Neurocomputational mechanisms of proactive social behavior deficits in autism spectrum disorder

项目摘要 社会交往缺陷是自闭症谱系障碍(ASD)的症结所在，并对 功能障碍，包括自闭症患者的关系质量较差和就业率较低。 尽管投入了大量的研究资金，数千篇研究论文发表在 在这个话题上，我们仍然远远不能理解自闭症患者潜在的社会过程的基本神经计算。在……里面 目前的建议，我们假设，这种信息差距的部分原因是稀有的计算模型- 在ASD神经影像研究中使用了基于分析的方法。此外，大多数研究都使用被动范式(例如： 面孔感知)，而不是在参与者从事与生态相关的活动时检查大脑功能， 互动式社交任务更类似于自闭症患者在日常生活中挣扎的互动类型 活着。这项提议采用了一种创新的计算精神病学方法来理解异常神经。 使用高分辨率(7T)功能磁共振成像计算自闭症患者的社会互动 (FMRI)和类似虚拟现实的任务，测试个人主动和动态参与的能力 模拟的社交互动。特别是，我们关注自闭症患者的能力：1)辨别和 以自己选择的冒险方式导航社交时，跟踪亲密度和力量的级别 互动范式，以及2)理解和适应社会规范，并对社会他人施加控制 积极主动的社会交流范例的背景。我们使用新的计算模型来检查神经 以主动性社交行为为基础的计算和连通性，关注大脑区域(例如， 在自闭症社会缺陷的背景下一直未得到充分的研究。最后，我们使用机器 沿着动态和主动的社会维度探索ASD异质性的学习方法 相互作用，并应用这些指数作出有临床意义的预测。我们假设：1) 与神经典型对照相比，ASD患者海马体对社交空间的跟踪能力较差 与社会症状相关；2)自闭症患者会表现出较慢的常模适应速度，对 违反规范，降低社会可控性，同时减少对社会价值观的神经编码 3)这些参数将有助于识别ASD的亚型并预测ASD。 相关结果(例如，社交技能、适应社会功能、生活质量)。我们期待从这一发现中 该项目将开辟新的天地，填补有关ASD神经生物学的关键知识空白。特别是， 我们希望我们的发现将极大地提高对神经和计算机制的理解 ASD患者主动性社会行为的潜在缺陷，将使我们能够从神经生物学角度识别不同的- 驱动型集群。这样，这个项目的结果可以提供新的工具，用来对ASD进行亚型划分 并为治疗靶点提供了新的见解。

Project Summary Social interaction deficits are at the crux of autism spectrum disorder (ASD) and contribute to significant functional impairment, including poorer relationship quality and low employment rates in individuals with ASD. Despite an enormous amount of research dollars invested and thousands of research papers published on the topic, we remain far from understanding the basic neural computations underlying social processes in ASD. In the current proposal, we posit that this information gap is due in part to the rarity with which computational model- based analyses are used in ASD neuroimaging research. Additionally, most studies use passive paradigms (e.g. face perception) rather than examining brain functioning while participants engage in ecologically-relevant, interactive social tasks more akin to the type of interactions with which people with ASD struggle in their daily lives. This proposal takes an innovative computational psychiatry approach to understanding aberrant neural computations of social interactions in ASD, using high-resolution (7T) functional magnetic resonance imaging (fMRI) and virtual reality-like tasks that test individuals’ abilities to proactively and dynamically engage in simulated social interactions. In particular, we focus on the ability of individuals with ASD to: 1) discriminate and track levels of closeness and power when navigating social interactions in a choose-your-own-adventure style interactive paradigm, and 2) understand and adapt to social norms and exert control over social others in the context of a proactive social exchange paradigm. We use novel computational models to examine the neural computations and connectivity underlying proactive social behavior, focusing on brain regions (e.g., hippocampus) that have been understudied in the context of social deficits in ASD. Finally, we use machine learning approaches to explore ASD heterogeneity along dimensions of dynamic and proactive social interactions and apply these indices to make clinically-meaningful predictions. We hypothesize that: 1) hippocampal tracking of social space will be less robust in ASD as compared to neurotypical controls and will correlate with social symptoms; 2) ASD individuals will show slower norm adaptation rate, greater aversion to norm violation, and reduced social controllability, accompanied by reduced neural encoding of social values in anterior insula and ventral striatum; and 3) these parameters will help identify subtypes of ASD and predict ASD- relevant outcomes (e.g. social skills, adaptive social functioning, quality of life). We expect that findings from this project will break new ground and fill critical knowledge gaps regarding the neurobiology of ASD. In particular, we expect our findings will greatly enhance understanding of the neural and computational mechanisms underlying deficits in proactive social behavior in ASD and will allow us to identify distinct, neurobiologically- driven clusters. In so doing, the results of this project could offer new tools by which to subtype the ASD phenotype and provide novel insights into treatment targets.