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

项目摘要/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。 在社交和沟通功能方面的核心缺陷。试图描绘出潜在的神经机制 对于ASD，神经成像研究主要集中在社交网络--情感知觉和社交 认知过程，如心理理论(Tom)。社会情绪调节(ER)的作用，是一种 在自闭症患者中，有效和连贯的社会情绪功能的组成部分是 研究不足，尽管最近的证据表明，ER异常是ASD核心和非ASD的潜在机制 核心(如焦虑)症状。尽管了解社交情绪背后的神经网络 感知、认知和调节都有所进步，他们的时间和结构架构还不完全 以典型发育的大脑和自闭症为特征。这可以部分归因于当前的方法 (1)忽略ASD相关症状的维度，(2)测量功能连通性(FC；a 表征神经网络的度量)作为静态过程，忽略内部的时间动态相互作用 以及网络之间的通信。目前的方案主要集中在神经网络子服务的动态FC 快乐和悲伤模拟社会互动的社会情绪加工和调节 在生态有效任务期间的功能磁共振成像(FMRI)。200名年龄在18岁至40岁之间的成年人，其中50人年龄较高- 功能性自闭症和150名混合的非自闭症对照将接受社会生活质量的全面评估 加工、ER以及内在化症状(如抑郁和焦虑)。在两次核磁共振检查中，他们 我会观看演员在谈论快乐、悲伤或中性情况时的视频短片，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，但在 第二期课程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 将使用包括独立成分分析(ICA)和动态分析在内的多元验证方法 功能网络连接(DFNC)，以评估社交网络内部和之间的时间动态FC- 情绪网络，和域转换分析(DTA)来评估外显ER对这些的影响 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(与绝对维度对比) 分析方法，检验它们对动态FC的预测值。如果成功，这项研究将揭示 自闭症和健康人的社会情绪处理和调节的神经生物学基础 个人，对开发这种和其他精神疾病的新治疗策略的影响 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.