Assessing non-invasive quantitative methods for [18F]SynVesT-1 PET imaging of synaptic vesicle glycoprotein 2A in the rat brain

Abstract

Purpose: Synaptic vesicle glycoprotein 2A (SV2A) is a critical biomarker for evaluating synaptic density in neurological research. Among available radioligands, [18F]SynVesT-1 is increasingly used in PET research because of its extended half-life, while having comparable pharmacokinetic properties to the widely used [11C]UCB-J. However, quantitative application in rat models remains unexplored for [18F]SynVesT-1. This study aims to validate quantitative kinetic modelling methods for [18F]SynVesT-1 and develop non-invasive quantification methods for synaptic density in rats. Methods: First, blood analysis of [18F]SynVesT-1 was performed to generate metabolite-corrected plasma input functions. Then, kinetic modelling was evaluated using compartmental analysis approaches, as well as Logan plot. Furthermore, non-invasive image-derived input functions (IDIF), with and without non-negative matrix factorization (NMF) were compared against the arterial input function (AIF). Results: Blood analysis showed that the parent fraction of the tracer decreased over time following a sigmoid curve, while the plasma-to-whole blood ratio remained stable over time (0.89 ± 0.02). The two-tissue compartmental model (2TCM) and Logan plot were determined to be the most accurate methods for quantification of [18F]SynVesT-1 kinetics in rats. Additionally, the results demonstrated strong agreement between AIF-derived and image-derived volume of distribution (VT) values, with both image-derived input approaches (IDIF and IDIF-NMF) performing equally well. Conclusion: These findings validate kinetic modelling methods for [18F]SynVesT-1 PET, enabling their application in further rat studies for preclinical neuroscience research and prove that image-derived input functions are reliable non-invasive alternatives to AIF.