Empirical Power Analysis Tool for fMRI

项目总结 功能磁共振成像(FMRI)研究改变了我们对人脑的理解 功能和疾病，并在前所未有的国际资金包括专门的支持下蓬勃发展 来自大脑计划。然而，最近的工作暴露出普遍缺乏统计能力(即 在典型的fMRI研究中检测感兴趣的影响)，导致发现不复制或仅发现 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来指导 功能磁共振成像研究的设计并不简单。首先，根据文献很难估计预期的效果， 而且研究样本量已经很小，即使是更小的试点数据也可能无法产生有用的估计。 此外，功能磁共振成像数据和推断算法是复杂的，而现有的功能磁共振成像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。结果, FMRI研究人员经常执行误导性的功率分析，或者完全避免进行功率分析，从而遗漏了关键的 有机会对研究进行优化设计，以检测预期效果。为了解决这一差距，我们将创造一种力量 为标准fMRI研究量身定做的分析算法和工具，它利用：1)大型现有数据集来定义 典型的研究效果，以及2)最近开发的复杂推理能力的标杆方法 程序。最后，它将被设计成提供量身定制的建议，并易于使用，从而促进 它对日常研究人员来说很有用。在目标1(K99)中，我们将为典型研究创建效果尺寸图数据库 使用大型公开可用的数据集进行设计，并构建一个Web应用程序来探索这些地图。我们将使用这个 Aim 2(R00)中的数据库设计了一种后自组织功率计算器算法来估计典型研究的功率 设计。Aim 3(R00)将通过创建一个整合了其他 研究和参与者因素，为个人研究人员提供更有针对性的力量估计。最后，在 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“量身定做”的力量 分析，特别是只需要用户指定他们随时可以获得的信息。这项提议将导致 在第一个算法和工具中执行fMRI研究规划的经验功率分析，具有潜在的 用户群，包括所有计划使用典型设计进行功能磁共振研究的研究人员。这将使研究人员能够 更容易和更准确地计划有的放矢的研究，从而促进更有力和可重复的研究结果 在战场上。此外，该提案还将提供可投入生产的网络开发方面的培训 聚合方法和以独立为导向的专业能力，这将促进我的过渡 在功能磁共振成像领域领导统计方法开发的独立研究生涯。

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.