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 社交互动定义是自闭症谱系障碍（ASD）的症结症，并有助于重要 功能障碍，包括ASD患者的关系质量较差和较低的就业率。 尽管投资了大量的研究资金，并发表了数千份研究论文 主题，我们远没有理解ASD中社会过程的基本神经计算。 当前的建议，我们认为此信息差距部分是由于计算模型的罕见性 - 基于的分析用于ASD神经影像研究。此外，大多数研究都使用被动范式（例如 面对感知），而不是在参与者参与与生态相关的同时检查大脑功能，而是 互动社会任务更类似于与ASD的人每天挣扎的互动类型 生命。该建议采用创新的计算精神病学方法来理解异常神经元 使用高分辨率（7T）功能磁共振成像的ASD社交互动计算 （fMRI）和类似于现实的任务，这些任务测试个人积极主动地参与的能力 模拟社交互动。特别是，我们专注于ASD患者的能力：1）歧视和 当您以选择自己的冒险风格导航社交互动时，接近和力量的跟踪水平 交互式范式，以及2）理解和适应社会规范，并在社会中施加控制 主动社会交流范式的背景。我们使用新颖的计算模型来检查中性 计算和连通性是主动社会行为的基础，重点是大脑区域（例如， 海马）在ASD的社会缺陷背景下已被理解。最后，我们使用机器 学习方法探索ASD异质性，沿动态和主动社会的维度 相互作用并应用这些指数来做出临床上的预测。我们假设：1） 与神经型对照相比 与社会症状相关； 2）ASD个人将显示较慢的规范适应率，对 违反规范的行为和降低社会可控性，并伴随着社会价值的神经编码减少 前岛和腹侧纹状体； 3）这些参数将有助于识别ASD的亚型并预测ASD- 相关结果（例如社交技能，适应性社交功能，生活质量）。我们希望从中发现 项目将打破新的基础，并填补有关ASD神经生物学的关键知识空白。尤其， 我们希望我们的发现将大大增强对神经和计算机制的理解 潜在的基本定义了ASD积极主动的社会行为，将使我们能够识别出不同的神经生物学 - 驱动的集群。这样，该项目的结果可能会提供新工具来亚型亚型 表型并提供对治疗靶标的新颖见解。