A neurocomputational hypothesis for nicotine addiction

Abstract

We present a hypothetical neurocomputational model that combines a set of neural circuits at the molecular, cellular, and system levels and accounts for several neurobiological and behavioral processes leading to nicotine addiction. We propose that combining changes in the nicotinic receptor response, expressed by mesolimbic dopaminergic neurons, with dopamine-gated learning in action-selection circuits, suffices to capture the acquisition of nicotine addiction. We show that an opponent process enhanced by persistent nicotine-taking renders self-administration rigid and habitual by inhibiting the learning process, resulting in long-term impairments in the absence of the drug. The model implies distinct thresholds on the dosage and duration for the acquisition and persistence of nicotine addiction. Our hypothesis unites a number of prevalent ideas on nicotine action into a coherent formal network for further understanding of compulsive drug addiction.computational model ͉ reward T obacco addiction is a multistage process involving persistent cycles of chronic smoking (1, 2). It is tied to long-lasting effects on mesolimbic dopaminergic (DA) pathways by nicotine, the main addictive substance in tobacco smoke (2, 3). Although the pharmacological target of nicotine is now well identified (4), how nicotine binding translates into addictive behavior remains enigmatic. Particularly puzzling is the ease with which nicotine addiction is acquired and resists despite its limited (or even negative) hedonic impact (3).Both reinforcement learning and opponent processes have been proposed to play a significant role in the development of addiction (5, 6). However, direct DA-dependent reinforcement learning does not conclusively treat the issue of persistence of addictive behaviors to extinction (5), and the opponent process or allostasis (6) theory does not provide a specific computational account of how addiction is acquired. To unravel two such aspects, the acquisition and persistence, we model the interplay between the phasic and the persistent effects of nicotine on the DA pathway and its assumed control of the plasticity in corticostriatal action-selection (A-S) circuits (7). We propose a hypothetical minimal computational circuit of neuronal and pharmacological processes (see Fig. 1) and apply it to a specific animal model of smoking in humans: self-administration of nicotine. Our framework implements the dynamics of interaction between the effect of nicotinic acetylcholine receptors (nAChRs) on the DA neuronal population and learning in a model of A-S. We specify the differential roles of nicotine in the positive (direct) DA response on the acquisition of nicotinetaking and the slow opponent process in its persistence.At the molecular level, nicotine actions are mediated by persistent changes in brain nAChRs involving, among others, the ␤2-subunit (8-11). These nAChRs modulate the excitability of the DA neurons in the ventral tegmental area (VTA) that project to striatal structures (e.g., nucleus accumbens and striatum) ...

Modeling nicotinic neuromodulation from global functional and network levels to nAChR based mechanisms

“…Gutkin and colleagues introduced a neuro-computational framework for nicotine addiction that integrated nicotine effects on the dopaminergic (DA) neuron population at the receptor population level (signaling the reward-related information), together with a simple model of action-selection ( Figure 1) [26] . This model also incorporated a novel dopamine-dependent learning rule that gives distinct roles to the phasic and tonic dopamine neurotransmission.…”

“…The authors strove to tease out the relative roles of the positive (rewarding) and opponent processes in the acquisition and maintenance of drug taking behavior, and the development of such behavior into a rigid habit. The details of the mathematical methods, equations and simulation details can be found in [26] , below we give an overview of the model and the major results.…”

“…Gutkin et al [26] specifically hypothesized that nicotine affects the DA response through a three-time scale model of drug action on the nAChRs: (i) the phasic nicotine dependent activation of nicotinic ACh receptors, (ii) slower nicotine dependent upregulation of nAChRs, (iii) and subsequent upregulation-evoked opponent homeostatic down-regulation of nAChRs (and hence their responses to nicotine). In the model, nicotine causes activation and upregulation of nicotinic acetylcholine receptors (nAChRs) in the VTA [29,30] , that in turn changes the gain of the DA signaling.…”

“…This disrupts DA neurotransmission blocking the plasticity. Hence, nicotine self-administration progressively escapes from the control of the ventral DA signal [26] . This effectively models the ventral-dorsal progression of long-term addiction [37] .…”

“…As all computational models, the framework of Gutkin and colleagues [26] has a number of strengths and shortcomings. The major strength of the model framework is that it neatly integrates the various processes involved in nicotine self-administration identifying the various functional effects with biological mechanisms and brain structures.…”