Cortical control of VTA function and influence on nicotine reward

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Biochemical Pharmacology journal homepage: www.elsevier.com/locate/biochempharm

Review

Cortical control of VTA function and influence on nicotine reward Jie Wu a,b,*, Ming Gao a, Jian-Xin Shen b, Wei-Xing Shi c, Andrew M. Oster d, Boris S. Gutkin e a

Divisions of Neurology, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix, AZ 85013-4496, USA Departments of Physiology, Shantou University Medical College, Shantou, Guangdong, China c Department of Pharmaceutical and Basic Sciences, Loma Linda University Schools of Pharmacy and Medicine, Loma Linda, CA 92350, USA d Department of Mathematics, Eastern Washington University, Cheney, WA 99004, USA e Group for Neural Theory, LNC INSERM U960, Department d‘Etudes Cognitive, Ecole Normale Superieure, 29, rue d‘Ulm, 75005 Paris, France b

A R T I C L E I N F O

A B S T R A C T

Article history: Received 1 May 2013 Accepted 16 July 2013 Available online xxx

Tobacco use is a major public health problem. Nicotine acts on widely distributed nicotinic acetylcholine receptors (nAChRs) in the brain and excites dopamine (DA) neurons in the ventral tegmental area (VTA). The elicited increase of DA neuronal activity is thought to be an important mechanism for nicotine reward and subsequently the transition to addiction. However, the current understanding of nicotine reward is based predominantly on the data accumulated from in vitro studies, often from VTA slices. Isolated VTA slices artificially terminate communications between neurons in the VTA and other brain regions that may significantly alter nicotinic effects. Consequently, the mechanisms of nicotinic excitation of VTA DA neurons under in vivo conditions have received only limited attention. Building upon the existing knowledge acquired in vitro, it is now time to elucidate the integrated mechanisms of nicotinic reward on intact systems that are more relevant to understanding the action of nicotine or other addictive drugs. In this review, we summarize recent studies that demonstrate the impact of prefrontal cortex (PFC) on the modulation of VTA DA neuronal function and nicotine reward. Based on existing evidence, we propose a new hypothesis that PFC–VTA functional coupling serves as an integration mechanism for nicotine reward. Moreover, addiction may develop due to nicotine perturbing the PFC–VTA coupling and thereby eliminating the PFC-dependent cognitive control over behavior. ß 2013 Elsevier Inc. All rights reserved.

Keywords: Prefrontal cortex Nicotinic acetylcholine receptor Nicotine reward Ventral tegmental area Dopamine neurons

Contents 1. 2. 3. 4. 5. 6. 7.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cortical control of VTA neurons: anatomical and functional evidence . . . . . . . . . . . . . . . . . . . Neuronal slow oscillation (SO) as an important indicator of PFC–VTA functional coupling. . . Role of computational modeling in order to understand cortical control of nicotine reward. . How does PFC–VTA coupling mediate nicotine reward? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hypothesis of two systems for drug addiction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prospective and future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1. Introduction

Abbreviations: nAChR(s), nicotinic acetylcholine receptor(s); PFC, prefrontal cortex; VTA, ventral tegmental area; DA, dopamine; ACh, acetylcholine. * Corresponding author at: Division of Neurology, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, 350 West Thomas Road, Phoenix, AZ 85013-4496, USA. Tel.: +1 602 406 6376; fax: +1 602 406 7172. E-mail address: [email protected] (J. Wu).

Tobacco use is a major public health problem. Nicotine acts on widely distributed nicotinic acetylcholine receptors (nAChRs) in the brain and excites dopamine (DA) neurons in the ventral tegmental area (VTA), which elevates DA release from VTA to the nucleus accumbens (NA) and the prefrontal cortex (PFC) with both nicotine reward and reinforcement generated as a result [1–3]. The mammalian VTA (A10) is a midbrain region that acts as an integrative center mediating incentive and motivational effects for

0006-2952/$ – see front matter ß 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.bcp.2013.07.013

Please cite this article in press as: Wu J, et al. Cortical control of VTA function and influence on nicotine reward. Biochem Pharmacol (2013), http://dx.doi.org/10.1016/j.bcp.2013.07.013

