Biology subjects

Godbersen, G. M.

Publications and source records attributed to Godbersen, G. M..

17 recordsLinked to original sources

bioRxiv2026

Network-specific metabolic cost of functional connectivity in the human brain

Despite decades of extensive research, the complex architecture underlying human brain signaling remains incompletely understood. Previous work investigating the relationship between brain glucose metabolism and functional connectivity (FC) employed whole-brain analysis, without accounting for network interactions, dependencies or hierarchies. Here, we assess network-specific differences in the relationship between FC and metabolic demand, using [18F]FDG PET/MR data from three independent datasets. The metabolic cost of FC, i.e., the change in glucose demand associated with corresponding changes in FC, was modeled at rest, during cognitive task performance and in Alzheimer's disease (AD). Our findings reveal network-specific differences in metabolic cost, with the default mode (DM), somatomotor (SM) and fronto-temporal networks accounting for highest, intermediate and lowest metabolic demands, respectively. Similarly, time-resolved variability of FC demonstrated highest costs for states with DM network involvement and lowest for SM network participation. This relationship was reversed in participants with AD, who exhibited decreased demands in the DM network and increased costs in the SM network. Cognitive performance consistently revealed cost reductions in the DM and SM networks, as well as increases in task relevant networks. Together, these results highlight the flexibility of functional network architecture associated with cognitive demands and neuropathology, and shed light on the complex interplay between glucose metabolism and network interactions.

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bioRxiv2025

Open-access template and database approaches for pseudo-CT generation in brain PET/MRI attenuation correction

Positron emission tomography (PET) provides high-sensitivity molecular information in neuroimaging. However, its quantitative accuracy critically depends on attenuation correction (AC). Unlike PET/CT, hybrid PET/MRI systems cannot directly measure photon attenuation, necessitating alternative AC strategies. While several MRI-based AC tools exist, they often raise concerns regarding data privacy, long-term accessibility and reproducibility. We present an open-access framework for PET AC using pseudo-CT (pCT) methods. A database of 60 paired MRI-CT scans was curated and normalized to stereotactic MNI space. Three established pCT approaches (Boston, MaxProb, UCL) were adapted to enable their use with the normalized database. Method performance was assessed by comparison with a CT-based AC reference in an independent validation sample of 28 subjects, using the Jaccard index and [11C]DASB PET-derived serotonin transporter quantification as evaluation metrics. Boston and MaxProb demonstrated consistent performance across database subsets, while the UCL achieved the closest agreement with CT-derived AC and yielded the lowest quantification error (mean relative error <0.5%). This work demonstrates the feasibility of different pCT approaches using a database in MNI space thereby introducing an openly available reference database to support fast, reproducible and continued integration of MRI-based AC into PET reconstruction pipelines.

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bioRxiv2025

A Comparative Evaluation of Molecular Connectivity and Covariance Approaches

Advances in high-temporal-resolution functional positron emission tomography (fPET) now enable the assessment of metabolic associations between brain regions, providing a molecular complement to functional connectivity derived from fMRI. However, the distinction between molecular connectivity (MC) and covariance (mCov) remains conceptually and methodologically inconsistent across studies. This work systematically compares major analytical approaches for MC and mCov to clarify their assumptions, dependencies, and interpretational boundaries with the most used radiotracer [18F]fluorodeoxyglucose to obtain metabolic connectivity (M-MC). Twenty healthy participants underwent ultra-high-sensitivity fPET acquisitions on a large axial field-of-view scanner. M-MC was estimated using CompCor, spatio-temporal filtering, third-order polynomial detrending, baseline normalization and Euclidean distance at multiple temporal resolutions. mCov was assessed from SUVR images with subsequent network- or subject-specific matrix computation using independent component analysis, principal component analysis, or jackknife perturbation. Results demonstrate that while all MC methods are valid, CompCor and Euclidean distance perform optimally at high temporal resolutions (1-16s), whereas polynomial and spatio-temporal filters are more robust at lower sampling rates (>16s). mCov offers a population-level characterization of metabolic organization with the option to derive relative-to-group single-subject maps. This comparison provides methodological clarity and supports standardized use of molecular network analyses now integrated into the open-source fPET toolbox.

