Quantification of SV2A Binding in Rodent Brain Using [18F]SynVesT-1 and PET Imaging
Open accessAbstract
Purpose Synapse loss is a hallmark of Alzheimer's disease (AD) and correlates with cognitive decline. The validation of a noninvasive in vivo imaging approach to quantify synapse would greatly facilitate our understanding of AD pathogenesis and assist drug developments for AD. As animal models of neurodegenerative and neuropsychiatric disorders play a critical role in the drug discovery and development process, a robust, objective, and translational method for quantifying therapeutic drug efficacy in animal models will facilitate the drug development process. In this study, we tested the quantification reliability of the SV2A PET tracer, [18 F]SynVesT-1, in a mouse model of AD (APP/PS1) and wild-type controls, and developed a simplified quantification method to facilitate large cohort preclinical imaging studies.Procedures We generated nondisplaceable binding potential (BP ND) and distribution volume ratio (DVR) values using the simplified reference tissue model (SRTM) on the 90-min dynamic PET imaging data, with brain stem and cerebellum as the reference region, respectively. Then, we correlated the standardized uptake value ratio (SUVR)-1 and SUVR averaged from different imaging windows with BP ND and DVR, using brain stem and cerebellum as the reference region, respectively. We performed homologous competitive binding assay and autoradiographic saturation binding assay using [18 F]SynVesT-1 to calculate the B max and K d.Results Using brain stem as the reference region, the averaged SUVR-1 from 30 to 60 min postinjection correlated well with the BP ND calculated using SRTM. Using cerebellum as the reference region, the averaged SUVR from 30 to 60 min postinjection correlated well with the SRTM DVR. From the homologous competitive binding assay and autoradiographic saturation binding assay, the calculated the B max and K d were 4.5-18 pmol/mg protein and 9.8-19.6 nM, respectively, for rodent brain tissue.Conclusions This simplified SUVR method provides reasonable SV2A measures in APP/PS1 mice and their littermate controls. Our data indicate that, in lieu of a full 90-min dynamic scan, a 30-min static PET scan (from 30 to 60 min postinjection) would be sufficient to provide quantification data on SV2A expression, equivalent to the data generated from kinetic modeling. The methods developed here are readily applicable to the evaluation of therapeutic effects of novel drugs in this rodent model using [18 F]SynVesT-1 and small animal PET.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In rodents and nonhuman primates, SV2A tracers map regional SV2A with reliability and support longitudinal designs, including occupancy and therapeutic challenge paradigms.
“In rodents and nonhuman primates, these tracers map regional SV2A with reliability and support longitudinal designs, including occupancy and therapeutic challenge paradigms [209].”
From Synaptic Plasticity—Intrinsic Excitability and Antidepressant Discovery (2026) · cites this paper as [209]
The Results (Rodent PET imaging and data analysis; page unavailable) report reliable regional binding estimates in mice, and the Conclusions describe the method as suitable for future evaluation of drug effects. The Introduction mentions pharmacokinetic findings in nonhuman primates, but this paper does not establish reliable regional mapping or longitudinal use in that population. It also does not validate longitudinal occupancy or therapeutic-challenge paradigms with [18F]SynVesT-1. (LLM, conf=0.86)
Experimental — accuracy not yet measured[18F]SynVesT-1 and [18F]SynVesT-2 extend accessibility through longer half-life, favorable kinetics, and validated simplified quantification protocols suited to clinical workflows.
“[11C]UCB-J established high brain uptake and specificity, creating the benchmark for human and preclinical studies, while [18F]SynVesT-1 and [18F]SynVesT-2 extend accessibility through longer half-life, favorable kinetics, and validated simplified quantification protocols suited to clinical workflows [174,208,209].”
