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

A bstract Compartmental modeling of 11 C-raclopride (RAC) is commonly used to measure dopamine response to intra-scan behavioral tasks. Bias in estimates of binding potential (BP ND ) and its dynamic changes (ΔBP ND ) 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. Methods 69 healthy young adult participants were scanned using RAC PET/MR while simultaneously performing a rewarded behavioral task. BP ND and ΔBP ND 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 BP ND for both simulations and participant data. Results Bias in ΔBP ND was observed to vary both spatially with BP ND and with the assumed value of k 4 . At the most likely value of k 4 (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 ΔBP ND to -0.3% and 2.4% respectively. In the acquired participant data, DE-MRTM2 produced modest changes in the experimental estimates of striatal ΔBP ND , while extrastriatal bias patterns were greatly reduced. DE-MRTM2 also considerably reduced the dependence of ΔBP ND upon the first-pass selection of k 2 ’. Conclusion Selectively discounting the contribution of the initial uptake period can help mitigate BP ND - and k 4 -dependent bias in single compartment models of ΔBP ND , while also reducing the dependence of ΔBP ND on the first-pass estimation of k 2 ’.