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almost all addictive drugs, including nicotine. The VTA DA neurons and their associated ascending projections to their targets (NA and PFC) comprise the well-characterized mesolimbic and mesocortical pathways. The VTA receives both direct and indirect excitatory glutamatergic as well as indirect inhibitory GABAergic inputs from the mPFC [4,5]. During cigarette smoking, nicotine rapidly acts on widely distributed nicotinic acetylcholine receptors (nAChRs) in the brain, which in turn, alters a number of neuronal circuits and leads to an increase in VTA DA neural firing and elevates the extracellular dopamine levels in DA targets, such as the NA and the medial prefrontal cortex (mPFC). This process is likely a key pathway responsible for nicotine reward and reinforcement [6]. Although it is well known that the PFC efficiently controls VTA function [7–11], the impact of this cortical control in nicotine reward is not fully understood [12]. In this review, we summarize recent advances that validate the role of the PFC in modulation of VTA neuronal function and nicotine reward. 2. Cortical control of VTA neurons: anatomical and functional evidence Anatomically, mPFC pyramidal neurons send descending glutamatergic projections to the VTA both directly and indirectly. It is known that VTA DA neurons receive glutamatergic innervations from the mPFC and that the enhancement of this glutamatergic pathway underlies drug addiction [13,14]. However, emerging data indicate that most mPFC projections do not directly terminate on VTA DA neurons [15,16], but rather form direct contacts onto GABAergic cells [7], and then, indirectly innervate VTA DA neurons. Therefore, the mPFC modulates VTA DA neuronal function through complex mechanisms including direct (and/or indirect) excitatory and indirect inhibitory innervations on VTA DA neurons. Functionally, the mPFC and VTA are closely coupled. Stimulation of the mPFC increases burst firing in VTA DA neurons, while inhibition of the PFC induces the opposite effect [7–11]. Under non-stimulation conditions, the activity of VTA DA neurons covaries with mPFC neuronal activity [17]. A slow oscillation (of approximately 4 Hz) has been identified that is thought to adaptively synchronize the PFC, VTA and hippocampal activities [18]. Further experimental studies, using simultaneous dual field potential recordings from the mPFC and the VTA, showed that slow oscillations (0.5–1.5 Hz) in the mPFC lead those in the VTA by approximately 30 ms, suggesting that these slow oscillations from the mPFC propagate to the VTA [17]. This idea is further supported by experiments, in which, a dysfunction of the mPFC abolished the neuronal slow oscillations within the VTA. These lines of evidence suggest that the mPFC control of VTA neuronal firing may be through a slow oscillatory pattern [17]. In addition, accumulating studies demonstrate that the mPFC–VTA circuit plays a key role in

the attribution of incentive value to addictive drug-associated cues. The mPFC is an associative brain region that contributes to cognitive processes such as attention, spatial learning, behavioral planning and working memory [19]. Nicotine, acting on mPFC nAChRs, enhances working memory and attention [20–22]. There is strong evidence suggesting that mPFC is instrumental to many dimensions of drug abuse, including the expression of behavioral sensitization to psychostimulants [23–25] and drug-primed reinstatement of drug-seeking behavior [26]. Therefore, the mPFC provides functionally important input to the VTA neurons, which may directly control their function. Moreover, systemic exposure to nicotine alters mPFC–VTA coupling, which may also underlie an executive mechanism for both nicotine reward and reinforcement. 3. Neuronal slow oscillation (SO) as an important indicator of PFC–VTA functional coupling 1) What is the slow-oscillation? VTA DA neurons exhibit robust oscillatory activity in anesthetized animals over a range frequencies (0–10 Hz). After spectral analysis, oscillations between 0.5 and 1.5 Hz were found to be most dominant in VTA DA neurons, with this range defined as the slow oscillation (SO) [27]. Neurons demonstrating considerable spontaneous SO constituted 50–70% of all VTA DA neurons under resting conditions; we refer to these neurons as high SO neurons. The remaining neurons are collectively referred to as non-SO or low-SO neurons. 2) How is the SO produced? Experimental results from rat brain slices and rats with forebrain hemisections show a lack of SO, suggesting that forebrain input plays an important role in generating the SO (Fig. 1). Consistent with this possibility, SO has been observed in mPFC pyramidal neurons [28,29] and in NA neurons [30–33]. Both structures are located in the forebrain and both regions project to VTA DA neurons. Peters et al. show that local field potential recordings in the VTA display a SO that was highly synchronized with oscillations observed in the mPFC [34]. The findings of Peters et al. and Gao et al. suggest that the SO in VTA neurons is related to the activity in the mPFC [17,34]. 3) What is the significance of neuronal SO? The frequency of the SO is in the low delta band. At the electroencephalographic (EEG) level, delta rhythms are most apparent in slow wave sleep and in general anesthesia but are also detected in wakeful states. For example, human cortical delta oscillations are significantly increased while performing the Wisconsin Card Sorting Test [35]. In the NA of freely moving rats, delta rhythms are highest in amplitude and regularity during wakeful immobility and face washing [35]. Oscillatory firing (1 Hz) of VTA DA neurons may lead to increased DA release [36]. Oscillations may also play a role in somatic and dendritic information processing in DA neurons. Neuronal oscillations functionally regulate input selection, facilitation of synaptic plasticity, and the promotion

