Neurocomputational mechanisms of proactive social behavior deficits in autism spectrum disorder

自闭症谱系障碍主动社会行为缺陷的神经计算机制

• 批准号: 10261593
• 负责人: Jennifer Foss-Feig
• 金额: $ 74.59万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10261593
• 关键词: Affect; Anterior; Biology; Brain; Brain imaging; Brain region; Caregivers; Child; Clinical; Communities; Complex; Computer Models; Corpus striatum structure; Data; Development; Dimensions; Employment; Environment; Face; Functional Magnetic Resonance Imaging; Goals; Heterogeneity; Hippocampal Formation; Hippocampus (Brain); Human; Image; Imaging Device; Impairment; Individual; Inferior; Insula of Reil; Intervention; Knowledge; Lead; Learning; Length; Location; Machine Learning; Maps; Measures; Modeling; Neurobiology; Neurodevelopmental Disorder; Neurosciences Research; Outcome; Paper; Parietal Lobe; Participant; Phenotype; Process; Psychiatry; Publishing; Quality of life; Reporting; Research; Resolution; Signal Transduction; Social Adjustment; Social Behavior; Social Controls; Social Environment; Social Functioning; Social Interaction; Social Processes; Social Values; Structure; Symptoms; System; Testing; Thinking; Time; United States; Ventral Striatum; Work; autism spectrum disorder; base; cingulate cortex; computational neuroscience; computer framework; cost; disorder control; effective intervention; face perception; functional disability; functional outcomes; geometric structure; improved; indexing; individuals with autism spectrum disorder; innovation; insight; neural circuit; neuroimaging; novel; rapid growth; relating to nervous system; response; social; social communication; social deficits; social learning; social neuroscience; social norm; social relationships; social skills; social space; supervised learning; tool; unsupervised learning; vector; virtual; virtual reality

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.

项目摘要 社会互动缺陷是自闭症谱系障碍（ASD）的关键， 功能障碍，包括ASD患者的关系质量较差和就业率低。 尽管投入了大量的研究资金，并发表了数千篇研究论文， 虽然我们的研究主题是自闭症，但我们仍然远远没有理解ASD中社会过程背后的基本神经计算。在 目前的建议，我们认为，这种信息差距部分是由于罕见的计算模型- 在ASD神经影像学研究中使用基于的分析。此外，大多数研究使用被动范式（例如， 面部感知），而不是在参与者参与生态相关的过程中检查大脑功能， 互动式社交任务更类似于ASD患者在日常生活中挣扎的互动类型 生活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的神经元 使用高分辨率（7 T）功能磁共振成像计算ASD中的社交互动 （功能磁共振成像）和虚拟现实类任务，测试个人的能力，主动和动态地参与 模拟社交互动。特别是，我们专注于ASD患者的能力：1）辨别， 在以自己的冒险风格进行社交互动时，跟踪亲密度和权力水平 互动范式，2）理解和适应社会规范，并在社会中对社会其他人施加控制 积极主动的社会交换模式。我们使用新的计算模型来检查神经 计算和连接的基础积极的社会行为，重点是大脑区域（例如， 海马体），这已经在ASD的社会缺陷的背景下研究不足。最后，我们使用机器 学习方法，探索ASD异质性沿着维度的动态和积极的社会 相互作用，并应用这些指标进行临床有意义的预测。我们假设：1） 与神经型对照相比，ASD中海马体对社交空间的追踪将不太稳健， 与社会症状相关; 2）ASD个体将表现出较慢的规范适应率，更大的厌恶， 规范违反，减少社会可控性，伴随着减少神经编码的社会价值观， 前纹状体和腹侧纹状体; 3）这些参数将有助于识别ASD的亚型并预测ASD- 相关成果（如社会技能、适应性社会功能、生活质量）。我们预计， 该项目将开辟新天地，填补关于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大大提高对神经和计算机制的理解， ASD患者主动社会行为的潜在缺陷，并将使我们能够识别不同的神经生物学- 驱动集群。在这样做的过程中，该项目的结果可以提供新的工具，通过这些工具可以对ASD进行分型 表型和提供新的见解治疗目标。