[11C]UCB-J is used to assess synaptic density and occupancy of SV2A binding drugs in vivo in CNS (Finnema et al., 2016; Nicolas et al., 2016; Finnema et al., 2019). [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). Specificity of [11C]UCB-J to SV2A was shown by displacing the radioligand binding with levetiracetam and padsevonil, which are SV2A-selective drugs (Finnema et al., 2016; Koole et al., 2019). 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 human subjects (Finnema et al., 2016). Binding was reduced in the seizure onset zone, with larger asymmetry index than observed with FDG PET (Finnema et al., 2020), which has been commonly used as a (non-specific) marker of active synaptic neurotransmission.

[11C]UCB-J PET has revealed synaptic loss in brainstem nuclei in Parkinson's disease (Matuskey et al., 2020; Wilson et al., 2020), and in the frontal and anterior cingulate cortices in schizophrenia (Onwordi et al., 2020). In Alzheimer's disease (AD), widespread decrease in [11C]UCB-J uptake has been observed (Chen et al., 2018; Mecca et al., 2020 and 2022a), although with marked overlap between patients and control subjects (Tuncel et al., 2021). In AD, [11C]UCB-J and [18F]FDG show high inter-tracer regional correlations, especially in medial temporal regions (Chen et al., 2021). [11C]UCB-J PET has shown severe and disease severity dependent synaptic loss in the primary tauopathies of progressive supranuclear palsy and amyloid-negative corticobasal syndrome (Holland et al., 2020). In patients with mild cognitive impairment, the brain regions with higher uptake of tau protein tracer [18F]MK-6240 have shown lower uptake of [11C]UCB-J (Vanhaute et al., 2021).

SV2A is highly expressed in endocrine pancreas, but [11C]UCB-J PET not found sensitive enough for imaging islets of Langerhans (Puuvuori et al., 2021).

[11C]UCB-J production is robust with high yield (Rokka et al., 2019) and it has favourable dosimetry (Nabulsi et al., 2016; Bini et al., 2020; Cawthorne et al., 2021). 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 same-day test-retest variability is excellent in the brain imaging (Finnema et al., 2018). In 60-min PET scans of Alzheimer's disease patients and healthy controls, 28-day repeatability is adequate to reveal changes of ∼15% (Tuncel et al., 2021). Negative bias in 28-day retest scans, but not same-day retest scans, of healthy subjects has been observed, possibly caused by acute stress (Tuncel et al., 2021).

Increased neural firing does not affect [11C]UCB-J binding in the brain, despite vesicle exocytosis, and marked increase in blood flow (Smart et al., 2021). Age and sex does not seem to affect [11C]UCB-J binding (Michiels et al., 2021; Andersen et al., 2022), although men have higher synaptic density than women (Alonso-Nanclares et al., 2008). [11C]UCB-J binding may be reduced in overweight/obese subjects (Asch et al., 2022).

[18F]UCB-J is an analogue of [11C]UCB-J, developed by the same group (Li et al., 2019).

Blood data

Plasma free fraction (fp), the fraction of tracer not bound to plasma proteins, was 0.32 (range 0.29-0.34) in the test-retest study of five human volunteers (Finnema et al., 2018). In twelve healthy subjects fp was 0.18-0.28, and appeared to decrease with age (Mansur et al., 2020). In rhesus monkeys fp is 0.43±0.05 for [11C]UCB-J and 0.42±0.06 for [18F]UCB-J (Li et al., 2019).

The fraction of non-metabolized radioligand dropped to ∼0.6 already in 5 minutes (Tuncel et al., 2021), is ∼0.3 20 minutes after injection (Finnema et al., 2018; figure 1), and after that the fraction drops only slowly, being ∼0.2 at 55 min and up to 120 min (Tuncel et al., 2021; Finnema et al., 2018; Mansur et al., 2020). Individual variation in fractions seems to be high, probably preventing the usage of population average with [11C]UCB-J; with [18F]UCB-H individual variance was low, and population average was used to correct image-derived input function (Bastin et al., 2020). Finnema et al (2018) fitted an inverted integrated gamma function to the non-metabolized parent fraction data. The parent fractions in Göttingen minipigs (Thomsen et al., 2020) seem to be similar than in humans. In mice the metabolism is faster, being ∼0.2 already at 15 min p.i., and a sigmoid function was fitted to the ratio data (Bertoglio et al., 2020). Sigmoid function has been applied also to human data (Mansur et al., 2020). There however seems to be slow but steady decrease in the parent fraction at late times (example in Fig 2). A combination of power and exponential functions may be better suited to this data with some parameters fixed to population means; program fit_ppf with options -model=EP -a=1.5 -b=1 -e=22 could be used to accomplish this.

