RT Journal Article
SR Electronic
T1 Brain-Wide Inferiority and Equivalence Tests in fMRI Group Analyses: Selected Applications
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.04.22.440880
DO 10.1101/2021.04.22.440880
A1 Martin Fungisai Gerchen
A1 Peter Kirsch
A1 Gordon Benedikt Feld
YR 2021
UL http://biorxiv.org/content/early/2021/04/22/2021.04.22.440880.abstract
AB Null hypothesis significance testing is the major statistical procedure in the field of fMRI, but provides only a rather limited picture of the effects in a data set. When sample size and power is low relying only on strict significance testing may lead to a host of false negative findings. In contrast, with very large data sets virtually every voxel might become significant.It is thus desirable to complement significance testing with procedures like inferiority and equivalence tests that allow to formally compare effect sizes within and between data sets and offer novel approaches to obtain insight into fMRI data. The major component of these tests are estimates of standardized effect sizes and their confidence intervals. Here we show how Hedge’s g, the bias corrected version of Cohen’s d, and its confidence interval can be obtained from SPM t maps. We then demonstrate how these values can be used to evaluate whether non-significant effects are really statistically smaller than significant effects to obtain “regions of undecidability” within a data set, and to test for the replicability and lateralization of effects.This method allows the analysis of fMRI data beyond point estimates enabling researchers to take measurement uncertainty into account when interpreting their findings.Competing Interest StatementThe authors have declared no competing interest.