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Preventive Medicine 55 (2012) S17–S23

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Preventive Medicine

journal homepage: www.elsevier.com/locate/ypmed

Review

The neurobiology of reward and cognitive control systems and their role in incentivizing health behavior

Hugh Garavan ⁎, Karen Weierstall Department of Psychiatry, University of Vermont, USA Department of Psychology, University of Vermont, USA

⁎ Corresponding author. Fax: +1 802 656 9628. E-mail address: Hugh.Garavan@uvm.edu (H. Garava

0091-7435/$ – see front matter © 2012 Elsevier Inc. All doi:10.1016/j.ypmed.2012.05.018

a b s t r a c t

a r t i c l e i n f o

Available online 20 June 2012

Keywords: Drug addiction Neuroimaging Reward Cognitive control Treatment Recovery

Objective: This article reviews the neurobiology of cognitive control and reward processes and addresses their role in the treatment of addiction. We propose that the neurobiological mechanisms involved in treat- ment may differ from those involved in the etiology of addiction and consequently are worthy of increased investigation.

Method: We review the literature on reward and control processes and evidence of differences in these systems in drug addicted individuals. We also review the relatively small literature on neurobiological predictors of abstinence.

Results: We conclude that prefrontal control systems may be central to a successful recovery from addiction.

The frontal lobes have been shown to regulate striatal reward-related processes, to be among the regions that predict treatment outcome, and to show elevated functioning in those who have succeeded in maintaining abstinence.

Conclusion: The evidence of the involvement of the frontal lobes in recovery is consistent with the hypothesis that recovery is a distinct process that is more than the undoing of those processes involved in becoming addicted and a return to the pre-addiction state of the individual. The extent to which these frontal systems are engaged by treatment interventions may contribute to their efficacy.

© 2012 Elsevier Inc. All rights reserved.

Contents

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S17 Neurobiology of reinforcement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S18 Neurobiology of control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S19 Interaction of reward and control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S20 Role of the prefrontal cortex in recovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S20 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S21 Conflict of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S22 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S22

Introduction

This article adopts a neurobiological approach to the study of in- centivizing health behaviors with a specific focus on drug addiction. Despite the broad application and critical value that incentivizing health has in clinical practice (e.g., the frequency of voucher-based contingency management programs has risen dramatically in recent

n).

rights reserved.

years (Higgins et al., 2011)), there is relatively little known about the brain-based mechanisms underlying these clinically efficacious treatments. In part, this may reflect the fact that the vast majority of neuroscience research focuses predominantly on the etiology of ad- diction rather than recovery. This bias toward the neuroscience of dis- ease cause rather than disease recovery may reflect an implicit assumption that, for example, recovery from addiction simply in- volves undoing the brain changes that accompanied the development of the addiction. However, this article will attempt to show that this is a very uncertain assumption and, instead, we will propose that

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additional psychological processes, especially those involving the pre- frontal cortex, play a central role in successful recovery.

This article will first review the neurobiology of cognitive control and reward reinforcement. In discussing reinforcement processes, we will address both hedonic and reinforcement processes and also drug incentive processes related to the salience that is attributed to drugs and associated drug cues. We focus on the control and reward domains as successful abstinence can be viewed within a framework whereby prefrontal cognitive systems seek to control pathological re- ward processes. For example, a body of evidence identifies the sub- cortical ventral striatum system with drug wanting (Robinson and Berridge, 2003) and the prefrontal cortex with behavioral and emo- tional regulations (Goldstein and Volkow, 2002); successful absti- nence may rest on the outcome of the antagonism between these drug-wanting and drug-denying systems. A key question arising from this framework is how to incorporate the influence of incentiv- izing healthy behaviors. Do interventions such as voucher-based con- tingency management, clinical approaches such as cognitive behavior therapy or support-group structures such as Alcoholics Anonymous exert their effects by increasing control over drug urges or by diminishing those urges directly? We propose that the former, char- acterized by the ability to monitor and exert control over drug de- sires, is central.

