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

项目摘要 据估计，ASD发生在1.6%的儿童（CDC，2014）和约30%的ASD人群中 保持最低限度的口头（MV）。这部分MV人群经历了相当大的临床损害 需要密集的干预，但往往无法取得更好的结果。研究相对较少 重点是了解为什么这些孩子无法获得语言。作为一名心理学家和翻译家， 作为一名研究人员，我的工作重点是确定ASD中语言障碍的潜在机制， 以更好地预测发展轨迹和个性化干预。拟议的研究将使用EEG来 (1)测量语言处理的多个方面，以评估MV中语言障碍的原因 ASD儿童;（2）分析EEG信号的频谱成分，以揭示可能的生物学特征， 影响ASD语言发展的机制;（3）使用先进的生物统计学方法 专为分析丰富的多方面数据集而开发，例如EEG记录产生的数据。这 严格的推理水平将有助于发现异质性背后的神经生物学特征 在ASD极少数电生理学研究包括MV参与者，可能是由于固有的困难 与这些人一起工作。这项研究的发现将成为我申请NIH的基础 R 01奖，专注于发现这些ASD儿童语言障碍的EEG生物标志物 与发展轨迹和干预进展有关。我的长期学术生涯将 针对这一服务不足的人群，通过了解神经机制， 语言障碍，并促进基于机制的个性化干预的发展 战略布局

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.