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

Abstract

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., K_i, 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, K_i, 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.

Keywords: Euclidean similarity; [18F]FDG; dynamic PET; individual-level metabolic connectivity; kinetic modeling.

Figure 1.

Analysis pipeline for inferring metabolic connectivity at the individual (within-individual approach) and group level (across-individual approach). [¹⁸F]FDG PET dynamic data (far left) is the source of all inferences of metabolic connectivity. A static SUVR image (top left) is obtained from frames in the 40–60 min window of the dynamic PET data; in parallel, a two-tissue compartment model is fitted to dynamic PET data to estimate [¹⁸F]FDG kinetic parameters at the voxel level (using Variational Bayesian inference and an image-derived input function as a surrogate for the [¹⁸F]FDG plasma concentration C_p), in particular K_i, K₁ and k₃ (center), and reconstruct the time courses of compartments 1 and 2 (bottom center). From the subject series of SUVR, K_i, K₁ and k₃, parcellated thanks to the chosen atlas ROIs registered to individual PET space, we calculate across-individual MC via Pearson’s correlation (top right), while from the time series of the tissue TACs, compartments 1 and 2, individual-level MC is obtained via Euclidean similarity and averaged across participants (bottom right).

Figure 2.

Matrix representation of within-individual metabolic connectivity. Matrix elements indicate Euclidean similarity of tracer kinetics from dynamic PET data of different regions. The Hammers parcellation is used to sample dynamic PET time series at the individual level. Following matrix computation for individual subjects, we averaged matrices for the group of 54 individuals. The average matrices for the full tissue TAC (a), its early part (b) and late part (c), the kinetics of $C_1\left(t\right)$ (d) and $C_2\left(t\right)$ (e) are displayed. For clarity and for conforming to conventions for identifying network hubs, matrices are visualized with 80% enforced sparsity. Homotopic inter-hemispheric connections are most evident in (a) and (c), i.e., matrices which are more related to metabolic events, while in (b), which highlights the early, flow-related information, there is little difference between inter-hemispheric and intra-hemispheric connections. Pearson’s correlation coefficients (Mantel’s test, all p < 10⁻⁷, Bonferroni corrected) between the upper triangular elements of the five wi-MC matrices are also reported (f), confirming the full tissue TACs associate most with the late part.

Figure 3.

Matrix representation of across-individual metabolic connectivity. Matrix elements indicate Pearson’s correlation coefficients between [¹⁸F]FDG parameter values of different regions across our group of 54 individuals. The Hammers parcellation is used to sample individual-level [¹⁸F]FDG parametric maps. The across-individual MC matrices for SUVR (a), K_i (b), K₁ (c) and k₃ (d) are reported. For clarity and for conforming to conventions for identifying network hubs, matrices are visualized with 80% enforced sparsity. The Pearson’s correlation coefficients (Mantel’s test, all p < 10⁻⁷, Bonferroni corrected) between the upper triangular elements of the four ai-MC matrices are also reported (e), highlighting how k₃ MC has the lower similarity to traditionally calculated SUVR MC.

Figure 4.

Comparison of wi-MC (a) vs. ai-MC (b) ‘hubs’, identified as the top DEG and EC nodes for each matrix. Hub nodes are shown on the Hammers atlas regions in red (a) and blue (b) respectively. A matrix of Dice similarity values between wi-MC and ai-MC hubs is shown in the central panel. Early-time and late-time dynamic PET MC have the highest association with across-individual covariation of k₃.

Figure 5.

Dice similarity (stem plots) of the wi-MC (red) and ai-MC (blue) binarized matrices (80th percentile) with the SC template (a) and group-average FC matrix (b).