The investigation of cerebral metabolic rates 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 assess baseline glucose metabolism and task-specific changes in a single measurement with constant infusion of ¹⁸F-FDG. Methods: Fifteen healthy subjects underwent two positron emission tomography (PET) measurements with arterial blood sampling. As reference, baseline CMRGlu was quantified from a 60min scan after ¹⁸F-FDG bolus application using the Patlak plot (eyes closed). For the other scan, constant radioligand infusion was applied for 95min, where subjects opened their eyes (10-20min and 60-70min) or tapped their right thumb to the fingers (35-45min and 85-95min). Quantification for the constant infusion scan was carried out in two steps. First, the general linear model (GLM) was used to fit regional time activity curves (TAC) with regressors for baseline metabolism, task-specific changes for eyes open and finger tapping conditions and movement parameters. Second, the Patlak plot was used for quantification of CMRGlu. Multiplication of the baseline regressor with beta-values from the GLM yielded regionally specific TACs for baseline metabolism. Further, task-specific changes in metabolism are directly proportional to changes in the slope of the TAC and hence proportional to changes in CMRGlu. Results: Baseline CMRGlu from the constant infusion scan matched with that from 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 FWE-corrected), with absolute changes up to 2.1 µmol/100g/min and 6.3% relative to baseline. For opened eyes decreased CMRGlu was observed in default mode regions (p 0.99). Conclusion: Baseline glucose metabolism as well as task-specific changes can be quantified in a single measurement with constant infusion of ¹⁸F-FDG and venous blood samples. The high sensitivity and regional specificity of the approach offers novel possibilities for functional and multimodal brain imaging.

Purpose: Positron emission tomography (PET) provides precise molecular information on physiological processes, but its low temporal resolution is a major obstacle. Consequently, we characterized the metabolic response of the human brain to working memory performance using an optimized functional PET (fPET) framework at a temporal resolution of 3 s. Methods: Thirty-five healthy volunteers underwent fPET with [¹⁸F]FDG bolus plus constant infusion, 19 of those at a hybrid PET/MRI scanner. During the scan, an n-back working memory paradigm was completed. fPET data were reconstructed to 3 s temporal resolution and processed with a novel sliding window filter to increase signal to noise ratio. BOLD fMRI signals were acquired at 2 s. Results: Consistent with simulated kinetic modeling, we observed a constant increase in the [¹⁸F]FDG signal during task execution, followed by a rapid return to baseline after stimulation ceased. These task-specific changes were robustly observed in brain regions involved in working memory processing. The simultaneous acquisition of BOLD fMRI revealed that the temporal coupling between hemodynamic and metabolic signals in the primary motor cortex was related to individual behavioral performance during working memory. Furthermore, task-induced BOLD deactivations in the posteromedial default mode network were accompanied by distinct temporal patterns in glucose metabolism, which were dependent on the metabolic demands of the corresponding task-positive networks. Conclusions: In sum, the proposed approach enables the advancement from parallel to truly synchronized investigation of metabolic and hemodynamic responses during cognitive processing. This allows to capture unique information in the temporal domain, which is not accessible to conventional PET imaging.

This continuing educational article gives an introduction to radiochemistry of positron emission tomography (PET)-tracers, which exhibit a covalently bound radiolabel with the nuclides carbon-11, nitrogen-13, and fluorine-18. The overall process of a PET-tracer production is explained starting from the production of the radionuclide in a cyclotron, followed by the automatization process of the radiosynthesis including the necessary steps for the respective synthesis and finalized with the requirements for quality control.

Predicting blood-brain barrier (BBB) penetration is crucial for developing central nervous system (CNS) drugs, representing a significant hurdle in successful clinical phase I studies. One of the most valuable properties for this prediction is the polar surface area (PSA). However, molecular structures are missing geometric optimization, which, together with lack of standardization, leads to variations in calculation. Additionally, prediction rules have been established by combining different molecular properties such as the BBB score or CNS multiparameter optimization (CNS MPO). This study aims to create an approach for 3D PSA calculation, to directly apply this value in combination with a set of 23 other parameters in a novel machine learning (ML)-based scoring, and to further evaluate existing prediction models using a standardized database. We developed and analyzed a standardized data set derived from the same laboratory, encompassing 24 calculated and experimentally determined molecular parameters such as PSA from various models, HPLC log P values, and hydrogen bond characteristics for 154 radiolabeled molecules and licensed or well-characterized drugs. These molecules were classified into categories based on BBB penetration, nonpenetration, and interactions with efflux transporters. We supplemented these with a novel in silico 3D calculation of nonclassical PSA. Additionally, we have calculated published prediction rules based on this standardized and transparent database. Using these data, we trained various ML models within a 100-fold Monte Carlo cross-validation framework to derive a novel ML-based prediction score for BBB penetration and validated the three most used existing predictive rules. To interpret the influence of individual molecular parameters and different existing predictive rules, we employed explainable artificial intelligence methods including Shapley additive explanations (SHAP) and surrogate modeling. The ML approach outperformed existing scores for BBB penetration by applying a complex nonlinear integration of molecular properties, with the random forest model achieving the best performance for predicting binary BBB penetration (area under the receiver operating characteristic curve (AUC) 0.88, 95% confidence intervals: 0.87-0.90), and multiclass efflux transporter versus CNS-positive and CNS-negative prediction (AUC 0.82, 95% CI: 0.81-0.82). SHAP analysis revealed the multifactorial nature of the problem, highlighting the advantage of multivariate models over single predictive parameters. The ML model's superior predictive capability was demonstrated in comparison with existing scoring systems, like the CNS MPO (AUC 0.53), the CNS MPO Positron emission tomography (PET) (AUC 0.51), and BBB score (AUC 0.68) while also enabling the identification of efflux transporter substrates and inhibitors. Our integrated ML approach, combining experimental and in silico measurements with novel in silico methods based on a standardized database including a plethora of different substance groups (licensed drugs and in vivo evaluated PET tracers), enhances the prediction of BBB penetration. This approach may reduce the reliance on extensive experimental measurements and animal testing, accelerating CNS drug development.

Increased cerebral monoamine oxidase A (MAO-A) levels have been shown in non-seasonal depression using positron emission tomography (PET). Seasonal affective disorder (SAD) is a sub-form of major depressive disorder and is typically treated with bright light therapy (BLT). The serotonergic system is affected by season and light. Hence, this study aims to assess the relevance of brain MAO-A levels to the pathophysiology and treatment of SAD. Changes to cerebral MAO-A distribution (1) in SAD in comparison to healthy controls (HC), (2) after treatment with BLT and (3) between the seasons, were investigated in 24 patients with SAD and 27 HC using [¹¹C]harmine PET. PET scans were performed in fall/winter before and after 3 weeks of placebo-controlled BLT, as well as in spring/summer. Cerebral MAO-A distribution volume (V_T, an index of MAO-A density) did not differ between patients and HC at any of the three time-points. However, MAO-A V_T decreased from fall/winter to spring/summer in the HC group (F_{1, 187.84} = 4.79, p < 0.050), while SAD showed no change. In addition, BLT, but not placebo, resulted in a significant reduction in MAO-A V_T (F_{1, 208.92} = 25.96, p < 0.001). This is the first study to demonstrate an influence of BLT on human cerebral MAO-A levels in vivo. Furthermore, we show that SAD may lack seasonal dynamics in brain MAO-A levels. The lack of a cross-sectional difference between patients and HC, in contrast to studies in non-seasonal depression, may be due to the milder symptoms typically shown by patients with SAD.