Dopamine D2 −141C Ins/Del and Taq1A polymorphisms, body mass index, and prediction error brain response

Abstract

The prediction error model is a widely used paradigm that is conceptually based on neuronal dopamine function. However, whether dopamine receptor gene alleles contribute to human neuroimaging prediction error results is uncertain. Recent research implicated the dopamine D2 receptor in behavior response during a prediction error paradigm and we expected that polymorphisms of that receptor would contribute to prediction error brain response. In this study, healthy female participants in the early follicular phase of the menstrual cycle underwent a taste prediction error paradigm during functional magnetic resonance imaging. Participants were also genotyped for dopamine receptor polymorphisms. Our data suggest that the dopamine D2 receptor −141C Ins/Del and Taq1A polymorphisms together with body mass index selectively explain putamen prediction error response. This was true using a region of interest analysis as well as for a whole-brain analysis (FWE corrected). Polymorphisms for dopamine D1 or D4 receptors, dopamine transporter, or COMT did not significantly contribute to prediction error activation. The prediction error model is a computational reward-learning paradigm that is important in psychiatric research and has been associated with dopamine. The results from this study indicate that dopamine D2 receptor polymorphisms together with body mass index are important determinants to include in research that tests prediction error response of the brain. Psychiatric disorders are frequently associated with elevated or reduced body weight. Adding BMI to genetic information in brain-imaging studies that use reward and the prediction error paradigm may be important to increase validity and reliability of results.