The brain’s “dark energy” puzzle upgraded

[18F]FDG uptake, delivery and phosphorylation, and their coupling with resting-state brain activity

  • Volpi, Tommaso
  • Lee, John J
  • Vlassenko, Andrei G
  • Goyal, Manu S
  • Corbetta, Maurizio
  • Bertoldo, Alessandra

  • 1Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA

    2Padova Neuroscience Center, University of Padova, Padova, Italy

    3Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA

    4Department of Neuroscience, University of Padova, Padova, Italy

    5Department of Information Engineering, University of Padova, Padova, Italy

Tommaso Volpi, 801 Howard Ave, 06519 New Haven, CT, USA. Email: [email protected]

Alessandra Bertoldo, Via Gradenigo 6/B, 35122 Padova, Italy. Email: [email protected]

Received October 9, 2024

Received in revised form March 1, 2025

Accepted March 9, 2025

Journal of Cerebral Blood Flow & Metabolism 45(9):p 1799-1815, September 2025. | DOI: 10.1177/0271678X251329707

The brain’s resting-state energy consumption is expected to be driven by spontaneous activity. We previously used 50 resting-state fMRI (rs-fMRI) features to predict [18F]FDG SUVR as a proxy of glucose metabolism. Here, we expanded on our effort by estimating [18F]FDG kinetic parameters Ki (irreversible uptake), K1 (delivery), k3 (phosphorylation) in a large healthy control group (n = 47). Describing the parameters’ spatial distribution at high resolution (216 regions), we showed that K1 is the least redundant (strong posteromedial pattern), and Ki and k3 have relevant differences (occipital cortices, cerebellum, thalamus). Using multilevel modeling, we investigated how much spatial variance of [18F]FDG parameters could be explained by a combination of a) rs-fMRI variables, b) cerebral blood flow (CBF) and metabolic rate of oxygen (CMRO2) from O PET. Rs-fMRI-only models explained part of the individual variance in Ki (35%), K1 (14%), k3 (21%), while combining rs-fMRI and CMRO2 led to satisfactory description of Ki (46%) especially. Ki was sensitive to both local rs-fMRI variables (ReHo) and CMRO2, k3 to ReHo, K1 to CMRO2. This work represents a comprehensive assessment of the complex underpinnings of brain glucose consumption, and highlights links between 1) glucose phosphorylation and local brain activity, 2) glucose delivery and oxygen consumption.

Copyright © 2025 International Society of Cerebral Blood Flow and Metabolism