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The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments.

Scientific data

Gorgolewski KJ, Auer T, Calhoun VD, Craddock RC, Das S, Duff EP, Flandin G, Ghosh SS, Glatard T, Halchenko YO, Handwerker DA, Hanke M, Keator D, Li X, Michael Z, Maumet C, Nichols BN, Nichols TE, Pellman J, Poline JB, Rokem A, Schaefer G, Sochat V, Triplett W, Turner JA, Varoquaux G, Poldrack RA.
PMID: 27326542
Sci Data. 2016 Jun 21;3:160044. doi: 10.1038/sdata.2016.44.

The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain....

Cite
Gorgolewski KJ, Auer T, Calhoun VD, et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data. 2016;3:160044doi: 10.1038/sdata.2016.44.
Gorgolewski, K. J., Auer, T., Calhoun, V. D., Craddock, R. C., Das, S., Duff, E. P., Flandin, G., Ghosh, S. S., Glatard, T., Halchenko, Y. O., Handwerker, D. A., Hanke, M., Keator, D., Li, X., Michael, Z., Maumet, C., Nichols, B. N., Nichols, T. E., Pellman, J., Poline, J. B., Rokem, A., Schaefer, G., Sochat, V., Triplett, W., Turner, J. A., Varoquaux, G., & Poldrack, R. A. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific data, 3160044. https://doi.org/10.1038/sdata.2016.44
Gorgolewski, Krzysztof J, et al. "The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments." Scientific data vol. 3 (2016): 160044. doi: https://doi.org/10.1038/sdata.2016.44
Gorgolewski KJ, Auer T, Calhoun VD, Craddock RC, Das S, Duff EP, Flandin G, Ghosh SS, Glatard T, Halchenko YO, Handwerker DA, Hanke M, Keator D, Li X, Michael Z, Maumet C, Nichols BN, Nichols TE, Pellman J, Poline JB, Rokem A, Schaefer G, Sochat V, Triplett W, Turner JA, Varoquaux G, Poldrack RA. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data. 2016 Jun 21;3:160044. doi: 10.1038/sdata.2016.44. PMID: 27326542; PMCID: PMC4978148.
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RRIDs: A Simple Step toward Improving Reproducibility through Rigor and Transparency of Experimental Methods.

Neuron

Bandrowski AE, Martone ME.
PMID: 27151636
Neuron. 2016 May 04;90(3):434-6. doi: 10.1016/j.neuron.2016.04.030.

With the call for more rigorous scientific reporting, authentication, and transparency from the scientific community and funding agencies, one critical step is to make finding and identifying key resources in the published literature tractable. We discuss here the use...

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Bandrowski AE, Martone ME. RRIDs: A Simple Step toward Improving Reproducibility through Rigor and Transparency of Experimental Methods. Neuron. 2016;90(3):434-6doi: 10.1016/j.neuron.2016.04.030.
Bandrowski, A. E., & Martone, M. E. (2016). RRIDs: A Simple Step toward Improving Reproducibility through Rigor and Transparency of Experimental Methods. Neuron, 90(3), 434-6. https://doi.org/10.1016/j.neuron.2016.04.030
Bandrowski, Anita E, and Martone, Maryann E. "RRIDs: A Simple Step toward Improving Reproducibility through Rigor and Transparency of Experimental Methods." Neuron vol. 90,3 (2016): 434-6. doi: https://doi.org/10.1016/j.neuron.2016.04.030
Bandrowski AE, Martone ME. RRIDs: A Simple Step toward Improving Reproducibility through Rigor and Transparency of Experimental Methods. Neuron. 2016 May 04;90(3):434-6. doi: 10.1016/j.neuron.2016.04.030. PMID: 27151636; PMCID: PMC5854161.
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From the Wet Lab to the Web Lab: A Paradigm Shift in Brain Imaging Research.

Frontiers in neuroinformatics

Keshavan A, Poline JB.
PMID: 30881299
Front Neuroinform. 2019 Mar 01;13:3. doi: 10.3389/fninf.2019.00003. eCollection 2019.

Web technology has transformed our lives, and has led to a paradigm shift in the computational sciences. As the neuroimaging informatics research community amasses large datasets to answer complex neuroscience questions, we find that the web is the best...

