Neural Prediction to Enhance Language Outcomes in Children with Cochlear Implant

神经预测可提高人工耳蜗植入儿童的语言效果

基本信息

批准号：
10676076
负责人：
PATRICK C M WONG
金额：
$ 60.22万
依托单位：
LURIE CHILDREN'S HOSPITAL OF CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-05 至 2027-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10676076
关键词：
Acclimatization Address Adopted Affect Age Area Attention Auditory Auditory area Behavior Therapy Brain Brain scan Chicago Child Child Language Childhood Clinical Cochlear Implants Cochlear implant procedure Cognitive Communication Communication impairment Data Dependence Development Effectiveness Enrollment Future Goals Habilitation Hong Kong Household Individual Inferior Investigation Knowledge Language Language Development Learning Left Longitudinal Studies MRI Scans Magnetic Resonance Imaging Measures Methods Modeling Operative Surgical Procedures Outcome Parents Pattern Performance Prediction of Response to Therapy Protocols documentation Randomized, Controlled Trials Readiness Reporting Research Research Personnel Resources Sensorineural Hearing Loss Specificity Standardization Subgroup Technology Testing Time analytical method auditory deprivation bilingualism brain magnetic resonance imaging cost cost effective follow-up frontal lobe hearing impairment implantation improved improved outcome innovation language impairment language outcome lexical member neural neurodevelopment neuroimaging normal hearing oral communication outcome prediction peer personalized learning phonology precision medicine prediction algorithm predictive modeling programs research clinical testing success syntax theories tool translational research program treatment program

项目摘要

PROJECT SUMMARY/ABSTRACT Although cochlear implantation (CI) is the most effective method for managing severe to profound sensorineural hearing loss, children with CI as a group perform at about 15th percentile of their normal-hearing peers on language measures. Most intriguingly, their language outcomes are highly variable at the individual level, despite implantation at a young age. Using pre-surgical brain magnetic resonance imaging (MRI) scans, done as part of the routine clinical evaluation, as well as AI-enabled analytical methods, our research will construct neural predictive models to forecast individual-level language outcomes in English- and Spanish-learning children up to 4 years after surgery. The clinical utility of these models will also be evaluated by investigating the extent to which the models' prediction is associated with the degree to which a child responds to a program of intensive communication treatment. We hypothesize that during the 4 years immediately after surgery, young children with CI follow three stages of language development: 1) global attention to spoken language as the child acclimatizes to electric auditory input about language, 2) encoding of phonological patterns with sufficient information to develop auditory-based lexical representations, and 3) development of spoken language syntax to communicate orally in longer utterances. We also hypothesize that the integrity of brain networks associated with higher-order cognitive, auditory and syntactic processing differentially contributes to language outcomes across these three stages in monolingual English-learning children with CI (Aim 1). We further hypothesize that these networks are largely invariant for typologically similar languages during the first stage, but that the contribution of the auditory network would be prolonged and require higher-order cognitive networks to an even greater extent to language outcomes for Spanish-English bilingual children with CI (Aim 2). As part of our current R21 project, we have developed a standardized clinical evaluation and follow-up protocol across different CI centers that will facilitate the investigations required to achieve Aims 1 and 2. Aim 3 concerns the interaction between neural prediction of outcomes and behavioral treatment. The main CI center of this project will enroll monolingual English-learning children for an intensive, Parent-Implemented Communication Treatment (PICT) program, which is the only treatment program to date whose effectiveness has been supported by a randomized controlled trial. We will evaluate whether neural prediction of language outcomes is inversely related to the degree of language gains from PICT. Specifically, we hypothesize that the more severe the predicted language impairment based on our neural predictive algorithms, the more the child could benefit from PICT. Our translational research program will advance the field of communication disorders in technological, theoretical and clinical innovations. It will be among the first to demonstrate that a predict-to- prescribe approach to holistically treat hearing loss is feasible, cost-effective and can lead to optimization of language outcomes of all children with CI.

项目摘要/摘要尽管人工耳蜗植入（CI）是管理严重至深刻感官的最有效方法听力损失，CI作为一个小组的儿童在约15％的正常同伴中表现语言措施。最有趣的是，他们的语言结果在个人层面上是高度变化的，尽管年轻时植入。使用手术前脑磁共振成像（MRI）扫描，完成作为常规临床评估的一部分以及支持AI的分析方法，我们的研究将构建神经预测模型，以预测英语和西班牙学习中的个人级语言结果手术后4年的儿童。这些模型的临床实用性也将通过研究来评估模型的预测与儿童对计划的响应程度相关的程度密集的沟通处理。我们假设在手术后的4年中， CI的幼儿遵循语言发展的三个阶段：1）全球对口语的关注儿童适应有关语言的电气输入，2）用语音模式编码足够的信息来开发基于听觉的词汇表示，3）开发口语语言语法以口语进行口头交流。我们还假设大脑的完整性与高阶认知，听觉和句法处理相关的网络有助于在这三个阶段的语言结果在具有CI的单语英文学习儿童中（AIM 1）。我们进一步假设这些网络在第一种中对于类型上类似语言而言是不变的阶段，但是听觉网络的贡献将延长，需要高阶认知与CI的西班牙语 - 英语双语儿童的语言成果相比，网络在更大程度上（AIM 2）。作为当前R21项目的一部分，我们已经开发了标准化的临床评估和后续方案在不同的CI中心，将促进实现目标1和2的调查。目标3 涉及神经预测结果与行为治疗之间的相互作用。主要CI中心这个项目将注册单语英文学习的孩子通信处理（PICT）计划，这是迄今为止唯一有效性的治疗计划已得到一项随机对照试验的支持。我们将评估语言的神经预测结果与Pict的语言收益成反比。具体来说，我们假设基于我们的神经预测算法的预测语言障碍越严重，孩子越多可以从Pict中受益。我们的翻译研究计划将推进沟通障碍领域在技术，理论和临床创新中。这将是第一个证明预测到的人之一规定整体治疗听力损失的方法是可行的，具有成本效益的，并且可能导致优化所有CI儿童的语言结果。