Brain Metabolic Network Redistribution in Patients with White Matter Hyperintensities on MRI Analyzed with an Individualized Index Derived from 18F-FDG-PET/MRI
- Author:
Jie MA
1
;
Xu-Yun HUA
;
Mou-Xiong ZHENG
;
Jia-Jia WU
;
Bei-Bei HUO
;
Xiang-Xin XING
;
Xin GAO
;
Han ZHANG
;
Jian-Guang XU
Author Information
- Publication Type:Original Article
- From:Korean Journal of Radiology 2022;23(10):986-997
- CountryRepublic of Korea
- Language:English
-
Abstract:
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 18F-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 (SUVmean) 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) x 10-3 and (0.0967 ± 0.0545) x 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 SUVmean 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.