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Keshavan A, Poline JB. From the Wet Lab to the Web Lab: A Paradigm Shift in Brain Imaging Research. Front Neuroinform. 2019;13:3doi: 10.3389/fninf.2019.00003.
Keshavan, A., & Poline, J. B. (2019). From the Wet Lab to the Web Lab: A Paradigm Shift in Brain Imaging Research. Frontiers in neuroinformatics, 133. https://doi.org/10.3389/fninf.2019.00003
Keshavan, Anisha, and Poline, Jean-Baptiste. "From the Wet Lab to the Web Lab: A Paradigm Shift in Brain Imaging Research." Frontiers in neuroinformatics vol. 13 (2019): 3. doi: https://doi.org/10.3389/fninf.2019.00003
Keshavan A, Poline JB. From the Wet Lab to the Web Lab: A Paradigm Shift in Brain Imaging Research. Front Neuroinform. 2019 Mar 01;13:3. doi: 10.3389/fninf.2019.00003. eCollection 2019. PMID: 30881299; PMCID: PMC6405692.
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A very simple, re-executable neuroimaging publication.

F1000Research

Ghosh SS, Poline JB, Keator DB, Halchenko YO, Thomas AG, Kessler DA, Kennedy DN.
PMID: 28781753
F1000Res. 2017 Feb 10;6:124. doi: 10.12688/f1000research.10783.2. eCollection 2017.

Reproducible research is a key element of the scientific process. Re-executability of neuroimaging workflows that lead to the conclusions arrived at in the literature has not yet been sufficiently addressed and adopted by the neuroimaging community. In this paper,...

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Ghosh SS, Poline JB, Keator DB, et al. A very simple, re-executable neuroimaging publication. F1000Res. 2017;6:124doi: 10.12688/f1000research.10783.2.
Ghosh, S. S., Poline, J. B., Keator, D. B., Halchenko, Y. O., Thomas, A. G., Kessler, D. A., & Kennedy, D. N. (2017). A very simple, re-executable neuroimaging publication. F1000Research, 6124. https://doi.org/10.12688/f1000research.10783.2
Ghosh, Satrajit S, et al. "A very simple, re-executable neuroimaging publication." F1000Research vol. 6 (2017): 124. doi: https://doi.org/10.12688/f1000research.10783.2
Ghosh SS, Poline JB, Keator DB, Halchenko YO, Thomas AG, Kessler DA, Kennedy DN. A very simple, re-executable neuroimaging publication. F1000Res. 2017 Feb 10;6:124. doi: 10.12688/f1000research.10783.2. eCollection 2017. PMID: 28781753; PMCID: PMC5516225.
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Uniform resolution of compact identifiers for biomedical data.

Scientific data

Wimalaratne SM, Juty N, Kunze J, Janée G, McMurry JA, Beard N, Jimenez R, Grethe JS, Hermjakob H, Martone ME, Clark T.
PMID: 29737976
Sci Data. 2018 May 08;5:180029. doi: 10.1038/sdata.2018.29.

Most biomedical data repositories issue locally-unique accessions numbers, but do not provide globally unique, machine-resolvable, persistent identifiers for their datasets, as required by publishers wishing to implement data citation in accordance with widely accepted principles. Local accessions may however...

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Wimalaratne SM, Juty N, Kunze J, et al. Uniform resolution of compact identifiers for biomedical data. Sci Data. 2018;5:180029doi: 10.1038/sdata.2018.29.
Wimalaratne, S. M., Juty, N., Kunze, J., Janée, G., McMurry, J. A., Beard, N., Jimenez, R., Grethe, J. S., Hermjakob, H., Martone, M. E., & Clark, T. (2018). Uniform resolution of compact identifiers for biomedical data. Scientific data, 5180029. https://doi.org/10.1038/sdata.2018.29
Wimalaratne, Sarala M, et al. "Uniform resolution of compact identifiers for biomedical data." Scientific data vol. 5 (2018): 180029. doi: https://doi.org/10.1038/sdata.2018.29
Wimalaratne SM, Juty N, Kunze J, Janée G, McMurry JA, Beard N, Jimenez R, Grethe JS, Hermjakob H, Martone ME, Clark T. Uniform resolution of compact identifiers for biomedical data. Sci Data. 2018 May 08;5:180029. doi: 10.1038/sdata.2018.29. PMID: 29737976; PMCID: PMC5944906.
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Uncovering the important acoustic features for detecting vocal fold paralysis with explainable machine learning.

medRxiv : the preprint server for health sciences

Low DM, Randolph G, Rao V, Ghosh SS, Song PC.
PMID: 33501466
medRxiv. 2021 Jun 24; doi: 10.1101/2020.11.23.20235945.

