In this #BucketListPapers, software used for statistical analysis of fMRI data, specifically those that are designed to correct for familywise error (FWE), are validated against real-world, resting-state fMRI data from healthy controls. FWE-correction is important in experiments where multiple measurements are taken in parallel and combined to infer a statistical signal, as the statistical error is amplified leading to a higher overall false positive rate. Eklund et al. compare the FWE rates for three standard fMRI analysis software packages (SPM, FSL and AFNI), using a variety of commonly-employed methods and parameters, and identified up to 70 % false positive rates. Importantly, the paper concludes by recommending that the fMRI community focuses on validating existing statistical analysis methods and highlights the importance of real-world data sharing in efforts to do this; these are considerations that apply in many areas of the life-sciences.
Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates.
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