Cargando…

Correlation of [(18)F]florbetaben textural features and age of onset of Alzheimer’s disease: a principal components analysis approach

BACKGROUND: When Alzheimer’s disease (AD) is occurring at an early onset before 65 years old, its clinical course is generally more aggressive than in the case of a late onset. We aim at identifying [[Formula: see text] F]florbetaben PET biomarkers sensitive to differences between early-onset Alzhei...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Jing, Antonecchia, Emanuele, Camerlenghi, Marco, Chiaravalloti, Agostino, Chu, Qian, Costanzo, Alfonso Di, Li, Zhen, Wan, Lin, Zhang, Xiangsong, D’Ascenzo, Nicola, Schillaci, Orazio, Xie, Qingguo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8060386/
https://www.ncbi.nlm.nih.gov/pubmed/33881633
http://dx.doi.org/10.1186/s13550-021-00774-x
_version_ 1783681350442680320
author Li, Jing
Antonecchia, Emanuele
Camerlenghi, Marco
Chiaravalloti, Agostino
Chu, Qian
Costanzo, Alfonso Di
Li, Zhen
Wan, Lin
Zhang, Xiangsong
D’Ascenzo, Nicola
Schillaci, Orazio
Xie, Qingguo
author_facet Li, Jing
Antonecchia, Emanuele
Camerlenghi, Marco
Chiaravalloti, Agostino
Chu, Qian
Costanzo, Alfonso Di
Li, Zhen
Wan, Lin
Zhang, Xiangsong
D’Ascenzo, Nicola
Schillaci, Orazio
Xie, Qingguo
author_sort Li, Jing
collection PubMed
description BACKGROUND: When Alzheimer’s disease (AD) is occurring at an early onset before 65 years old, its clinical course is generally more aggressive than in the case of a late onset. We aim at identifying [[Formula: see text] F]florbetaben PET biomarkers sensitive to differences between early-onset Alzheimer’s disease (EOAD) and late-onset Alzheimer’s disease (LOAD). We conducted [[Formula: see text] F]florbetaben PET/CT scans of 43 newly diagnosed AD subjects. We calculated 93 textural parameters for each of the 83 Hammers areas. We identified 41 independent principal components for each brain region, and we studied their Spearman correlation with the age of AD onset, by taking into account multiple comparison corrections. Finally, we calculated the probability that EOAD and LOAD patients have different amyloid-[Formula: see text] ([Formula: see text] ) deposition by comparing the mean and the variance of the significant principal components obtained in the two groups with a 2-tailed Student’s t-test. RESULTS: We found that four principal components exhibit a significant correlation at a 95% confidence level with the age of onset in the left lateral part of the anterior temporal lobe, the right anterior orbital gyrus of the frontal lobe, the right lateral orbital gyrus of the frontal lobe and the left anterior part of the superior temporal gyrus. The data are consistent with the hypothesis that EOAD patients have a significantly different [[Formula: see text] F]florbetaben uptake than LOAD patients in those four brain regions. CONCLUSIONS: Early-onset AD implies a very irregular pattern of [Formula: see text] deposition. The authors suggest that the identified textural features can be used as quantitative biomarkers for the diagnosis and characterization of EOAD patients.
