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

通过基于人类内耳病理学的建模和编程策略改善人工耳蜗的效果

• 批准号: 10825043
• 负责人: JULIE G Arenberg
• 金额: $ 45.35万
• 依托单位: MASSACHUSETTS EYE AND EAR INFIRMARY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-18 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10825043
• 关键词: 3-Dimensional; Acclimatization; Acoustic Nerve; Action Potentials; Adult; Assessment tool; Audiology; Auditory; Axon; Basic Science; Behavioral; Bone Growth; Child; Chronic; Clinical; Clinical Data; Cochlea; Cochlear Implants; Cochlear implant procedure; Collection; Complex; Computer Models; Data; Detection; Devices; Distant; Electric Stimulation; Electrodes; Environment; Evoked Potentials; Foreign Bodies; Goals; Growth; Health; Hearing; Histology; Histopathology; Human; Implant; Individual; Knowledge; Labyrinth; Learning; Machine Learning; Maps; Measures; Methods; Modeling; Neurons; Noise; Operative Surgical Procedures; Outcome; Output; Participant; Pathology; Patients; Pattern; Performance; Peripheral; Physiologic Ossification; Physiologic pulse; Positioning Attribute; Research; Speech Perception; Stimulus; Temporal bone structure; Testing; Time; Translating; Trauma; Update; Variant; clinical care; clinical practice; clinical predictors; density; electric field; experimental study; hearing impairment; hearing restoration; implantation; improved; improved outcome; insight; nerve supply; neural; neural prosthesis; programs; reconstruction; response; sound; speech in noise; spiral ganglion; success; tool; translational approach

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.

项目总结/摘要 耳蜗植入物是非常成功的神经假体，可以增强或恢复听力， 听力受损的成人和儿童。来自环境的声音被转换成电脉冲， 通过电极阵列传送到听觉神经。耳蜗植入物提供重要的光谱和 时间听觉信息。然而，语音感知性能差异很大 在耳蜗植入者中，特别是在嘈杂的环境中和对于复杂的刺激。显著 这种性能可变性的一个影响因素是电极-神经元界面的质量， 通过单个电极的位置，神经元、骨骼和组织的相对健康和完整性， 与电极阵列插入造成的创伤相关的生长。 目前，还没有一种临床工具来查询耳蜗植入者中的电极-神经元界面， 也没有基于人类耳蜗组织病理学的精确计算模型来估计电极， 神经元接口提高我们理解的一步是更新经典方法， 定量的密度和完整性的听觉神经元在捐赠的人颞骨从 人工耳蜗植入者我们将利用机器学习的力量来生成3D重建 神经元相对于耳蜗植入阵列中电极的位置。开发临床措施来预测 在电极神经元接口的变化，我们将利用跨学科的专家团队，机器 学习，计算建模，无与伦比的颞骨收集，以及丰富的临床数据。 拟议实验的长期目标是改善人工耳蜗编程和语音 通过提高我们对电极-神经元界面的理解来改善感知结果。 提出了三个目标：1）在耳蜗组织病理学的定量三维地图， 耳蜗植入者颞骨内电极-神经元界面的变化及其与听力的关系 措施和结果; 2）开发和验证电极-神经元界面的计算模型 通过组织病理学、研究和临床措施; 3）改善人工耳蜗植入者的言语感知 通过基于电极-神经元接口估计的个性化编程来帮助听众。 这些研究的结果有望改变我们对耳蜗的看法。 组织病理学和人工耳蜗植入患者的临床护理。我们将描述最大的一组 迄今为止粒度最大的颞骨我们将提供洞察力和工具， 评估组织病理学、研究和临床措施之间的关系。最后，我们将开发 基于鲁棒性的人工耳蜗植入者个性化编程的可翻译方法 了解和验证模型的电极神经元接口在人类。