Example of UCB-J parent fractions in plasma Example plasma-to-blood ratios
Figure 2. Example of parent [11C]UCB-J fractions in plasma and plasma-to-blood ratio in one subject.

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 is feasible even in mice studies (Bertoglio et al., 2020; Glorie et al., 2020), and has been applied to human studies with [18F]UCB-H (Bahri et al., 2017; Bastin et al., 2020). Conversion of concentration in blood to that in plasma may be relatively simple, because plasma-to-blood ratio is stable at ∼1.3 (Mansur et al., 2020). Careful sampling reveals that there is an initial small and rapid drop in the plasma-to-blood ratio (example in Fig 2), and if this needs to be accounted for, then the ratio data can be fitted for example using program fit_pbr with option -model=FM2, with parameters constrained to tight limits.

The image-derived input method still requires blood samples for metabolite correction and scaling. In animal studies, a separate group of animals can be used for assessing the metabolism, and if group differences in metabolism are not observed the metabolite correction can be omitted (Bertoglio et al., 2020; Akkermans et al., 2022). Use of population-based input data was validated in Göttingen minipigs (Thomsen et al., 2020).

Brain data analysis methods

The brain uptake of [11C]UCB-J is highest in the striatum and cortex, and moderate in the thalamus and cerebellum, and low in white matter (Finnema et al., 2016 and 2018). Distribution of specific [11C]UCB-J binding sites in human and non-human primate brain, based on autoradiographic mapping, has been reported by Varnäs et al (2020).

Compartmental models with arterial plasma input function

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. In mice studies, VB has been fixed to 3.6% (Bertoglio et al., 2020). Tuncel et al (2021) fitted VB in their test-retest study.

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; Mansur et al., 2020). 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]SynVesT-1 ([18F]MNI-1126, [18F]SDM-8) the 1TCM performed better (Constantinescu et al., 2019). With [18F]UCB-H, 2TCM model is preferred over 1TCM, but a coupled fit with global K1/k2 must be used to avoid unstable fits (Goutal et al., 2021). 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). Tuncel et al (2021) noted that 1TCM with VB as one of the model parameters is sufficient to fit 60-min [11C]UCB-J data.

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).

Calculation of parametric VT image provides also a map of K1, representing perfusion and passage through blood brain barrier. Visual stimulation increases brain perfusion, which is seen as increased 1TCM K1, with no effect on VT and BPND (Smart et al., 2021). The pattern of K1 and R1 (K1 divided by cerebellar K1) reduction in AD patients is similar to the pattern of hypometabolism in AD seen with [18F]FDG (Chen et al., 2018 and 2021). A single [11C]UCB-J PET study can thus provide information on both neuronal activity and synaptic density (Chen et al., 2021).

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. In their study, 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. Shortening the scan duration further from 60 min to 45 and 30 min led to overestimation of VT and poor reliability (Tuncel et al., 2021). Also Mansur et al (2020) recommended the 60 min scan length. For mice studies, Bertoglio et al (2020) concluded that 60 min scan length is sufficient.

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. Yet, VT/fp test-retest performance was good, and in a later study fp was found to contribute to the variability in VT so much that some group differences were seen in VT/fp but not in VT, while there was no group difference in fp (Angarita et al., 2022).

Binding potential has often been calculated from 1TCM based VTs of region of interest and reference region, usually centrum semiovale:

Logan plot

In mice and rat studies, 1TCM and Logan plot have been applied (Bertoglio et al., 2020; Glorie et al., 2020; Thomsen et al., 2021; Akkermans et al., 2022). Logan plot has also been used with human [18F]UCB-H data (Bastin et al., 2020). In calculation of parametric images Logan plot leads to negatively biased VT (Tuncel et al., 2022).

Spectral analysis

Bases function based spectral analysis performs best in calculation of parametric VT images, as compared to the 1TCM fitting (Tuncel et al., 2022).

Tissue-to-blood ratio

[11C]UCB-J brain-to-blood ratio (0-60 min) correlated strongly with VT (calculated using whole blood curve as input function) in mice models of PD and AD (Xiong et al., 2021).

