首页> 美国卫生研究院文献>Frontiers in Behavioral Neuroscience >Open(G)PIAS: An Open-Source Solution for the Construction of a High-Precision Acoustic Startle Response Setup for Tinnitus Screening and Threshold Estimation in Rodents
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Open(G)PIAS: An Open-Source Solution for the Construction of a High-Precision Acoustic Startle Response Setup for Tinnitus Screening and Threshold Estimation in Rodents

机译:Open(G)PIAS:构造用于耳鸣筛查和阈值估计的高精度声震响应设置的开源解决方案

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摘要

The modulation of the acoustic startle reflex (ASR) by a pre-stimulus called pre-pulse inhibition (PPI, for gap of silence pre-stimulus: GPIAS) is a versatile tool to, e.g., estimate hearing thresholds or identify subjective tinnitus percepts in rodents. A proper application of these paradigms depends on a reliable measurement of the ASR amplitudes and an exact stimulus presentation in terms of frequency and intensity. Here, we introduce a novel open-source solution for the construction of a low-cost ASR setup. The complete software for data acquisition and stimulus presentation is written in Python 3.6 and is provided as an Anaconda package. Furthermore, we provide a construction plan for the sensor system based on low-cost hardware components. Exemplary GPIAS data from two animal models (Mus musculus, Meriones unguiculatus) show that the ratio histograms (1-GPIAS) of the gap-pre-stimulus and no pre-stimulus ASR amplitudes can be well described by a log-normal distribution being in good accordance to previous studies with already established setups. Furthermore, it can be shown that the PPI as a function of pre-stimulus intensity (threshold paradigm) can be approximated with a hard-sigmoid function enabling a reproducible sensory threshold estimation. Thus, we show that the open-source solution could help to further establish the ASR method in many laboratories and, thus, facilitate and standardize research in animal models of tinnitus and/or hearing loss.
机译:通过称为“脉冲抑制”的预刺激(PPI,用于沉默间隙的预刺激:GPIAS)对声惊吓反射(ASR)的调制是一种通用工具,例如,可以估计听力阈值或确定主观耳鸣感觉。啮齿动物。这些范例的正确应用取决于对ASR振幅的可靠测量以及在频率和强度方面精确的刺激表现。在这里,我们介绍了一种新颖的开源解决方案,用于构建低成本的ASR安装程序。完整的数据采集和激励表示软件用Python 3.6编写,并作为Anaconda软件包提供。此外,我们提供了基于低成本硬件组件的传感器系统的建设计划。来自两个动物模型(小家鼠,无子鱼)的示例GPIAS数据显示,间隙前刺激的比率直方图(1-GPIAS)和无刺激前ASR振幅可以通过对数正态分布表示为与以前的研究非常吻合,已经建立了设置。此外,可以证明,可以使用可重现的感觉阈值估计的硬S形函数来近似PPI作为刺激前强度的函数(阈值范式)。因此,我们表明,开源解决方案可以帮助许多实验室进一步建立ASR方法,从而促进耳鸣和/或听力损失动物模型的研究并使之标准化。

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