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<jats:sec><jats:title>Highlights</jats:title><jats:p><jats:list list-type="bullet"><jats:list-item><jats:p>The manuscript presents a method to calculate sample sizes for fMRI experiments</jats:p></jats:list-item><jats:list-item><jats:p>The power analysis is based on the estimation of the mixture distribution of null and active peaks</jats:p></jats:list-item><jats:list-item><jats:p>The methodology is validated with simulated and real data.</jats:p></jats:list-item></jats:list></jats:p></jats:sec><jats:sec><jats:label>1</jats:label><jats:title>Abstract</jats:title><jats:p>Mounting evidence over the last few years suggest that published neuroscience research suffer from low power, and especially for published fMRI experiments. Not only does low power decrease the chance of detecting a true effect, it also reduces the chance that a statistically significant result indicates a true effect (Ioannidis, 2005). Put another way, findings with the least power will be the least reproducible, and thus a (prospective) power analysis is a critical component of any paper. In this work we present a simple way to characterize the spatial signal in a fMRI study with just two parameters, and a direct way to estimate these two parameters based on an existing study. Specifically, using just (1) the proportion of the brain activated and (2) the average effect size in activated brain regions, we can produce closed form power calculations for given sample size, brain volume and smoothness. This procedure allows one to minimize the cost of an fMRI experiment, while preserving a predefined statistical power. The method is evaluated and illustrated using simulations and real neuroimaging data from the Human Connectome Project. The procedures presented in this paper are made publicly available in an online web-based toolbox available at <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://www.neuropowertools.org">www.neuropowertools.org</jats:ext-link>.</jats:p></jats:sec>

Original publication

DOI

10.1101/049429

Type

Journal article

Publisher

Cold Spring Harbor Laboratory

Publication Date

21/04/2016