Biology subjects

Murgas, M.

Publications and source records attributed to Murgas, M..

13 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

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.

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

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.

neuroscience↗

bioRxiv2021

Correlation of receptor density and mRNA expression patterns in the human cerebral cortex

Changes in distribution of associated molecular targets have been reported across several neuropsychiatric disorders. However, the high-resolution topology of most proteins is unknown and simultaneous in vivo measurement in multi-receptor systems is complicated. To account for the missing proteomic information, mRNA transcripts are typically used as a surrogate. Nonetheless, post-transcriptional and post-translational processes might cause the discrepancy between the final distribution of proteins and gene expression patterns. Therefore, this study aims to investigate ex vivo links between mRNA expression and corresponding receptor density in the human cerebral cortex. To this end, autoradiography data on the density of 15 different receptors in 38 brain regions were correlated with the expression patterns of 50 associated genes derived from microarray data (mA), RNA sequencing data (RNA-Seq) provided by the Allen Human Brain Atlas and predicted mRNA expression patterns (pred-mRNA). Spearmans rank correlation was used to evaluate the possible links between proteomic data and mRNA expression patterns. Correlations between mRNA and protein density varied greatly between targets: Positive associations were found for e.g. the serotonin 1A (pred-mRNA: rs = 0.708; mA: rs = 0.601) or kainate receptor (pred-mRNA: rs = 0.655; mA: rs = 0.601; RNA-Seq: rs = 0.575), while most of the investigated target receptors showed low or negative correlations. The high variability in the correspondence of mRNA expression and receptor warrants caution when inferring the topology of molecular targets in the brain from transcriptome data. This highlights the longstanding value of molecular imaging data and the need for comprehensive proteomic data.

neuroscience↗

bioRxiv2020

Regional expression profiles of risk genes for depression are associated with brain activation patterns in emotion and reward tasks

The spatial relationship between gene expression profiles and neural response patterns known to be altered in neuropsychiatric disorders, e.g. depression, can guide the development of more targeted therapies. Here, we estimated the correlation between human transcriptome data and two different brain activation maps measured with functional magnetic resonance imaging (fMRI) in healthy subjects. Whole-brain activation patterns evoked during an emotional face recognition task were associated with topological mRNA expression of genes involved in cellular transport. In contrast, fMRI activation patterns related to the acceptance of monetary rewards were associated with genes implicated in neuronal development, synapse regulation, and gene transcription. An overlap of these genes with risk genes from major depressive disorder genome-wide associations studies revealed the involvement of the master regulators TCF4 and MEF2C. These results were further validated by means of meta-analytic fMRI data. Overall, the identification of stable relationships between spatial gene expression profiles and fMRI data may reshape the prospects for imaging transcriptomics studies.

neuroscience↗

bioRxiv2020

Hierarchical clustering of the human cerebral cortex based on comprehensive transcriptome data

The parcellation of the cerebral cortex serves the investigation of the emergence of uniquely human brain functions and disorders. We employed hierarchical clustering based on comprehensive transcriptomic data of the human cortex in order to delineate areas with distinct gene expression profiles. These profiles were analyzed for the enrichment of gene sets associated with brain disorders by genome-wide studies (GWAS) and expert curation. This suggested new roles of specific cortical areas in psychiatric, neurodegenerative, congenital and other neurological disorders while reproducing some well-established links for movement disorders and dementias. GWAS-derived gene sets for psychiatric disorders exhibited similar enrichment patterns in the posterior fusiform gyrus and inferior parietal lobule driven by pleiotropic genes. This implies that the effects of risk variants shared between neuropsychiatric disorders might converge in these areas. For several diseases, specific genes were highlighted, which may aid the discovery of novel disease mechanisms and urgently needed treatments.

neuroscience↗