A Novel Model Incorporating Two Variability Sources for Describing Motor Evoked Potentials
A Novel Model Incorporating Two Variability Sources for Describing Motor Evoked Potentials
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DOI：
10.1016/j.brs.2014.03.002
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发表时间：
2014-07-01
影响因子：
7.7
通讯作者：
Peterchev, Angel V.
中科院分区：
文献类型：
--
作者：
Goetz, Stefan M.;Luber, Bruce;Peterchev, Angel V.
目的：运动诱发电位（motor evoked potentials，MEP）在经颅磁刺激（transcranial magnetic stimulation，TMS）中起着关键作用，用于确定运动阈值和探测皮层兴奋性。在刺激强度范围内采样，MEP勾勒出输入-输出（IO）曲线，该曲线通常用于表征皮质脊髓束。更详细的了解信号的产生和变化的MEP将提供深入了解潜在的生理和援助正确的统计处理MEP data.Methods：一种新的回归模型进行测试，使用测得的IO数据的12个科目。该模型将MEP变异性分为两个独立的贡献，作用于代表神经募集的强S形非线性的两侧。传统的sigmoidal回归后的非线性与一个单一的可变性来源是用于comparation.Results：MEP振幅的分布在不同的刺激强度，违反了传统的回归模型的统计假设。与传统的回归模型相比，双变量源模型更好地描述了IO特征，包括IO曲线沿着分布的变化、分布的扩展和偏度等现象。MEP变异性最好由两个来源来描述，这两个来源最有可能将初始兴奋过程中的变异性与随后发生的效应分开。新模型能够更准确和灵敏地估计IO曲线因此，本发明提供了一种用于增强脑刺激特性的方法，增强了其作为检测工具的能力，并且可以应用于其他脑刺激模态。此外，它提取新的信息，从IO数据有关的神经变异信息，以前被视为噪声。(C)2014爱思唯尔公司All rights reserved.
Objective: Motor evoked potentials (MEPs) play a pivotal role in transcranial magnetic stimulation (TMS), e.g., for determining the motor threshold and probing cortical excitability. Sampled across the range of stimulation strengths, MEPs outline an input-output (IO) curve, which is often used to characterize the corticospinal tract. More detailed understanding of the signal generation and variability of MEPs would provide insight into the underlying physiology and aid correct statistical treatment of MEP data.Methods: A novel regression model is tested using measured IO data of twelve subjects. The model splits MEP variability into two independent contributions, acting on both sides of a strong sigmoidal nonlinearity that represents neural recruitment. Traditional sigmoidal regression with a single variability source after the nonlinearity is used for comparison.Results: The distribution of MEP amplitudes varied across different stimulation strengths, violating statistical assumptions in traditional regression models. In contrast to the conventional regression model, the dual variability source model better described the IO characteristics including phenomena such as changing distribution spread and skewness along the IO curve.Conclusions: MEP variability is best described by two sources that most likely separate variability in the initial excitation process from effects occurring later on. The new model enables more accurate and sensitive estimation of the IO curve characteristics, enhancing its power as a detection tool, and may apply to other brain stimulation modalities. Furthermore, it extracts new information from the IO data concerning the neural variability-information that has previously been treated as noise. (C) 2014 Elsevier Inc. All rights reserved.