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Population-specific brain [(18)F]-FDG PET templates of Chinese subjects for statistical parametric mapping

Statistical Parametric Mapping (SPM) is a computational approach for analysing functional brain images like Positron Emission Tomography (PET). When performing SPM analysis for different patient populations, brain PET template images representing population-specific brain morphometry and metabolism...

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Autores principales: Wang, Hongkai, Tian, Yang, Liu, Yang, Chen, Zhaofeng, Zhai, Haoyu, Zhuang, Mingrui, Zhang, Nan, Jiang, Yuanfang, Gao, Ya, Feng, Hongbo, Zhang, Yanjun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8626451/
https://www.ncbi.nlm.nih.gov/pubmed/34836985
http://dx.doi.org/10.1038/s41597-021-01089-1
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author Wang, Hongkai
Tian, Yang
Liu, Yang
Chen, Zhaofeng
Zhai, Haoyu
Zhuang, Mingrui
Zhang, Nan
Jiang, Yuanfang
Gao, Ya
Feng, Hongbo
Zhang, Yanjun
author_facet Wang, Hongkai
Tian, Yang
Liu, Yang
Chen, Zhaofeng
Zhai, Haoyu
Zhuang, Mingrui
Zhang, Nan
Jiang, Yuanfang
Gao, Ya
Feng, Hongbo
Zhang, Yanjun
author_sort Wang, Hongkai
collection PubMed
description Statistical Parametric Mapping (SPM) is a computational approach for analysing functional brain images like Positron Emission Tomography (PET). When performing SPM analysis for different patient populations, brain PET template images representing population-specific brain morphometry and metabolism features are helpful. However, most currently available brain PET templates were constructed using the Caucasian data. To enrich the family of publicly available brain PET templates, we created Chinese-specific template images based on 116 [(18)F]-fluorodeoxyglucose ([(18)F]-FDG) PET images of normal participants. These images were warped into a common averaged space, in which the mean and standard deviation templates were both computed. We also developed the SPM analysis programmes to facilitate easy use of the templates. Our templates were validated through the SPM analysis of Alzheimer’s and Parkinson’s patient images. The resultant SPM t-maps accurately depicted the disease-related brain regions with abnormal [(18)F]-FDG uptake, proving the templates’ effectiveness in brain function impairment analysis.
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spelling pubmed-86264512021-12-10 Population-specific brain [(18)F]-FDG PET templates of Chinese subjects for statistical parametric mapping Wang, Hongkai Tian, Yang Liu, Yang Chen, Zhaofeng Zhai, Haoyu Zhuang, Mingrui Zhang, Nan Jiang, Yuanfang Gao, Ya Feng, Hongbo Zhang, Yanjun Sci Data Data Descriptor Statistical Parametric Mapping (SPM) is a computational approach for analysing functional brain images like Positron Emission Tomography (PET). When performing SPM analysis for different patient populations, brain PET template images representing population-specific brain morphometry and metabolism features are helpful. However, most currently available brain PET templates were constructed using the Caucasian data. To enrich the family of publicly available brain PET templates, we created Chinese-specific template images based on 116 [(18)F]-fluorodeoxyglucose ([(18)F]-FDG) PET images of normal participants. These images were warped into a common averaged space, in which the mean and standard deviation templates were both computed. We also developed the SPM analysis programmes to facilitate easy use of the templates. Our templates were validated through the SPM analysis of Alzheimer’s and Parkinson’s patient images. The resultant SPM t-maps accurately depicted the disease-related brain regions with abnormal [(18)F]-FDG uptake, proving the templates’ effectiveness in brain function impairment analysis. Nature Publishing Group UK 2021-11-26 /pmc/articles/PMC8626451/ /pubmed/34836985 http://dx.doi.org/10.1038/s41597-021-01089-1 Text en © The Author(s) 2021 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/) . The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) applies to the metadata files associated with this article.
spellingShingle Data Descriptor
Wang, Hongkai
Tian, Yang
Liu, Yang
Chen, Zhaofeng
Zhai, Haoyu
Zhuang, Mingrui
Zhang, Nan
Jiang, Yuanfang
Gao, Ya
Feng, Hongbo
Zhang, Yanjun
Population-specific brain [(18)F]-FDG PET templates of Chinese subjects for statistical parametric mapping
title Population-specific brain [(18)F]-FDG PET templates of Chinese subjects for statistical parametric mapping
title_full Population-specific brain [(18)F]-FDG PET templates of Chinese subjects for statistical parametric mapping
title_fullStr Population-specific brain [(18)F]-FDG PET templates of Chinese subjects for statistical parametric mapping
title_full_unstemmed Population-specific brain [(18)F]-FDG PET templates of Chinese subjects for statistical parametric mapping
title_short Population-specific brain [(18)F]-FDG PET templates of Chinese subjects for statistical parametric mapping
title_sort population-specific brain [(18)f]-fdg pet templates of chinese subjects for statistical parametric mapping
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8626451/
https://www.ncbi.nlm.nih.gov/pubmed/34836985
http://dx.doi.org/10.1038/s41597-021-01089-1
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