PyBIDS: Python tools for BIDS datasets

doi:10.21105/joss.01294

. 2019;4(40):1294.

doi: 10.21105/joss.01294. Epub 2019 Aug 12.

PyBIDS: Python tools for BIDS datasets

Tal Yarkoni¹, Christopher J Markiewicz², Alejandro de la Vega¹, Krzysztof J Gorgolewski², Taylor Salo³, Yaroslav O Halchenko⁴, Quinten McNamara¹, Krista DeStasio⁵, Jean-Baptiste Poline⁶, Dmitry Petrov⁷, Valérie Hayot-Sasson⁸, Dylan M Nielson⁹, Johan Carlin¹⁰, Gregory Kiar¹¹, Kirstie Whitaker¹², Elizabeth DuPre¹¹, Adina Wagner¹³, Lee S Tirrell¹⁴, Mainak Jas¹⁵, Michael Hanke¹³, Russell A Poldrack², Oscar Esteban², Stefan Appelhoff¹⁶, Chris Holdgraf¹⁷, Isla Staden¹⁸, Bertrand Thirion¹⁹, Dave F Kleinschmidt²⁰, John A Lee⁹, Matteo Visconti di Oleggio Castello¹⁷, Michael P Notter²¹, Ross Blair²

Affiliations

¹ University of Texas at Austin.
² Stanford University.
³ Florida International University.
⁴ Dartmouth College.
⁵ University of Oregon.
⁶ McGill University.
⁷ University of Southern California.
⁸ Concordia University.
⁹ National Institute of Mental Health.
¹⁰ MRC Cognition and Brain Sciences Unit.
¹¹ Montreal Neurological Institute and Hospital.
¹² Alan Turing Institute.
¹³ Otto-von-Guericke University Magdeburg.
¹⁴ CorticoMetrics LLC.
¹⁵ Télécom ParisTech, France.
¹⁶ Max Planck Institute for Human Development, Berlin, Germany.
¹⁷ University of California at Berkeley.
¹⁸ Queen Mary University London.
¹⁹ INRIA.
²⁰ Rutgers University.
²¹ University of Lausanne.

PMID: 32775955
PMCID: PMC7409983
DOI: 10.21105/joss.01294

PyBIDS: Python tools for BIDS datasets

Tal Yarkoni et al. J Open Source Softw. 2019.

. 2019;4(40):1294.

doi: 10.21105/joss.01294. Epub 2019 Aug 12.

Authors

Affiliations

¹ University of Texas at Austin.
² Stanford University.
³ Florida International University.
⁴ Dartmouth College.
⁵ University of Oregon.
⁶ McGill University.
⁷ University of Southern California.
⁸ Concordia University.
⁹ National Institute of Mental Health.
¹⁰ MRC Cognition and Brain Sciences Unit.
¹¹ Montreal Neurological Institute and Hospital.
¹² Alan Turing Institute.
¹³ Otto-von-Guericke University Magdeburg.
¹⁴ CorticoMetrics LLC.
¹⁵ Télécom ParisTech, France.
¹⁶ Max Planck Institute for Human Development, Berlin, Germany.
¹⁷ University of California at Berkeley.
¹⁸ Queen Mary University London.
¹⁹ INRIA.
²⁰ Rutgers University.
²¹ University of Lausanne.

PMID: 32775955
PMCID: PMC7409983
DOI: 10.21105/joss.01294

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References

1. Esteban O, Birman D, Schaer M, Koyejo OO, Poldrack RA, & Gorgolewski KJ (2017). MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites. PLOS One, 12(9), e0184661. doi:10.1371/journal.pone.0184661 - DOI - PMC - PubMed
1. Esteban O, Markiewicz CJ, Blair RW, Moodie CA, Isik AI, Erramuzpe A, Kent JD, et al. (2019). FMRIPrep: A robust preprocessing pipeline for functional MRI. Nature Methods, 16(1), 111. doi:10.1038/s41592-018-0235-4 - DOI - PMC - PubMed
1. Gorgolewski KJ, Alfaro-Almagro F, Auer T, Bellec P, Capotă M, Chakravarty MM, Churchill NW, et al. (2017). BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods. PLOS Computational Biology, 13(3), e1005209. doi:10.1371/journal.pcbi.1005209 - DOI - PMC - PubMed
1. Gorgolewski KJ, Auer T, Calhoun VD, Craddock RC, Das S, Duff EP, Flandin G, et al. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data, 3, 160044. doi:10.1038/sdata.2016.44 - DOI - PMC - PubMed

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[1] Esteban O, Birman D, Schaer M, Koyejo OO, Poldrack RA, & Gorgolewski KJ (2017). MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites. PLOS One, 12(9), e0184661. doi:10.1371/journal.pone.0184661 - DOI - PMC - PubMed

[2] Esteban O, Birman D, Schaer M, Koyejo OO, Poldrack RA, & Gorgolewski KJ (2017). MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites. PLOS One, 12(9), e0184661. doi:10.1371/journal.pone.0184661 - DOI - PMC - PubMed

[3] Esteban O, Markiewicz CJ, Blair RW, Moodie CA, Isik AI, Erramuzpe A, Kent JD, et al. (2019). FMRIPrep: A robust preprocessing pipeline for functional MRI. Nature Methods, 16(1), 111. doi:10.1038/s41592-018-0235-4 - DOI - PMC - PubMed

[4] Esteban O, Markiewicz CJ, Blair RW, Moodie CA, Isik AI, Erramuzpe A, Kent JD, et al. (2019). FMRIPrep: A robust preprocessing pipeline for functional MRI. Nature Methods, 16(1), 111. doi:10.1038/s41592-018-0235-4 - DOI - PMC - PubMed

[5] Gorgolewski KJ, Alfaro-Almagro F, Auer T, Bellec P, Capotă M, Chakravarty MM, Churchill NW, et al. (2017). BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods. PLOS Computational Biology, 13(3), e1005209. doi:10.1371/journal.pcbi.1005209 - DOI - PMC - PubMed

[6] Gorgolewski KJ, Alfaro-Almagro F, Auer T, Bellec P, Capotă M, Chakravarty MM, Churchill NW, et al. (2017). BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods. PLOS Computational Biology, 13(3), e1005209. doi:10.1371/journal.pcbi.1005209 - DOI - PMC - PubMed

[7] Gorgolewski KJ, Auer T, Calhoun VD, Craddock RC, Das S, Duff EP, Flandin G, et al. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data, 3, 160044. doi:10.1038/sdata.2016.44 - DOI - PMC - PubMed

[8] Gorgolewski KJ, Auer T, Calhoun VD, Craddock RC, Das S, Duff EP, Flandin G, et al. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data, 3, 160044. doi:10.1038/sdata.2016.44 - DOI - PMC - PubMed

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PyBIDS: Python tools for BIDS datasets

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PyBIDS: Python tools for BIDS datasets

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