An improved approach to separating startle data from noise
An improved approach to separating startle data from noise
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DOI：
10.1016/j.jneumeth.2015.07.001
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发表时间：
2015-09-30
影响因子：
3
通讯作者：
Galazyuk, Alexander V.
中科院分区：
文献类型：
--
作者：
Grimsley, Calum A.;Longenecker, Ryan J.;Galazyuk, Alexander V.
背景：声惊吓反射（ASR）是一种对声音的快速、不自主的运动，在许多物种中都有发现。ASR可以通过外部刺激和内部状态进行调节，使其成为许多学科的有用工具。不同实验室的ASR数据收集和解释差异很大，这使得比较成为一项挑战。新方法：在这里，我们研究了与小鼠惊吓相关的动物运动（CBA/CAJ）。运动被同时捕捉到高速视频和压电惊吓板。我们还使用简单的数学外推惊吓数据（力）转换成中心的质量位移（“高度”），其中包括动物的mass.Results：惊吓板力的数据揭示了一个刻板印象的波形与惊吓，包含三个不同的峰值。这种波形允许研究人员将试验分为“惊吓”和“无惊吓”（称为“手动分类”）。Fleiss的kappa和Krippendorff的alpha（两者均为0.865）表明研究人员之间的一致性非常好。进一步的工作使用该波形来开发自动惊吓分类器。自动分类器与人工分类相比毫不逊色。双向方差分析显示，通过手动和自动方法分类的3个峰的幅度没有显着差异（P1：p = 0.526，N1：p = 0.488，P2：p = 0.529）。与现有方法的比较：将自动分类器的分类能力与其他三种常用的分类方法进行比较;自动分类器的性能远远优于这些方法。结论：所做的改进使研究人员能够自动将惊吓数据与噪音分离，并将单个动物的质量标准化。这些步骤简化了惊吓数据的动物间和实验室间比较。由爱思唯尔公司出版
Background: The acoustic startle reflex (ASR) is a rapid, involuntary movement to sound, found in many species. The ASR can be modulated by external stimuli and internal state, making it a useful tool in many disciplines. ASR data collection and interpretation varies greatly across laboratories making comparisons a challenge.New method: Here we investigate the animal movement associated with a startle in mouse (CBA/CAJ). Movements were simultaneously captured with high-speed video and a piezoelectric startle plate. We also use simple mathematical extrapolations to convert startle data (force) into center of mass displacement ("height"), which incorporates the animal's mass.Results: Startle plate force data revealed a stereotype waveform associated with a startle that contained three distinct peaks. This waveform allowed researchers to separate trials into 'startles' and 'no-startles' (termed 'manual classification). Fleiss' kappa and Krippendorff's alpha (0.865 for both) indicate very good levels of agreement between researchers. Further work uses this waveform to develop an automated startle classifier. The automated classifier compares favorably with manual classification. A two-way ANOVA reveals no significant difference in the magnitude of the 3 peaks as classified by the manual and automated methods (P1: p = 0.526, N1: p = 0.488, P2: p = 0.529).Comparison with existing method(s): The ability of the automated classifier was compared with three other commonly used classification methods; the automated classifier far outperformed these methods.Conclusions: The improvements made allow researchers to automatically separate startle data from noise, and normalize for an individual animal's mass. These steps ease inter-animal and inter-laboratory comparisons of startle data. Published by Elsevier B.V.