Electrophysiological correlates of language processing in minimally verbal children with ASD: Elucidating pathways to language impairment

自闭症谱系障碍儿童语言处理的电生理相关性：阐明语言障碍的途径

• 批准号: 10614364
• 负责人: Charlotte Alcestis DiStefano
• 金额: $ 16.95万
• 依托单位: CHILDREN'S HOSPITAL OF LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10614364
• 关键词: Age; Attenuated; Auditory; Award; Behavior assessment; Biological; Biological Assay; Biological Markers; Biostatistical Methods; Child; Clinical; Cluster Analysis; Cognitive; Communication; Data; Data Set; Development; Electroencephalography; Electrophysiology (science); Evolution; Foundations; Growth; Heterogeneity; Impairment; Intervention; Language; Language Development; Language Disorders; Longitudinal trends; Measures; Methodology; Methods; Modeling; Neurobiology; Participant; Pathway interactions; Pattern; Population; Psychologist; Research; Signal Transduction; Source; Speech Sound; Testing; Underserved Population; United States National Institutes of Health; Work; autism spectrum disorder; autistic children; behavior measurement; career; density; experience; experimental study; improved outcome; inter-individual variation; language impairment; language processing; minimally verbal; neural; neural correlate; neuroimaging; neuromechanism; response; semantic processing; skills; social; speech processing; statistical learning; trait; translational scientist; verbal

Project Abstract ASD is estimated to occur in 1.6% of children (CDC, 2014), and approximately 30% of the ASD population remains minimally verbal (MV). This MV portion of the population experiences considerable clinical impairment and requires intensive intervention that often fails to achieve improved outcomes. Relatively little research focuses on understanding why these children fail to gain language. As a psychologist and translational researcher, my work focuses on identifying the mechanisms underlying language impairment in ASD, in order to better predict developmental trajectories and individualize intervention. The proposed study will use EEG to (1) measure multiple facets of language processing in order to assess causes of language impairment in MV children with ASD; (2) analyze spectral components of the EEG signal in order to reveal possible biological mechanisms influencing language development in ASD; and (3) use advanced biostatistics methods specifically developed for analyzing rich, multifaceted datasets such as what is yielded by EEG recording. This level of rigorous inference will be instrumental in discovering neurobiological traits that underlie heterogeneity in ASD. Very few electrophysiology studies have included MV participants, likely due to the difficulties inherent in working with that population. Findings from this study will form the foundation for my application for an NIH R01 award, focused on discovering how these EEG biomarkers of language impairment in children with ASD are related to developmental trajectories and progress in intervention. My long-term academic career will target this underserved population, improving outcomes by understanding neural mechanisms underlying language impairment and contributing to the development of mechanism-based, individualized intervention strategies.

项目摘要