Noise, stress, and the perception of loudness: exploring the mechanistic bases of tinnitus and hyperacusis with the use of technological advances

在大多数情况下，耳鸣与听力功能的自然下降有关。最近的数据显示，它在年轻群体中的患病率正在上升。听力亢进是一种对外界声音的敏感性增加，可能与耳鸣有共同的机制，包括上调中枢增益功能。值得注意的是，这两种情况似乎都因个体的压力状况而加剧。然而，关于正常听力成年人的噪音和压力相互作用的情况却知之甚少。因此，我们建议研究对噪声和应激的动态反应，包括：1)自主心脏对噪声相关的生理和心理应激的反应(第1部分)；以及II)解决响度的感觉和情感维度与它们可能与之相关的不同面部表情模式的关系(第2部分)。在第一部分中，我们建议将S使用的听觉研究平台从听力保护设备转变为强大的研究工具，利用位于闭塞耳道的内部麦克风提取有节奏的心脏信号，用于评估自主神经对噪音和应激的反应，并测试有关男女以及年轻人和老年人之间差异的假设。ARP声学记录的心跳检测将被用来从暴露在几个实验条件(~5分钟持续时间)的正常听力成年人的正常心跳(NN)之间得出时间序列。然后对数据进行心率变异性(HRV)分析，以确定高频(HF，0.15-0.4赫兹)和低频(LF，0.04-0.15赫兹)频谱分量的功率，以衡量副交感反应和交感/副交感平衡的变化，跑步ANCOVA(性别和年龄作为受试者之间的变量，调查问卷分数作为协变量)对HR和HRV指数进行测量。在第二部分中，我们将研究一种假设，即正常听力的成年人在听到感觉(强度)和情感维度(厌恶与中性或愉快)不同的声音时，会显示出特定的面部表情模式。因此，我们将记录参与者的动态面部表情。与使用经典的手动和耗时的面部动作编码系统(FACS)不同，我们建议使用一种机器学习方法，该方法可以快速处理具有不同分布的大量复杂数据，同时还允许识别新的模式来预测特定的分类器，这里是感觉和情感响度评级，作为这些维度中每个维度的最相关指数。这些研究解决了对噪音和压力的动态生理反应，这两种反应在我们当代的环境中都在上升，使用新的方法有望在现实生活中提供对听力障碍的机制基础的洞察。

Tinnitus is associated in most cases with a natural decline in hearing function over a lifetime. Recent data show that its prevalence is on the rise among younger age groups. Hyperacusis, an increased sensitivity to external sounds, possibly shares with tinnitus common mechanisms including upregulated central gain function. Remarkably, both appear to be exacerbated by the individual's stress status. However, little is known regarding noise and stress interactions in normal hearing adults. We therefore propose to investigate dynamic responses to noise and stress, characterizing i) autonomic cardiac responses to noise-related, physical and psychological stress (Part 1), and ii) addressing the sensory and affective dimensions of loudness in relation to differential facial expression patterns with which they are putatively associated (Part 2). In Part 1, we propose to transform the Auditory Research Platform (ARP)'s use from a hearing protection device into a powerful research instrument taking advantage of the internal microphone located in the occluded auditory canal to extract rhythmic cardiac signals for assessment of autonomic responses to noise and stress and test hypotheses concerning differences between male and female as well as young and aging adults. Heart beat detection from ARP acoustic recordings will be used to derive times series of intervals between normal beats (NN) from normal-hearing adults exposed to several experimental conditions (~5 min duration). The data will then be subjected to heart rate variability (HRV) analysis to determine power for the high (HF, 0.15-0.4 Hz) and low frequency (LF, 0.04-0.15 Hz) spectral components as measures of parasympathetic responses and changes in sympathetic/ parasympathetic balance, running ANCOVAs (Sex and Age as between- and Conditions as within- subjects variables, and questionnaire scores as co-variates) on HR and HRV indices. In Part 2, we will investigate the hypothesis that normal hearing adults, when exposed to sounds varying in their sensory (intensity) and affective dimensions (aversive versus neutral or pleasant), display specific patterns of facial expressions. We will therefore record dynamic facial expressions of participants when. Rather than using the classical manual and time-consuming Facial Action Coding System (FACS), we propose to make use of a machine-learning approach which can rapidly handle large bodies of complex data with heterogeneous distributions while also - in contrast to classical FACS - allowing the identification of novel patterns to predict specific classifiers, here sensory and affective loudness ratings, as the most relevant indices for each of these dimensions. These studies address dynamic physiological responses to noise and stress, both on the rise in our contemporary environment, using novel approaches that hold the promise of providing - in a real-life ambulatory context - insights into the mechanistic bases of hearing disturbances.