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Comparison of [¹¹C]UCB-J and [¹⁸F]FDG PET in Alzheimer's disease: A tracer kinetic modeling study.

ABSTRACT: [¹¹C]UCB-J PET for synaptic vesicle glycoprotein 2 A (SV2A) has been proposed as a suitable marker for synaptic density in Alzheimer's disease (AD). We compared [¹¹C]UCB-J binding for synaptic density and [¹⁸F]FDG uptake for metabolism (correlated with neuronal activity) in 14 AD and 11 cognitively normal (CN) participants. We assessed both absolute and relative outcome measures in brain regions of interest, i.e., K₁ or R₁ for [¹¹C]UCB-J perfusion, V_T (volume of distribution) or DVR to cerebellum for [¹¹C]UCB-J binding to SV2A; and K_i or K_iR to cerebellum for [¹⁸F]FDG metabolism. [¹¹C]UCB-J binding and [¹⁸F]FDG metabolism showed a similar magnitude of reduction in the medial temporal lobe of AD -compared to CN participants. However, the magnitude of reduction of [¹¹C]UCB-J binding in neocortical regions was less than that observed with [¹⁸F]FDG metabolism. Inter-tracer correlations were also higher in the medial temporal regions between synaptic density and metabolism, with lower correlations in neocortical regions. [¹¹C]UCB-J perfusion showed a similar pattern to [¹⁸F]FDG metabolism, with high inter-tracer regional correlations. In summary, we conducted the first in vivo PET imaging of synaptic density and metabolism in the same AD participants and reported a concordant reduction in medial temporal regions but a discordant reduction in neocortical regions.

SUBMITTER: Chen MK

PROVIDER: S-EPMC8393289 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Comparison of [11C]UCB-J and [18F]FDG PET in Alzheimer's disease: A tracer kinetic modeling study.

Chen Ming-Kai MK Mecca Adam P AP Naganawa Mika M Gallezot Jean-Dominique JD Toyonaga Takuya T Mondal Jayanta J Finnema Sjoerd J SJ Lin Shu-Fei SF O'Dell Ryan S RS McDonald Julia W JW Michalak Hannah R HR Vander Wyk Brent B Nabulsi Nabeel B NB Huang Yiyun Y Arnsten Amy Ft AF van Dyck Christopher H CH Carson Richard E RE

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism 20210324 9

[11C]UCB-J PET for synaptic vesicle glycoprotein 2 A (SV2A) has been proposed as a suitable marker for synaptic density in Alzheimer's disease (AD). We compared [11C]UCB-J binding for synaptic density and [18F]FDG uptake for metabolism (correlated with neuronal activity) in 14 AD and 11 cognitively normal (CN) participants. We assessed both absolute and relative outcome measures in brain regions of interest, i.e., K1 or R1 for ...[more]

PMID: 33757318

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Project description:BackgroundAntiepileptic 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.MethodDisplacement (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.ResultsThe 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.ConclusionsUnder 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.

Project description:ObjectivesTo decipher the correlations between PET and DCE kinetic parameters in non-small-cell lung cancer (NSCLC), by using voxel-wise analysis of dynamic simultaneous [18F]FDG PET-MRI.Material and methodsFourteen treatment-naïve patients with biopsy-proven NSCLC prospectively underwent a 1-h dynamic [18F]FDG thoracic PET-MRI scan including DCE. The PET and DCE data were normalized to their corresponding T1-weighted MR morphological space, and tumors were masked semi-automatically. Voxel-wise parametric maps of PET and DCE kinetic parameters were computed by fitting the dynamic PET and DCE tumor data to the Sokoloff and Extended Tofts models respectively, by using in-house developed procedures. Curve-fitting errors were assessed by computing the relative root mean square error (rRMSE) of the estimated PET and DCE signals at the voxel level. For each tumor, Spearman correlation coefficients (rs) between all the pairs of PET and DCE kinetic parameters were estimated on a voxel-wise basis, along with their respective bootstrapped 95% confidence intervals (n = 1000 iterations).ResultsCurve-fitting metrics provided fit errors under 20% for almost 90% of the PET voxels (median rRMSE = 10.3, interquartile ranges IQR = 8.1; 14.3), whereas 73.3% of the DCE voxels showed fit errors under 45% (median rRMSE = 31.8%, IQR = 22.4; 46.6). The PET-PET, DCE-DCE, and PET-DCE voxel-wise correlations varied according to individual tumor behaviors. Beyond this wide variability, the PET-PET and DCE-DCE correlations were mainly high (absolute rs values > 0.7), whereas the PET-DCE correlations were mainly low to moderate (absolute rs values < 0.7). Half the tumors showed a hypometabolism with low perfused/vascularized profile, a hallmark of hypoxia, and tumor aggressiveness.ConclusionA dynamic "one-stop shop" procedure applied to NSCLC is technically feasible in clinical practice. PET and DCE kinetic parameters assessed simultaneously are not highly correlated in NSCLC, and these correlations showed a wide variability among tumors and patients. These results tend to suggest that PET and DCE kinetic parameters might provide complementary information. In the future, this might make PET-MRI a unique tool to characterize the individual tumor biological behavior in NSCLC.

Dataset Information

Comparison of [¹¹C]UCB-J and [¹⁸F]FDG PET in Alzheimer's disease: A tracer kinetic modeling study.

Publications

Comparison of [<sup>11</sup>C]UCB-J and [<sup>18</sup>F]FDG PET in Alzheimer's disease: A tracer kinetic modeling study.

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