fMRIPrep: a robust preprocessing pipeline for functional MRI. Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., Snyder, M., Oya, H., Ghosh, S. S., Wright, J., Durnez, J., Poldrack, R. A., & Gorgolewski, K. J. Nature Methods, 16(1):111–116, January, 2019. Number: 1 Publisher: Nature Publishing Group![fMRIPrep: a robust preprocessing pipeline for functional MRI [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Preprocessing of functional magnetic resonance imaging (fMRI) involves numerous steps to clean and standardize the data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each dataset, building upon a large inventory of available tools. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for fMRI data. fMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing without manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than observed with commonly used preprocessing tools. fMRIPrep equips neuroscientists with an easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of results.
@article{esteban_fmriprep_2019,
title = {{fMRIPrep}: a robust preprocessing pipeline for functional {MRI}},
volume = {16},
copyright = {2018 The Author(s), under exclusive licence to Springer Nature America, Inc.},
issn = {1548-7105},
shorttitle = {{fMRIPrep}},
url = {https://www.nature.com/articles/s41592-018-0235-4},
doi = {10.1038/s41592-018-0235-4},
abstract = {Preprocessing of functional magnetic resonance imaging (fMRI) involves numerous steps to clean and standardize the data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each dataset, building upon a large inventory of available tools. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for fMRI data. fMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing without manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than observed with commonly used preprocessing tools. fMRIPrep equips neuroscientists with an easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of results.},
language = {en},
number = {1},
urldate = {2023-03-11},
journal = {Nature Methods},
author = {Esteban, Oscar and Markiewicz, Christopher J. and Blair, Ross W. and Moodie, Craig A. and Isik, A. Ilkay and Erramuzpe, Asier and Kent, James D. and Goncalves, Mathias and DuPre, Elizabeth and Snyder, Madeleine and Oya, Hiroyuki and Ghosh, Satrajit S. and Wright, Jessey and Durnez, Joke and Poldrack, Russell A. and Gorgolewski, Krzysztof J.},
month = jan,
year = {2019},
note = {Number: 1
Publisher: Nature Publishing Group},
keywords = {Computational neuroscience, Magnetic resonance imaging, Software, Image processing, Standards},
pages = {111--116},
file = {Full Text PDF:/home/tchaase/snap/zotero-snap/common/Zotero/storage/IR9XZ46Q/Esteban et al. - 2019 - fMRIPrep a robust preprocessing pipeline for func.pdf:application/pdf},
}
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