The multiverse of data preprocessing and analysis in graph-based fMRI: A systematic literature review of analytical choices fed into a decision support tool for informed analysis

The large number of different analytical choices researchers use may be partly responsible for the replication challenge in neuroimaging studies. For robustness analysis, knowledge of the full space of options is essential. We conducted a systematic literature review to identify the analytical decisions in functional neuroimaging data preprocessing and analysis in the emerging field of cognitive network neuroscience. We found 61 different steps, with 17 of them having debatable options. Scrubbing, global signal regression, and spatial smoothing are among the controversial steps. There is no standardized order in which different steps are applied, and the options within several steps vary widely across studies. By aggregating the pipelines across studies, we propose three taxonomic levels to categorize analytical choices: 1) inclusion or exclusion of specific steps, 2) distinct sequencing of steps, and 3) parameter tuning within steps. To facilitate access to the data, we developed a decision support app with high educational value called METEOR, which allows researchers to explore the space of choices as reference for well-informed robustness (multiverse) analysis. HighlightsO_LIData analysis variability in neuroimaging hinders replicability. C_LIO_LIAnalysis across multiple defensible options examines the robustness of results. C_LIO_LIWe conducted a systematic literature review to identify analytical options. C_LIO_LIWe identified 61 steps and 102 options in performing graph-fMRI analysis. C_LIO_LIInteractive visualization of these steps and options is available as a Shiny app. C_LI