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. Standardized uptake value ratio (SUVR) using the brainstem as a reference region was the primary outcome measure for FDG-PET. Partial volume correction was applied to PET data to account for disease-related atrophy. RESULTS: Significant positive associations were found between whole-cortex GM volume and MoCA scores (r = 0.46, p = .04). The association between whole-cortex FDG SUVR and MoCA scores was not significant (r = 0.37, p = .09). GM volumes of the frontal cortex (r = 0.54, p = .01), caudate (r = 0.62, p<.01), and insula (r = 0.57, p<.01) were also significantly correlated with MoCA, as were SUVR values of the insula (r = 0.51, p = .02), thalamus (r = 0.48, p = .03), and posterior cingulate cortex (PCC) (r = 0.47, p = .03). CONCLUSIONS: Whole-cortex atrophy is associated with cognitive dysfunction, and this association is larger than for whole-cortex hypometabolism as measured with FDG-PET. At the regional level, focal atrophy and/or hypometabolism in the frontal cortex, insula, PCC, thalamus, and caudate seem to be important for the decline of cognitive function in FTD. Furthermore, these results highlight how functional and structural changes may not overlap and might contribute to cognitive dysfunction in FTD in different ways.

Population-based deep image prior for dynamic PET denoising: A data-driven approach to improve parametric quantification.

The high noise level of dynamic Positron Emission Tomography (PET) images degrades the quality of parametric images. In this study, we aim to improve the quality and quantitative accuracy of Ki images by utilizing deep learning techniques to reduce the noise in dynamic PET images. We propose a novel denoising technique, Population-based Deep Image Prior (PDIP), which integrates population-based prior information into the optimization process of Deep Image Prior (DIP). Specifically, the population-based prior image is generated from a supervised denoising model that is trained on a prompts-matched static PET dataset comprising 100 clinical studies. The 3D U-Net architecture is employed for both the supervised model and the following DIP optimization process. We evaluated the efficacy of PDIP for noise reduction in 25%-count and 100%-count dynamic PET images from 23 patients by comparing with two other baseline techniques: the Prompts-matched Supervised model (PS) and a conditional DIP (CDIP) model that employs the mean static PET image as the prior. Both the PS and CDIP models show effective noise reduction but result in smoothing and removal of small lesions. In addition, the utilization of a single static image as the prior in the CDIP model also introduces a similar tracer distribution to the denoised dynamic frames, leading to lower Ki in general as well as incorrect Ki in the descending aorta. By contrast, as the proposed PDIP model utilizes intrinsic image features from the dynamic dataset and a large clinical static dataset, it not only achieves comparable noise reduction as the supervised and CDIP models but also improves lesion Ki predictions.

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. Methods: We examined the association of cognition using Montreal Cognitive Assessment (MoCA) with both GM volume and glucose metabolism using structural magnetic resonance imaging (MRI) and 18F-fluorodeoxyglucose positron emission tomography scanning ([18F]FDG PET) in 21 patients diagnosed with FTD. Standardized uptake value ratio (SUVR) using the brainstem as a reference region was the primary outcome measure for [18F]FDG PET. Partial volume correction was applied to PET data to account for disease-related atrophy. Results: Significant positive associations were found between whole-cortex GM volume and MoCA scores (r = 0.461, p = 0.035). The association between whole-cortex [18F]FDG SUVR and MoCA scores was not Significant (r = 0.374, p = 0.094). GM volumes of the frontal cortex (r = 0.540, p = 0.011), caudate (r = 0.616, p = 0.002), and insula (r = 0.568, p = 0.007) were also Significantly correlated with MoCA, as were SUVR values of the insula (r = 0.508, p = 0.018), thalamus (r = 0.478, p = 0.028), and posterior cingulate cortex (PCC) (r = 0.472, p = 0.030). Discussion: Whole-cortex atrophy is associated with cognitive dysfunction, and this effect is larger than for cortical hypometabolism as measured with [18F]FDG PET. At the regional level, focal atrophy and/or hypometabolism in the frontal lobe, insula, PCC, thalamus, and caudate seem to imply the importance of these regions for the decline of cognitive function in FTD. Furthermore, these results highlight how functional and structural changes may not overlap and might contribute to cognitive dysfunction in FTD in different ways. Our findings provide insight into the relationships between structural, metabolic, and cognitive changes due to FTD.

Synaptic loss and its association with symptom severity in Parkinson's disease.

