Gerum Richard, Rahlfs Hinrich, Streb Matthias, Krauss Patrick, Grimm Jannik, Metzner Claus, Tziridis Konstantin, Günther Michael, Schulze Holger, Kellermann Walter, Schilling Achim
Biophysics Group, Department of Physics, Center for Medical Physics and Technology, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany.
Experimental Otolaryngology, ENT-Hospital, Head and Neck Surgery, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany.
Front Behav Neurosci. 2019 Jun 25;13:140. doi: 10.3389/fnbeh.2019.00140. eCollection 2019.
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 () 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.
一种称为预脉冲抑制(PPI,对于沉默间隙预刺激:GPIAS)的刺激前调制声学惊吓反射(ASR)是一种通用工具,例如,用于估计啮齿动物的听力阈值或识别主观耳鸣感知。这些范式的正确应用取决于对ASR幅度的可靠测量以及在频率和强度方面精确的刺激呈现。在此,我们介绍一种用于构建低成本ASR设置的新型开源解决方案。用于数据采集和刺激呈现的完整软件用Python 3.6编写,并作为Anaconda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构建计划。来自两种动物模型的示例性GPIAS数据表明,间隙预刺激和无预刺激ASR幅度的比率直方图(1 - GPIAS)可以通过对数正态分布很好地描述,这与先前使用已建立设置的研究结果高度一致。此外,可以表明,作为预刺激强度函数的PPI(阈值范式)可以用硬Sigmoid函数近似,从而实现可重复的感觉阈值估计。因此,我们表明开源解决方案有助于在许多实验室中进一步建立ASR方法,从而促进和规范耳鸣和/或听力损失动物模型的研究。