Fully Integrated PET/MR Imaging for the Assessment of the Relationship Between Functional Connectivity and Glucose Metabolic Rate - Shiyam Sundar, Lalith Kumar; Baajour, Shahira; Beyer, Thomas; Lanzenberger, Rupert; Traub-Weidinger, Tatjana; Rausch, Ivo; Pataraia, Ekaterina; Hahn, Andreas; Rischka, Lucas; Hienert, Marius; Klebermass, Eva-Maria; Muzik, Otto (Frontiers in Neuroscience, 2020)
Determination of the cerebral metabolic rate of glucose (CMRGlc) in clinical routine represents one of the most widely used clinical applications of PET imaging. However, CMRGlc values display high physiological variability, presumably due to fluctuations in the intrinsic activity of the brain at rest. With the advent of combined PET/MR imaging technology, CMRGlc values can be acquired simultaneously with the intrinsic brain activity as obtained with functional MRI. To reduce CMRGlc variability associated with these fluctuations, the objective of this study was to determine whether functional connectivity measures derived from resting-state fMRI (rs-fMRI) could be used to correct for these fluctuations in intrinsic brain activity. We studied 10 healthy volunteers who underwent a test-retest dynamic [18F]FDG-PET study using a fully-integrated PET/MR system (Siemens Biograph mMR). Using the arterial input function, parametric images representing CMRGlc were determined using the Patlak graphical approach. Both directed (dFC) and undirected (uFC) functional connectivity was determined between nodes in six major networks (Default-mode network, Salience, L/R Executive, Attention, and Sensory-motor network) using either a bivariate-correlation (R-coefficient) or a Multi-Variate AutoRegressive (MVAR) model. The average intrasubject variability for CMRGlc values between test and retest was determined as (14 + 8%) with an average inter-subject variability of 25.4% at test and 15.1% at retest. The average CMRGlc value (umol/100g/min) across all networks was 39 + 10 at test and increased slightly to 43 + 6 at retest. The R and MVAR-coefficients were in general reproducible across time with a stable network pattern. However, no significant relationship was found between either R-coefficients (for uFC) or MVAR coefficients (for dFC) and corresponding CMRGlc values for any of the six major networks. Measurement of functional connectivity within established brain networks did not provide a means to decrease the inter- or intrasubject variability of CMRGlc values. As such, our results indicate that connectivity measures derived from rs-fMRI acquired contemporaneously with PET imaging are not suited for correction of CMRGlc variability associated with intrinsic fluctuations of resting-state brain activity. Thus, given the observed substantial inter- and intrasubject variability of CMRGlc values, the relevance of absolute quantification for clinical routine is presently uncertain.