基于运动单元累计尖峰序列的脑肌耦合分析

doi:10.3969/j.issn.0258-8021.2023.04.001

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摘要脑肌耦合能够反映大脑皮层和肌肉在感觉运动之间的联系。针对传统表面肌电信号输入-输出非线性的问题,本研究提出一种新的脑肌耦合分析方法。将运动单元分解后能线性传输神经驱动的累计尖峰序列(CST)与脑电信号进行相干性分析,定量描述上肢抓握运动中不同收缩力水平下,不同频段特征的脑肌耦合强度和神经元的共同轴突输入。在10名健康人的指浅屈肌(FDS)和尺侧腕屈肌(FCU)的同步脑肌电数据进行了测试和分析。结果表明,在上肢抓握运动中,频段(F(4, 8)=337.2,P<0.01)与收缩力水平(F(2, 8)=12.15,P<0.01)均对肌间耦合影响显著,其中β与α频段最为明显,在30%MVC下,β频段相干性均值为0.23±0.10,α频段相干性均值为0.47±0.02。轴突共同输入控制收缩力水平。脑肌耦合整体强度较小,耦合强度最大的为β频段,30%MVC下相干性均值为0.12±0.02。CST脑肌耦合分析显示了脑肌间各个频段、各个收缩力水平的耦合特性和共同轴突输入,为脑肌耦合分析提供了一种新的方法。

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	苏佳豪
	佘青山
	张建海
	马玉良
	范影乐

关键词 ：脑肌耦合, 运动单元分解, 相干性, 累计尖峰序列, 轴突输入

Abstract：Cortico-muscular coupling can reflect the connection between cerebral cortex and muscle in sensorimotor. This paper proposed a new cortico-muscular coupling analysis method, that is utilizing EEG and cumulative spike train (CST) obtained after the decomposition of the motor unit to linearly transmit the neural drive for coherence analysis, quantitatively assessing cortico-muscular coupling and common synaptic input of neurons under different frequency band at different contraction force levels during upper limb grasping. The synchronous EEG and sEMG data of flexor digitorum superficialis (FDS) and flexor carpi ulnaris (FCU) were measured and analyzed in 10 healthy subjects. Results showed that both frequency band (F (4, 8)=337.2, P<0.01) and contractile force level (F (2,8)=12.15, P<0.01) had significant effects on the intermuscular coupling during upper limb grasping exercise, especially in β and α band. At 30% MVC, the mean coherence of β frequency band was 0.23 ± 0.10, and that of α frequency band was 0.47 ± 0.02. The synaptic input controls the level of contraction force. Cortico-muscular coupling was relatively low. The highest coupling strength was in β band with a coherence value of 0.12 ± 0.01 at 30% MVC. The CST brain muscle coupling analysis can reflect the coupling characteristics and common synaptic inputs of various frequency bands and contraction levels between brain muscles, providing a new method for brain muscle coupling analysis.

Key words： cortico-muscular motor unit decomposition coherence cumulative spike train synaptic input

收稿日期: 2022-10-11

PACS:

R318

基金资助:浙江省自然科学基金重点项目(LZ22F010003);国家自然科学基金(61871427) ;国家重点研发计划(2017YFE0116800)

通讯作者: ^*E-mail: qsshe@hdu.edu.cn

引用本文:

苏佳豪, 佘青山, 张建海, 马玉良, 范影乐. 基于运动单元累计尖峰序列的脑肌耦合分析[J]. 中国生物医学工程学报, 2023, 42(4): 385-393.
Su Jiahao, She Qingshan, Zhang Jianhai, Ma Yuliang, Fan Yingle. Cortico-Muscular Coupling Analysis Based on Cumulative Spike Sequence of Motor Unit. Chinese Journal of Biomedical Engineering, 2023, 42(4): 385-393.

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http://cjbme.csbme.org/CN/10.3969/j.issn.0258-8021.2023.04.001 或 http://cjbme.csbme.org/CN/Y2023/V42/I4/385

