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.

摘要 大约7%的学龄儿童患有发展性语言障碍（DLD），使其成为世界上最严重的语言障碍之一。 儿童语言障碍患病率最高。DLD将个人置于学术失败的风险中，社会 孤独、焦虑、抑郁、情绪调节不良、青少年监禁（65%）和重复犯罪 (70%). DLD持续到成年，保守估计表明，美国有1200万成年人 国家有DLD，但因为行为表型可以与典型的个人重叠，他们可能会 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“看起来”正常，但他们的 语言能力与大脑结构和皮质动力学有关，这些结构和动力学与典型个体的性质不同， 提示DLD的神经信号可能是该疾病的关键标志。新的机器学习方法 彻底改变了神经发育障碍的神经科学，并在记录光学 功能性近红外光谱（fNIRS）的信号神经解剖数据，现在使捕捉的空间- 口语处理的时间动力学对言语、语言和听力是可行的和经济有效的 人口。这两个领域的专业知识对于高影响力的语音语言研究至关重要。候选 是一个既定的研究员与研究和校外资金跨越30多个强大的记录 在DLD领域，增强的目标是增强候选人目前在DLD方面的专业知识 通过获得fNIRS神经成像和更新的计算建模技术的高级培训， 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同步 接近。提高的目标是：（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、光学成像师和计算建模师的研究合作。说教 应用机器学习和计算建模课程（学期课程），1：1会议， 学术旅行和小规模的研究项目将提供增强的经验， 增强候选人的研究技能，与其他领域的科学家进行新的合作， 与DLD有关。该研究项目将为候选人提供实践机会， 在上级团队的指导下，拥有光学专业知识， 成像（fNIRS），功能和结构脑成像（fMRI，MRI），以及一组高级合作者 在计算建模和认知神经科学方面有专长。一群18岁的年轻人;0 - 21岁;0 有/无DLD的患者（n = 44）将完成标准化评估、结构MRI和一系列fNIRS 任务，然后将其用作输入，以导出DLD的多维模型。总体而言， 将大大增加一个成熟的DLD研究人员目前的研究轨迹，并提供 基金会为新的NIH资金开发DLD的多维神经生物学衍生模型。