Drug occupancy studies

Lassen plot can be used to assess receptor occupancy from VT in the absence of reference region (Koole et al., 2019; Finnema et al., 2019; Bertoglio et al., 2020; Rossano et al., 2020).

Occupancy and displacement studies with [11C]UCB-J can be conducted using bolus plus constant infusion protocol (Finnema et al., 2019). At equilibrium, VT can be calculated simply as the ratio of radioactivity concentrations in tissue and plasma; true equilibrium however is difficult to achieve, causing marked bias unless corrected (Hillmer & Carson, 2020). More complex compartmental model can be used to assess detailed drug pharmacokinetics (Naganawa et al., 2022).


In rat brain study with [18F]UCB-H, SUV correlated well with VT from Logan plot using population-based input function; static 20-40 min scan was recommended (Serrano et al., 2019).

Reference region

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.

Subcortical white matter

In minipigs the centrum semiovale (white matter underneath the cerebral cortex) appeared to contain specific binding, but it might have been artefact caused by spillover from cortex (Thomsen et al., 2020). Image reconstruction with increased OSEM iterations and careful ROI placement on centrum semiovale reduces the spill-in from grey matter but marked bias still remains (Rossano et al., 2020). 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]SynVesT-1 ([18F]MNI-1126, [18F]SDM-8) the bias in occupancy studies was minimal (Constantinescu et al., 2019). VT in the white matter (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).

Due to the very low uptake of SV2A ligands, centrum semiovale has been used as reference region in numerous studies, especially in comparative studies where strict quantification is not necessary. Applied analysis methods include SRTM, SRTM2, or one-tissue compartmental model based DVR (Finnema et al., 2016; Toyonaga et al., 2018; Rossano et al., 2020; Mertens et al., 2020; Mecca et al., 2020), and 60-90 min tissue-to-reference ratio (SUVR) (Naganawa et al., 2018; Koole et al., 2019; Delva et al., 2020; Andersen et al., 2021; Naganawa et al., 2021; Vanhaute et al., 2021).

Cerebellum as reference region

Cerebellum has considerable SV2A-specific binding, and therefore cannot be used for computing true BPND. However, VT (based on 1TCM) difference is minimal (∼1%) between cerebellum of healthy and AD subjects (Mecca et al., 2020). Compared to cerebellum, centrum semiovale ROI is smaller and activity level lower, leading to greater variability. Thus, in practice, cerebellum may be superior to centrum semiovale as reference region in AD studies (Mecca et al., 2020), and cerebellum-based DVR images have been used in AD studies (Mecca et al., 2020, 2022a, 2022b; O'Dell et al., 2021).

SUVR from static late scans

In a mice study time range 30-60 min was found to be sufficient for SUVR (tissue-to-reference ratio) calculation when brain stem was used as the reference region (Toyonaga et al., 2019). In drug occupancy study in humans, SUVR60-90 with centrum semiovale as the reference region was found to lead to small but significant bias as compared to VT and Lassen plot (Koole et al., 2019). SUVR images from time ranges 50-80 min or 60-90 min were found to provide good results as compared to reference tissue input methods (Mertens et al., 2020). SUVR from 40-60 min correlated well with 1TCM-derived DVR (Tuncel et al., 2021). The best correlation between SUVR and BPND was obtained from 60-90 min scan, with only ‑1±7% difference between SUVR‑1 and BPND in the whole brain of healthy subjects, and ‑1±8% difference in neuropsychiatric subjects (Naganawa et al., 2021). SUVR60-90 is not affected by sex or healthy ageing, except for small age-related SUVR decrease in caudate nucleus (Michiels et al., 2021). SUVR60-90 has been used for example to study synaptic loss in ischaemic stroke (Michiels et al., 2022 and 2023), and Lewy body dementia (Andersen et al., 2021).


SRTM may require at least 70 min dynamic PET scan (Koole et al., 2019), but parametric BP images produced with SRTM2 using only the first 60 min of the dynamic data were not much different from the maps computed using 90 min of the data (Mertens et al., 2020). From 60-min scans, SRTM-derived BPND was underestimated by ∼25% as compared to DVR-1 calculated from 1TCM results, with adequate repeatability in AD patients and healthy control subjects (Tuncel et al., 2021). In calculation of parametric images, SRTM2 was found to be the best reference tissue input method to obtain quantitatively accurate and repeatable results (Tuncel et al., 2022). SRTM2 from 60 min data using centrum semiovale as reference has been used to analyse data in a study comparing PD patients and control subjects (Matuskey et al., 2020). K1 is much lower in white matter than in grey matter, leading to high R1 estimate from SRTM methods. In SRTM2 the SRTM is performed twice, fixing the reference region k'2 on the second round to the mean value obtained on the first round. With centrum semiovale as input function, k'2 was fixed to 0.027 min-1 (Smart et al., 2021).

