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Neuroinflammation is associated with a broad spectrum of neurodegenerative and psychiatric diseases. The core process in neuroinflammation is activation of microglia, the innate immune cells of the brain. We measured the neuroinflammatory response produced by a systemic administration of theEscherichia colilipopolysaccharide (LPS; also called endotoxin) in humans with the positron emission tomography (PET) radiotracer [11C]PBR28, which binds to translocator protein, a molecular marker that is up-regulated by microglial activation. In addition, inflammatory cytokines in serum and sickness behavior profiles were measured before and after LPS administration to relate brain microglial activation with systemic inflammation and behavior. Eight healthy male subjects each had two 120-min [11C]PBR28 PET scans in 1 d, before and after an LPS challenge. LPS (1.0 ng/kg, i.v.) was administered 180 min before the second [11C]PBR28 scan. LPS administration significantly increased [11C]PBR28 binding 30–60%, demonstrating microglial activation throughout the brain. This increase was accompanied by an increase in blood levels of inflammatory cytokines, vital sign changes, and sickness symptoms, well-established consequences of LPS administration. To our knowledge, this is the first demonstration in humans that a systemic LPS challenge induces robust increases in microglial activation in the brain. This imaging paradigm to measure brain microglial activation with [11C]PBR28 PET provides an approach to test new medications in humans for their putative antiinflammatory effects.

Microglia play an essential role in many brain diseases. Microglia are activated by local tissue damage or inflammation, but systemic inflammation can also activate microglia. An important clinical question is whether the effects of systemic inflammation on microglia mediate the deleterious effects of systemic inflammation in diseases such as Alzheimer's dementia, multiple sclerosis, and stroke. Positron Emission Tomography (PET) imaging with ligands that bind to Translocator Protein (TSPO) can be used to detect activated microglia. The aim of this study was to evaluate whether the effect of systemic inflammation on microglia could be measured with PET imaging in nonhuman primates, using the TSPO ligand [11C]PBR28. Six female baboons (Papio anubis) were scanned before and at 1h and/or 4h and/or 22h after intravenous administration of E. coli lipopolysaccharide (LPS; 0.1mg/kg), which induces systemic inflammation. Regional time-activity data from regions of interest (ROIs) were fitted to the two-tissue compartmental model, using the metabolite-corrected arterial plasma curve as input function. Total volume of distribution (VT) of [11C]PBR28 was used as a measure of total ligand binding. The primary outcome was change in VT from baseline. Serum levels of tumor necrosis factor alpha (TNFα), interleukin-1 beta (IL-1β), interleukin-6 (IL-6), and interleukin-8 (IL-8) were used to assess correlations between systemic inflammation and microglial activation. In one baboon, immunohistochemistry was used to identify cells expressing TSPO. LPS administration increased [11C]PBR28 binding (F(3,6)=5.1, p=.043) with a 29±16% increase at 1h (n=4) and a 62±34% increase at 4h (n=3) post-LPS. There was a positive correlation between serum IL-1β and IL-6 levels and the increase in [11C]PBR28 binding. TSPO immunoreactivity occurred almost exclusively in microglia and rarely in astrocytes. In the nonhuman-primate brain, LPS-induced systemic inflammation produces a robust increase in the level of TSPO that is readily detected with [11C]PBR28 PET. The effect of LPS on [11C]PBR28 binding is likely mediated by inflammatory cytokines. Activation of microglia may be a mechanism through which systemic inflammatory processes influence the course of diseases such as Alzheimer's, multiple sclerosis, and possibly depression. ► PET imaging can detect microglial activation after lipopolysaccharide administration. ► Systemic inflammation led to increased [11C]PBR28 binding in the brain. ► The increase in binding correlated with blood levels of inflammatory cytokines. ► The effect of systemic inflammation on microglia can be detected with PET. ► Relevant to brain diseases in which systemic inflammation affects disease progression.