OBJECTIVE: To detect unilateral vocal fold paralysis (UVFP) from voice recordings using an explainable model of machine learning.STUDY DESIGN: Case series - retrospective with a control group.SETTING: Tertiary care laryngology practice between 2009 to 2019.METHODS: Patients with confirmed UVFP...

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Low DM, Randolph G, Rao V, et al. Uncovering the important acoustic features for detecting vocal fold paralysis with explainable machine learning. medRxiv. 2021;doi: 10.1101/2020.11.23.20235945.
Low, D. M., Randolph, G., Rao, V., Ghosh, S. S., & Song, P. C. (2021). Uncovering the important acoustic features for detecting vocal fold paralysis with explainable machine learning. medRxiv : the preprint server for health sciences, . https://doi.org/10.1101/2020.11.23.20235945
Low, Daniel M, et al. "Uncovering the important acoustic features for detecting vocal fold paralysis with explainable machine learning." medRxiv : the preprint server for health sciences vol. (2021). doi: https://doi.org/10.1101/2020.11.23.20235945
Low DM, Randolph G, Rao V, Ghosh SS, Song PC. Uncovering the important acoustic features for detecting vocal fold paralysis with explainable machine learning. medRxiv. 2021 Jun 24; doi: 10.1101/2020.11.23.20235945. PMID: 33501466; PMCID: PMC7836138.
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Standardizing workflows in imaging transcriptomics with the abagen toolbox.

eLife

Markello RD, Arnatkeviciute A, Poline JB, Fulcher BD, Fornito A, Misic B.
PMID: 34783653
Elife. 2021 Nov 16;10. doi: 10.7554/eLife.72129.

Gene expression fundamentally shapes the structural and functional architecture of the human brain. Open-access transcriptomic datasets like the Allen Human Brain Atlas provide an unprecedented ability to examine these mechanisms in vivo; however, a lack of standardization across research...

Cite
Markello RD, Arnatkeviciute A, Poline JB, et al. Standardizing workflows in imaging transcriptomics with the abagen toolbox. Elife. 2021;10doi: 10.7554/eLife.72129.
Markello, R. D., Arnatkeviciute, A., Poline, J. B., Fulcher, B. D., Fornito, A., & Misic, B. (2021). Standardizing workflows in imaging transcriptomics with the abagen toolbox. eLife, 10. https://doi.org/10.7554/eLife.72129
Markello, Ross D, et al. "Standardizing workflows in imaging transcriptomics with the abagen toolbox." eLife vol. 10 (2021). doi: https://doi.org/10.7554/eLife.72129
Markello RD, Arnatkeviciute A, Poline JB, Fulcher BD, Fornito A, Misic B. Standardizing workflows in imaging transcriptomics with the abagen toolbox. Elife. 2021 Nov 16;10. doi: 10.7554/eLife.72129. PMID: 34783653; PMCID: PMC8660024.
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Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging.

Frontiers in neuroinformatics

Kennedy DN, Abraham SA, Bates JF, Crowley A, Ghosh S, Gillespie T, Goncalves M, Grethe JS, Halchenko YO, Hanke M, Haselgrove C, Hodge SM, Jarecka D, Kaczmarzyk J, Keator DB, Meyer K, Martone ME, Padhy S, Poline JB, Preuss N, Sincomb T, Travers M.
PMID: 30792636
Front Neuroinform. 2019 Feb 07;13:1. doi: 10.3389/fninf.2019.00001. eCollection 2019.

There has been a recent major upsurge in the concerns about reproducibility in many areas of science. Within the neuroimaging domain, one approach is to promote reproducibility is to target the re-executability of the publication. The information supporting such...

