Neural Processing of Speech Signals in Children Who Stutter

口吃儿童语音信号的神经处理

• 批准号: 10589099
• 负责人: Amanda M Hampton Wray
• 金额: $ 50.96万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-09 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10589099
• 关键词: Acoustics; Attention; Auditory; Auditory area; Behavioral; Categories; Child; Childhood; Cognitive; Communication; Complex; Comprehension; Computer Models; Data; Data Set; Development; Developmental Stuttering; Electroencephalography; Environment; Event-Related Potentials; Frequencies; Functional Magnetic Resonance Imaging; Future; Grain; Inferior frontal gyrus; Intervention; Left; Linguistics; Machine Learning; Measures; Mediating; Methods; Modeling; Motor; Motor Cortex; Neurobiology; Neurodevelopmental Disorder; Participant; Pattern; Perception; Phonetics; Physiological; Process; Production; Psychophysiology; Quality of life; Research Personnel; Resources; Sensory; Signal Transduction; Speech; Speech Sound; Stimulus; Stuttering; Subgroup; System; Task Performances; Work; aged; attentional modulation; cognitive neuroscience; collaborative environment; directed attention; experience; innovation; millisecond; multimodal neuroimaging; neural; neural network; neuroimaging; neurophysiology; peer; response; speech processing; temporal measurement

PROJECT SUMMARY/ABSTRACT Developmental stuttering is a dynamic, multifactorial neurodevelopmental disorder characterized by unintended disruptions in fluent speech production. Speech planning and production rely on intact speech sound processing, which helps develop and maintain internal speech sound models. Unstable internal speech sound models, which regulate motor signals in the speech motor articulatory network (SMAN), may contribute to disfluent speech in children who stutter (CWS). In concert with frontoparietal attention network, SMAN also modulates attention to phonetic/syllabic information in speech, particularly in difficult listening conditions. CWS often perform worse on speech processing tasks than fluent peers, especially on more challenging tasks, potentially due to inefficiencies in these auxiliary networks. However, the underlying causes of speech processing deficits in CWS remain unclear. A mechanistic understanding of speech sound processing will facilitate future development of neurobiologically informed stuttering interventions that target the specific neural deficits in CWS. The current proposal extends previous findings of atypical speech sound processing in CWS. Combining the complementary expertise of a cross-disciplinary team of investigators, the current project will evaluate the integrity of neural processes underlying speech sound encoding and the ways in which these processes are modulated by task demands using multimodal neuroimaging and systems-level computational modeling approaches. Aim 1 will measure electroencephalography (EEG) in 150 CWS and 150 fluent peers, aged 7-15 years, while children complete four tasks of varying difficulty: A) a syllable identification task (/ba/ vs /da/) in quiet; B) a continuous speech narrative comprehension task in quiet; and C & D) complex speech encoding tasks with syllables and continuous speech presented simultaneously, with attention directed either toward syllables (C) or toward the narrative (D). Directly comparing neural responses elicited in simpler and more complex listening conditions (A/C, B/D) and responses to the same stimuli when attended vs. ignored (C/D) is critical for characterizing effects of task demands on speech sound processing. State-of-the-art machine-learning approaches for EEG will enable simultaneous extraction of temporally precise neural representations of fast and slow temporal fluctuations in speech in the transformation from acoustic to syllable representations. Aim 2 will leverage functional MRI (fMRI) to assess multiple neural systems underlying speech sound processing in CWS. Employing the same tasks in the same participants as Aim 1 will allow for quantifying neural activations and representations in auditory, SMAN, and attention networks during simple and complex speech tasks. Aim 3 will develop a systems-level computational model of speech sound processing in CWS. The model, based on combined EEG and fMRI data, will simulate how interactions between neural networks mediate task performance across listening conditions. This project will provide a mechanistic understanding of speech sound processing in CWS and a unique, curated, open access, multimodal neuroimaging dataset that will be a lasting resource for the field of stuttering.

项目总结/摘要 发育性口吃是一种动态的、多因素的神经发育障碍，其特征是无意识地 中断流畅的言语表达。语音规划和制作依赖于完整的语音声音处理， 这有助于开发和维护内部语音声音模型。不稳定的内部语音声音模型， 调节言语运动发音网络（SMAN）中的运动信号，可能有助于言语不流利。 口吃儿童（CWS）与额顶叶注意网络一致，SMAN也调节注意， 语音中的语音/音节信息，特别是在困难的听力条件下。CWS通常在以下情况下表现更差： 语音处理任务，特别是在更具有挑战性的任务，可能是由于效率低下 这些辅助网络。然而，CWS的语音处理缺陷的根本原因仍然存在， 不清楚对语音处理机制的理解将有助于语音处理技术的进一步发展。 神经生物学信息的口吃干预措施，针对特定的神经缺陷在CWS。当前 该提案扩展了之前关于CWS中非典型语音声音处理的发现。结合互补性 一个跨学科的调查小组的专业知识，目前的项目将评估神经系统的完整性， 语音编码的基本过程以及这些过程被任务调制的方式 使用多模态神经成像和系统级计算建模方法的需求。目标1将 对150名7 - 15岁的CWS和150名7-15岁的流利同龄人进行了脑电图（EEG）测量， 完成四个不同难度的任务：A）安静的音节识别任务（/ba/ vs /da/）; B）连续的 安静条件下的言语叙事理解任务; C & D）复杂的语音编码任务， 同时出现的连续语音，注意力指向音节（C）或 叙述（D）。直接比较在简单和更复杂的听力条件下引起的神经反应 （A/C，B/D）和对相同刺激的反应时，关注与忽略（C/D）是表征效果的关键 对语音处理的要求。最先进的EEG机器学习方法将使 快速和缓慢的时间波动的时间精确神经表征的同时提取， 语音从声学到音节表示的转变。Aim 2将利用功能性MRI（fMRI） 评估CWS中语音声音处理的多个神经系统。同样的任务， 与目标1相同的参与者将允许量化听觉中的神经激活和表征， SMAN和注意力网络在简单和复杂的语音任务。目标3将开发一个系统级 CWS中语音处理的计算模型。该模型基于EEG和fMRI数据， 将模拟神经网络之间的相互作用如何在听力条件下调节任务表现。 该项目将提供一个在CWS语音声音处理的机械理解和一个独特的，策划， 开放获取，多模式神经成像数据集，将成为口吃领域的持久资源。