ALE meta‐analysis: Controlling the false discovery rate and performing statistical contrasts

TLDR

ALE has greatly advanced voxel‑based meta‑analysis in functional neuroimaging. The study aims to improve ALE by evaluating multiple‑comparison corrections and proposing a method for statistical comparisons of meta‑analyses. The authors tested the correction methods on foci from overt speech in stuttering, Stroop, picture‑naming, painful stimulation, and random data, and illustrated how comparison techniques can inform future meta‑analytic designs. The FDR approach better controls false positives across meta‑analyses of varying focus counts. Published in Hum Brain Mapp 25:155–164, 2005; © 2005 Wiley‑Liss, Inc.

Abstract

Abstract Activation likelihood estimation (ALE) has greatly advanced voxel‐based meta‐analysis research in the field of functional neuroimaging. We present two improvements to the ALE method. First, we evaluate the feasibility of two techniques for correcting for multiple comparisons: the single threshold test and a procedure that controls the false discovery rate (FDR). To test these techniques, foci from four different topics within the literature were analyzed: overt speech in stuttering subjects, the color‐word Stroop task, picture‐naming tasks, and painful stimulation. In addition, the performance of each thresholding method was tested on randomly generated foci. We found that the FDR method more effectively controls the rate of false positives in meta‐analyses of small or large numbers of foci. Second, we propose a technique for making statistical comparisons of ALE meta‐analyses and investigate its efficacy on different groups of foci divided by task or response type and random groups of similarly obtained foci. We then give an example of how comparisons of this sort may lead to advanced designs in future meta‐analytic research. Hum Brain Mapp 25:155–164, 2005. © 2005 Wiley‐Liss, Inc.