Discovering Neural Biomarkers of Language and Social Development in ASD Toddlers

发现自闭症谱系障碍 (ASD) 幼儿语言和社会发展的神经生物标志物

• 批准号: 10239178
• 负责人: ERIC COURCHESNE
• 金额: $ 56.21万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10239178
• 关键词: 2 year old; Address; Age; Animal Model; Area; Behavior; Behavioral; Biological Markers; Brain; Brain Diseases; Cell model; Child; Clinical; Complex; Data; Defect; Development; Developmental Delay Disorders; Diagnosis; Diagnostic; Distal; Emotional; Emotions; Functional Imaging; Functional Magnetic Resonance Imaging; Future; General Population; Genetic; Genomics; Goals; Growth; Heterogeneity; Image; Individual; Infant; Intervention; Investigational Therapies; Knowledge; Language; Language Delays; Language Development; Language Disorders; Least-Squares Analysis; Life; Lobar; Magnetic Resonance Imaging; Maps; Measures; Modeling; Mothers; Music; Neuronal Plasticity; Noise; Nurseries; Outcome; Outcome Measure; Pattern; Prognosis; Prognostic Marker; Research; Research Domain Criteria; Resources; Rest; Sampling; Seeds; Series; Siblings; Signal Transduction; Social Development; Social outcome; Speech; Structure; Subgroup; Surface; Techniques; Testing; Thick; Toddler; Variant; Voice; analytical method; autism spectrum disorder; autistic children; base; care costs; causal model; clinical biomarkers; clinical subtypes; clinically relevant; cost; denoising; design; effective therapy; experience; language outcome; longitudinal design; molecular modeling; neural model; neural network; neurodevelopment; neuroimaging; novel; population based; precision medicine; predictive marker; prognostic; prospective; recruit; relating to nervous system; response; screening; social; social relationships; treatment services

Project Summary/Abstract Despite the annual $268 billion cost of ASD in the U.S. and the tens of millions spent annually on research, “precision” medicine does not exist in any meaningful way for ASD infants and toddlers. The heterogeneity of early neural and behavioral developmental trajectories in ASD has stymied the search for explanations, and the identification of clinically useful biomarkers of prognosis as well as the discovery of biotargets that could be used to develop maximally effective treatments. In our proposed studies, 175 ASD, typical, language delayed (LD) and global developmental delayed (GDD) toddlers will participate in a series of language- relevant (nursery rhymes vs music) and social emotion fMRI paradigms (own mother’s voice vs stranger’s) as well as resting state connectivity paradigms to begin to address this major gap in the field. Toddlers will be recruited using our novel general population based screening approach that provides unique and complementary data to those from baby sibling studies. In order to generate a rich clinical profile of each toddler, multiple language and social measures will be taken, including CELF-R, Mullen and Vineland. In order to examine change and leverage powerful longitudinal modeling approaches, toddlers will be clinically assessed and imaged at both 1-2 and 3-4 years. State-of-the-field MEMB and ME-ICA denoising approaches will be utilized that yield highly reliable high signal-to-noise functional imaging that outperforms previous fMRI approaches and enhances effect size estimates and statistical power; this greatly benefits robustness in our analyses, reliability, split sample feasibility, and exploratory prognostic biomarker modeling. Multiple analytic methods (e.g., PLS, seed-PLS, ICA, spectral DCM, PPI) will be applied to identify brain-language and brain- social emotion relationships; model neural and clinical trajectories from 1-2 to 3-4 years; reveal language- and social emotion-relevant fMRI activation and connectivity patterns at 1-2 years that are predictive of language and social outcomes; discover underlying neural-clinical subtypes; model continuous variation in still other neural measures that predict continuous language and social measures; identify fMRI-MRI relationships; define how language and social emotion neural deficits tap into shared neural network resources in early development; and examine similarities and differences in brain-behavioral relationships across multiple groups which then allows for sensitive tests of whether brain-behavioral patterns are common across diagnostic boundaries (e.g., LD, GDD and ASD poor language toddlers in an RDoC fashion) or specific to a subgroup of individuals. Our studies will identify clinically meaningful early-age neural biomarkers that predict which ASD children will go on to have good language outcomes and which poor ones, and others that predict social outcome. Compelling ASD language and social biotargets will be found that can be tested for response to specific, targeted interventions in future experimental therapeutic paradigms.

项目概要/摘要 尽管美国每年因自闭症谱系障碍 (ASD) 花费 2680 亿美元，并且每年在研究上花费数千万美元， 对于自闭症谱系障碍婴儿和幼儿来说，“精准”医学并不存在任何有意义的方式。异质性 自闭症谱系障碍早期神经和行为发展轨迹的研究阻碍了对解释的探索， 以及临床上有用的预后生物标志物的鉴定以及生物靶标的发现 可用于开发最有效的治疗方法。在我们提出的研究中，175 ASD，典型，语言 发育迟缓（LD）和整体发育迟缓（GDD）幼儿将参加一系列语言- 相关（童谣与音乐）和社会情感功能磁共振成像范式（自己母亲的声音与陌生人的声音） 以及静止状态连接范式，以开始解决该领域的这一重大差距。幼儿会 使用我们新颖的基于一般人群的筛选方法来招募，该方法提供了独特和 对婴儿兄弟姐妹研究的补充数据。为了生成每个患者丰富的临床资料 对于幼儿，将采取多种语言和社交措施，包括 CELF-R、Mullen 和 Vineland。在 为了检查变化并利用强大的纵向建模方法，幼儿将在临床上 在 1-2 年和 3-4 年进行评估和成像。最先进的 MEMB 和 ME-ICA 去噪方法 将用于产生高度可靠的高信噪比功能成像，其性能优于以前的功能磁共振成像 方法并增强效应大小估计和统计功效；这极大地有利于我们的鲁棒性 分析、可靠性、分割样本可行性和探索性预后生物标志物建模。多重解析 方法（例如 PLS、seed-PLS、ICA、频谱 DCM、PPI）将用于识别大脑语言和大脑语言 社会情感关系；建立 1-2 至 3-4 年神经和临床轨迹模型；揭示语言—— 1-2 岁时与社会情绪相关的 fMRI 激活和连接模式可预测 语言和社会成果；发现潜在的神经临床亚型；模型连续变化 还有其他预测连续语言和社交测量的神经测量；识别功能磁共振成像 关系；定义语言和社交情感神经缺陷如何利用共享神经网络 早期开发资源；并检查大脑行为关系的相似性和差异 跨越多个群体，然后可以对大脑行为模式是否常见进行敏感测试 跨越诊断界限（例如，以 RDoC 方式诊断 LD、GDD 和 ASD 语言障碍幼儿）或 特定于某个人的子群体。我们的研究将确定具有临床意义的早期神经生物标志物 预测哪些自闭症谱系障碍儿童将继续取得良好的语言成绩，哪些儿童将取得较差的语言成绩，以及其他 预测社会结果。将发现令人信服的 ASD 语言和社交生物目标并进行测试 以应对未来实验治疗范式中的具体、有针对性的干预措施。