(2) Deep learning  
Keyword(3) Electric field estimation  
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1st Author's Name Toyohiro Maki  
1st Author's Affiliation Nagoya Institute of Technology (Nitech)
2nd Author's Name Tatsuya Yokota  
2nd Author's Affiliation Nagoya Institute of Technology (Nitech)
3rd Author's Name Akimasa Hirata  
3rd Author's Affiliation Nagoya Institute of Technology (Nitech)
4th Author's Name Hontani Hidekata  
4th Author's Affiliation Nagoya Institute of Technology (Nitech)
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Speaker Author-1 
Date Time 2025-03-20 11:26:00 
Presentation Time 12 minutes 
Registration for MI 
Paper # MI2024-82 
Volume (vol) vol.124 
Number (no) no.448 
Page pp.126-127 
#Pages 2 
Date of Issue 2025-03-12 (MI) 

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