Brain-wide inferiority and equivalence tests in fMRI group analyses: Selected applications,Human Brain Mapping

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Brain-wide inferiority and equivalence tests in fMRI group analyses: Selected applications
Human Brain Mapping ( IF 3.5 ) Pub Date : 2021-09-16 , DOI: 10.1002/hbm.25664
Martin Fungisai Gerchen _{1,

2,

3} , Peter Kirsch _{1,

2,

3} , Gordon Benedikt Feld _{1,

3,

4,

5}

Affiliation

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.

中文翻译：

fMRI 组分析中的全脑劣势和等效性测试：选定的应用

零假设显着性检验是 fMRI 中的主要统计程序，但仅提供了数据集中效果的相当有限的图片。当样本量和功效较低时，仅依靠严格的显着性检验可能会导致大量假阴性结果。相反，对于非常大的数据集，几乎每个体素都可能变得重要。因此，需要使用自卑性和等效性测试等程序来补充显着性测试，从而允许正式比较数据集内和数据集之间的效应大小，并提供新的方法来深入了解 fMRI 数据。这些检验的主要组成部分是对标准化效应大小及其置信区间的估计。在这里，我们展示了 Hedges 的g，即 Cohen 的d的偏差校正版本, 其置信区间可以从 SPM t图得到。然后，我们展示了如何使用这些值来评估非显着效应在统计上是否真的小于显着效应，从而在数据集中获得“不可判定区域”，并测试效应的可复制性和横向化。这种方法允许分析超出点估计的 fMRI 数据，使研究人员能够在解释他们的发现时考虑测量不确定性。

更新日期：2021-11-17

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