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Wang Y, Lin J, Zhu K, Nie Y, Wang M, Ma X, Liu X, Wang R, Mai W, Chu F, Liu R, Wu J, Jin J, Zhou X, Ma R, Wang X, Yin T, Liu Z, Zhang S. Precision neuroregulation combining liquid metal and magnetic stimulation. J Neuroeng Rehabil 2025; 22:76. [PMID: 40197274 PMCID: PMC11974191 DOI: 10.1186/s12984-025-01575-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Accepted: 02/11/2025] [Indexed: 04/10/2025] Open
Abstract
BACKGROUND Electromagnetic field-based neuroregulation technology is a crucial technique for treating central nervous system and peripheral nervous system disorders. However, the use of invasive electrodes has unavoidable problems such as the risk of inflammation due to high hardness, electrical connections and the need for batteries. On the other hand, non-invasive magnetic stimulation has limitations such as centimeter-level focal areas and shallow stimulation depth. METHODS To enhance the precision and effectiveness of wireless magnetic stimulation, we employed a figure-8 magnetic stimulation coil (8-coil) to generate a magnetic field, combined with an injectable, highly conductive, and flexible liquid metal (LM) to produce a millimeter-scale focused electric field. A coaxial electric field measurement electrode was used to establish an agar phantom-based electric field measurement platform. The sciatic nerve of C57 mice was stimulated under acute anesthesia conditions, and electromyography (EMG) signals were collected to evaluate the enhancement of stimulation effects. Long-term safety was assessed through four weeks of implantation. RESULTS Theoretical analysis and finite element simulations demonstrated that the combination of LM and the 8-coil generated a millimeter-scale enhanced vector electric field within the tissue. Measured electric field distributions closely aligned with theoretical and simulation results. In the sciatic nerve experiments on mice, 1 µL of LM under a 0.45 T magnetic field significantly increased EMG signals and leg movement amplitude by approximately 500%. Long-term implantation under magnetic stimulation revealed no adverse effects. CONCLUSIONS This method utilizes focused electric fields to improve the precision and effectiveness of neuro-magnetic stimulation. It holds promise as a novel approach for precise stimulation. Preliminary evidence was provided for the safety of in vivo LM implantation under external magnetic fields.
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Affiliation(s)
- Yuheng Wang
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, 300192, China
- Tianjin Key Laboratory of Neuroregulation and Neurorepair, Tianjin, 300192, China
| | - Junjie Lin
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, 300192, China
- Tianjin Key Laboratory of Neuroregulation and Neurorepair, Tianjin, 300192, China
| | - Kai Zhu
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, 300192, China
- Tianjin Key Laboratory of Neuroregulation and Neurorepair, Tianjin, 300192, China
| | - Yuhui Nie
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, 300192, China
- Tianjin Key Laboratory of Neuroregulation and Neurorepair, Tianjin, 300192, China
| | - Mengyuan Wang
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, 300192, China
- Tianjin Key Laboratory of Neuroregulation and Neurorepair, Tianjin, 300192, China
| | - Xiaoxu Ma
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, 300192, China
- Tianjin Key Laboratory of Neuroregulation and Neurorepair, Tianjin, 300192, China
| | - Xu Liu
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, 300192, China
- Tianjin Key Laboratory of Neuroregulation and Neurorepair, Tianjin, 300192, China
| | - Ruru Wang
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, 300192, China
- Tianjin Key Laboratory of Neuroregulation and Neurorepair, Tianjin, 300192, China
| | - Wenshu Mai
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, 300192, China
- Tianjin Key Laboratory of Neuroregulation and Neurorepair, Tianjin, 300192, China
| | - Fangxuan Chu
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, 300192, China
- Tianjin Key Laboratory of Neuroregulation and Neurorepair, Tianjin, 300192, China
| | - Ruixu Liu
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, 300192, China
- Tianjin Key Laboratory of Neuroregulation and Neurorepair, Tianjin, 300192, China
| | - Jiankang Wu
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, 300192, China
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin, 300192, China
- Tianjin Key Laboratory of Neuroregulation and Neurorepair, Tianjin, 300192, China
- Tianjin Institutes of Health Science, Tianjin, 301600, China
| | - Jingna Jin
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, 300192, China
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin, 300192, China
- Tianjin Key Laboratory of Neuroregulation and Neurorepair, Tianjin, 300192, China
- Tianjin Institutes of Health Science, Tianjin, 301600, China
| | - Xiaoqing Zhou
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, 300192, China
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin, 300192, China
- Tianjin Key Laboratory of Neuroregulation and Neurorepair, Tianjin, 300192, China
- Tianjin Institutes of Health Science, Tianjin, 301600, China
| | - Ren Ma
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, 300192, China
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin, 300192, China
- Tianjin Key Laboratory of Neuroregulation and Neurorepair, Tianjin, 300192, China
- Tianjin Institutes of Health Science, Tianjin, 301600, China
| | - Xin Wang
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, 300192, China
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin, 300192, China
- Tianjin Key Laboratory of Neuroregulation and Neurorepair, Tianjin, 300192, China
- Tianjin Institutes of Health Science, Tianjin, 301600, China
| | - Tao Yin
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, 300192, China.