From Synaptic Plasticity—Intrinsic Excitability and Antidepressant Discovery (2026) · cites this paper as [209]
The Introduction and Discussion describe both tracers’ pharmacokinetic profiles and explain how fluorine-18’s longer half-life could enable distribution to PET centers. The Conclusions validate a simplified quantification method only for [18F]SynVesT-1 in rodent PET; they do not establish a simplified protocol for [18F]SynVesT-2 or validate either protocol for clinical workflows. The supplied passages have section labels but no page numbers. (LLM, conf=0.89)
Experimental — accuracy not yet measured[11C]UCB-J established high brain uptake and specificity, creating the benchmark for human and preclinical studies.
“[11C]UCB-J established high brain uptake and specificity, creating the benchmark for human and preclinical studies, while [18F]SynVesT-1 and [18F]SynVesT-2 extend accessibility through longer half-life, favorable kinetics, and validated simplified quantification protocols suited to clinical workflows [174,208,209].”
From Synaptic Plasticity—Intrinsic Excitability and Antidepressant Discovery (2026) · cites this paper as [209]
The Discussion calls [11C]UCB-J a lead SV2A PET tracer and describes its use in clinical studies; it also compares [18F]SynVesT-1 with prior [11C]UCB-J imaging in mice. This supports its role as a reference for human and preclinical work, but the passages do not establish the claimed high brain uptake and specificity of [11C]UCB-J. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Complement and microglia mediate early synapse loss in Alzheimer mouse models
Hong, S., Beja-Glasser, V. F., Nfonoyim, B. M., Frouin, A., Li, S., Ramakrishnan, S., Merry, K. M., Shi, Q., Rosenthal, A., Barres, B. A., et al. (2016) · Science · doi:10.1126/science.aad8373 · Cited by 3,343 · see citing works
Simplified Reference Tissue Model for PET Receptor Studies
Lammertsma, A. A., & Hume, S. P. (1996) · NeuroImage · doi:10.1006/nimg.1996.0066 · Cited by 2,025 · see citing works
The synaptic vesicle protein SV2A is the binding site for the antiepileptic drug levetiracetam
Lynch, B. A., Lambeng, N., Nocka, K., Kensel-Hammes, P., Bajjalieh, S. M., Matagne, A., & Fuks, B. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308208101 · Cited by 1,610 · see citing works
Jankowsky, J. L., Fadale, D. J., Anderson, J., Xu, G. M., Gonzales, V., Jenkins, N. A., Copeland, N. G., Lee, M. K., Younkin, L. H., Wagner, S. L., et al. (2004) · Human Molecular Genetics · doi:10.1093/hmg/ddh019 · Cited by 1,575 · see citing works
Distribution Volume Ratios without Blood Sampling from Graphical Analysis of PET Data
Logan, J., Fowler, J. S., Volkow, N. D., Wang, G., Ding, Y., & Alexoff, D. L. (1996) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1097/00004647-199609000-00008 · Cited by 1,515 · see citing works
Alzheimer amyloid-β oligomer bound to postsynaptic prion protein activates Fyn to impair neurons
Um, J. W., Nygaard, H. B., Heiss, J. K., Kostylev, M. A., Stagi, M., Vortmeyer, A. O., Wısnıewskı, T., Gunther, E. C., & Strittmatter, S. M. (2012) · Nature Neuroscience · doi:10.1038/nn.3178 · Cited by 654 · see citing works
Self-citationImaging synaptic density in the living human brain
