Cochlear synaptopathy and audiometric measures from human temporal-bone cases of sensorineural hearing loss

项目2摘要-摘要 由于内耳不能活检，临床成像不能产生细胞水平的 解决方案，唯一的方法来揭示功能重要的结构变化， 感音神经性听力损失是死后人类颞叶的显微镜检查 骨头该P50中心专注于内耳中的原发性神经变性（即， 存活的感觉细胞和听觉神经细胞之间的突触连接），及其 在限制理解复杂刺激（如语音）的能力方面， 耳鸣和听觉过敏的诱发者。 在过去的5年里，项目2表明，正如动物模型所预测的那样， 正常老化的人耳中听觉神经损失的速度超过毛细胞损失的速度2：1， 在那些有噪音暴露史的人中，这种神经损失进一步加速。严格的 统计模型表明，虽然听力阈值预测良好， 毛细胞损失的模式，神经损失不影响阈值，但有助于 具有相似听力图的人之间单词识别能力的差异。 在接下来的5年里，项目2将在此基础上探索更广泛的 获得性听力损失病因，即耳毒性抗生素和化疗药物，突发性 感音神经性听力损失和梅尼埃病。我们选择这个是因为它们很常见， 在我们的颞骨档案中有很好的代表性，通常与耳鸣有关， 言语辨别困难。我们正在开发机器学习方法， 自动获取定量组织病理学数据，因为我们的目标是增加 尽可能快地分析案件数量。随着我们进入临床时代， 对于耳聋治疗的临床试验，迫切需要稳健的统计模型来准确地 从听力图、单词评分、听力损失预测细胞损失的程度和模式 候选患者的病因、年龄和性别。

Project 2 Summary - Abstract Since the inner ear cannot be biopsied, and clinical imaging cannot produce cellular-level resolution, the only way to uncover the functionally important structural changes underlying sensorineural hearing loss is the microscopic examination of post-mortem human temporal bones. This P50 Center focuses on primary neural degeneration in the inner ear (i.e. the loss of synaptic connections between surviving sensory cells and auditory nerve cells), and its hypothesized role in limiting the ability to understand complex stimuli like speech and as a key elicitor of tinnitus and hyperacusis. Over the past 5 years, Project 2 showed, as predicted from animal models, that the rate of auditory nerve loss in normal-aging human ears out-paced the rate of hair cell loss by 2:1, and that this neural loss was further accelerated in those with a history of noise exposure. A rigorous statistical model showed that while the audiometric thresholds were well predicted by the patterns of hair cell loss, the neural loss did not affect threshold but contributed to the differences in word identification abilities among those with similar audiograms. Over the next 5 years, Project 2 builds on this foundation to explore a wider range of acquired hearing loss etiologies, i.e. ototoxic antibiotics and chemotherapeutics, sudden sensorineural hearing loss and Ménière’s disease. We choose this because they are common, well represented in our temporal bone archives, and are often associated with tinnitus and difficulties in speech discrimination. We are developing machine-learning approaches to automate the acquisition of quantitative histopathological data, because we aim to grow the number of cases analyzed as rapidly as possible. As we have now entered the era of clinical trials for deafness therapeutics, there is a critical need for robust statistical models to accurately predict the degree and pattern of cellular loss from the audiogram, word score, hearing loss etiology, age and sex of a candidate patient.