Related Experiment Video
Updated: Aug 29, 2025
![Studying Metabolic Brain Connectivity Using 2-Deoxy-2-[18F]Fluoro-D-Glucose Dynamic Positron Emission Tomography at the Single-subject Level](/_next/image?url=https%3A%2F%2Fcloudfront.jove.com%2FCDNSource%2Fteasers%2F67458.jpg&w=3840&q=50)
Studying Metabolic Brain Connectivity Using 2-Deoxy-2-[18F]Fluoro-D-Glucose Dynamic Positron Emission Tomography at the Single-subject Level
Published on: January 24, 2025
Brain Metabolic Network Redistribution in Patients with White Matter Hyperintensities on MRI Analyzed with an
Jie Ma1,2, Xu-Yun Hua3, Mou-Xiong Zheng3
1Center of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
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) × 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 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.
Insights
This study introduces a new brain metabolic network measure, the individual contribution index, to detect metabolic redistribution in patients with white matter hyperintensities (WMHs). The index effectively differentiates WMH patients from controls and correlates with disease severity.
Area of Science:
- Neuroimaging
- Metabolic Neuroscience
- Brain Network Analysis
Background:
- White matter hyperintensities (WMHs) are common in aging and cerebrovascular disease, but their impact on brain metabolism is not fully understood.
- Assessing brain metabolic network alterations in individuals with WMHs is crucial for understanding disease mechanisms and progression.
Purpose of the Study:
- To develop and validate a novel measure, the individual contribution index, for assessing brain metabolic network properties in individual patients.
- To investigate whether metabolic redistribution occurs in patients with WMHs using this new index.
- To explore the clinical and imaging correlates of metabolic redistribution in WMH patients.
Main Methods:
- Utilized 18F-fluorodeoxyglucose-positron emission tomography/magnetic resonance imaging (FDG-PET/MRI) in 50 WMH patients and 70 healthy controls (HCs).
- Calculated global network properties and the individual contribution index, a novel parameter for brain metabolic networks.
- Employed graph theory, receiver operating characteristic (ROC) curve analysis, and correlation analyses to assess the index's performance and associations.
Main Results:
- The individual contribution index was significantly higher in WMH patients compared to HCs (p < 0.001), with an area under the ROC curve (AUC) of 0.864, indicating good discriminative ability.
- A positive correlation was found between the individual contribution index and Fazekas scores (r = 0.57, p < 0.001), suggesting a link to WMH severity.
- The index showed a significant association with the mean standardized uptake value (SUVmean) of the limbic network (p < 0.001).
Conclusions:
- The individual contribution index shows promise as a tool to identify metabolic redistribution in the brain's metabolic network of patients with WMHs.
- This measure may offer insights into the functional consequences of WMHs and their impact on brain metabolism.
![Semi-quantitative Assessment Using [18F]FDG Tracer in Patients with Severe Brain Injury](/_next/image?url=https%3A%2F%2Fcloudfront.jove.com%2FCDNSource%2Fteasers%2F58641.jpg&w=3840&q=50)