Positron emission tomography, an imaging tool using radiolabeled tracers in humans and preclinical species, has been widely used in recent years in drug development, particularly in the central nervous system. One important goal of PET in drug development is assessing the occupancy of various molecular targets (e.g., receptors, transporters, enzymes) by exogenous drugs. The current linear mathematical approaches used to determine occupancy using PET imaging experiments are presented. These algorithms use results from multiple regions with different target content in two scans, a baseline (pre-drug) scan and a post-drug scan. New mathematical estimation approaches to determine target occupancy, using maximum likelihood, are presented. A major challenge in these methods is the proper definition of the covariance matrix of the regional binding measures, accounting for different variance of the individual regional measures and their nonzero covariance, factors that have been ignored by conventional methods. The novel methods are compared to standard methods using simulation and real human occupancy data. The simulation data showed the expected reduction in variance and bias using the proper maximum likelihood methods, when the assumptions of the estimation method matched those in simulation. Between-method differences for data from human occupancy studies were less obvious, in part due to small dataset sizes. These maximum likelihood methods form the basis for development of improved PET covariance models, in order to minimize bias and variance in PET occupancy studies.

Head motion during PET scans causes image quality degradation, decreased concentration in regions with high uptake and incorrect outcome measures from kinetic analysis of dynamic datasets. Previously, we proposed a data-driven method, center of tracer distribution (COD), to detect head motion without an external motion tracking device. There, motion was detected using one dimension of the COD trace with a semiautomatic detection algorithm, requiring multiple user defined parameters and manual intervention. In this study, we developed a new data-driven motion detection algorithm, which is automatic, self-adaptive to noise level, does not require user-defined parameters and uses all three dimensions of the COD trace (3DCOD). 3DCOD was first validated and tested using 30 simulation studies (18F-FDG, N = 15; 11C-raclopride (RAC), N = 15) with large motion. The proposed motion correction method was tested on 22 real human datasets, with 20 acquired from a high resolution research tomograph (HRRT) scanner (18F-FDG, N = 10; 11C-RAC, N = 10) and 2 acquired from the Siemens Biograph mCT scanner. Real-time hardware-based motion tracking information (Vicra) was available for all real studies and was used as the gold standard. 3DCOD was compared to Vicra, no motion correction (NMC), one-direction COD (our previous method called 1DCOD) and two conventional frame-based image registration (FIR) algorithms, i.e., FIR1 (based on predefined frames reconstructed with attenuation correction) and FIR2 (without attenuation correction) for both simulation and real studies. For the simulation studies, 3DCOD yielded -2.3 ± 1.4% (mean ± standard deviation across all subjects and 11 brain regions) error in region of interest (ROI) uptake for 18F-FDG (-3.4 ± 1.7% for 11C-RAC across all subjects and 2 regions) as compared to Vicra (perfect correction) while NMC, FIR1, FIR2 and 1DCOD yielded -25.4 ± 11.1% (-34.5 ± 16.1% for 11C- RAC), -13.4 ± 3.5% (-16.1 ± 4.6%), -5.7 ± 3.6% (-8.0 ± 4.5%) and -2.6 ± 1.5% (-5.1 ± 2.7%), respectively. For real HRRT studies, 3DCOD yielded -0.3 ± 2.8% difference for 18F-FDG (-0.4 ± 3.2% for 11C-RAC) as compared to Vicra while NMC, FIR1, FIR2 and 1DCOD yielded -14.9 ± 9.0% (-24.5 ± 14.6%), -3.6 ± 4.9% (-13.4 ± 14.3%), -0.6 ± 3.4% (-6.7 ± 5.3%) and -1.5 ± 4.2% (-2.2 ± 4.1%), respectively. In summary, the proposed motion correction method yielded comparable performance to the hardware-based motion tracking method for multiple tracers, including very challenging cases with large frequent head motion, in studies performed on a non-TOF scanner.

The energy-dissipating properties of brown adipose tissue (BAT) have been proposed as therapeutic targets for obesity and diabetes. Little is known about basal BAT activity. Capitalizing on the dense sympathetic innervation of BAT, we have previously shown that BAT can be detected in humans under resting room temperature (RT) conditions by using (S,S)-11C–O-methylreboxetine (MRB), a selective ligand for the norepinephrine transporter (NET). In this study, we determine whether MRB labeling of human BAT is altered by obesity. Fifteen healthy, nondiabetic Caucasian women (nine lean, age 25.6 ± 1.7, BMI 21.8 ± 1.3 kg/m2; six obese age 30.8 ± 8.8 BMI 37.9 ± 6.6 kg/m2) underwent PET-CT imaging of the neck/supraclavicular region using 11C-MRB under RT conditions. The distribution volume ratio (DVR) for 11C-MRB was estimated via multilinear reference tissue model 2 (MRTM2) referenced to the occipital cortex. Two women (one lean and one with obesity) had no detectable BAT. Of the women with detectable BAT, women with obesity had lower 11C-MRB DVR (0.80 ± 0.12 BAT DVR) compared to lean (1.15 ± 0.19 BAT DVR) (p = 0.004). Our findings are consistent with reports that NET is decreased in obesity and suggest that the sympathetic innervation of BAT is altered in obesity.

