Neural Processing of Speech Signals in Children Who Stutter

项目摘要/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 在流利的演讲过程中受到干扰。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，这 调节言语运动发音网络中的运动信号，可能会导致言语不流畅 口吃儿童(CWS)。与额叶顶叶注意网络相一致，SMAN还调节注意 语音中的语音/音节信息，特别是在听力困难的情况下。CWS在以下方面的表现通常较差 语音处理任务比流利的同行更多，特别是在更具挑战性的任务上，可能是因为效率低下 在这些辅助网络中。然而，CWS语音处理缺陷的根本原因仍然存在 不清楚。对语音处理的机械性理解将有助于未来语音处理的发展 针对CWS中特定神经缺陷的神经生物学信息口吃干预。海流 该建议扩展了CWS中非典型语音处理的先前发现。结合互补性 一个跨学科的研究团队的专业知识，目前的项目将评估神经的完整性 语音编码的基本过程以及这些过程被任务调制的方式 使用多模式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CWS和150名流利同龄人进行脑电检测 完成四项难度不同的任务：A)安静地完成音节识别任务(/ba/vs/da/)；B)连续的 安静中的语音叙事理解任务；以及C&D)带音节的复杂语音编码任务和 同时呈现的连续语音，注意力指向音节(C)或指向 叙事(D)。在更简单和更复杂的听力条件下直接比较神经反应 (A/C、B/D)和对相同刺激的反应(C/D)是表征效果的关键 语音处理的任务需求。最先进的脑电机器学习方法将使 快速和缓慢时间波动的时间精确神经表示的同时提取 语音在从声学到音节的转化中表现出来。AIM 2将利用功能磁共振成像(FMRI) 评估CWS中语音处理的多个神经系统。使用相同的任务 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示， SMAN和注意网络在简单和复杂的语音任务中。AIM 3将开发一个系统级的 CWS中语音处理的计算模型。该模型基于组合的脑电和功能磁共振数据， 将模拟神经网络之间的交互如何在不同的收听条件下调节任务性能。 该项目将提供对CWS中语音处理的机械理解，并提供一个独特的、经过策划的、 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。

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.