Fig. 1. Two functional states of VTA DA neurons in vivo. (A) Representative typical traces of high slow oscillation (h-SO) and low slow oscillation (l-SO) DA neurons recorded from the VTA, shown in (Aa) and (Ab), respectively. Identification of DA neurons was performed using a well-documented protocol. The upper trace illustrates segments of spontaneous spiking, and the bottom trace shows the corresponding smoothed 50 ms bin-width firing-rate histograms. (B) Spectral analysis showed an averaged slowoscillation power of high slow oscillation (red) and low slow oscillation (black) neurons. (C) Local infusion of TTX (10 ng/ml in PBS, 2 ml) into the mPFC abolished high slow oscillations. (D) In VTA slices, all recorded DA neurons are low slow oscillation neurons (n = 16).

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of synchrony between coupled neurons [37]. The potential significance of the SO is also highlighted by the fact that the SO can be differentially modulated by addictive drugs [38]. Thus, neuronal SO is potentially an important index for the functional coupling of PFC and VTA neurons. The evaluation of the effects of addictive drugs on the SO may provide significant information in drug-induced alterations of neuronal network function.

4. Role of computational modeling in order to understand cortical control of nicotine reward. Nicotine reward is inexorably tied to the endogenous actions of acetylcholine. Several general approaches to understanding its role have been developed, with some efforts focusing specifically on the functional mechanisms of nicotine reward. The benefit of computational modeling, in this context, is to build upon our knowledge of the specific receptor mechanisms and to infer functional consequences of such. Here we review a selection of such modeling studies: (1) A top-down computational view of cholinergic neuromodulation: uncertainty and ACh. Top-down functional approaches to modeling the consequence of neuromodulators come from machine learning literature. Primarily, theories have built on reinforcement learning algorithms that are capable of modeling behavioral conditioning [39]. A functional top-down methodology has been applied to the role of ACh, where Yu and Dayan utilized a Bayesian statistical framework that incorporated ACh as a measure of the uncertainty of a stimulus in a given task [40]. That is, ACh signifies contextual uncertainty between top-down, bottom-up, and contextbound processing. In layman’s terms, ACh tracks and signals the ‘expected’ uncertainty associated with an environment. They found that high levels of ACh resulted in an improved estimation of the stimulus via top-down processing. In further work, they went on to test their Bayesian statistical model framework with ACh level representing the degree of expected uncertainty as it relates to the context-dependent Posner task [41]. Nicotine acts through the same receptor pathways as the ACh, hence, within this framework, nicotine would functionally alter the estimation of ‘expected’’ uncertainty – biasing it to higher levels and hence altering behavior [39]. (2) A dynamical view of cholinergic neuromodulation of neuronal network function: memory read-in and recall. Computational approaches to study how ACh influences neural population dynamics have recently received significant attention in the literature. ACh has been equated with attention and, at times, network models of attentional modulation make a heuristic argument that parameter changes reflecting attentional modulation (e.g., [42]) are due to increases in ACh inputs to the cortex. Notably, attentional modulation has been modeled as an increase in the excitability of local neuronal populations, with the justification that such an increase is due to muscarinic AChR-dependent down-regulation of slow potassium channels (e.g., the M current) [43]. Alternatively, attentional modulation has also been modeled as an increase in a top-down excitatory input [44]. In addition to the above mechanisms, cholinergic influence has been suggested to give rise to selective glutamatergic neurotransmission. Synaptic modulatory effects of ACh are thought to be mediated through nicotinic receptors. In this framework, ACh would bias the dynamics of the network between memory encoding and memory consolidation. Obviously, nicotine, acting through nAChRs, would disturb this process, potentially preferentially consolidating smoking related memories and behavioral patterns over others.

