A neurocomputational account of catalepsy sensitization induced by D2 receptor blockade in rats: context dependency, extinction, and renewal

Abstract

Rationale Repeated haloperidol treatment in rodents results in a day-to-day intensification of catalepsy (i.e., sensitization). Prior experiments suggest that this sensitization is context-dependent and resistant to extinction training. Objectives The aim of this study was to provide a neurobiological mechanistic explanation for these findings. Materials and methodsWe use a neurocomputational model of the basal ganglia and simulate two alternative models based on the reward prediction error and novelty hypotheses of dopamine function. We also conducted a behavioral rat experiment to adjudicate between these models. Twenty male Sprague-Dawley rats were challenged with 0.25 mg/kg haloperidol across multiple days and were subsequently tested in either a familiar or novel context. Results Simulation results show that catalepsy sensitization, and its context dependency, can be explained by "NoGo" learning via simulated D2 receptor antagonism in striatopallidal neurons, leading to increasingly slowed response latencies. The model further exhibits a nonextinguishable component of catalepsy sensitization due to latent NoGo representations that are prevented from being expressed, and therefore from being unlearned, during extinction. In the rat experiment, context dependency effects were not dependent on the novelty of the context, ruling out the novelty model's account of context dependency. Conclusions Simulations lend insight into potential complex mechanisms leading to context-dependent catalepsy sensitization, extinction, and renewal.

Neurogenetics and Pharmacology of Learning, Motivation, and Cognition

“…It is noteworthy that the striatal polymorphisms again were associated with measures of Go and NoGo learning: the DARPP-32 genotype was predictive of the extent to which participants incrementally speeded their responses as function of positive reward prediction error, whereas the DRD2 genotype was predictive of incremental slowing because of negative prediction errors (Figure 3). These effects again converge with those observed as a function of pharmacological manipulations of striatal dopamine in Parkinson's patients performing the same task (Moustafa et al, 2008a), and with effects of striatal D1 and D2 receptor manipulation on approach and avoidance as a function of reinforcement outcomes in rodents and primates (Dalley et al, 2005;Nakamura and Hikosaka, 2006;Klein and Schmidt, 2003;Wiecki et al, 2009).…”

“…Conversely, D2 receptor blockade actually improves NoGo learning from negative prediction errors in patients with Tourette's syndrome (Palminteri et al, 2009). This latter result is also supported by both theoretical and empirical data: D2-blockade and resulting enhancement of striatopallidal excitability and plasticity promotes avoidance learning in both models and rats (Wiecki et al, 2009).…”

“…First, faster responses are made as a function of the relative difference in activity levels between striatonigral/Go and striatopallidal/NoGo cells coding for the executed action. These relative activation differences can arise either because of previous learning (such that responses with higher reinforcement probabilities are more swiftly executed), or because of greater induced DA release (such that DA bursts as a function of novelty or reward predicting variables may directly influence Go vs NoGo activity, and hence response speed), or both (Moustafa et al, 2008a;Wiecki et al, 2009). It is to be noted that the same Go-NoGo mechanisms, operating in the ventral striatum as opposed to dorsal striatum coding of specific instrumental actions, can support the selection of general Pavlovian approach 'actions' in pursuit of rewards.…”

Computational models of reinforcement learning: the role of dopamine as a reward signal

“…The model has been used to simulate the effects of Parkinson's disease and pharmacological manipulations on learning and decision making processes in humans, and predicted that learning from positive and negative outcomes would be modulated in opposite directions by such treatments, as confirmed empirically (Frank et al 2004; for review see Cohen and Frank 2009). More recently, the same model has accounted for some of the pharmacological effects of D2 receptor antagonists on the acquisition, extinction and renewal of motivated behavior in rats (Wiecki et al 2009). Of course, several biophysical details are omitted in these models of corticostriatal circuitry.…”

“…While this study did not explicitly model neural mechanisms, but behavioral data, the requirement of having parallel state representations illustrated the way parallel neural networks might be working together to form different stimulus representations depending on the situation. Other related examples of extinction and renewal have been modeled in basal ganglia networks to account for the effects of dopaminergic pharmacological agents on the development and extinction of motor behaviors in rats (Wiecki et al 2009). Finally, fear conditioning is another well documented conditioning paradigm amenable to extinction studies (reviewed in Ehrlich et al 2009;Quirk and Mueller 2007).…”