Parkinson's disease (PD) is the fastest growing neurodegenerative disease, but at present there is no cure, nor any disease-modifying treatments. Synaptic biomarkers from in vivo imaging have shown promise in imaging loss of synapses in PD and other neurodegenerative disorders. Here, we provide new clinical insights from a cross-sectional, high-resolution positron emission tomography (PET) study of 30 PD individuals and 30 age- and sex-matched healthy controls (HC) with the radiotracer [11C]UCB-J, which binds to synaptic vesicle glycoprotein 2A (SV2A), and is therefore, a biomarker of synaptic density in the living brain. We also examined a measure of relative brain perfusion from the early part of the same PET scan. Our results provide evidence for synaptic density loss in the substantia nigra that had been previously reported, but also extend this to other early-Braak stage regions known to be affected in PD (brainstem, caudate, olfactory cortex). Importantly, we also found a direct association between synaptic density loss in the nigra and severity of symptoms in patients. A greater extent and wider distribution of synaptic density loss in PD patients with longer illness duration suggests that [11C]UCB-J PET can be used to measure synapse loss with disease progression. We also demonstrate lower brain perfusion in PD vs. HC groups, with a greater extent of abnormalities in those with longer duration of illness, suggesting that [11C]UCB-J PET can simultaneously provide information on changes in brain perfusion. These results implicate synaptic imaging as a useful PD biomarker for future disease-modifying interventions.

First-in-Human Study of 18F-SynVesT-2: An SV2A PET Imaging Probe with Fast Brain Kinetics and High Specific Binding.

PET imaging of synaptic vesicle glycoprotein 2A allows for noninvasive quantification of synapses. This first-in-human study aimed to evaluate the kinetics, test-retest reproducibility, and extent of specific binding of a recently developed synaptic vesicle glycoprotein 2A PET ligand, (R)-4-(3-(18F-fluoro)phenyl)-1-((3-methylpyridin-4-yl)methyl)pyrrolidine-2-one (18F-SynVesT-2), with fast brain kinetics. Methods: Nine healthy volunteers participated in this study and were scanned on a High Resolution Research Tomograph scanner with 18F-SynVesT-2. Five volunteers were scanned twice on 2 different days. Five volunteers were rescanned with preinjected levetiracetam (20 mg/kg, intravenously). Arterial blood was collected to calculate the plasma free fraction and generate the arterial input function. Individual MR images were coregistered to a brain atlas to define regions of interest for generating time-activity curves, which were fitted with 1- and 2-tissue-compartment (1TC and 2TC) models to derive the regional distribution volume (V T). The regional nondisplaceable binding potential (BP ND) was calculated from 1TC V T, using the centrum semiovale (CS) as the reference region. Results: 18F-SynVesT-2 was synthesized with high molar activity (187 ± 69 MBq/nmol, n = 19). The parent fraction of 18F-SynVesT-2 in plasma was 28% ± 8% at 30 min after injection, and the plasma free fraction was high (0.29 ± 0.04). 18F-SynVesT-2 entered the brain quickly, with an SUVpeak of 8 within 10 min after injection. Regional time-activity curves fitted well with both the 1TC and the 2TC models; however, V T was estimated more reliably using the 1TC model. The 1TC V T ranged from 1.9 ± 0.2 mL/cm3 in CS to 7.6 ± 0.8 mL/cm3 in the putamen, with low absolute test-retest variability (6.0% ± 3.6%). Regional BP ND ranged from 1.76 ± 0.21 in the hippocampus to 3.06 ± 0.29 in the putamen. A 20-min scan was sufficient to provide reliable V T and BP ND Conclusion: 18F-SynVesT-2 has fast kinetics, high specific uptake, and low nonspecific uptake in the brain. Consistent with the nonhuman primate results, the kinetics of 18F-SynVesT-2 is faster than the kinetics of 11C-UCB-J and 18F-SynVesT-1 in the human brain and enables a shorter dynamic scan to derive physiologic information on cerebral blood flow and synapse density.

The regional pattern of age-related synaptic loss in the human brain differs from gray matter volume loss: in vivo PET measurement with [11C]UCB-J.

