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Multiple Comparisons Correction in Qdec

Introduction

Muliple comparisons correction refers to the need to correct a significance level for the number of hypothesis tests performed. In brain mapping, the number of hypothesis tests is typically associated with the number of voxels or surface vertices and is therefore massive. Methods for multiple comparisons correction include:

• Bonferroni Correction

• False Discovery Rate

• Random Field Theory

• Permutation testing

• Simulation (cluster analysis)

With so many vertices in the significance maps produced in Qdec, it is likely that many vertices will appear signficant purely by random chance (ie, a false positive). This is known as the "Problem of Multiple Comparisons". Qdec implements two forms of correction: False Discovery Rate (FDR), and simulation (cluster analysis). In a cluster analysis, only clustered vertices are retained, the idea being that false positives will not appear next to each other.

Qdec

FDR

In qdec, once your analysis is complete, and the Design tab displays your hypothesis in the 'Scalars' menu, and you have selected one of these hypothesis for display, such that their significance values are painted on the average surface, then to use the FDR method of correcting for multiple comparisons, you simply press the Set Using FDR button found beneath the Scalars menu list. The rate used defaults to 0.05, and is adjustable via the entry box next to the FDR button. When the FDR button is pressed, the minimum threshold is set to that found from the FDR calculation. The mid-point is merely 1.5 above that minimum threshold (where 1.5 is an arbitrarily chosen value), and the max is 2.25 above the min (2.25 is also arbitrarily chosen).