Data Science & Management Core

摘要 数据科学与管理核心（Timothy Verstynen，CL） 本计划项目旨在阐明行为和社会环境影响的神经生物学， 中年人患心血管疾病（CVD）的风险。满足该计划的特殊分析需求 项目需要将传统的统计方法与尖端的机器学习和数据相结合 用于大型、高维数据集的科学工具（即，多变量神经影像学、生物学和行为学 在多个时间点收集的数据）。因此，核心C是针对这些新的分析挑战。 具体而言，核心C的综合方法包括结合各种方法，这些方法可以（i）确定具体的 功能或结构的大脑网络，从成千上万的测量整个大脑， 可靠地预测生物，行为和心血管健康相关的变量和（ii）产生适当的 来自这些大脑测量的度量可以被整合到使用神经， 行为和生理因素来预测CVD风险的介质和标志物随时间的变化。到 为此，核心C将雇用：（i）机器学习、多层次建模方法和生物统计学方面的专家 用于生成脑表型指标（Aim 1）和开发CVD风险出现的前瞻性模型 (Aim（ii）有经验的数据管理员，以制定安全收集和维护 项目数据（目标4）。核心C还将与行政核心（核心A）合作，确保 通过提供定期更新的测量数据， 核心（Core B）和3个项目（Ps），以及向每个项目分发数据。 Timothy Verstynen（一位接受过博士培训的认知神经科学家，在定量研究方面拥有先进的专业知识， 建模和机器学习），并通过Aarti Singh博士（统计学家）的协调努力得到加强 核心C将（i）提供集中的研究支持，（ii）提供先进的 统计专门知识和统一数据分析; ㈢就新统计方法提供咨询; ㈣提供培训 统计方法;以及（v）存档和电子共享数据。一家国际性的外部咨询公司 公认的统计学家和定量方法学家也将提供持续的咨询， 预期的分析挑战的类型（即，纵向结构方程模型，缺失数据， 纵向数据、心理测量学和潜在变量建模的多层次建模）。在其面板上绘制 专家统计和方法顾问以及数据科学团队，核心C还将提供教育 为项目调查人员、受训人员和科学家提供创新的分析和研究方法服务。 社区（目标3）。最后，与核心A协调，核心C将实施P01数据共享计划， 使所有项目数据在公共领域可用。核心成员将定期会晤，并将合作 与P调查员就传播其协同活动产生的研究结果进行合作。

ABSTRACT Data Science & Management Core (Timothy Verstynen, CL) This Program Project aims to explicate the neurobiology of behavioral and socio-environmental influences on risk for cardiovascular disease (CVD) in midlife adults. Meeting the special analytic demands of the Program Project requires integrating traditional statistical approaches with cutting-edge machine learning and data science tools for large, high-dimensional data sets (i.e., multivariate neuroimaging, biological, and behavioral data collected at multiple time points). Accordingly, Core C is tailored to these novel analytical challenges. Specifically, the integrative approach of Core C consists of combining approaches that can (i) identify specific functional or structural brain networks from hundreds of thousands of measurements across the brain that reliably predict biological, behavioral, and cardiovascular health-related variables and (ii) generate appropriate metrics from these brain measurements that can be integrated into structural equation models that use neural, behavioral, and physiological factors to predict changes in mediators and markers of CVD risk over time. To this end, Core C will employ: (i) experts in machine learning, multi-level modeling methods, and biostatistics for generating brain phenotype metrics (Aim 1) and developing prospective models of CVD risk emergence (Aim 2); and (ii) experienced data managers to develop protocols for the secure collection and maintenance of Project data (Aim 4). Core C will also work with the Administrative Core (Core A) to ensure efficient flow of data through the Program Project by providing regular updates on the receipt of data from the Measurement Core (Core B) and the 3 Projects (Ps), as well as data distribution to each P. Under the leadership of Dr. Timothy Verstynen (a doctorally-trained cognitive neuroscientist with advanced expertise in quantitative modeling and machine learning) and as reinforced by the coordinating efforts of Dr. Aarti Singh (a statistician and authority in machine learning), Core C will (i) provide centralized research support, (ii) provide advanced statistical expertise and harmonize data analyses; (iii) consult on novel statistical methods; (iv) deliver training on statistical methods; and (v) archive and publically share data. An external consultancy of internationally recognized statisticians and quantitative methodologists will also provide ongoing consultation for the specific types of analytic challenges anticipated (i.e., longitudinal structural equation modeling, missing data, multilevel modeling of longitudinal data, psychometrics and latent variable modeling). Drawing on its panel of expert statistical and methodological consultants and the data science team, Core C will also offer educational services on innovative analytic and research methods to Program investigators, trainees, and the scientific community (Aim 3). Lastly, in coordination with Core A, Core C will implement the P01 data sharing plan to make all Project data available in the public domain. Core members will meet regularly and will collaborate with P investigators on the dissemination of research findings emanating from their synergistic activities.