期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 113, 期 28, 页码 7900-7905出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1602413113
关键词
fMRI; statistics; false positives; cluster inference; permutation test
资金
- McDonnell Center for Systems Neuroscience at Washington University
- National Science Foundation [OCI-1131441]
- Neuroeconomic Research Initiative at Linkoping University
- Swedish Research Council [2013-5229]
- Information Technology for European Advancement 3 Project BENEFIT (better effectiveness and efficiency by measuring and modelling of interventional therapy)
- Wellcome Trust
- [1U54MH091657]
- Wellcome Trust [100309/A/12/Z] Funding Source: researchfish
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.
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