Medial Prefrontal Cortex Serotonin Input Regulates Cognitive Flexibility in Mice

The role of the GABAergic cells of the median raphe region in reinforcement-based learning

Learning and memory are important in everyday life as well as in pathological conditions. The median raphe region (MRR) contributes to memory formation; however, its precise role and the neurotransmitters involved have yet to be elucidated. To address this issue, we stimulated the MRR neurons of mice by chemogenetic technique and studied them in the operant conditioning and active avoidance tests. The virus carrier infected a variety of neuron types including both GABAergic and glutamatergic ones. Behavior was not influenced by stimulation. We hypothesize that the lack of effect was due to opposing effects exerted via GABAergic and glutamatergic neurons. Therefore, next we used VGAT-Cre mice that allowed the specific manipulation of MRR-GABAergic neurons. The stimulation did not affect behavior in the learning phase of the operant conditioning task, but increased reward preference and total responses when operant contingencies were reversed. The enhanced responsiveness might be a proclivity to impulsive behavior. Stimulation facilitated learning in the active avoidance test but did not affect reversal learning in this paradigm. Our findings suggest that MRR-GABAergic neurons are involved in both learning and reversal learning, but the type of learning that is affected depends on the task.

An experimental platform for stochastic analyses of single serotonergic fibers in the mouse brain

The self-organization of the serotonergic matrix, a massive axon meshwork in all vertebrate brains, is driven by the structural and dynamical properties of its constitutive elements. Each of these elements, a single serotonergic axon (fiber), has a unique trajectory and can be supported by a soma that executes one of the many available transcriptional programs. This "individuality" of serotonergic neurons necessitates the development of specialized methods for single-fiber analyses, both at the experimental and theoretical levels. We developed an integrated platform that facilitates experimental isolation of single serotonergic fibers in brain tissue, including regions with high fiber densities, and demonstrated the potential of their quantitative analyses based on stochastic modeling. Single fibers were visualized using two transgenic mouse models, one of which is the first implementation of the Brainbow toolbox in this system. The trajectories of serotonergic fibers were automatically traced in the three spatial dimensions with a novel algorithm, and their properties were captured with a single parameter associated with the directional von Mises-Fisher probability distribution. The system represents an end-to-end workflow that can be imported into various studies, including those investigating serotonergic dysfunction in brain disorders. It also supports new research directions inspired by single-fiber analyses in the serotonergic matrix, including supercomputing simulations and modeling in physics.

Dorsal raphe neurons signal integrated value during multi-attribute decision-making

The dorsal raphe nucleus (DRN) is implicated in psychiatric disorders that feature impaired sensitivity to reward amount, impulsivity when facing reward delays, and risk-seeking when grappling with reward uncertainty. However, whether and how DRN neurons signal reward amount, reward delay, and reward uncertainty during multi-attribute value-based decision-making, where subjects consider all these attributes to make a choice, is unclear. We recorded DRN neurons as monkeys chose between offers whose attributes, namely expected reward amount, reward delay, and reward uncertainty, varied independently. Many DRN neurons signaled offer attributes. Remarkably, these neurons commonly integrated offer attributes in a manner that reflected monkeys' overall preferences for amount, delay, and uncertainty. After decision-making, in response to post-decision feedback, these same neurons signaled signed reward prediction errors, suggesting a broader role in tracking value across task epochs and behavioral contexts. Our data illustrate how DRN participates in integrated value computations, guiding theories of DRN in decision-making and psychiatric disease.