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. BenoitRuud 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, Erin W. Dickie, Ekaterina Dobryakova, Claire L. Donnat, Juergen Dukart, Niall W. Duncan, Joke Durnez, Amr Eed, Simon B. Eickhoff, Andrew Erhart, Laura Fontanesi, G. Matthew Fricke, Shiguang Fu, Adriana Galván, Remi Gau, Sarah Genon, Tristan Glatard, Enrico Glerean, Jelle J. Goeman, Sergej A.E. Golowin, Carlos González-García, Krzysztof J. Gorgolewski, Cheryl L. Grady, Mikella A. Green, João F. Guassi Moreira, Olivia Guest, Shabnam Hakimi, J. Paul Hamilton, Roeland Hancock, Giacomo Handjaras, Bronson B. Harry, Colin Hawco, Peer Herholz, Gabrielle Herman, Stephan Heunis, Felix Hoffstaedter, Jeremy Hogeveen, Susan Holmes, Chuan-Peng Hu, Scott A. Huettel, Matthew E. Hughes, Vittorio Iacovella, Alexandru D. Iordan, Peder M. Isager, Ayse I. Isik, Andrew Jahn, Matthew R. Johnson, Tom Johnstone, Michael J.E. Joseph, Anthony C. Juliano, Joseph W. Kable, Michalis Kassinopoulos, Cemal Koba, Xiang-Zhen Kong, Timothy R. Koscik, Nuri Erkut Kucukboyaci, Brice A. Kuhl, Sebastian Kupek, Angela R. Laird, Claus Lamm, Robert Langner, Nina Lauharatanahirun, Hongmi Lee, Sangil Lee, Alexander Leemans, Andrea Leo, Elise Lesage, Flora Li, Monica Y.C. Li, Phui Cheng Lim, Evan N. Lintz, Schuyler W. Liphardt, Annabel B. Losecaat Vermeer, Bradley C. Love, Michael L. Mack, Norberto Malpica, Theo Marins, Camille Maumet, Kelsey McDonald, Joseph T. McGuire, Helena Melero, Adriana S. Méndez Leal, Benjamin Meyer, Kristin N. Meyer, Glad Mihai, Georgios D. Mitsis, Jorge Moll, Dylan M. Nielson, Gustav Nilsonne, Michael P. Notter, Emanuele Olivetti, Adrian I. Onicas, Paolo Papale, Kaustubh R. Patil, Jonathan E. Peelle, Alexandre Pérez, Doris Pischedda, Jean-Baptiste Poline, Yanina Prystauka, Shruti Ray, Patricia A. Reuter-Lorenz, Richard C. Reynolds, Emiliano Ricciardi, Jenny R. Rieck, Anais M. Rodriguez-Thompson, Anthony Romyn, Taylor Salo, Gregory R. Samanez-Larkin, Emilio Sanz-Morales, Margaret L. Schlichting, Douglas H. Schultz, Qiang Shen, Margaret A. Sheridan, Jennifer A. Silvers, Kenny Skagerlund, Alec Smith, David V. Smith, Peter Sokol-Hessner, Simon R. Steinkamp, Sarah M. Tashjian, Bertrand Thirion, John N. Thorp, Gustav Tinghög, Loreen Tisdall, Steven H. Tompson, Claudio Toro-Serey, Juan Jesus Torre Tresols, Leonardo Tozzi, Vuong Truong, Luca Turella, Anna E. van 't Veer, Tom Verguts, Jean M. Vettel, Sagana Vijayarajah, Khoi Vo, Matthew B. Wall, Wouter D. Weeda, Susanne Weis, David J. White, David Wisniewski, Alba Xifra-Porxas, Emily A. Yearling, Sangsuk Yoon, Rui Yuan, Kenneth S.L. Yuen, Joshua E. Zosky, Thomas E. Nichols, Russell A. Poldrack, Tom Schonberg (Corresponding author)

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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.

Original languageEnglish
Pages (from-to)84-88
Number of pages5
Issue number7810
Publication statusPublished - 4 Jun 2020


