Eaton-Peabody Laboratories, Massachusetts Eye and Ear, Boston, MA, 02114, USA; Department of Otolaryngology, Harvard Medical School, Boston, MA, 02115, USA.
Eaton-Peabody Laboratories, Massachusetts Eye and Ear, Boston, MA, 02114, USA; Department of Otolaryngology, Harvard Medical School, Boston, MA, 02115, USA.
Hear Res. 2019 Sep 15;381:107782. doi: 10.1016/j.heares.2019.107782. Epub 2019 Aug 8.
The auditory brainstem response (ABR) is a sound-evoked neural response commonly used to assess auditory function in humans and laboratory animals. ABR thresholds are typically chosen by visual inspection, leaving the procedure susceptible to user bias. We sought to develop an algorithm to automate determination of ABR thresholds to eliminate such biases and to standardize approaches across investigators and laboratories. Two datasets of mouse ABR waveforms obtained from previously published studies of normal ears as well as ears with varying degrees of cochlear-based threshold elevations (Maison et al., 2013; Sergeyenko et al., 2013) were reanalyzed using an algorithm based on normalized cross-covariation of adjacent level presentations. Correlation-coefficient vs. level data for each ABR level series were fit with both a sigmoidal and two-term power function. From these fits, threshold was interpolated at different criterion values of correlation-coefficient ranging from 0 to 0.5. The criterion value of 0.35 was selected by comparing visual thresholds to computed thresholds across all frequencies tested. With such a criterion, the mean algorithm-computed thresholds were comparable to the visual thresholds noted by two independent observers for each data set. The success of the algorithm was also qualitatively assessed by comparing averaged waveforms at the thresholds determined by the two methods, and quantitatively assessed by comparing peak 1 amplitude growth functions expressed as dB re each of the two threshold measures. Application of a cross-covariance analysis to ABR waveforms can emulate visual thresholding decisions made by highly trained observers. Unlike previous applications of similar methodologies using template matching, our algorithm performs only intrinsic comparisons within ABR sets, and therefore is more robust to equipment and investigator differences in assessing waveforms, as evidenced by similar results across the two datasets.
听觉脑干反应（ABR）是一种声音诱发的神经反应，常用于评估人类和实验室动物的听觉功能。ABR 阈值通常通过视觉检查来选择，这使得该过程容易受到用户偏见的影响。我们试图开发一种算法来自动确定 ABR 阈值，以消除这种偏见，并在研究人员和实验室之间标准化方法。使用基于相邻水平呈现的归一化互协方差的算法，重新分析了以前发表的关于正常耳朵以及耳蜗基础阈值升高程度不同的耳朵的两项小鼠 ABR 波形数据集（Maison 等人，2013 年；Sergeyenko 等人，2013 年）。对于每个 ABR 水平系列的相关系数与水平数据，使用 sigmoidal 和两项幂函数进行拟合。根据这些拟合，在不同的相关系数准则值（范围从 0 到 0.5）下对阈值进行插值。通过将视觉阈值与所有测试频率的计算阈值进行比较，选择了 0.35 作为准则值。使用这种准则，算法计算的平均阈值与两个数据集的每个数据集中两个独立观察员记录的视觉阈值相当。通过比较两种方法确定的阈值处的平均波形，以及通过比较以两种阈值测量中的每一种表示的 dB 表示的峰 1 幅度增长函数，对算法的成功进行了定性和定量评估。应用互协方差分析对 ABR 波形进行分析可以模拟由经过高度训练的观察者做出的视觉阈值决策。与使用模板匹配的类似方法的先前应用不同，我们的算法仅在 ABR 集合内执行内在比较，因此在评估波形时对设备和研究人员的差异更具鲁棒性，这从两个数据集的相似结果中可以得到证明。