PURPOSE: Aging is a major societal concern due to age-related functional losses. Synapses are crucial components of neural circuits, and synaptic density could be a sensitive biomarker to evaluate brain function. [11C]UCB-J is a positron emission tomography (PET) ligand targeting synaptic vesicle glycoprotein 2A (SV2A), which can be used to evaluate brain synaptic density in vivo. METHODS: We evaluated age-related changes in gray matter synaptic density, volume, and blood flow using [11C]UCB-J PET and magnetic resonance imaging (MRI) in a wide age range of 80 cognitive normal subjects (21-83 years old). Partial volume correction was applied to the PET data. RESULTS: Significant age-related decreases were found in 13, two, and nine brain regions for volume, synaptic density, and blood flow, respectively. The prefrontal cortex showed the largest volume decline (4.9% reduction per decade: RPD), while the synaptic density loss was largest in the caudate (3.6% RPD) and medial occipital cortex (3.4% RPD). The reductions in caudate are consistent with previous SV2A PET studies and likely reflect that caudate is the site of nerve terminals for multiple major tracts that undergo substantial age-related neurodegeneration. There was a non-significant negative relationship between volume and synaptic density reductions in 16 gray matter regions. CONCLUSION: MRI and [11]C-UCB-J PET showed age-related decreases of gray matter volume, synaptic density, and blood flow; however, the regional patterns of the reductions in volume and SV2A binding were different. Those patterns suggest that MR-based measures of GM volume may not be directly representative of synaptic density.

Differences in Quantification of the Metabotropic Glutamate Receptor 5 Across Bipolar Disorder and Major Depressive Disorder.

BACKGROUND: Understanding the neurobiology underlying bipolar disorder (BD) versus major depressive disorder (MDD) is crucial for accurate diagnosis and for driving the discovery of novel treatments. A promising target is the metabotropic glutamate receptor 5 (mGluR5), a modulator of glutamate transmission associated with synaptic plasticity. We measured mGluR5 availability in individuals with MDD and BD for the first time using positron emission tomography. METHODS: Individuals with BD (n = 17 depressed; n = 10 euthymic) or MDD (n = 17) and healthy control (HC) individuals (n = 18) underwent imaging with [18F]FPEB positron emission tomography to quantify mGluR5 availability in regions of the prefrontal cortex, which was compared across groups and assessed in relation to depressive symptoms and cognitive function. RESULTS: Prefrontal cortex mGluR5 availability was significantly different across groups (F6,116 = 2.18, p = .050). Specifically, mGluR5 was lower in BD versus MDD and HC groups, with no difference between MDD and HC groups. Furthermore, after dividing the BD group, mGluR5 was lower in both BD-depression and BD-euthymia groups versus both MDD and HC groups across regions of interest. Interestingly, lower dorsolateral prefrontal cortex mGluR5 was associated with worse depression in MDD (r = -0.67, p = .005) but not in BD. Significant negative correlations were observed between mGluR5 and working memory in MDD and BD-depression groups. CONCLUSIONS: This work suggests that mGluR5 could be helpful in distinguishing BD and MDD as a possible treatment target for depressive symptoms in MDD and for cognitive alterations in both disorders. Further work is needed to confirm differentiating roles for mGluR5 in BD and MDD and to probe modulation of mGluR5 as a preventive/treatment strategy.

Neurotransmitter transporter occupancy following administration of centanafadine sustained-release tablets: A phase 1 study in healthy male adults.

BACKGROUND: Centanafadine is an inhibitor of reuptake transporters for norepinephrine (NET), dopamine (DAT) and serotonin (SERT). AIMS: This phase 1, adaptive-design positron emission tomography study investigated the occupancy time course of NET, DAT, and SERT and the relationship to centanafadine plasma concentrations. METHODS: Healthy adult males received centanafadine sustained-release 400 mg/day for 4 days (N = 6) or 800 mg in a single day (N = 4). Assessments included safety monitoring; time course of occupancy of NET, DAT, and SERT; and centanafadine plasma concentrations. RESULTS: Transporter occupancy was numerically higher for NET versus DAT or SERT. For NET, estimated (mean ± standard error [SE]) maximal observable target occupancy (TOmax) and concentration at half maximal occupancy (IC50) were 64 ± 7% and 132 ± 65 ng/mL, respectively, for all regions and 82 ± 13% and 135 ± 97 ng/mL after excluding the thalamus, which showed high nonspecific binding. For DAT and SERT, TOmax could not be established and was assumed to be 100%; estimated IC50 (mean ± SE) values were 1580 ± 186 ng/mL and 1,760 ± 309 ng/mL, respectively. For centanafadine, the estimated in vivo affinity ratio was 11.9 ± 6.0 (mean ± SE) for NET/DAT, 13.3 ± 7.0 for NET/SERT, and 1.1 ± 0.2 for DAT/SERT. DAT and SERT occupancies at a plasma concentration of 1400 ng/mL were estimated to be 47 and 44%, respectively. CONCLUSIONS: High occupancy at NET and moderate occupancy at DAT and SERT was observed at peak concentrations achieved following 400 mg total daily doses of centanafadine.

