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Journal Articles Nature Year : 2020

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

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Magnus Johannesson
Roni Iwanir
  • Function : Author
Stefan Czoschke
Joke Durnez
  • Function : Author
Remi Gau
Tristan Glatard
  • Function : Author
  • PersonId : 867504
Enrico Glerean
  • Function : Author
Jelle J Goeman
  • Function : Author
  • PersonId : 987058
Susan Holmes
  • Function : Author
  • PersonId : 943876
Claus Lamm
Sangil Lee
  • Function : Author
  • PersonId : 1000034
Flora Li
  • Function : Author
Michael L Mack
  • Function : Author
  • PersonId : 892724
Camille Maumet
Benjamin Meyer
  • Function : Author
  • PersonId : 782319
  • IdRef : 200081810
Gustav Tinghög
Khoi Vo
  • Function : Author
Wouter D Weeda
  • Function : Author
Sangsuk Yoon
Lei Zhang
  • Function : Author
  • PersonId : 1011221
Xu Zhang
  • Function : Author
  • PersonId : 1012368
Thomas E. Nichols
  • Function : Correspondent author
Russell A Poldrack
  • Function : Correspondent author
Tom Schonberg

Abstract

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.

Dates and versions

inserm-02914443 , version 1 (11-08-2020)

Identifiers

Cite

Rotem Botvinik-Nezer, Felix Holzmeister, Colin F Camerer, Anna Dreber, Juergen Huber, et al.. Variability in the analysis of a single neuroimaging dataset by many teams. Nature, 2020, 582 (7810), pp.84-88. ⟨10.1038/s41586-020-2314-9⟩. ⟨inserm-02914443⟩
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