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 [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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:43 |
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| Enthalten in: |
Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism - 43(2023), 11 vom: 19. Nov., Seite 1905-1918 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Volpi, Tommaso [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 09.11.2023 Date Revised 13.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1177/0271678X231184365 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM358784123 |
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| 245 | 1 | 2 | |a A new framework for metabolic connectivity mapping using bolus [18F]FDG PET and kinetic modeling |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Euclidean similarity | |
| 650 | 4 | |a [18F]FDG | |
| 650 | 4 | |a dynamic PET | |
| 650 | 4 | |a individual-level metabolic connectivity | |
| 650 | 4 | |a kinetic modeling | |
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| 700 | 1 | |a Vallini, Giulia |e verfasserin |4 aut | |
| 700 | 1 | |a Silvestri, Erica |e verfasserin |4 aut | |
| 700 | 1 | |a Francisci, Mattia De |e verfasserin |4 aut | |
| 700 | 1 | |a Durbin, Tony |e verfasserin |4 aut | |
| 700 | 1 | |a Corbetta, Maurizio |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, John J |e verfasserin |4 aut | |
| 700 | 1 | |a Vlassenko, Andrei G |e verfasserin |4 aut | |
| 700 | 1 | |a Goyal, Manu S |e verfasserin |4 aut | |
| 700 | 1 | |a Bertoldo, Alessandra |e verfasserin |4 aut | |
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