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(3) Large-scale neurodynamical framework for nicotine in DA signaling and action-selection: Gutkin and colleagues introduced a neuro-computational framework for nicotine addiction that integrated nicotine effects on the DA neuron population at the receptor population level (signaling reward-related information), together with a simple model of action-selection (Fig. 2) [45]. This model 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, as well as, the development of such behavior into a rigid habit. The details of the mathematical methods, equations, and simulation details can be found in [45]. The model showed that the transition to addiction (here specifically modeled as self-administration of nicotine) is critically dependent on complementary mechanisms: the ability of nicotine to evoke a phasic dopamine response the leads to over-learning and behaviorally apparent over-valuation of drug-seeking choices, and tonic dopamine control that in turn gates cortical plasticity, and the subsequent druginduced opponent process that withdraws tonic dopamine and stops plasticity – thereby instilling the habitual drug-seeking behavior. In this sense, this model marries together the positive and the negative reinforcement theories of addiction, and it predicts a generalized learning deficit in addicted individuals. Recently this global approach was focused to examine the role of local VTA circuitry on the nicotine-evoked changes in DA cell activity, proposing that the endogenous ACh levels in consort with the descending glutamatergic inputs play a central role in determining the various mechanisms for nicotine action [39].

5. How does PFC–VTA coupling mediate nicotine reward? Nicotine stimulates brain reward circuits and promotes the release of the neurotransmitter dopamine in the reward pathways of the brain, which leads to a reinforcement of associated behaviors, termed nicotine reward. Nicotine reward is critical for the initiation of the resulting nicotine dependence and addiction when nicotine is repetitively used. Our current understanding of the mechanisms underlying nicotine-induced excitation of VTA DA neurons is mainly based on studies using brain slices [46–48]. For instance, in VTA slices, nicotine activates a7-nAChRs on glutamatergic terminals to increase glutamate release onto DA neurons with excitatory consequences [47,49,50]. At the same time, nicotine activates and desensitizes a4b2 nAChRs. Although a4b2 nAChRs are expressed on both VTA DA and GABA neurons, the effects of smoking-relevant concentrations of nicotine (e.g., 500 nM) on the VTA and DA and GABA neural responses to nicotine differ since the endogenous cholinergic innervations on these two types of VTA neurons differ. Based on a previous report, most cholinergic terminals innervate GABA neurons, while only few cholinergic terminals innervate the DA neurons [51]. That is, while nearly all DA neurons in the VTA express nAChRs, only 5% of the DA neurons actually receive cholinergic projections [52]. One might posit that the receptors on the DA neurons remain in a non-activated state, while the receptors on the GABA neurons are tonically activated by endogenous ACh inputs (see discussion in [39]). Thus, nicotine acts on VTA neuronal somatic/dendritic b2-containing nAChRs (e.g., a4b2-nAChRs) to directly depolarize and activate those neurons [50,53]. Thereafter, with relevantly long-lasting exposure, nicotine desensitizes a4b2-nAChRs on VTA both DA and GABA neurons, yet the nicotine-induced nAChR desensitization mainly eliminates the tonic cholinergic excitation of GABA neurons

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Fig. 2. A computational model to simulate nicotinic effects on VTA DA neurons. (Aa) A scheme of the VTA circuits and nAChR distribution, in which r is a parameter introduced in the model to change continuously the dominant site of a4b2 nAChR action. (Ab) Different states of the nAChRs, in which, a indicates an activated state and s indicates a desensitized state. a (purple lines) and s (orange lines) variables are shown during and after the exposure to a constant nicotine concentration of 10 mM (c) and 100 mM (d) for 200 ms starting at t = 50 ms. The normalized receptor activation (a–s) is shown in blue. Peak current and net charges mediated during the exposure are illustrated in the panels. The insets show the dynamics of the same variables on a longer time scale. (Ba) Disinhibition of the DA circuit can be achieved in the computational model. Here, ACh is taken to be at a high level and the expression level of the a4b2 nAChR subtype is more heavily expressed by the VTA GABA cells. However, in Bb, direct excitation of the DA circuit occurs when ACh is low and the DA cells have a high degree of a4b2 nAChR subtype. These results are interesting when compared to experiment findings (Bc,d), where similar dynamics were found for the type I and type II DA cell dynamics (associated with high SO and low SO, respectively).

