Performance evaluation and personalized electric field prediction of the deep H1 coil in the human brain based on simulation and machine learning.

IF 1.5 4区 生物学 Q3 BIOLOGY
Xinhua Tan, Ao Guo, Yifan Wang, Jiasheng Tian, Jian Shi, Yingwei Li
{"title":"Performance evaluation and personalized electric field prediction of the deep H1 coil in the human brain based on simulation and machine learning.","authors":"Xinhua Tan, Ao Guo, Yifan Wang, Jiasheng Tian, Jian Shi, Yingwei Li","doi":"10.1080/15368378.2025.2561001","DOIUrl":null,"url":null,"abstract":"<p><p>Deep transcranial magnetic stimulation (DTMS) has been increasingly used to treat neurological disorders in recent years. However, owing to the complicated configuration of DTMS coils, such as the H1 coil, the electric field induced by it in the personalized human brain is so varied and complex that its transcranial magnetic stimulation performances, especially focusing behavior and depth characteristics, have to be studied and evaluated further before clinical application. Therefore, besides the effects of the excitation frequency of the H1 coils, two types of magnetic shielding blocks (MSBs) with various dimensions were analyzed, and the H1 coil circuit structure with flexible length adjustment and its coil spacing were also investigated in this study. Finally, a machine learning model based on an optimizable tree algorithm was established to rapidly predict the induced electric field in the personalized human brain. Results demonstrated that the half-value depth <i>D</i><sub>1/2</sub> of the electric field induced by the H1 coil could reach 3.67 cm, which was deeper than that by the figure-of-eight (FOE) coil (<1.6 cm), but its focusing (half-value) volume <i>V</i><sub>1/2</sub> was 567.94 cm<sup>3</sup>, larger than that of the FOE coil. After introducing MSBs, reasonably adjusting the coil circuit length and the coil spacing, <i>V</i><sub>1/2</sub> was reduced to 81.748 cm<sup>3</sup>, with a slight increase in <i>D</i><sub>1/2</sub>. The proposed machine learning model exhibited a good prediction performance (<i>R</i><sup>2</sup> = 0.99, etc.) and only took about 0.014 s to finish predicting the induced electric field in the personalized human brain for rapidly evaluating the H1 coil performance in clinical practices.</p>","PeriodicalId":50544,"journal":{"name":"Electromagnetic Biology and Medicine","volume":" ","pages":"1-26"},"PeriodicalIF":1.5000,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electromagnetic Biology and Medicine","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1080/15368378.2025.2561001","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Deep transcranial magnetic stimulation (DTMS) has been increasingly used to treat neurological disorders in recent years. However, owing to the complicated configuration of DTMS coils, such as the H1 coil, the electric field induced by it in the personalized human brain is so varied and complex that its transcranial magnetic stimulation performances, especially focusing behavior and depth characteristics, have to be studied and evaluated further before clinical application. Therefore, besides the effects of the excitation frequency of the H1 coils, two types of magnetic shielding blocks (MSBs) with various dimensions were analyzed, and the H1 coil circuit structure with flexible length adjustment and its coil spacing were also investigated in this study. Finally, a machine learning model based on an optimizable tree algorithm was established to rapidly predict the induced electric field in the personalized human brain. Results demonstrated that the half-value depth D1/2 of the electric field induced by the H1 coil could reach 3.67 cm, which was deeper than that by the figure-of-eight (FOE) coil (<1.6 cm), but its focusing (half-value) volume V1/2 was 567.94 cm3, larger than that of the FOE coil. After introducing MSBs, reasonably adjusting the coil circuit length and the coil spacing, V1/2 was reduced to 81.748 cm3, with a slight increase in D1/2. The proposed machine learning model exhibited a good prediction performance (R2 = 0.99, etc.) and only took about 0.014 s to finish predicting the induced electric field in the personalized human brain for rapidly evaluating the H1 coil performance in clinical practices.

基于仿真和机器学习的人脑深H1线圈性能评价与个性化电场预测
近年来,深经颅磁刺激(DTMS)越来越多地用于治疗神经系统疾病。然而,由于H1线圈等DTMS线圈结构复杂,其在个性化人脑中产生的电场变化复杂,其经颅磁刺激性能,特别是聚焦行为和深度特征,在临床应用前还需进一步研究和评估。因此,除了H1线圈激励频率的影响外,本研究还分析了两种不同尺寸的磁屏蔽块(msb),并对具有柔性长度调节的H1线圈电路结构及其线圈间距进行了研究。最后,建立了一种基于可优化树算法的机器学习模型,用于快速预测个性化人脑中的感应电场。结果表明,H1线圈感应电场的半值深度D1/2可达3.67 cm,比FOE线圈的半值深度深(V1/2为567.94 cm3,比FOE线圈的半值深度大)。引入msb后,合理调整线圈电路长度和线圈间距,V1/2减小到81.748 cm3, D1/2略有增加。所提出的机器学习模型具有良好的预测性能(R2 = 0.99等),仅需0.014 s左右即可完成对个性化人脑中的感应电场的预测,用于临床实践中快速评估H1线圈的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
3.60
自引率
11.80%
发文量
33
审稿时长
>12 weeks
期刊介绍: Aims & Scope: Electromagnetic Biology and Medicine, publishes peer-reviewed research articles on the biological effects and medical applications of non-ionizing electromagnetic fields (from extremely-low frequency to radiofrequency). Topic examples include in vitro and in vivo studies, epidemiological investigation, mechanism and mode of interaction between non-ionizing electromagnetic fields and biological systems. In addition to publishing original articles, the journal also publishes meeting summaries and reports, and reviews on selected topics.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信