[1] Xi Xugang, Ding Jinsuo, Wang Junhong, et al. Analysis of functional cortico-muscular coupling based on multiscale transfer spectral entropy [J]. IEEE Journal of Biomedical and Health Informatics, 2022,26(10): 5085-5096.
[2] 王洪安,佘青山,马玉良,等. 基于时变copula互信息的肌间耦合分析 [J]. 中国生物医学工程学报, 2022, 41(2): 140-150.
[3] Chen Xiaoling, Xie Ping, Zhang Yuanyuan, et al. Multiscale information transfer in functional corticomuscular coupling estimation following stroke: a pilot study [J]. Frontiers in Neurology, 2018, 9, 287.
[4] Xi Xugang, Wu Xiangxiang, Zhao Yunbo, et al. Cortico-muscular functional network: an exploration of cortico-muscular coupling in hand movements [J]. Journal of Neural Engineering, 2021, 4(18): 046084.
[5] Enoka RM, Duchateau J. Inappropriate interpretation of surface EMG signals and muscle fiber characteristics impedes understanding of the control of neuromuscular function [J]. Journal of Applied Physiology, 2015, 119: 1516-1518.
[6] Holobar A, Zazula D. Blind deconvolution of close-to-orthogonal pulse sources applied to surface electromyograms [C] // Independent Component Analysis and Blind Signal Separation. Berlin: Heidelberg, 2004: 1056-1063.
[7] Del Vecchio A, Jones RHA, Schofield IS, et al. Interfacing motor units in nonhuman primates identifies a principal neural component for force control constrained by the size principle [J]. The Journal of Neuroscience, 2022, 42(39): 7386-7399.
[8] 何金保,管冰蕾,黄凯,等. 一种肌肉动态收缩的高密度表面肌电信号分解新方法 [J]. 生物医学工程学杂志, 2021, 38(6): 1081-1086.
[9] Clarke A, Atashzar S, Del Vecchio A, et al. Deep learning for robust decomposition of High-Density surface EMG signals [J]. IEEE Transactions on Biomedical Engineering, 2021, 68(2): 526-534.
[10] Chen Chen, Ma Shihan, Sheng Xinjun, et al. Adaptive real-time identification of motor unit discharges from non-stationary high-density surface electromyographic signals [J]. IEEE Transactions on Biomedical Engineering, 2020, 67(12): 3501-3509.
[11] Liu Yang, Chen Yenting, Zhang Yingchun, et al. Motor unit number estimation in spastic biceps brachii muscles of chronic stroke survivors before and after BoNT injection. [J]. IEEE Transactions on Biomedical Engineering, 2022, 99: 1-8.
[12] Del Vecchio A, Holobar A, Falla D, et al. Tutorial: analysis of motor unit discharge characteristics from high-density surface EMG signals [J]. Journal of Electromyography and Kinesiology, 2020, 53: 1024-1026.
[13] Farina D, Negro F, Jiang N. Identification of common synaptic inputs to motor neurons from the rectified electromyogram [J]. Journal of Physiology, 2013, 591: 2403-2418.
[14] Castronovo M, Negro F, Farina D, et al. The proportion of common synaptic input to motor neurons increases with an increase in net excitatory input [J]. Journal of Applied Physiology, 2015, 11(119): 1337-1346.
[15] Barsakcioglu DY, Bracklein M, Holobar A, et al. Control of spinal motoneurons by feedback from a non-invasive real-time interface [J]. IEEE Transactions on Biomedical Engineering, 2020, 68(3): 926-935.
[16] De Luca CJ, Erim Z. Common drive of motor units in regulation of muscle force [J]. Trends in Neurosciences, 1994, 17(7): 299-305.
[17] Laine CM, Martinez-Valdes E, Falla D, et al. Motor neuron pools of synergistic thigh muscles share most of their synaptic input [J]. The Journal of Neuroscience, 2015, 35(35): 12207-12216.
[18] Wang Lejun, Yu Xiaoming, Shao Qineng, et al. Muscle fatigue enhance beta band EMG-EMG coupling of antagonistic muscles in patients with post-stroke spasticity [J]. Frontiers in Bioengineering and Biotechnology, 2020, 8: 1007.
[19] Dai Chenyun, Suresh NL, Suresh AK, et al. Altered motor unit discharge coherence in paretic muscles of stroke survivors[J]. Frontiers in Neurology, 2017, 8: 202.
[20] Bräcklein M, Barsakcioglu DY. Reading and modulating cortical βbursts from motor unit spiking activity[J]. The Journal of Neuroscience, 2022, 42(17): 3611-3621.
[21] Croft RJ, Chandler JS, Barry RJ, et al. EOG correction: a comparison of four methods [J]. Psychophysiology, 2005, 42: 16-24.
[22] Nawab SH, Chang SS, De Luca CJ. High-yield decomposition of surface EMG signals [J]. Clinical Neurophysiology, 2010, 121: 1602-1615.
[23] Grosse P, Guerrini R, Parmeggiani L, et al. Abnormal corticomuscular and intermuscular coupling in high-frequency rhythmic myoclonus [J]. Brain, 2003, 126(2): 326-342.
[24] Mima T, Hallett M. Corticomuscular coherence: a review [J]. Journal of Clinical Neurophysiology, 1999, 16(6): 501-511.
[25] Yin F, Daly JJ, Sun J, et al. Functional corticomuscular connection during reaching is weakened following stroke [J]. Clinical Neurophysiology, 2009, 120(5): 994-1002.
[26] Terry K, Griffin L. How computational technique and spike train properties affect coherence detection [J]. Journal of Neuroscience Methods, 2008, 168(1): 212-223.
[27] Negro F, Yavuz U, Farina D. The human motor neuron pools receive a dominant slow-varying common synaptic input [J]. Journal of Physiology, 2016, 594(19): 5491-5505.
[28] Nakao M, Norimatsu M, Mizutani Y, et al. Spectral distortion properties of the integral pulse frequency modulation model [J]. IEEE Transactions on Biomedical Engineering, 2002, 44(5): 419-426.
[29] Farina D, Negro F. Common synaptic input to motor neurons, motor unit synchronization, and force control [J]. Exercise & Sport Sciences Reviews, 2015, 43(1): 23-33.
[30] Thompson CK, Negro F, Johnson MD, et al. Robust and accurate decoding of motoneuron behaviour and prediction of the resulting force output [J]. The Journal of Physiology, 2018, 596(14): 2643-2659.
[31] Farina D, Negro F, Muceli S, et al. Principles of motor unit physiology evolve with advances in technology [J]. Physiology, 2016, 31(2): 83-94.
[32] Farina D, Negro F, Dideriksen JL. The effective neural drive to muscles is the common synaptic input to motor neurons [J]. Journal of Physiology, 2015, 592(16): 3427-3441.
[33] Echeverria-Altuna I, Quinn AJ, Zokaei N, et al. Transient beta activity and cortico-muscular connectivity during sustained motor behaviour [J]. Progress in Neurobiology, 2021, 214(11): 102281.
[34] Chen Xiaoling, Zhang Yuanyuan, Yang Yinan, et al. Beta-range corticomuscular coupling reflects asymmetries in hand movement [J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020, 11(28): 2575-2585.
[35] Grosse P, Cassidy MJ, Brown P. EEG-EMG, MEG-EMG and EMG-EMG frequency analysis: physiological principles and clinical applications [J]. Clinical Neurophysiology, 2002, 113: 1523-1531.