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin, 300192, China.
- Tianjin Key Laboratory of Neuroregulation and Neurorepair, Tianjin, 300192, China.
- Tianjin Institutes of Health Science, Tianjin, 301600, China.
| | - Zhipeng Liu
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, 300192, China.
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin, 300192, China.
- Tianjin Key Laboratory of Neuroregulation and Neurorepair, Tianjin, 300192, China.
- Tianjin Institutes of Health Science, Tianjin, 301600, China.
| | - Shunqi Zhang
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, 300192, China.
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin, 300192, China.
- Tianjin Key Laboratory of Neuroregulation and Neurorepair, Tianjin, 300192, China.
- Tianjin Institutes of Health Science, Tianjin, 301600, China.
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Gutiérrez-Muto AM, Bestmann S, Sánchez de la Torre R, Pons JL, Oliviero A, Tornero J. The complex landscape of TMS devices: A brief overview. PLoS One 2023; 18:e0292733. [PMID: 38015924 PMCID: PMC10684101 DOI: 10.1371/journal.pone.0292733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 09/27/2023] [Indexed: 11/30/2023] Open
Abstract
The increasing application of TMS in research and therapy has spawned an ever-growing number of commercial and non-commercial TMS devices and technology development. New CE-marked devices appear at a rate of approximately one every two years, with new FDA-approved application of TMS occurring at a similar rate. With the resulting complex landscape of TMS devices and their application, accessible information about the technological characteristics of the TMS devices, such as the type of their circuitry, their pulse characteristics, or permitted protocols would be beneficial. We here present an overview and open access database summarizing key features and applications of available commercial and non-commercial TMS devices (http://www.tmsbase.info). This may guide comparison and decision making about the use of these devices. A bibliometric analysis was performed by identifying commercial and non-commercial TMS devices from which a comprehensive database was created summarizing their publicly available characteristics, both from a technical and clinical point of view. In this document, we introduce both the commercial devices and prototypes found in the literature. The technical specifications that unify these devices are briefly analysed in two separate tables: power electronics, waveform, protocols, and coil types. In the prototype TMS systems, the proposed innovations are focused on improving the treatment regarding the patient: noise cancellation, controllable parameters, and multiple stimulation. This analysis shows that the landscape of TMS is becoming increasingly fragmented, with new devices appearing ever more frequently. The review provided here can support development of benchmarking frameworks and comparison between TMS systems, inform the choice of TMS platforms for specific research and therapeutic applications, and guide future technology development for neuromodulation devices. This standardisation strategy will allow a better end-user choice, with an impact on the TMS manufacturing industry and a homogenisation of patient samples in multi-centre clinical studies. As an open access repository, we envisage the database to grow along with the dynamic development of TMS devices and applications through community-lead curation.
