Dopaminergic and prefrontal dynamics co-determine mouse decisions in a spatial gambling task.

The neural mechanisms by which animals initiate goal-directed actions, choose between options, or explore opportunities remain unknown. Here, we develop a spatial gambling task in which mice, to obtain intracranial self-stimulation rewards, self-determine the initiation, direction, vigor, and pace of their actions based on their knowledge of the outcomes. Using electrophysiological recordings, pharmacology, and optogenetics, we identify a sequence of oscillations and firings in the ventral tegmental area (VTA), orbitofrontal cortex (OFC), and prefrontal cortex (PFC) that co-encodes and co-determines self-initiation and choices. This sequence appeared with learning as an uncued realignment of spontaneous dynamics. Interactions between the structures varied with the reward context, particularly the uncertainty associated with the different options. We suggest that self-generated choices arise from a distributed circuit based on an OFC-VTA core determining whether to wait for or initiate actions, while the PFC is specifically engaged by reward uncertainty in action selection and pace.

Chronic nicotine increases midbrain dopamine neuron activity and biases individual strategies towards reduced exploration in mice.

Long-term exposure to nicotine alters brain circuits and induces profound changes in decision-making strategies, affecting behaviors both related and unrelated to drug seeking and consumption. Using an intracranial self-stimulation reward-based foraging task, we investigated in mice the impact of chronic nicotine on midbrain dopamine neuron activity and its consequence on the trade-off between exploitation and exploration. Model-based and archetypal analysis revealed substantial inter-individual variability in decision-making strategies, with mice passively exposed to nicotine shifting toward a more exploitative profile compared to non-exposed animals. We then mimicked the effect of chronic nicotine on the tonic activity of dopamine neurons using optogenetics, and found that photo-stimulated mice adopted a behavioral phenotype similar to that of mice exposed to chronic nicotine. Our results reveal a key role of tonic midbrain dopamine in the exploration/exploitation trade-off and highlight a potential mechanism by which nicotine affects the exploration/exploitation balance and decision-making.

Nicotine inhibits the VTA-to-amygdala dopamine pathway to promote anxiety.

Nicotine stimulates dopamine (DA) neurons of the ventral tegmental area (VTA) to establish and maintain reinforcement. Nicotine also induces anxiety through an as yet unknown circuitry. We found that nicotine injection drives opposite functional responses of two distinct populations of VTA DA neurons with anatomically segregated projections: it activates neurons that project to the nucleus accumbens (NAc), whereas it inhibits neurons that project to the amygdala nuclei (Amg). We further show that nicotine mediates anxiety-like behavior by acting on ß2-subunit-containing nicotinic acetylcholine receptors of the VTA. Finally, using optogenetics, we bidirectionally manipulate the VTA-NAc and VTA-Amg pathways to dissociate their contributions to anxiety-like behavior. We show that inhibition of VTA-Amg DA neurons mediates anxiety-like behavior, while their activation prevents the anxiogenic effects of nicotine. These distinct subpopulations of VTA DA neurons with opposite responses to nicotine may differentially drive the anxiogenic and the reinforcing effects of nicotine.

Author Correction: Mice adaptively generate choice variability in a deterministic task.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Mice adaptively generate choice variability in a deterministic task.

Can decisions be made solely by chance? Can variability be intrinsic to the decision-maker or is it inherited from environmental conditions? To investigate these questions, we designed a deterministic setting in which mice are rewarded for non-repetitive choice sequences, and modeled the experiment using reinforcement learning. We found that mice progressively increased their choice variability. Although an optimal strategy based on sequences learning was theoretically possible and would be more rewarding, animals used a pseudo-random selection which ensures high success rate. This was not the case if the animal is exposed to a uniform probabilistic reward delivery. We also show that mice were blind to changes in the temporal structure of reward delivery once they learned to choose at random. Overall, our results demonstrate that a decision-making process can self-generate variability and randomness, even when the rules governing reward delivery are neither stochastic nor volatile.