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

基本信息

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
项目类别：
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

项目摘要

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% 的学龄儿童患有发展性语言障碍 (DLD)，使其成为儿童语言障碍的患病率最高。 DLD 使个人面临学业失败、社交失败的风险孤立、焦虑、抑郁、情绪调节不良、青少年监禁（65%）和重复犯罪（70%）。 DLD 持续到成年期，保守估计表明，美国有 1200 万成年人各州都有 DLD，但由于行为表型可能与典型个体的行为表型重叠，因此它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“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，与典型个体的性质不同，表明 DLD 的神经特征可能是该疾病的一个关键标志。新的机器学习方法彻底改变了神经发育障碍的神经科学，并在记录光学方面取得了进展功能性近红外光谱 (fNIRS) 信号到神经解剖数据现在可以捕获空间- 口语处理的时间动态对于语音、语言和听力来说是可行且具有成本效益的人口。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。候选人是一位知名研究者，拥有丰富的研究记录和外部资助，涉及 30 多个领域在 DLD 领域的年数。增强的目标是增强候选人当前在 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，以保持候选人的研究计划与新兴和不断发展的神经影像学和计算模型同步接近。增强的目标是：(1) 提高考生在尖端 fNIRS 方面的技能方法，（2）将计算模型纳入候选人的研究计划中，以及（3）催化新的与认知神经科学家、光学成像师和计算建模师进行研究合作。说教式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开展新的合作与 DLD 相关。该研究项目将为候选人提供实践机会在一支由年轻、有前途、具有光学专业知识的导师组成的优秀团队的指导下，获得专业知识成像（fNIRS）、功能和结构脑成像（fMRI、MRI）以及一组高级合作者拥有计算建模和认知神经科学方面的专业知识。一群年龄在 18;0 - 21;0 的年轻人有/无 DLD (n = 44) 将完成标准化评估、结构 MRI 和一系列 fNIRS 任务，然后将其用作派生 DLD 多维模型的输入。总体而言，增强将显着增强成熟 DLD 研究人员当前的研究轨迹，并提供 NIH 新资助的基础，用于开发 DLD 的多维神经生物学模型。