Toward Imaging of the GABA Transporter Type 1 In Vivo: Quantitative Evaluation of 4 Novel PET Radiotracers in Nonhuman Primates.

The main inhibitory neurotransmitter in the central nervous system is γ-aminobutyric acid (GABA). GABA transporter type 1 (GAT-1) is the principal GABA transporter in the brain, and it plays a crucial role in modulating GABA signaling. Its potential role in several neuropsychiatric disorders makes it an important target to study.

Condensates of synaptic vesicles and synapsin-1 mediate actin sequestering and polymerization.

Neuronal communication relies on precisely maintained synaptic vesicle (SV) clusters, which assemble via liquid-liquid phase separation. This process requires synapsins, the major synaptic phosphoproteins, which are known to bind actin. Reorganization of SVs, synapsins, and actin is a hallmark of synaptic activity, but the molecular details of the interactions between these components remain unclear.

Molecular connectivity studies in neurotransmission: a scoping review.

Positron emission tomography (PET) and single photon emission computed tomography (SPECT) are essential molecular imaging tools for the in vivo investigation of neurotransmission. Traditionally, PET and SPECT images are analysed in a univariate manner, testing for changes in radiotracer binding in regions or voxels of interest independently of each other. Over the past decade, there has been an increasing interest in the so-calledapproach that captures relationships of molecular imaging measures in different brain regions.

Glucose Metabolism echoes Long-Range Temporal Correlations in the Human Brain.

Intrinsic brain activity is characterized by pervasive long-range temporal correlations. While these scale-invariant dynamics are a fundamental hallmark of brain function, their implications for individual-level metabolic regulation remain poorly understood. Here, we address this gap by integrating resting-state functional Magnetic Resonance Imaging (fMRI) and dynamic [F]FDG Positron Emission Tomography (PET) data acquired from the same cohort of participants.

Unveiling functional-metabolic synergy in the healthy brain: multivariate integration of dynamic [F]FDG-PET and resting-state fMRI.

Introduction: Despite accounting for only 2% of body weight, the human brain requires significant amounts of glucose, even at rest, underscoring the importance of functional-metabolic relationships. Previous studies revealed moderate associations between resting-state fMRI functional connectivity (FC) and local metabolism via [F]FDG-PET, yet much remains to be understood, particularly regarding their coupling between functional and metabolic networks.

Methods: To this end, we employed multivariate Partial Least Squares Correlation (PLSC) to investigate the functional-metabolic relationship at both nodal and network level.

Connecting the dots: approaching a standardized nomenclature for molecular connectivity in positron emission tomography.

Positron emission tomography (PET)-based connectivity analysis provides a molecular perspective that complements fMRI-derived functional connectivity. However, lack of standardized terminology and diverse methodologies in PET connectivity studies has resulted in inconsistencies, complicating the interpretation and comparison of results across studies. A standardized nomenclature is thus needed to reduce ambiguity, enhance reproducibility, and facilitate interpretability across radiotracers, imaging modalities and studies.

The brain's "dark energy" puzzle : [F]FDG uptake, delivery and phosphorylation, and their coupling with resting-state brain activity.

The brain's resting-state energy consumption is expected to be driven by spontaneous activity. We previously used 50 resting-state fMRI (rs-fMRI) features to predict [F]FDG SUVR as a proxy of glucose metabolism. Here, we expanded on our effort by estimating [F]FDG kinetic parameters (irreversible uptake), (delivery), (phosphorylation) in a large healthy control group (n = 47).

Current Advances in PARP1-Targeted Theranostics.

Poly (ADP-ribose) polymerase 1 (PARP1) plays critical roles in DNA repair, chromatin regulation, and cellular equilibrium, positioning it as a pivotal target for therapeutic interventions in cancer and central nervous system (CNS) disorders. PARP1 responds to oxidative stress and DNA damage through PARylation, influencing energy depletion, survival, inflammation, and genomic regulation in many biological scenarios. PARP inhibitors (PARPis) have demonstrated efficacy against cancers harboring defective homologous recombination repair pathways, notably those linked to BRCA mutations.

EMATA: a toolbox for the automatic extraction and modeling of arterial inputs for tracer kinetic analysis in [F]FDG brain studies.

Purpose: PET imaging is a pivotal tool for biomarker research aimed at personalized medicine. Leveraging the quantitative nature of PET requires knowledge of plasma radiotracer concentration. Typically, the arterial input function (AIF) is obtained through arterial cannulation, an invasive and technically demanding procedure.

Individual-level metabolic connectivity from dynamic [F]FDG PET reveals glioma-induced impairments in brain architecture and offers novel insights beyond the SUVR clinical standard.

Purpose: This study evaluates the potential of within-individual Metabolic Connectivity (wi-MC), from dynamic [F]FDG PET data, based on the Euclidean Similarity method. This approach leverages the biological information of the tracer's full temporal dynamics, enabling the direct extraction of individual metabolic connectomes. Specifically, the proposed framework, applied to glioma pathology, seeks to assess sensitivity to metabolic dysfunctions in the whole brain, while simultaneously providing further insights into the pathophysiological mechanisms regulating glioma progression.

The brain's "dark energy" puzzle : [F]FDG uptake, delivery and phosphorylation, and their coupling with resting-state brain activity.

The brain's resting-state energy consumption is expected to be mainly driven by spontaneous activity. In our previous work, we extracted a wide range of features from resting-state fMRI (rs-fMRI), and used them to predict [F]FDG PET SUVR as a proxy of glucose metabolism. Here, we expanded upon our previous effort by estimating [F]FDG kinetic parameters according to Sokoloff's model, i.

Condensates of synaptic vesicles and synapsin are molecular beacons for actin sequestering and polymerization.