Arising from this review, the second half of this article will address the evidence implicating the prefrontal cortex in abstinence and re- covery. With a focus on the neurobiology of recovery, we will address what characterizes successful abstinence, what neurobiological sys- tems predict abstinence, change with abstinence and, finally, what are the possible neurobiological effects of treatment interventions. Given the emphasis on the prefrontal cortex and the scarcity of ani- mal models of recovery, this review will draw heavily on neuroimag- ing studies of drug addicts. Existing animal models of addiction may not be ideal for studying treatment as abstinence can be enforced rather easily with such models and therefore may not describe the human condition and the psychological resources upon which the re- covering human addict must draw.

Neurobiology of reinforcement

Numerous brain regions including the ventral striatum, insula, orbitofrontal cortex, amygdala, hypothalamus, periaqueductal gray and other brainstem nuclei (Bechara, 2005; Olds, 1977; Robinson and Berridge, 1993) are all considered to be important components of the brain's reward system. Reward-related functions performed by these regions include maintaining representations of reward states and incentivized, salient stimuli (Everitt et al., 1999; Robinson and Berridge, 1993), representing interoceptive, subjective feelings such as craving (Garavan, 2010), and motivating consummatory behavior. The ventral striatum, a key hub in the mesocorticolimbic dopamine system, has been the focus of considerable research in addiction and a large body of research directly identifies it with the reinforcing as- pects of drugs of abuse (Koob, 2000; Koob et al., 1998; Nestler, 2005).

The bulk of the neurobiological research exploring reward pro- cesses has been conducted on animal models with the emphasis being placed on a drug's reinforcement value rather than its reward value, based in part, on the difficulty of determining subjective he- donic effects in animal models. However, recent non-invasive brain imaging techniques allow these processes to be assayed with probes such as the monetary incentive delay task (MID) (Knutson et al., 2001). Here, a visual cue such as a colored square informs the partic- ipant that a target is imminent and, should the participant make a sufficiently fast button press response to the target, that a financial reward will be forthcoming. A relatively simple task such as this al- lows one to image brain activity during the cue period (i.e., cue- related reward anticipation) and after the target response, when feedback informing the participant of a win or miss, loss or loss

avoidance is presented. Numerous studies identify the ventral striatum with the anticipation of the win and distributed subcortical regions in- cluding the ventral striatum, insula, and ventromedial and orbitofrontal cortices with the actual reward delivery (Bjork et al., 2009; Jia et al., 2011; Knutson and Bossaerts, 2007; Knutson et al., 2001).

Research using these tasks with drug users has typically shown anticipation-related hypoactivity in the ventral striatum. This has been reported for users of nicotine (Buhler et al., 2010), alcohol (Beck et al., 2009; Wrase et al., 2007), and cannabis (Van Hell et al., 2010). One interpretation of this striatal hypoactivity is that it reflects a reward deficiency in the user (Blum et al., 2000) and may underpin the user's motivation for reward and especially the potent reward as- sociated with drug use. The reward deficiency theory, and related theories such as allostasis (Koob and Le Moal, 2005), propose that the addicted individual is in a state of relative anhedonia and conse- quently finds drugs of abuse especially potent and reinforcing. This tonic state of striatal hypoactivity has been linked to reductions in D2 dopamine receptor numbers which has been considered a risk fac- tor for drug abuse (Peters et al., 2011; Schneider et al., 2011; Volkow et al., 1999).

