Neural Processing of Speech Signals in Children Who Stutter

口吃儿童语音信号的神经处理

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要/摘要发育口吃是一种动态的多因素神经发育障碍，其特征是意外流利的语音产生中断。语音计划和生产依赖于完整的语音处理，这有助于开发和维护内部语音模型。不稳定的内部语音模型，在语音电动网络（SMAN）中调节电机信号，可能会导致语音不足口吃的孩子（CWS）。与额叶注意力网络一致，Sman还调节了关注语音中的语音/音节信息，尤其是在困难的听力条件下。 CW通常在语音处理任务比流利的同伴，尤其是在更具挑战性的任务上，可能由于效率低下在这些辅助网络中。但是，CWS中语音处理缺陷的根本原因仍然存在不清楚。对语音声音处理的机械理解将有助于未来的发展针对CWS中特定神经缺陷的神经生物学知情干预措施。电流提案扩展了CWS中非典型语音处理的先前发现。结合补充一个跨学科团队的专业知识，当前项目将评估神经的完整性语音声音编码的基础流程以及这些过程通过任务调节的方式使用多模式神经影像学和系统级计算建模方法的需求。目标1意志测量150 CW和150名流利的同龄人，年龄7-15岁的脑电图（EEG），儿童完成不同难度的四个任务：a）安静的音节标识任务（/ba/vs/da/）； b）连续安静的语音叙事理解任务；和C＆D）复杂的语音编码任务，并用音节和连续言语同时提出，注意力指向音节（C）或朝向叙事（D）。直接比较在更简单，更复杂的聆听条件下引起的神经反应（a/c，b/d）和对忽略（c/d）时对同一刺激的响应对于表征效果至关重要对语音处理的任务需求。脑电图的最先进的机器学习方法将启用同时提取快速和缓慢的时间波动的时间精确的神经表示从声学到音节表示的转换中的语音。 AIM 2将利用功能性MRI（fMRI）评估CWS中语音处理的多个神经系统。在与AIM 1相同的参与者将允许量化听觉中的神经激活和表示形式，在简单而复杂的语音任务中，SMAN和注意力网络。 AIM 3将开发系统级 CWS中语音处理的计算模型。该模型基于组合的脑电图和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介导任务性能。该项目将对CWS中的语音处理以及独特的，精心策划的，开放访问，多模式神经影像学数据集将是口吃领域的持久资源。