Specificity of Action Representations in the Lateral Occipitotemporal Cortex (2006, methodology): “For each contrast of interest, we used permutation methods to determine a cluster size threshold
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for voxels significant at p .001 (two-tailed, uncorrected) that corresponded to a corrected significance of p .05 (Nichols & Holmes, 2002).”
Modeling the Hemodynamic Response Function in fMRI: Efficiency, Bias and Mis-modeling (2008, methodology): “…these models may be advantageous when testing effects that do not have clear parametric pvalues, such as the distribution of maxima used in multiple comparisons correction (Nichols & Holmes, 2002), or for which parametric p-values are insensitive (such as mediation tests; (Shrout & Bolger, 2002)).”
On the distribution of cross-validated Mahalanobis distances (2016, background): “In sum, the normal approximation provides a simple and straightforward way of testing linear combinations of distances, freeing the user of the need to perform permutation statistics [21, 24].”
Network-based statistic: Identifying differences in brain networks (2010, methodology): “This kind of permutation testing is not new and is synonymous with conventional cluster-based thresholding of statistical parametric maps (Bullmore et al., 1999; Hayasaka & Nichols, 2004; Nichols & Holmes, 2001).”
Statistical Analysis of Longitudinal Neuroimage Data with Linear Mixed Effects Models (2012, methodology): “…approach relies on assumptions that are weaker than those required for the parametric p-values and is known to yield an accurate assessment of the probability of false positive (type 1 error, p-value, or equivalently specificity) when the number of permutations is large (Nichols and Holmes, 2002).”