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 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. Eklund et al. (1) attribute the invalid cluster-based inferences to the incorrect assumption of squared exponential structure in the spatial autocorrelation function of the parametric models. The authors suggest nonparametric methods as a more appropriate way to achieve targeted error rates, and conclude that statistical methods for fMRI data analysis should be validated. In addition, Eklund et al. state that their findings “question the validity of some 40,000 fMRI studies and may have a large impact on the interpretation of neuroimaging results” (1). This sentence from the Significance section of the original paper was picked up by the press and yielded the alarming negative headline that fMRI analyses produce incorrect results … [↵][1]1To whom correspondence should be addressed. Email: enb{at}neurostat.mit.edu. [1]: #xref-corresp-1-1