Academic Journal
Performance evaluation and personalized electric field prediction of the deep H1 coil in the human brain based on simulation and machine learning.
| Τίτλος: | Performance evaluation and personalized electric field prediction of the deep H1 coil in the human brain based on simulation and machine learning. |
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| Συγγραφείς: | Tan X; School of Information Science and Engineering, Yanshan University, Qinhuangdao, China., Guo A; School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, Hubei, China., Wang Y; School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, Hubei, China., Tian J; School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, Hubei, China., Shi J; School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, Hubei, China., Li Y; School of Information Science and Engineering, Yanshan University, Qinhuangdao, China.; Hebei Key Laboratory of Information Transmission and Signal Processing, Qinhuangdao, China. |
| Πηγή: | Electromagnetic biology and medicine [Electromagn Biol Med] 2026; Vol. 45 (1), pp. 22-47. Date of Electronic Publication: 2025 Sep 23. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Informa Healthcare Country of Publication: England NLM ID: 101133002 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1536-8386 (Electronic) Linking ISSN: 15368386 NLM ISO Abbreviation: Electromagn Biol Med Subsets: MEDLINE |
| Imprint Name(s): | Publication: London : Informa Healthcare Original Publication: Monticello, NY : Marcel Dekker, Inc., c2002- |
| Ιατρικοί όροι (MeSH): | Brain*/physiology , Transcranial Magnetic Stimulation*/instrumentation , Machine Learning* , Electricity* , Computer Simulation*, Humans |
| Περίληψη: | 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 D |
| Contributed Indexing: | Keywords: H1 coil; Transcranial magnetic stimulation; deep coil; electric field prediction; electromagnetic field simulation; machine learning Local Abstract: [plain-language-summary] Deep transcranial magnetic stimulation (DTMS) is widely used in treating neurological disorders; however, the focusing ability and depth properties of DTMS coils, such as the H1 coil, remain poorly understood and require detailed study and analysis prior to clinical application. First, the effects of the excitation frequency of the H1 coil on its focusing behavior and depth characteristics were examined using a human head model derived from magnetic resonance images (MRIs). Second, two types of magnetic shielding blocks (MSBs) with varying dimensions and a coil circuit structure with flexible adjustable length were employed to investigate the focusing and depth characteristics of the H1 coil. Third, the effects of the spacing of the H1 coils on its performance were evaluated. Finally, a machine learning model based on an optimizable tree algorithm was trained and developed by using those induced electric fields simulated by COMSOL to rapidly predict the induced electric field in the personalized human brain. All results demonstrated that the excitation frequency could increase the maximum induced electric field strength Emax, the first type of MSBs could effectively improve the focusing behavior of the H1 coil, and the reasonable adjustment of the H1 coil circuit length and its spacing could achieve ideal focusing behavior and depth characteristics. In clinical practices, a machine learning model based on the optimizable tree algorithm could effectively and rapidly predict the induced electric field in a personalized human brain for promptly obtaining the focusing and depth characteristics of the H1 coil. The main contributions of this study were as follows: some sensitive factors were discussed in detail, and the methods of using the MSBs and coil circuit length adjustment could effectively improve the performance of the H1 coil. The presented machine learning algorithm could rapidly and effectively predict the induced electric field in a personalized human brain. |
| Entry Date(s): | Date Created: 20250923 Date Completed: 20260121 Latest Revision: 20260121 |
| Update Code: | 20260130 |
| DOI: | 10.1080/15368378.2025.2561001 |
| PMID: | 40985741 |
| Βάση Δεδομένων: | MEDLINE |
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