Biology subjects

Hahn, A.

Publications and source records attributed to Hahn, A..

At least 19 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↗

bioRxiv2026

Germline mitophagy selectively eliminates deleterious mitochondrial DNA across generations

Faithful transmission of genetic information through the immortal germline is essential for organismal health and species survival, yet how mutant mitochondrial genomes (mtDNAs) are selectively eliminated across generations remains unclear. Here, we show that germline mitophagy functions as a mutation-responsive surveillance system that selectively eliminates mutant mtDNAs and shapes inheritance across generations. In C. elegans, mitochondria enriched for mutant mtDNAs are selectively removed in the maternal germline prior to oocyte fertilization via PINK1/Parkin-dependent and BNIP3-mediated mitophagy pathways activated by mtDNA defects. Germline mitophagy declines with age, resulting in offspring that inherit increased burdens of mutant mtDNAs. Conversely, enhancing mitophagy within germ cells restricts the transmission of deleterious genomes in a compounding manner, ultimately driving their complete elimination from the matrilineal lineage. Together, our findings demonstrate that germline mitophagy is a critical determinant of intergenerational mitochondrial genome inheritance, establishing its role in restricting the transmission of defective genetic information.

Cell Biology↗

bioRxiv2026

Bacterial Virulence Genes Detected by Metagenomic Sequencing in the Cystic Fibrosis Airway Microbiome

BackgroundCystic fibrosis (CF) is an autosomal recessive genetic disorder that leads to chronic infection and mucus retention in the lungs, with lung function gradually deteriorating through recurrent pulmonary exacerbations (PEx). Virulence factors (VFs) of Pseudomonas aeruginosa and Staphylococcus aureus are thought to contribute to pulmonary exacerbations. Our study objective was to identify VF genes related to PEx, high Pseudomonas abundance, and high Staphylococcus abundance in persons with CF (pwCF). MethodsThis was an ancillary study of pwCF treated with IV antibiotics for PEx between 2016-2020 at Childrens National Hospital. Using shotgun metagenomics and ShortBRED, we identified bacterial VF genes and used DESeq2 to determine differential expression of VF genes across comparators. ResultsTwenty-two PwCF experienced 43 PEx. The study cohort had a mean age of 14.6 years, 41% female, 59% white, 36% Hispanic, and 45% had an F508del homozygous CFTR mutation. Minimal differences in VF gene abundance were identified across clinical state. The most differentially increased VF genes found in Pseudomonas high samples were associated with an aminotransferase (log2FC 25.9), flagellar biosynthesis (log2FC 8.3), and type VI secretion systems (log2FC 8.2). The most differentially increased VF genes found in Staphylococcus high samples were an exotoxin (log2FC 26.7), macrolide phosphotransferase (log2FC 25.8), pathogenicity island proteins (log2FC 25.2 and 24.7), and VOC family proteins (log2FC 24.8). ConclusionsThese findings demonstrate that specific VFs associated with immune modulation, motility secretion systems, bacterial motility, and antibiotic resistance are related to P. aeruginosa and S. aureus abundance, providing potential targets for more personalized antimicrobial interventions.

microbiology↗

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

Dynamic neural states underpin bradykinesia severity in Parkinsons disease

BackgroundBradykinesia in Parkinsons disease (PD) may arise due to transient, network-wide neural dynamics that extend beyond beta-band oscillatory activity within the motor cortical-subthalamic nucleus (STN) circuit. MethodsWe address this question by using Hidden Markov Models (HMMs) to identify neural states from chronic motor cortical and STN recordings in five PD patients (1,046 hours from 10 hemispheres), with concurrent measurements of bradykinesia using wearable sensors. FindingsWe identified four neural states with distinct spectral and temporal features. Two states exhibited spectral signatures--particularly STN low and high gamma, STN delta/alpha, cortical beta, and cortico-STN beta coherence--that predicted worsening bradykinesia. However, STN beta power alone was not consistently predictive, challenging traditional beta-centric views. These states also displayed compensatory features associated with bradykinesia amelioration, including cortical delta/alpha activity, cortical high gamma, and cortico-STN high gamma coherence. Two additional states affected bradykinesia through temporal rather than spectral properties. Prolonged lifetimes of one of these state worsened symptoms, whereas increased occurrences of another, marked by local beta without cortico-STN beta coherence, improved motor function. InterpretationOur findings highlight the multidimensional nature of bradykinesia and suggest that state-aware, adaptive interventions targeting state features--rather than single frequency bands--may offer new opportunities for improved deep brain stimulation in PD. FundingAO is supported by an MRC Clinician Scientist Fellowship (MR/W024810/1) and a Rosetrees Trust/Race Against Dementia Team award. BA and AO acknowledge funding support from the Oxford University Hospitals Charity and Jon Moulton Trust. TL acknowledges funding support from the China Scholarship Council. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSPrevious work has demonstrated that subthalamic nucleus oscillatory activity at beta (15-30 Hz) frequencies correlates positively with motor symptoms in Parkinsons disease. This has led to beta activity being used as a biomarker for adaptive Deep Brain Stimulation. It remains unclear however whether other oscillatory features within the broader motor cortical-subthalamic nucleus network could provide improved biomarkers for tracking symptom severity. Added value of this studyWe address this by performing chronic motor cortical and subthalamic nucleus recordings in Parkinsons disease patients during activities of daily living. Simultaneous measurements of symptom severity were captured using wearable sensors. We used Hidden Markov Models to identify transient states of neural activity and related these to symptom severity. Although cortical beta and cortico-STN beta coherence predicted worsening motor symptoms, STN beta activity alone was not a consistent predictor. Interestingly, we identified spectral features associated with motor symptom improvements, including cortical delta/alpha activity, cortical high gamma, and cortico-STN high gamma coherence. Additionally, there was a compensatory state characterised by short-lived cortical and subthalamic nucleus beta activity, whose increased occurrence was associated with symptomatic improvements. Implications of all the available evidenceOur findings highlight the importance of prolonged, high temporal resolution measurements of both neural activity and symptom severity for discovering adaptive Deep Brain Stimulation biomarkers. Crucially, we identify new target states and spectral features for improving motor symptoms in Parkinsons disease.

