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neuRosim: An R Package for Generating fMRI Data

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  • Welvaert, Marijke
  • Durnez, Joke
  • Moerkerke, Beatrijs
  • Berdoolaege, Geert
  • Rosseel, Yves

Abstract

Studies that validate statistical methods for functional magnetic resonance imaging (fMRI) data often use simulated data to ensure that the ground truth is known. However, simulated fMRI data are almost always generated using in-house procedures because a well-accepted simulation method is lacking. In this article we describe the R package neuRosim, which is a collection of data generation functions for neuroimaging data. We will demonstrate the possibilities to generate data from simple time series to complete 4D images and the possibilities for the user to create her own data generation method.

Suggested Citation

  • Welvaert, Marijke & Durnez, Joke & Moerkerke, Beatrijs & Berdoolaege, Geert & Rosseel, Yves, 2011. "neuRosim: An R Package for Generating fMRI Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 44(i10).
    • Handle: RePEc:jss:jstsof:v:044:i10
    DOI: http://hdl.handle.net/10.18637/jss.v044.i10
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    Cited by:

    1. Cardona Jiménez, Johnatan & de B. Pereira, Carlos A., 2021. "Assessing dynamic effects on a Bayesian matrix-variate dynamic linear model: An application to task-based fMRI data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 163(C).
    2. Samaddar, Arunava & Jackson, Brooke S. & Helms, Christopher J. & Lazar, Nicole A. & McDowell, Jennifer E. & Park, Cheolwoo, 2022. "A group comparison in fMRI data using a semiparametric model under shape invariance," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    3. 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.
    4. Park, Jun Young & Polzehl, Joerg & Chatterjee, Snigdhansu & Brechmann, André & Fiecas, Mark, 2020. "Semiparametric modeling of time-varying activation and connectivity in task-based fMRI data," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
    5. Sam Efromovich & Jiayi Wu, 2018. "Wavelet Analysis of Big Data Contaminated by Large Noise in an fMRI Study of Neuroplasticity," Methodology and Computing in Applied Probability, Springer, vol. 20(4), pages 1381-1402, December.
    6. Cheng‐Han Yu & Raquel Prado & Hernando Ombao & Daniel Rowe, 2023. "Bayesian spatiotemporal modeling on complex‐valued fMRI signals via kernel convolutions," Biometrics, The International Biometric Society, vol. 79(2), pages 616-628, June.
    7. Zhao, Yuxuan & Matteson, David S. & Mostofsky, Stewart H. & Nebel, Mary Beth & Risk, Benjamin B., 2022. "Group linear non-Gaussian component analysis with applications to neuroimaging," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
    8. Daniel Spencer & Rajarshi Guhaniyogi & Raquel Prado, 2020. "Joint Bayesian Estimation of Voxel Activation and Inter-regional Connectivity in fMRI Experiments," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 845-869, December.
    9. Ani Eloyan & Shanshan Li & John Muschelli & Jim J Pekar & Stewart H Mostofsky & Brian S Caffo, 2014. "Analytic Programming with fMRI Data: A Quick-Start Guide for Statisticians Using R," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-13, February.
    10. Marijke Welvaert & Yves Rosseel, 2013. "On the Definition of Signal-To-Noise Ratio and Contrast-To-Noise Ratio for fMRI Data," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-10, November.
    11. Mohsen Soltanifar & Chel Hee Lee, 2023. "SimSST: An R Statistical Software Package to Simulate Stop Signal Task Data," Mathematics, MDPI, vol. 11(3), pages 1-15, January.

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