The Influence of Study-Level Inference Models and Study Set Size on Coordinate-Based fMRI Meta-Analyses
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The Influence of Study-Level Inference Models and Study Set Size on Coordinate-Based fMRI Meta-Analyses. / Bossier, Han; Seurinck, Ruth; Kühn, Simone; Banaschewski, Tobias; Barker, Gareth J; Bokde, Arun L W; Martinot, Jean-Luc; Lemaitre, Herve; Paus, Tomáš; Millenet, Sabina; Moerkerke, Beatrijs.
In: FRONT NEUROSCI-SWITZ, Vol. 11, 2017, p. 745.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - The Influence of Study-Level Inference Models and Study Set Size on Coordinate-Based fMRI Meta-Analyses
AU - Bossier, Han
AU - Seurinck, Ruth
AU - Kühn, Simone
AU - Banaschewski, Tobias
AU - Barker, Gareth J
AU - Bokde, Arun L W
AU - Martinot, Jean-Luc
AU - Lemaitre, Herve
AU - Paus, Tomáš
AU - Millenet, Sabina
AU - Moerkerke, Beatrijs
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Journal Article
U2 - 10.3389/fnins.2017.00745
DO - 10.3389/fnins.2017.00745
M3 - SCORING: Journal article
C2 - 29403344
VL - 11
SP - 745
JO - FRONT NEUROSCI-SWITZ
JF - FRONT NEUROSCI-SWITZ
SN - 1662-453X
ER -