Finnema, S. J., Nabulsi, N. B., Eid, T., Detyniecki, K., Lin, S.-F., Chen, M.-K., Dhaher, R., Matuskey, D., Baum, E., Holden, D., et al. (2016) · Science Translational Medicine · doi:10.1126/scitranslmed.aaf6667 · Cited by 575 · see citing works
Self-citationUm, J. W., Kaufman, A. C., Kostylev, M., Heiss, J. K., Stagi, M., Takahashi, H., Kerrisk, M. E., Vortmeyer, A., Wisniewski, T., Koleske, A. J., et al. (2013) · Neuron · doi:10.1016/j.neuron.2013.06.036 · Cited by 572 · see citing works
Self-citationLower synaptic density is associated with depression severity and network alterations
Holmes, S. E., Scheinost, D., Finnema, S. J., Naganawa, M., Davis, M. T., DellaGioia, N., Nabulsi, N., Matuskey, D., Angarita, G. A., Pietrzak, R. H., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09562-7 · Cited by 484 · see citing works
Self-citationDifferential expression of synaptic vesicle protein 2 (SV2) isoforms
Bajjalieh, S., Frantz, G., Weimann, J., McConnell, S., & Scheller, R. (1994) · The Journal of Neuroscience · doi:10.1523/jneurosci.14-09-05223.1994 · Cited by 440 · see citing works
Chen, M.-K., Mecca, A. P., Naganawa, M., Finnema, S. J., Toyonaga, T., Lin, S.-F., Najafzadeh, S., Ropchan, J., Lu, Y., McDonald, J. W., et al. (2018) · JAMA Neurology · doi:10.1001/jamaneurol.2018.1836 · Cited by 431 · see citing works
Self-citationMemory Impairment in Transgenic Alzheimer Mice Requires Cellular Prion Protein
Gimbel, D. A., Nygaard, H. B., Coffey, E. E., Gunther, E. C., Laurén, J., Gimbel, Z. A., & Strittmatter, S. M. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.0395-10.2010 · Cited by 418 · see citing works
Self-citationSynaptic changes in Alzheimer’s disease and its models
Pozueta, J., Lefort, R., & Shelanski, M. L. (2012) · Neuroscience · doi:10.1016/j.neuroscience.2012.05.050 · Cited by 305 · see citing works
Nabulsi, N. B., Mercier, J., Holden, D., Carré, S., Najafzadeh, S., Vandergeten, M.-C., Lin, S.-F., Deo, A., Price, N., Wood, M., et al. (2016) · Journal of Nuclear Medicine · doi:10.2967/jnumed.115.168179 · Cited by 296 · see citing works
Self-citationIn vivo measurement of widespread synaptic loss in Alzheimer’s disease with SV2A PET
Mecca, A. P., Chen, M., O'Dell, R. S., Naganawa, M., Toyonaga, T., Godek, T. A., Harris, J. E., Bartlett, H. H., Zhao, W., Nabulsi, N. B., et al. (2020) · Alzheimer's & Dementia · doi:10.1002/alz.12097 · Cited by 288 · see citing works
Self-citationGillard, M. R., Fuks, B., Leclercq, K., & Matagne, A. (2011) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2011.04.064 · Cited by 252 · see citing works
Onwordi, E. C., Halff, E. F., Whitehurst, T., Mansur, A., Cotel, M.-C., Wells, L., Creeney, H., Bonsall, D., Rogdaki, M., Shatalina, E., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-14122-0 · Cited by 243 · see citing works
Synaptic Changes in Parkinson Disease Assessed with in vivo Imaging
Matuskey, D., Tinaz, S., Wilcox, K. C., Naganawa, M., Toyonaga, T., Dias, M., Henry, S. E., Pittman, B. P., Ropchan, J. R., Nabulsi, N., et al. (2020) · Annals of Neurology · doi:10.1002/ana.25682 · Cited by 180 · see citing works
Self-citationNaganawa, M., Li, S., Nabulsi, N., Henry, S., Zheng, M.-Q., Pracitto, R., Cai, Z., Gao, H., Kapinos, M., Labaree, D., et al. (2021) · Journal of Nuclear Medicine · doi:10.2967/jnumed.120.249144 · Cited by 138 · see citing works
Self-citationPET imaging of synaptic density: A new tool for investigation of neuropsychiatric diseases