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bioRxiv2025

Test-Retest Reliability of Dopaminergic fPET and fMRI Measures During Reward Processing

Reward processing is essential to human brain function, with dopamine signalling in the nucleus accumbens (NAcc) as key element. The monetary incentive delay task is widely studied with functional magnetic resonance imaging (fMRI), measuring indirect hemodynamic changes. Functional positron emission tomography (fPET) with 6-[18F]FDOPA directly quantifies dopamine synthesis enabling dynamic assessment during task performance within a single scan. We investigated the reliability of 6-[18F]FDOPA fPET and blood oxygenation level dependent (BOLD) fMRI during a modified monetary incentive delay task in 25 healthy participants across two PET/MRI sessions. Intraclass correlation coefficients and coefficients of variance were computed for BOLD beta estimates and striatal dopamine synthesis at 30s and 2s resolutions. fPET showed fair to good reliability in the NAcc and putamen at rest, fair reliability during the win condition in the caudate and putamen, but poor reliability across the loss condition in all regions. Conversely, fMRI showed good reliability in the NAcc during feedback and in the caudate during feedback loss, but fair reliability elsewhere except for poor reliability in the caudate during cue loss. These findings indicate that both methods achieve comparable reliability but in different target areas, with the molecular specificity of fPET offering dynamic assessment of dopaminergic function. Clinicaltrials.gov IdentifierNCT06675851

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bioRxiv2025

Subacute effects of ketamine on neural correlates of reward processing

ObjectivesKetamines prohedonic properties have been linked to enhanced reward-related brain activation during the early post-infusion phase. Its effects during the subacute period ([~]2-24 h post-infusion), when psychotomimetic symptoms fade and neuroplastic adaptations emerge, are less well characterised. This study assessed ketamines subacute effects on reward processing using the Monetary Incentive Delay (MID) task. MethodsIn a randomised, placebo-controlled, crossover study, 28 healthy participants received 0.5 mg/kg racemic ketamine or placebo via 40-minute intravenous infusion. Functional magnetic resonance imaging (fMRI) was acquired [~]5 h post-infusion. Plasma concentrations of ketamine and norketamine were obtained for individual area under the curve (AUC) estimation. Analyses focused on the contrast between expected and actual trial outcomes. ResultsAt five hours post-infusion, ketamine did not significantly modulate MID task-related brain activation, despite pronounced subjective drug effects. Pharmacokinetic modelling confirmed expected ketamine and norketamine profiles, but neither drug exposure (AUC) nor subjective measures correlated with neural activation. ConclusionsProhedonic effects of ketamine may not sufficiently manifest in MID task-related activation in healthy individuals [~]5 hours after infusion. The lack of significant effects provides valuable extension of the existing literature, as ketamines effects might be confined to a more acute time window or differ in clinical populations.

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bioRxiv2025

Evaluation of Data-based Motion Correction Techniques for High Temporal Resolution Functional PET

Functional Positron Emission Tomography (fPET) data offers novel insights into brain energy demands and molecular connectivity. Recent advances in improving temporal resolutions for this imaging technique have opened up new research possibilities. However, lower signal-to-noise ratios (SNR) inherent to short PET frames bring into question whether current realignment approaches still provide appropriate motion correction. Thus, we aimed to evaluate the effectiveness of standard motion correction methods and explore potential improvements for high temporal resolution fPET with 3s frames. We investigated two techniques aimed at improving the SNR to facilitate more accurate realignment of fPET images, in comparison to conventional motion correction: a deep-learning technique based on the application of a conditional generative adversarial network and an exponentially weighted sliding window average. Performance was evaluated by correlating rigid motion parameters between approaches and with simultaneously acquired fMRI data, and by assessing magnitudes of task-induced activation. Our results indicate that neither of the two methods substantially improve mitigation of motion artefacts. Given the increased computational effort of both techniques, we propose that the standard motion correction procedure is adequate for processing high temporal resolution fPET data. Nevertheless, future development of targeted strategies to enhance motion correction may further advance this imaging technique.

