Analysis of task-based functional MRI data preprocessed with fMRIPrep. Esteban, O., Ciric, R., Finc, K., Blair, R. W., Markiewicz, C. J., Moodie, C. A., Kent, J. D., Goncalves, M., DuPre, E., Gomez, D. E. P., Ye, Z., Salo, T., Valabregue, R., Amlien, I. K., Liem, F., Jacoby, N., Stojić, H., Cieslak, M., Urchs, S., Halchenko, Y. O., Ghosh, S. S., De La Vega, A., Yarkoni, T., Wright, J., Thompson, W. H., Poldrack, R. A., & Gorgolewski, K. J. Nature Protocols, 15(7):2186–2202, July, 2020. Number: 7 Publisher: Nature Publishing Group![Analysis of task-based functional MRI data preprocessed with fMRIPrep [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Functional magnetic resonance imaging (fMRI) is a standard tool to investigate the neural correlates of cognition. fMRI noninvasively measures brain activity, allowing identification of patterns evoked by tasks performed during scanning. Despite the long history of this technique, the idiosyncrasies of each dataset have led to the use of ad-hoc preprocessing protocols customized for nearly every different study. This approach is time consuming, error prone and unsuitable for combining datasets from many sources. Here we showcase fMRIPrep (http://fmriprep.org), a robust tool to prepare human fMRI data for statistical analysis. This software instrument addresses the reproducibility concerns of the established protocols for fMRI preprocessing. By leveraging the Brain Imaging Data Structure to standardize both the input datasets (MRI data as stored by the scanner) and the outputs (data ready for modeling and analysis), fMRIPrep is capable of preprocessing a diversity of datasets without manual intervention. In support of the growing popularity of fMRIPrep, this protocol describes how to integrate the tool in a task-based fMRI investigation workflow.
@article{esteban_analysis_2020,
title = {Analysis of task-based functional {MRI} data preprocessed with {fMRIPrep}},
volume = {15},
copyright = {2020 The Author(s), under exclusive licence to Springer Nature Limited},
issn = {1750-2799},
url = {https://www.nature.com/articles/s41596-020-0327-3},
doi = {10.1038/s41596-020-0327-3},
abstract = {Functional magnetic resonance imaging (fMRI) is a standard tool to investigate the neural correlates of cognition. fMRI noninvasively measures brain activity, allowing identification of patterns evoked by tasks performed during scanning. Despite the long history of this technique, the idiosyncrasies of each dataset have led to the use of ad-hoc preprocessing protocols customized for nearly every different study. This approach is time consuming, error prone and unsuitable for combining datasets from many sources. Here we showcase fMRIPrep (http://fmriprep.org), a robust tool to prepare human fMRI data for statistical analysis. This software instrument addresses the reproducibility concerns of the established protocols for fMRI preprocessing. By leveraging the Brain Imaging Data Structure to standardize both the input datasets (MRI data as stored by the scanner) and the outputs (data ready for modeling and analysis), fMRIPrep is capable of preprocessing a diversity of datasets without manual intervention. In support of the growing popularity of fMRIPrep, this protocol describes how to integrate the tool in a task-based fMRI investigation workflow.},
language = {en},
number = {7},
urldate = {2023-03-11},
journal = {Nature Protocols},
author = {Esteban, Oscar and Ciric, Rastko and Finc, Karolina and Blair, Ross W. and Markiewicz, Christopher J. and Moodie, Craig A. and Kent, James D. and Goncalves, Mathias and DuPre, Elizabeth and Gomez, Daniel E. P. and Ye, Zhifang and Salo, Taylor and Valabregue, Romain and Amlien, Inge K. and Liem, Franziskus and Jacoby, Nir and Stojić, Hrvoje and Cieslak, Matthew and Urchs, Sebastian and Halchenko, Yaroslav O. and Ghosh, Satrajit S. and De La Vega, Alejandro and Yarkoni, Tal and Wright, Jessey and Thompson, William H. and Poldrack, Russell A. and Gorgolewski, Krzysztof J.},
month = jul,
year = {2020},
note = {Number: 7
Publisher: Nature Publishing Group},
keywords = {Computational neuroscience, Magnetic resonance imaging, Neurological models, Software},
pages = {2186--2202},
file = {Accepted Version:/home/tchaase/snap/zotero-snap/common/Zotero/storage/NHJAVWLK/Esteban et al. - 2020 - Analysis of task-based functional MRI data preproc.pdf:application/pdf},
}
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