Prospective power estimation for peak inference with the toolbox neuropower

There is increasing concern about statistical power in neuroscience research. Critically, an underpowered study has poor predictive power. That is, findings from a low-power study are unlikely to be reproducible, and thus a power analysis is a critical component of any paper. We present a simple way to characterize the spatial signal in an fMRI study, and a direct way to estimate power based on an existing pilot study, thus allowing to minimize the cost of an fMRI experiment, while attaining a given level of power. The procedure estimates the proportion of active peaks (π1) and the average effect peak height, which is a function of effect size δ and sample size n. Using an evaluation procedure based on simulations and data from the Human Connectome Project, we found that our method predicted well, for sufficiently high power, the effect size in a given study, which enables power and sample size calculations for new studies.
 We provide a user-friendly toolbox neuropower, which shows easy-to-interpret figures and results. This work enables researchers to get the best trade-off between cost and statistical power for any fMRI study.

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