Improving Cochlear Implant Outcomes Through Modeling and Programming Strategies Based on Human Inner Ear Pathology

项目摘要/摘要 人工耳蜗是一种非常成功的神经假体，可以严重增强或恢复听力。 听力受损的成年人和儿童。来自环境的声音被转化为电脉冲，并 通过一组电极传送到听神经。人工耳蜗术提供了重要的频谱和 传递给听者的时间听觉信息。然而，语音感知性能差异很大 尤其是在嘈杂的环境和复杂的刺激下。一个重要的 造成这种性能差异的因素是电极-神经元接口的质量，其定义如下 根据单个电极的位置，神经元、骨骼和组织的相对健康和完整性 生长与电极阵列插入造成的创伤有关。 目前，还没有一种临床工具来查询人工耳蜗听者的电极-神经元界面， 也没有一个基于人类耳蜗组织病理学的准确计算模型来估计电极- 神经元接口。提高我们理解的一个步骤是更新经典的方法，以 供体人颞骨中听神经元密度和完整性的定量研究 接受人工耳蜗术的患者。我们将利用机器学习的力量来生成3D重建 相对于人工耳蜗阵列中的电极的神经元。制定临床措施以预测 对于电极-神经元接口的变化，我们将利用一个跨学科的专家团队，机器 学习，计算建模，无与伦比的颞骨集合，以及丰富的临床数据。 拟议中的实验的长期目标是改进人工耳蜗的编程和语音 通过提高我们对电极-神经元界面的理解，感知结果。 提出了三个目标：1)制作耳蜗区组织病理学的定量三维地图 人工耳蜗术后患者颞骨中电极-神经元界面及其与测听的关系 测量和结果；2)开发和验证电极-神经元接口的计算模型 利用组织病理学、研究和临床措施；3)改善人工耳蜗术后的言语知觉 通过基于电极-神经元接口估计的个性化编程来识别听者。 拟议中的研究结果预计将导致我们对耳蜗的看法发生变化 人工耳蜗植入者的组织病理学和临床护理。我们将描述最大的一组 植入了迄今为止粒度最高的颞骨。我们将提供洞察力和工具来 评估组织病理学、研究和临床措施之间的关系。最后，我们将发展 基于健壮性的人工耳蜗听者个性化可翻译编程方法 对人类电极-神经元界面的理解和验证模型。

Project Summary/Abstract Cochlear implants are highly successful neural prostheses that enhance or restore hearing to severely hearing-impaired adults and children. Sound from the environment is converted into electrical pulses and conveyed to the auditory nerve by an array of electrodes. The cochlear implant provides important spectral and temporal auditory information to the listener. However, speech perception performance varies considerably among cochlear implant listeners, particularly in noisy environments and for complex stimuli. A significant contributing factor to this performance variability is the quality of the electrode-neuron interface, which is defined by the position of individual electrodes, the relative health and integrity of the neurons and bone and tissue growth related to trauma from electrode array insertion. Currently, there is not a clinical tool to query the electrode-neuron interface in cochlear implant listeners, nor is there an accurate computational model based on human cochlear histopathology to estimate the electrode- neuron interface. One step towards improving our understanding is to update the classical approach to quantification of the density and integrity of the auditory neurons in donated human temporal bones from recipients with cochlear implants. We will harness the power of machine learning to generate 3-D reconstructions of the neurons relative to the electrodes in the cochlear implant array. To develop clinical measures to predict variations in the electrode-neuron interface, we will leverage an interdisciplinary team of experts, machine learning, computational modeling, an unparalleled collection of temporal bones, and a wealth of clinical data. The long-term goal of the proposed experiments is to improve cochlear implant programming and speech perception outcomes by improving our understanding of the electrode-neuron interface. Three aims are proposed: 1) To produce quantitative 3-D maps of cochlear histopathology at the electrode-neuron interface in temporal bones from cochlear implant recipients and relate them to audiometric measures and outcomes; 2) To develop and validate a computational model of the electrode-neuron interface using histopathology, research and clinical measures; and 3) To improve speech perception for cochlear implant listeners by individualizing programming based on electrode-neuron interface estimates. The results of the proposed studies are expected to lead to a shift in how we think about cochlear histopathology and clinical care of individuals with cochlear implants. We will characterize the largest set of implanted temporal bones with the highest degree of granularity to date. We will provide insight and tools for assessing the relationship between histopathology, research, and clinical measures. Finally, we will develop translatable methods for individualized programming for cochlear implant listeners based on a robust understanding and a validated model of the electrode-neuron interface in humans.