FMRI and EEG approaches to the resting state in ASD

描述（由申请人提供）：在患病率不断增加的背景下，自闭症谱系障碍（ASD）的大脑基础仍然不完全清楚。越来越多的人认为ASD是一种大脑连接障碍，但功能性连接的发现并不一致。静息状态功能性磁共振成像（rs-fMRI）数据，被认为是研究内在功能连接的理想方法，正在被许多团体获得，现在可以在一个大型的公共数据库中获得。然而，已知的行为和认知特征的差异可能会显着影响功能磁共振成像数据收集在'休息状态'和鲜为人知的潜力 可能导致的混乱。鉴于对人权的定义不清，这种缺乏知识的情况令人不安。 “静止状态”，这在很大程度上逃避了实验控制。具体而言，rs-fMRI连接已单独使用静态方法检查。目前的建议将使用动态fMRI滑动窗口分析结合EEG来研究静息状态下功能连接的动态变化。 将使用rs-fMRI和同步EEG对ASD青少年和来自现有队列的匹配的典型发育（TD）参与者的样本进行扫描。在两个目标中，我们将（1）使用滑动窗口rs-fMRI分析来测试连接性随时间的动态变化;（2）使用EEG数据来检查高时间频带的动态变化，并描述目标1中观察到的电生理变化的时间模式。我们假设，ASD参与者将显示更大的变异性，随着时间的推移，在几个感兴趣的网络（默认模式，背侧注意，显着性）之间的连接，伴随着电生理状态的变化。 该项目将是第一个将功能磁共振成像和脑电图联合收割机用于深入研究ASD“静息状态”期间动态变化的项目。更好地了解ASD和TD参与者之间的潜在系统性差异，以应对静息状态是一个先决条件，充分解释组的差异检测到的rs-fMRI功能连接研究，因此迫切需要。

DESCRIPTION (provided by applicant): In the context of increasing prevalence the brain bases of autism spectrum disorder (ASD) remain incompletely understood. There is growing consensus that ASD is a disorder of brain connectivity, but functional connectivity findings are inconsistent. Resting state functional MRI (rs-fMRI) data, considered ideal for the study of intrinsic functional connectivity, are being acquired by numerous groups and are now available in a large public database. However, known differences in behavioral and cognitive traits may significantly affect fMRI data collected in the 'resting state' and little is known about potential confounds that may result. This lack of knowledge is troubling in view of the ill-defined nature of the 'resting state', which largely eludes experimental control. Specifically, rs-fMRI connectivity has been solely examined using static approaches. The current proposal will use dynamic fMRI sliding window analyses in combination with EEG to investigate the dynamic variability of functional connectivity during the resting state. Samples of adolescents with ASD and matched typically developing (TD) participants from existing cohorts will be scanned using rs-fMRI with concurrent EEG. In two aims we will (1) test dynamic changes in connectivity across time, using a sliding window rs-fMRI analysis; and (2) use EEG data to examine dynamic changes in high temporal frequency bands and characterize temporal patterns observed in Aim 1 with respect to electrophysiological changes. We hypothesize that ASD participants will show greater variability of connectivity across time in and between several networks of interest (default mode, dorsal attention, saliency), accompanied by variability in electrophysiological states. The proposed project will be the first to combine fMRI and EEG for an in-depth investigation of dynamic changes during the 'resting state' in ASD. A better understanding of potentially systematic differences between ASD and TD participants in response to the resting state is a prerequisite for the adequate interpretation of group differences detected in rs-fMRI functional connectivity studies and is therefore urgently needed.