Neuronal communication relies on precisely maintained synaptic vesicle (SV) clusters, which assemble via liquid-liquid phase separation (LLPS). This process requires synapsins, the major synaptic phosphoproteins, which are known to bind actin. The reorganization of SVs, synapsins and actin is a hallmark of synaptic activity, but their interplay is still unclear.

Performance Characteristics of the NeuroEXPLORER, a Next-Generation Human Brain PET/CT Imager.

The collaboration of Yale, the University of California, Davis, and United Imaging Healthcare has successfully developed the NeuroEXPLORER, a dedicated human brain PET imager with high spatial resolution, high sensitivity, and a built-in 3-dimensional camera for markerless continuous motion tracking. It has high depth-of-interaction and time-of-flight resolutions, along with a 52.4-cm transverse field of view (FOV) and an extended axial FOV (49.

Relationship between neuroimaging and cognition in frontotemporal dementia: An FDG-PET and structural MRI study.

Background And Purpose: Frontotemporal dementia (FTD) is a clinically and pathologically heterogeneous neurodegenerative condition with a prevalence comparable to Alzheimer's disease for patients under 65 years of age. Limited studies have examined the association between cognition and neuroimaging in FTD using different imaging modalities.

Methods: We examined the association of cognition using Montreal Cognitive Assessment (MoCA) with both gray matter (GM) volume and glucose metabolism using magnetic resonance imaging and fluorodeoxyglucose (FDG)-PET in 21 patients diagnosed with FTD.

The brain's "dark energy" puzzle: How strongly is glucose metabolism linked to resting-state brain activity?

Brain glucose metabolism, which can be investigated at the macroscale level with [F]FDG PET, displays significant regional variability for reasons that remain unclear. Some of the functional drivers behind this heterogeneity may be captured by resting-state functional magnetic resonance imaging (rs-fMRI). However, the full extent to which an fMRI-based description of the brain's spontaneous activity can describe local metabolism is unknown.

Relationship between Neuroimaging and Cognition in Frontotemporal Dementia: A [18 F]FDG PET and Structural MRI Study.

Background: Frontotemporal dementia (FTD) is a clinically and pathologically heterogeneous condition with a prevalence comparable to Alzheimer's Disease for patients under sixty-five years of age. Gray matter (GM) atrophy and glucose hypometabolism are important biomarkers for the diagnosis and evaluation of disease progression in FTD. However, limited studies have systematically examined the association between cognition and neuroimaging in FTD using different imaging modalities in the same patient group.

An update on the use of image-derived input functions for human PET studies: new hopes or old illusions?

Background: The need for arterial blood data in quantitative PET research limits the wider usability of this imaging method in clinical research settings. Image-derived input function (IDIF) approaches have been proposed as a cost-effective and non-invasive alternative to gold-standard arterial sampling. However, this approach comes with its own limitations-partial volume effects and radiometabolite correction among the most important-and varying rates of success, and the use of IDIF for brain PET has been particularly troublesome.

A new framework for metabolic connectivity mapping using bolus [F]FDG PET and kinetic modeling.

Metabolic connectivity (MC) has been previously proposed as the covariation of static [F]FDG PET images across participants, i.e., MC (ai-MC).

Linking resting-state network fluctuations with systems of coherent synaptic density: A multimodal fMRI and C-UCB-J PET study.

Resting-state network (RSN) connectivity is a widely used measure of the brain's functional organization in health and disease; however, little is known regarding the underlying neurophysiology of RSNs. The aim of the current study was to investigate associations between RSN connectivity and synaptic density assessed using the synaptic vesicle glycoprotein 2A radioligand C-UCB-J PET. Independent component analyses (ICA) were performed on resting-state fMRI and PET data from 34 healthy adult participants (16F, mean age: 46 ± 15 years) to identify RSNs of interest (default-mode, right frontoparietal executive-control, salience, and sensorimotor networks) and select sources of C-UCB-J variability (medial prefrontal, striatal, and medial parietal).

Modeling venous plasma samples in [F] FDG PET studies: a nonlinear mixed-effects approach.

The gold-standard approach to quantifying dynamic PET images relies on using invasive measures of the arterial plasma tracer concentration. An attractive alternative is to employ an image-derived input function (IDIF), corrected for spillover effects and rescaled with venous plasma samples. However, venous samples are not always available for every participant.

Image-derived Input Function in brain [F]FDG PET data: which alternatives to the carotid siphons?

Quantification of brain [F] fluorodeoxyglucose ([F]FDG) positron emission tomography (PET) data requires an input function. A noninvasive alternative to gold-standard arterial sampling is the image-derived input function (IDIF), typically extracted from the internal carotid arteries (ICAs), which are however difficult to segment and subjected to spillover effects. In this work, we evaluated the feasibility of extracting the IDIF from two different vascular sites, i.

Variability of regional glucose metabolism and the topology of functional networks in the human brain.

The brain consumes the most energy per relative mass amongst the organs in the human body. Theoretical and empirical studies have shown that behavioral processes are relatively inexpensive metabolically, and that most energy goes to maintaining the status quo, i.e.

Assessing different approaches to estimate single-subject metabolic connectivity from dynamic [F]fluorodeoxyglucose Positron Emission Tomography data.

Metabolic connectivity is conventionally calculated in terms of correlation of static positron emission tomography (PET) measurements across subjects. There is increasing interest in deriving metabolic connectivity at the single-subject level from dynamic PET data, in a similar way to functional magnetic resonance imaging. However, the strong multicollinearity among region-wise PET time-activity curves (TACs), their non-Gaussian distribution, and the choice of the best strategy for TAC standardization before metabolic connectivity estimation, are non-trivial methodological issues to be tackled.