Functional brain developments during movie watching and resting-state in autism spectrum disorder

项目总结 自闭症谱系障碍(ASD)通常在儿童时期就被诊断出来。理解发展的 典型发育儿童和自闭症儿童的脑功能曲线有助于了解大脑 房间隔缺损的相关因素。在这个项目中，我们将分析1000多名儿童的功能磁共振(FMRI)数据，并 成年人描绘大脑发育曲线。 功能磁共振成像已被广泛用于研究大脑活动中的功能。通常，参与者被要求做一些 在计算机上严格设计任务，或者干脆躺在扫描仪里，没有特别考虑任何事情。A更多 已经提出了一种执行扫描的自然方法，即要求参与者观看简短的电影剪辑 在扫描过程中。然而，对电影观看数据的数据分析变得复杂。我们已经开发出 一种新的方法来检查电影观看过程中大脑区域之间的功能交流。在这 项目中，我们将比较我们的新方法和现有方法。 该项目分为两个部分。在第一部分中，我们将研究数百个典型的大脑发育 发展中的儿童。我们将比较电影观看和休息状态的数据，并比较不同的 分析方法。结果将帮助我们找到更好的方法来绘制发育中的大脑。第二部分， 我们将比较ASD患者和典型患者之间的发展曲线。 个人。这些数据可能有助于我们建立一种更好的扫描ASD个体的方法，以及一种更好的方法来 描述ASD患者的脑功能。

Project summary Autism spectrum disorder (ASD) is usually diagnosed early in childhood. Understanding the developmental curves of brain functions in typically developing children and children with ASD will help to understand the brain correlates of ASD. In this project, we will analyze functional MRI (fMRI) data of more than 1,000 children and adults to portrait brain developmental curves. FMRI has been commonly used to study brain functions in action. Usually, participants are asked to do some strictly designed tasks on a computer, or simply lay in the scanner without thinking a thing in particular. A more natural way to perform scanning has been proposed, where the participants are asked to watch short movie clips during the scanning. However, data analysis for movie watching data becomes complicated. We have developed a novel method to examine functional communications between brain regions during movie watching. In this project, we will compare our new method with existing methods. The project is divided into two parts. In the first part, we will study brain developments in hundreds of typically developing children. We will compare data from movie watching and resting-state, and compare different analysis methods. The results will help us find better methods to chart the developing brain. In the second part, we will compare the developing curves between individuals with ASD and those in typically developing individuals. The data may help us to establish a better way to scan individuals of ASD, and a better way to characterize brain functions in ASD.