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Statistically rigorous calculations do not support common input and long-term synchronization of motor-unit firings

机译:统计上严格的计算不支持通用输入和电机单元点火的长期同步

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摘要

Over the past four decades, various methods have been implemented to measure synchronization of motor-unit firings. In this work, we provide evidence that prior reports of the existence of universal common inputs to all motoneurons and the presence of long-term synchronization are misleading, because they did not use sufficiently rigorous statistical tests to detect synchronization. We developed a statistically based method (SigMax) for computing synchronization and tested it with data from 17,736 motor-unit pairs containing 1,035,225 firing instances from the first dorsal interosseous and vastus lateralis muscles—a data set one order of magnitude greater than that reported in previous studies. Only firing data, obtained from surface electromyographic signal decomposition with >95% accuracy, were used in the study. The data were not subjectively selected in any manner. Because of the size of our data set and the statistical rigor inherent to SigMax, we have confidence that the synchronization values that we calculated provide an improved estimate of physiologically driven synchronization. Compared with three other commonly used techniques, ours revealed three types of discrepancies that result from failing to use sufficient statistical tests necessary to detect synchronization. 1) On average, the z-score method falsely detected synchronization at 16 separate latencies in each motor-unit pair. 2) The cumulative sum method missed one out of every four synchronization identifications found by SigMax. 3) The common input assumption method identified synchronization from 100% of motor-unit pairs studied. SigMax revealed that only 50% of motor-unit pairs actually manifested synchronization.
机译:在过去的四十年中,已经采用了各种方法来测量电机点火的同步性。在这项工作中,我们提供的证据表明,先前关于所有运动神经元通用通用输入的存在以及长期同步的存在的报告具有误导性,因为它们没有使用足够严格的统计检验来检测同步。我们开发了一种基于统计的方法(SigMax),用于计算同步,并用来自第1骨间背侧和股外侧肌的1,736,225个发射实例的17,736个运动单元对的数据进行了测试,该数据集比以前的报告大一个数量级。学习。在这项研究中,仅使用从表面肌电信号分解中获得的准确度> 95%的点火数据。没有以任何方式主观选择数据。由于我们的数据集的大小和SigMax固有的统计严格性,我们有信心所计算出的同步值可提供对生理驱动同步的改进估计。与其他三种常用技术相比,我们的技术揭示了三种类型的差异,这是由于未能使用足够的统计测试来检测同步所导致的。 1)平均而言,z评分方法错误地在每个电机对中以16个独立的延迟检测到同步。 2)累积总和方法错过了SigMax发现的每四个同步标识中的一个。 3)通用输入假设方法从研究的100%电机对中识别出同步。 SigMax透露,只有50%的电机对实际上显示出同步。

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