Assessing publication bias in coordinate-based meta-analysis techniques?

fMRI is an important neuroimaging technique to localize brain functions. Since publications of single fMRI studies have flourished, it is increasingly recognized that progress in understanding human brain function requires the integration of data across studies. Meta-analysis is a promising tool to achieve this. Furthermore, meta-analysis can address some of the shortcomings of fMRI studies such as lack of power and the large multiple testing problem. Recently coordinate-based methods have been specifically developed for fMRI data that combine the limited amount of voxels that survive a statistical threshold or peaks into a summary map. The most frequently employed meta-analysis toolbox for fMRI data is Activation Likelihood Estimation(ALE) (Eickhoff et al., 2009). ALE verifies whether the congruency of location of activation across studies is larger than can be expected by chance. However, while it is possible to assess publication bias with standard meta-analyses, no such procedures exist for meta-analyses of fMRI studies. Therefore we have developed a procedure to add null-studies to an ALE-meta-analysis and calculate a Fail-Safe N (Rosenthal, 1979). The Fail-Safe N is the minimum amount of null studies necessary to reduce a statistically significant effect to a non-significant effect. We simulated data-sets, consisting of studies with real activation and up to 100 null studies with no activation at a predefined location in the brain. The Fail-Safe N was evaluated in function of different multiple testing corrections.

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