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

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

Curiosity for information predicts wellbeing mediated by loneliness during COVID-19 pandemic

The COVID-19 pandemic confronted humans with high uncertainty and lockdowns, which severely disrupted people’s daily social and health lifestyles, enhanced loneliness, and reduced well-being. Curiosity and information-seeking are central to behavior, fostering well-being and adaptation in changing environments. They may be particularly important to maintain well-being during the pandemic. Here, we investigated which motives drive information-seeking, and whether and how curiosity and information-seeking related to well-being and mood (excitement, anxiety). Additionally, we tested whether daily diet contributed to this relationship during lockdown. Participants (N = 183) completed questionnaires measuring curiosity, information-seeking, social and mental health. Using a smartphone app, participants submitted their daily food intake and lifestyle ratings for a week. We found participants had highest motivation to seek positive (vs. negative) information, concerning themselves more than others. Both trait curiosity and information-seeking predicted higher well-being, mediated by loneliness. Trait curiosity also predicted well-being and excitement days later. Considering diet, participants with lower trait curiosity ate food containing more tyrosine (i.e., dopamine precursor). Furthermore, participants consuming food high in sugar reported higher anxiety, which was specifically found in participants with relatively low, but not high, trait curiosity. Taken together, curiosity and information-seeking may benefit well-being and mood in high uncertain and challenging times, by interacting with lifestyle measures (loneliness and nutrition)

Relationships between serotonin transporter rate and brain activity associated with learning of social ranks: a simultaneous TEP([11C]-DASB)-fMRI study in humans

International audienc

Regulation of social hierarchy learning by serotonin transporter availability

Learning one's status in a group is a fundamental process in building social hierarchies. Although animal studies suggest that serotonin (5-HT) signaling modulates learning social hierarchies, direct evidence in humans is lacking. Here we determined the relationship between serotonin transporter (SERT) availability and brain systems engaged in learning social ranks combining computational approaches with simultaneous PET-fMRI acquisition in healthy males. We also investigated the link between SERT availability and brain activity in a non-social control condition involving learning the payoffs of slot machines. Learning social ranks was modulated by the dorsal raphe nucleus (DRN) 5-HT function. BOLD ventral striatal response, tracking the rank of opponents, decreased with DRN SERT levels. Moreover, this link was specific to the social learning task. These findings demonstrate that 5-HT plays an influence on the computations required to learn social ranks

Testosterone affects learning of implicit social dominance relationships from competitive interaction

International audienc