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Studying Metabolic Brain Connectivity Using 2-Deoxy-2-[18F]Fluoro-D-Glucose Dynamic Positron Emission Tomography at the Single-subject Level
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Studying Metabolic Brain Connectivity Using 2-Deoxy-2-[18F]Fluoro-D-Glucose Dynamic Positron Emission Tomography at the Single-subject Level

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High-temporal resolution metabolic connectivity resolved by component-based noise correction.

Murray B Reed1,2, Samantha Graf1,2, Matej Murgaš1,2

  • 1Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.

Journal of Cerebral Blood Flow and Metabolism : Official Journal of the International Society of Cerebral Blood Flow and Metabolism
|March 13, 2026
PubMed
Summary
This summary is machine-generated.

High-temporal functional PET (fPET) imaging can now model molecular connectivity. A new component-based noise reduction method (CompCor) improves signal extraction, enabling reliable resting-state metabolic connectivity analysis across different scanners.

Keywords:
Molecular connectivity[18F]FDG[18F]fludeoxyglucoseband-passwithin-subject connectivity

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Area of Science:

  • Neuroimaging
  • Molecular Imaging
  • Biophysics

Background:

  • Functional PET (fPET) offers second-level temporal resolution for metabolic process modeling.
  • High-temporal fPET is sensitive to noise, challenging signal extraction for applications like molecular connectivity imaging.
  • Existing preprocessing methods may not optimally handle the noise characteristics of high-temporal fPET data.

Purpose of the Study:

  • To adapt and evaluate a component-based noise reduction strategy (CompCor) for high-temporal [18F]FDG-fPET scans.
  • To assess the impact of different filtering methods and scanner types (PET/CT vs. PET/MR) on metabolic connectivity (M-MC) estimation.
  • To compare the characteristics of fPET-derived networks with those from functional MRI (fMRI).

Main Methods:

  • Applied a CompCor preprocessing pipeline, adapted from fMRI, to high-temporal [18F]FDG-fPET data from PET/CT (1s frames) and PET/MR (3s frames) scanners.
  • Compared various filtering strategies across different frequency bands to assess their effect on M-MC.
  • Analyzed the consistency, structure, and spatial characteristics of resulting connectivity networks.

Main Results:

  • CompCor yielded more consistent and structured metabolic networks compared to standard bandpass filters.
  • Intermediate frequency bands (0.01-0.1 Hz) demonstrated the most reliable M-MC across both PET/CT and PET/MR scanners (r=0.89).
  • High-sensitivity PET/CT also showed structured patterns at higher frequencies (0.1-0.2 Hz). fPET networks were spatially cohesive but less differentiated than fMRI networks.

Conclusions:

  • High-temporal [18F]FDG-fPET, when combined with appropriate denoising techniques like CompCor, allows for reliable estimation of resting-state metabolic connectivity.
  • Scanner characteristics and preprocessing choices critically influence signal quality and network reliability.
  • This physiologically informed pipeline enhances cross-system and cross-study comparability in molecular imaging.