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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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Sleutjes BTHM, Ruisch J, Nassi TE, Buitenweg JR, van Schelven LJ, van den Berg LH, Franssen H, Stephan Goedee H. Impact of stimulus duration on motor unit thresholds and alternation in compound muscle action potential scans. Clin Neurophysiol 2021; 132:323-331. [PMID: 33450554 DOI: 10.1016/j.clinph.2020.10.026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 09/30/2020] [Accepted: 10/26/2020] [Indexed: 11/17/2022]
Abstract
OBJECTIVE To investigate the impact of stimulus duration on motor unit (MU) thresholds and alternation within compound muscle action potential (CMAP) scans. METHODS The stimulus duration (0.1, 0.2, 0.6, and 1.0 ms) in thenar CMAP scans and individual MUs of 14 healthy subjects was systematically varied. We quantified variability of individual MU's thresholds by relative spread (RS), MU thresholds by stimulus currents required to elicit target CMAPs of 5% (S5), 50% (S50) and 95% (S95) of the maximum CMAP, and relative range (RR) by 100*[S95-S5]/S50. We further assessed the strength-duration time constant (SDTC). Experimental observations were subsequently simulated to quantify alternation. RESULTS RS, unaffected by stimulus duration, was 1.65% averaged over all recordings. RR increased for longer stimulus duration (11.4% per ms, p < 0.001). SDTC shortened with higher target CMAPs (0.007 ms per 10% CMAP, p < 0.001). Experiments and simulations supported that this may underlie the increased RR. A short compared to long stimulus duration recruited relative more MUs at S50 (more alternation) than at the tails (less alternation). CONCLUSIONS The stimulus duration significantly affects MU threshold distribution and alternation within CMAP scans. SIGNIFICANCE Stimulation settings can be further optimized and their standardization is preferred when using CMAP scans for monitoring neuromuscular diseases.
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Affiliation(s)
- Boudewijn T H M Sleutjes
- Department of Neurology, Brain Centre Utrecht, University Medical Centre Utrecht, Utrecht, the Netherlands.
| | - Janna Ruisch
- Biomedical Signals and Systems, Technical Medical Centre, University of Twente, Enschede, the Netherlands
| | - Thijs E Nassi
- Biomedical Signals and Systems, Technical Medical Centre, University of Twente, Enschede, the Netherlands
| | - Jan R Buitenweg
- Biomedical Signals and Systems, Technical Medical Centre, University of Twente, Enschede, the Netherlands
| | - Leonard J van Schelven
- Department of Medical Technology and Clinical Physics, University Medical Center Utrecht, the Netherlands
| | - Leonard H van den Berg
- Department of Neurology, Brain Centre Utrecht, University Medical Centre Utrecht, Utrecht, the Netherlands
| | - Hessel Franssen
- Department of Neurology, Brain Centre Utrecht, University Medical Centre Utrecht, Utrecht, the Netherlands
| | - H Stephan Goedee
- Department of Neurology, Brain Centre Utrecht, University Medical Centre Utrecht, Utrecht, the Netherlands
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Wang Y, Wu Q, Dey N, Fong S, Ashour AS. Deep back propagation–long short-term memory network based upper-limb sEMG signal classification for automated rehabilitation. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.05.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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4
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Theme 8 Clinical imaging and electrophysiology. Amyotroph Lateral Scler Frontotemporal Degener 2019; 20:246-261. [DOI: 10.1080/21678421.2019.1646996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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5
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Tankisi H, Burke D, Cui L, de Carvalho M, Kuwabara S, Nandedkar SD, Rutkove S, Stålberg E, van Putten MJAM, Fuglsang-Frederiksen A. Standards of instrumentation of EMG. Clin Neurophysiol 2019; 131:243-258. [PMID: 31761717 DOI: 10.1016/j.clinph.2019.07.025] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2018] [Revised: 07/12/2019] [Accepted: 07/14/2019] [Indexed: 12/14/2022]
Abstract
Standardization of Electromyography (EMG) instrumentation is of particular importance to ensure high quality recordings. This consensus report on "Standards of Instrumentation of EMG" is an update and extension of the earlier IFCN Guidelines published in 1999. First, a panel of experts in different fields from different geographical distributions was invited to submit a section on their particular interest and expertise. Then, the merged document was circulated for comments and edits until a consensus emerged. The first sections in this document cover technical aspects such as instrumentation, EMG hardware and software including amplifiers and filters, digital signal analysis and instrumentation settings. Other sections cover the topics such as temporary storage, trigger and delay line, averaging, electrode types, stimulation techniques for optimal and standardised EMG examinations, and the artefacts electromyographers may face and safety rules they should follow. Finally, storage of data and databases, report generators and external communication are summarized.
