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.

摘要 该项目将对150名患有和没有自闭症谱系障碍的儿童（~ 12岁）进行成像，以揭示 与自闭症严重程度和相关行为测量相关的功能性大脑组织的变化。 这些儿童将代表3个不同的组，每组50名儿童（自闭症高危儿童，自闭症高危兄弟姐妹 没有自闭症和低风险的健康对照），我们将对比这些组，以了解主要的 与自闭症相关的功能网络变化。我们还将开发基于连接体的预测 模型将个体功能连接概况与自闭症评分（使用ADOS-2社会影响作为 主要措施和RRB作为次要措施），并检查模型 定位到特定的网络，包括执行控制，突出，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20分钟的静止状态 提供高度可靠的单一受试者数据的数据）。除了休息状态，我们将包括数据 在连续执行任务（注意力任务：gradCPT和选择性社会注意力）期间收集 先前用眼睛跟踪数据表征的任务）。性别差异将特别感兴趣， 了解自闭症严重程度的网络变化。我们还将识别出与之相关的改变的网络， 并提供这些网络用于项目1（婴儿）和项目4（胎儿）的探索性分析 大脑）来检查这些网络在发育早期被改变的程度。神经 在项目3中进行的表征将是在这个项目中研究的主题的子集，因此，我们 将具有直接的神经特征，与我们将量化的连接变化相关。这将是一个 神经元结构特征首次与宏观连通性数据相关。