Functional Positron Emission Tomography (fPET) is an effective tool for studying dynamic processes in glucose metabolism and neurotransmitter action, providing insights into brain function and disease progression. However, optimizing signal processing to extract stimulation-specific information remains challenging. This study systematically evaluates state-of-the-art filtering techniques for fPET imaging. Forty healthy participants performed a cognitive task (Tetris®) during [ 18 F]FDG PET/MR scans. Seven filtering techniques and multiple hyperparameters were tested: including 3D and 4D Gaussian smoothing, highly constrained backprojection (HYPR), iterative HYPR (IHYPR4D), MRI-Markov Random Field (MRI-MRF) filters, and dynamic/extended dynamic Non-Local Means (dNLM/edNLM). Filters were assessed based on test-retest reliability, task signal identifiability (temporal signal-to-noise ratio, tSNR), spatial task-based activation, and sample size calculations were assessed. Compared to 3D Gaussian smoothing, edNLM, dNLM, MRI-MRF L = 10, and IHYPR4D filters improved tSNR, while edNLM and HYPR enhanced test-retest reliability. Spatial task-based activation was enhanced by NLM filters and MRI-MRF approaches. The edNLM filter reduced the required sample size by 15.4%. Simulations supported these findings. This study highlights the strengths and limitations of fPET filtering techniques, emphasizing how hyperparamter adjustments affect outcome parameters. The edNLM filter shows promise with improved performance across all metrics, but filter selection should consider specific study objectives and resource constraints.

Purpose: The human brain is characterized by interacting large-scale functional networks fueled by glucose metabolism. Since former studies could not sufficiently clarify how these functional connections shape glucose metabolism, we aimed to provide a neurophysiologically-based approach.Methods: 51 healthy volunteers underwent simultaneous PET/MRI to obtain BOLD functional connectivity and [18F]FDG glucose metabolism. These multimodal imaging proxies of fMRI and PET were combined in a whole-brain extension of metabolic connectivity mapping. Specifically, functional connectivity of all brain regions were used as input to explain glucose metabolism of a given target region. This enabled the modeling of postsynaptic energy demands by incoming signals from distinct brain regions.Results: Functional connectivity input explained a substantial part of metabolic demands but with pronounced regional variations (34 - 76%). During cognitive task performance this multimodal association revealed a shift to higher network integration compared to resting state. In healthy aging, a dedifferentiation (decreased segregated/modular structure of the brain) of brain networks during rest was observed. Furthermore, by including data from mRNA maps, [11C]UCB-J synaptic density and aerobic glycolysis (oxygen-to-glucose index from PET data), we show that whole-brain functional input reflects non-oxidative, on-demand metabolism of synaptic signaling. The metabolically-derived directionality of functional inputs further marked them as top-down predictions. In addition, the approach uncovered formerly hidden networks with superior efficiency through metabolically informed network partitioning.Conclusions: Applying multimodal imaging, we decipher a crucial part of the metabolic and neurophysiological basis of functional connections in the brain as interregional on-demand synaptic signaling fueled by anaerobic metabolism. The observed task- and age-related effects indicate promising future applications to characterize human brain function and clinical alterations.

Serotonin (5-HT) plays an essential role in reward processing, however, the possibilities to investigate 5-HT action in humans during emotional stimulation are particularly limited. Here we demonstrate the feasibility of assessing reward-specific dynamics in 5-HT synthesis using functional PET (fPET), combining its molecular specificity with the high temporal resolution of blood oxygen level dependent (BOLD) fMRI. Sixteen healthy volunteers underwent simultaneous fPET/fMRI with the radioligand [11C]AMT, a substrate for tryptophan hydroxylase. During the scan, participants completed the monetary incentive delay task and arterial blood samples were acquired for quantifying 5-HT synthesis rates. BOLD fMRI was recorded as a proxy of neuronal activation, allowing differentiation of reward anticipation and feedback. Monetary gain and loss resulted in substantial increases in 5-HT synthesis in the ventral striatum (VStr, +21% from baseline) and the anterior insula (+41%). In the VStr, task-specific 5-HT synthesis was further correlated with BOLD signal changes during reward feedback (ρ = −0.65), but not anticipation. Conversely, 5-HT synthesis in the anterior insula correlated with BOLD reward anticipation (ρ = −0.61), but not feedback. In sum, we provide a robust tool to identify task-induced changes in 5-HT action in humans, linking the dynamics of 5-HT synthesis to distinct phases of reward processing in a regionally specific manner. Given the relevance of altered reward processing in psychiatric disorders such as addiction, depression and schizophrenia, our approach offers a tailored assessment of impaired 5-HT signaling during cognitive and emotional processing.