Acknowledgements Neuroimaging data collection, performed at Tel Aviv University, was supported by the Austrian Science Fund (P29362-G27), the Israel Science Foundation (ISF 2004/15 to T. Schonberg) and the Swedish Foundation for Humanities and Social Sciences (NHS14-1719:1). Hosting of the data on OpenNeuro was supported by a National Institutes of Health (NIH) grant (R24MH117179). We thank M. C. Frank, Y. Assaf and N. Daw for comments on an earlier draft; the Texas Advanced Computing Center for providing computing resources for preprocessing of the data; the Stanford Research Computing Facility for hosting the data; and D. Roll for assisting with data processing. T. Schonberg thanks The Alfredo Federico Strauss Center for Computational Neuroimaging at Tel Aviv University; A.D. thanks the Knut and Alice Wallenberg Foundation and the Marianne and Marcus Wallenberg Foundation (A.D. is a Wallenberg Scholar), the Austrian Science Fund (FWF, SFB F63) and the Jan Wallander and Tom Hedelius Foundation (Svenska Handelsbankens Forskningsstiftelser); F. Holzmeister, J. Huber and M. Kirchler thank the Austrian Science Fund (FWF, SFB F63); D.W. was supported by the Research Foundation Flanders (FWO) and the European Union’s Horizon 2020 research and innovation programme ( under the Marie Skłodowska-Curie grant agreement no. 665501; L. Tisdall was supported by the University of Basel Research Fund for Junior Researchers; C.B.C. was supported by grant 12O7719N from the Research Foundation Flanders; E.L. was supported by grant 12T2517N from the Research Foundation Flanders and Marie Skłodowska-Curie Actions under COFUND grant agreement 665501; A. Eed was supported by a predoctoral fellowship La Caixa-Severo Ochoa from Obra Social La Caixa and also acknowledges Comunidad de Cálculo Científico del CSIC for the high-performance computing (HPC) use; C.L. was supported by the Vienna Science and Technology Fund (WWTF VRG13-007) and Austrian Science Fund (FWF P 32686); A.B.L.V. was supported by the Vienna Science and Technology Fund (WWTF VRG13-007); L.Z. was supported by the Vienna Science and Technology Fund (WWTF VRG13-007), the National Natural Science Foundation of China (no. 71801110), MOE (Ministry of Education in China) Project of Humanities and Social Sciences (no. 18YJC630268) and China Postdoctoral Science Foundation (no. 2018M633270); D.P. is currently supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy ‘Science of Intelligence’ (EXC 2002/1, project number 390523135); P.H. was supported in part by funding provided by Brain Canada, in partnership with Health Canada, for the Canadian Open Neuroscience Platform initiative; J.-B.P. was partially funded by the NIH (NIH-NIBIB P41 EB019936 (ReproNim), NIH-NIMH R01 MH083320 (CANDIShare) and NIH RF1 MH120021 (NIDM)) and the National Institute Of Mental Health of the NIH under award number R01MH096906 (Neurosynth), as well as the Canada First Research Excellence Fund, awarded to McGill University for the Healthy Brains for Healthy Lives initiative and the Brain Canada Foundation with support from Health Canada; S.B.E. was supported by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 785907 (HBP SGA2); G.M. was supported by the Max Planck Society; S. Heunis has received funding from the Dutch foundation LSH-TKI (grant LSHM16053-SGF); J.F.G.M. was supported by a Graduate Research Fellowship from the NSF and T32 Predoctoral Fellowship from the NIH; B.M. was supported by the Deutsche Forschungsgemeinschaft (grant CRC1193, subproject B01); A.R.L. was supported by NSF 1631325 and NIH R01 DA041353; M.E.H., T.J. and D.J.W. were supported by the Australian National Imaging Facility, a National Collaborative Research Infrastructure Strategy (NCRIS) capability; P.M.I. was supported by VIDI grant 452-17-013 from the Netherlands Organisation for Scientific Research; B.M.B. was supported by the Max Planck Society; J.P.H. was supported by a grant from the Swedish Research Council; R.W.C. and R.C.R. were supported by NIH IRP project number ZICMH002888; D.M.N., R.W.C., and R.C.R. used the computational resources of the National Institutes of Health High Performance Computing Biowulf cluster (; D.M.N. was supported by NIH IRP project number ZICMH002960; C.F.C. was supported by the Tianqiao and Chrissy Center for Social and Decision Neuroscience Center Leadership Chair; R.G.B. was supported by the Max Planck Society; R.M.W.J.B. was supported by the Max Planck Society; M.B., O.C. and R.G. were supported by the Belgian Excellence of Science program (EOS project 30991544) from the FNRS-Belgium; O.C. is a research associate at the FRS-FNRS of Belgium; A.D.L. was supported by grant R4195 “Repimpact” of EraNET Neuron; Q.S. was funded by grant no. 71971199,71602175 and 71942004 from the National Natural Science Foundation of China and no. 16YJC630103 of the Ministry of Education of Humanities and Social Science; and T.E.N. was supported by the Wellcome Trust award 100309/Z/12/Z.

FundersFunder number
Belgian Excellence of Science program30991544
Dutch foundation LSH-TKILSHM16053-SGF
FNRS-BelgiumR4195, 71971199,71602175, 16YJC630103, 71942004
Jan Wallander and Tom Hedelius Foundation
NIH NIMHRF1 MH120021, R01 MH083320
National Institutes of Health High Performance Computing Biowulf clusterZICMH002960
Marie Skłodowska‐Curie
Svenska Handelsbankens Forskningsstiftelser
Swedish Foundation for Humanities and Social SciencesNHS14-1719:1
National Science FoundationCRC1193, R01 DA041353, 1631325
National Institutes of Health
National Institute of Mental HealthR01MH096906, R24MH117179
University of Basel12T2517N, 12O7719N
McGill University
Fondation Brain Canada
Wellcome Trust100309/Z/12/Z.
Horizon 2020 Framework Programme785907, 665501, 945539
Health Canada
Deutsche ForschungsgemeinschaftEXC 2002/1, 390523135
National Natural Science Foundation of China71801110
Vienna Science and Technology FundFWF P 32686, WWTF VRG13-007
Ministry of Education of the People's Republic of China18YJC630268
Austrian Science FundP29362-G27
China Postdoctoral Science Foundation2018M633270
Fonds Wetenschappelijk Onderzoek
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Consejo Superior de Investigaciones Científicas (IEM-CSIC)
Israel Science FoundationISF 2004/15
Knut och Alice Wallenbergs Stiftelse
Max-Planck Society
Tel Aviv University
Marcus och Amalia Wallenbergs minnesfondSFB F63


    • Brain/diagnostic imaging
    • Data Analysis
    • Data Science/methods
    • Datasets as Topic/statistics & numerical data
    • Female
    • Functional Neuroimaging
    • Humans
    • Logistic Models
    • Magnetic Resonance Imaging
    • Male
    • Meta-Analysis as Topic
    • Models, Neurological
    • Reproducibility of Results
    • Research Personnel/organization & administration
    • Software


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