Quantification of Task-Specific Glucose Metabolism with Constant Infusion of¹⁸F-FDG

Abstract

The investigation of cerebral metabolic rate of glucose (CMRGlu) at baseline and during specific tasks previously required separate scans with the drawback of high intrasubject variability. We aimed to validate a novel approach to assessing baseline glucose metabolism and task-specific changes in a single measurement with a constant infusion of 18 F-FDG. Methods: Fifteen healthy subjects underwent two PET measurements with arterial blood sampling. As a reference, baseline CMRGlu was quantified from a 60-min scan after 18 F-FDG bolus application using the Patlak plot (eyes closed). For the other scan, a constant radioligand infusion was applied for 95 min, during which the subjects opened their eyes at 10-20 min and 60-70 min and tapped their right thumb to their fingers at 35-45 min and 85-95 min. The constant-infusion scan was quantified in two steps. First, the general linear model was used to fit regional time-activity curves with regressors for baseline metabolism, task-specific changes for the eyes-open and finger-tapping conditions, and movement parameters. Second, the Patlak plot was used for quantification of CMRGlu. Multiplication of the baseline regressor by β-values from the general linear model yielded regionally specific time-activity curves for baseline metabolism. Further, taskspecific changes in metabolism are directly proportional to changes in the slope of the time-activity curve and hence to changes in CMRGlu. Results: Baseline CMRGlu from the constant-infusion scan matched that from the bolus application (test-retest variability, 1.1% ± 24.7%), which was not the case for a previously suggested approach (variability, −39.9% ± 25.2%, P , 0.001). Task-specific CMRGlu increased in the primary visual and motor cortices for eyes open and finger tapping, respectively (P , 0.05, familywise errorcorrected), with absolute changes of up to 2.1 μmol/100 g/min and 6.3% relative to baseline. For eyes open, a decreased CMRGlu was observed in default-mode regions (P , 0.05, familywise errorcorrected). CMRGlu quantified with venous blood samples (n 5 6) showed excellent agreement with results obtained from arterial samples (r . 0.99). Conclusion: Baseline glucose metabolism and taskspecific changes can be quantified in a single measurement with constant infusion of 18 F-FDG and venous blood sampling. The high sensitivity and regional specificity of the approach offer novel possibilities for functional and multimodal brain imaging.

From simultaneous to synergistic MR‐PET brain imaging: A review of hybrid MR‐PET imaging methodologies

“…Given that the BOLD-fMRI signal indexes recent neural activity with a temporal resolution of 0.5-3 s, there is a significant temporal confound when comparing simultaneously acquired FDG-PET and BOLD-fMRI activation maps. Comparable results were recently obtained by (Hahn et al, 2016) in the motor cortex. One procedure is to dynamically track FDG uptake by slowly infusing FDG over the course of the scan.…”

“…In a proof-of-concept study, Villien et al (2014) demonstrated that a slow continuous infusion technique yields sufficient FDG-PET signal in the occipital cortex during a checkerboard stimulation to be able to identify activation in the primary visual cortex. Comparable results were recently obtained by (Hahn et al, 2016) in the motor cortex.…”

Analysis of continuous infusion functional PET (fPET) in the human brain

“…Using GLM, the FDG time activity signal can be modelled as a linear combination of a baseline metabolic signal and a task induced activation signal. The baseline represents a glucose measure that is independent of any task stimulation, and the activation signal is defined as the task induced FDG activity changes (Hahn et al 2016;Villien et al 2014 , which includes head motion, physiological noise and other noise terms. These design matrices incorporate a priori knowledge to parameterize the linear model.…”

“…The aim of the fPET approach is to maintain a continuous plasma supply of FDG to provide improved sensitivity of brain-state changes, and better temporal dynamics than bolus PET to track dynamic change of glucose uptake over time. Several studies have demonstrated that fPET can isolate task related changes in glucose uptake (Hahn et al 2016;Hahn et al 2018;Rischka et al 2018;Villien et al 2014;Jamadar, Ward, Carey, et al 2019).…”

“…The estimated parameters are then submitted to parametric statistical tests to generate brain activation maps (Friston 2007). The GLM model-based approach has been widely used in the analysis of functional MRI (fMRI), EEG and recently in fPET (Hahn et al 2016;Villien et al 2014;Jamadar, Ward, Carey, et al 2019). In the fPET GLM analysis, the time activity signal is represented by the combination of the task regressors, baseline regressor, and other nuisance regressors accounting for motion and physiological noises (Hahn et al 2016;Villien et al 2014).…”