Modeling Social and Non-Social Learning in Autism

项目总结/摘要 遗传学、分子生物学和认知神经科学的进展为个性化治疗提供了希望。 治疗和改善自闭症谱系障碍（ASD）患者的结果。然而，Promise 由于缺乏解释表型和病因学的机制模型， 异质性;而不是使用这样的模型来识别可能对特定治疗有反应的亚组， 该领域依赖于服务的可用性、试错法和临床判断来做出治疗决策。这是一 有效治疗ASD的主要障碍。该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，以建立一个神经认知模型。 模型，可以成功地预测个别青少年的社会和非社会学习概况的关键 发展时间点。具体来说，这项工作比较了各种强化学习的适用性 模型来捕捉ASD青少年社会学习的选择性缺陷，以及社会学习和社交能力的变化。 和非社会性学习在典型发展（TD）青少年和ASD。确定这些 基于模型的预测在脑电路中实现，可以允许表征神经元 在治疗相关的背景下学习的基础架构。这个项目的重点是未被研究的 鉴于最近的研究表明小脑后叶在感兴趣的学习过程中的作用，该区域 参与更新社会信息。拟议的工作将确定和表征神经认知 ASD内学习基质的可变性，长期目标是应用这些模型提供信息， 完善和个性化的诊断，预后，教育和青少年ASD的治疗。

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.