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because of the high ACh innervation of these GABA neurons [51]. Consequently, nicotine reduces the GABA neuron firing, and leads to an increase in VTA DA neuron firing through a disinhibition mechanism [48,50,53]. These collective results lead to the hypothesis that nicotine reward is promoted by nicotinic stimulation of the nAChRs through multiple pathways (both direct stimulation on the DA neurons and disinhibition via the VTA GABA cells). Activation of these nAChR-associated pathways increases DA release from the VTA to its DA targets: NA and PFC. On the other hand, systemic exposure to nicotine affects the activity of DA neurons through mechanisms that are more complex, involving the VTA and other brain regions [54,55], like the mPFC [56,57]. Emerging evidence demonstrates that the mPFC plays an important role in controlling VTA DA neuronal function [58]. The mPFC contributes to cognitive processes such as attention, spatial learning, behavioral planning and working memory [19]. Nicotine has been reported to act through mPFC nAChRs to enhance working memory and attention [20–22,59,60]. In addition, the mPFC has been suggested to be a key brain region underlying the neural mechanisms of drug addiction and craving [61]. Chen et al. examined the effects of PFC dysfunction on the VTA neuronal function and on the nicotinic effects. They found that a systemic injection of nicotine (single i.p. injection) induced enhanced AMPA/NMDA ratios in VTA DA neurons (after 24 h) with no significant difference between intact and dysfunctional PFC [62]. They used wavelet analysis to show that in PFC intact rats, systemic nicotine increased energy contents of the 1–1.5 Hz frequency band in VTA DA neurons, while in PFC dysfunctional rats, nicotine failed to induce a similar effect [63]. In addition, using the Lempel-Ziv estimator, this group further found that systemic nicotine triggered a significant increase in the complexity of VTA DA neuronal firing patterns when communication between PFC and VTA was present, while transection of PFC obliterated the effect of nicotine [64]. Together, these studies suggest the importance of PFC in the control VTA DA neuronal function and nicotinic effects on VTA DA neural firing. Therefore, an important question is whether mPFC participates in (or controls) nicotine reward. We recently addressed this question using electrophysiological recording from single VTA DA neurons (extracellular single unit recording) in anesthetized rats [65]. We found that systemic injection of nicotine (0.5 mg/kg, i.v.) induced two types of changes to the firing rate of VTA DA neurons. One group of neurons exhibited a bi-phasic change of firing rate, an initial decrease with a subsequent increase in firing rate for a prolonged duration, while another group of neurons exhibited a mono-phasic increase of firing rate. We classified the VTA DA neurons as either type-I or type-II according to their response to nicotine with either a biphasic or monophasic response, respectively (Fig. 2Bc and Bd). Further analysis showed that the VTA DA

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neurons classified by these two response types exhibited distinct firing characteristics. Prior to nicotine exposure, the type-I neurons displayed irregular firing patterns with strong variations of the average firing frequency and more pronounced slow oscillations, whereas the type-II neurons maintained a regular firing pattern with a low slow oscillation power. These results suggest the possible existence of two subtypes of VTA DA neurons: differentiable by their firing characteristics and response to systemic nicotine. However, there may be only one group of VTA DA neurons with different functional states that respond differently to systemic nicotine. Further study is needed in order to distinguish between these possibilities. Beyond identifying two types of DA neurons, we examined the effects of systemic nicotine with the control of the VTA by PFC disrupted. We altered mPFC function with local infusion of TTX to both sides of mPFC or by physical transection of mPFC, then tested the effects of systemic injection of nicotine. The results demonstrated that with a disruption of the mPFC circuit, nicotine failed to elicit an increase of the firing rate for the type-I neurons, but the type-II DA neural response remained unperturbed. In addition, after the inactivation of the mPFC, the power of the slow oscillation for type-I neurons was dramatically reduced (to levels similar to that of the type-II neurons). These results suggest that the VTA type-I, but not type-II, DA neurons are functionally coupled with mPFC. Fig. 3 shows typical traces (Fig. 3A) that present the effects of systemic nicotine on VTA firing rate (top trace) and on both PFC and VTA SO (bottom traces). The results demonstrate that systemic nicotine administration increases VTA DA neuron firing (e.g., at 12 min) but reduces SO power during simultaneous dual recording of field potentials from the PFC and the VTA. These results suggest that systemic exposure to nicotine perturbs this coupling (indicated by the reduction of the slow oscillations in VTA) and increases nicotinic excitation of type-I neurons. We, also, considered whether nicotine activates PFC pyramidal neurons, which would alter the descending cortical input from the PFC to the VTA. To address this question, we performed three experiments. (1) We directly recorded pyramidal neuronal firing in PFC layer 5 in anesthetized rats and found that systemic nicotine initially increased PFC neuron firing, which then declined to a firing rate below the baseline level (Fig. 3B). (2) We locally infused nAChR antagonist mecamylamine into both sides of mPFC and found that pharmacological inhibition of PFC nAChRs eliminated systemic nicotine-induced excitation in VTA type-I neurons. (3) We locally infused 0.5 mM nicotine into the mPFC and examined the response of VTA type-I DA neurons. We observed that the local nicotine infusion was insufficient to increase the DA neuronal firing rate but did enhance the power of the SO. Together, these results suggest that systemic nicotine acts on nAChRs in PFC neurons, thereby altering PFC neuronal function, and, in turn, alters the descending