Purpose Functional positron emission tomography (fPET) with [ 18 F]FDG allows quantification of stimulation-induced changes in glucose metabolism independent of neurovascular coupling. However, the gold standard for quantification requires invasive arterial blood sampling, limiting its widespread use. Here, we introduce a novel fPET method without the need for an input function. Methods We validated the approach using two datasets (DS). For DS1, 52 volunteers (23.2 ± 3.3 years, 24 females) performed Tetris® during a [ 18 F]FDG fPET scan (bolus + constant infusion). For DS2, 18 participants (24.2 ± 4.3 years, 8 females) performed an eyes-open/finger tapping task (constant infusion). Task-specific changes in metabolism were assessed with the general linear model (GLM) and cerebral metabolic rate of glucose (CMRGlu) was quantified with the Patlak plot as reference. We then estimated simplified outcome parameters, including GLM beta values and percent signal change (%SC), and compared them, region and whole-brain-wise. Results We observed higher agreement with the reference for DS1 than DS2. Both DS resulted in strong correlations between regional task-specific beta estimates and CMRGlu ( r = 0.763…0.912). %SC of beta values exhibited strong agreement with %SC of CMRGlu ( r = 0.909…0.999). Average activation maps showed a high spatial similarity between CMRGlu and beta estimates (Dice = 0.870…0.979) as well as %SC (Dice = 0.932…0.997), respectively. Conclusion The non-invasive method reliably estimates task-specific changes in glucose metabolism without blood sampling. This streamlines fPET, albeit with the trade-off of being unable to quantify baseline metabolism. The simplification enhances its applicability in research and clinical settings.

Functional PET (fPET) is a novel technique for studying dynamic changes in brain metabolism and neurotransmitter signaling. Accurate quantification of fPET relies on measuring the arterial input function (AIF), traditionally achieved through invasive arterial blood sampling. While non-invasive image-derived input functions (IDIF) offer an alternative, they suffer from limited spatial resolution and field of view. To overcome these issues, we developed and validated a scan protocol for brain fPET utilizing cardiac IDIF, aiming to mitigate known IDIF limitations. Twenty healthy individuals underwent fPET/MR scans using [18F]FDG or 6-[18F]FDOPA, utilizing bed motion shuttling to capture cardiac IDIF and brain task-induced changes. Arterial and venous blood sampling was used to validate IDIFs. Participants performed a monetary incentive delay task. IDIFs from various blood pools and composites estimated from a linear fit over all IDIF blood pools (3VOI) and further supplemented with venous blood samples (3VOIVB) were compared to the AIF. Quantitative task-specific images from both tracers were compared to assess the performance of each input function to the gold standard. For both radiotracer cohorts, moderate to high agreement (r: 0.60–0.89) between IDIFs and AIF for both radiotracer cohorts was observed, with further improvement (r: 0.87–0.93) for composite IDIFs (3VOI and 3VOIVB). Both methods showed equivalent quantitative values and high agreement (r: 0.975–0.998) with AIF-derived measurements. Our proposed protocol enables accurate non-invasive estimation of the input function with full quantification of task-specific changes, addressing the limitations of IDIF for brain imaging by sampling larger blood pools over the thorax. These advancements increase applicability to any PET scanner and clinical research setting by reducing experimental complexity and increasing patient comfort.

External tasks evoke characteristic fMRI BOLD signal deactivations in the default mode network (DMN). However, for the corresponding metabolic glucose demands both decreases and increases have been reported. To resolve this discrepancy, functional PET/MRI data from 50 healthy subjects performing Tetris were combined with previously published data sets of working memory, visual and motor stimulation. We show that the glucose metabolism of the posteromedial DMN is dependent on the metabolic demands of the correspondingly engaged task-positive networks. Specifically, the dorsal attention and frontoparietal network shape the glucose metabolism of the posteromedial DMN in opposing directions. While tasks that mainly require an external focus of attention lead to a consistent downregulation of both metabolism and the BOLD signal in the posteromedial DMN, cognitive control during working memory requires a metabolically expensive BOLD suppression. This indicates that two types of BOLD deactivations with different oxygen-to-glucose index may occur in this region. We further speculate that consistent downregulation of the two signals is mediated by decreased glutamate signaling, while divergence may be subject to active GABAergic inhibition. The results demonstrate that the DMN relates to cognitive processing in a flexible manner and does not always act as a cohesive task-negative network in isolation.

The nervous and circulatory system interconnects the various organs of the human body, building hierarchically organized subsystems, enabling fine-tuned, metabolically expensive brain-body and inter-organ crosstalk to appropriately adapt to internal and external demands. A deviation or failure in the function of a single organ or subsystem could trigger unforeseen biases or dysfunctions of the entire network, leading to maladaptive physiological or psychological responses. Therefore, quantifying these networks in healthy individuals and patients may help further our understanding of complex disorders involving body-brain crosstalk.Here we present a generalized framework to automatically estimate metabolic inter-organ connectivity utilizing whole-body functional positron emission tomography (fPET). The developed framework was applied to 16 healthy subjects (mean age ± SD, 25 ± 6 years; 13 female) that underwent one dynamic 18F-FDG PET/CT scan. Multiple procedures of organ segmentation (manual, automatic, circular volumes) and connectivity estimation (polynomial fitting, spatiotemporal filtering, covariance matrices) were compared to provide an optimized thorough overview of the workflow.The proposed approach was able to estimate the metabolic connectivity patterns within brain regions and organs as well as their interactions. Automated organ delineation, but not simplified circular volumes, showed high agreement with manual delineation. Polynomial fitting yielded similar connectivity as spatiotemporal filtering at the individual subject level. Furthermore, connectivity measures and group-level covariance matrices did not match. The strongest brain-body connectivity was observed for the liver and kidneys.The proposed framework offers novel opportunities towards analyzing metabolic function from a systemic, hierarchical perspective in a multitude of physiological pathological states.

