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: 10447647
• 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/10447647
• 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.

项目概要 社交互动缺陷是自闭症谱系障碍 (ASD) 的症结所在，并导致显着的 功能障碍，包括自闭症患者的人际关系质量较差和就业率较低。 尽管投入了大量的研究资金并在该网站上发表了数千篇研究论文 尽管这个话题，我们还远远没有理解自闭症谱系障碍中社会过程的基本神经计算。 在当前的提案中，我们认为这种信息差距部分是由于计算模型的稀有性造成的 此外，大多数研究都使用被动范式（例如，自闭症谱系障碍）。 面部感知），而不是在参与者参与与生态相关的活动时检查大脑功能， 互动社交任务更类似于自闭症谱系障碍患者在日常生活中遇到的互动类型 该提案采用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 ASD 中的社交互动 （功能磁共振成像）和类似虚拟现实的任务，测试个人主动和动态参与的能力 我们特别关注自闭症谱系障碍患者的模拟社交互动能力：1）歧视和歧视。 以选择自己的冒险方式进行社交互动时跟踪亲密程度和权力水平 互动范式，2）理解并适应社会规范并对社会他人施加控制 我们使用新颖的计算模型来检查神经网络。 主动社交行为背后的计算和连接，重点关注大脑区域（例如， 海马体）在自闭症谱系障碍的社会缺陷的背景下得到了充分研究最后，我们使用机器。 沿着动态和主动的社交维度探索 ASD 异质性的学习方法 我们发现：1) 与神经典型对照组相比，自闭症谱系障碍患者的海马体对社交空间的追踪能力较差，并且会 2）自闭症谱系障碍（ASD）个体会表现出较慢的规范适应速度，更厌恶 违反规范，社会可控性降低，同时社会价值观的神经编码减少 前岛叶和腹侧纹状体；3) 这些参数将有助于识别 ASD 亚型并预测 ASD- 相关结果（例如社交技能、适应性社会功能、生活质量）。 该项目将开辟新领域，填补自闭症谱系障碍神经生物学方面的关键知识空白。 我们期望我们的发现将极大地增强对神经和计算机制的理解 自闭症谱系障碍（ASD）中主动社会行为的潜在缺陷，将使我们能够识别独特的、神经生物学的 通过这样做，该项目的结果可以提供新的工具来对 ASD 进行子类型化。 表型并为治疗目标提供新的见解。