Variations in the fat mass and obesity-associated (FTO) gene are linked to obesity. However, the underlying neurobiological mechanisms by which these genetic variants influence obesity, behavior, and brain are unknown. Given that Fto regulates D2/3R signaling in mice, we tested in humans whether variants in FTO would interact with a variant in the ANKK1 gene, which alters D2R signaling and is also associated with obesity. In a behavioral and fMRI study, we demonstrate that gene variants of FTO affect dopamine (D2)-dependent midbrain brain responses to reward learning and behavioral responses associated with learning from negative outcome in humans. Furthermore, dynamic causal modeling confirmed that FTO variants modulate the connectivity in a basic reward circuit of meso-striatoprefrontal regions, suggesting a mechanism by which genetic predisposition alters reward processing not only in obesity, but also in other disorders with altered D2R-dependent impulse control, such as addiction.

BACKGROUND: The autistic spectrum is characterized by profound impairments of social interaction. The exact subpersonal processes, however, that underlie the observable lack of social reciprocity are still a matter of substantial controversy. Recently, it has been suggested that the autistic spectrum might be characterized by alterations of the brain's inference about the causes of socially relevant sensory signals. METHODS: We used a novel reward-based learning task that required integration of nonsocial and social cues in conjunction with computational modeling. Thirty-six healthy subjects were selected based on their score on the Autism-Spectrum Quotient (AQ), and AQ scores were assessed for correlations with cue-related model parameters and task scores. RESULTS: Individual differences in AQ scores were significantly correlated with participants' total task scores, with high AQ scorers performing more poorly in the task (r = 2.39, 95% confidence interval = 20.68 to 20.13). Computational modeling of the behavioral data unmasked a learning deficit in high AQ scorers, namely, the failure to integrate social context to adapt one's belief precision-the precision afforded to prior beliefs about changing states in the world-particularly in relation to the nonsocial cue. CONCLUSIONS: More pronounced autistic traits in a group of healthy control subjects were related to lower scores associated with misintegration of the social cue. Computational modeling further demonstrated that these traitrelated performance differences are not explained by an inability to process the social stimuli and their causes, but rather by the extent to which participants consider social information to infer the nonsocial cue.

Our results demonstrate that higher autistic traits in healthy subjects are related to lower scores in a learning task that requires social cue integration. Computational modeling further demonstrates that these trait-related performance differences are not explained by an inability to process the social stimuli and its causes, but rather by the extent to which participants take into account social information during decision making.