Fig. 3. Effects of systemic nicotine on mPFC pyramidal neuron firing. (A) A typical case of nicotinic effects on mPFC pyramidal cells. Inset showed Normalized changes of firing rate (FR), bursting fraction (BF) and slow-oscillation power (Pso) after nicotine. *p < 0.05. **p < 0.01. (B) Simultaneous recordings of field potential (FP) from left side of PFC (PFC LFP) and VTA (VTA LFP). (Ba) A typical response of hSO VTA DA neuron to nicotine. The slow-oscillation power (Bb) was simultaneously measured between 0 and 2 min (baseline) and 12 and 14 min after nicotine injection.

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information from the PFC to the VTA. This signal from the PFC to the VTA modulates VTA DA neuronal (type-I) function. However, smoking-relevant levels of nicotine in PFC alone (locally infused into PFC) are insufficient to alter VTA DA neuron firing rate in in vivo conditions. Finally, we evaluated the role of GABA neurons in nicotinic modulation of PFC–VTA coupling and nicotine reward. After systemic injection of GABAA receptor antagonist bicuculline (BMI), systemic injection nicotine failed to alter VTA type-I DA neuron firing (both initial negative and followed positive phases). A systemic injection of bicuculline did not alter VTA DA neuron firing rate and bursting fraction, yet significantly reduced slow oscillation power before nicotine injection. These data suggest that bicuculline eliminates the PFC–VTA functional coupling. Our previous work [17] showed that the PFC negatively couples to the VTA, suggesting that the PFC may couple to VTA DA neurons through the GABAergic neurons. Based on these lines of evidence, it is likely that GABA neurons between PFC and VTA DA neurons are important for nicotinic excitation in DA neurons. Taken together, systemic nicotine administration significantly reduces the power of the slow oscillation in type-I VTA DA neurons while increasing their firing rate. The findings in our study are consistent with nicotine acting to functionally uncouple the mPFC–VTA circuit. In addition, exposure to BMI lowers type-I VTA DA neuronal Pso and blocks both the initial inhibitory and the late excitatory effects of nicotine on type-I VTA DA neuronal firing. This is consistent with nicotine’s mediatory effects and the mPFC–VTA DA neuronal negative coupling via an inhibitory, GABAergic connection. It has been reported that GABAergic neurons in the VTA fire at rate of higher than 10 Hz and exhibit underlying oscillations at a frequency range between 1 and 2 Hz [66] and that VTA GABAergic neurons receive glutamatergic innervations from the PFC [15,67]. Thus, VTA GABAergic neurons may play a role in the transfer of the SO from the mPFC to the type-I VTA DA neurons. The mPFC nAChRs influences the excitatory response of type-I VTA DA neurons as is evidenced by the response sensitivity to mecamylamine applied locally in the mPFC [65]. Moreover, the ability of BMI administration to prevent the late inhibitory phase of nicotine’s actions on mPFC neuronal firing suggests that GABA and GABAA receptors, at least partly, mediate that effect. However, the insensitivity to BMI of the initial excitatory effect of nicotine exposure on mPFC neuronal firing is consistent with an effect by nAChR-mediated activation of release from glutamatergic terminals in the mPFC [68–70]. These results also suggest that nicotineinduced initial inhibition on type-I DA neuron firing may be mediated by the GABA neurons that may be subject to a degree of PFC control. In the mPFC, nicotine exposure culminates in a delayed but longer-lasting reduction in mPFC neuron firing, likely through activation of GABAergic mechanisms [71]. Such a process would lead to lasting activation of VTA DA neurons and has been implicated in morphine-, muscimol- and benzodiazepine-induced VTA DA neuron excitation [72,73]. However, our studies suggest that consideration should be given to the nicotine exposureinduced impairment of inverse coupling between the mPFC and the VTA via the GABA neurons as defined by diminished power of the SO as a novel alternate mechanism for nicotine-induced DA neuron excitation in vivo. 6. Hypothesis of two systems for drug addiction The major feature of drug addiction is a reduced ability to regulate control over the desire to procure drugs regardless of the risks involved. Traditional models implicated the neural ‘reward’ (i.e., the VTA) system as the key brain region in providing a neurobiological model of addiction. Computational models of the local VTA circuitry, suggest that a biphasic pattern of response to

