Empirical Power Analysis Tool for fMRI

fMRI 经验功率分析工具

• 批准号: 10868802
• 负责人: Stephanie Noble
• 金额: $ 24.9万
• 依托单位: NORTHEASTERN 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 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10868802
• 关键词: Address; Algorithmic Analysis; Algorithms; BRAIN initiative; Benchmarking; Brain; Characteristics; Complex; Computer software; Data; Data Set; Databases; Dedications; 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; Training; Work; base; career; design; 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.

项目摘要 功能性磁共振成像（fMRI）研究已经改变了我们对人类大脑的理解 在前所未有的国际资金支持下， 来自大脑计划。然而，最近的工作暴露了统计力量的普遍缺乏（即，能力 在典型的功能磁共振成像研究中，检测到感兴趣的效应），导致无法复制或仅发现少量 真实效果的冰山一角。这在很大程度上是因为执行适当的功率分析， fMRI研究设计并不简单。首先，很难根据文献估计预期效果， 研究样本量已经很小，即使是更小的试点数据也可能无法得出有用的估计。 此外，fMRI数据和推理算法是复杂的，然而现有的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程序。因此，在本发明中， 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， 有机会优化设计研究，以检测预期效果。为了弥补这一差距，我们将建立一个权力 为标准fMRI研究量身定制的分析算法和工具，利用：1）大型现有数据集来定义 典型的学习效果，以及2）最近开发的用于基准测试复杂推理能力的方法 程序.最后，它的设计将提供量身定制的建议，并易于使用，从而促进 它对日常研究人员的效用。在目标1（K99）中，我们将为典型研究创建效应量图数据库 设计使用大型，公开可用的数据集，并建立一个网络应用程序来探索这些地图。我们将使用这个 Aim 2（R00）中的数据库，以设计事后功效计算器算法来估计典型研究的功效 的设计.目标3（R00）将通过创建一个元回归模型来完善该算法，该模型包含额外的 研究和参与者的因素，以提供一个更有针对性的估计功率为个人研究人员。最后在 目标4（R00）我们将创建和传播一个易于使用的基于网络的工具，用于执行“量身定制”的权力 分析，特别是只要求用户指定他们随时可用的信息。这一提议将导致 在第一个算法和工具，执行实证功率分析的功能磁共振成像研究规划，具有潜在的 用户群，包括使用典型设计计划fMRI研究的所有研究人员。这将使研究人员 更容易和准确地计划良好的动力研究，从而促进更强大和可重复的发现 在外地此外，该提案将提供生产就绪的Web开发培训， 聚合方法和独立导向的专业能力，这将有助于我的过渡 到一个独立的研究生涯领先的统计方法的发展功能磁共振成像。