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Affiliation(s)
- Hatice Tankisi
- Department of Clinical Neurophysiology, Aarhus University Hospital & Dept of Clinical Medicine, Aarhus University, Aarhus, Denmark.
| | - David Burke
- Royal Prince Alfred Hospital and University of Sydney, Australia
| | - Liying Cui
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Mamede de Carvalho
- Faculdade de Medicina-iMM, Universidade de Lisboa, Lisbon, Portugal; Department of Neurosciences, Centro Hospitalar Universitário de Lisboa, Portugal
| | - Satoshi Kuwabara
- Department of Neurology, Graduate School of Medicine, Chiba University, Japan
| | | | | | - Erik Stålberg
- Department Clin Neurophysiology, Inst Neurosciences, Uppsala University, Sweden
| | | | - Anders Fuglsang-Frederiksen
- Department of Clinical Neurophysiology, Aarhus University Hospital & Dept of Clinical Medicine, Aarhus University, Aarhus, Denmark
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Sleutjes BTHM, Kovalchuk MO, Durmus N, Buitenweg JR, van Putten MJAM, van den Berg LH, Franssen H. Simulating perinodal changes observed in immune-mediated neuropathies: impact on conduction in a model of myelinated motor and sensory axons. J Neurophysiol 2019; 122:1036-1049. [PMID: 31291151 DOI: 10.1152/jn.00326.2019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Immune-mediated neuropathies affect myelinated axons, resulting in conduction slowing or block that may affect motor and sensory axons differently. The underlying mechanisms of these neuropathies are not well understood. Using a myelinated axon model, we studied the impact of perinodal changes on conduction. We extended a longitudinal axon model (41 nodes of Ranvier) with biophysical properties unique to human myelinated motor and sensory axons. We simulated effects of temperature and axonal diameter on conduction and strength-duration properties. We then studied effects of impaired nodal sodium channel conductance and paranodal myelin detachment by reducing periaxonal resistance, as well as their interaction, on conduction in the 9 middle nodes and enclosed paranodes. Finally, we assessed the impact of reducing the affected region (5 nodes) and adding nodal widening. Physiological motor and sensory conduction velocities and changes to axonal diameter and temperature were observed. The sensory axon had a longer strength-duration time constant. Reducing sodium channel conductance and paranodal periaxonal resistance induced progressive conduction slowing. In motor axons, conduction block occurred with a 4-fold drop in sodium channel conductance or a 7.7-fold drop in periaxonal resistance. In sensory axons, block arose with a 4.8-fold drop in sodium channel conductance or a 9-fold drop in periaxonal resistance. This indicated that motor axons are more vulnerable to developing block. A boundary of block emerged when the two mechanisms interacted. This boundary shifted in opposite directions for a smaller affected region and nodal widening. These differences may contribute to the predominance of motor deficits observed in some immune-mediated neuropathies.NEW & NOTEWORTHY Immune-mediated neuropathies may affect myelinated motor and sensory axons differently. By the development of a computational model, we quantitatively studied the impact of perinodal changes on conduction in motor and sensory axons. Simulations of increasing nodal sodium channel dysfunction and paranodal myelin detachment induced progressive conduction slowing. Sensory axons were more resistant to block than motor axons. This could explain the greater predisposition of motor axons to functional deficits observed in some immune-mediated neuropathies.
