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Reproducibility of findings in modern PET neuroimaging: insight from the NRM2018 grand challenge
The reproducibility of findings is a compelling methodological problem that the neuroimaging community is facing these days. The lack of standardized pipelines for image processing, quantification and statistics plays a major role in the variability and interpretation of results, even when the same...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8504414/ https://www.ncbi.nlm.nih.gov/pubmed/33993794 http://dx.doi.org/10.1177/0271678X211015101 |
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author | Veronese, Mattia Rizzo, Gaia Belzunce, Martin Schubert, Julia Searle, Graham Whittington, Alex Mansur, Ayla Dunn, Joel Reader, Andrew Gunn, Roger N |
author_facet | Veronese, Mattia Rizzo, Gaia Belzunce, Martin Schubert, Julia Searle, Graham Whittington, Alex Mansur, Ayla Dunn, Joel Reader, Andrew Gunn, Roger N |
author_sort | Veronese, Mattia |
collection | PubMed |
description | The reproducibility of findings is a compelling methodological problem that the neuroimaging community is facing these days. The lack of standardized pipelines for image processing, quantification and statistics plays a major role in the variability and interpretation of results, even when the same data are analysed. This problem is well-known in MRI studies, where the indisputable value of the method has been complicated by a number of studies that produce discrepant results. However, any research domain with complex data and flexible analytical procedures can experience a similar lack of reproducibility. In this paper we investigate this issue for brain PET imaging. During the 2018 NeuroReceptor Mapping conference, the brain PET community was challenged with a computational contest involving a simulated neurotransmitter release experiment. Fourteen international teams analysed the same imaging dataset, for which the ground-truth was known. Despite a plurality of methods, the solutions were consistent across participants, although not identical. These results should create awareness that the increased sharing of PET data alone will only be one component of enhancing confidence in neuroimaging results and that it will be important to complement this with full details of the analysis pipelines and procedures that have been used to quantify data. |
format | Online Article Text |
id | pubmed-8504414 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-85044142021-10-12 Reproducibility of findings in modern PET neuroimaging: insight from the NRM2018 grand challenge Veronese, Mattia Rizzo, Gaia Belzunce, Martin Schubert, Julia Searle, Graham Whittington, Alex Mansur, Ayla Dunn, Joel Reader, Andrew Gunn, Roger N J Cereb Blood Flow Metab Original Articles The reproducibility of findings is a compelling methodological problem that the neuroimaging community is facing these days. The lack of standardized pipelines for image processing, quantification and statistics plays a major role in the variability and interpretation of results, even when the same data are analysed. This problem is well-known in MRI studies, where the indisputable value of the method has been complicated by a number of studies that produce discrepant results. However, any research domain with complex data and flexible analytical procedures can experience a similar lack of reproducibility. In this paper we investigate this issue for brain PET imaging. During the 2018 NeuroReceptor Mapping conference, the brain PET community was challenged with a computational contest involving a simulated neurotransmitter release experiment. Fourteen international teams analysed the same imaging dataset, for which the ground-truth was known. Despite a plurality of methods, the solutions were consistent across participants, although not identical. These results should create awareness that the increased sharing of PET data alone will only be one component of enhancing confidence in neuroimaging results and that it will be important to complement this with full details of the analysis pipelines and procedures that have been used to quantify data. SAGE Publications 2021-05-17 2021-10 /pmc/articles/PMC8504414/ /pubmed/33993794 http://dx.doi.org/10.1177/0271678X211015101 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Articles Veronese, Mattia Rizzo, Gaia Belzunce, Martin Schubert, Julia Searle, Graham Whittington, Alex Mansur, Ayla Dunn, Joel Reader, Andrew Gunn, Roger N Reproducibility of findings in modern PET neuroimaging: insight from the NRM2018 grand challenge |
title | Reproducibility of findings in modern PET neuroimaging: insight from the NRM2018 grand challenge |
title_full | Reproducibility of findings in modern PET neuroimaging: insight from the NRM2018 grand challenge |
title_fullStr | Reproducibility of findings in modern PET neuroimaging: insight from the NRM2018 grand challenge |
title_full_unstemmed | Reproducibility of findings in modern PET neuroimaging: insight from the NRM2018 grand challenge |
title_short | Reproducibility of findings in modern PET neuroimaging: insight from the NRM2018 grand challenge |
title_sort | reproducibility of findings in modern pet neuroimaging: insight from the nrm2018 grand challenge |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8504414/ https://www.ncbi.nlm.nih.gov/pubmed/33993794 http://dx.doi.org/10.1177/0271678X211015101 |
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