In the absence of overt cellular pathology but profound perceptual disorganization and cognitive deficits, schizophrenia is increasingly considered a disorder of neural coordination. Thus, different causal factors can similarly interrupt the dynamic function of neuronal ensembles and networks, in particular in the prefrontal cortex (PFC), leading to behavioral disorganization. The importance of establishing preclinical biomarkers for this aberrant function has prompted investigations into the nature of psychotomimetic drug effects on PFC neuronal activity. The drugs used in this context include serotonergic hallucinogens, amphetamine, and NMDA receptor antagonists. A prominent line of thinking is that these drugs create psychotomimetic states by similarly disinhibiting the activity of PFC pyramidal neurons. In the present study we did not find evidence in support of this mechanism in PFC subregions of freely moving rats. Whereas the NMDA receptor antagonist MK801 increased PFC population activity, the serotonergic hallucinogen DOI dose-dependently decreased population activity. Amphetamine did not strongly affect this measure. Despite different effects on the direction of change in activity, all three drugs caused similar net disruptions of population activity and modulated gamma oscillations. We also observed reduced correlations between spikerate and LFP power selectively in the gamma band suggesting that these drugs disconnect spike-discharge from PFC gamma oscillators. Gamma band oscillations support cognitive functions affected in schizophrenia. These findings provide insight into mechanisms that may lead to cortical processing deficits in schizophrenia and provide a novel electrophysiological approach for phenotypic characterization of animal models of this disease.

The mesocorticolimbic system, consisting, at its core, of the ventral tegmental area, the nucleus accumbens, and medial prefrontal cortex, has historically been investigated primarily for its role in positively motivated behaviors and reinforcement learning, and its dysfunction in addiction, schizophrenia, depression, and other mood disorders. Recently, researchers have undertaken a more comprehensive analysis of this system, including its role in not only reward but also punishment, as well as in both positive and negative reinforcement. This focus has been facilitated by new anatomical, physiological, and behavioral approaches to delineate functional circuits underlying behaviors and to determine how this system flexibly encodes and responds to positive and negative states and events, beyond simple associative learning. This review is a summary of topics covered in a mini-symposium at the 2013 Society for Neuroscience annual meeting. IntroductionThe dopamine neurons in the ventral tegmental area (VTA) and their targets in the nucleus accumbens (NAc) and the medial prefrontal cortex (mPFC) are often considered the nexus of the brain's mesocorticolimbic "reward circuit," although it has long been known that neurons in these brain areas are also influenced by aversive stimuli and events (e.g., Bromberg-Martin et al., 2010). With the advent of new anatomical, physiological, and behavioral approaches, and critical attention to psychological constructs, a more sophisticated understanding is emerging of the role of the mesocorticolimbic system in motivated behavior.One area of current investigation is the delineation of functional heterogeneity and specificity of subcircuits in the VTA (reviewed by Ungless and Grace, 2012;Lammel et al., 2013;. It is now apparent that the dopamine (DA) neurons in the VTA comprise several subpopulations distinguished by their afferent and efferent projections, gene expression profiles, electrophysiological properties, and participation in reward and aversion. Similarly, the GABAergic neurons in the VTA display diversity . Tract-tracing experiments in

Dopamine neurons of the ventral tegmental area (VTA) signal the occurrence of a reward-predicting conditioned stimulus (CS) with a subsecond duration increase in post-CS firing rate. Important theories about reward-prediction error and reward expectancy have been informed by the substantial number of studies that have examined post-CS phasic VTA neuron activity. On the other hand, the role of VTA neurons in anticipation of a reward-predicting CS and analysis of prestimulus spike rate rarely has been studied. We recorded from the VTA in rats during the 3-choice reaction time task, which has a fixed-duration prestimulus period and a difficult-to-detect stimulus. Use of a stimulus that was difficult to detect led to behavioral errors, which allowed us to compare VTA activity between trials with correct and incorrect stimulus-guided choices. We found a sustained increase in firing rate of both putative dopamine and GABA neurons during the pre-CS period of correct and incorrect trials. The poststimulus phasic response, however, was absent on incorrect trials, suggesting that the stimulus-evoked phasic response of dopamine neurons may relate to stimulus detection. The prestimulus activation of VTA neurons may modulate cortical systems that represent internal states of stimulus expectation and provide a mechanism for dopamine neurotransmission to influence preparatory attention to an expected stimulus.

Our understanding of how value-related information is encoded in the ventral tegmental area (VTA) is based mainly on the responses of individual putative dopamine neurons. In contrast to cortical areas, the nature of coordinated interactions between groups of VTA neurons during motivated behavior is largely unknown. These interactions can strongly affect information processing, highlighting the importance of investigating network level activity. We recorded the activity of multiple single units and local field potentials (LFP) in the VTA during a task in which rats learned to associate novel stimuli with different outcomes. We found that coordinated activity of VTA units with either putative dopamine or GABA waveforms was influenced differently by rewarding versus aversive outcomes. Specifically, after learning, stimuli paired with a rewarding outcome increased the correlation in activity levels between unit pairs whereas stimuli paired with an aversive outcome decreased the correlation. Paired single unit responses also became more redundant after learning. These response patterns flexibly tracked the reversal of contingencies, suggesting that learning is associated with changing correlations and enhanced functional connectivity between VTA neurons. Analysis of LFP recorded simultaneously with unit activity showed an increase in the power of theta oscillations when stimuli predicted reward but not an aversive outcome. With learning, a higher proportion of putative GABA units were phase locked to the theta oscillations than putative dopamine units. These patterns also adapted when task contingencies were changed. Taken together, these data demonstrate that VTA neurons organize flexibly as functional networks to support appetitive and aversive learning.

The prefrontal cortex (PFC) is thought to play a critical role in behavioral flexibility by monitoring action-outcome contingencies. How PFC ensembles represent shifts in behavior in response to changes in these contingencies remains unclear. We recorded single-unit activity and local field potentials in the dorsomedial PFC (dmPFC) of male rats during a set-shifting task that required them to update their behavior, among competing options, in response to changes in action-outcome contingencies. As behavior was updated, a subset of PFC ensembles encoded the current trial outcome before the outcome was presented. This novel outcome-prediction encoding was absent in a control task, in which actions were rewarded pseudorandomly, indicating that PFC neurons are not merely providing an expectancy signal. In both control and set-shifting tasks, dmPFC neurons displayed postoutcome discrimination activity, indicating that these neurons also monitor whether a behavior is successful in generating rewards. Gamma-power oscillatory activity increased before the outcome in both tasks but did not differentiate between expected outcomes, suggesting that this measure is not related to set-shifting behavior but reflects expectation of an outcome after action execution. These results demonstrate that PFC neurons support flexible rule-based action selection by predicting outcomes that follow a particular action.