Structural and functional connectivity markers of developmental speech and language disorders

摘要 据估计，15%的儿童会受到发育性言语和语言障碍的影响，并对他们的一生产生影响 关于社会和情感发展和就业。两种常见的神经发育障碍是 发育性语言障碍(DLD；也称为特殊语言障碍)和发育性口吃， 分别影响7%和5%的儿童。尽管它们的流行和巨大的影响，人们对此知之甚少 这些常见的神经发育障碍的神经原因、相互关系和后果；因此， 有效的治疗方法仍然难以捉摸。在拟议的项目中，我们将研究这些神经基础 使用磁共振成像(MRI)研究结构和功能神经连接的障碍。 以前对这些人群连通性的研究是有限的，几乎没有达成共识，可能部分原因是 样本量小。对这两种疾病的理论描述都涉及到神经回路功能障碍 基底节。在目前的方案中，我们将测试和比较神经的结构和功能的完整性 DLD患者(N=80)和口吃者(PWS；N=80)大队列中的通路，并进行比较 在年龄和性别匹配的对照组中，具有典型发育的人(N=160)获得了类似的数据。 首先，我们将评估语音/语言特定网络中的连通性，使用扩散数据来评估结构 连接性和休眠状态数据，以评估功能连接性。结果将会显示出 在大量PWS和DLD人群中的连接性。在每一种疾病中，我们还将确定连接性 对个体行为差异的贡献。这将揭示连接模式的不同之处 与相关维度(例如，流利度、语言测量)的严重程度差异相关，理想情况下 导致了这些疾病的神经关联。最后，我们将评估全脑功能连接 基于数据驱动机器学习的各障碍组与其匹配对照组的差异 接近了。结果将显示神经活动的模式，将这些疾病与对照组区分开来。这个 这一提议的结果将是描述这些人群中潜在的网络差异， 这将理想地导致对这些疾病的有针对性的行为和神经调节治疗的发展 多方面的和普遍的疾病。研究和培训将在牛津大学进行，这是一个理想的选择 从事这一领域研究的环境。申请者将由世界领先的研究人员指导 所需的知识来指导他的这项工作，包括在发育的神经基础方面的专业知识 语言和语言障碍，神经成像的前沿方法，以及机器学习。实现 这些目标将阐明这些言语和语言障碍的神经关联，以及准备 申请在这一领域从事独立研究工作。

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.