Empirical Power Analysis Tool for fMRI

fMRI 经验功率分析工具

• 批准号: 10503064
• 负责人: Stephanie Noble
• 金额: $ 12.27万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10503064
• 关键词: Address; Algorithmic Analysis; Algorithmic Software; Algorithms; BRAIN initiative; Benchmarking; Brain; Characteristics; Complex; Computer software; Data; Data Set; Databases; Development; Dimensions; Disease; Documentation; Functional Magnetic Resonance Imaging; Funding; Human; Individual; International; Internet; Literature; Maps; Memory; Methodology; Methods; Modeling; Modernization; Online Systems; Participant; Patients; Pilot Projects; Procedures; Production; Professional Competence; Recommendation; Reproducibility of Results; Research; Research Design; Research Personnel; Sample Size; Sampling; Sampling Studies; Scanning; Specific qualifier value; Testing; To specify; Training; Work; base; career; design; imaging study; interactive tool; interest; power analysis; simulation; study characteristics; symposium; tool; web app; web-based tool

PROJECT SUMMARY Functional magnetic resonance imaging (fMRI) research has transformed our understanding of human brain function and disease and is flourishing under unprecedented international funding, including dedicated support from the BRAIN Initiative. However, recent work has exposed an endemic lack of statistical power (i.e., ability to detect effects of interest) in typical fMRI studies, leading to findings that do not replicate or uncover only a small tip of the iceberg of true effects. This arises in large part because performing proper power analyses to guide fMRI study design is not straightforward. First, it is difficult to estimate expected effects based on the literature, and study sample sizes are already so small that even smaller pilot data may not yield helpful estimates. Furthermore, fMRI data and inferential algorithms are complex, yet existing fMRI power analysis tools rely on relatively limited simulations, parametric estimates, and omit the most popular inferential procedures. As a result, fMRI researchers often perform misleading power analyses or avoid power analyses altogether, missing a critical opportunity to optimally design studies to detect desired effects. To address this gap, we will create a power analysis algorithm and tool tailored for standard fMRI studies that leverages: 1) large existing datasets to define typical study effects, and 2) recently developed methods for benchmarking power of complex inferential procedures. Finally, it will be designed to provide tailored recommendations and be easy to use, thus promoting its utility to everyday researchers. In Aim 1 (K99), we will create database of effect size maps for typical study designs using large, publicly available datasets and build a web app for exploring these maps. We will use this database in Aim 2 (R00) to design a post hoc power calculator algorithm to estimate power for typical study designs. Aim 3 (R00) will refine this algorithm by creating a meta-regression model that incorporates additional study and participant factors to provide a more tailored estimate of power for an individual researcher. Finally, in Aim 4 (R00) we will create and disseminate an easy-to use web-based tool for performing the “tailored” power analysis, notably only requiring the user to specify information readily available to them. This proposal will result in the first algorithm and tool to perform an empirical power analysis for fMRI study planning, with a potential user base that includes all researchers planning an fMRI study using typical designs. This will enable researchers to more easily and accurately plan well-powered studies, thus promoting more robust and reproducible findings in the field. Furthermore, this proposal will provide training in production-ready web development, study aggregation methods, and independence-oriented professional competencies, which will facilitate my transition to an independent research career leading statistical methodology development in fMRI.

项目概要 功能磁共振成像（fMRI）研究改变了我们对人类大脑的理解 功能和疾病，并在前所未有的国际资助下蓬勃发展，包括专门的支持 然而，最近的工作暴露了统计能力的普遍缺乏（即能力）。 在典型的功能磁共振成像研究中检测感兴趣的效应），导致结果不能复制或仅揭示一小部分 这在很大程度上是因为进行了适当的功率分析来指导。 首先，功能磁共振成像研究设计并不简单，很难根据文献估计预期效果。 而且研究样本量已经很小，即使较小的试点数据也可能无法产生有用的估计。 此外，fMRI 数据和推理算法很复杂，而现有的 fMRI 功率分析工具依赖于 相对有限的模拟、参数估计，并省略了最流行的推理过程。 fMRI 研究人员经常进行误导性的功效分析或完全避免功效分析，从而错过了关键的 优化设计研究以检测预期效果的机会 为了解决这一差距，我们将创造一种力量。 专为标准功能磁共振成像研究量身定制的分析算法和工具，利用：1）现有的大型数据集来定义 典型的研究效果，以及 2) 最近开发的复杂推理能力基准测试方法 最后，它将被设计为提供量身定制的建议并且易于使用，从而促进。 在目标 1 (K99) 中，我们将为典型研究创建效应大小图数据库。 使用大型、公开可用的数据集进行设计，并构建一个用于探索这些地图的网络应用程序。 目标 2 (R00) 中的数据库设计事后功效计算器算法来估计典型研究的功效 Aim 3 (R00) 将通过创建一个包含附加功能的元回归模型来完善该算法。 最后，为个别研究人员提供更量身定制的能力估计。 目标 4 (R00) 我们将创建并传播一个易于使用的基于网络的工具，用于执行“定制”功能 分析，特别是只需要用户指定他们容易获得的信息即可产生此建议。 第一个对功能磁共振成像研究计划进行实证功效分析的算法和工具，具有潜在的潜力 用户群，包括所有计划使用典型设计进行功能磁共振成像研究的研究人员，这将使研究人员能够使用。 更轻松、更准确地规划有力的研究，从而促进更稳健和可重复的研究结果 此外，该提案将提供生产就绪型网络开发、研究方面的培训 聚合方法和独立导向的专业能力，这将有助于我的过渡 领导功能磁共振成像统计方法开发的独立研究生涯。