neuroscience↗

bioRxiv2025

CEREBRAL GLUCOSE UTILISATION DURING MUSICAL EMOTIONS: A MULTIMODAL FUNCTIONAL PET/MRI STUDY.

Functional magnetic resonance imaging (fMRI) studies have demonstrated music-induced activation of the blood-oxygen-level-dependent (BOLD) signal across brain networks associated with auditory perception, motor control, and emotion. However, BOLD-fMRI reflects vascular responses that may not fully capture underlying neural activity. Here, we used simultaneous [18F]fluorodeoxyglucose (FDG) functional positron emission tomography (fPET) and fMRI to examine glucose metabolism closely linked to neural activity, alongside hemodynamic responses during pleasurable music listening. Thirty-five female participants listened to self-selected pleasurable music and control stimuli while undergoing 90-minute PET-MRI scans. fPET revealed music-evoked increase in glucose consumption in auditory and motor cortices, as well as reward-related regions, including the nucleus accumbens (NAcc), caudate, insula, and orbitofrontal cortex. The fPET and fMRI results showed substantial overlap though some discrepancies were also observed. Notably, the NAcc exhibited increased glucose consumption in fPET but showed no activation in fMRI. Conversely, deactivation of the default mode network during music processing was only observed with fMRI. These results highlight the complementary nature of neurometabolic and neurovascular processes and offer novel insights into their dynamics during the processing of aesthetic rewards.

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

Local Adaptation and Transcriptomic Plasticity of the Copepod Acartia tonsa Under Low Salinity Stress

Salinity--an essential factor shaping marine species distributions--is rapidly shifting due to global change, yet the mechanisms of salinity tolerance and adaptation remain poorly understood. We investigated local adaptation in the calanoid copepod Acartia tonsa, a broadly distributed marine species that thrives in the brackish Baltic Sea. Using a common-garden design, we compared physiological and transcriptomic responses to low salinity between populations from the North Sea (>25 PSU) and the Baltic Sea (<15 PSU). Baltic copepods exhibited significantly higher survival under low salinity, indicating local adaptation. While both populations shared a core osmoregulatory strategy involving active ion transport and regulation of amino acids, transcriptomic profiles revealed population-specific differences. Baltic individuals showed a reduced overall gene expression response, yet maintained higher relative expression of osmoregulatory genes--suggesting higher plasticity and a primed response. In contrast, North Sea copepods exhibited broader transcriptional shifts, including downregulation of metabolic and developmental pathways after prolonged stress exposure, possibly reflecting energy conservation mechanisms. These findings reveal that A. tonsa possesses both a plastic osmoregulatory strategy and population-level adaptation that enable survival in extreme salinity conditions. While both populations tolerate short-term exposure to low salinity, local adaptation has enhanced the Baltic populations resilience. This suggests that A. tonsa is broadly tolerant of moderate climate-driven salinity declines across most of its distribution. However, our data also indicate potential range contractions in the lowest salinity zones of the Baltic Sea, underscoring the importance of identifying physiological and genetic thresholds in climate resilience studies.

molecular biology↗

bioRxiv2025

Behavior decoding delineates seizure microfeatures and associated sudden death risks in mice

Behavior and motor manifestations are distinctive yet often overlooked features of epileptic seizures. Seizures can result in transient disruptions in motor control, often organized into specific behavioral patterns that can inform seizure types, onset zones, and outcomes. However, refined analysis of behaviors in epilepsy remains challenging in both clinical and preclinical settings. Current manual video inspection approaches are subjective, time-consuming, and often focus on gross and ambiguous descriptions of seizure behaviors, overlooking much of the intricate behavioral dynamics and action kinematics. Here, we utilized two machine learning-aided tools, DeepLabCut (DLC) and Behavior Segmentation of Open Field in DLC (B-SOiD), to decode previously underexplored behavior and action domains of epilepsy. We identified 63 interpretable behavior groups during seizures in a population of 32 genetically diverse mouse strains. Analysis of these behavior groups demonstrates significant differential behavior expression and complexity that can delineate distinct seizure states, unravel intrinsic seizure progression over time, and inform mouse strain backgrounds and genotypes. We also identified seizure behavior patterns and action/subaction kinematics that determine the risks of sudden unexpected death in epilepsy (SUDEP). These findings underscore the significant potential for translation into inpatient settings for video analysis in epilepsy monitoring units and outpatient settings via home surveillance devices and smartphones. One Sentence SummaryAI-aided behavior decoding delineates intricate seizure microfeatures in mice.

