dc.contributor.author | Lerma-Usabiaga, Garikoitz | |
dc.contributor.author | Mukherjee, Pratik | |
dc.contributor.author | Perry, Michael L. | |
dc.contributor.author | Wandell, Brian A. | |
dc.date.accessioned | 2020-12-17T13:31:45Z | |
dc.date.available | 2020-12-17T13:31:45Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Lerma-Usabiaga, G., Mukherjee, P., Perry, M.L. et al. Data-science ready, multisite, human diffusion MRI white-matter-tract statistics. Sci Data 7, 422 (2020). https://doi.org/10.1038/s41597-020-00760-3 | es_ES |
dc.identifier.issn | 2052-4463 | |
dc.identifier.uri | http://hdl.handle.net/10810/49147 | |
dc.description | Published 30 November 2020 | es_ES |
dc.description.abstract | The white matter tracts in the living human brain are critical for healthy function, and the diffusion MRI measured in these tracts is correlated with diverse behavioral measures. The technical skills required to analyze diffusion MRI data are complex: data acquisition requires MRI sequence development and acquisition expertise, analyzing raw-data into meaningful summary statistics requires computational neuroimaging and neuroanatomy expertise. The human white matter study field will advance faster if the tract summaries are available in plain data-science-ready format for non-diffusion MRI experts, such as statisticians, computer graphic researchers or data scientists in general. Here, we share a curated and processed dataset from three different MRI centers in a format that is data-science ready. The multisite data we share include measures of within and between MRI center variation in white-matter-tract diffusion measurements. Along with the dataset description and summary statistics, we describe the state-of-the-art computational system that guarantees reproducibility and provenance from the original scanner output. | es_ES |
dc.description.sponsorship | This work was supported by a Marie Sklodowska-Curie (H2020-MSCA-IF-2017-795807-ReCiModel) grant to G.L.-U. We thank the Simons Foundation Autism Research Initiative and Weston Havens foundation for support. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Scientific Data | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/H2020- MSCA-IF-2017-795807-ReCiModel | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.title | Data-science ready, multisite, human diffusion MRI whitematter- tract statistics | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article. | es_ES |
dc.relation.publisherversion | https://www.nature.com/sdata/ | es_ES |
dc.identifier.doi | 10.1038/s41597-020-00760-3 | |