Related Experiment Video
Updated: Jun 4, 2026
Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)
Published on: November 27, 2019
Increasing statistical power in functional MRI through permutation and multivariate statistics
1Division of Medical Informatics, Department of Biomedical Engineering, Division of Statistics and Machine Learning, Department of Computer and Information Science, Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
Abstract:
Slotnick (2026) provides a large number of simulations to demonstrate that statistical power in fMRI can be improved by including the sample size N when calculating an appropriate cluster extent threshold for thresholding statistical maps. I argue that the problems acknowledged by Slotnick can instead be solved using threshold free cluster enhancement (TFCE) and a permutation test, which together apply a large number of cluster forming thresholds and implicitly model the sample size as well as the spatial autocorrelation. Furthermore, I briefly mention some other approaches for increasing statistical power in fMRI.
