Quantitative Methods For Detection of Transient Changes in Endogenous Dopamine For Preclinical PET Studies

This thesis investigates quantitative methods for the characterisation of dopamine (DA) release in preclinical PET. The goal was to develop methodologies that improve quantification of the PET signal and test to what extent these techniques can reliably detect subtle DA fluctuations. Ultimately, reliable quantification could help to better understand the role of DA in human brain conditions. The focus of this work is kinetic modelling, whose accuracy depends on different factors, from the quality of the measured imaging data to the interpretation of parameter estimates. An investigation of the model, linear parametric neurotransmitter PET (lp-ntPET), that enables the description of time-varying DA changes, was performed in 3 contexts. First, its performance was tested in a Bayesian framework, the PET-ABC method. Second, it was evaluated in 1D-simulations by varying the activations and the priors, to study their effects on uncertainty in the estimates. Third, a [11C]raclopride study was performed in monkeys using the high sensitivity MiniExplorer, where different amphetamine doses were injected to induce variable DA releases. Finally, towards exploiting the synergies of PET/fMRI data in the future, a preclinical PET insert was evaluated in 3 configurations: as a standalone unit, in the MR bore and with the MR pulsing. In conclusion, the results of the studies showed that the lp-ntPET model used in conjunction with PET-ABC is a robust quantitative method that not only provides parameter estimates and their uncertainties but also statistically sound inferences about model preference. Further, PET-ABC is able to reliably estimate parameters describing transient DA release in preclinical studies. The PET insert presented good performance in all configurations, enabling future investigation of parallel analysis of fMRI and PET data for improved parameter estimation