The Influence of Study-Level Inference Models and Study Set Size on Coordinate-Based fMRI Meta-Analyses.
Front. Neurosci.. 2018-01-18; 11:
DOI: 10.3389/fnins.2017.00745
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Given the increasing amount of neuroimaging studies, there is a growing need to
summarize published results. Coordinate-based meta-analyses use the locations of
statistically significant local maxima with possibly the associated effect sizes
to aggregate studies. In this paper, we investigate the influence of key
characteristics of a coordinate-based meta-analysis on (1) the balance between
false and true positives and (2) the activation reliability of the outcome from a
coordinate-based meta-analysis. More particularly, we consider the influence of
the chosen group level model at the study level [fixed effects, ordinary least
squares (OLS), or mixed effects models], the type of coordinate-based
meta-analysis [Activation Likelihood Estimation (ALE) that only uses peak
locations, fixed effects, and random effects meta-analysis that take into account
both peak location and height] and the amount of studies included in the analysis
(from 10 to 35). To do this, we apply a resampling scheme on a large dataset (N =
1,400) to create a test condition and compare this with an independent evaluation
condition. The test condition corresponds to subsampling participants into
studies and combine these using meta-analyses. The evaluation condition
corresponds to a high-powered group analysis. We observe the best performance
when using mixed effects models in individual studies combined with a random
effects meta-analysis. Moreover the performance increases with the number of
studies included in the meta-analysis. When peak height is not taken into
consideration, we show that the popular ALE procedure is a good alternative in
terms of the balance between type I and II errors. However, it requires more
studies compared to other procedures in terms of activation reliability. Finally,
we discuss the differences, interpretations, and limitations of our results.