format Online
Article
Text
id pubmed-8060386
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Springer Berlin Heidelberg
record_format MEDLINE/PubMed
spelling pubmed-80603862021-05-05 Correlation of [(18)F]florbetaben textural features and age of onset of Alzheimer’s disease: a principal components analysis approach Li, Jing Antonecchia, Emanuele Camerlenghi, Marco Chiaravalloti, Agostino Chu, Qian Costanzo, Alfonso Di Li, Zhen Wan, Lin Zhang, Xiangsong D’Ascenzo, Nicola Schillaci, Orazio Xie, Qingguo EJNMMI Res Original Research BACKGROUND: When Alzheimer’s disease (AD) is occurring at an early onset before 65 years old, its clinical course is generally more aggressive than in the case of a late onset. We aim at identifying [[Formula: see text] F]florbetaben PET biomarkers sensitive to differences between early-onset Alzheimer’s disease (EOAD) and late-onset Alzheimer’s disease (LOAD). We conducted [[Formula: see text] F]florbetaben PET/CT scans of 43 newly diagnosed AD subjects. We calculated 93 textural parameters for each of the 83 Hammers areas. We identified 41 independent principal components for each brain region, and we studied their Spearman correlation with the age of AD onset, by taking into account multiple comparison corrections. Finally, we calculated the probability that EOAD and LOAD patients have different amyloid-[Formula: see text] ([Formula: see text] ) deposition by comparing the mean and the variance of the significant principal components obtained in the two groups with a 2-tailed Student’s t-test. RESULTS: We found that four principal components exhibit a significant correlation at a 95% confidence level with the age of onset in the left lateral part of the anterior temporal lobe, the right anterior orbital gyrus of the frontal lobe, the right lateral orbital gyrus of the frontal lobe and the left anterior part of the superior temporal gyrus. The data are consistent with the hypothesis that EOAD patients have a significantly different [[Formula: see text] F]florbetaben uptake than LOAD patients in those four brain regions. CONCLUSIONS: Early-onset AD implies a very irregular pattern of [Formula: see text] deposition. The authors suggest that the identified textural features can be used as quantitative biomarkers for the diagnosis and characterization of EOAD patients. Springer Berlin Heidelberg 2021-04-21 /pmc/articles/PMC8060386/ /pubmed/33881633 http://dx.doi.org/10.1186/s13550-021-00774-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Research
Li, Jing
Antonecchia, Emanuele
Camerlenghi, Marco
Chiaravalloti, Agostino
Chu, Qian
Costanzo, Alfonso Di
Li, Zhen
Wan, Lin
Zhang, Xiangsong
D’Ascenzo, Nicola
Schillaci, Orazio
Xie, Qingguo
Correlation of [(18)F]florbetaben textural features and age of onset of Alzheimer’s disease: a principal components analysis approach
title Correlation of [(18)F]florbetaben textural features and age of onset of Alzheimer’s disease: a principal components analysis approach
title_full Correlation of [(18)F]florbetaben textural features and age of onset of Alzheimer’s disease: a principal components analysis approach
title_fullStr Correlation of [(18)F]florbetaben textural features and age of onset of Alzheimer’s disease: a principal components analysis approach
title_full_unstemmed Correlation of [(18)F]florbetaben textural features and age of onset of Alzheimer’s disease: a principal components analysis approach
title_short Correlation of [(18)F]florbetaben textural features and age of onset of Alzheimer’s disease: a principal components analysis approach
title_sort correlation of [(18)f]florbetaben textural features and age of onset of alzheimer’s disease: a principal components analysis approach
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8060386/
https://www.ncbi.nlm.nih.gov/pubmed/33881633
http://dx.doi.org/10.1186/s13550-021-00774-x
work_keys_str_mv AT lijing correlationof18fflorbetabentexturalfeaturesandageofonsetofalzheimersdiseaseaprincipalcomponentsanalysisapproach
AT antonecchiaemanuele correlationof18fflorbetabentexturalfeaturesandageofonsetofalzheimersdiseaseaprincipalcomponentsanalysisapproach
AT camerlenghimarco correlationof18fflorbetabentexturalfeaturesandageofonsetofalzheimersdiseaseaprincipalcomponentsanalysisapproach
AT chiaravallotiagostino correlationof18fflorbetabentexturalfeaturesandageofonsetofalzheimersdiseaseaprincipalcomponentsanalysisapproach
AT chuqian correlationof18fflorbetabentexturalfeaturesandageofonsetofalzheimersdiseaseaprincipalcomponentsanalysisapproach
AT costanzoalfonsodi correlationof18fflorbetabentexturalfeaturesandageofonsetofalzheimersdiseaseaprincipalcomponentsanalysisapproach
AT lizhen correlationof18fflorbetabentexturalfeaturesandageofonsetofalzheimersdiseaseaprincipalcomponentsanalysisapproach
AT wanlin correlationof18fflorbetabentexturalfeaturesandageofonsetofalzheimersdiseaseaprincipalcomponentsanalysisapproach
AT zhangxiangsong correlationof18fflorbetabentexturalfeaturesandageofonsetofalzheimersdiseaseaprincipalcomponentsanalysisapproach
AT dascenzonicola correlationof18fflorbetabentexturalfeaturesandageofonsetofalzheimersdiseaseaprincipalcomponentsanalysisapproach
AT schillaciorazio correlationof18fflorbetabentexturalfeaturesandageofonsetofalzheimersdiseaseaprincipalcomponentsanalysisapproach
AT xieqingguo correlationof18fflorbetabentexturalfeaturesandageofonsetofalzheimersdiseaseaprincipalcomponentsanalysisapproach