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The influence of study-level inference models and study set size on coordinate-based fMRI meta-analyses

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Abstract
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.
Keywords
NEUROIMAGING DATA, BRAIN-FUNCTION, ACTIVATION, LIKELIHOOD, REPRODUCIBILITY, POWER, NEUROSCIENCE, BEHAVIOR, FAILURE, coordinate-based meta-analysis, fMRI, group modeling, mixed effects, models, random effects models, reliability

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Citation

Please use this url to cite or link to this publication:

Chicago
Bossier, Han, Ruth Seurinck, Simone Kuehn, Tobias Banaschewski, Gareth J Barker, Arun LW Bokde, Jean-Luc Martinot, et al. 2018. “The Influence of Study-level Inference Models and Study Set Size on Coordinate-based fMRI Meta-analyses.” Frontiers in Neuroscience 11.
APA
Bossier, H., Seurinck, R., Kuehn, S., Banaschewski, T., Barker, G. J., Bokde, A. L., Martinot, J.-L., et al. (2018). The influence of study-level inference models and study set size on coordinate-based fMRI meta-analyses. FRONTIERS IN NEUROSCIENCE, 11.
Vancouver
1.
Bossier H, Seurinck R, Kuehn S, Banaschewski T, Barker GJ, Bokde AL, et al. The influence of study-level inference models and study set size on coordinate-based fMRI meta-analyses. FRONTIERS IN NEUROSCIENCE. 2018;11.
MLA
Bossier, Han et al. “The Influence of Study-level Inference Models and Study Set Size on Coordinate-based fMRI Meta-analyses.” FRONTIERS IN NEUROSCIENCE 11 (2018): n. pag. Print.
@article{8552076,
  abstract     = {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.},
  articleno    = {745},
  author       = {Bossier, Han and Seurinck, Ruth and Kuehn, Simone and Banaschewski, Tobias and Barker, Gareth J and Bokde, Arun LW and Martinot, Jean-Luc and Lemaitre, Herve and Paus, Tom{\'a}\v{s} and Millenet, Sabina and Moerkerke, Beatrijs},
  issn         = {1662-453X},
  journal      = {FRONTIERS IN NEUROSCIENCE},
  language     = {eng},
  pages        = {22},
  title        = {The influence of study-level inference models and study set size on coordinate-based fMRI meta-analyses},
  url          = {http://dx.doi.org/10.3389/fnins.2017.00745},
  volume       = {11},
  year         = {2018},
}

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