Multisensory Integration and Cortical Plasticity in Cochlear Implant Users

摘要 听力损失是残疾的主要原因，影响超过4800万美国人。髋关节植入物（CI）是 神经修复设备，使严重听力损失的人恢复听力和语言能力 理解力然而，CI手术的结果是高度可变的，难以预测， 临床医生指导患者决策和期望的挑战。语音识别是一种多感官 过程虽然已知视觉语音提示可以改善听觉语音识别，但是视觉和听觉语音提示可以改善听觉语音识别。 人工耳蜗植入前后，人工耳蜗使用者的视听能力还没有得到很好的表征。在我 初步数据，我表明，植入前的视觉和视听语音识别预测后， 植入听觉语音识别，这表明多感觉整合可能发挥作用， 在CI结果中的作用被低估。在拟议的实验中，我将探讨视觉和 通过一系列感觉实验（目标1），评估CI手术后的视听表现。我也会 使用创新的方法评估任何行为变化的神经相关性， 记录脑电图（EEG）和功能性近红外光谱（fNIRS）反应， 时间（目标2）。最后，我将确定预测植入后语音识别的植入前因素 并使用机器学习将这些数据合成为预测模型（目标3）。通过实验 在这次奖学金申请中提出，我将全面描述感觉的纵向变化， 人工耳蜗植入后的感知和皮层组织。我还将利用这些发现来制定一个 用于预测CI结局的新临床工具。该计划结合了我在听觉方面的研究兴趣 神经科学与我在耳鼻喉科的临床兴趣，它将为我的成功奠定了未来 物理学家兼科学家

ABSTRACT Hearing loss is a major cause of disability that affects over 48 million Americans. Cochlear implants (CIs) are neuroprosthetic devices that allow people with profound hearing loss to recover hearing and speech comprehension. However, CI surgery outcomes are highly variable and difficult to predict, which creates a challenge for clinicians to guide patient decisions and expectations. Speech recognition is a multisensory process. Although it is known that visual speech cues can improve auditory speech recognition, the visual and audiovisual abilities of CI users have not been well characterized before and after cochlear implantation. In my preliminary data, I show that pre-implantation visual and audiovisual speech recognition predicts post- implantation auditory speech recognition, suggesting that multisensory integration may play an underappreciated role in CI outcomes. In the proposed experiments, I will explore changes in visual and audiovisual performance following CI surgery through a battery of sensory experiments (Aim 1). I will also assess the neural correlates of any behavioral changes using an innovative approach by simultaneously recording electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) responses over time (Aim 2). Finally, I will identify pre-implantation factors that predict post-implantation speech recognition and synthesize these data into a prediction model using machine learning (Aim 3). Through the experiments proposed in this fellowship application, I will comprehensively characterize the longitudinal changes in sensory perception and cortical organization following cochlear implantation. I will also use these findings to develop a novel clinical tool for predicting CI outcomes. The proposed plan integrates my research interests in auditory neuroscience with my clinical interests in otolaryngology, and it will set me up for success as a future physician-scientist.