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噪声诱导毛细胞损伤对耳蜗电机械反应的影响：使用生物物理模型的计算方法。

The effects of noise-induced hair cell lesions on cochlear electromechanical responses: A computational approach using a biophysical model.

机构信息

Department of Applied Physics and Electronics, Umeå University, Umeå, Sweden.

Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden.

出版信息

Int J Numer Method Biomed Eng. 2022 May;38(5):e3582. doi: 10.1002/cnm.3582. Epub 2022 Feb 21.

DOI:10.1002/cnm.3582
PMID:35150464
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9286811/
Abstract

A biophysically inspired signal processing model of the human cochlea is deployed to simulate the effects of specific noise-induced inner hair cell (IHC) and outer hair cell (OHC) lesions on hearing thresholds, cochlear compression, and the spectral and temporal features of the auditory nerve (AN) coding. The model predictions were evaluated by comparison with corresponding data from animal studies as well as human clinical observations. The hearing thresholds were simulated for specific OHC and IHC damages and the cochlear nonlinearity was assessed at 0.5 and 4 kHz. The tuning curves were estimated at 1 kHz and the contributions of the OHC and IHC pathologies to the tuning curve were distinguished by the model. Furthermore, the phase locking of AN spikes were simulated in quiet and in presence of noise. The model predicts that the phase locking drastically deteriorates in noise indicating the disturbing effect of background noise on the temporal coding in case of hearing impairment. Moreover, the paper presents an example wherein the model is inversely configured for diagnostic purposes using a machine learning optimization technique (Nelder-Mead method). Accordingly, the model finds a specific pattern of OHC lesions that gives the audiometric hearing loss measured in a group of noise-induced hearing impaired humans.

摘要

一个受生物启发的人类耳蜗信号处理模型被用来模拟特定噪声诱导的内毛细胞（IHC）和外毛细胞（OHC）损伤对内耳听力阈值、耳蜗压缩以及听觉神经（AN）编码的光谱和时间特征的影响。通过与动物研究和人类临床观察的相应数据进行比较，评估了模型的预测结果。针对特定的 OHC 和 IHC 损伤模拟了听力阈值，并在 0.5 和 4 kHz 评估了耳蜗的非线性。在 1 kHz 估计了调谐曲线，并通过模型区分了 OHC 和 IHC 病变对调谐曲线的贡献。此外，还模拟了安静和噪声环境下 AN 尖峰的相位锁定。该模型预测，在噪声中相位锁定会急剧恶化，这表明在听力受损的情况下，背景噪声会对时间编码产生干扰。此外，本文还介绍了一个示例，其中使用机器学习优化技术（Nelder-Mead 方法）反向配置模型用于诊断目的。因此，该模型找到了一种特定的 OHC 病变模式，这种模式可以解释一组噪声诱导听力受损人群中测量到的听力损失。

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