Controversy in statistical analysis of functional magnetic resonance imaging data
To test the validity of statistical methods for fMRI data analysis, Eklund et al. (1) used, for the first time, large-scale experimental data rather than simulated data. Using resting-state fMRI measurements to represent a null hypothesis of no task-induced activation, the authors compare familywise...
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| Format: | Article |
| Published: |
National Academy of Sciences (U.S.)
2017
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| Online Access: | http://hdl.handle.net/1721.1/112127 https://orcid.org/0000-0003-2668-7819 |
| Summary: | To test the validity of statistical methods for fMRI data analysis, Eklund et al. (1) used, for the first time, large-scale experimental data rather than simulated data. Using resting-state fMRI measurements to represent a null hypothesis of no task-induced activation, the authors compare familywise error rates for voxel-based and cluster-based inferences for both parametric and nonparametric methods. Eklund et al.’s study used three fMRI statistical analysis packages. They found that, for a target familywise error rate of 5%, the parametric methods gave invalid cluster-based inferences and conservative voxel-based inferences. |
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