Drug characteristics derived from kinetic modeling: combined 11C-UCB-J human PET imaging with levetiracetam and brivaracetam occupancy of SV2A.

BACKGROUND: Antiepileptic drugs, levetiracetam (LEV) and brivaracetam (BRV), bind to synaptic vesicle glycoprotein 2A (SV2A). In their anti-seizure activity, speed of brain entry may be an important factor. BRV showed faster entry into the human and non-human primate brain, based on more rapid displacement of SV2A tracer 11C-UCB-J. To extract additional information from previous human studies, we developed a nonlinear model that accounted for drug entry into the brain and binding to SV2A using brain 11C-UCB-J positron emission tomography (PET) data and the time-varying plasma drug concentration, to assess the kinetic parameter K1 (brain entry rate) of the drugs. METHOD: Displacement (LEV or BRV p.i. 60 min post-tracer injection) and post-dose scans were conducted in five healthy subjects. Blood samples were collected for measurement of drug concentration and the tracer arterial input function. Fitting of nonlinear differential equations was applied simultaneously to time-activity curves (TACs) from displacement and post-dose scans to estimate 5 parameters: K1 (drug), K1(11C-UCB-J, displacement), K1(11C-UCB-J, post-dose), free fraction of 11C-UCB-J in brain (fND(11C-UCB-J)), and distribution volume of 11C-UCB-J (VT(UCB-J)). Other parameters (KD(drug), KD(11C-UCB-J), fP(drug), fP(11C-UCB-J, displacement), fP(11C-UCB-J, post-dose), fND(drug), koff(drug), koff(11C-UCB-J)) were fixed to literature or measured values. RESULTS: The proposed model described well the TACs in all subjects; however, estimates of drug K1 were unstable in comparison with 11C-UCB-J K1 estimation. To provide a conservative estimate of the relative speed of brain entry for BRV vs. LEV, we determined a lower bound on the ratio BRV K1/LEV K1, by finding the lowest BRV K1 or highest LEV K1 that were statistically consistent with the data. Specifically, we used the F test to compare the residual sum of squares with fixed BRV K1 to that with floating BRV K1 to obtain the lowest possible BRV K1; the same analysis was performed to find the highest LEV K1. The lower bound of the ratio BRV K1/LEV K1 was ~ 7. CONCLUSIONS: Under appropriate conditions, this advanced nonlinear model can directly estimate entry rates of drugs into tissue by analysis of PET TACs. Using a conservative statistical cutoff, BRV enters the brain at least sevenfold faster than LEV.

An objective evaluation method for head motion estimation in PET-Motion corrected centroid-of-distribution.

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.

Age, gender and body-mass-index relationships with in vivo CB1 receptor availability in healthy humans measured with [11C]OMAR PET.

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.

Mapping neurotransmitter systems to the structural and functional organization of the human neocortex.

Neurotransmitter receptors support the propagation of signals in the human brain. How receptor systems are situated within macro-scale neuroanatomy and how they shape emergent function remain poorly understood, and there exists no comprehensive atlas of receptors. Here we collate positron emission tomography data from more than 1,200 healthy individuals to construct a whole-brain three-dimensional normative atlas of 19 receptors and transporters across nine different neurotransmitter systems. We found that receptor profiles align with structural connectivity and mediate function, including neurophysiological oscillatory dynamics and resting-state hemodynamic functional connectivity. Using the Neurosynth cognitive atlas, we uncovered a topographic gradient of overlapping receptor distributions that separates extrinsic and intrinsic psychological processes. Finally, we found both expected and novel associations between receptor distributions and cortical abnormality patterns across 13 disorders. We replicated all findings in an independently collected autoradiography dataset. This work demonstrates how chemoarchitecture shapes brain structure and function, providing a new direction for studying multi-scale brain organization.

Adaptive data-driven motion detection and optimized correction for brain PET.

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.

