Characterization of the Neurobiological Profiles of Young Adults with and without Developmental Language Disorder (DLD)

患有和不患有发育性语言障碍 (DLD) 的年轻人的神经生物学特征的表征

• 批准号: 10721464
• 负责人: Julia L Evans
• 金额: $ 21.3万
• 依托单位: UNIVERSITY OF TEXAS DALLAS
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-08 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10721464
• 关键词: Adolescent; Adolescent and Young Adult; Adult; Age; Anatomy; Anxiety; Area; Auditory; Award; Biological Markers; Brain; Brain imaging; Child; Child Language; Classification; Clinical; Cognition; Cognitive; Collaborations; Communication; Complex; Computer Models; Coupled; Data; Development; Diagnostic; Dimensions; Disease; Dyslexia; Elasticity; Electroencephalography; Emerging Technologies; Emotional; Environment; Exclusion Criteria; Extramural Activities; Failure; Family; Foundations; Friends; Functional Magnetic Resonance Imaging; Funding; Genetic; Goals; Grant; Head; Hearing; Heterogeneity; High Prevalence; Human; Image; Impairment; Imprisonment; Individual; Individual Differences; Knowledge acquisition; Language; Language Development Disorders; Language Disorders; Language Tests; Learning Skill; Link; Logistic Regressions; Longevity; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Maps; Measures; Medical; Mental Depression; Mentors; Mentorship; Metals; Methods; Modeling; Morphologic artifacts; Motion; Neuroanatomy; Neurobiology; Neurodevelopmental Disorder; Neurologic; Neurosciences; Noise; Occupational; Optical Tomography; Optics; Participant; Pattern; Population; Production; Qualifying; Recording of previous events; Research; Research Personnel; Research Project Grants; Resolution; Risk; Sample Size; Sampling; School-Age Population; Scientist; Sensory; Series; Services; Severities; Short-Term Memory; Signal Transduction; Social isolation; Societies; Speech; Standardization; Structure; Target Populations; Techniques; Three-Dimensional Image; Training; Travel; United States; United States National Institutes of Health; Williams Syndrome; Work; autism spectrum disorder; automated analysis; behavioral phenotyping; cognitive neuroscience; cost effective; density; design; developmental disease; diffuse optical tomography; emotion regulation; executive function; experience; functional near infrared spectroscopy; hemodynamics; indexing; interest; language comprehension; language impairment; language processing; machine learning method; meetings; member; multimodality; neural; neuroimaging; novel; novel strategies; optical imager; optical imaging; peer; portability; programs; research study; response; skills; source localization; specific language impairment; success; temporal measurement; trait; verbal; young adult

ABSTRACT Approximately 7% of school-aged children have Developmental Language Disorder (DLD), making it one of the highest prevalence of the child language disorders. DLD places individuals at risk for academic failure, social isolation, anxiety, depression, poor emotional regulation, juvenile incarceration (65%), and repeat offending (70%). DLD persists into adulthood, with conservative estimates indicating that 12 million adults in the United States have DLD, but because the behavioral phenotype can overlap with that of typical individuals', they may no longer qualify for support services. Notably, although these young adults with DLD "appear" normal, their language abilities are linked to brain structure and cortical dynamics that differ qualitative from typical individuals, suggesting that neural signature of DLD may be a critical marker of the disorder. New machine learning methods have revolutionized the neuroscience of neurodevelopmental disorders and advances in registering the optical signal of functional near infrared spectroscopy (fNIRS) to neuroanatomical data now make capturing the spatial- temporal dynamics of spoken language processing feasible and cost-effective for speech, language, and hearing populations. Expertise in these two domains is critical for high impact speech-language research. The candidate is an established investigator with a strong record of research and extramural funding spanning more than 30 years in the area of DLD. The goal of the enhancement is to augment the candidate's current expertise in DLD by gaining advanced training in fNIRS neuroimaging and newer computational modeling techniques to keep the candidate's program of research in-step with emerging and evolving neuroimaging and computational modeling approaches. The goals of the enhancement are to: (1) advance the candidate's skills in cutting-edge fNIRS methods, (2) incorporate computational modeling into the candidate's program of research, and (3) catalyze new research collaborations with cognitive neuroscientists, optical imagers, and computational modelers. Didactic course work in applied machine learning and computational modeling (semester long courses), 1:1 meetings, scholarly travel and a small-scale research project will provide the enhanced experience to substantially augment the research skills of the candidate, seed new collaborations with scientists in other fields whose work is relevant to DLD. The research project will provide the hands-on opportunity for the candidate to acquire expertise under the mentorship of a superior team of young, up and coming mentors with expertise in optical imaging (fNIRS), and functional and structural brain imaging (fMRI, MRI), and a group of senior collaborators with expertise in computational modeling and cognitive neuroscience. A group of young adults ages 18;0 - 21;0 with/without DLD (n = 44) will complete standardized assessments, a structural MRI, and a series of fNIRS tasks, which will then be used as inputs to derive multidimensional models of DLD. Overall, the enhancement will significantly augment the current research trajectory of a well-established DLD researcher and provide the foundation for new NIH funding to develop multidimensional neurobiologically derived models of DLD.

抽象的 大约7％的学龄儿童患有发育性语言障碍（DLD），使其成为其中之一 儿童语言障碍的患病率最高。 DLD使个人面临学术失败，社会的风险 隔离，焦虑，抑郁，情绪调节不良，青少年监禁（65％）和重复犯罪 （70％）。 DLD一直持续到成年，保守的估计表明，统一的1200万成年人 各州有DLD，但是由于行为表型可以与典型个体的表型重叠，因此他们可能 不再有资格获得支持服务。值得注意的是，尽管这些患有DLD的年轻人显得正常，但他们 语言能力与大脑结构和皮质动态有关，这些动态与典型个体不同， 表明DLD的神经特征可能是该疾病的关键标志。新的机器学习方法 已经彻底改变了神经发育障碍的神经科学和注册光学的进步 神经解剖学数据的功能近红外光谱（FNIRS）的信号现在使捕获空间 语言处理的时间动态可行和具有成本效益的语音，语言和听力 人群。这两个领域的专业知识对于高影响语音语言研究至关重要。候选人 是一名既定的研究者 在DLD地区多年。增强的目的是增强候选人在DLD中的当前专业知识 通过在FNIRS神经影像学和新的计算建模技术中获得高级培训，以保持 候选人的研究计划与新兴和不断发展的神经成像和计算建模计划 方法。增强的目标是：（1）提高候选人在尖端FNIRS方面的技能 方法，（2）将计算建模纳入候选人的研究计划，（3）催化新的 与认知神经科学家，光学成像器和计算建模者的研究合作。教学 应用机器学习和计算建模（学期长课程）的课程工作，1：1会议， 学术旅行和小规模的研究项目将为实质上提供增强的体验 增强候选人的研究技能，种子与其他领域的科学家进行新的合作 与DLD有关。研究项目将为候选人提供动手的机会 在一支具有光学专业知识的年轻，新兴和即将到来的导师的指导下的专业知识 成像（FNIRS）以及功能和结构性脑成像（fMRI，MRI）和一组高级合作者 具有计算建模和认知神经科学方面的专业知识。一群18岁的年轻人； 0-21; 0 使用/没有DLD（n = 44）将完成标准化评估，结构性MRI和一系列FNIRS 任务，然后将其用作输入，以得出DLD的多维模型。总体而言，增强功能 将大大扩大良好的DLD研究人员的当前研究轨迹，并提供 新的NIH资金基金会开发了DLD的多维神经生物学模型。