neuroscience↗

bioRxiv2025

Regulation of eye movements and pupil size in natural scenes

The visual diet of humans is complex in space, time, spectrum, and conse- quently the activation of the retinal photoreceptors. While analyses of natural scenes have yielded valuable insights, the naturalistic natural stimulus on the retina is not very well understood. In the present study, we performed eye tracking in naturalistic indoor and outdoor real-world scenes. We recorded pupil size, several saccade and fixation metrics, as well as subjective scene perception ratings derived from subjective questionnaires. For the first five seconds of eye tracking, the descriptive data analysis revealed significantly increased average saccade frequency (p =.0025), amplitude (p =.0049), peak velocity (p =.0072), as well as pupil size (p = 1.307e-09) in the indoor environ- ments. After this initial phase, these differences vanished, except for pupil size. Using an exploratory analysis on the whole 4-minute measurement, we found that saccade and fixation metrics, along with scene ratings, showed significantly different correlations between indoor and outdoor conditions. Despite the inherent constraints of such a naturalistic study (reduced ability to exert control over the precise task and the environmental conditions), we contend that the dataset holds substantial value for field of eye movement research, as it has effectively minimized confounding factors that have been prevalent in previous eye tracking studies. HighlightsO_LIIncreased amplitude, frequency, and peak velocity of saccades in real- world indoor environments within the initial 5 seconds of eye tracking C_LIO_LIDifferent correlations of eye movement metrics and subjective scene per- ception between real-world indoor and outdoor environments C_LIO_LIEstablishing an eye movement dataset of various saccade in fixation metrics in naturalistic real-world environments with reduced confounding factors C_LI

neuroscience↗

bioRxiv2024

Beta burst characteristics and coupling within the sensorimotor cortical-subthalamic nucleus circuit in Parkinson's disease

BackgroundBursts of exaggerated subthalamic nucleus (STN) beta activity contribute to clinical impairments in Parkinsons disease (PD). Few studies have explored the characteristics and coupling of bursts across the sensorimotor cortical-STN circuit. ObjectiveWe sought to (1) establish the characteristics of sensorimotor cortical and STN bursts during naturalistic behaviours, and (2) determine the predictability of STN bursts from motor cortical recordings. MethodsWe analysed 1,478 hours of wirelessly streamed bilateral sensorimotor cortical and STN recordings from 5 PD patients. ResultsSTN bursts were longer than cortical bursts and had shorter inter-burst intervals. Long bursts (>200ms) in both structures displayed temporal overlap (>30%), with an estimated cortico-STN conduction delay of 8ms. Furthermore, approximately 27% of all STN bursts were preceded by a cortical burst. ConclusionCortical beta bursts tend to precede STN beta bursts, with short delays. However, subcortical mechanisms are also likely to contribute to STN burst initiation and propagation.

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

On the analysis of functional PET (fPET)-FDG: baseline mischaracterization can introduce artifactual metabolic (de)activations

Functional Positron Emission Tomography (fPET) with (bolus plus) constant infusion of [18F]-fluorodeoxyglucose FDG), known as fPET-FDG, is a recently introduced technique in human neuroimaging, enabling the detection of dynamic glucose metabolism changes within a single scan. However, the statistical analysis of fPET-FDG data remains challenging because its signal and noise characteristics differ from both classic bolus-administration FDG PET and from functional Magnetic Resonance Imaging (fMRI), which together compose the primary sources of inspiration for analytical methods used by fPET-FDG researchers. In this study, we present an investigate of how inaccuracies in modeling baseline FDG uptake can introduce artifactual patterns to detrended TAC residuals, potentially introducing spurious (de)activations to general linear model (GLM) analyses. By combining simulations and empirical data from both constant infusion and bolus-plus-constant infusion protocols, we evaluate the effects of various baseline modeling methods, including polynomial detrending, regression against the global mean time-activity curve, and two analytical methods based on tissue compartment model kinetics. Our findings indicate that improper baseline removal can introduce statistically significant artifactual effects, although these effects characterized in this study ([~]2-8%) are generally smaller than those reported by previous literature employing robust sensory stimulation ([~]10-30%). We discuss potential strategies to mitigate this issue, including informed baseline modeling, optimized tracer administration protocols, and careful experimental design. These insights aim to enhance the reliability of fPET-FDG in capturing true metabolic dynamics in neuroimaging research.

neuroscience↗