RT Journal Article
SR Electronic
T1 Power and sample size calculations for fMRI studies based on the prevalence of active peaks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 049429
DO 10.1101/049429
A1 Joke Durnez
A1 Jasper Degryse
A1 Beatrijs Moerkerke
A1 Ruth Seurinck
A1 Vanessa Sochat
A1 Russell A. Poldrack
A1 Thomas E. Nichols
YR 2016
UL http://biorxiv.org/content/early/2016/04/20/049429.abstract
AB HighlightsThe manuscript presents a method to calculate sample sizes for fMRI experimentsThe power analysis is based on the estimation of the mixture distribution of null and active peaksThe methodology is validated with simulated and real data.Abstract 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 www.neuropowertools.org.