In-vivo verified anatomically aware deep learning for real-time electric field simulation
文献类型：期刊论文
| 作者 | Ma, Liang1,2 ; Zhong, Gangliang2; Yang, Zhengyi2; Lu, Xuefeng2; Fan, Lingzhong2,3; Liu, Hao2; Chu, Congying2; Xiong, Hui2; Jiang, Tianzi1,2,3,4,5,6
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| 刊名 | JOURNAL OF NEURAL ENGINEERING
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| 出版日期 | 2023-12-01 |
| 卷号 | 20期号:6页码:17 |
| ISSN号 | 1741-2560 |
| 关键词 | electric field transcranial magnetic stimulation coil placement deep learning in-vivo verification |
| DOI | 10.1088/1741-2552/ad0add |
| 通讯作者 | Jiang, Tianzi(jiangtz@nlpr.ia.ac.cn) |
| 英文摘要 | Objective. Transcranial magnetic stimulation (TMS) has emerged as a prominent non-invasive technique for modulating brain function and treating mental disorders. By generating a high-precision magnetically evoked electric field (E-field) using a TMS coil, it enables targeted stimulation of specific brain regions. However, current computational methods employed for E-field simulations necessitate extensive preprocessing and simulation time, limiting their fast applications in the determining the optimal coil placement. Approach. We present an attentional deep learning network to simulate E-fields. This network takes individual magnetic resonance images and coil configurations as inputs, firstly transforming the images into explicit brain tissues and subsequently generating the local E-field distribution near the target brain region. Main results. Relative to the previous deep-learning simulation method, the presented method reduced the mean relative error in simulated E-field strength of gray matter by 21.1%, and increased the correlation between regional E-field strengths and corresponding electrophysiological responses by 35.0% when applied into another dataset. In-vivo TMS experiments further revealed that the optimal coil placements derived from presented method exhibit comparable stimulation performance on motor evoked potentials to those obtained using computational methods. The simplified preprocessing and increased simulation efficiency result in a significant reduction in the overall time cost of traditional TMS coil placement optimization, from several hours to mere minutes. Significance. The precision and efficiency of presented simulation method hold promise for its application in determining individualized coil placements in clinical practice, paving the way for personalized TMS treatments. |
| WOS关键词 | TRANSCRANIAL MAGNETIC STIMULATION ; DORSOLATERAL PREFRONTAL CORTEX ; COIL PLACEMENT ; TMS ; DEPRESSION ; FRAMEWORK ; LOCALIZATION ; PARCELLATION ; VARIABILITY ; NETWORKS |
| 资助项目 | National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809 |
| WOS研究方向 | Engineering ; Neurosciences & Neurology |
| 语种 | 英语 |
| 出版者 | IOP Publishing Ltd |
| WOS记录号 | WOS:001123483800001 |
| 资助机构 | National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/55039]  |
| 专题 | 脑图谱与类脑智能实验室 |
| 通讯作者 | Jiang, Tianzi |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Automat, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China 4.Artificial Intelligence Res Inst, Res Ctr Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, Zhejiang, Peoples R China 5.Xiaoxiang Inst Brain Hlth, Yongzhou 425000, Hunan, Peoples R China 6.Yongzhou Cent Hosp, Yongzhou 425000, Hunan, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ma, Liang,Zhong, Gangliang,Yang, Zhengyi,et al. In-vivo verified anatomically aware deep learning for real-time electric field simulation[J]. JOURNAL OF NEURAL ENGINEERING,2023,20(6):17. |
| APA | Ma, Liang.,Zhong, Gangliang.,Yang, Zhengyi.,Lu, Xuefeng.,Fan, Lingzhong.,...&Jiang, Tianzi.(2023).In-vivo verified anatomically aware deep learning for real-time electric field simulation.JOURNAL OF NEURAL ENGINEERING,20(6),17. |
| MLA | Ma, Liang,et al."In-vivo verified anatomically aware deep learning for real-time electric field simulation".JOURNAL OF NEURAL ENGINEERING 20.6(2023):17. |
入库方式： OAI收割
来源：自动化研究所
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