Neural Architecture of Social Emotional Processing and Regulation in Autism Spectrum Disorder: A Dynamic Connectivity Perspective

项目总结/摘要 自闭症谱系障碍（ASD）是一种严重的异质性神经发育障碍， 社交和沟通功能的核心缺陷。在试图描绘潜在的神经机制 对于ASD，神经影像学研究集中在有助于社会情感感知和社会认知的网络上。 心理理论（Theory of Mind，ToM）。社会情绪调节（ER）的作用， 在ASD中，有效和连贯的社会情感功能与治疗意义的组成部分是 研究不足，尽管最近的证据表明异常ER是ASD核心和非ASD的潜在机制， 核心（例如焦虑）症状。虽然理解了社交情绪背后的神经网络 知觉、认知和调节已经进步，它们的时间和结构架构还不完全 在典型的大脑发育和ASD中表现为特征。这可以部分归因于当前的方法 （1）忽略ASD相关症状的维度，以及（2）测量功能连接（FC; a 测量表征神经网络）作为静态过程，忽略内部的时间动态相互作用 在网络之间。目前的建议集中在动态FC的神经网络subserving 社会情绪处理和调节快乐和悲伤的模拟社会互动， 功能性磁共振成像（fMRI）在生态有效的任务。200名18至40岁的成年人，50名高- 功能ASD和150混合，非ASD控制，将进行全面的社会评估 加工，ER以及内化症状（例如抑郁和焦虑）。在两次MRI检查中， 我将观看演员谈论快乐，悲伤或中性情况的短视频剪辑，同时直接看着 摄像头，模拟现实生活中的社交互动。虽然第一次会议将需要被动观看， 第二次会议的参与者将被要求应用明确的ER重新评估策略，他们将接受培训。我们 将使用多元验证的方法，包括独立成分分析（伊卡）和动态 功能网络连接（dFNC），以评估社会内部和之间的时间动态FC， 情感网络，和域转换分析（DTA），以评估外显ER对这些 dFNC模式（即FC状态）。由于社会过程和功能，包括ER，自闭症特征和其他 相关症状（例如焦虑）是维度结构，我们将采用维度（与分类） 分析方法，以测试其预测值的动态FC。如果成功，这项研究将揭示 社会情绪处理和调节的神经生物学基础在ASD和健康 个人，与发展的新的治疗策略，在这个和其他精神病的影响 人口。

PROJECT SUMMARY/ABSTRACT Autism spectrum disorder (ASD) is a severe and heterogeneous neurodevelopmental disorder characterized by core deficits in social and communication functions. In trying to delineate the underlying neural mechanisms of ASD, neuroimaging studies have focused on networks subserving social-emotional perception and social cognitive processes such as theory of mind (ToM). The role of social-emotional regulation (ER), a central component of effective and coherent social-emotional functioning with treatment implications, in ASD is understudied, albeit recent evidence points to abnormal ER as a potential mechanism for ASD core and non- core (e.g. anxiety) symptoms. Although understanding the neural networks underlying social-emotional perception, cognition and regulation has advanced, their temporal and structural architecture is not fully characterized in the typically developed brain and in ASD. This can be partially attributed to current methods that (1) ignore the dimensionality of ASD related symptoms, and (2) measure functional connectivity (FC; a measure characterizing neural networks) as a static process, ignoring the temporal dynamic interactions within and between networks. The current proposal focuses on the dynamic FC of the neural networks subserving social-emotion processing and regulation of happy and sad simulated social interactions as measured by functional MRI (fMRI) during an ecologically valid task. Two-hundred adults ages 18 to 40, 50 with high- functioning ASD and 150 mixed, non-ASD controls, will undergo a comprehensive assessment of social processing, ER as well as internalizing symptoms (e.g. depression and anxiety). During two MRI sessions they will view short video clips of actors talking about happy, sad or neutral situations while looking directly at the camera, simulating real-life social interactions. While the first session will entail passive viewing, during the second session participants will be asked to apply explicit ER reappraisal strategies they will be trained on. We will use multivariate validated approach that includes independent component analysis (ICA) and dynamic functional network connectivity (dFNC) to assess the temporal dynamic FC within and between social- emotional networks, and domain transition analysis (DTA) to assess the influence of explicit ER on these dFNC patterns (i.e. FC states). Since social processes and functioning, including ER, autistic traits and other related symptoms (e.g. anxiety) are dimensional constructs, we will take a dimensional (vs. categorical) analysis approach to test their predictive values on dynamic FC. If successful, this study will shed light on the neurobiological underpinnings of social-emotional processing and regulation across ASD and healthy individuals, with implications to development of new treatment strategies in this and other psychiatric populations.