Facilitating open-science with realistic fMRI simulation: validation and application.
| Autor: | Ellis CT; Department of Psychology, Yale University, New Haven, CT, United States of America., Baldassano C; Department of Psychology, Columbia University, New York, NY, United States of America., Schapiro AC; Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States of America., Cai MB; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States of America., Cohen JD; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States of America. |
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| Jazyk: | angličtina |
| Zdroj: | PeerJ [PeerJ] 2020 Feb 19; Vol. 8, pp. e8564. Date of Electronic Publication: 2020 Feb 19 (Print Publication: 2020). |
| DOI: | 10.7717/peerj.8564 |
| Abstrakt: | 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 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. 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. Competing Interests: The authors declare there are no competing interests. (©2020 Ellis et al.) |
| Databáze: | MEDLINE |
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