[89Zr]ZrDFO-CR011 PET Correlates with Response to Glycoprotein Nonmetastatic Melanoma B-targeted Therapy in Triple-negative Breast Cancer.

There is a need for prognostic markers to select patients most likely to benefit from antibody-drug conjugate (ADC) therapy. We quantified the relationship between pretreatment PET imaging of glycoprotein nonmetastatic melanoma B (gpNMB) with 89Zr-labeled anti-gpNMB antibody ([89Zr]ZrDFO-CR011) and response to ADC therapy (CDX-011) in triple-negative breast cancer. First, we compared different PET imaging metrics and found that standardized uptake values (SUV) and tumor-to-heart SUV ratios were sufficient to delineate differences in radiotracer uptake in the tumor of four different cell- and patient-derived tumor models and achieved high standardized effect sizes. These tumor models with varying levels of gpNMB expression were imaged with [89Zr]ZrDFO-CR011 followed by treatment with a single bolus injection of CDX-011. The percent change in tumor volume relative to baseline (% CTV) was then correlated with SUVmean of [89Zr]ZrDFO-CR011 uptake in the tumor. All gpNMB-positive tumor models responded to CDX-011 over 6 weeks of treatment, except one patient-derived tumor regrew after 4 weeks of treatment. As expected, the gpNMB-negative tumor increased in volume by 130 ± 59% at endpoint. The magnitude of pretreatment SUV had the strongest inverse correlation with the % CTV at 2-4 weeks after treatment with CDX-011 (Spearman ρ = -0.8). However, pretreatment PET imaging with [89Zr]ZrDFO-CR011 did not inform on which tumor types will regrow over time. Other methods will be needed to predict resistance to treatment.

Relationships between dopamine D2/3 receptor availability and social-environmental factors in humans.

Social factors are associated with psychiatric outcomes and brain function. Relationships between local population data obtained from Social Explorer analyses of the American Community Survey (2014-2018) and dopamine D2/3 receptor (D2/3R) availability were explored in this retrospective analysis of [11C]PHNO positron emission tomography (PET) imaging data (n = 70). Larger local population size and lower percentage of the population with a bachelor's degree or higher were significantly associated with higher striatal D2/3R availability, suggesting that living in a populous area with fewer educational resources may be accompanied by stressors with concomitant dopaminergic changes. Future prospective, collaborative studies are needed to better understand the precise etiology of the observed relationships.

Association of entorhinal cortical tau deposition and hippocampal synaptic density in older individuals with normal cognition and early Alzheimer's disease.

Sites of early neuropathologic change provide important clues regarding the initial clinical features of Alzheimer's disease (AD). We have shown significant reductions in hippocampal synaptic density in participants with AD, consistent with the early degeneration of entorhinal cortical (ERC) cells that project to hippocampus via the perforant path. In this study, [11C]UCB-J binding to synaptic vesicle glycoprotein 2A (SV2A) and [18F]flortaucipir binding to tau were measured via PET in 10 participants with AD (5 mild cognitive impairment, 5 mild dementia) and 10 cognitively normal participants. In the overall sample, ERC tau was inversely associated with hippocampal synaptic density (r = -0.59, p = 0.009). After correction for partial volume effects, the association of ERC tau with hippocampal synaptic density was stronger in the overall sample (r = -0.61, p = 0.007) and in the AD group where the effect size was large, but not statistically significant (r = -0.58, p = 0.06). This inverse association of ERC tau and hippocampal synaptic density may reflect synaptic failure due to tau pathology in ERC neurons projecting to the hippocampus.

Feasibility study of PET dynamic imaging of [18F]DHMT for quantification of reactive oxygen species in the myocardium of large animals.

OBJECTIVES: We aimed to develop a dynamic imaging technique for a novel PET superoxide tracer, [18F]DHMT, to allow for absolute quantification of myocardial reactive oxygen species (ROS) production in a large animal model. METHODS: Six beagle dogs underwent a single baseline dynamic [18F]DHMT PET study, whereas one animal underwent three serial dynamic studies over the course of chronic doxorubicin administration (1 mg·kg-1·week-1 for 15 weeks). During the scans, sequential arterial blood samples were obtained for plasma metabolite correction. The optimal compartment model and graphical analysis method were identified for kinetic modeling. Values for the left ventricular (LV) net influx rate, Ki, were reported for all the studies and compared with the LV standard uptake values (SUVs) and the LV-to-blood pool SUV ratios from the 60 to 90 minute static images. Parametric images were also generated. RESULTS: [18F]DHMT followed irreversible kinetics once oxidized within the myocardium in the presence of superoxide, as evidenced by the fitting generated by the irreversible two-tissue (2Ti) compartment model and the linearity of Patlak analysis. Myocardial Ki values showed a weak correlation with LV SUV (R2 = 0.27), but a strong correlation with LV-to-blood pool SUV ratio (R2 = 0.92). Generation of high-quality parametric images showed superior myocardial to blood contrast compared to static images. CONCLUSIONS: A dynamic PET imaging technique for [18F]DHMT was developed with full and simplified kinetic modeling for absolute quantification of myocardial superoxide production in a large animal model.