Alternative interpretations stress the role of the ventral striatum in attributing incentive salience to drug-predictive cues which would not necessarily lead to differences in activity related to non- drug reward anticipation. Indeed, some studies that have modified the MID to separate reward anticipation from outcomes have ob- served no differences in reward-anticipation between drug users and controls (Bjork et al., 2008; Jia et al., 2011) while others have reported increased ventral striatal activity during reward anticipation in drug users (Nestor et al., 2010). Evidence of elevated ventral striatal activity on receipt of the reward (Bjork et al., 2008; Jia et al., 2011) and diminished activity on failures to receive rewards suggests further that the reward systems of drug users might be especially sensitive to positive rewards and relatively blunted to poor outcomes or punishments (Garavan and Stout, 2005; Nestor et al., 2010). In ad- dition to variations in task design, a further contributing factor to the discrepancies in the literature may be the abstinence status of the users. Most studies reporting ventral striatal hypoactivity have tested users in abstinence, raising the possibility that the effects may reflect a state-like abstinence-related anhedonia (although there is evidence that withdrawal does not affect reward anticipation in dependent cig- arette smokers, Buhler et al., 2010).

A recent review addressing striatal responses to drug administra- tion and to drug-related cues concluded that drug dependence is as- sociated with reduced dopaminergic responses to acute drug administration (in both abstinent and current users) but increased re- sponses to the cues associated with drug use (Volkow et al., 2011). This conclusion would therefore suggest that the striatum mediates a heightened incentive salience process that may be particular to the salient drug cues. For example, Wrase and colleagues report that detoxified alcoholics (abstinent for a mean of 11 days) relative to healthy controls showed reduced ventral striatal activation in antici- pation of monetary rewards but increased ventral striatal activity when shown alcohol-related stimuli (Wrase et al., 2007). At odds with these findings, Buhler et al. (2010) contrasted reward anticipa- tion for cigarettes and money in dependent and occasional smokers (both groups were smoking as usual and not abstinent or in treat- ment). They observed a greater striatal activity in response to stimuli which predicted money relative to stimuli which predicted cigarettes in occasional smokers, but this difference was not observed in the de- pendent smokers (Buhler et al., 2010). Phrasing this in another way, they found that the biggest cue-related difference in striatal activa- tion between the groups was in their response to the non-drug cues (i.e., money cues) rather than the drug cues.

In overview, there remains a lack of consensus in both the empir- ical literature on the status of the reward systems of drug users and the theoretical interpretation of the observed effects. Numerous

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factors may contribute to this lack of consensus including the severity of the addiction, the drug in question, and the stage at which testing occurred (i.e., during current use or abstinence). Clarity in this line of research is vital to the understanding of the reward system in drug users. A sound understanding is imperative as we consider the effects of treatment interventions, especially voucher-based treatments that provide alternative reinforcements to motivate drug-abstinence. There is, for example, evidence that these reward systems show atyp- ical responses to financial rewards in cocaine users (Goldstein et al., 2007). In contrast, the success of voucher-based treatments in a size- able proportion of drug addicts, as well as the lawful relationships that exist between the magnitude of the voucher and its efficacy in maintaining abstinence, suggests that the reward-system of drug users remain sufficiently intact to be capable of motivating absti- nence. Recently, Jia et al. (2011) reported that neural responses to both the anticipation and receipt of financial rewards, obtained prior to abstinence in current cocaine users, predicted subsequent outcome measures demonstrated by drug-negative urines and treat- ment retention duration (Jia et al., 2011). Specifically, greater activa- tion in the subcortical regions including the thalamus, amygdala, parahippocampus and striatum in response to the financial rewards was associated with worse outcomes. In some cases, the ventral striatum's response to reward anticipation was elevated in users compared to controls, demonstrating that the less hyperactive the user's response the better the treatment outcome. Thus, the integrity of the subcortical processing of financial reward may be a useful pre- dictor in determining which users are most likely to respond positive- ly to treatment.

Another important line of research of direct relevance to incentiv- izing behavior is understanding the similarities and differences in the neural correlates of drug and non-drug rewards. If contingency man- agement using financial rewards is to effect behavior change then it will be valuable to understand the (neurobiological) metric on which drug users compare financial and drug rewards and choose be- tween them. A body of research implicates the ventromedial prefron- tal cortex in this process (Chib et al., 2009; Kim et al., 2011; Lim et al., 2011). For example, Kim et al. (2011) showed overlapping regions in the ventromedial prefrontal cortex and anterior insula that responded to the anticipation of both juice and money reward out- comes. Similarly, activity in the ventromedial prefrontal cortex reflected subjective valuations of food, nonfood consumables, and monetary gambles (Chib et al., 2009) suggesting that this region may be a crucial node in evaluating competing rewards.