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Affiliation(s)
- Boudewijn T H M Sleutjes
- Department of Neurology, Brain Center Utrecht, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Maria O Kovalchuk
- Department of Neurology, Brain Center Utrecht, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Naric Durmus
- Biomedical Signals and Systems, MIRA, Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands.,Department of Clinical Neurophysiology, University of Twente, Enschede, The Netherlands
| | - Jan R Buitenweg
- Biomedical Signals and Systems, MIRA, Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands
| | - Michel J A M van Putten
- Department of Clinical Neurophysiology, University of Twente, Enschede, The Netherlands.,Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, Enschede, The Netherlands
| | - Leonard H van den Berg
- Department of Neurology, Brain Center Utrecht, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Hessel Franssen
- Department of Neurology, Brain Center Utrecht, University Medical Center Utrecht, Utrecht, The Netherlands
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7
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Howells J, Matamala JM, Park SB, Garg N, Vucic S, Bostock H, Burke D, Kiernan MC. In vivo evidence for reduced ion channel expression in motor axons of patients with amyotrophic lateral sclerosis. J Physiol 2018; 596:5379-5396. [PMID: 30175403 DOI: 10.1113/jp276624] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Accepted: 08/31/2018] [Indexed: 12/11/2022] Open
Abstract
KEY POINTS The progressive loss of motor units in amyotrophic lateral sclerosis (ALS) is initially compensated for by the reinnervation of denervated muscle fibres by surviving motor axons. A disruption in protein homeostasis is thought to play a critical role in the pathogenesis of ALS. The changes in surviving motor neurons were studied by comparing the nerve excitability properties of moderately and severely affected single motor axons from patients with ALS with those from single motor axons in control subjects. A mathematical model indicated that approximately 99% of the differences between the ALS and control units could be explained by a non-selective reduction in the expression of all ion channels. These changes in ALS patients are best explained by a failure in the supply of ion channel and other membrane proteins from the diseased motor neuron. ABSTRACT Amyotrophic lateral sclerosis (ALS) is characterised by a progressive loss of motor units and the reinnervation of denervated muscle fibres by surviving motor axons. This reinnervation preserves muscle function until symptom onset, when some 60-80% of motor units have been lost. We have studied the changes in surviving motor neurons by comparing the nerve excitability properties of 31 single motor axons from patients with ALS with those from 21 single motor axons in control subjects. ALS motor axons were classified as coming from moderately or severely affected muscles according to the compound muscle action potential amplitude of the parent muscle. Compared with control units, thresholds were increased, and there was reduced inward and outward rectification and greater superexcitability following a conditioning impulse. These abnormalities were greater in axons from severely affected muscles, and were correlated with loss of fine motor skills. A mathematical model indicated that 99.1% of the differences between the moderately affected ALS and control units could be explained by a reduction in the expression of all ion channels. For the severely affected units, modelling required, in addition, an increase in the current leak through and under the myelin sheath. This might be expected if the anchoring proteins responsible for the paranodal seal were reduced. We conclude that changes in axonal excitability identified in ALS patients are best explained by a failure in the supply of ion channel and other membrane proteins from the diseased motor neuron, a conclusion consistent with recent animal and in vitro human data.
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Affiliation(s)
- James Howells
- Brain & Mind Centre, University of Sydney, Sydney, Australia
| | | | - Susanna B Park
- Brain & Mind Centre, University of Sydney, Sydney, Australia
| | - Nidhi Garg
- Brain & Mind Centre, University of Sydney, Sydney, Australia.,Institute of Clinical Neurosciences, Royal Prince Alfred Hospital and University of Sydney, Sydney, Australia
| | - Steve Vucic
- Departments of Neurology and Neurophysiology, Westmead Hospital and University of Sydney, Sydney, Australia
| | - Hugh Bostock
- MRC Centre for Neuromuscular Diseases, National Hospital for Neurology and Neurosurgery, Queen Square, London, UK.,Institute of Neurology, UCL, Queen Square, London, WC1N 3BG, UK
| | - David Burke
- Institute of Clinical Neurosciences, Royal Prince Alfred Hospital and University of Sydney, Sydney, Australia
| | - Matthew C Kiernan
- Brain & Mind Centre, University of Sydney, Sydney, Australia.,Institute of Clinical Neurosciences, Royal Prince Alfred Hospital and University of Sydney, Sydney, Australia
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