Selective Effects of the Loss of NMDA or mGluR5 Receptors in the Reward System on Adaptive Decision-Making.

Cieślak PE
Ahn WY
Bogacz R
Rodriguez Parkitna J

The chemical messenger dopamine is important for brain activity. This study tested the effects of gene mutations connected with dopamine on the behavior of animals in a learning task. It shows that some of the mutations make the behaviour of mice more random.

Scientific Abstract

Selecting the most advantageous actions in a changing environment is a central feature of adaptive behavior. The midbrain dopamine (DA) neurons along with the major targets of their projections, including dopaminoceptive neurons in the frontal cortex and basal ganglia, play a key role in this process. Here, we investigate the consequences of a selective genetic disruption of NMDA receptor and metabotropic glutamate receptor 5 (mGluR5) in the DA system on adaptive choice behavior in mice. We tested the effects of the mutation on performance in the probabilistic reinforcement learning and probability-discounting tasks. In case of the probabilistic choice, both the loss of NMDA receptors in dopaminergic neurons or the loss mGluR5 receptors in D1 receptor-expressing dopaminoceptive neurons reduced the probability of selecting the more rewarded alternative and lowered the likelihood of returning to the previously rewarded alternative (win-stay). When observed behavior was fitted to reinforcement learning models, we found that these two mutations were associated with a reduced effect of the expected outcome on choice (i.e., more random choices). None of the mutations affected probability discounting, which indicates that all animals had a normal ability to assess probability. However, in both behavioral tasks animals with targeted loss of NMDA receptors in dopaminergic neurons or mGluR5 receptors in D1 neurons were significantly slower to perform choices. In conclusion, these results show that glutamate receptor-dependent signaling in the DA system is essential for the speed and accuracy of choices, but at the same time probably is not critical for correct estimation of probable outcomes.

Estimated values of parameters of a reinforcement learning model
Estimated values of parameters of a reinforcement learning model. Each panel compares parameters of mice with different mutation to normal mice. In panels A and B there are significant differences in a parameter describing randomness of animals' choices (beta).
2018. eNeuro. 5(4):e0331-18.2018
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