A browser-based tool for visualization and analysis of diffusion MRI data
Abstract
Human neuroscience research faces several challenges with regards to reproducibility. While scientists are generally aware that data sharing is important, it is not always clear how to share data in a manner that allows other labs to understand and reproduce published findings. Here we report a new open source tool, AFQ-Browser, that builds an interactive website as a companion to a diffusion MRI study. Because AFQ-Browser is portable—it runs in any web-browser—it can facilitate transparency and data sharing. Moreover, by leveraging new web-visualization technologies to create linked views between different dimensions of the dataset (anatomy, diffusion metrics, subject metadata), AFQ-Browser facilitates exploratory data analysis, fueling new discoveries based on previously published datasets. In an era where Big Data is playing an increasingly prominent role in scientific discovery, so will browser-based tools for exploring high-dimensional datasets, communicating scientific discoveries, aggregating data across labs, and publishing data alongside manuscripts.
- Authors:
-
[1];
[5]
- Univ. of Washington, Seattle, WA (United States). Inst. for Learning & Brain Sciences and Department of Speech and Hearing Sciences
- Univ. of Washington, Seattle, WA (United States). Dept. of Physics
- Univ. of Washington, Seattle, WA (United States). Dept of Chemical Engineering
- Univ. of Washington, Seattle, WA (United States). Inst. for Learning & Brain Sciences, Department of Speech and Hearing Sciences, and eScience Institute
- Univ. of Washington, Seattle, WA (United States). eScience Institute
- Publication Date:
- Research Org.:
- Krell Institute, Ames, IA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC); USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1529919
- Grant/Contract Number:
- FG02-97ER25308
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nature Communications
- Additional Journal Information:
- Journal Volume: 9; Journal Issue: 1; Journal ID: ISSN 2041-1723
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 60 APPLIED LIFE SCIENCES
Citation Formats
Yeatman, Jason D., Richie-Halford, Adam, Smith, Josh K., Keshavan, Anisha, and Rokem, Ariel. A browser-based tool for visualization and analysis of diffusion MRI data. United States: N. p., 2018.
Web. doi:10.1038/s41467-018-03297-7.
Yeatman, Jason D., Richie-Halford, Adam, Smith, Josh K., Keshavan, Anisha, & Rokem, Ariel. A browser-based tool for visualization and analysis of diffusion MRI data. United States. https://doi.org/10.1038/s41467-018-03297-7
Yeatman, Jason D., Richie-Halford, Adam, Smith, Josh K., Keshavan, Anisha, and Rokem, Ariel. Mon .
"A browser-based tool for visualization and analysis of diffusion MRI data". United States. https://doi.org/10.1038/s41467-018-03297-7. https://www.osti.gov/servlets/purl/1529919.
@article{osti_1529919,
title = {A browser-based tool for visualization and analysis of diffusion MRI data},
author = {Yeatman, Jason D. and Richie-Halford, Adam and Smith, Josh K. and Keshavan, Anisha and Rokem, Ariel},
abstractNote = {Human neuroscience research faces several challenges with regards to reproducibility. While scientists are generally aware that data sharing is important, it is not always clear how to share data in a manner that allows other labs to understand and reproduce published findings. Here we report a new open source tool, AFQ-Browser, that builds an interactive website as a companion to a diffusion MRI study. Because AFQ-Browser is portable—it runs in any web-browser—it can facilitate transparency and data sharing. Moreover, by leveraging new web-visualization technologies to create linked views between different dimensions of the dataset (anatomy, diffusion metrics, subject metadata), AFQ-Browser facilitates exploratory data analysis, fueling new discoveries based on previously published datasets. In an era where Big Data is playing an increasingly prominent role in scientific discovery, so will browser-based tools for exploring high-dimensional datasets, communicating scientific discoveries, aggregating data across labs, and publishing data alongside manuscripts.},
doi = {10.1038/s41467-018-03297-7},
journal = {Nature Communications},
number = 1,
volume = 9,
place = {United States},
year = {2018},
month = {3}
}
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Figures / Tables:
Fig. 1 : Development of the corticospinal tract, arcuate fasciculus, and cingulum. Tract profiles of mean diffusivity (top) and fractional anisotropy (bottom) are shown for the left hemisphere corticospinal tract (CST, orange), arcuate fasciculus (blue), and cingulum (green). Splitting the group by age, and selecting 3 bins, displays mean linesmore »
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