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Affiliation(s)
| | - Sven Bestmann
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | | | - José L. Pons
- Legs and Walking Lab, Shirley Ryan Ability Laboratory (Formerly Rehabilitation Institute of Chicago), Chicago, IL, United States of America
- Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America
| | - Antonio Oliviero
- Center for Clinical Neuroscience, Hospital Los Madroños, Brunete, Madrid, Spain
- Advanced Neurorehabilitation Unit, Hospital Los Madroños, Brunete, Madrid, Spain
| | - Jesús Tornero
- Center for Clinical Neuroscience, Hospital Los Madroños, Brunete, Madrid, Spain
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Wang B, Zhang J, Li Z, Grill WM, Peterchev AV, Goetz SM. Optimized monophasic pulses with equivalent electric field for rapid-rate transcranial magnetic stimulation. J Neural Eng 2023; 20:10.1088/1741-2552/acd081. [PMID: 37100051 PMCID: PMC10464893 DOI: 10.1088/1741-2552/acd081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Accepted: 04/26/2023] [Indexed: 04/28/2023]
Abstract
Objective.Transcranial magnetic stimulation (TMS) with monophasic pulses achieves greater changes in neuronal excitability but requires higher energy and generates more coil heating than TMS with biphasic pulses, and this limits the use of monophasic pulses in rapid-rate protocols. We sought to design a stimulation waveform that retains the characteristics of monophasic TMS but significantly reduces coil heating, thereby enabling higher pulse rates and increased neuromodulation effectiveness.Approach.A two-step optimization method was developed that uses the temporal relationship between the electric field (E-field) and coil current waveforms. The model-free optimization step reduced the ohmic losses of the coil current and constrained the error of the E-field waveform compared to a template monophasic pulse, with pulse duration as a second constraint. The second, amplitude adjustment step scaled the candidate waveforms based on simulated neural activation to account for differences in stimulation thresholds. The optimized waveforms were implemented to validate the changes in coil heating.Main results.Depending on the pulse duration and E-field matching constraints, the optimized waveforms produced 12%-75% less heating than the original monophasic pulse. The reduction in coil heating was robust across a range of neural models. The changes in the measured ohmic losses of the optimized pulses compared to the original pulse agreed with numeric predictions.Significance.The first step of the optimization approach was independent of any potentially inaccurate or incorrect model and exhibited robust performance by avoiding the highly nonlinear behavior of neural responses, whereas neural simulations were only run once for amplitude scaling in the second step. This significantly reduced computational cost compared to iterative methods using large populations of candidate solutions and more importantly reduced the sensitivity to the choice of neural model. The reduced coil heating and power losses of the optimized pulses can enable rapid-rate monophasic TMS protocols.
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Affiliation(s)
- Boshuo Wang
- Department of Psychiatry and Behavior Sciences, School of Medicine, Duke University, Durham, NC, USA
| | - Jinshui Zhang
- Department of Electrical and Computer Engineering, School of Engineering, Duke University, Durham, NC, USA
| | - Zhongxi Li
- Department of Electrical and Computer Engineering, School of Engineering, Duke University, Durham, NC, USA
| | - Warren M. Grill
- Department of Electrical and Computer Engineering, School of Engineering, Duke University, Durham, NC, USA
- Department of Biomedical Engineering, School of Engineering, Duke University, Durham, NC, USA
- Department of Neurosurgery, School of Medicine, Duke University, NC, USA
- Department of Neurobiology, School of Medicine, Duke University, NC, USA
| | - Angel V. Peterchev
- Department of Psychiatry and Behavior Sciences, School of Medicine, Duke University, Durham, NC, USA
- Department of Electrical and Computer Engineering, School of Engineering, Duke University, Durham, NC, USA
- Department of Biomedical Engineering, School of Engineering, Duke University, Durham, NC, USA
- Department of Neurosurgery, School of Medicine, Duke University, NC, USA
| | - Stefan M. Goetz
- Department of Psychiatry and Behavior Sciences, School of Medicine, Duke University, Durham, NC, USA
- Department of Electrical and Computer Engineering, School of Engineering, Duke University, Durham, NC, USA
- Department of Neurosurgery, School of Medicine, Duke University, NC, USA
- Department of Engineering, School of Technology, University of Cambridge, Cambridge, UK