Generation of parametric Ki images for FDG PET using two 5-min scans.

PURPOSE: The net uptake rate constant (Ki ) derived from dynamic imaging is considered the gold standard quantification index for FDG PET. In this study, we investigated the feasibility and assessed the clinical usefulness of generating Ki images for FDG PET using only two 5-min scans with population-based input function (PBIF). METHODS: Using a Siemens Biograph mCT, 10 subjects with solid lung nodules underwent a single-bed dynamic FDG PET scan and 13 subjects (five healthy and eight cancer patients) underwent a whole-body dynamic FDG PET scan in continuous-bed-motion mode. For each subject, a standard Ki image was generated using the complete 0-90 min dynamic data with Patlak analysis (t* = 20 min) and individual patient's input function, while a dual-time-point Ki image was generated from two 5-min scans based on the Patlak equations at early and late scans with the PBIF. Different start times for the early (ranging from 20 to 55 min with an increment of 5 min) and late (ranging from 50 to 85 min with an increment of 5 min) scans were investigated with the interval between scans being at least 30 min (36 protocols in total). The optimal dual-time-point protocols were then identified. Regions of interest (ROI) were drawn on nodules for the lung nodule subjects, and on tumors, cerebellum, and bone marrow for the whole-body-imaging subjects. Quantification accuracy was compared using the mean value of each ROI between standard Ki (gold standard) and dual-time-point Ki , as well as between standard Ki and relative standardized uptake value (SUV) change that is currently used in clinical practice. Correlation coefficients and least squares fits were calculated for each dual-time-point protocol and for each ROI. Then, the predefined criteria for identifying a reliable dual-time-point Ki estimation for each ROI were empirically determined as: (1) the squared correlation coefficient (R2 ) between standard Ki and dual-time-point Ki is larger than 0.9; (2) the absolute difference between the slope of the equality line (1.0) and that of the fitted line when plotting standard Ki versus dual-time-point Ki is smaller than 0.1; (3) the absolute value of the intercept of the fitted line when plotting standard Ki versus dual-time-point Ki normalized by the mean of the standard Ki across all subjects for each ROI is smaller than 10%. Using Williams' one-tailed t test, the correlation coefficient (R) between standard Ki and dual-time-point Ki was further compared with that between standard Ki and relative SUV change, for each dual-time-point protocol and for each ROI. RESULTS: Reliable dual-time-point Ki images were obtained for all the subjects using our proposed method. The percentage error introduced by the PBIF on the dual-time-point Ki estimation was smaller than 1% for all 36 protocols. Using the predefined criteria, reliable dual-time-point Ki estimation could be obtained in 25 of 36 protocols for nodules and in 34 of 36 protocols for tumors. A longer time interval between scans provided a more accurate Ki estimation in general. Using the protocol of 20-25 min plus 80-85 or 85-90 min, very high correlations were obtained between standard Ki and dual-time-point Ki (R2  = 0.994, 0.980, 0.971 and 0.925 for nodule, tumor, cerebellum, and bone marrow), with all the slope values with differences ≤0.033 from 1 and all the intercept values with differences ≤0.0006 mL/min/cm3 from 0. The corresponding correlations were much lower between standard Ki and relative SUV change (R2  = 0.673, 0.684, 0.065, 0.246). Dual-time-point Ki showed a significantly higher quantification accuracy with respect to standard Ki than relative SUV change for all the 36 protocols (p < 0.05 using Williams' one-tailed t test). CONCLUSIONS: Our proposed approach can obtain reliable Ki images and accurate Ki quantification from dual-time-point scans (5-min per scan), and provide significantly higher quantification accuracy than relative SUV change that is currently used in clinical practice.