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

, Rotem Botvinik-Nezer, Felix Holzmeister, Colin F. Camerer, Anna Dreber, Juergen Huber, Magnus Johannesson, Michael Kirchler, Roni Iwanir, Jeanette A. Mumford, R. Alison Adcock, Paolo Avesani, Blazej M. Baczkowski, Aahana Bajracharya, Leah Bakst, Sheryl Ball, Marco Barilari, Nadège Bault, Derek Beaton, Julia Beitner, Roland G. Benoit, Ruud M.W.J. Berkers, Jamil P. Bhanji, Bharat B. Biswal, Sebastian Bobadilla-Suarez, Tiago Bortolini, Katherine L. Bottenhorn, Alexander Bowring, Senne Braem, Hayley R. Brooks, Emily G. Brudner, Cristian B. Calderon, Julia A. Camilleri, Jaime J. Castrellon, Luca Cecchetti, Edna C. Cieslik, Zachary J. Cole, Olivier Collignon, Robert W. Cox, William A. Cunningham, Stefan Czoschke, Kamalaker Dadi, Charles P. Davis, Alberto De Luca, Mauricio R. Delgado, Lysia Demetriou, Jeffrey B. Dennison, Xin Di, Claus Lamm, Annabel B. Losecaat Vermeer, Lei Zhang

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. 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 dataset2–5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

Department of Cognition, Emotion, and Methods in Psychology, Vienna Cognitive Science Hub
External organisation(s)
Tel Aviv University, Dartmouth College, Leopold-Franzens-Universität Innsbruck, California Institute of Technology (Caltech), Stockholm School of Economics, University of Wisconsin, Madison, Duke University, Fondazione Bruno Kessler, Università degli Studi di Trento, Max-Planck-Institut für Kognitions- und Neurowissenschaften, Washington University in St. Louis, Boston University, Virginia Polytechnic Institute and State University , Université catholique de Louvain, Plymouth University , Baycrest Health Sciences Centre, University of Amsterdam (UvA), Johann Wolfgang Goethe-Universität Frankfurt am Main, Rutgers University, Newark, New Jersey Institute of Technology, University of Electronic Science and Technology of China, D’Or Institute for Research and Education (IDOR), Florida International University, University of Oxford, Ghent University , Vrije Universiteit Brussel, University of Denver, Forschungszentrum Jülich, Heinrich-Heine-Universität Düsseldorf, IMT School for Advanced Studies Lucca, University of Nebraska-Lincoln, National Institute of Mental Health (NIMH), University of Toronto, Université Paris-Saclay, University of Connecticut, PROVIDI Lab, Image Sciences Institute, Imperial College London, Temple University, Philadelphia
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Peer reviewed
Austrian Fields of Science 2012
302043 Magnetic resonance imaging (MRI), 501030 Cognitive science
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