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 [ 18 F]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 [ 18 F]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.

The neurobiological basis of learning is reflected in adaptations of brain structure, network organization and energy metabolism. However, it is still unknown how different neuroplastic mechanisms act together and if cognitive advancements relate to general or task-specific changes. Therefore, we tested how hierarchical network interactions contribute to improvements in the performance of a visuo-spatial processing task by employing simultaneous PET/MR neuroimaging before and after a 4-week learning period. We combined functional PET and metabolic connectivity mapping (MCM) to infer directional interactions across brain regions. Learning altered the top-down regulation of the salience network onto the occipital cortex, with increases in MCM at resting-state and decreases during task execution. Accordingly, a higher divergence between resting-state and task-specific effects was associated with better cognitive performance, indicating that these adaptations are complementary and both required for successful visuo-spatial skill learning. Simulations further showed that changes at resting-state were dependent on glucose metabolism, whereas those during task performance were driven by functional connectivity between salience and visual networks. Referring to previous work, we suggest that learning establishes a metabolically expensive skill engram at rest, whose retrieval serves for efficient task execution by minimizing prediction errors between neuronal representations of brain regions on different hierarchical levels.

Impaired cognitive flexibility represents a widespread symptom in psychiatric disorders, including major depressive disorder (MDD), a disease, characterized by an imbalance of neurotransmitter concentrations. While memory formation is mostly associated with glutamate, also gamma-Aminobutyric acid (GABA) and serotonin show attributions in a complex interplay between neurotransmitter systems. Treatment with selective serotonin reuptake inhibitors (SSRIs) does not solely affect the serotonergic system but shows downstream effects on GABA- and glutamatergic neurotransmission, potentially helping to restore cognitive function via neuroplastic effects. Hence, this study aims to elaborate the effects of associative relearning and SSRI treatment on GABAergic and glutamatergic function within and between five brain regions using magnetic resonance spectroscopy imaging (MRSI).In this study, healthy subjects were randomized into four groups which underwent three weeks of an associative relearning paradigm, with or without emotional connotation, under SSRI (10mg escitalopram) or placebo administration. MRSI measurements, using a spiral-encoded, 3D-GABA-edited MEGA-LASER sequence at 3T, were performed on the first and last day of relearning. Mean GABA+/tCr (GABA+ = GABA + macromolecules; tCr = total creatine) and Glx/tCr (Glx = glutamate + glutamine) ratios were quantified in a ROI-based approach for the hippocampus, insula, putamen, pallidum and thalamus, using LCModel. A total of 66 subjects ((37 female, mean age ± SD = 25.4±4.7) for Glx/tCr and 58 subjects (32 female, mean age ± SD = 25.1±4.7) for GABA+/tCr were included in the final analysis.A significant measurement by region and treatment (SSRI vs placebo) interaction on Glx/tCr ratios was found (pcor=0.017), with post hoc tests confirming differential effects on hippocampus and thalamus (pcor=0.046). Moreover, treatment by time comparison, for each ROI independently, showed a reduction of hippocampal Glx/tCr ratios after SSRI treatment (puncor=0.033). No significant treatment effects on GABA+/tCr ratios or effects of relearning condition on any neurotransmitter ratio could be found.Here, we showed a significant SSRI- and relearning-driven interaction effect of hippocampal and thalamic Glx/tCr levels, suggesting differential behavior based on different serotonin transporter and receptor densities. Moreover, an indication for Glx/tCr adaptions in the hippocampus after three weeks of SSRI treatment could be revealed. Our findings are in line with animal studies reporting glutamate adaptions in the hippocampus following chronic SSRI intake. Due to the complex interplay of serotonin and hippocampal function, involving multiple serotonin receptor subtypes on glutamatergic cells and GABAergic interneurons, the interpretation of underlying neurobiological actions remains challenging.

Mapping the neuronal response during cognitive processing is of crucial importance to gain new insights into human brain function. BOLD imaging and ASL are established MRI methods in this endeavor. Recently, the novel approach of functional PET (fPET) was introduced, enabling absolute quantification of glucose metabolism at rest and during task execution in a single measurement. Here, we report test-retest reliability of fPET in direct comparison to BOLD imaging and ASL. Twenty healthy subjects underwent two PET/MRI measurements, providing estimates of glucose metabolism, cerebral blood flow (CBF) and blood oxygenation. A cognitive task was employed with different levels of difficulty requiring visual-motor coordination. Task-specific neuronal activation was robustly detected with all three imaging approaches. The highest reliability was obtained for glucose metabolism at rest. Although this dropped during task performance it was still comparable to that of CBF. In contrast, BOLD imaging yielded high performance only for qualitative spatial overlap of task effects but not for quantitative comparison. Hence, the combined assessment of fPET and ASL offers reliable and simultaneous absolute quantification of glucose metabolism and CBF at rest and task.