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The most widely used task functional magnetic resonance imaging (fMRI) analyses use parametric statistical methods that depend on a variety of assumptions. In this work, we use real resting-state data and a total of 3 million random task group analyses to compute empirical familywise error rates for the fMRI software packages SPM, FSL, and AFNI, as well as a nonparametric permutation method. For a nominal familywise error rate of 5%, the parametric statistical methods are shown to be conservative for voxelwise inference and invalid for clusterwise inference. Our results suggest that the principal cause of the invalid cluster inferences is spatial autocorrelation functions that do not follow the assumed Gaussian shape. By comparison, the nonparametric permutation test is found to produce nominal results for voxelwise as well as clusterwise inference. These findings speak to the need of validating the statistical methods being used in the field of neuroimaging.

Original publication

DOI

10.1073/pnas.1602413113

Type

Journal article

Journal

Proceedings of the National Academy of Sciences of the United States of America

Publication Date

07/2016

Volume

113

Pages

7900 - 7905

Addresses

Division of Medical Informatics, Department of Biomedical Engineering, Linköping University, S-581 85 Linköping, Sweden; Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University, S-581 83 Linköping, Sweden; Center for Medical Image Science and Visualization, Linköping University, S-581 83 Linköping, Sweden; anders.eklund@liu.se.

Keywords

Humans, False Positive Reactions, Magnetic Resonance Imaging, Statistics as Topic, Functional Neuroimaging