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Motometrics:运动诱发电位注释与高效分析工具箱

Motometrics: A Toolbox for Annotation and Efficient Analysis of Motor Evoked Potentials.

作者信息

Ratnadurai Giridharan Shivakeshavan, Gupta Disha, Pal Ajay, Mishra Asht M, Hill N Jeremy, Carmel Jason B

机构信息

Motor Recovery Lab, Burke Neurological Institute, White Plains, NY, United States.

Department of Neurology, New York University School of Medicine, New York, NY, United States.

出版信息

Front Neuroinform. 2019 Mar 26;13:8. doi: 10.3389/fninf.2019.00008. eCollection 2019.

Abstract

Stimulating the nervous system and measuring muscle response offers a unique opportunity to interrogate motor system function. Often, this is performed by stimulating motor cortex and recording muscle activity with electromyography; the evoked response is called the motor evoked potential (MEP). To understand system dynamics, MEPs are typically recorded through a range of motor cortex stimulation intensities. The MEPs increase with increasing stimulation intensities, and these typically produce a sigmoidal response curve. Analysis of MEPs is often complex and analysis of response curves is time-consuming. We created an MEP analysis software, called Motometrics, to facilitate analysis of MEPs and response curves. The goal is to combine robust signal processing algorithms with a simple user interface. Motometrics first enables the user to annotate data files acquired from the recording system so that the responses can be extracted and labeled with the correct subject and experimental condition. The software enables quick visual representations of entire datasets, to ensure uniform quality of the signal. It then enables the user to choose a variety of response curve analyses and to perform near real time quantification of the MEPs for quick feedback during experimental procedures. This is a modular open source tool that is compatible with several popular electrophysiological systems. Initial use indicates that Motometrics enables rapid, robust, and intuitive analysis of MEP response curves by neuroscientists without programming or signal processing expertise.

摘要

刺激神经系统并测量肌肉反应为探究运动系统功能提供了一个独特的机会。通常,这是通过刺激运动皮层并用肌电图记录肌肉活动来进行的;诱发反应称为运动诱发电位(MEP)。为了了解系统动态,通常会在一系列运动皮层刺激强度下记录MEP。MEP随着刺激强度的增加而增加,并且这些通常会产生一条S形反应曲线。MEP的分析通常很复杂,而反应曲线的分析则很耗时。我们创建了一个名为Motometrics的MEP分析软件,以促进对MEP和反应曲线的分析。目标是将强大的信号处理算法与简单的用户界面相结合。Motometrics首先允许用户注释从记录系统获取的数据文件,以便可以提取反应并用正确的受试者和实验条件进行标记。该软件能够快速直观地呈现整个数据集,以确保信号质量的一致性。然后,它允许用户选择各种反应曲线分析,并对MEP进行近实时量化,以便在实验过程中快速获得反馈。这是一个模块化的开源工具,与几种流行的电生理系统兼容。初步使用表明,Motometrics使神经科学家能够在无需编程或信号处理专业知识的情况下,对MEP反应曲线进行快速、可靠且直观的分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f733/6444173/747c8501b0af/fninf-13-00008-g0001.jpg

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