Neurophysiological Mechanisms of Speech Intelligibility in Noise - A Quantitative Framework

噪声中言语清晰度的神经生理学机制 - 定量框架

• 批准号: 9788035
• 负责人: Vibha Viswanathan
• 金额: $ 4.5万
• 依托单位: PURDUE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9788035
• 关键词: Acoustic Nerve; Address; Age; Attention; Auditory; Auditory system; Brain; Brain Stem; Clinic; Clinical; Cochlea; Cochlear Implants; Code; Cognitive; Complement; Computer Simulation; Data; Data Collection; Development; Diagnostic; Electroencephalography; Environment; Etiology; Foundations; Functional disorder; Future; Goals; Hearing; Hearing Aids; Human; Impairment; Individual; Individual Differences; Inner Hair Cells; Intervention; Knowledge; Labyrinth; Link; Maps; Measurement; Measures; Modeling; Nature; Neurosciences; Noise; Output; Participant; Performance; Process; Psychophysics; Research; Resources; Sensory; Shapes; Solid; Source; Speech; Speech Intelligibility; Stimulus; Techniques; Testing; Training; Variant; attentional control; attentional modulation; aural rehabilitation; base; career; cochlear synaptopathy; cognitive ability; cognitive process; design; experience; experimental study; hearing impairment; individual patient; interest; neurophysiology; neurotransmission; noise perception; normal hearing; otoacoustic emission; rehabilitation strategy; relating to nervous system; response; selective attention; signal processing; sound; speech in noise; statistics; success

Project Summary/Abstract Communicating in noisy environments with many sound sources places enormous demands on the auditory system. Successfully extracting target speech information in such scenarios requires a combination of precise cochlear transduction and neural coding of sound information, and effective downstream cognitive processes that use the encoded information to segregate and selectively process the target speech of interest. Speech-in- noise problems (e.g., in old age or hearing loss) can thus arise from impaired “bottom-up” coding of information, or from declines in cognitive ability. Although the existence of these two components is well recognized, there is currently no integrative framework for quantifying the relative contributions of each to speech intelligibility in noise, and for identifying which aspect is deficient in an individual who is experiencing listening problems. The specific aims of this proposal are designed to address this gap by measuring the neurophysiological representation of envelope information and investigating how that varies with both “bottom- up” manipulations and “top-down” manipulations in the same individuals. First, envelope coding in the brainstem and cortex will be directly measured using electroencephalography (EEG). The EEG metrics will then be linked to perceptual intelligibility by examining how they covary as the speech is presented with different levels and types of background noise, and how they covary across individuals (Aim 1). Next, for the same individuals, the effect of attentional focus on the same envelope coding measures as Aim 1 will be examined by keeping the input speech mixture constant and manipulating which speech source is the focus of selective attention (Aim 2). This approach helps isolate the top-down component. Importantly, conducting all intelligibility and EEG measurements in the same individual subjects allows us to leverage individual differences and use regression techniques to characterize the relative contributions of bottom-up and top-down mechanisms to performance. Finally, for the speech-noise mixtures used in Aim 1, we will calculate the same envelope coding metrics at the output of a computational auditory-nerve model (Aim 3). Key mechanisms of cochlear dysfunction will be incorporated into the model to characterize their effects on envelope coding of speech in noise. By comparing “model” (Aim 3) and “neural” metrics (Aims 1 and 2), we will test whether cochlear mechanisms can account for the individual differences in bottom-up coding. The knowledge gained through the proposed set of experiments will be foundational in the development of objective diagnostics and interventions tailored for the specific nature of speech-in-noise problems that an individual patient is experiencing. Project completion will also provide the applicant with training in computational modeling, psychophysical and EEG experiment design, data collection, analysis, and interpretation, as well as scientific hypothesis testing. This complements her existing background in signal processing and statistics, and will set her on a solid path towards her long-term goal of an academic research career in auditory neuroscience.

项目概要/摘要 在有许多声源的嘈杂环境中进行交流对听觉提出了巨大的要求 系统在此类场景中成功提取目标语音信息需要精确的组合。 声音信息的耳蜗转导和神经编码，以及有效的下游认知过程 使用编码信息来分离和选择性地处理感兴趣的目标语音。 因此，噪声问题（例如，老年或听力损失）可能是由于“自下而上”的编码受损而引起的。 信息，或来自认知能力的下降，尽管这两个成分的存在是很好的。 认识到，目前还没有一个综合框架来量化每个因素对 噪音中的言语清晰度，以及识别正在经历的个人的哪个方面存在缺陷 该提案的具体目标是通过衡量听力问题来解决这一差距。 包络信息的神经生理学表征，并研究其如何随“底- 同一个体中的“向上”操作和“自上而下”操作首先，信封编码。 脑干和皮质将使用脑电图（EEG）直接测量。 然后通过检查他们在演讲时如何覆盖来与感知清晰度联系起来 不同级别和类型的背景噪声，以及它们如何覆盖个体（下一步，目标 1）。 相同的个体，注意力集中在与目标 1 相同的包络编码措施上的效果将是 通过保持输入语音混合恒定并操纵哪个语音源是焦点来进行检查 选择性注意（目标 2）。重要的是，这种方法有助于隔离自上而下的部分。 同一个体受试者的可懂度和脑电图测量使我们能够利用个体 差异并使用回归技术来表征自下而上和自上而下的相对贡献 最后，对于目标 1 中使用的语音噪声混合，我们将计算相同的结果。 计算听觉神经模型输出的包络编码度量（目标 3）。 耳蜗功能障碍将被纳入包络模型中，以表征其对编码的影响 通过比较“模型”（目标 3）和“神经”指标（目标 1 和 2），我们将测试是否存在噪声中的语音。 耳蜗机制可以解释自下而上编码中的个体差异。 通过拟议的一组实验将成为客观诊断和发展的基础 针对个别患者所遇到的噪声中言语问题的具体性质而量身定制的干预措施 项目完成还将为申请人提供计算建模方面的培训， 心理物理学和脑电图实验设计、数据收集、分析和解释，以及科学 这补充了她现有的信号处理和统计学背景，并将设置。 她正在朝着听觉神经科学学术研究生涯的长期目标迈出坚实的道路。