Neural Pathophysiology and Suprathreshold Processing in Older Adults with Elevated Thresholds

项目摘要 听力障碍是一种常见的老年人慢性健康状况，与不利的 社会和情感福祉的变化。陡坡、高频听力损失(HFHL)是最严重的 既往有噪声史的中老年人的常见感音神经性听力损失特征 曝光。HFHL受试者的一种常见抱怨是在波动中理解语音的困难 背景噪音。在噪声中识别语音的个体阈值差异很大，不能很好地预测 从听力图上看，并不是通过放大就能得到可靠的改善。这个项目的根本动机是 为了在噪声识别中识别更准确地预测语音的生理和感知生物标记物 与听力正常(NH)的年龄匹配的受试者进行比较。我们的基础 假设HFHL受试者在噪声处理过程中的语音受损可以从异常中预测出来 对阈值正常的低频信号进行神经编码。在目标1中，我们采用了一系列 用于确定神经处理阶段(从听神经到皮质)的生理和心理物理测试 以及神经处理模式(从听神经复合动作电位到皮层下编码 刺激精细结构)，在HFHL和NH受试者的噪声结果中最直接地映射到语音。至 进一步探讨噪声识别中低频信号的神经处理与语音之间的联系， 我们将采用一种新的方法，通过身临其境的闭环系统来增强噪声处理中的语音 音响运动软件培训界面。我们的初步数据表明，噪声识别中的语音可以 在随机分配到闭环组的感音神经性听力损失患者中显著改善 听觉运动训练，与被分配到安慰剂听觉训练界面的受试者进行比较。然而，它是 不知道语音处理的哪些生理和感知预测器也被修改为支持 语音识别阈值的变化。目标2将通过随机、双盲的方法解决这一问题 安慰剂对照研究设计，将比较语音处理的神经和生理预测因素 在训练前、训练后和训练结束后进行跟踪测试。通过识别 神经处理的生物标记物，不仅预测基线条件下的语音结果，还跟踪 在干预过程中，语音处理的动态变化，这些研究可能识别出最 噪声处理中语音的稳健神经预测器以及未来治疗的可能目标。

Project Summary Hearing impairment is a common chronic health condition of older age that has been linked to adverse changes in social and emotional well-being. Steeply sloping, high-frequency hearing loss (HFHL) is the most common sensorineural hearing loss profile for middle-aged and older adults with a past history of noise exposure. A common complaint of subjects with HFHL relates to a difficulty understanding speech in fluctuant background noise. Individual thresholds for recognizing speech in noise vary widely, are not well predicted from the audiogram, and are not reliably improved by amplification. The underlying motivation for this project is to identify physiological and perceptual biomarkers that more accurately predict speech in noise recognition in subject with HFHL, as compared to age-matched subjects with normal hearing (NH). Our underlying hypothesis is that impaired speech in noise processing for subjects with HFHL can be predicted from abnormal neural coding of low-frequency signals, where thresholds are normal. In Aim 1, we employ a series of physiological and psychophysical tests to identify the stage of neural processing (from auditory nerve to cortex) and mode of neural processing (from the auditory nerve compound action potential to subcortical encoding of stimulus fine structure) that most directly map onto speech in noise outcomes in HFHL and NH subjects. To further probe the linkage between neural processing of low-frequency signals and speech in noise recognition, we will employ a new approach to enhance speech in noise processing through an immersive, closed-loop audiomotor software training interface. Our preliminary data suggest that speech in noise recognition can be significantly improved in subjects with sensorineural hearing loss that were randomly assigned to closed-loop audiomotor training, as compared to subjects assigned to a placebo auditory training interface. However, it is not known which physiological and perceptual predictors of speech processing are also modified to support a change in speech recognition thresholds. Aim 2 will address this point through a randomized, double-blind placebo-controlled study design that will compare the neural and physiological predictors of speech processing before training, after training and at a follow-up test after training has been discontinued. By identifying the biomarkers of neural processing that not only predict speech outcomes in a baseline condition, but also track dynamic shifts in speech processing over the course of an intervention, these studies may identify the most robust neural predictors of speech in noise processing as well as possible targets for future therapies.