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Related Experiment Video

Updated: Feb 12, 2026

Determining Glucose Metabolism Kinetics Using 18F-FDG Micro-PET/CT
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Inter-subject FDG PET Brain Networks Exhibit Multi-scale Community Structure with Different Normalization Techniques.

Megan M Sperry1, Sonia Kartha1, Eric J Granquist2

  • 1Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.

Annals of Biomedical Engineering
|April 13, 2018
PubMed
Summary
This summary is machine-generated.

Inter-subject brain networks derived from 18F-2-deoxy-2-(18F)fluoro-D-glucose (FDG) positron emission tomography (PET) are stable for group sizes of 10 or more. Network properties are consistent across normalization methods, suggesting reliability in metabolic imaging studies.

Keywords:
BrainFDG PETModularityNetworks

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Area of Science:

  • Neuroscience
  • Metabolic Imaging
  • Network Science

Background:

  • Inter-subject networks model brain region correlations, valuable for metabolic imaging like FDG PET.
  • FDG PET yields single images, limiting temporal correlation analysis.
  • Basic properties of inter-subject networks concerning group size and normalization remain under-explored.

Purpose of the Study:

  • Investigate the impact of group size and image normalization on inter-subject FDG PET network properties.
  • Assess the stability and reliability of these networks for metabolic imaging research.

Main Methods:

  • FDG PET images from rats (n=18) were acquired and normalized using whole brain, visual cortex, or cerebellar uptake.
  • Correlation matrices were constructed to form inter-subject networks.
  • Network stability was evaluated by varying group size and analyzing local connectivity (node strength, clustering coefficient).
  • Modularity and community structure were assessed across different normalization strategies.

Main Results:

  • Local network properties demonstrated stability irrespective of normalization region for groups of at least 10 rats.
  • Whole brain-normalized networks exhibited significantly higher modularity compared to visual cortex or cerebellum normalization (p < 0.00001).
  • Community structure remained consistent across network resolutions, even where modularity varied.
  • Hierarchical analysis revealed stable modules at multiple scales and clustering of proximate brain regions.

Conclusions:

  • Inter-subject FDG PET networks are robust and stable with adequate group sizes (≥10 rats).
  • Network stability is maintained across different normalization techniques.
  • The findings support the use of inter-subject FDG PET networks in metabolic imaging research due to their consistent properties and multi-scale modularity.