神经力学:电生理学和计算方法,用于准确估算体内神经对人体肌肉的驱动力

IF 2 4区 医学 Q3 NEUROSCIENCES Journal of Electromyography and Kinesiology Pub Date : 2024-03-07 DOI:10.1016/j.jelekin.2024.102873
Arnault H. Caillet , Andrew T.M. Phillips , Luca Modenese , Dario Farina
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引用次数: 0

摘要

肌肉控制的最终神经信号是从脊髓发送到肌肉的神经驱动力。这种神经信号由活跃的脊髓运动神经元释放的动作电位集合组成,并传递给受支配的肌肉纤维以产生力量。准确估算人体体内肌肉的神经驱动力具有挑战性,因为这需要识别能代表活跃运动单元群的运动单元(MU)样本的活动。目前的电生理记录通常无法完成这项任务,因为所识别的运动单元样本较小,阈值较高的运动单元与阈值较低的运动单元相比代表性过高。在这里,我们将介绍电生理方法的最新进展,与以前相比,这些方法可以识别出更多具有代表性的 MU 样本。这可以通过大型高密度肌电图电极阵列来实现。此外,最近开发的数据增强计算方法进一步扩展了实验性肌单位样本,从而推断出整个肌单位池的活动。总之,新电极技术与计算建模相结合,可以准确估计肌肉的神经驱动力,为运动神经控制和神经接口研究开辟了新的前景。
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NeuroMechanics: Electrophysiological and computational methods to accurately estimate the neural drive to muscles in humans in vivo

The ultimate neural signal for muscle control is the neural drive sent from the spinal cord to muscles. This neural signal comprises the ensemble of action potentials discharged by the active spinal motoneurons, which is transmitted to the innervated muscle fibres to generate forces. Accurately estimating the neural drive to muscles in humans in vivo is challenging since it requires the identification of the activity of a sample of motor units (MUs) that is representative of the active MU population. Current electrophysiological recordings usually fail in this task by identifying small MU samples with over-representation of higher-threshold with respect to lower-threshold MUs. Here, we describe recent advances in electrophysiological methods that allow the identification of more representative samples of greater numbers of MUs than previously possible. This is obtained with large and very dense arrays of electromyographic electrodes. Moreover, recently developed computational methods of data augmentation further extend experimental MU samples to infer the activity of the full MU pool. In conclusion, the combination of new electrode technologies and computational modelling allows for an accurate estimate of the neural drive to muscles and opens new perspectives in the study of the neural control of movement and in neural interfacing.

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来源期刊
CiteScore
4.70
自引率
8.00%
发文量
70
审稿时长
74 days
期刊介绍: Journal of Electromyography & Kinesiology is the primary source for outstanding original articles on the study of human movement from muscle contraction via its motor units and sensory system to integrated motion through mechanical and electrical detection techniques. As the official publication of the International Society of Electrophysiology and Kinesiology, the journal is dedicated to publishing the best work in all areas of electromyography and kinesiology, including: control of movement, muscle fatigue, muscle and nerve properties, joint biomechanics and electrical stimulation. Applications in rehabilitation, sports & exercise, motion analysis, ergonomics, alternative & complimentary medicine, measures of human performance and technical articles on electromyographic signal processing are welcome.
期刊最新文献
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