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Brain Metabolic Network Redistribution in Patients with White Matter Hyperintensities on MRI Analyzed with an Individualized Index Derived from (18)F-FDG-PET/MRI

OBJECTIVE: Whether metabolic redistribution occurs in patients with white matter hyperintensities (WMHs) on magnetic resonance imaging (MRI) is unknown. This study aimed 1) to propose a measure of the brain metabolic network for an individual patient and preliminarily apply it to identify impaired m...

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Autores principales: Ma, Jie, Hua, Xu-Yun, Zheng, Mou-Xiong, Wu, Jia-Jia, Huo, Bei-Bei, Xing, Xiang-Xin, Gao, Xin, Zhang, Han, Xu, Jian-Guang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Society of Radiology 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9523232/
https://www.ncbi.nlm.nih.gov/pubmed/36098344
http://dx.doi.org/10.3348/kjr.2022.0320
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author Ma, Jie
Hua, Xu-Yun
Zheng, Mou-Xiong
Wu, Jia-Jia
Huo, Bei-Bei
Xing, Xiang-Xin
Gao, Xin
Zhang, Han
Xu, Jian-Guang
author_facet Ma, Jie
Hua, Xu-Yun
Zheng, Mou-Xiong
Wu, Jia-Jia
Huo, Bei-Bei
Xing, Xiang-Xin
Gao, Xin
Zhang, Han
Xu, Jian-Guang
author_sort Ma, Jie
collection PubMed
description OBJECTIVE: Whether metabolic redistribution occurs in patients with white matter hyperintensities (WMHs) on magnetic resonance imaging (MRI) is unknown. This study aimed 1) to propose a measure of the brain metabolic network for an individual patient and preliminarily apply it to identify impaired metabolic networks in patients with WMHs, and 2) to explore the clinical and imaging features of metabolic redistribution in patients with WMHs. MATERIALS AND METHODS: This study included 50 patients with WMHs and 70 healthy controls (HCs) who underwent (18)F-fluorodeoxyglucose-positron emission tomography/MRI. Various global property parameters according to graph theory and an individual parameter of brain metabolic network called “individual contribution index” were obtained. Parameter values were compared between the WMH and HC groups. The performance of the parameters in discriminating between the two groups was assessed using the area under the receiver operating characteristic curve (AUC). The correlation between the individual contribution index and Fazekas score was assessed, and the interaction between age and individual contribution index was determined. A generalized linear model was fitted with the individual contribution index as the dependent variable and the mean standardized uptake value (SUV(mean)) of nodes in the whole-brain network or seven classic functional networks as independent variables to determine their association. RESULTS: The means ± standard deviations of the individual contribution index were (0.697 ± 10.9) × 10(-3) and (0.0967 ± 0.0545) × 10(-3) in the WMH and HC groups, respectively (p < 0.001). The AUC of the individual contribution index was 0.864 (95% confidence interval, 0.785–0.943). A positive correlation was identified between the individual contribution index and the Fazekas scores in patients with WMHs (r = 0.57, p < 0.001). Age and individual contribution index demonstrated a significant interaction effect on the Fazekas score. A significant direct association was observed between the individual contribution index and the SUV(mean) of the limbic network (p < 0.001). CONCLUSION: The individual contribution index may demonstrate the redistribution of the brain metabolic network in patients with WMHs.
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spelling pubmed-95232322022-10-08 Brain Metabolic Network Redistribution in Patients with White Matter Hyperintensities on MRI Analyzed with an Individualized Index Derived from (18)F-FDG-PET/MRI Ma, Jie Hua, Xu-Yun Zheng, Mou-Xiong Wu, Jia-Jia Huo, Bei-Bei Xing, Xiang-Xin Gao, Xin Zhang, Han Xu, Jian-Guang Korean J Radiol Nuclear Medicine OBJECTIVE: Whether metabolic redistribution occurs in patients with white matter hyperintensities (WMHs) on magnetic resonance imaging (MRI) is unknown. This study aimed 1) to propose a measure of the brain metabolic network for an individual patient and preliminarily apply it to identify impaired metabolic networks in patients with WMHs, and 2) to explore the clinical and imaging features of metabolic redistribution in patients with WMHs. MATERIALS AND METHODS: This study included 50 patients with WMHs and 70 healthy controls (HCs) who underwent (18)F-fluorodeoxyglucose-positron emission tomography/MRI. Various global property parameters according to graph theory and an individual parameter of brain metabolic network called “individual contribution index” were obtained. Parameter values were compared between the WMH and HC groups. The performance of the parameters in discriminating between the two groups was assessed using the area under the receiver operating characteristic curve (AUC). The correlation between the individual contribution index and Fazekas score was assessed, and the interaction between age and individual contribution index was determined. A generalized linear model was fitted with the individual contribution index as the dependent variable and the mean standardized uptake value (SUV(mean)) of nodes in the whole-brain network or seven classic functional networks as independent variables to determine their association. RESULTS: The means ± standard deviations of the individual contribution index were (0.697 ± 10.9) × 10(-3) and (0.0967 ± 0.0545) × 10(-3) in the WMH and HC groups, respectively (p < 0.001). The AUC of the individual contribution index was 0.864 (95% confidence interval, 0.785–0.943). A positive correlation was identified between the individual contribution index and the Fazekas scores in patients with WMHs (r = 0.57, p < 0.001). Age and individual contribution index demonstrated a significant interaction effect on the Fazekas score. A significant direct association was observed between the individual contribution index and the SUV(mean) of the limbic network (p < 0.001). CONCLUSION: The individual contribution index may demonstrate the redistribution of the brain metabolic network in patients with WMHs. The Korean Society of Radiology 2022-10 2022-09-05 /pmc/articles/PMC9523232/ /pubmed/36098344 http://dx.doi.org/10.3348/kjr.2022.0320 Text en Copyright © 2022 The Korean Society of Radiology https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0 (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Nuclear Medicine
Ma, Jie
Hua, Xu-Yun
Zheng, Mou-Xiong
Wu, Jia-Jia
Huo, Bei-Bei
Xing, Xiang-Xin
Gao, Xin
Zhang, Han
Xu, Jian-Guang
Brain Metabolic Network Redistribution in Patients with White Matter Hyperintensities on MRI Analyzed with an Individualized Index Derived from (18)F-FDG-PET/MRI
title Brain Metabolic Network Redistribution in Patients with White Matter Hyperintensities on MRI Analyzed with an Individualized Index Derived from (18)F-FDG-PET/MRI
title_full Brain Metabolic Network Redistribution in Patients with White Matter Hyperintensities on MRI Analyzed with an Individualized Index Derived from (18)F-FDG-PET/MRI
title_fullStr Brain Metabolic Network Redistribution in Patients with White Matter Hyperintensities on MRI Analyzed with an Individualized Index Derived from (18)F-FDG-PET/MRI
title_full_unstemmed Brain Metabolic Network Redistribution in Patients with White Matter Hyperintensities on MRI Analyzed with an Individualized Index Derived from (18)F-FDG-PET/MRI
title_short Brain Metabolic Network Redistribution in Patients with White Matter Hyperintensities on MRI Analyzed with an Individualized Index Derived from (18)F-FDG-PET/MRI
title_sort brain metabolic network redistribution in patients with white matter hyperintensities on mri analyzed with an individualized index derived from (18)f-fdg-pet/mri
topic Nuclear Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9523232/
https://www.ncbi.nlm.nih.gov/pubmed/36098344
http://dx.doi.org/10.3348/kjr.2022.0320
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