Quantification of synaptic density with [11C]UCB-J PET
Finnema et al (2016) demonstrated that [11C]UCB-J can be used as synaptic density marker in humans. [11C]UCB-J binds to synaptic vesicle glycoprotein 2A (SV2A), which is ubiquitously and homogeneously located in synapses across the brain (Bajjalieh et al., 1994; Janz & Südhof, 1999). [11C]UCB-J could be displaced by levetiracetam and padsevonil, which are SV2A-selective drugs (Finnema et al., 2016; Koole et al., 2019); this confirms that [11C]UCB-J is specifically binding to SV2A. PET imaging of patients with temporal lobe epilepsy revealed unilateral decreased binding, suggesting that [11C]UCB-J can be used to detect synaptic loss in vivo in human subjects (Finnema et al., 2016). In addition, [11C]UCB-J can be used to assess occupancy of SV2A binding drugs (Nicolas et al., 2016).
[11C]UCB-J has favourable dosimetry (Nabulsi et al., 2016) and excellent test-retest variability (Finnema et al., 2018). Binding is specific; only ∼20% of the volume of distribution (VT) of [11C]UCB-J is due to the nondisplaceable binding (VND); therefore VT can be used as outcome parameter (Rabiner, 2018), representing the SV2A concentration in the brain.
The fraction of non-metabolized radioligand drops to ∼0.3 in 20 minutes after injection, and after that the fraction drops only slowly, being >0.2 at 120 min p.i. (Finnema et al., 2018; figure 1). Individual variation in fractions seems to be high, probably preventing the usage of population average. Finnema et al. (2018) fitted an inverted integrated gamma function to the unmetabolized parent fraction data. Radiometabolites do not seem to penetrate the blood-brain barrier (Finnema et al., 2016).
As an alternative to arterial blood sampling, extraction of image-derived input function could be feasible, as such method has been applied to [18F]UCB-H (Bahri et al., 2017); the method still required blood samples for metabolite correction and scaling.
Brain data analysis methods
Peak radioactivity concentration in the plasma is about two times higher than the peak radioactivity in the brain; thus the vascular radioactivity can be neglected in compartmental model analysis, but it may have an impact in time delay correction (Finnema et al., 2018). Koole et al (2019) fixed VB to 5% in compartmental model fitting.
Based on AIC, the two-tissue compartmental model (2TCM) fitted the regional data better than the one-tissue compartmental model (1TCM), but the difference in VT estimates was small; additionally, 2TCM did not provide reasonable parameter values for some data sets, and therefore 1TCM was selected for the final data analysis (Finnema et al., 2018). Mean VT ranged from 5.3 ± 0.5 in the centrum semiovale to 22.4 ± 1.8 in the putamen (Finnema et al., 2018). Based on AIC and F-test Koole et al (2019) ended up using 1TCM, and also for the F-18 labelled UCB-J derivative [18F]MNI-1126 1TCM performed better (Constantinescu et al., 2018). In the occupancy studies, when the occupancy was highest, 2TCM performed better than 1TCM, probably because of the relatively increased contribution of nonspecific binding; yet, VT from 2TCM and 1TCM did not differ significantly, supporting the use of 1TCM in occupancy studies as well (Koole et al., 2019).
Parametric VT images can be calculated using 1TCM with basis functions method, with k2 limits set to 0.01-1.0 min-1 (Finnema et al., 2018). Highest VT, 34±4, was seen in parietal cortex (Chen et al., 2018). Parametric image of K1, representing perfusion and delivery, can also be produced simultaneously; the pattern of K1 reduction in AD patients is similar to the pattern of hypometabolism in AD seen with [18F]FDG (Chen et al., 2018).
Finnema et al. (2018) assessed the time stability of the 1TCM , and noticed that the study length could be reduced from 120 to 60 min with no impairment in ICC or test-retest variability. Shorter scan duration will lead to somewhat lower VT estimates (maximally about -5% when comparing 60 min and 120 min scan lengths), but there is also possibility that the longer scan duration may lead to overestimated VT due to uptake of radioactive metabolite(s) in the brain, especially in the low uptake regions.
Correction of VT for plasma protein binding (VT/fp) worsened ICC and test-retest variability, and thus Finnema et al. (2018) recommended that VT/fp would be used as outcome parameter only in cross-sectional studies, if group differences or treatment effects in plasma protein binding could be expected.
Ubiquitous distribution of SV2A means also that there is no true reference region in the brain that could be used as input function or to determine the SV2A binding potential. Therefore arterial input function must be measured, and VT is the only possible quantitative parameter; because of the relatively low nonspecific binding (VNS) of [11C]UCB-J in the brain VT can be assumed to well represent the SV2A density. Lassen plot can be used to assess receptor occupancy from VT in the absence of reference region (Koole et al., 2019).
Yet, for comparative studies where strict quantification is not necessary, centrum semiovale, that has low uptake of SV2A ligands, can be used as reference region in [11C]UCB-J data analysis, applying either SRTM or one-tissue compartmental model based DVR (Finnema et al., 2016; Toyonaga et al., 2018), or 60-90 min tissue-to-reference ratio (Naganawa et al., 2018; Koole et al., 2019). SRTM requires at least 70 min dynamic PET scan (Koole et al., 2019). Although VT in centrum semiovale was decreased in an occupancy study, the occupancy results were not statistically different between VT-based Lassen plot analysis and binding potential-based analysis using centrum semiovale as reference region (Koole et al., 2019). When centrum semiovale was used as reference region for UCB-J derivative [18F]MNI-1126, the bias in occupancy studies was minimal (Constantinescu et al., 2018).
VT in the centrum semiovale was virtually identical between AD patients and age matched control subjects (Chen et al., 2018). Highest BPND, 6.09±0.33 in control subjects, was seen in parietal cortex (Chen et al., 2018).
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Updated at: 2019-01-27
Created at: 2017-11-29
Written by: Vesa Oikonen