Functional connectomics associated with ASD

与 ASD 相关的功能连接组学

基本信息

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
项目类别：
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

项目摘要

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 150 名患有或不患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。这些孩子将代表 3 个不同的组，每组 50 名孩子（患有自闭症的高危儿童、患有自闭症的高危儿童、兄弟姐妹没有自闭症和低风险健康对照），我们将对比这些群体以了解主要与自闭症相关的功能网络变化。我们还将开发基于连接组的预测将个人功能连接概况与自闭症评分相关联的模型（使用 ADOS-2 社会影响作为主要措施和 RRB 作为次要措施）并检查模型的程度定位到特定网络，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 20 分钟的静息状态数据提供高度可靠的单一受试者数据）。除了静息状态之外，我们还将包括数据在连续表现任务期间收集（注意力任务：gradCPT 和选择性社会注意力之前用眼动追踪数据表征的任务）。性别差异将特别令人感兴趣了解网络变化与自闭症严重程度的关系。我们还将识别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）中的探索性分析大脑）来检查这些网络在发育早期改变的程度。神经元项目 3 中进行的表征将针对该项目中研究的主题的子集，因此我们将具有与我们将量化的连接变化相关的直接神经特征。这将是一个神经元结构特征首次与宏观连接数据相关。