Logan plot with reference tissue input

Logan plot with centrum semiovale as reference input performed clearly worse than other methods in calculation of parametric images (Mertens et al., 2020; Tuncel et al., 2022).

Spinal cord

SUV from 25-45 min after injection has been used to analyse spinal cord [11C]UCB-J data in rat model of traumatic spinal cord injury (Bertoglio et al., 2022a). 1TCM has been used to calculate VT in spinal cord from mouse HD model (Bertoglio et al., 2022b).

In humans, parametric VT images have been computed using 1TCM (Rossano et al., 2022). Baseline and blocking scans were analyzed using Lassen occupancy plot to estimate the non-specific and specific binding components of VT. The VND was 2.15±0.24, VS was 0.92±1.06, and BPND was 0.43. Since SV2A Bmax may be ∼20-fold lower than in the cerebral cortex, a radioligand with higher affinity to SV2A might provide better estimate of BPND. SRTM2 with whole brain grey matter as reference region, and k'2 fixed to 0.018 min-1, was also used (Rossano et al., 2022).

See also:


Chen M, Mecca AP, Naganawa M, Finnema SJ, Toyonaga T, Lin S, Najafzadeh S, Ropchan J, Lu Y, McDonald JW, Michalak HR, Nabulsi NB, Arnsten AFT, Huang Y, Carson RE, van Dyck CH. Assessing synaptic density in Alzheimer disease with synaptic vesicle glycoprotein 2A positron emission tomographic imaging. JAMA Neurol. 2018; 75(10): 1215-1224. doi: 10.1001/jamaneurol.2018.1836.

Finnema SJ, Nabulsi NB, Eid T, Detyniecki K, Lin S, Chen M-K, Dhaher R, Matuskey D, Baum E, Holden D, Spencer DD, Mercier J, Hannestad J, Huang Y, Carson RE. Imaging synaptic density in the living human brain. Sci Transl Med. 2016; 8: 348ra96. doi: 10.1126/scitranslmed.aaf6667.

Finnema SJ, Nabulsi NB, Mercier J, Lin SF, Chen MK, Matuskey D, Gallezot JD, Henry S, Hannestad J, Huang Y, Carson RE. Kinetic evaluation and test-retest reproducibility of [11C]UCB-J, a novel radioligand for positron emission tomography imaging of synaptic vesicle glycoprotein 2A in humans. J Cereb Blood Flow Metab. 2018; 38(11): 2041-2052. doi: 10.1177/0271678X17724947.

Koole M, van Aalst J, Devrome M, Mertens N, Serdons K, Lacroix B, Mercier J, Sciberras D, Maguire P, Van Laere K. Quantifying SV2A density and drug occupancy in the human brain using [11C]UCB-J PET imaging and subcortical white matter as reference tissue. Eur J Nucl Med Mol Imaging 2019; 46: 396-406. doi: 10.1007/s00259-018-4119-8.

Löscher W, Gillard M, Sands ZA, Kaminski RM, Klitgaard H. Synaptic vesicle glycoprotein 2A ligands in the treatment of epilepsy and beyond. CNS Drugs 2016; 30(11): 1055-1077. doi: 10.1007/s40263-016-0384-x.

Nabulsi NB, Mercier J, Holden D, Carré S, Najafzadeh S, Vandergeten MC, Lin SF, Deo A, Price N, Wood M, Lara-Jaime T, Montel F, Laruelle M, Carson RE, Hannestad J, Huang Y. Synthesis and preclinical evaluation of 11C-UCB-J as a PET tracer for imaging the synaptic vesicle glycoprotein 2A in the brain. J Nucl Med. 2016; 57(5): 777-784. doi: 10.2967/jnumed.115.168179.

Rabiner EA. Imaging synaptic density: a different look at neurological diseases. J Nucl Med. 2018; 59(3): 380-381. doi: 10.2967/jnumed.117.198317.

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Updated at: 2023-05-09
Created at: 2017-11-29
Written by: Vesa Oikonen