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bioRxiv2025

High-temporal resolution metabolic connectivity resolved by component-based noise correction

Recent advances in functional PET (fPET) allow for accurate modelling of metabolic processes with a temporal resolution in the range of seconds. This enables new applications such as imaging molecular connectivity at temporal resolutions comparable to fMRI. However, high-temporal resolution fPET data are more sensitive to noise and the extraction of a meaningful signal remains a challenge. We developed a component-based preprocessing approach adapted from fMRI, which models structured noise using tissue-specific regressors and removes low-frequency uptake trends from the fPET signal (CompCor). We applied this method to 20 high-temporal [18F]FDG fPET scans from a next-generation long-axial field of view PET/CT system (1s frames) and 16 scans from a conventional PET/MR scanner (3s frames). We compared filtering methods across frequency bands and examined their effects on metabolic connectivity (M-MC) estimates. Metabolic connectivity was markedly influenced by filtering strategy and scanner type. The CompCor filter produced more consistent and structured networks than standard bandpass filters. Intermediate frequency bands (0.01-0.1 Hz) yielded the most reliable connectivity patterns between PET/CT and PET/MR data (r=0.89). High sensitivity PET/CT data revealed structured connectivity patterns also at a higher frequency band (0.1-0.2 Hz). Compared to fMRI functional connectivity, fPET-derived networks were more spatially cohesive but less differentiated. High-temporal [18F]FDG fPET enables reliable estimation of individual resting-state M-MC when paired with appropriate denoising. Scanner choice and preprocessing significantly affect signal quality and interpretation, whereas the proposed physiologically informed pipeline improves comparability across systems and studies.

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bioRxiv2024

Connecting the Dots: Approaching a Standardized Nomenclature for Molecular Connectivity Combining Data and Literature

PET-based connectivity computation is a molecular approach that complements fMRI-derived functional connectivity. However, the diversity of methodologies and terms employed in PET connectivity analysis has resulted in ambiguities and confounded interpretations, highlighting the need for a standardized nomenclature. Drawing parallels from other imaging modalities, we propose "molecular connectivity" as an umbrella term to characterize statistical dependencies between PET signals across brain regions at the individual level (within-subject). Like fMRI resting-state functional connectivity, "molecular connectivity" leverages temporal associations in the PET signal to derive brain network associations. Another within-subject approach evaluates regional similarities of tracer kinetics, which are unique in PET imaging, thus referred to as "kinetic connectivity". On the other hand, "molecular covariance" denotes group-level computations of covariance matrices across-subject. Further specification of the terminology can be achieved by including the employed radioligand, such as "metabolic connectivity/covariance" for [18F]FDG as well as "tau/amyloid covariance" for [18F]flutemetamol / [18F]flortaucipir. To augment these distinctions, high-temporal resolution functional [18F]FDG PET scans from 17 healthy participants were analysed with common techniques of molecular connectivity and covariance, allowing for a data-driven support of the above terminology. Our findings demonstrate that temporal band-pass filtering yields structured network organization, whereas other techniques like 3rd order polynomial fitting, spatio-temporal filtering and baseline normalization require further methodological refinement for high-temporal resolution data. Conversely, molecular covariance from across-subject data provided a simple means to estimate brain region interactions through regularized or sparse inverse covariance estimation. A standardized nomenclature in PET-based connectivity research can reduce ambiguity, enhance reproducibility, and facilitate interpretability across radiotracers and imaging modalities. Via a data-driven approach, this work provides a transparent framework for categorizing and comparing PET-derived connectivity and covariance metrics, laying the foundation for future investigations in the field.

