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Identification of common synaptic inputs to motor neurons from the rectified electromyogram

机译:从矫正肌电图识别运动神经元的常见突触输入

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Oscillatory common inputs of cortical or peripheral origin can be identified from the motor neuron output with coherence analysis. Linear transmission is possible despite the motor neuron non-linearity because the same input is sent commonly to several neurons. Because of the linear transmission, common input components to motor neurons can be investigated from the surface EMG, for example by EEG-EMG or EMG-EMG coherence. In these studies, there is an open debate on the utility and appropriateness of EMG rectification. The present study addresses this issue using an analytical, simulation and experimental approach. The main novel theoretical contribution that we report is that the spectra of both the rectified and the raw EMG contain input spectral components to motor neurons. However, they differ by the contribution of amplitude cancellation which influences the rectified EMG spectrum when extracting common oscillatory inputs. Therefore, the degree of amplitude cancellation has an impact on the effectiveness of EMG rectification in extracting input spectral peaks. The theoretical predictions were exactly confirmed by realistic simulations of a pool of motor neurons innervating a muscle in a cylindrical volume conductor of EMG generation and by experiments conducted on the first dorsal interosseous and the abductor pollicis brevis muscles of seven healthy subjects during pinching. It was concluded that when the contraction level is relatively low, EMG rectification may be preferable for identifying common inputs to motor neurons, especially when the energy of the action potentials in the low frequency range is low. Nonetheless, different levels of cancellation across conditions influence the relative estimates of the degree of linear transmission of oscillatory inputs to motor neurons when using the rectified EMG.
机译:可以通过运动神经元输出通过相干分析来识别皮层或周围来源的振荡性常见输入。尽管运动神经元是非线性的,但仍可以进行线性传输,因为相同的输入通常会发送到多个神经元。由于是线性传输,因此可以通过表面EMG来研究运动神经元的常见输入成分,例如通过EEG-EMG或EMG-EMG相干性。在这些研究中,关于肌电图校正的实用性和适当性存在公开辩论。本研究使用分析,模拟和实验方法解决了这个问题。我们报告的主要新颖理论贡献是,整流的和原始的EMG的光谱都包含运动神经元的输入光谱成分。但是,它们的区别在于幅度抵消的作用,后者在提取公共振荡输入时会影响整流后的EMG频谱。因此,幅度抵消的程度会影响EMG整流在提取输入频谱峰中的有效性。理论预测已通过逼真的模拟肌电图生成的圆柱形体导体中支配肌肉的运动神经元池以及在捏捏过程中对七名健康受试者的第一背骨间质和短骨外展肌进行了实验证实。结论是,当收缩水平相对较低时,EMG整流可能更适合于识别运动神经元的常见输入,尤其是在低频范围内的动作电位能量较低时。尽管如此,当使用整流的EMG时,跨条件的不同取消水平会影响振荡输入到运动神经元的线性传递程度的相对估计。

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