Cai, Z., Li, S., Matuskey, D., Nabulsi, N., & Huang, Y. (2019) · Neuroscience Letters · doi:10.1016/j.neulet.2018.07.038 · Cited by 131 · see citing works
Self-citationAssessment of a white matter reference region for <sup>11</sup> C-UCB-J PET quantification
Rossano, S., Toyonaga, T., Finnema, S. J., Naganawa, M., Lu, Y., Nabulsi, N., Ropchan, J., De Bruyn, S., Otoul, C., Stockis, A., et al. (2020) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x19879230 · Cited by 126 · see citing works
Self-citationLi, S., Cai, Z., Wu, X., Holden, D., Pracitto, R., Kapinos, M., Gao, H., Labaree, D. C., Nabulsi, N., Carson, R. E., et al. (2018) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.8b00526 · Cited by 124 · see citing works
Self-citationKaminski, R. M., Gillard, M. R., Leclercq, K., Hanon, É., Lorent, G., Dassesse, D., Matagne, A., & Klitgaard, H. (2009) · Epilepsia · doi:10.1111/j.1528-1167.2009.02089.x · Cited by 116 · see citing works
Evaluation of<sup>18</sup>F-UCB-H as a Novel PET Tracer for Synaptic Vesicle Protein 2A in the Brain
Warnock, G. I., Aerts, J., Bahri, M. A., Bretin, F., Lemaire, C., Giacomelli, F., Mievis, F., Mestdagh, N., Buchanan, T., Valade, A., et al. (2014) · Journal of Nuclear Medicine · doi:10.2967/jnumed.113.136143 · Cited by 85 · see citing works
Toyonaga, T., Smith, L. M., Finnema, S. J., Gallezot, J.-D., Naganawa, M., Bini, J., Mulnix, T., Cai, Z., Ropchan, J., Huang, Y., et al. (2019) · Journal of Nuclear Medicine · doi:10.2967/jnumed.118.223867 · Cited by 79 · see citing works
Self-citation[11C]UCB-A, a novel PET tracer for synaptic vesicle protein 2A
Estrada, S., Lubberink, M., Thibblin, A., Sprycha, M., Buchanan, T., Mestdagh, N., Kenda, B., Mercier, J., Provins, L., Gillard, M., et al. (2016) · Nuclear Medicine and Biology · doi:10.1016/j.nucmedbio.2016.03.004 · Cited by 71 · see citing works
Mercier, J., Archen, L., Bollu, V., Carré, S., Evrard, Y., Jnoff, E., Kenda, B., Lallemand, B., Michel, P., Montel, F., et al. (2014) · ChemMedChem · doi:10.1002/cmdc.201300482 · Cited by 68 · see citing works
Li, S., Cai, Z., Zhang, W., Holden, D., Lin, S.-F., Finnema, S. J., Shirali, A., Ropchan, J., Carre, S., Mercier, J., et al. (2019) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-019-04357-w · Cited by 59 · see citing works
Self-citationValidation and noninvasive kinetic modeling of [ <sup>11</sup> C]UCB-J PET imaging in mice
Bertoglio, D., Verhaeghe, J., Miranda, A., Kertesz, I., Cybulska, K., Korat, Š., Wyffels, L., Stroobants, S., Mrzljak, L., Dominguez, C., et al. (2020) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x19864081 · Cited by 54 · see citing works
Cai, Z., Li, S., Zhang, W., Pracitto, R., Wu, X., Baum, E. J., Finnema, S. J., Holden, D., Toyonaga, T., Lin, S.-F., et al. (2020) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.9b00618 · Cited by 54 · see citing works
Self-citationPatel, S., Knight, A. C., Krause, S., Teceno, T. J., Tresse, C., Li, S., Cai, Z., Gouasmat, A., Carroll, V. M., Barret, O., et al. (2019) · Molecular Imaging and Biology · doi:10.1007/s11307-019-01428-0 · Cited by 32 · see citing works
Self-citationSimplified Quantification of 11C-UCB-J PET Evaluated in a Large Human Cohort
Naganawa, M., Gallezot, J., Finnema, S. J., Matuskey, D., Mecca, A. P., Nabulsi, N., Labaree, D. C., Ropchan, J. R., Malison, R. T., D’Souza, D. C., et al. (2020) · Journal of Nuclear Medicine · doi:10.2967/jnumed.120.243949 · Cited by 28 · see citing works