nicotine could be mediated by nicotine action on the nAChRs at local GABA neurons and the subsequent disinhibition of the DA neurons [39,74]. However, this single rewarding system theory has been expanded as two separate, but interconnected systems, the limbic system in the incentive sensitization of drugs (VTA) and the PFC in regulating inhibitory cognitive control over drug use. Our experimental lines of evidence support this novel two-system model; this is particularly significant when put into the larger context of drug addiction theory. The development of drug addiction has been likened to a progressive habitualization of behavior and a loss of cognitive control [75,76], possibly through a disconnection of higher cortical influence over reward circuitry [77]. Consistent with the notion that the mPFC plays a role in drug addiction through the modulation of descending inhibitory system [76], and in line with the concept that the mPFC mediates cognitive control (and executive function), the inverse coupling that we show here suggests that the mPFC exerts this control through multi-synaptic gating of inhibition and excitation of DA transmission and that nicotine selectively disrupts this inhibitory control, leading to a dysfunctional modulation of type-I VTA DA neurons. Furthermore, recent computational theories propose an opposition of cognitive control versus reward-driven habitual action [78], suggesting that a model-based cortical system can ensure flexible achievement of long-term goals, whereas a model-free, rewardbased, DA system promotes habitual seeking of immediate rewards. The theory proposes that an optimal integration between the two systems for control of behavior is key and may be mediated by cholinergic signals encoding the uncertainty of the behavioral context [41]. Based on our results, we speculate that nicotine would pathologically remove the influence of the cortical modelbased cognitive control system over the DA circuitry and promote habitual immediate-reward-seeking behavior. Thus, our results, which point to a nicotine-induced uncoupling of the mPFCmediated inhibitory control over reward circuitry, provide a potential neural basis for the bias toward impulsivity and loss of cognitive control seen in nicotine-dependent individuals and possibly other types of drug dependencies. 7. Prospective and future directions Mechanisms of nicotine reward are very complex including nicotine-induced alterations from the levels of genes, molecules, synapses, receptors, circuits, networks and behaviors. Although extensive efforts have been made, our understanding is still immature, especially at the circuit and network levels. In this article, we summarized recent advances aimed at understanding the roles of the PFC in nicotine-induced excitation of VTA DA neurons. Based on the existing data and computational models, we proposed a new hypothesis of how nicotine modulates PFC–VTA coupling and how PFC neurons control nicotinic excitation of VTA DA neurons. However, there are still many important questions that need to be addressed in future studies. (1) In VTA DA neurons, one group is coupled to PFC (type-I) and another group is not (typeII). However, the properties of these two groups of neurons are not well understood and it remains to be seen whether they belong to two subtypes or are, in fact, of the same type, but in two different states. For example, do they have different locations (ventral or dorsal region in the VTA), different projections (to NA or PFC) or different innervations from PFC? In addition, it is of interest to determine whether these two groups of VTA DA neurons have different functional roles (e.g., reward vs. aversive). (2) VTA DA neuron SO is a useful index to show the functional coupling between PFC and VTA in anesthetized animals, but it is much weaker in waking animals, thus the significance of this SO in VTA DA neurons needs to be carefully validated. (3) Although we have demonstrated the importance of the PFC in controlling nicotinic

