A new framework for metabolic connectivity mapping using bolus [18F]FDG PET and kinetic modeling

Tommaso Volpi, Giulia Vallini, Erica Silvestri, Mattia De Francisci, Tony Durbin, Maurizio Corbetta, John J. Lee, Andrei G. Vlassenko, Manu S. Goyal, Alessandra Bertoldo

Research output: Contribution to journalArticlepeer-review


Metabolic connectivity (MC) has been previously proposed as the covariation of static [18F]FDG PET images across participants, i.e., across-individual MC (ai-MC). In few cases, MC has been inferred from dynamic [18F]FDG signals, i.e., within-individual MC (wi-MC), as for resting-state fMRI functional connectivity (FC). The validity and interpretability of both approaches is an important open issue. Here we reassess this topic, aiming to 1) develop a novel wi-MC methodology; 2) compare ai-MC maps from standardized uptake value ratio (SUVR) vs. [18F]FDG kinetic parameters fully describing the tracer behavior (i.e., Ki, K1, k3); 3) assess MC interpretability in comparison to structural connectivity and FC. We developed a new approach based on Euclidean distance to calculate wi-MC from PET time-activity curves. The across-individual correlation of SUVR, Ki, K1, k3 produced different networks depending on the chosen [18F]FDG parameter (k3 MC vs. SUVR MC, r = 0.44). We found that wi-MC and ai-MC matrices are dissimilar (maximum r = 0.37), and that the match with FC is higher for wi-MC (Dice similarity: 0.47–0.63) than for ai-MC (0.24–0.39). Our analyses demonstrate that calculating individual-level MC from dynamic PET is feasible and yields interpretable matrices that bear similarity to fMRI FC measures.

Original languageEnglish
Pages (from-to)1905-1918
Number of pages14
JournalJournal of Cerebral Blood Flow and Metabolism
Issue number11
StatePublished - Nov 2023


  • Euclidean similarity
  • [F]FDG
  • dynamic PET
  • individual-level metabolic connectivity
  • kinetic modeling


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