Head motion presents a continuing problem in brain PET studies. A wealth of motion correction (MC) algorithms had been proposed in the past, including both hardware-based methods and data-driven methods. However, in most real brain PET studies, in the absence of ground truth or gold standard of motion information, it is challenging to objectively evaluate MC quality. For MC evaluation, image-domain metrics, e.g., standardized uptake value (SUV) change before and after MC are commonly used, but this measure lacks objectivity because 1) other factors, e.g., attenuation correction, scatter correction and parameters used in the reconstruction, will confound MC effectiveness; 2) SUV only reflects final image quality, and it cannot precisely inform when an MC method performed well or poorly during the scan time period; 3) SUV is tracer-dependent and head motion may cause increases or decreases in SUV for different tracers, so evaluating MC effectiveness is complicated. Here, we present a new algorithm, i.e., motion corrected centroid-of-distribution (MCCOD) to perform objective quality control for measured or estimated rigid motion information. MCCOD is a three-dimensional surrogate trace of the center of tracer distribution after performing rigid MC using the existing motion information. MCCOD is used to inform whether the motion information is accurate, using the PET raw data only, i.e., without PET image reconstruction, where inaccurate motion information typically leads to abrupt changes in the MCCOD trace. MCCOD was validated using simulation studies and was tested on real studies acquired from both time-of-flight (TOF) and non-TOF scanners. A deep learning-based brain mask segmentation was implemented, which is shown to be necessary for non-TOF MCCOD generation. MCCOD is shown to be effective in detecting abrupt translation motion errors in slowly varying tracer distribution caused by the motion tracking hardware and can be used to compare different motion estimation methods as well as to improve existing motion information.

Abstract Context Hypoglycemia, one of the major factors limiting optimal glycemic control in insulin-treated patients with diabetes, elicits a brain response to restore normoglycemia by activating counterregulation. Animal data indicate that local release of norepinephrine (NE) in the hypothalamus is important for triggering hypoglycemia-induced counterregulatory (CR) hormonal responses. Objective To examine the potential role of brain noradrenergic (NA) activation in humans during hypoglycemia. Design A hyperinsulinemic-hypoglycemic clamp was performed in conjunction with positron emission tomographic imaging. Participants Nine lean healthy volunteers were studied during the hyperinsulinemic-hypoglycemic clamp. Design Participants received intravenous injections of (S,S)-[11C]O-methylreboxetine ([11C]MRB), a highly selective NE transporter (NET) ligand, at baseline and during hypoglycemia. Results Hypoglycemia increased plasma epinephrine, glucagon, cortisol, and growth hormone and decreased [11C]MRB binding potential (BPND) by 24% ± 12% in the raphe nucleus (P < 0.01). In contrast, changes in [11C]MRB BPND in the hypothalamus positively correlated with increments in epinephrine and glucagon levels and negatively correlated with glucose infusion rate (all P < 0.05). Furthermore, in rat hypothalamus studies, hypoglycemia induced NET translocation from the cytosol to the plasma membrane. Conclusions Insulin-induced hypoglycemia initiated a complex brain NA response in humans. Raphe nuclei, a region involved in regulating autonomic output, motor activity, and hunger, had increased NA activity, whereas the hypothalamus showed a NET-binding pattern that was associated with the individual’s CR response magnitude. These findings suggest that NA output most likely is important for modulating brain responses to hypoglycemia in humans. Hypoglycemia increases noradrenergic activity in the raphe nuclei, whereas changes in hypothalamic noradrenergic activity correlate with the counterregulatory response to hypoglycemia in humans.

