Characterization and Mitigation of Model Bias in Parametric Mapping of Dopamine Response to Behavioral Challenge

AO_SCPLOWBSTRACTC_SCPLOWCompartmental modeling of 11C-raclopride (RAC) is commonly used to measure dopamine response to intra-scan behavioral tasks. Bias in estimates of binding potential (BPND) and its dynamic changes ({Delta}BPND) can arise when the selected compartmental model deviates from the underlying biology. In this work, we characterize the bias associated with assuming a single target compartment and propose a model for reducing this bias by selectively discounting the contribution of the initial uptake period. Methods69 healthy young adult participants were scanned using RAC PET/MR while simultaneously performing a rewarded behavioral task. BPND and {Delta}BPND were estimated using an extension of the Multilinear Reference Tissue Model (MRTM2) with the task challenge encoded as a Heaviside step function. Bias was estimated using simulations designed to match the acquired data and was reduced by introducing a new model (DE-MRTM2) that reduces the biasing influence of the initial uptake period in the modeled estimation of BPND for both simulations and participant data. ResultsBias in {Delta}BPND was observed to vary both spatially with BPND and with the assumed value of k4. At the most likely value of k4 (0.13 min-1), the average bias and the maximum voxel bias magnitude in the nucleus accumbens were estimated to be 1.2% and 3.9% respectively. Simulations estimated that debiasing the contribution of the first 27 minutes of acquired data reduced average bias and maximum voxel bias in the nucleus accumbens {Delta}BPND to -0.3% and 2.4% respectively. In the acquired participant data, DE-MRTM2 produced modest changes in the experimental estimates of striatal {Delta}BPND, while extrastriatal bias patterns were greatly reduced. DE-MRTM2 also considerably reduced the dependence of {Delta}BPND upon the first-pass selection of k2. ConclusionSelectively discounting the contribution of the initial uptake period can help mitigate BPND- and k4-dependent bias in single compartment models of {Delta}BPND, while also reducing the dependence of {Delta}BPND on the first-pass estimation of k2.