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慢性健康状况，与不良的 社会和情感健康的变化。陡峭倾斜，高频听力损失（HFHL）是最 有噪声史的中老年人的常见感音神经性听力损失概况 exposure. HFHL受试者的一个常见主诉与难以理解波动性语言中的言语有关 背景噪音在噪声中识别语音的个体阈值变化很大，不能很好地预测 从听力图，并没有可靠地改善放大。这个项目的潜在动机是 识别生理和感知生物标志物，更准确地预测噪声识别中的语音， HFHL受试者与听力正常（NH）的年龄匹配受试者相比。我们的基本 假设HFHL受试者在噪声处理中的言语受损可以从异常 低频信号的神经编码，阈值正常。在目标1中，我们采用了一系列 生理和心理物理测试，以确定神经处理的阶段（从听觉神经到皮层） 和神经处理模式（从听神经复合动作电位到皮层下编码， 刺激精细结构）最直接映射到HFHL和NH受试者噪声结果中的语音。到 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，通过沉浸式闭环， 音频软件培训界面。我们的初步数据表明，噪声中的语音识别可以 在随机分配到闭环系统的感音神经性听力损失受试者中， 听觉训练，与分配到安慰剂听觉训练接口的受试者相比。但据 不知道语音处理的哪些生理和感知预测器也被修改以支持 语音识别阈值的变化。目标2将通过随机、双盲 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 训练前、训练后和训练停止后的随访测试。通过识别 神经处理的生物标志物，不仅可以预测基线条件下的语音结果， 在干预过程中语音处理的动态变化，这些研究可能会确定最 噪声处理中语音的鲁棒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.