ReviewEJNMMI reports2025
FDG-PET-based brain network analysis: a brief review of metabolic connectivity.
Review in EJNMMI reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- A review of FDG-PET biomarker analysis methods in Parkinson's disease.Journal of Parkinson's disease · 2026Review
- Mapping the Synaptome in Neurodegeneration: Emerging Clinical Applications of SV2A PET Imaging.Molecular diagnosis & therapy · 2026Article
- Graph-Based White Matter Tractometry: Methods, Applications, and the Path to Validation.Neuroinformatics · 2026Review
- SPECT and PET imaging of Alzheimer's disease revisited: from biomarkers to artificial intelligence-based prediction.Annals of nuclear medicine · 2026Review
- From synapse to system: mechanistic pathways of neural signaling dysfunction in psychiatric disorders.Frontiers in cell and developmental biology · 2026Review
- Comparing three methodologies for network analysis of human [EJNMMI research · 2025Article
- Alteration of brain metabolism in lacunar stroke based onFrontiers in neurology · 2025Article
- Precision neurodiversity: personalized brain network architecture as a window into cognitive variability.Frontiers in human neuroscience · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundOver the past decades, the analysis metabolic connectivity patterns has received significant attention in exploring the underlying mechanism of human behaviors, and the neural underpinnings of brain neurological disorders. Brain network can be considered a powerful tool and play an important role in the analysis and understanding of brain metabolic patterns. With the advantages and emergence of metabolic-based network analysis, this study aims to systematically review how brain properties, under various conditions, can be studied using Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) images and graph theory, as well as applications of this approach. Additionally, this study provides a brief summary of graph metrics and their uses in studying and diagnosing different types of brain disorders using FDG-PET images. MAIN BODY: In this study, we used several databases in Web of Science including Web of Science Core Collection, MEDLINE to search for related studies from 1980 up to the present, focusing on FDG-PET images and graph theory. From 68 articles that matched our keywords, we selected 28 for a full review in order to find out the most recent findings and trends. Our results reveal that graph theory and its applications in analyzing metabolic connectivity patterns have attracted the attention of researchers since 2015. While most of the studies are focusing on group-level based analysis, there is a growing trend in individual-based network analysis. Although metabolic connectivity can be applied to both neurological and psychiatric disorders, the majority of studies concentrate on neurological disorders, particularly Alzheimer's Disease and Parkinson's Disease. Most of the findings focus on changes in brain network topology, including brain segregation and integration.
conclusionThis review provides an insight into how graph theory can be used to study metabolic connectivity patterns under various conditions including neurological and psychiatric disorders.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.