Self-citationUm, J. W., Kaufman, A. C., Kostylev, M. A., et al. (2013) · Neuron · doi:10.1016/j.neuron.2013.10.001 · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [27]Nabulsi N, Mercier J, Holden D, (2016) 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. 26848175
- [32]Cai Z, Li S, Zhang W, (2020) Synthesis and Preclinical Evaluation of an 18F-Labeled Synaptic Vesicle Glycoprotein 2A PET Imaging Probe: [18F]SynVesT-2. ACS Chem Neurosci. 31961649
Cited by
Sorted by citations
Synaptic Vesicle Glycoprotein 2A: Features and Functions
Rossi, R., Arjmand, S., Bærentzen, S. L., Gjedde, A., & Landau, A. M. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.864514 · Cited by 123 · see citing works
Imaging Synaptic Density: The Next Holy Grail of Neuroscience?
Serrano, M. E., Kim, E., Petrinovic, M. M., Turkheimer, F., & Cash, D. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.796129 · Cited by 67 · see citing works
Bertoglio, D., Zajicek, F., Lombaerde, S. D., Miranda, A., Stroobants, S., Wang, Y., Dominguez, C., Munoz-Sanjuan, I., Bard, J., Liu, L., et al. (2022) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x221101648 · Cited by 29 · see citing works
Xiong, M., Roshanbin, S., Rokka, J., Schlein, E., Ingelsson, M., Sehlin, D., Eriksson, J., & Syvänen, S. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118302 · Cited by 24 · see citing works
Toyonaga, T., Fesharaki-Zadeh, A., Strittmatter, S. M., Carson, R. E., & Cai, Z. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.787404 · Cited by 20 · see citing works
Self-citationSynaptic Plasticity—Intrinsic Excitability and Antidepressant Discovery
Tanaka, M. (2026) · Biomedicines · doi:10.3390/biomedicines14061265
- Authors
- Sadasivam, P., Fang, X. T., Toyonaga, T., Lee, S., Xu, Y., Zheng, M.-Q., Spurrier, J., Huang, Y., Strittmatter, S. M., Carson, R. E., & Cai, Z.
- Affiliations
- Yale University (US)
- Corresponding author
- Cai, Zhengxin
- ORCID
- Venue
- Molecular Imaging and Biology
- Published
- 2020-11-30
- Volume
- 23
- Issue
- 3
- Pages
- 372-381
- ISSN
- 1536-1632 (ISSN-L)
- 1860-2002
- PMC
- PMC8105262
- OpenAlex
- W3110370901
- Cited by
- 31
- References
- 33
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 3 · 2023: 8 · 2022: 9 · 2021: 3
- FWCI
- 3.16
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Molecular Imaging and Biology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute on Aging (R01AG058773, R01AG052560)
- National Institute of Biomedical Imaging and Bioengineering (K01EB023312)
- Topics
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- SV2A, [18F]SynVesT-1, positron emission tomography, Alzheimer's disease, synapse quantification, APP/PS1 mice, simplified reference tissue model, binding potential, distribution volume ratio, standardized uptake value ratio, binding affinity, competitive binding assay, rodent brain, animal models
- MeSH terms
- Alzheimer Disease, Animals, Brain, Brain Stem, Cerebellum, Female, Fluorine Radioisotopes, Kinetics, Membrane Glycoproteins, Nerve Tissue Proteins, Protein Binding, Pyridines, Pyrrolidines, Drug Design, Radiopharmaceuticals, Inhibitory Concentration 50, Positron-Emission Tomography, Mice
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
