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Detecting and Interpreting Heterogeneity and Publication Bias in Image-Based Meta-Analyses

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Abstract

With the increase of data sharing, meta-analyses are becoming increasingly important in the neuroimaging community. They provide a quantitative summary of published results and heightened confidence due to higher statistical power. The gold standard approach to combine results from neuroimaging studies is an Image-Based Meta-Analysis (IBMA) [1] in which group-level maps from different studies are combined. Recently, we have introduced the IBMA toolbox, an extension for SPM that provides methods for combining image maps from multiple studies [2]. However, the current toolbox lacks diagnostic tools used to assess critical assumptions of meta-analysis, in particular whether there is inter-study variation requiring random-effects IBMA, and whether publication bias is present. Here, we present two new tools added to the IBMA toolbox to detect heterogeneity and to assess evidence of publication bias.
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Dates and versions

inserm-01933023 , version 1 (29-11-2018)

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  • HAL Id : inserm-01933023 , version 1

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Thomas Maullin-Sapey, Camille Maumet, Thomas E. Nichols. Detecting and Interpreting Heterogeneity and Publication Bias in Image-Based Meta-Analyses. OHBM 2018 - 24th Annual Meeting of the Organization for Human Brain Mapping, Jun 2018, Singapore, Singapore. ⟨inserm-01933023⟩
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