RT Journal Article
SR Electronic
T1 Power, positive predictive value, and sample size calculations for random field theory-based fMRI inference
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 613331
DO 10.1101/613331
A1 Dirk Ostwald
A1 Sebastian Schneider
A1 Rasmus Bruckner
A1 Lilla Horvath
YR 2019
UL http://biorxiv.org/content/early/2019/06/01/613331.abstract
AB Recent discussions on the reproducibility of task-related functional magnetic resonance imaging (fMRI) studies have emphasized the importance of power and sample size calculations in fMRI study planning. In general, statistical power and sample size calculations are dependent on the statistical inference framework that is used to test hypotheses. Bibliometric analyses suggest that random field theory (RFT)-based voxel- and cluster-level fMRI inference are the most commonly used approaches for the statistical evaluation of task-related fMRI data. However, general power and sample size calculations for these inference approaches remain elusive. Based on the mathematical theory of RFT-based inference, we here develop power and positive predictive value (PPV) functions for voxel- and cluster-level inference in both uncorrected single test and corrected multiple testing scenarios. Moreover, we apply the theoretical results to evaluate the sample size necessary to achieve desired power and PPV levels based on an fMRI pilot study.