It is also worthwhile to note that another theoretical approach emphasizes a role for the ventral striatum in impulsive choice behav- ior. For instance, a delayed discounting task wherein a ventral striatal activity is associated with selecting immediate rewards over larger delayed rewards is evidenced (McClure et al., 2004). As summarized below, overcoming this desire for immediate gratification involves the prefrontal cortex and may provide valuable insights into the pro- cesses underlying successful abstinence.

Neurobiology of control

Cognitive control processes (Atkinson and Shiffrin, 1968; Schneider and Shiffrin, 1977; Shiffrin and Schneider, 1977), also commonly re- ferred to as executive functions, are attentionally-demanding, and consciously-available, volitional processes that initiate a certain action or interrupt ongoing actions. Baddeley's influential model of working memory (Baddeley, 2001) likens a “co-ordinator,” labeled the central executive, to Norman and Shallice's Supervisory Attentional System, thus emphasizing the role that the central executive plays in allocating attentional resources (Baddeley, 2002: Norman and Shallice, 1985) In attempting to identify their anatomical loci, cognitive neuroimaging experiments initially operationalized executive functions in various ways, including dual-task coordination (D' Esposito et al., 1995), task

switching (Dove et al., 2000; Sohn et al., 2000), memory updating (Salmon et al., 1996), and response sequencing, monitoring, and manip- ulation (Owen et al., 1996). From these studies a consensus emerged, implicating the dorsolateral prefrontal cortex as the brain region critical for executive functioning. This is consistent with the human lesion literature implicating the frontal lobes in organizing, regulating and producing coherent behavior (Luria and Pribram, 1973; Stuss and Benson, 1987). Frontal lobe damaged patients appear to lose important aspects of autonomous executive control as evidenced by the loss of behavioral control to environmental contingencies (e.g., capture errors and utilization behaviors) (Lhermitte, 1986). Although the focus of this paper will be on prefrontal systems mediating control, it should be appreciated that neuroimaging studies have also localized executive functions to the dorsolateral prefrontal cortex and have observed ex- tensive parietal, premotor, cingulate, occipital and cerebellar activation in control. Consistent with these findings, functional imaging studies of classic executive tasks such as the Tower of London, the Wisconsin Card Sorting Task, and Stroop Test reveal extensive activation in the frontal, temporal, parietal, and occipital lobes and the cerebellum (Berman et al., 1995; Monchi et al., 2001; Newman et al., 2003; Prabhakaran et al., 1997).

Even if one limits one's focus to the frontal lobes, determining the precise localization of cognitive processes within the frontal lobes has been proven problematic. One of the defining characteristics of the frontal lobes resides in its ability to flexibly adapt to task demands. A number of large-scale organizational structures have been postulat- ed. For example, while the dorsolateral prefrontal cortex is associated with the working memory, the most anterior portions of the frontal cortex have been implicated in coordinating the activities of the working memory. Koechlin and Hyafil (2007) have suggested that this anterior region enables the switching back and forth between multiple mental tasks or behavioral plans (Koechlin and Hyafil, 2007). In keeping with this notion, Badre et al. (2010) have argued that progressively more anterior prefrontal regions are involved in maintaining progressively more abstract rules. A rostral–caudal gradient of this sort enables the organism to represent information at increasingly abstract levels which facilitates generalization from one learning context to perhaps another novel context. Dosenbach et al. (2007) suggests that different brain networks are involved in distinct aspects of control with the fronto-parietal cortex implicated in initiating and adapting behavior while sustained stable task per- formance is associated with the anterior cingulate, anterior insula, frontal operculum and anterior prefrontal cortex (Dosenbach et al., 2007).

