A new framework for metabolic connectivity mapping using bolus FDG PET and kinetic modelling

Volpi, T.; Vallini, G.; Silvestri, E.; De Francisci, M.; Durbin, T.; Corbetta, M.; Lee, J. J.; Vlassenko, A. G.; Goyal, M. S.; Bertoldo, A.

doi:10.1101/2022.12.27.522050

bioRxiv · 10.1101/2022.12.27.522050

A new framework for metabolic connectivity mapping using bolus FDG PET and kinetic modelling

Volpi, T.·Vallini, G.·Silvestri, E.·De Francisci, M.·Durbin, T.·Corbetta, M.·Lee, J. J.·Vlassenko, A. G.·Goyal, M. S.·Bertoldo, A.

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Abstract

PurposeMetabolic connectivity (MC) has been previously proposed as the covariation of static [18F]FDG PET images across participants, which we call across-individual MC (ai-MC). In few cases, MC has also been inferred from dynamic [18F]FDG signals, similarly to fMRI functional connectivity (FC), which we term within-individual MC (wi-MC). The validity and interpretability of both MC approaches is an important open issue. Here we reassess this topic, aiming to 1) develop a novel methodology for wi-MC estimation; 2) compare ai-MC maps obtained using different [18F]FDG parameters (K1, i.e. tracer transport rate, k3, i.e. phosphorylation rate, Ki, i.e. tracer uptake rate, and the standardized uptake value ratio, SUVR); 3) assess the interpretability of ai-MC and wi-MC in comparison to structural and functional connectivity (FC) measures. MethodsWe analyzed dynamic [18F]FDG data from 54 healthy adults using kinetic modelling to quantify the macro- and microparameters describing the tracer behavior (i.e. Ki, K1, k3). We also calculated SUVR. From the across-individual correlation of SUVR, Ki, K1, k3, we obtained four different ai-MC matrices. A new approach based on Euclidean distance was developed to calculate wi-MC from PET time-activity curves. ResultsWe identified Euclidean similarity as the most appropriate metric to calculate wi-MC. ai-MC networks changed with different [18F]FDG parameters (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). ConclusionOur data demonstrate that individual-level MC from dynamic [18F]FDG data using Euclidean similarity is feasible and yields interpretable matrices that bear similarity to resting-state fMRI FC measures.

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Volpi, T., Vallini, G., Silvestri, E., De Francisci, M., Durbin, T., Corbetta, M., Lee, J. J., Vlassenko, A. G., Goyal, M. S., Bertoldo, A.. 2022-12-30. A new framework for metabolic connectivity mapping using bolus FDG PET and kinetic modelling. https://doi.org/10.1101/2022.12.27.522050

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