Biology subjects

Milz, C.

Publications and source records attributed to Milz, C..

10 recordsLinked to original sources

bioRxiv2026

Cellular and mitochondrial correlates of regional glucose metabolism in the human cortex

Regional differences in human brain glucose metabolism reflect local energy demands, but their cellular and mitochondrial determinants remain unclear. We combined [18F]FDG positron emission tomography in healthy human participants with estimates of cellular composition and mitochondrial density to unravel (sub)cellular correlates of heterogeneity in brain glucose metabolism. By correcting for RNA content and differential gene expression due to postmortem sampling, we addressed methodological shortcomings in multimodal neuroimaging integration. We introduce transcriptionally derived cytoarchitectonic maps of the human cortex that are consistent with histological findings, providing a biologically grounded framework for relating in vivo neuroimaging signals. Accordingly, mitochondrial distribution showed strong association with the intrinsic energy requirements at resting state. In contrast, relations between metabolism and neuronal composition were driven by sensory input and mitochondria-sparse thin-axon neurons, suggesting that stimulation-induced energy demand is met rather by neuronal processes that are independent of mitochondrial abundance to maintain efficient information processing.

neuroscience↗

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.

neuroscience↗

bioRxiv2026

Differential upregulation of metabolic demands and functional integration of the default mode network during stress

Psychosocial stress engages coordinated physiological and neural responses that enable adaptation to environmental challenges. However, maladaptive stress and reduced resilience are major risk factors for psychiatric and neurodegenerative disorders. As the brains metabolic response to stress remains largely unexplored, we used simultaneous [18F]FDG PET/MRI during performance of the Montreal Imaging Stress Task to assess cerebral glucose metabolism, BOLD activation and functional connectivity. On top of activation in relation to cognitive processing, psychosocial stress specifically recruits the posterior cingulate cortex (PCC) with increased glucose metabolism and attenuated BOLD deactivations. This was accompanied by reduced PCC integration within the default mode network and increased influence onto frontoparietal and dorsal attention networks. Moreover, individuals exhibiting an endocrine stress response showed lower resilience scores, failed to downregulate anterior cingulate cortex (ACC) metabolism during stress, and displayed an inverse relationship between ACC glucose metabolism and anterior insula functional connectivity. Together, these results demonstrate that acute psychosocial stress induces coordinated alterations in brain metabolism and large-scale network organization. Our findings show that metabolic imaging provides complementary information, revealing stress-related brain responses not captured by hemodynamics alone, thereby providing a multimodal framework for understanding human stress processing and individual vulnerability to stress-related psychiatric disorders.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

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

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

bioRxiv2024

A Unified Approach for Identifying PET-based Neuronal Activation and Molecular Connectivity with the functional PET toolbox

PurposeFunctional PET (fPET) enables the identification of stimulation-specific changes of various physiological processes (e.g., glucose metabolism, neurotransmitter synthesis) as well as computation of individual molecular connectivity and group-level molecular covariance. However, currently no consistent analysis approach is available for these techniques. We present a versatile, freely available toolbox designed for the analysis of fPET data, thereby filling a gap in the assessment of neuroimaging data. MethodsThe fPET toolbox supports analyses for a variety of radiotracers, scanners, experimental protocols, cognitive tasks and species. It includes general linear model (GLM)-based assessment of task-specific effects, percent signal change and absolute quantification, as well as independent component analysis (ICA) for data-driven analyses. Furthermore, it allows computation of molecular connectivity via temporal correlations of PET signals between regions and molecular covariance as between-subject covariance using static images. ResultsToolbox performance was validated by analysis protocols established in previous work. Stimulation-induced changes in [18F]FDG metabolic demands and neurotransmitter dynamics obtained with 6-[18F]FDOPA and [11C]AMT were robustly detected across different cognitive tasks. Molecular connectivity analysis demonstrated metabolic interactions between different networks, whereas group-level covariance analysis highlighted interhemispheric relationships. These results underscore the flexibility of fPET in capturing dynamic molecular processes. ConclusionsThe toolbox offers a comprehensive, unified and user-friendly platform for analyzing fPET data across a variety of experimental settings. It provides a reproducible analysis approach, which in turn facilitates sharing of analyses pipelines and comparison across centers to advance the study of brain metabolism and neurotransmitter dynamics in health and disease.

neuroscience↗

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.

neuroscience↗