The influence of study-level inference models and study set size on coordinate-based fMRI meta-analyses
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 in...
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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
18 January 2018
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| In: |
Frontiers in neuroscience
Year: 2018, Volume: 11 |
| ISSN: | 1662-453X |
| DOI: | 10.3389/fnins.2017.00745 |
| Online Access: | Verlag, kostenfrei, Volltext: http://dx.doi.org/10.3389/fnins.2017.00745 Verlag, kostenfrei, Volltext: https://www.frontiersin.org/articles/10.3389/fnins.2017.00745/full 
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| Author Notes: | Han Bossier, Ruth Seurinck, Simone Kühn, Tobias Banaschewski, Gareth J. Barker, Arun L. W. Bokde, Jean-Luc Martinot, Herve Lemaitre, Tomáš Paus, Sabina Millenet, Beatrijs Moerkerke |
| Summary: | 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 or mixed effects models), the type of coordinate-based meta-analysis (Activation Likelihood Estimation 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 = 1400) 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 Activation Likelihood Estimation 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. |
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| Item Description: | Gesehen am 07.05.2018 |
| Physical Description: | Online Resource |
| ISSN: | 1662-453X |
| DOI: | 10.3389/fnins.2017.00745 |