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effects on some VTA DA neurons, the details of neuronal circuits (e.g., direct and indirect innervations) underlying this modulation are unclear. (4) GABA neurons are likely very important for PFC– VTA coupling, but it is uncertain where these GABA neurons are located, within the VTA or outside of the VTA (or both). (5) Computational simulation provides a powerful approach for studying complex systems such as this. Mathematical models may provide important predictions of how PFC exerts control over VTA function and nicotine reward. The present models mainly focus on the VTA local circuit, and there is need for the development of a more complex PFC–VTA model. Nevertheless, recent advancements have furthered our understanding of the integrated signals between the PFC and the VTA and provided insight into the impact of the PFC in nicotine reward and dependence. With new conceptual insights, methodologies, and advances in computational modeling, there is great promise that the role of cortical modulations underlying nicotine reward and addiction will be elucidated. Acknowledgements Work toward this project was supported by the Barrow Neurological Foundation and by grants from the Arizona Biomedical Research Commission (0028 and 0057), the Institute for Mental Health Research, Philip Morris International through their External Research Program, and the National Institutes of Health (R01 NS040417 and R01 DA015389). BSG and AMO were supported in part by the ANR ‘‘Dopanic’’ grant, and AMO was supported in part by a NERF grant. References [1] Wise RA. Dopamine, learning and motivation. Nature Reviews Neuroscience 2004;5:483–94. [2] Mansvelder HD, McGehee DS. Cellular and synaptic mechanisms of nicotine addiction. Journal of Neurobiology 2002;53:606–17. [3] Fields HL, Hjelmstad GO, Margolis EB, Nicola SM. Ventral tegmental area neurons in learned appetitive behavior and positive reinforcement. Annual Review of Neuroscience 2007;30:289–316. [4] Kalivas PW. Neurotransmitter regulation of dopamine neurons in the ventral tegmental area. Brain Research 1993;18:75–113. [5] Charara A, Smith Y, Parent A. Glutamatergic inputs from the pedunculopontine nucleus to midbrain dopaminergic neurons in primates: phaseolus vulgarisleucoagglutinin anterograde labeling combined with postembedding glutamate and GABA immunohistochemistry. Journal of Comparative Neurology 1996;364:254–66. [6] Mathon DS, Kamal A, Smidt MP, Ramakers GM. Modulation of cellular activity and synaptic transmission in the ventral tegmental area. European Journal of Pharmacology 2003;480:97–115. [7] Tong ZY, Overton PG, Clark D. Stimulation of the prefrontal cortex in the rat induces patterns of activity in midbrain dopaminergic neurons which resemble natural burst events. Synapse 1996;22:195–208. [8] Gariano RF, Groves PM. Burst firing induced in midbrain dopamine neurons by stimulation of the medial prefrontal and anterior cingulate cortices. Brain Research 1988;462:194–8. [9] Svensson TH, Tung CS. Local cooling of pre-frontal cortex induces pacemakerlike firing of dopamine neurons in rat ventral tegmental area in vivo. Acta Physiologica Scandinavica 1989;136:135–6. [10] Murase S, Grenhoff J, Chouvet G, Gonon FG, Svensson TH. Prefrontal cortex regulates burst firing and transmitter release in rat mesolimbic dopamine neurons studied in vivo. Neuroscience Letters 1993;157:53–6. [11] Overton PG, Tong ZY, Brain PF, Clark D. Preferential occupation of mineralocorticoid receptors by corticosterone enhances glutamate-induced burst firing in rat midbrain dopaminergic neurons. Brain Research 1996;737:146–54. [12] Tolu S, Eddine R, Marti F, David V, Graupner M, Pons S, et al. Co-activation of VTA DA and GABA neurons mediates nicotine reinforcement. Molecular Psychiatry 2013;18:382–93. [13] Takahata R, Moghaddam B. Activation of glutamate neurotransmission in the prefrontal cortex sustains the motoric and dopaminergic effects of phencyclidine. Neuropsychopharmacology 2003;28:1117–24. [14] Kalivas PW, Lalumiere RT, Knackstedt L, Shen H. Glutamate transmission in addiction. Neuropharmacology 2009;56(Suppl 1):169–73. [15] Carr DB, Sesack SR. Projections from the rat prefrontal cortex to the ventral tegmental area: target specificity in the synaptic associations with mesoaccumbens and mesocortical neurons. Journal of Neuroscience 2000;20: 3864–73.

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