FMRI and EEG approaches to the resting state in ASD

描述(申请人提供)：在日益流行的背景下，自闭症谱系障碍(ASD)的大脑基础仍未完全了解。越来越多的人一致认为ASD是一种大脑连接障碍，但功能连接的研究结果并不一致。静息状态功能磁共振成像(rs-fMRI)数据被认为是研究内在功能连通性的理想数据，许多小组正在获取这些数据，现在可以在大型公共数据库中获得。然而，已知的行为和认知特征的差异可能会显著影响静息状态下收集的功能磁共振数据，而且对潜在的影响知之甚少 这可能会导致混乱。考虑到定义不明确的性质，这种知识的缺乏令人担忧 “休眠状态”，这在很大程度上躲避了实验控制。具体地说，仅使用静态方法检查了RS-fMRI的连通性。目前的方案将使用动态fMRI滑动窗口分析结合EEG来研究静息状态下功能连接性的动态变异性。来自现有队列的患有自闭症的青少年和匹配的典型发育中(TD)参与者的样本将使用RS-fMRI和同步EEG进行扫描。在两个目标中，我们将(1)使用滑动窗口RS-fMRI分析来测试连通性随时间的动态变化；以及(2)使用脑电数据来检查高时间频带中的动态变化，并针对电生理变化来表征在目标1中观察到的时间模式。我们假设，ASD参与者将在几个感兴趣的网络(默认模式、背部注意、突显)中和几个感兴趣网络之间表现出更大的连通性随时间的变异性，并伴随着电生理状态的变异性。这项拟议的项目将是第一个将功能磁共振成像和EEG相结合的项目，以深入研究自闭症患者在静息状态下的动态变化。更好地了解ASD和TD参与者对静息状态反应的潜在系统性差异是充分解释RS-fMRI功能连接性研究中检测到的组差异的先决条件，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.