Empirical Power Analysis Tool for fMRI - 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.
