Structural and functional connectivity markers of developmental speech and language disorders

发育性言语和语言障碍的结构和功能连接标记

• 批准号: 9757857
• 负责人: Gabriel Cler
• 金额: $ 6.54万
• 依托单位: UNIVERSITY OF OXFORD
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-04 至 2022-03-03
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9757857
• 关键词: Address; Adolescent; Adult; Affect; Age; Area; Auditory; Basal Ganglia; Behavior; Behavioral; Brain; Cerebral cortex; Child; Classification; Clinical; Consensus; Control Groups; Corpus striatum structure; Data; Data Set; Development; Developmental Stuttering; Diffusion; Diffusion Magnetic Resonance Imaging; Dimensions; Disease; Economics; Emotional; Employment; Environment; Event; Functional Imaging; Future; Heterogeneity; Image; Impairment; Individual; Individual Differences; Inferior frontal gyrus; Investigation; Knowledge; Language; Language Delays; Language Development; Language Development Disorders; Language Disorders; Lead; Length; Life; Machine Learning; Magnetic Resonance Imaging; Measures; Mentors; Methodology; Motor; Network-based; Neural Pathways; Neurodevelopmental Disorder; Neurophysiology - biologic function; Outcome; Participant; Pathway interactions; Patients; Pattern; Population; Prevalence; Process; Reporting; Research; Research Personnel; Research Training; Rest; Sample Size; Scanning; Semantics; Severities; Speech; Speech Development; Speech Disorders; Structure; Stuttering; Superior temporal gyrus; Testing; Universities; Work; base; career; caudate nucleus; cohort; developmental disease; disorder control; effective therapy; interest; language impairment; motor control; neural circuit; neural correlate; neural patterning; neuroimaging; neuroregulation; putamen; relating to nervous system; sex; social; specific language impairment; standardize measure

ABSTRACT Developmental speech and language disorders affect an estimated 15% of children and have lifelong impacts on social and emotional development and employment. Two common neurodevelopmental disorders are developmental language disorder (DLD; also called specific language impairment) and developmental stuttering, affecting 7% and 5% of children respectively. Despite their prevalence and immense impact, little is known of the neural causes, correlates, and consequences of these common neurodevelopmental disorders; thus, effective treatment remains elusive. In the proposed project, we will study the neural underpinnings of these disorders using magnetic resonance imaging (MRI) to study structural and functional neural connectivity. Previous studies of connectivity in these populations are limited and show little consensus, likely due in part to small sample sizes. Theoretical accounts of both disorders implicate dysfunctional neural circuits through the basal ganglia. In the current proposal, we will test and compare the structural and functional integrity of neural pathways in large cohorts of people with DLD (N=80) and people who stutter (PWS; N=80) and compare them with similar data obtained in age- and sex-matched control groups of people with typical development (N=160). First, we will assess connectivity in speech/language-specific networks, using diffusion data to assess structural connectivity and resting-state data to assess functional connectivity. Results will indicate abnormalities in connectivity in large cohorts of PWS and people with DLD. In each disorder, we will also determine connectivity contributions to individual differences in behavior. This will reveal how different connectivity patterns are correlated to differences in severity along relevant dimensions (e.g., fluency, language measures), ideally resulting in neural correlates of the disorders. Finally, we will evaluate whole-brain functional connectivity differences between each disorder group and its matched control group using data-driven machine learning approaches. Results will indicate patterns of neural activity that differentiate these disorders from controls. The outcome of this proposal will be the characterization of underlying network differences in these populations, which will ideally lead to the development of targeted behavioral and neuro-modulatory treatments of these multifaceted and pervasive disorders. Research and training will take place at the University of Oxford, an ideal environment in which to pursue this line of research. The applicant will be mentored by world-leading researchers with the knowledge needed to guide him in this work, including expertise in the neural bases of developmental speech and language disorders, cutting-edge methodology in neuroimaging, and machine learning. Achieving these aims will illuminate the neural correlates of these speech and language disorders as well as prepare the applicant for an independent research career in this area.

抽象的 发育语音和语言障碍会影响约15％的儿童，并产生终生影响 关于社会和情感发展和就业。两个常见的神经发育障碍是 发育语言障碍（DLD；也称为特定语言障碍）和开发性口吃， 分别影响7％和5％的儿童。尽管他们的盛行和巨大影响，但知之甚少 这些常见神经发育障碍的神经原因，相关性和后果；因此， 有效的治疗仍然难以捉摸。在拟议的项目中，我们将研究其中的神经基础 使用磁共振成像（MRI）研究结构和功能神经连通性的疾病。 这些人群中有关连通性的先前研究受到限制，几乎没有共识，可能部分归因于 小样本量。两种疾病的理论说法都暗示了通过 基底神经节。在当前的建议中，我们将测试和比较神经的结构和功能完整性 大量DLD（n = 80）和口吃（PWS; n = 80）的人群中的途径并进行比较 在年龄和性别匹配的对照组中获得的类似数据具有典型发育的人（n = 160）。 首先，我们将使用扩散数据评估语音/语言特定网络中的连接性 连接性和静止状态数据以评估功能连接性。结果将表明异常 大量PWS和DLD的人的连通性。在每种疾病中，我们还将确定连通性 对行为个体差异的贡献。这将揭示连接模式的不同 与沿相关维度（例如流利度，语言度量）的严重程度差异相关，理想情况下 导致疾病的神经相关性。最后，我们将评估全脑功能连接 使用数据驱动的机器学习，每个疾病组与其匹配的对照组之间的差异 方法。结果将表明神经活动的模式将这些疾病与对照区分开。这 该建议的结果将是这些人群中潜在网络差异的表征， 理想情况下，这将导致靶向行为和神经调节治疗的发展 多方面和普遍的疾病。研究和培训将在牛津大学举行，这是一个理想的 追求这一研究的环境。申请人将受到世界领先的研究人员的指导 有了指导他在这项工作中所需的知识，包括在发展的神经基础上的专业知识 语音和语言障碍，神经影像学中的尖端方法和机器学习。实现 这些目标将阐明这些语音和语言障碍的神经相关性，并准备 该领域独立研究职业的申请人。