A key control function that has been repeatedly implicated in human addiction and may be especially relevant for treatment suc- cess is inhibitory control. Impulsivity has been identified as a risk factor for drug use in both animal models (Dalley et al., 2007) and in human studies (Volkow et al., 1999). Although a multi-faceted con- cept, one broad decomposition separates impulsivity into impulsive action and impulsive choice (Reynolds et al., 2006). The former, im- pulsive action can be assessed with tests of motor response inhibition. Human lesion studies, using transcranial magnetic stimulation (TMS), which temporarily disrupts the function of a brain region, along with functional MRI studies measuring blood flow responses triggered by increased brain activity, have linked motor response inhibition to the right inferior frontal gyrus (IFG) (Aron et al., 2003; Chambers et al., 2006; Garavan et al., 1999, 2006). More recent studies suggest that this may reflect a broader role for this region in detecting attentionally salient events (Hampshire et al., 2010), although it may be the case that in order to evoke right IFG activity, the salience of these events must be relevant to response control (Dodds et al., 2011). Indeed, evidence from other paradigms would seem to extend the right PFC's role beyond control over motor responses. For exam- ple, the magnitude of fMRI activation change in the right inferior frontal gyrus when inhibiting craving responses to a cocaine video,

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relative to when not inhibiting, was negatively correlated with a sim- ilar difference score in the right nucleus accumbens (Volkow et al., 2010). The active suppression of the sensory components of memo- ries in a think/no think paradigm was associated with activation in the right inferior frontal cortex (Depue et al., 2007). In a study of at- tentional biases to irrelevant but distracting drug-related stimuli in cocaine users, those users who showed the greatest activity in the right prefrontal cortex showed the smallest behavioral interference caused by the distracting drug stimuli (Hester and Garavan, 2009). Following from this result, Roberts et al. (unpublished results) showed that ecstasy users relative to drug-naïve controls show in- creased visual activity and decreased right prefrontal activity when presented with distracting drug-related images.

Curiously, the left rather than the right prefrontal cortex has been linked to impulsive choice; one operationalization is the preference for immediate rewards relative to larger temporally delayed rewards. Figner et al. (2010) showed that choices for smaller, more immediate rewards over delayed rewards increased following disruptive rTMS over the left but not the right lateral PFC. This result is consistent with earlier studies that have linked both increased gray matter vol- ume and increased functional activation of the lateral PFC to smaller discounting of delayed rewards (Bjork et al., 2009; McClure et al., 2004). Further support for a role for the left PFC in impulsive choice comes from studies that require the modulation or regulation of re- ward processes and are described next.

Interaction of reward and control

The interaction of reward and control is central to the framework of this paper. If recovery from addiction can be usefully conceived of as the struggle between control and reward (perhaps, more precisely, over reinforcement or incentive salience processes) then neurobio- logical insights into recovery might come from studying how individ- uals actively suppress reward drives. In one recent example, Diekhof and Gruber (2010) required participants to either select immediate rewards or to withhold selecting an immediate reward in favor of long-term goals, with the latter yielding a delayed but larger reward. Selections in favor of immediate rewards were associated with in- creased activity in the ventral tegmental area and the ventral stria- tum, which are key structures as noted above in the brain's mesocorticolimbic reinforcement system. Activity levels in these re- gions were reduced when subjects selected in favor of the long- term goal. Significantly, when selecting in favor of the long-term goal, activity levels in the ventral striatum were negatively correlated with activity in the left inferior prefrontal cortex and lateral orbitofrontal cortex. Similarly, Delgado et al. (2008) showed that par- ticipants actively attempting to regulate their anticipation for a re- ward in the monetary incentive delay task (participants were instructed to think of something other than the potential reward dur- ing the pre-reward period) produced increased activity in the left frontal cortex and decreased activity in the ventral striatum. Staudinger et al. (2011), using a similar paradigm (participants were trained to distance themselves from the upcoming pleasant feelings associated with a reward and, instead to behave as if they were a neutral observer), showed similar reductions in the putamen when participants were trying to regulate their anticipation of re- ward, while also showing that the regulation condition altered the strength of the correlation between the putamen and the dorsolateral prefrontal cortex.