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bioRxiv2024

Optimal filtering strategies for task-specific functional PET imaging

Functional Positron Emission Tomography (fPET) has advanced as an effective tool for investigating dynamic processes in glucose metabolism and neurotransmitter action, offering potential insights into brain function, disease progression, and treatment development. Despite significant methodological advances, extracting stimulation-specific information presents additional challenges in optimizing signal processing across both spatial and temporal domains, which are essential for obtaining clinically relevant insights. This study aims to provide a systematic evaluation of state-of-the-art filtering techniques for fPET imaging. Forty healthy participants underwent a single [18F]FDG PET/MR scan, engaging in the cognitive task Tetris(R). Twenty thereof also underwent a second PET/MR session. Eight filtering techniques, including 3D and 4D Gaussian smoothing, highly constrained backprojection (hypr), iterative hypr (Ihypr4D), two MRI-Markov Random Field (MRI-MRF) filters (L=10 and 14 mm neighborhood) as well as static and dynamic Non-Local Means (sNLM and dNLM respectively) approaches, were applied to fPET data. Test-retest reliability (intraclass correlation coefficient), the identifiability of the task signal (temporal signal-to-noise ratio (tSNR)), spatial task-based activation (group level t-values), and sample size calculations were assessed. Results indicate distinct performance between filtering techniques. Compared to standard 3D Gaussian smoothing, dNLM, sNLM, MRI-MRF L=10 and Ihypr4D filters exhibited superior tSNR, while only dNLM and hypr showed improved test-retest reliability. Spatial task-based activation was enhanced by both NLM filters and MRI-MRF approaches. The dNLM enabled a minimum reduction of 15.4% in required sample size. The study systematically evaluated filtering techniques in fPET data processing, highlighting their strengths and limitations. The dNLM filter emerges as a promising choice, with improved performance across all metrics. However, filter selection should align with specific study objectives, considering factors like processing time and resource constraints.

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bioRxiv2023

Non-invasive assessment of stimulation-specific changes in cerebral glucose metabolism with functional PET

Functional positron emission tomography (fPET) with [18F]FDG allows one to quantify stimulation-induced dynamics in glucose metabolism independent of neurovascular coupling. However, the gold standard for quantification requires arterial blood sampling, which can cause discomfort for the participant and increases complexity of the experimental protocol. These constraints have limited the widespread applicability of fPET, especially in the clinical routine. Therefore, we introduce a novel approach, which enables the assessment of the dynamics in cerebral glucose metabolism without the need for an input function. MethodsWe tested the validity of a mathematical derivation on the basis of two independent data sets (DS). For DS1, 52 healthy volunteers (23.2 {+/-} 3.3 years, 24 females) completed a visuo-spatial motor coordination task (the video game Tetris(R)) and for DS2, 18 healthy participants (24.2 {+/-} 4.3 years, 8 females) performed an eyes-open/finger tapping task, both during a [18F]FDG fPET scan. 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 the reference standard. Simplified outcome parameters, such as GLM beta values of task effects and percent signal change (%SC) of both parameters were estimated. These were compared for task-relevant brain regions and on a whole-brain level. ResultsIn general, we observed higher agreement with the reference standard for DS1 (radiotracer administration as bolus + constant infusion) compared to DS2 (constant infusion only). Across both data sets, strong correlations were found between regional task-specific beta estimates and CMRGlu (r = 0.763...0.912). Additionally, %SC of beta values exhibited excellent 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. ConclusionTask-specific changes in glucose metabolism can be reliably estimated using %SC of GLM beta values, eliminating the need for any blood sampling. This approach streamlines fPET imaging, albeit with the trade-off of being unable to quantify baseline metabolism. The proposed simplification enhances the applicability of fPET, allowing for widespread employment in research settings and clinical investigations. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/558617v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@77faeaorg.highwire.dtl.DTLVardef@fa3495org.highwire.dtl.DTLVardef@926353org.highwire.dtl.DTLVardef@b70da0_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2023

High-temporal resolution functional PET/MRI reveals coupling between human metabolic and hemodynamic brain response

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 framework at a temporal resolution of 3 seconds. 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. 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 depended on the task-positive network metabolic demands. In sum, the proposed approach enables the advancement from parallel to truly synchronized investigation of metabolic and hemodynamic responses during cognitive processing.