Brain cannabinoid 1 receptors (CB1Rs) contribute importantly to the regulation of autonomic tone, appetite, mood and cognition. Inconsistent results have been reported from positron emission tomography (PET) studies using different radioligands to examine relationships between age, gender and body mass index (BMI) and CB1R availability in healthy individuals. In this study, we examined these variables in 58 healthy individuals (age range: 18–55 years; 44 male; BMI=27.01±5.56), the largest cohort of subjects studied to date using the CB1R PET ligand [11C]OMAR. There was a significant decline in CB1R availability (VT) with age in the pallidum, cerebellum and posterior cingulate. Adjusting for BMI, age-related decline in VT remained significant in the posterior cingulate among males, and in the cerebellum among women. CB1R availability was higher in women compared to men in the thalamus, pallidum and posterior cingulate. Adjusting for age, CB1R availability negatively correlated with BMI in women but not men. These findings differ from those reported using [11C]OMAR and other radioligands such as [18F]FMPEP-d2 and [18F]MK-9470. Although reasons for these seemingly divergent findings are unclear, the choice of PET radioligand and range of BMI in the current dataset may contribute to the observed differences. This study highlights the need for cross-validation studies using both [11C]OMAR and [18F]FMPEP-d2 within the same cohort of subjects.

Stimulant-use disorders have been associated with lower availability of dopamine type-2 receptors (D2R) and greater availability of type-3 receptors (D3R). Links between D2R levels, cognitive performance, and suppression of the default mode network (DMN) during executive functioning have been observed in healthy and addicted populations; however, there is limited evidence regarding a potential role of elevated D3R in influencing cognitive control processes in groups with and without addictions. Sixteen individuals with cocaine-use disorder (CUD) and 16 healthy comparison (HC) participants completed [11C]-(+)-PHNO PET imaging of D2R and D3R availability and fMRI during a Stroop task of cognitive control. Independent component analysis was performed on fMRI data to assess DMN suppression during Stroop performance. In HC individuals, lower D2R-related binding in the dorsal putamen was associated with improved task performance and greater DMN suppression. By comparison, in individuals with CUD, greater D3R-related binding in the substantia nigra was associated with improved performance and greater DMN suppression. Exploratory moderated-mediation analyses indicated that DMN suppression was associated with Stroop performance indirectly through D2R in HC and D3R in CUD participants, and these indirect effects were different between groups. To our knowledge, this is the first evidence of a dissociative and potentially beneficial role of elevated D3R availability in executive functioning in cocaine-use disorder.

BackgroundArterial blood sampling is the gold standard method to obtain the arterial input function (AIF) for quantification of whole body (WB) dynamic 18F-FDG PET imaging. However, this procedure is invasive and not typically available in clinical environments. As an alternative, we compared AIFs to population-based input functions (PBIFs) using two normalization methods: area under the curve (AUC) and extrapolated initial plasma concentration (CP*(0)). To scale the PBIFs, we tested two methods: (1) the AUC of the image-derived input function (IDIF) and (2) the estimated CP*(0). The aim of this study was to validate IDIF and PBIF for FDG oncological WB PET studies by comparing to the gold standard arterial blood sampling.MethodsThe Feng 18F-FDG plasma concentration model was applied to estimate AIF parameters (n = 23). AIF normalization used either AUC(0–60 min) or CP*(0), estimated from an exponential fit. CP*(0) is also described as the ratio of the injected dose (ID) to initial distribution volume (iDV). iDV was modeled using the subject height and weight, with coefficients that were estimated in 23 subjects. In 12 oncological patients, we computed IDIF (from the aorta) and PBIFs with scaling by the AUC of the IDIF from 4 time windows (15–45, 30–60, 45–75, 60–90 min) (PBIFAUC) and estimated CP*(0) (PBIFiDV). The IDIF and PBIFs were compared with the gold standard AIF, using AUC values and Patlak Ki values.ResultsThe IDIF underestimated the AIF at early times and overestimated it at later times. Thus, based on the AUC and Ki comparison, 30–60 min was the most accurate time window for PBIFAUC; later time windows for scaling underestimated Ki (− 6 ± 8 to − 13 ± 9%). Correlations of AUC between AIF and IDIF, PBIFAUC(30–60), and PBIFiDV were 0.91, 0.94, and 0.90, respectively. The bias of Ki was − 9 ± 10%, − 1 ± 8%, and 3 ± 9%, respectively.ConclusionsBoth PBIF scaling methods provided good mean performance with moderate variation. Improved performance can be obtained by refining IDIF methods and by evaluating PBIFs with test-retest data.