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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.

项目概要/摘要尽管人工耳蜗植入 (CI) 是治疗严重至极重度感音神经病的最有效方法听力损失，患有 CI 的儿童作为一个群体在听力正常的同龄人中表现大约为 15% 语言措施。最有趣的是，他们的语言结果在个人层面上差异很大，尽管在年轻时就着床了。使用术前脑部磁共振成像 (MRI) 扫描，完成作为常规临床评估以及人工智能分析方法的一部分，我们的研究将构建用于预测英语和西班牙语学习中个人语言结果的神经预测模型手术后 4 岁以内的儿童。这些模型的临床实用性也将通过调查来评估模型的预测与儿童对计划的反应程度相关的程度的强化沟通治疗。我们假设在手术后的 4 年内，患有 CI 的幼儿遵循三个语言发展阶段：1）全球对口语的关注孩子适应有关语言的电听觉输入，2）语音模式的编码足够的信息来发展基于听觉的词汇表征，以及3）口语的发展以较长的话语进行口头交流的语言语法。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对单语英语学习的 CI 儿童在这三个阶段的语言结果（目标 1）。我们进一步假设这些网络在第一阶段对于类型相似的语言来说在很大程度上是不变的阶段，但听觉网络的贡献会延长并且需要更高阶的认知网络在更大程度上影响患有 CI 的西班牙语-英语双语儿童的语言结果（目标 2）。作为我们当前 R21 项目的一部分，我们开发了标准化的临床评估和随访方案跨不同 CI 中心，这将促进实现目标 1 和 2 所需的调查。目标 3 涉及结果的神经预测与行为治疗之间的相互作用。主要 CI 中心该项目将招收单语英语学习儿童参加由家长实施的强化课程沟通治疗（PICT）计划，这是迄今为止唯一有效的治疗计划已得到随机对照试验的支持。我们将评估语言的神经预测是否结果与 PICT 中语言增益的程度成反比。具体来说，我们假设根据我们的神经预测算法预测的语言障碍越严重，孩子的语言障碍就越严重可以从 PICT 中受益。我们的转化研究计划将推动沟通障碍领域的发展技术、理论和临床创新。它将是第一个证明预测到制定全面治疗听力损失的方法是可行的、具有成本效益的，并且可以优化听力损失所有 CI 儿童的语言结果。