Analysis of family-wise error rates in statistical parametric mapping using random field theory

Guillaume Flandin, Karl J Friston, Guillaume Flandin, Karl J Friston

Abstract

This technical report revisits the analysis of family-wise error rates in statistical parametric mapping-using random field theory-reported in (Eklund et al. []: arXiv 1511.01863). Contrary to the understandable spin that these sorts of analyses attract, a review of their results suggests that they endorse the use of parametric assumptions-and random field theory-in the analysis of functional neuroimaging data. We briefly rehearse the advantages parametric analyses offer over nonparametric alternatives and then unpack the implications of (Eklund et al. []: arXiv 1511.01863) for parametric procedures. Hum Brain Mapp, 40:2052-2054, 2019. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Keywords: family-wise error rate; random field theory; statistical parametric mapping; topological inference.

© 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Figures

Figure 1
Figure 1
Cluster‐level inference results for a two‐sample t‐test (two groups of 10 random subjects, repeated a thousand times) with the Beijing dataset using a cluster forming threshold of P = 0.001 (uncorrected) and the SPM12 software (r6685). Five levels of spatial smoothing were evaluated (4, 6, 8, 10 and 12 mm isotropic Gaussian kernels) with four different regressors (see [Eklund et al., 2015] for details).

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Source: PubMed

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