DAT1-Genotype and Menstrual Cycle, but Not Hormonal Contraception, Modulate Reinforcement Learning: Preliminary Evidence

Kristina Jakob, Hanna Ehrentreich, Sarah K. C. Holtfrerich, Luise Reimers, Esther K. Diekhof

Hormone by genotype interactions have been widely ignored by cognitive neuroscience. Yet, the dependence of cognitive performance on both baseline dopamine (DA) and current 17ß-estradiol (E2) level argues for their combined effect also in the context of reinforcement learning. Here, we assessed how the interaction between the natural rise of E2 in the late follicular phase (FP) and the 40 base-pair variable number tandem repeat polymorphism of the dopamine transporter (DAT1) affects reinforcement learning capacity. 30 women with a regular menstrual cycle performed a probabilistic feedback learning task twice during the early and late FP. In addition, 39 women, who took hormonal contraceptives (HC) to suppress natural ovulation, were tested during the "pill break" and the intake phase of HC. The present data show that DAT1-genotype may interact with transient hormonal state, but only in women with a natural menstrual cycle. We found that carriers of the 9-repeat allele (9RP) experienced a significant decrease in the ability to avoid punishment from early to late FP. Neither homozygote subjects of the 10RP allele, nor subjects from the HC group showed a change in behavior between phases. These data are consistent with neurobiological studies that found that rising E2 may reverse DA transporter function and could enhance DA efflux, which would in turn reduce punishment sensitivity particularly in subjects with a higher transporter density to begin with. Taken together, the present results, although based on a small sample, add to the growing understanding of the complex interplay between different physiological modulators of dopaminergic transmission. They may not only point out the necessity to control for hormonal state in behavioral genetic research, but may offer new starting points for studies in clinical settings.