Publications

Cues in the environment can elicit complex emotional states, and thereby maladaptive behavior, as a function of their ascribed value. Here we capture individual variation in the propensity to attribute motivational value to reward-cues using the sign-tracker/goal-tracker animal model. Goal-trackers attribute predictive value to reward-cues, and sign-trackers attribute both predictive and incentive value. Using chemogenetics and microdialysis, we show that, in sign-trackers, stimulation of the neuronal pathway from the prelimbic cortex (PrL) to the paraventricular nucleus of the thalamus (PVT) decreases the incentive value of a reward-cue. In contrast, in goal-trackers, inhibition of the PrL-PVT pathway increases both the incentive value and dopamine levels in the nucleus accumbens shell. The PrL-PVT pathway, therefore, exerts top-down control over the dopamine-dependent process of incentive salience attribution. These results highlight PrL-PVT pathway as a potential target for treating psychopathologies associated with the attribution of excessive incentive value to reward-cues, including addiction.

The application of theoretical and computational approaches to the analysis of complex behavior has a rich history in psychology. A shining example of this is the modeling of learning encapsulated elegantly by Rescorla and Wagner (1972), who demonstrated that classical conditioning can be described by a simple mathematical equation. The explanatory power of the Rescorla-Wagner rule and its subsequent expansion into additional areas of behavioral plasticity has enabled the precise mapping of learning parameters onto neural structures and even individual neurons. A plethora of other mathematical models have since been used to describe a variety of behaviors, and to map those behaviors onto their underlying neurobiology. This so-called computational phenotyping is now gaining momentum as a translational tool that can be used to identify process characteristics in both humans and animals with the potential of transforming the field of psychopharmacology. The contributions to this Special Issue on Computational and Translational Psychopharmacology stem from the European Behavioural Pharmacology Society (EBPS) Workshop that was held at the University of Cambridge, in August of 2018. The overarching goal of the workshop was to foster discussion around the nascent subfield we refer to as Computational and Translational Psychopharmacology, and to identify points of convergence for which computational approaches could be used to enhance the translational value of animal and human studies. The manuscripts contained herein demonstrate the potential utility of such approaches and provide a foundation for continuous growth towards a better mechanistic understanding of the complex behaviors that characterize psychiatric conditions and the development of more predictive translational probes.

There are a number of traits that are thought to increase susceptibility to addiction, and some of these are modeled in preclinical studies. For example, “sensation-seeking” is predictive of the initial propensity to take drugs; whereas “novelty-seeking” predicts compulsive drug-seeking behavior. In addition, the propensity to attribute incentive salience to reward cues can predict the propensity to approach drug cues, and reinstatement or relapse, even after relatively brief periods of drug exposure. The question addressed here is the extent to which these three ‘vulnerability factors’ are related; that is, predictive of one another. Some relationships have been reported in small samples, but here a large sample of 1,598 outbred male and female heterogeneous stock rats were screened for Pavlovian conditioned approach behavior (to obtain an index of incentive salience attribution; ‘sign-tracking’), and subsequently tested for sensation-seeking and novelty-seeking. Despite the large N there were no significant correlations between these traits, in either males or females. There were, however, novel relationships between multiple measures of incentive salience attribution and, based on these findings, we generated a new metric that captures “incentive value”. Furthermore, there were sex differences on measures of incentive salience attribution and sensation-seeking behavior that were not previously apparent.