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

Metabolic connectivity (MC) has been previously proposed as the covariation of static [ 18 F]FDG PET images across participants, i.e., across-individual MC (ai-MC). In few cases, MC has been inferred from dynamic [ 18 F]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. [ 18 F]FDG kinetic parameters fully describing the tracer behavior (i.e., K i , K 1 , k 3 ); 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, K i , K 1 , k 3 produced different networks depending on the chosen [ 18 F]FDG parameter ( k 3 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.