Brain Signal Variability as a Novel Marker of Flexible Cognition in Autism

项目摘要 目前，自闭症谱系障碍(ASD)的患病率为每59人中有1人(CDC，2018年)。 目前的研究方案旨在引入一种新的ASD特征描述方法 神经生物学是由计算神经科学启发的，它依赖于对大脑信号变异性的测量。 先前对典型个体的研究表明，大脑信号变异性增加的原因更多 年轻人和那些在灵活认知任务中表现更好的人更有优势。这使得 大脑信号变异性--一种很有前途的新的发育和灵活认知的测量方法 研究ASD的神经生物学和相关的僵硬行为 失序(即限制性和重复性行为)。目前的研究将有两个主要目标：1) 确定患有和不患有脑血管紧张素转换酶障碍的个体在发育过程中大脑信号变异性的差异 ASD的诊断，以及2)确定大脑信号变异性与受限和 使用分类方法对被诊断为ASD的个体的重复行为，以及在 使用维度方法诊断患有和不患有自闭症的个体。这项建议旨在 此外，机构间自闭症协调委员会了解神经的战略计划 自闭症患者大脑功能的潜在回路。结果将有助于创建更复杂的模型 ASD神经生物学，融合了脑功能的多种成像测量。反过来，更多 ASD神经生物学的复杂模型将有助于指导和评估旨在 旨在提高被诊断为自闭症患者的生活质量。

Project Summary The prevalance rate of autism spectrum disorder (ASD) is currently 1 out of 59 individuals (CDC, 2018). The current research proposal aims to introduce a new approach for characterization of ASD neurobiology inspired by computational neuroscience that relies on measures of brain signal variability. Previous research in typical individuals has shown that increased brain signal variability is more prevelant in younger individuals and those who perform better on tasks of flexible cognition. This makes brain signal variability a promising new measure of development and flexible cognition that can be used to investigate the neurobiology of ASD and associated inflexible behaviors that characterize the disorder (i.e., restrictive and repetitive behaviors). The current study will have two major goals: 1) identify differences in brain signal variability across development between individuals with and without a diagnosis of ASD, and 2) identify relationships between brain signal variability and restricted and repetitive behaviors in individuals with a diagnosis of ASD using a categorical approach, and in individuals with and without a diagnosis of ASD using a dimensional approach. This proposal aims to further the Interagency Autism Coordinating Committee’s strategic plan of understanding the neural circuitry underlying brain function in ASD. The results will help to create more sophisticated models of ASD neurobiology that incorporate multiple imaging measures of brain function. In turn, more sophisticated models of ASD neurobiology will help to guide and assess behavioral interventions aimed at improving the quality of life of individuals diagnosed with ASD.