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Yu S, Yue W, Guo T, Liu Y, Zhang Y, Khademi S, Zhou T, Xu Z, Song B, Wu T, Liu F, Tai Y, Yu X, Wang H. The effect of the subthreshold oscillation induced by the neurons' resonance upon the electrical stimulation-dependent instability. Front Neurosci 2023; 17:1178606. [PMID: 37229430 PMCID: PMC10203711 DOI: 10.3389/fnins.2023.1178606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 04/10/2023] [Indexed: 05/27/2023] Open
Abstract
Repetitive electrical nerve stimulation can induce a long-lasting perturbation of the axon's membrane potential, resulting in unstable stimulus-response relationships. Despite being observed in electrophysiology, the precise mechanism underlying electrical stimulation-dependent (ES-dependent) instability is still an open question. This study proposes a model to reveal a facet of this problem: how threshold fluctuation affects electrical nerve stimulations. This study proposes a new method based on a Circuit-Probability theory (C-P theory) to reveal the interlinkages between the subthreshold oscillation induced by neurons' resonance and ES-dependent instability of neural response. Supported by in-vivo studies, this new model predicts several key characteristics of ES-dependent instability and proposes a stimulation method to minimize the instability. This model provides a powerful tool to improve our understanding of the interaction between the external electric field and the complexity of the biophysical characteristics of axons.
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Affiliation(s)
- Shoujun Yu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, China
| | - Wenji Yue
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, China
| | - Tianruo Guo
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW, Australia
| | - Yonghong Liu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, China
| | - Yapeng Zhang
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, China
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Sara Khademi
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, China
- Institute of Polymeric Materials, Sahand University of Technology, Tabriz, Iran
| | - Tian Zhou
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, China
| | - Zhen Xu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, China
| | - Bing Song
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, China
| | - Tianzhun Wu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, China
- Key Laboratory of Health Bioinformatics, Chinese Academy of Sciences (CAS), Shenzhen, China
| | - Fenglin Liu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, China
| | - Yanlong Tai
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, China
| | - Xuefei Yu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Hao Wang
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, China
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Deletis V, Shils J, Anso J, Villar Ortega E, Marchal-Crespo L, Buetler KA, Raabe A, Seidel K. Effects of 10-kHz Subthreshold Stimulation on Human Peripheral Nerve Activation. Neuromodulation 2022; 26:614-619. [PMID: 35715282 DOI: 10.1016/j.neurom.2022.04.051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 04/02/2022] [Accepted: 04/28/2022] [Indexed: 10/18/2022]
Abstract
OBJECTIVE The mechanisms of action of high-frequency stimulation (HFS) are unknown. We investigated the possible mechanism of subthreshold superexcitability of HFS on the excitability of the peripheral nerve. MATERIALS AND METHODS The ulnar nerve was stimulated at the wrist in six healthy participants with a single (control) stimulus, and the responses were compared with the responses to a continuous train of 5 seconds at frequencies of 500 Hz, 2.5 kHz, 5 kHz, and 10 kHz. Threshold intensity for compound muscle action potential (CMAP) was defined as intensity producing a 100-μV amplitude in ten sequential trials and "subthreshold" as 10% below the CMAP threshold. HFS threshold was defined as stimulation intensity eliciting visible tetanic contraction. RESULTS Comparing the threshold of single pulse stimulation for eliciting CMAP vs threshold for HFS response and pooling data at different frequencies (500 Hz-10 kHz) revealed a significant difference (p = 0.00015). This difference was most obvious at 10 kHz, with a mean value for threshold reduction of 42.2%. CONCLUSIONS HFS with a stimulation intensity below the threshold for a single pulse induces axonal superexcitability if applied in a train. It can activate the peripheral nerve and produce a tetanic muscle response. Subthreshold superexcitability may allow new insights into the mechanism of HFS.
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Jang MH, Chae JW, Lim SH. Application of Intraoperative Neurophysiological Monitoring in Aortic Surgery. KOREAN JOURNAL OF CLINICAL LABORATORY SCIENCE 2022. [DOI: 10.15324/kjcls.2022.54.1.61] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Affiliation(s)
- Min Hwan Jang
- Department of Neurology, Institute of Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Ji Won Chae
- Department of Cardiology, The Catholic University of Korea, Incheon St. Mary’s Hospital, Incheon, Korea
| | - Sung Hyuk Lim
- Department of Neurology, Institute of Neuroscience Center, Samsung Medical Center, Seoul, Korea
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Kim SH, Park SB, Kang HC, Park SK. Intraoperative Neurophysiological Monitoring and Neuromuscular Anesthesia Depth Monitoring. KOREAN JOURNAL OF CLINICAL LABORATORY SCIENCE 2020. [DOI: 10.15324/kjcls.2020.52.4.317] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Affiliation(s)
- Sang-Hun Kim
- Department of Neurology, Kangbuk Samsung Hospital, Seoul, Korea
| | - Soon-Bu Park
- Physiologic Diagnostic Laboratory, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Hyo-Chan Kang
- Department of Biomedical Laboratory Science, Daegu Hanny University, Daegu, Korea
| | - Sang-Ku Park
- Department of Neurosurgery, Konkuk University Medical Center, Seoul, Korea
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