NeuroMAP Phase II - Data Management and Statistics Core

NeuroMAP 第二阶段 - 数据管理和统计核心

• 批准号: 10711138
• 负责人: Wesley Kurt Thompson
• 金额: $ 18.48万
• 依托单位: LAUREATE INSTITUTE FOR BRAIN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10711138
• 关键词: Accounting; Algorithms; Anxiety Disorders; Arkansas; Behavior; Behavioral Assay; Behavioral Paradigm; Biological Markers; Brain imaging; Businesses; Categories; Cities; Common Data Element; Communities; Complex; Consultations; Data; Data Collection; Data Science; Data Scientist; Disease; Ensure; Environment; Experimental Designs; Fee-for-Service Plans; Future; Grant; Health Sciences; Human Subject Research; Individual Differences; Infrastructure; Institution; Interoception; Leadership; Measurement; Mental Health; Methods; Mood Disorders; Neurosciences; Oklahoma; Outcome; Performance; Periodicals; Phase; Physiology; Pilot Projects; Procedures; Process; Protocols documentation; Randomized; Research; Research Design; Research Personnel; Research Project Grants; Research Support; Resources; Role; Sample Size; Sampling; Scheme; Services; Source; Statistical Data Interpretation; Statistical Methods; Statistical Models; Stimulus; Symptoms; Target Populations; Thinking; Universities; Variant; Visualization; Weight; Work; base; biomarker identification; causal model; computerized data processing; data management; data quality; data standards; data structure; design; falls; follow-up; health assessment; machine learning algorithm; machine learning prediction; machine learning prediction algorithm; multilevel analysis; operation; predict clinical outcome; programs; random forest; statistics; study population

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.

项目概要：数据管理和统计核心 一个为期3年的研究项目负责人（RPL）项目，以进行实验性人类受试者研究， 精神病目标人群和获得R级补助金的试点数据可能具有挑战性， 数据管理和处理以及统计分析视角。数据管理与统计 (DMS)核心将确保最严格的研究设计，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， 最大化样本外预测性能和评估作用机制的能力。芯会 与RPL和试点项目研究人员合作，以促进识别和验证疾病修饰 与情绪和焦虑障碍相关的过程（DMPs）。这将极大地提高 RPL进行的研究，用于制定目标并根据这些目标制定假设 初步数据和设计未来的研究，从而使R 01级的应用更有可能成功 同时也更有竞争力和可资助性。该中心提供的服务包括：（1）咨询 专家数据科学家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）本核心开发的程序和计划， 满足用户的刺激呈现、数据管理和统计需求。数据管理 该组件将有助于确保可靠有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。服务将在研究设置时开始，包括 行为范例的实现和配置，将原始数据转换为标准的流水线（例如， 脑成像数据结构：BIDS）格式，定期审计和共享的需要。该核心将提供 标准管道，以提取共同的数据元素和质量指标，并促进访问和使用 研究所的计算机基础设施核心方案的统计部分将侧重于开展研究， 适用于评估DMP无偏效应和预测性能的设计和分析程序 (e.g.,威胁敏感性、厌恶性内感受期间的回避、重复性消极思维）对心理健康的影响 结果。由于这些DMP将在几个分析水平上进行检查（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 并且需要考虑关联中的潜在偏差，例如，由于选择、测量误差和/或 真让人困惑该核心将专注于多层次模型，因果推理和机器学习预测， 考虑变化的来源（例如，嵌套数据）和混杂（例如，混杂偏倚），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有益的， 利用NeuroMAP项目产生的数据开展后续R级研究。