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A Review of fMRI Simulation Studies

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  • Marijke Welvaert
  • Yves Rosseel

Abstract

Simulation studies that validate statistical techniques for fMRI data are challenging due to the complexity of the data. Therefore, it is not surprising that no common data generating process is available (i.e. several models can be found to model BOLD activation and noise). Based on a literature search, a database of simulation studies was compiled. The information in this database was analysed and critically evaluated focusing on the parameters in the simulation design, the adopted model to generate fMRI data, and on how the simulation studies are reported. Our literature analysis demonstrates that many fMRI simulation studies do not report a thorough experimental design and almost consistently ignore crucial knowledge on how fMRI data are acquired. Advice is provided on how the quality of fMRI simulation studies can be improved.

Suggested Citation

  • Marijke Welvaert & Yves Rosseel, 2014. "A Review of fMRI Simulation Studies," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-10, July.
    • Handle: RePEc:plo:pone00:0101953
    DOI: 10.1371/journal.pone.0101953
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    References listed on IDEAS

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    1. Douglas Greve & Gregory Brown & Bryon Mueller & Gary Glover & Thomas Liu, 2013. "A Survey of the Sources of Noise in fMRI," Psychometrika, Springer;The Psychometric Society, vol. 78(3), pages 396-416, July.
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    Cited by:

    1. Garikoitz Lerma-Usabiaga & Noah Benson & Jonathan Winawer & Brian A Wandell, 2020. "A validation framework for neuroimaging software: The case of population receptive fields," PLOS Computational Biology, Public Library of Science, vol. 16(6), pages 1-18, June.
    2. Jonathan Schiefer & Alexander Niederbühl & Volker Pernice & Carolin Lennartz & Jürgen Hennig & Pierre LeVan & Stefan Rotter, 2018. "From correlation to causation: Estimating effective connectivity from zero-lag covariances of brain signals," PLOS Computational Biology, Public Library of Science, vol. 14(3), pages 1-18, March.
    3. Saikou Y. Diallo & Ross J. Gore & Jose J. Padilla & Christopher J. Lynch, 2015. "An overview of modeling and simulation using content analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(3), pages 977-1002, June.

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