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Gomez LJ, Goetz SM, Peterchev AV. Design of transcranial magnetic stimulation coils with optimal trade-off between depth, focality, and energy. J Neural Eng 2018; 15:046033. [PMID: 29855433 DOI: 10.1088/1741-2552/aac967] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Transcranial magnetic stimulation (TMS) is a noninvasive brain stimulation technique used for research and clinical applications. Existent TMS coils are limited in their precision of spatial targeting (focality), especially for deeper targets. This paper presents a methodology for designing TMS coils to achieve optimal trade-off between the depth and focality of the induced electric field (E-field), as well as the energy required by the coil. APPROACH A multi-objective optimization technique is used for computationally designing TMS coils that achieve optimal trade-offs between E-field focality, depth, and energy (fdTMS coils). The fdTMS coil winding(s) maximize focality (minimize the volume of the brain region with E-field above a given threshold) while reaching a target at a specified depth and not exceeding predefined peak E-field strength and required coil energy. Spherical and MRI-derived head models are used to compute the fundamental depth-focality trade-off as well as focality-energy trade-offs for specific target depths. MAIN RESULTS Across stimulation target depths of 1.0-3.4 cm from the brain surface, the suprathreshold volume can be theoretically decreased by 42%-55% compared to existing TMS coil designs. The suprathreshold volume of a figure-8 coil can be decreased by 36%, 44%, or 46%, for matched, doubled, or quadrupled energy. For matched focality and energy, the depth of a figure-8 coil can be increased by 22%. SIGNIFICANCE Computational design of TMS coils could enable more selective targeting of the induced E-field. The presented results appear to be the first significant advancement in the depth-focality trade-off of TMS coils since the introduction of the figure-8 coil three decades ago, and likely represent the fundamental physical limit.
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Affiliation(s)
- Luis J Gomez
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, United States of America
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Goetz SM, Deng ZD. The development and modelling of devices and paradigms for transcranial magnetic stimulation. Int Rev Psychiatry 2017; 29:115-145. [PMID: 28443696 PMCID: PMC5484089 DOI: 10.1080/09540261.2017.1305949] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Revised: 03/03/2017] [Accepted: 03/09/2017] [Indexed: 12/20/2022]
Abstract
Magnetic stimulation is a non-invasive neurostimulation technique that can evoke action potentials and modulate neural circuits through induced electric fields. Biophysical models of magnetic stimulation have become a major driver for technological developments and the understanding of the mechanisms of magnetic neurostimulation and neuromodulation. Major technological developments involve stimulation coils with different spatial characteristics and pulse sources to control the pulse waveform. While early technological developments were the result of manual design and invention processes, there is a trend in both stimulation coil and pulse source design to mathematically optimize parameters with the help of computational models. To date, macroscopically highly realistic spatial models of the brain, as well as peripheral targets, and user-friendly software packages enable researchers and practitioners to simulate the treatment-specific and induced electric field distribution in the brains of individual subjects and patients. Neuron models further introduce the microscopic level of neural activation to understand the influence of activation dynamics in response to different pulse shapes. A number of models that were designed for online calibration to extract otherwise covert information and biomarkers from the neural system recently form a third branch of modelling.
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Affiliation(s)
- Stefan M Goetz
- a Department of Psychiatry & Behavioral Sciences, Division for Brain Stimulation & Neurophysiology , Duke University , Durham , NC , USA
- b Department of Electrical & Computer Engineering , Duke University , Durham , NC , USA
- c Department of Neurosurgery , Duke University , Durham , NC , USA
| | - Zhi-De Deng
- a Department of Psychiatry & Behavioral Sciences, Division for Brain Stimulation & Neurophysiology , Duke University , Durham , NC , USA
- d Intramural Research Program, Experimental Therapeutics & Pathophysiology Branch, Noninvasive Neuromodulation Unit , National Institutes of Health, National Institute of Mental Health , Bethesda , MD , USA
- e Duke Institute for Brain Sciences , Duke University , Durham , NC , USA
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