Brain‐wide inferiority and equivalence tests in fMRI group analyses: Selected applications. Issue 18 (16th September 2021)

• Record Type: Journal Article
• Authors: Gerchen, Martin Fungisai; Kirsch, Peter; Feld, Gordon Benedikt
• Is Part Of: Human brain mapping. Volume 42:Issue 18(2021)
• Page Start: 5803
• Page End: 5813
• Publication Date: 2021-09-16
• Subjects: confidence interval -- equivalence tests -- functional magnetic resonance imaging -- Hedge's g -- null hypothesis significance testing; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.25664
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: Null hypothesis significance testing is the major statistical procedure in 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 Hedges' 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 nonsignificant 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. Abstract : Inferiority and equivalence tests allow to compare effect sizes within and between data sets and are based on standardized effect sizes and their confidence intervals. We show how Hedges' g and its confidence interval can be obtained from SPM t maps and demonstrate how these can be used to obtain "regions of undecidability" within a data set, to test for the replicability, and for the lateralization of effects.