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SummaryData 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.

We conducted a rapid review and quantitative summary of meta-analyses that have examined interventions which can be used by individuals during quarantine and social distancing to manage anxiety, depression, stress, and subjective well-being. A literature search yielded 34 meta-analyses (total number of studies k = 1,390, n = 145,744) that were summarized. Overall, self-guided interventions showed small to medium effects in comparison to control groups. In particular, self-guided therapeutic approaches (including cognitive-behavioral, mindfulness, and acceptance-based interventions), selected positive psychology interventions, and multi-component and activity-based interventions (music, physical exercise) showed promising evidence for effectiveness. Overall, self-guided interventions on average did not show the same degree of effectiveness as traditional guided individual or group therapies. There was no consistent evidence of dose effects, baseline differences, and differential effectiveness of eHealth interventions. More research on the effectiveness of interventions in diverse cultural settings is needed.