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bioRxiv2022

Metabolic demands of the posteromedial default mode network are shaped by dorsal attention and frontoparietal control networks

Although BOLD signal decreases in the default mode network (DMN) are commonly observed during attention-demanding tasks, their neurobiological underpinnings are not fully understood. Previous work has shown decreases but also increases in glucose metabolism that match with or dissociate from these BOLD signal decreases, respectively. To resolve this discrepancy, we analyzed functional PET/MRI data from 50 healthy subjects during the performance of the visuo-spatial processing game Tetris(R) and combined this with previously published data sets of working memory as well as visual and motor stimulation. Our findings show that the glucose metabolism of the posteromedial DMN is dependent on the metabolic demands of the correspondingly engaged task-positive brain networks. Specifically, the dorsal attention (involved in Tetris(R)) and frontoparietal networks (engaged during working memory) shape the glucose metabolism of the posteromedial DMN in opposing directions. External attention-demanding tasks lead to a downregulation of the posteromedial DMN with consistent decreases in the BOLD signal and glucose metabolism, whereas working memory is subject to metabolically expensive mechanisms of BOLD signal suppression. We suggest that the former finding is mediated by decreased glutamate signaling, while the latter results from active GABAergic inhibition, regulating the competition between self-generated and task-driven internal demands. 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.

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bioRxiv2021

Learning induces coordinated neuronal plasticity of metabolic demands and functional brain networks

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. To address these questions, 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 with metabolic connectivity mapping (MCM) to infer directional interactions across brain regions and subsequently performed simulations to disentangle the role of functional network dynamics and glucose metabolism. As a result, 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 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.

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bioRxiv2021

Escitalopram Administration, Neuroplastic Effects and Relearning: A Diffusion Tensor Imaging Study in Healthy Individuals

BackgroundNeuroplastic processes are influenced by selective serotonergic reuptake inhibitors, while learning in conjunction with the administration of serotonergic agents alters white matter microstructure in humans. The goal of this double-blind, placebo-controlled imaging study was to investigate the influence of escitalopram on white matter plasticity during (re)learning. MethodsSeventy-one healthy individuals (age = 25.6{+/-}5.0, 43 females) underwent 3 diffusion magnetic resonance imaging sessions: at baseline, after 3-weeks of associative learning (emotional/non-emotional content) and after relearning shuffled associations for an additional 3 weeks. During the relearning phase, subjects received daily escitalopram 10 mg or placebo orally. Statistical analysis was performed with statistical parametric mapping (SPM) and using sandwich estimator. ResultsA three-way and two-way rmANOVA was conducted to analyze the effects of escitalopram on AD, FA, MD and RD during the learning and relearning period. We found no significant three-way or two-way interactions for each DTI metrics (pFDR > 0.05), thus neither after 3 nor after 6 weeks we found significant changes in white matter microstructure. ConclusionWe examined neither an effect of escitalopram nor learning (or relearning) interventions on different DTI metrics. The duration and intensity of study interventions (i.e. administration of escitalopram and learning as the relearning task) might have been chosen insufficiently to induce detectable alterations. Previous studies examining the effects of SSRIs on white matter tracts in humans are underrepresented, but do mainly yield towards non-significant findings. The results implicate that escitalopram does not impact white matter microstructures in healthy subjects.

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bioRxiv2021

Effects of learning and escitalopram administration on serum BDNF levels, a randomised placebo-controlled trial

BackgroundThe brain-derived neurotrophic factor (BDNF) has been implicated in processes essential for neuroplasticity. Learning and serotonin reuptake inhibitors (SSRI) foster neuronal reorganization, a mechanism potentially related to BDNF. This study aims to assess the effects of associative learning and escitalopram on serum BDNF (sBDNF) levels, to gain further knowledge on their dynamic interplay. MethodsFor three weeks, 37 participants performed one of two associative learning paradigms with either emotional or semantic content daily. During a subsequent three-week period of reversal learning, subjects either received escitalopram (10mg per day) or placebo. Before and after each learning period sBDNF values were assessed. Citalopram plasma levels were measured at the last time point. Linear mixed effects models (LME) and partial Spearmans rank and Pearson correlations were used for statistical analyses. ResultsOne-way LME resulted in a significant effect of time during the first learning period over both groups (p<0.01). Two-way LME revealed a significant interaction effect of the emotional content learning group and time (p=0.02). Three-way LME (time x reversal learning group x substance) showed no significant effects (all p> 0.05). Furthermore, correlation between citalopram and sBDNF level after three weeks of escitalopram administration exhibit a negative trend (partial Pearson correlation: r=-0.30, p=0.05; partial Spearmans rank: r=-0.22, p=0.15). ConclusionThe results suggest that three weeks of associative emotional content learning affect sBDNF levels, while subsequently assessed citalopram plasma and sBDNF levels tend to correlate negatively. Key PointsO_LIEmotional learning may affect serum BDNF levels in healthy human subjects C_LIO_LIBlood levels of citalopram and serum BDNF exhibit a negative correlation C_LI

