Proceedings of the National Academy of Sciences· 2016Q1
Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates
- 3,673citations
- Q1SCImago
- 2016year
Short summary
Widely used fMRI analysis software (SPM, FSL, AFNI) inflates false-positive rates for cluster-based spatial inference, with parametric methods being invalid for clusterwise analysis.
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Key points
- Parametric fMRI analysis software (SPM, FSL, AFNI) produces invalid clusterwise inferences.
- False-positive rates are inflated for cluster-based spatial extent analysis in fMRI.
- Deviations from assumed Gaussian shapes in spatial autocorrelation are the primary cause.
- Nonparametric permutation tests provide valid inference for both voxelwise and clusterwise analysis.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. Proceedings of the National Academy of Sciences, 2016 · DOI ↗
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Field: Cognitive Neuroscience
Cognitive NeuroscienceNeuroscience