Multisensory Integration and Cortical Plasticity in Cochlear Implant Users

摘要 听力损失是影响超过4800万美国人的残疾的主要原因。人工耳蜗(CI)是 使严重听力损失的人能够恢复听力和语言的神经假体设备 理解。然而，CI手术的结果是高度可变和难以预测的，这造成了 临床医生在指导患者决策和预期方面面临的挑战。语音识别是一种多感官 进程。虽然众所周知，视觉语音提示可以改善听觉语音识别，但视觉和 人工耳蜗术前、术后CI患者的视听能力没有得到很好的评价。在我的 初步数据显示，植入前的视觉和视听语音识别预测植入后的 植入听觉语音识别，表明多感觉整合可能起到 在CI结果中被低估的作用。在提议的实验中，我将探索视觉和 通过一系列感官实验观察脑梗塞手术后的视听表现(目标1)。我也会 通过同时使用创新方法评估任何行为变化的神经关联 记录脑电(EEG)和功能近红外光谱(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到预测模型中(目标3)。通过实验 在这份奖学金申请中，我将全面描述感官的纵向变化 人工耳蜗术后的知觉和皮质组织。我还将利用这些发现来开发一种 预测脑梗塞预后的新的临床工具。拟议的计划将我在听觉方面的研究兴趣整合在一起 神经科学和我对耳鼻喉科的临床兴趣，它将为我未来的成功奠定基础 医生兼科学家。

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.