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A longitudinal multi-scanner multimodal human neuroimaging dataset
Human neuroimaging has led to an overwhelming amount of research into brain function in healthy and clinical populations. However, a better appreciation of the limitations of small sample studies has led to an increased number of multi-site, multi-scanner protocols to understand human brain function...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9198098/ https://www.ncbi.nlm.nih.gov/pubmed/35701471 http://dx.doi.org/10.1038/s41597-022-01386-3 |
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author | Hawco, Colin Dickie, Erin W. Herman, Gabrielle Turner, Jessica A. Argyelan, Miklos Malhotra, Anil K. Buchanan, Robert W. Voineskos, Aristotle N. |
author_facet | Hawco, Colin Dickie, Erin W. Herman, Gabrielle Turner, Jessica A. Argyelan, Miklos Malhotra, Anil K. Buchanan, Robert W. Voineskos, Aristotle N. |
author_sort | Hawco, Colin |
collection | PubMed |
description | Human neuroimaging has led to an overwhelming amount of research into brain function in healthy and clinical populations. However, a better appreciation of the limitations of small sample studies has led to an increased number of multi-site, multi-scanner protocols to understand human brain function. As part of a multi-site project examining social cognition in schizophrenia, a group of “travelling human phantoms” had structural T1, diffusion, and resting-state functional MRIs obtained annually at each of three sites. Scan protocols were carefully harmonized across sites prior to the study. Due to scanner upgrades at each site (all sites acquired PRISMA MRIs during the study) and one participant being replaced, the end result was 30 MRI scans across 4 people, 6 MRIs, and 4 years. This dataset includes multiple neuroimaging modalities and repeated scans across six MRIs. It can be used to evaluate differences across scanners, consistency of pipeline outputs, or test multi-scanner harmonization approaches. |
format | Online Article Text |
id | pubmed-9198098 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-91980982022-06-16 A longitudinal multi-scanner multimodal human neuroimaging dataset Hawco, Colin Dickie, Erin W. Herman, Gabrielle Turner, Jessica A. Argyelan, Miklos Malhotra, Anil K. Buchanan, Robert W. Voineskos, Aristotle N. Sci Data Data Descriptor Human neuroimaging has led to an overwhelming amount of research into brain function in healthy and clinical populations. However, a better appreciation of the limitations of small sample studies has led to an increased number of multi-site, multi-scanner protocols to understand human brain function. As part of a multi-site project examining social cognition in schizophrenia, a group of “travelling human phantoms” had structural T1, diffusion, and resting-state functional MRIs obtained annually at each of three sites. Scan protocols were carefully harmonized across sites prior to the study. Due to scanner upgrades at each site (all sites acquired PRISMA MRIs during the study) and one participant being replaced, the end result was 30 MRI scans across 4 people, 6 MRIs, and 4 years. This dataset includes multiple neuroimaging modalities and repeated scans across six MRIs. It can be used to evaluate differences across scanners, consistency of pipeline outputs, or test multi-scanner harmonization approaches. Nature Publishing Group UK 2022-06-14 /pmc/articles/PMC9198098/ /pubmed/35701471 http://dx.doi.org/10.1038/s41597-022-01386-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Hawco, Colin Dickie, Erin W. Herman, Gabrielle Turner, Jessica A. Argyelan, Miklos Malhotra, Anil K. Buchanan, Robert W. Voineskos, Aristotle N. A longitudinal multi-scanner multimodal human neuroimaging dataset |
title | A longitudinal multi-scanner multimodal human neuroimaging dataset |
title_full | A longitudinal multi-scanner multimodal human neuroimaging dataset |
title_fullStr | A longitudinal multi-scanner multimodal human neuroimaging dataset |
title_full_unstemmed | A longitudinal multi-scanner multimodal human neuroimaging dataset |
title_short | A longitudinal multi-scanner multimodal human neuroimaging dataset |
title_sort | longitudinal multi-scanner multimodal human neuroimaging dataset |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9198098/ https://www.ncbi.nlm.nih.gov/pubmed/35701471 http://dx.doi.org/10.1038/s41597-022-01386-3 |
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