NeuroMAP Phase II - Data Management and Statistics Core

项目摘要：数据管理和统计核心 研究项目负责人(RPL)的一个为期3年的项目，用来进行人体实验研究 从设计上来说，精神病学目标人群和获得R级拨款的试点数据可能是具有挑战性的， 数据管理和处理，以及统计分析的观点。数据管理与统计 (DMS)核心将确保研究设计的最高严格性，实施社区标准数据 管理和处理协议，以及尖端数据科学算法的应用 最大限度地提高样本外预测性能和评估行动机制的能力。核心意志 与RPL和试点项目调查人员合作，帮助识别和验证疾病修改 与情绪和焦虑症相关的过程(DMP)。这将大大增强 由RPL制作的研究，用于制定目标和开发基于这些目标的假设 初步数据和设计未来研究，从而使R01级应用程序更有可能成功 以及更具竞争力和资金来源。本中心提供的服务包括：(1)咨询 与专业数据科学家合作，他们将与调查人员合作开发和实例化操作环境 优化数据使用和分析；以及(2)该核心开发的程序和程序，以 适应用户的刺激性陈述、数据管理和统计需求。数据管理 组件将有助于确保可靠和高效地获取和处理数据 使用我们可扩展的数据管理基础设施。服务将从研究设置开始，包括 实现和配置行为范例、将原始数据转换为标准的管道(例如， 脑成像数据结构：BIDS)格式，定期审计并根据需要共享。这一核心将提供 标准管道，用于提取通用数据元素和质量指标，并便于访问和使用 该研究所的计算基础设施。核心的统计部分将侧重于开展研究 适用于评估DMPS的无偏效应和预测性能的设计和分析方法 (例如，威胁敏感度、厌恶内感中的回避、重复的消极思维)对心理健康的影响 结果。因为这些DMP将在几个层面上进行分析(症状、行为、生理、 电路和分子)、研究设计和分析将需要整合复杂的多方法关联 并且将需要考虑关联中的潜在偏差，例如由于选择、测量误差和/或 令人困惑。这一核心将专注于多级别模型、因果推理和机器学习预测 说明差异(例如嵌套数据)和混淆(例如混淆偏见)的来源，同时提供 最大的解释性和样本外预测性能。这个核心的产品将是工具性的 在利用NeuroMAP项目产生的数据进行后续R级研究方面。

PROJECT SUMMARY: Data Management and Statistics Core A 3-year project for a Research Project Leader (RPL) to conduct experimental human subjects research with psychiatric target populations and to obtain pilot data for an R-level grant can be challenging from a design, data management and processing, and statistical analysis perspective. The Data Management and Statistics (DMS) Core will ensure the highest rigor of study design, the implementation of community-standard data management and processing protocols, and the application of cutting-edge data science algorithms that maximize out-of-sample prediction performance and power for assessing mechanisms of action. The Core will work with RPLs and pilot project investigators to facilitate identifying and validating disease-modifying processes (DMPs) that are relevant for mood and anxiety disorders. This will greatly enhance the utility of the research produced by RPLs for use in formulating aims and developing hypotheses based on these preliminary data and for designing future studies, thereby making R01-level applications more likely to succeed as well as being more competitive and fundable. Services provided by this Core consist of: (1) consultations with expert data scientists who will work with investigators to develop and instantiate an operating environment that optimizes data use and analytics; and (2) procedures and programs developed by this Core to accommodate users' stimulus presentation, data management and statistical needs. The data management component will be instrumental in guaranteeing that data are acquired and processed reliably and efficiently using our scalable data management infrastructure. Services will begin at study setup and include implementation and configuration of behavioral paradigms, pipelines to convert raw data into standard (e.g., Brain Imaging Data Structure: BIDS) format, periodic auditing and sharing as needed. This Core will provide standard pipelines to extract common data elements and quality metrics and to facilitate access and usage of the institute's computing infrastructure. The statistics component of the Core will focus on developing study designs and analytic procedures applicable to assessing unbiased effects and predictive performance of DMPs (e.g., threat sensitivity, avoidance during aversive interoception, repetitive negative thinking) on mental health outcomes. As these DMPs will be examined on several levels of analysis (symptoms, behavior, physiology, circuits, and molecules), study designs and analyses will need to integrate complex multi-method associations and will need to account for potential biases in associations, e.g., due to selection, measurement error, and/or confounding. This Core will focus on multilevel models, causal inference and machine learning prediction that account for sources of variation (e.g., nested data) and confounding (e.g., confounding bias) while providing maximal explanatory and out-of-sample prediction performance. The products of this Core will be instrumental in developing follow-up R-level research leveraging data produced by NeuroMAP projects.