A linear model for estimation of neurotransmitter response profiles from dynamic PET data

Abstract

The parametric ntPET model (p-ntPET) estimates the kinetics of neurotransmitter release from dynamic PET data with receptor-ligand radiotracers. Here we introduce a linearization (lp-ntPET) that is computationally efficient and can be applied to single scan data. lp-ntPET employs a non-invasive reference region input function and extends the LSRRM of Alpert et al. (2003) using basis functions to characterize the time course of neurotransmitter activation. In simulation studies, the temporal precision of neurotransmitter profiles estimated by lp-ntPET was similar to that of p-ntPET (standard deviation ~3 min for responses early in the scan) while computation time was reduced by several orders of magnitude. Violations of model assumptions such as activation-induced changes in regional blood flow or specific binding in the reference tissue have negligible effects on lp-ntPET performance. Application of the lp-ntPET method is demonstrated on [11C]raclopride data acquired in rats receiving methamphetamine, which yielded estimated response functions that were in good agreement with simultaneous microdialysis measurements of extracellular dopamine concentration. These results demonstrate that lp-ntPET is a computationally efficient, linear variant of ntPET that can be applied to PET data from single or multiple scan designs to estimate the time course of neurotransmitter activation.