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Longitudinal multi-centre brain imaging studies: guidelines and practical tips for accurate and reproducible imaging endpoints and data sharing
BACKGROUND: Research involving brain imaging is important for understanding common brain diseases. Study endpoints can include features and measures derived from imaging modalities, providing a benchmark against which other phenotypical data can be assessed. In trials, imaging data provide objective...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6323670/ https://www.ncbi.nlm.nih.gov/pubmed/30616680 http://dx.doi.org/10.1186/s13063-018-3113-6 |
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author | Wiseman, Stewart J. Meijboom, Rozanna Valdés Hernández, Maria del C. Pernet, Cyril Sakka, Eleni Job, Dominic Waldman, Adam D. Wardlaw, Joanna M. |
author_facet | Wiseman, Stewart J. Meijboom, Rozanna Valdés Hernández, Maria del C. Pernet, Cyril Sakka, Eleni Job, Dominic Waldman, Adam D. Wardlaw, Joanna M. |
author_sort | Wiseman, Stewart J. |
collection | PubMed |
description | BACKGROUND: Research involving brain imaging is important for understanding common brain diseases. Study endpoints can include features and measures derived from imaging modalities, providing a benchmark against which other phenotypical data can be assessed. In trials, imaging data provide objective evidence of beneficial and adverse outcomes. Multi-centre studies increase generalisability and statistical power. However, there is a lack of practical guidelines for the set-up and conduct of large neuroimaging studies. METHODS: We address this deficit by describing aspects of study design and other essential practical considerations that will help researchers avoid common pitfalls and data loss. RESULTS: The recommendations are grouped into seven categories: (1) planning, (2) defining the imaging endpoints, developing an imaging manual and managing the workflow, (3) performing a dummy run and testing the analysis methods, (4) acquiring the scans, (5) anonymising and transferring the data, (6) monitoring quality, and (7) using structured data and sharing data. CONCLUSIONS: Implementing these steps will lead to valuable and usable data and help to avoid imaging data wastage. |
format | Online Article Text |
id | pubmed-6323670 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-63236702019-01-10 Longitudinal multi-centre brain imaging studies: guidelines and practical tips for accurate and reproducible imaging endpoints and data sharing Wiseman, Stewart J. Meijboom, Rozanna Valdés Hernández, Maria del C. Pernet, Cyril Sakka, Eleni Job, Dominic Waldman, Adam D. Wardlaw, Joanna M. Trials Methodology BACKGROUND: Research involving brain imaging is important for understanding common brain diseases. Study endpoints can include features and measures derived from imaging modalities, providing a benchmark against which other phenotypical data can be assessed. In trials, imaging data provide objective evidence of beneficial and adverse outcomes. Multi-centre studies increase generalisability and statistical power. However, there is a lack of practical guidelines for the set-up and conduct of large neuroimaging studies. METHODS: We address this deficit by describing aspects of study design and other essential practical considerations that will help researchers avoid common pitfalls and data loss. RESULTS: The recommendations are grouped into seven categories: (1) planning, (2) defining the imaging endpoints, developing an imaging manual and managing the workflow, (3) performing a dummy run and testing the analysis methods, (4) acquiring the scans, (5) anonymising and transferring the data, (6) monitoring quality, and (7) using structured data and sharing data. CONCLUSIONS: Implementing these steps will lead to valuable and usable data and help to avoid imaging data wastage. BioMed Central 2019-01-07 /pmc/articles/PMC6323670/ /pubmed/30616680 http://dx.doi.org/10.1186/s13063-018-3113-6 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Wiseman, Stewart J. Meijboom, Rozanna Valdés Hernández, Maria del C. Pernet, Cyril Sakka, Eleni Job, Dominic Waldman, Adam D. Wardlaw, Joanna M. Longitudinal multi-centre brain imaging studies: guidelines and practical tips for accurate and reproducible imaging endpoints and data sharing |
title | Longitudinal multi-centre brain imaging studies: guidelines and practical tips for accurate and reproducible imaging endpoints and data sharing |
title_full | Longitudinal multi-centre brain imaging studies: guidelines and practical tips for accurate and reproducible imaging endpoints and data sharing |
title_fullStr | Longitudinal multi-centre brain imaging studies: guidelines and practical tips for accurate and reproducible imaging endpoints and data sharing |
title_full_unstemmed | Longitudinal multi-centre brain imaging studies: guidelines and practical tips for accurate and reproducible imaging endpoints and data sharing |
title_short | Longitudinal multi-centre brain imaging studies: guidelines and practical tips for accurate and reproducible imaging endpoints and data sharing |
title_sort | longitudinal multi-centre brain imaging studies: guidelines and practical tips for accurate and reproducible imaging endpoints and data sharing |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6323670/ https://www.ncbi.nlm.nih.gov/pubmed/30616680 http://dx.doi.org/10.1186/s13063-018-3113-6 |
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