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Conference Papers Year : 2023

Systematic review and evaluation of meta-analysis methods for same data meta-analyses

Thomas E Nichols​
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  • PersonId : 1014943
Camille Maumet

Abstract

Researchers using fMRI data have a wide range of analysis tools to model brain activity. This diversity of analytical approaches means there are many possible variations of the same imaging result. Thus, analyzing a dataset with a single approach can be misleading. Alternatively a multiverse analysis can be used, where multiple sets of results are obtained from running different pipelines on the same single dataset. The starting assumption for traditional meta-analyses is the independence among input data. Thus, here, we present "same data meta analysis" methods for examining multiple sets of neuroimaging results derived from a multiverse analysis, accounting for the inter-analysis dependence. The validity of this method is evaluated and compared against established meta-analysis methods, and we demonstrate the method on real world data from "NARPS", a multiverse analysis with 70 different statistic maps originating from the same data.

Domains

Neuroscience
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Licence : CC BY - Attribution
Licence : CC BY - Attribution

Dates and versions

inserm-04222730 , version 1 (13-10-2023)

Licence

Attribution - NonCommercial

Identifiers

  • HAL Id : inserm-04222730 , version 1

Cite

Jeremy Lefort-Besnard, Thomas E Nichols​, Camille Maumet. Systematic review and evaluation of meta-analysis methods for same data meta-analyses. Cogbases-2023 - Workshop on open science methods for analyzing brain imaging data, Institut Pasteur, Paris; Parietal team, Inria, Paris Saclay, Oct 2023, Paris, France. pp.1-1. ⟨inserm-04222730⟩
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