Data Management and Statistics Core

项目总结 青少年节律、奖励和睡眠中心(CARRS)的中心假设是青少年 发育作用于潜在的睡眠和昼夜节律特征，以改变动态平衡睡眠驱动，昼夜节律阶段， 以及昼夜节律调整，进而影响对物质使用风险至关重要的皮质-边缘功能(例如，奖励 和认知控制)。我们进一步假设，睡眠和昼夜节律的特定操作在 青春期会对奖励反应和认知控制产生积极或消极的影响。这些 操作将为我们的模型提供实验支持，并为新的临床干预措施提供概念证明 以减少药物使用和滥用的风险。核心C：数据管理和统计将支持5 通过管理数据(例如，开发协议、表格和数据库并确保数据)在CARRS中实施项目 质量和安全)并执行统计分析(例如，初步、主要、次要和探索性 分析)。这样，Core C将保证高质量、透明和一致的数据标准 管理和跨项目的统计分析，并将最大限度地提高CARRS的严谨性和可重复性。 核心C还将开发和调整分析方法，以充分利用翻译和高 在CARRS内捕获的5个项目的维度数据。重点领域将包括：使用受监督的 利用人类和啮齿动物体内的高维数据进行预测的学习(例如随机森林)； 整合跨项目和物种的调查结果的方法(基于项目1-5的数据)，以及量化 整合rna-seq和质谱学数据的“多组学”方法。最后，我们将教育研究人员 在CARRS内部和研究社区中，关于与我们的 研究。主题将包括严谨性和重复性，睡眠和昼夜节律数据的分析，RNA的分析 测序和蛋白质组数据，以及核心C开发和应用的创新统计方法。

PROJECT SUMMARY The central hypothesis for the Center for Adolescent Rhythms, Reward, and Sleep (CARRS) is that adolescent development acts on underlying sleep and circadian traits to modify homeostatic sleep drive, circadian phase, and circadian alignment, which in turn impact cortico-limbic functions critical to substance use risk (e.g., reward and cognitive control). We further hypothesize that specific manipulations of sleep and circadian rhythms during adolescence will affect reward responsivity and cognitive control in either positive or negative directions. These manipulations will provide experimental support our model, and proof of concept for novel clinical interventions to reduce the risk of substance use and abuse. Core C: Data Management and Statistics will support the 5 projects in CARRS by managing data (e.g., developing protocols, forms, and databases and assuring data quality and security) and performing statistical analyses (e.g., preliminary, primary, secondary, and exploratory analyses). In this way, Core C will guarantee high-quality, transparent, and consistent standards for data management and statistical analyses across projects and will maximize rigor and reproducibility within CARRS. Core C will also develop and adapt analytic methods that take full advantage of the translational and high- dimensional data captured across the 5 projects within CARRS. Areas of focus will include: use of supervised learning (e.g., random forests) for prediction with high-dimensional data within humans and, separately, rodents; methods for integrating findings across projects and species (based on data from Projects 1-5), and quantitative “multi-omic” methods for integrating RNA-seq and mass spectrometry data. Finally, we will educate researchers within CARRS and in the research community on existing and cutting-edge statistical methods relevant for our research. Topics will include rigor and reproducibility, analysis of sleep and circadian data, analysis of RNA sequencing and proteomic data, and the innovative statistical methods developed and applied within Core C.