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使用高密度表面肌电图在不同实验阶段纵向追踪运动单位。

Tracking motor units longitudinally across experimental sessions with high-density surface electromyography.

作者信息

Martinez-Valdes E, Negro F, Laine C M, Falla D, Mayer F, Farina D

机构信息

Department of Sports Medicine and Sports Orthopaedics, University of Potsdam, Potsdam, Germany.

Institute of Neurorehabilitation Systems, Bernstein Focus Neurotechnology Göttingen (BFNT), Bernstein Centre for Computational Neuroscience (BCCN), University Medical Center Göttingen, Georg-August University, Göttingen, Germany.

出版信息

J Physiol. 2017 Mar 1;595(5):1479-1496. doi: 10.1113/JP273662.

Abstract

KEY POINTS

Classic motor unit (MU) recording and analysis methods do not allow the same MUs to be tracked across different experimental sessions, and therefore, there is limited experimental evidence on the adjustments in MU properties following training or during the progression of neuromuscular disorders. We propose a new processing method to track the same MUs across experimental sessions (separated by weeks) by using high-density surface electromyography. The application of the proposed method in two experiments showed that individual MUs can be identified reliably in measurements separated by weeks and that changes in properties of the tracked MUs across experimental sessions can be identified with high sensitivity. These results indicate that the behaviour and properties of the same MUs can be monitored across multiple testing sessions. The proposed method opens new possibilities in the understanding of adjustments in motor unit properties due to training interventions or the progression of pathologies.

ABSTRACT

A new method is proposed for tracking individual motor units (MUs) across multiple experimental sessions on different days. The technique is based on a novel decomposition approach for high-density surface electromyography and was tested with two experimental studies for reliability and sensitivity. Experiment I (reliability): ten participants performed isometric knee extensions at 10, 30, 50 and 70% of their maximum voluntary contraction (MVC) force in three sessions, each separated by 1 week. Experiment II (sensitivity): seven participants performed 2 weeks of endurance training (cycling) and were tested pre-post intervention during isometric knee extensions at 10 and 30% MVC. The reliability (Experiment I) and sensitivity (Experiment II) of the measured MU properties were compared for the MUs tracked across sessions, with respect to all MUs identified in each session. In Experiment I, on average 38.3% and 40.1% of the identified MUs could be tracked across two sessions (1 and 2 weeks apart), for the vastus medialis and vastus lateralis, respectively. Moreover, the properties of the tracked MUs were more reliable across sessions than those of the full set of identified MUs (intra-class correlation coefficients ranged between 0.63-0.99 and 0.39-0.95, respectively). In Experiment II, ∼40% of the MUs could be tracked before and after the training intervention and training-induced changes in MU conduction velocity had an effect size of 2.1 (tracked MUs) and 1.5 (group of all identified motor units). These results show the possibility of monitoring MU properties longitudinally to document the effect of interventions or the progression of neuromuscular disorders.

摘要

关键点

传统的运动单位(MU)记录和分析方法无法在不同的实验阶段追踪同一个运动单位,因此,关于训练后或神经肌肉疾病进展过程中运动单位特性的调整,实验证据有限。我们提出了一种新的处理方法,通过使用高密度表面肌电图,在不同的实验阶段(相隔数周)追踪同一个运动单位。在两个实验中应用该方法表明,在相隔数周的测量中可以可靠地识别单个运动单位,并且可以高度敏感地识别追踪到的运动单位在不同实验阶段特性的变化。这些结果表明,可以在多个测试阶段监测同一个运动单位的行为和特性。该方法为理解由于训练干预或疾病进展导致的运动单位特性调整开辟了新的可能性。

摘要

提出了一种在不同日期的多个实验阶段追踪单个运动单位(MU)的新方法。该技术基于一种用于高密度表面肌电图的新型分解方法,并通过两项实验研究测试了其可靠性和敏感性。实验I(可靠性):10名参与者在三个阶段以其最大自主收缩(MVC)力的10%、30%、50%和70%进行等长膝关节伸展,每个阶段相隔1周。实验II(敏感性):7名参与者进行了为期2周的耐力训练(骑自行车),并在干预前后以10%和30%MVC进行等长膝关节伸展测试。比较了在不同阶段追踪到的运动单位的测量运动单位特性的可靠性(实验I)和敏感性(实验II),与每个阶段识别出的所有运动单位相比。在实验I中,分别在内侧股四头肌和外侧股四头肌中,平均有38.3%和40.1%的识别出的运动单位可以在两个相隔1周和2周的阶段中被追踪到。此外,追踪到的运动单位的特性在不同阶段比所有识别出的运动单位的特性更可靠(组内相关系数分别在0.63 - 0.99和0.39 - 0.95之间)。在实验II中,约40%的运动单位在训练干预前后可以被追踪到,并且训练引起的运动单位传导速度变化的效应大小为2.1(追踪到的运动单位)和1.5(所有识别出的运动单位组)。这些结果表明纵向监测运动单位特性以记录干预效果或神经肌肉疾病进展的可能性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4d5/5330923/bbcab7cf31dd/TJP-595-1479-g001.jpg

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