Facilitating open-science with realistic fMRI simulation: Validation and application

Cameron T. Ellis, Christopher Baldassano, Anna C. Schapiro, Ming Bo Cai, Jonathan D. Cohen

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

With advances in methods for collecting and analyzing fMRI data, there is a concurrent need to understand how to reliably evaluate and optimally use these methods. Simulations of fMRI data can aid in both the evaluation of complex designs and the analysis of data. We present fmrisim, a new Python package for standardized, realistic simulation of fMRI data. This package is part of BrainIAK: a recently released opensource Python toolbox for advanced neuroimaging analyses. We describe how to use fmrisim to extract noise properties from real fMRI data and then create a synthetic dataset with matched noise properties and a user-specified signal. We validate the noise generated by fmrisim to show that it can approximate the noise properties of real data. We further show how fmrisim can help researchers find the optimal design in terms of power. The fmrisim package holds promise for improving the design of fMRI experiments, which may facilitate both the pre-registration of such experiments as well as the analysis of fMRI data.

Original languageEnglish (US)
Article numbere8564
JournalPeerJ
Volume8
DOIs
StatePublished - Feb 2020

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Keywords

  • fMRI
  • Multivariate design
  • Power analysis
  • Reproducibility
  • Simulation

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