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bioRxiv2021

Serotonin modulates learning content-specific neuroplasticity of functional brain networks

Learning-induced neuroplastic changes, further modulated by content and setting, are mirrored in brain functional connectivity (FC). In animal models, serotonergic agents were shown to facilitate neuroplasticity. This is especially prominent during emotional relearning, such as fear extinction, which may translate to clinical improvements in human patients. To investigate this assumption, 99 healthy subjects underwent six weeks of emotional or non-emotional learning and subsequent relearning. Resting-state functional magnetic resonance imaging was performed before and after the learning phases to investigate changes in FC. During relearning, subjects received either a daily dose of the selective serotonin reuptake inhibitor (SSRI) escitalopram or placebo. Escitalopram intake modulated FC changes in a network comprising Brocas area, the medial prefrontal cortex, the right inferior temporal and left lingual gyrus. More specifically, escitalopram increased the bidirectional connectivity between medial prefrontal cortex and lingual gyrus for non-emotional and additionally the connectivity from medial prefrontal cortex to Brocas area for emotional relearning. The context-dependence of these effects supports the assumption that SSRIs in clinical practice might improve neuroplasticity rather than psychiatric symptoms per se. Correlations with learning behavior further point towards a relationship with extinction processes in relearning. These results demonstrate that escitalopram intake during relearning results in content-dependent network adaptations and support the conclusion that enhanced neuroplasticity might be the major underlying mechanism also in humans. Beyond expanding the complexities of learning, these findings emphasize the influence of external factors on serotonin-facilitated neuroplasticity of the human brain.

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bioRxiv2019

Sex differences in monetary reward and punishment processing are associated with fast dynamics of dopamine synthesis

PurposeIn the human brain endogenous dopamine release is commonly assessed by the PET competition model. Although thoroughly validated, cognitive processing yields low signal changes and the assessment of several task conditions requires repeated scanning. Using the framework of functional PET imaging we introduce a novel approach which leverages the incorporation of the radioligand 6-[18F]FDOPA into the dynamic fast-acting regulation of the corresponding enzyme activities by neuronal firing and neurotransmitter release. We demonstrate the feasibility of the approach by the assessment of widely described sex differences in dopamine neurotransmission. MethodsReward and punishment processing was behaviorally investigated in 36 healthy participants, where 16 underwent fPET and fMRI while performing the monetary incentive delay task. 6-[18F]FDOPA was applied as bolus+infusion during a single 50 min PET acquisition. Task-specific changes in dopamine synthesis were identified with the general linear model and quantified with the Gjedde-Patlak plot. ResultsMonetary gain induced 78% increase in nucleus accumbens dopamine synthesis vs. 49% for loss in men. Interestingly, the opposite was discovered in women (gain: 51%, loss: 78%). Behavioral modeling revealed direct associations of task-specific dopamine synthesis with reward sensitivity in men (rho = -0.7) and with punishment sensitivity in women (rho = 0.89). As expected, fMRI showed robust task-specific neuronal activation but no sex difference. ConclusionsOur findings provide a dopaminergic basis for well-known behavioral differences in reward and punishment processing between women and men. This has important implications in psychiatric conditions showing sex-specific prevalence rates, altered reward processing and dopamine signaling. The high temporal resolution and pronounced magnitude of task-specific changes make fPET a promising tool to investigate functional neurotransmitter dynamics during cognitive or emotional processing in various brain disorders.

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