Functional connectomics associated with ASD

与 ASD 相关的功能连接组学

• 批准号: 10240560
• 负责人: R Todd Constable
• 金额: $ 37.66万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-07 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10240560
• 关键词: 12 year old; Affect; Age; Attention; Behavior; Behavioral; Birth; Brain; Categories; Characteristics; Child; Clinical; Data; Development; Dimensions; Female; Functional Imaging; Functional Magnetic Resonance Imaging; Image; Imaging Techniques; Individual; Infant; Intervention; Link; Measures; Methodology; Modeling; Neonatal; Neurons; Outcome; Population; Procedures; Rest; Risk; Sampling; School-Age Population; Severities; Sex Differences; Siblings; Structure; Study Subject; Symptoms; System; Task Performances; Time; Variant; Work; aged; autism spectrum disorder; autistic; autistic children; base; behavior measurement; behavior test; cellular imaging; cohort; connectome; connectome based predictive modeling; executive function; fetal; high risk; imaging approach; imaging study; improved; infancy; innovation; insight; interest; male; molecular imaging; neurodevelopment; neuronal growth; novel; predictive modeling; relating to nervous system; selective attention; sex; social; social attention; sustained attention; visual tracking

Abstract This project will image 150 children (~ aged 12) with and without autism spectrum disorder to reveal the changes in functional brain organization that relate to autism severity and associated behavioral measures. The children will represent 3 distinct groups of 50 kids each (high risk children with autism, high risk siblings without autism and low risk healthy controls) and we will contrast these groups to understand the major functional network changes associated with autism. We will also develop connectome based predictive models to relate individual functional connectivity profiles to autism scores (using ADOS-2 social affect as the primary measure and RRB as a secondary measure) and examine the extent to which the models localize to specific networks including the executive control, the salience, and the default mode networks. The connectivity input data will be of unprecedented quality and extent (at least 20minutes of resting-state data providing highly reliable single subject data). In addition to the resting-state, we will include data collected during continuous performance tasks (an attention task: gradCPT and a selective social attention task previously characterized with eye-tracking data). Sex differences will be of specific interest in understanding network changes with autism severity. We will also identify the altered networks associated with ASD and provide these networks for exploratory analysis in project 1 (infants) and project 4 (fetal brains) to examine the extent to which these networks are altered early in development. The neural characterization performed in project 3 will be on a subset of the subjects studied in this project and thus we will have direct neural characteristics to relate to the connectivity changes we will quantify. This will be one of the first times neuronal structural features have been related to macroscopic connectivity data.

抽象的 该项目将对有和没有自闭症谱系障碍的150名儿童（12岁）成像，以揭示 与自闭症严重程度和相关行为指标相关的功能性大脑组织的变化。 孩子们将代表3个不同的50个孩子（高风险儿童自闭症儿童，高风险兄弟姐妹） 没有自闭症和低风险健康控制），我们将对比这些群体以了解主要 功能网络变化与自闭症相关。我们还将开发基于连接的预测 将单个功能连接概况与自闭症分数联系起来的模型（使用ADOS-2社会影响 主要度量和RRB作为次要措施）并检查模型的程度 本地化到特定网络，包括执行控制，显着性和默认模式网络。 连接输入数据将具有前所未有的质量和范围（至少有20分钟的休息状态 数据提供高度可靠的单一主题数据）。除了静止状态，我们还将包括数据 在连续的绩效任务中收集（注意任务：garchcpt和选择性社会关注 以前用眼睛跟踪数据表征的任务）。性别差异将引起特别的兴趣 了解网络因自闭症的严重性而变化。我们还将确定关联的更改网络 使用ASD并提供这些网络，用于项目1（婴儿）和项目4（胎儿） 大脑）检查这些网络在开发早期发生变化的程度。神经 项目3中执行的表征将在该项目中研究的主题的一个子集上，因此我们 将具有直接的神经特征，以与我们将量化的连通性变化相关。这将是一个 第一次神经元结构特征与宏观连通性数据有关。