Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly
complex and flexible. To assess the impact of this flexibility on functional magnetic resonance
imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing
nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that
no two teams chose identical workflows to analyze the data. This flexibility resulted in
sizeable variation in hypothesis test results, even for teams whose statistical maps were highly
correlated at intermediate stages of their analysis pipeline. Variation in reported results was
related to several aspects of analysis methodology. Importantly, meta-analytic approaches
that aggregated information across teams yielded significant consensus in activated regions
across teams. Furthermore, prediction markets of researchers in the field revealed an
overestimation of the likelihood of significant findings, even by researchers with direct
knowledge of the dataset. Our findings show that analytic flexibility can have substantial
effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The
results emphasize the importance of validating and sharing complex analysis workflows, and
demonstrate the need for multiple analyses of the same data. Potential approaches to
mitigate issues related to analytical variability are discussed.