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Kennedy DN, Abraham SA, Bates JF, et al. Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging. Front Neuroinform. 2019;13:1doi: 10.3389/fninf.2019.00001.
Kennedy, D. N., Abraham, S. A., Bates, J. F., Crowley, A., Ghosh, S., Gillespie, T., Goncalves, M., Grethe, J. S., Halchenko, Y. O., Hanke, M., Haselgrove, C., Hodge, S. M., Jarecka, D., Kaczmarzyk, J., Keator, D. B., Meyer, K., Martone, M. E., Padhy, S., Poline, J. B., Preuss, N., Sincomb, T., & Travers, M. (2019). Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging. Frontiers in neuroinformatics, 131. https://doi.org/10.3389/fninf.2019.00001
Kennedy, David N, et al. "Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging." Frontiers in neuroinformatics vol. 13 (2019): 1. doi: https://doi.org/10.3389/fninf.2019.00001
Kennedy DN, Abraham SA, Bates JF, Crowley A, Ghosh S, Gillespie T, Goncalves M, Grethe JS, Halchenko YO, Hanke M, Haselgrove C, Hodge SM, Jarecka D, Kaczmarzyk J, Keator DB, Meyer K, Martone ME, Padhy S, Poline JB, Preuss N, Sincomb T, Travers M. Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging. Front Neuroinform. 2019 Feb 07;13:1. doi: 10.3389/fninf.2019.00001. eCollection 2019. PMID: 30792636; PMCID: PMC6374302.
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From data sharing to data publishing [version 2; peer review: 2 approved, 1 approved with reservations].

MNI open research

Poline JB.
PMID: 31157322
MNI Open Res. 2019;2. doi: 10.12688/mniopenres.12772.2. Epub 2019 Jan 31.

Data sharing, i.e. depositing data in research community accessiblerepositories, is not becoming as rapidly widespread across the life scienceresearch community as hoped or expected. I consider the sociological and cultural context of research and lay out why the community...

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Poline JB. From data sharing to data publishing [version 2; peer review: 2 approved, 1 approved with reservations]. MNI Open Res. 2019;2doi: 10.12688/mniopenres.12772.2.
Poline, J. B. (2019). From data sharing to data publishing [version 2; peer review: 2 approved, 1 approved with reservations]. MNI open research, 2. https://doi.org/10.12688/mniopenres.12772.2
Poline, Jean-Baptiste. "From data sharing to data publishing [version 2; peer review: 2 approved, 1 approved with reservations]." MNI open research vol. 2 (2019). doi: https://doi.org/10.12688/mniopenres.12772.2
Poline JB. From data sharing to data publishing [version 2; peer review: 2 approved, 1 approved with reservations]. MNI Open Res. 2019;2. doi: 10.12688/mniopenres.12772.2. Epub 2019 Jan 31. PMID: 31157322; PMCID: PMC6540973.
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The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments.

Scientific data

Gorgolewski KJ, Auer T, Calhoun VD, Craddock RC, Das S, Duff EP, Flandin G, Ghosh SS, Glatard T, Halchenko YO, Handwerker DA, Hanke M, Keator D, Li X, Michael Z, Maumet C, Nichols BN, Nichols TE, Pellman J, Poline JB, Rokem A, Schaefer G, Sochat V, Triplett W, Turner JA, Varoquaux G, Poldrack RA.
PMID: 27326542
Sci Data. 2016 Jun 21;3:160044. doi: 10.1038/sdata.2016.44.

The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain....

