Facilitating open-science with realistic fMRI simulation: validation and application
Cameron Thomas Ellisa b✉, Christopher A. Baldassanoc d e, Anna C. Schapirof g, Ming Bo Caid h, Jonathan D. Cohend h
- aDepartment of Psychology, Yale University, New Haven, CT, USA
- bDepartment of Psychology, Yale University, New Haven, CT, United States of America
- cDepartment of Psychology, Columbia University, New York, NY, USA
- dPrinceton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- eDepartment of Psychology, Columbia University, New York, NY, United States of America
- fDepartment of Psychiatry, Harvard Medical School, Harvard University, Boston, MA, USA
- gDepartment of Psychology, University of Pennsylvania, Philadelphia, PA, United States of America
- hPrinceton Neuroscience Institute, Princeton University, Princeton, NJ, United States of America
- ✉Corresponding author
Papers open inside SciRah from the original publisher or repository; SciRah never hosts them.
Abstract
Abstract Background 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. New Method We present fmrisim, a new Python package for standardized, realistic simulation of fMRI data. This package is part of BrainIAK: a recently released open-source 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. Results 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. Comparison with other methods fmrisim ports the functionality of other packages to the Python platform while extending what is available in order to make it seamless to simulate realistic fMRI data. Conclusions 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. Highlights fmrisim can simulate fMRI data matched to the noise properties of real fMRI. This can help researchers investigate the power of their fMRI designs. This also facilitates open science by making it easy to pre-register analysis pipelines.