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Choosing a practical and valid Image-Based Meta-Analysis

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Abstract

Meta-analysis provides a quantitative approach to summarise the rich functional Magnetic Resonance Imaging (fMRI) literature. When image data is available for each study, the optimal approach is to perform an Image-Based Meta-Analysis (IBMA) [1]. A number of IBMA approaches have been proposed including combination of standardised statistics (Z's), just effect estimates (E's) or both effect estimates and their standard errors (SE's). While using both E’s & SE’s and estimating between-study variance should be optimal, the methods are not guaranteed to work for small number of studies. Also, often only standardised estimates are shared, reducing the possible meta-analytic approaches. Finally, because the BOLD signal is non-quantitative care has to be taken in order to insure that E's are expressed in the same units [2,3], especially when combining data from different software packages. Given the growing interest in data sharing in the neuroimaging community there is a need to identify what is the minimal data to be shared in order to allow for future IBMAs. Here, we investigate the validity of 8 IBMA approaches.
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Dates and versions

inserm-01933032 , version 1 (23-11-2018)

Identifiers

  • HAL Id : inserm-01933032 , version 1

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Camille Maumet, Thomas E. Nichols. Choosing a practical and valid Image-Based Meta-Analysis. OHBM 2018 - 24th Annual Meeting of the Organization for Human Brain Mapping, Jun 2018, Singapore, Singapore. pp.1-3. ⟨inserm-01933032⟩
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