Modeling Social and Non-Social Learning in Autism

自闭症的社交和非社交学习建模

• 批准号: 9886754
• 负责人: Allison Elizabeth Jack
• 金额: $ 34.8万
• 依托单位: GEORGE WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9886754
• 关键词: Academic achievement; Academic skills; Address; Adolescent; Adolescent Development; Age; Architecture; Behavior; Belief; Biological Markers; Brain; Brain region; Categories; Cerebellum; Characteristics; Clinical; Computer Models; Data; Development; Diagnosis; Disease model; Education; Etiology; Feedback; Functional Magnetic Resonance Imaging; Genetic; Goals; Grain; Health Services Accessibility; Heterogeneity; Home environment; Individual; Judgment; Learning; Lobe; Measures; Medial; Modeling; Molecular Biology; Neurocognitive; Outcome; Participant; Pattern; Performance; Phenotype; Prefrontal Cortex; Process; Protocols documentation; Psychological reinforcement; Puberty; Research; Role; Schools; Services; Signal Transduction; Social Adjustment; Social Behavior; Specific qualifier value; Subgroup; Testing; Therapeutic; Time; To specify; Treatment outcome; Update; Work; Youth; age group; autism spectrum disorder; base; cognitive neuroscience; cognitive process; common symptom; effective therapy; improved outcome; interest; learned behavior; learning outcome; learning strategy; mathematical model; neuromechanism; neurotransmission; outcome forecast; personalized medicine; precision medicine; predictive modeling; preference; relating to nervous system; social; social deficits; social learning; social skills; theories; treatment planning

PROJECT SUMMARY/ABSTRACT Advances in genetics, molecular biology, and cognitive neuroscience offer promise towards personalized treatment and improved outcomes in individuals with Autism Spectrum Disorder (ASD). However, the promise of precision medicine has been hindered by a lack of mechanistic models that explain phenotypic and etiological heterogeneity; instead of using such models to identify subgroups likely to respond to specific treatments, the field relies on availability of service, trial-and-error, and clinical judgment to make treatment decisions. This is a major barrier to effective treatment of ASD. This project addresses this problem by integrating mathematical models of behavior and brain activity across adolescent development, in order to establish a neurocognitive model that can successfully predict individual adolescents' social and nonsocial learning profiles at key developmental time-points. Specifically, this work compares the suitability of various reinforcement learning models to capture selective deficits in social learning of adolescents with ASD, as well as variability in both social and nonsocial learning across typically developing (TD) adolescents and those with ASD. Identifying how these model-based predictions are implemented in brain circuits may allow for characterization of the neural architecture underlying learning in therapeutically relevant contexts. This project focuses on the understudied role of the cerebellar posterior lobe in learning processes of interest, given recent research indicating this region's involvement in updating social information. The proposed work will identify and characterize neurocognitive variability in the substrates of learning within ASD, with the long-term goal of applying these models to inform, refine, and individualize diagnosis, prognosis, education, and treatment of youth with ASD.

项目摘要/摘要 遗传学、分子生物学和认知神经科学的进步为个性化提供了希望 自闭症谱系障碍(ASD)患者的治疗和改善结果。然而，这一承诺 由于缺乏解释表型和病因的机械模型，精确医学的发展受到了阻碍 异质性；不是使用这样的模型来确定可能对特定治疗有反应的亚组，而是 菲尔德依靠服务的可用性、反复试验和临床判断来做出治疗决定。这是一个 ASD有效治疗的主要障碍。这个项目通过整合数学模型来解决这个问题 青少年发育过程中的行为和大脑活动模型，以建立神经认知 关键时刻能够成功预测青少年个体社会性和非社会性学习特征的模型 发育时间点。具体地说，本工作比较了各种强化学习的适用性。 捕捉患有自闭症青少年社交学习的选择性缺陷的模型，以及两种社交方式的变异性 以及典型发育期(TD)青少年和ASD患者的非社会性学习。确定这些因素如何 基于模型的预测是在大脑电路中实现的，可以允许对神经进行表征 在治疗相关的背景下进行学习的基础架构。这个项目关注的是未被研究的人 小脑后叶在感兴趣的学习过程中的作用，鉴于最近的研究表明，该区域 参与更新社会信息。这项拟议的工作将识别和表征神经认知 ASD内部学习基础的可变性，长期目标是应用这些模型来告知， 完善和个体化青少年自闭症的诊断、预后、教育和治疗。