Biology subjects

Falb, P.

Publications and source records attributed to Falb, P..

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

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

Warming winter disrupts mycorrhizal phenology and plant-fungal nutrient cycling.

Climate change is reshaping the timing of ecological processes in montane ecosystems, where short, snowpack-dependent growing seasons tightly constrain plant-microbe interactions. Arbuscular mycorrhizal (AM) fungi regulate plant acquisition of nitrogen (N) and phosphorus (P) during nutrient pulses triggered by melting snowpack. Yet the extent and consequences of warming-induced phenological asynchrony between AM fungi and host plants remains unknown. We experimentally advanced snowmelt in a subalpine meadow and monitored plant and AM fungal growth, soil nutrients, and AM fungal community composition throughout the growing season. Early snowmelt advanced plant greenness and root standing stock but suppressed AM fungal hyphal production, reducing available NH{square}{square} and PO43-. Hyphal allocation strategies shaped AM fungal temporal niches and species-specific responses to warming. Nutrient-foraging, edaphophilic AM fungi dominated early in the season, while rhizophilic AM fungi dominated later, following peak root growth. Our results reveal that fungal functional traits and nutrient dynamics govern the temporal niche partitioning of mycorrhizal fungi. By decoupling AM fungal activity from plant demand and nutrient mineralization windows, early snowmelt drives plant-fungal asynchrony. This decoupling weakens mycorrhizal symbioses and threatens nutrient retention and ecosystem stability under future warming. Such belowground temporal mismatches may determine the resilience of seasonally temperature-dependent ecosystems under warming.

ecology↗

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↗

bioRxiv2025

Earlier snowmelt reduces the strength of carbon sink in montane meadows

O_LIWarming temperatures are changing winters, leading to earlier snowmelt. This shift can lead to an earlier and potentially longer growing season, which in turn may affect various plant-mediated ecosystem functions. Despite its relevance in the carbon cycle, we still know little about how earlier snowmelt impacts the carbon balance in ecosystems over the growing season, e.g., does it only shift phenology, or does it affect the overall carbon uptake? Most studies rely on interannual variability in snowmelt timing, making it difficult to isolate snowmelt effects from other confounding variables, e.g, temperature and moisture anomalies. To address this uncertainty, we investigated how experimentally advancing snowmelt affects the carbon cycling of montane meadows across the growing season. C_LIO_LIWe experimentally advanced the snowmelt date in a montane meadow by approximately 12 days and collected data every two weeks throughout the growing season, including net ecosystem exchange (NEE), gross primary productivity (GPP), ecosystem respiration (ER), plant composition, and shrub, graminoid, and forb biomass. C_LIO_LIEarly in the growing season, GPP was higher in the early snowmelt plots, though this effect decreased as the growing season progressed. Our modeling of cumulative NEE showed that earlier snowmelt increased the carbon sink strength by 22%, with the strongest effect in the early spring and the effect diminishing as the growing season progressed, with control plots being a greater carbon sink in the later season. Graminoid biomass was 47% higher in plots with earlier snowmelt, but there was no change in total biomass. C_LIO_LISynthesis: As winters warm and snowmelt occurs earlier, plant productivity will shift earlier in the growing season, and montane meadows will become a stronger carbon sink. However, this effect will differ seasonally, altering the carbon balance in montane meadows. C_LI

ecology↗

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↗

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

neuroscience↗

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.

neuroscience↗