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

Authors :
Volpi, Tommaso
Vallini, Giulia
Silvestri, Erica
Francisci, Mattia De
Durbin, Tony
Corbetta, Maurizio
Lee, John J
Vlassenko, Andrei G
Goyal, Manu S
Bertoldo, Alessandra
Source :
Journal of Cerebral Blood Flow & Metabolism; Nov2023, Vol. 43 Issue 11, p1905-1918, 14p
Publication Year :
2023

Abstract

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

Details

Language :
English
ISSN :
0271678X
Volume :
43
Issue :
11
Database :
Complementary Index
Journal :
Journal of Cerebral Blood Flow & Metabolism
Publication Type :
Academic Journal
Accession number :
173490975
Full Text :
https://doi.org/10.1177/0271678X231184365