Cite
Gorgolewski KJ, Auer T, Calhoun VD, et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data. 2016;3:160044doi: 10.1038/sdata.2016.44.
Gorgolewski, K. J., Auer, T., Calhoun, V. D., Craddock, R. C., Das, S., Duff, E. P., Flandin, G., Ghosh, S. S., Glatard, T., Halchenko, Y. O., Handwerker, D. A., Hanke, M., Keator, D., Li, X., Michael, Z., Maumet, C., Nichols, B. N., Nichols, T. E., Pellman, J., Poline, J. B., Rokem, A., Schaefer, G., Sochat, V., Triplett, W., Turner, J. A., Varoquaux, G., & Poldrack, R. A. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific data, 3160044. https://doi.org/10.1038/sdata.2016.44
Gorgolewski, Krzysztof J, et al. "The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments." Scientific data vol. 3 (2016): 160044. doi: https://doi.org/10.1038/sdata.2016.44
Gorgolewski KJ, Auer T, Calhoun VD, Craddock RC, Das S, Duff EP, Flandin G, Ghosh SS, Glatard T, Halchenko YO, Handwerker DA, Hanke M, Keator D, Li X, Michael Z, Maumet C, Nichols BN, Nichols TE, Pellman J, Poline JB, Rokem A, Schaefer G, Sochat V, Triplett W, Turner JA, Varoquaux G, Poldrack RA. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data. 2016 Jun 21;3:160044. doi: 10.1038/sdata.2016.44. PMID: 27326542; PMCID: PMC4978148.
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Aberrant levels of cortical myelin distinguish individuals with depressive disorders from healthy controls.

NeuroImage. Clinical

Baranger DAA, Halchenko YO, Satz S, Ragozzino R, Iyengar S, Swartz HA, Manelis A.
PMID: 34455188
Neuroimage Clin. 2021;32:102790. doi: 10.1016/j.nicl.2021.102790. Epub 2021 Aug 23.

The association between depressive disorders and measures reflecting myelin content is underexplored, despite growing evidence of associations with white matter tract integrity. We characterized the T1w/T2w ratio using the Glasser atlas in 39 UD and 47 HC participants (ages...

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Baranger DAA, Halchenko YO, Satz S, et al. Aberrant levels of cortical myelin distinguish individuals with depressive disorders from healthy controls. Neuroimage Clin. 2021;32:102790doi: 10.1016/j.nicl.2021.102790.
Baranger, D. A. A., Halchenko, Y. O., Satz, S., Ragozzino, R., Iyengar, S., Swartz, H. A., & Manelis, A. (2021). Aberrant levels of cortical myelin distinguish individuals with depressive disorders from healthy controls. NeuroImage. Clinical, 32102790. https://doi.org/10.1016/j.nicl.2021.102790
Baranger, David A A, et al. "Aberrant levels of cortical myelin distinguish individuals with depressive disorders from healthy controls." NeuroImage. Clinical vol. 32 (2021): 102790. doi: https://doi.org/10.1016/j.nicl.2021.102790
Baranger DAA, Halchenko YO, Satz S, Ragozzino R, Iyengar S, Swartz HA, Manelis A. Aberrant levels of cortical myelin distinguish individuals with depressive disorders from healthy controls. Neuroimage Clin. 2021;32:102790. doi: 10.1016/j.nicl.2021.102790. Epub 2021 Aug 23. PMID: 34455188; PMCID: PMC8406024.
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Standardizing workflows in imaging transcriptomics with the abagen toolbox.

eLife

Markello RD, Arnatkeviciute A, Poline JB, Fulcher BD, Fornito A, Misic B.
PMID: 34783653
Elife. 2021 Nov 16;10. doi: 10.7554/eLife.72129.

Gene expression fundamentally shapes the structural and functional architecture of the human brain. Open-access transcriptomic datasets like the Allen Human Brain Atlas provide an unprecedented ability to examine these mechanisms in vivo; however, a lack of standardization across research...

Cite
Markello RD, Arnatkeviciute A, Poline JB, et al. Standardizing workflows in imaging transcriptomics with the abagen toolbox. Elife. 2021;10doi: 10.7554/eLife.72129.
Markello, R. D., Arnatkeviciute, A., Poline, J. B., Fulcher, B. D., Fornito, A., & Misic, B. (2021). Standardizing workflows in imaging transcriptomics with the abagen toolbox. eLife, 10. https://doi.org/10.7554/eLife.72129
Markello, Ross D, et al. "Standardizing workflows in imaging transcriptomics with the abagen toolbox." eLife vol. 10 (2021). doi: https://doi.org/10.7554/eLife.72129
Markello RD, Arnatkeviciute A, Poline JB, Fulcher BD, Fornito A, Misic B. Standardizing workflows in imaging transcriptomics with the abagen toolbox. Elife. 2021 Nov 16;10. doi: 10.7554/eLife.72129. PMID: 34783653; PMCID: PMC8660024.
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Showing 1 to 12 of 24 entries