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