Cntnap2 loss drives striatal neuron hyperexcitability and behavioral inflexibility.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by two major diagnostic criteria - persistent deficits in social communication and interaction, and the presence of restricted, repetitive patterns of behavior (RRBs). Evidence from both human and animal model studies of ASD suggest that alteration of striatal circuits, which mediate motor learning, action selection, and habit formation, may contribute to the manifestation of RRBs. CNTNAP2 is a syndromic ASD risk gene, and loss of function of Cntnap2 in mice is associated with RRBs. How loss of Cntnap2 impacts striatal neuron function is largely unknown. In this study, we utilized Cntnap2-/- mice to test whether altered striatal neuron activity contributes to aberrant motor behaviors relevant to ASD. We find that Cntnap2-/- mice exhibit increased cortical drive of striatal projection neurons (SPNs), with the most pronounced effects in direct pathway SPNs. This enhanced drive is likely due to increased intrinsic excitability of SPNs, which make them more responsive to cortical inputs. We also find that Cntnap2-/- mice exhibit spontaneous repetitive behaviors, increased motor routine learning, and cognitive inflexibility. Increased corticostriatal drive, in particular of the direct pathway, may contribute to the acquisition of repetitive, inflexible behaviors in Cntnap2 mice.

Nucleus accumbens dopamine release reflects Bayesian inference during instrumental learning.

Dopamine release in the nucleus accumbens has been hypothesized to signal reward prediction error, the difference between observed and predicted reward, suggesting a biological implementation for reinforcement learning. Rigorous tests of this hypothesis require assumptions about how the brain maps sensory signals to reward predictions, yet this mapping is still poorly understood. In particular, the mapping is non-trivial when sensory signals provide ambiguous information about the hidden state of the environment. Previous work using classical conditioning tasks has suggested that reward predictions are generated conditional on probabilistic beliefs about the hidden state, such that dopamine implicitly reflects these beliefs. Here we test this hypothesis in the context of an instrumental task (a two-armed bandit), where the hidden state switches repeatedly. We measured choice behavior and recorded dLight signals reflecting dopamine release in the nucleus accumbens core. Model comparison based on the behavioral data favored models that used Bayesian updating of probabilistic beliefs. These same models also quantitatively matched the dopamine measurements better than non-Bayesian alternatives. We conclude that probabilistic belief computation plays a fundamental role in instrumental performance and associated mesolimbic dopamine signaling.