Another useful approach to studying the dynamic between reward and control comes from studies that reward or punish control. Simoes-Franklin et al. (2010) studied the effect on response inhibi- tion if failures to inhibit incurred a financial loss. In brief, when per- forming a Go/NoGo task in which frequent responses (Go trials) are interrupted with infrequent and unpredictable response withholds (NoGo trials) participants showed improved performance (i.e.,

fewer commission errors) when financially motivated to avoid errors. Errors that incurred a financial penalty, relative to those that did not, produced a greater activity in the rostral anterior cingulate and the ventral striatum. Notably, during the entire period when errors might be financially costly, there was an elevated activity in numer- ous regions related to cognitive control including the dorsal anterior cingulate, the bilateral anterior insula and the fronto-parietal cortex. Hester et al. (unpublished results) showed that the same manipula- tion of financial punishment in abstinent cocaine users elevated inhibition-related activation and tonic levels of activation relative to controls (but did not elevate error-related activation). This result may be especially germane to the present topic as it demonstrates that monetary incentives (albeit, in this case financial punishments rather than financial rewards) can modulate some components of the cognitive control system thereby suggesting a route through which financial contingencies might affect behavior.

Two principal conclusions can be drawn from these studies. First, control over reward processes is possibly reflected by increases in prefrontal activity level and decreases in striatal activity levels. Sec- ond, and this might have important implications for treatment, con- trol can be improved through reward manipulations. Together, these observations suggest an important role for prefrontally- mediated control and its modulation by reward in treatment, the topic that we turn to next.

One final consideration is that the focus here on control over re- ward processes might also be extended to control over habitual pro- cesses or over the aversive processes associated with withdrawal that provide a negative reinforcement for continued use in the addict. For example, evidence exists that re-asserting control over an auto- matic response selectively engages the dorsolateral prefrontal cortex (Kuebler et al., 2006). Healthy participants searched for a target in a visual search task which, following extensive practice yielding behav- ioral markers of automatic performance, was changed requiring them to search for a new target and thereby having to suppress the auto- matic learned response. Similarly, prefrontal cognitive control pro- cesses may also be involved in regulating the aversive symptoms of withdrawal consistent with studies of other types of emotion regulation.

Role of the prefrontal cortex in recovery

The involvement of the prefrontal cortex in addiction recovery is consistent with current clinical practice. Both Cognitive Behavioral Treatment and AA programs stress cognitive approaches to identify- ing and surmounting relapse risk. We hypothesize that a core deficit in addicts that leads to relapse is the inability to maintain the absti- nence goal, allowing that goal to guide behavior and dominate over other goals that may yield more immediate rewards. Further, we hy- pothesize that treatment such as contingency management shows ef- ficacy by providing addicts with a more proximal, immediately rewarding goal which lends itself to a more robust goal representa- tion. Ballard et al. (2011) employed a dynamic causal modeling, a technique that tests for causal influences of one brain region on an- other, to identify the brain regions underlying motivated behavior. They showed that external cues indicating potential reward produced activity in the dorsolateral prefrontal cortex and that this activity was a necessary input to produce responses in the ventral tegmental area and ventral striatum. Thus, the prefrontal cortex would appear to be essential in initiating the subcortical responses that underlie a moti- vated behavior. Thus, one hypothesis concerning the mechanisms un- derlying clinical interventions is that they enhance the representation of the abstinence goal. In the case of contingency management, they do so by replacing the more abstract long-term goal of prolonged ab- stinence with the more immediate and reinforcing goal of a voucher or financial reward. This may prove to be especially effective in drug addicted individuals as the prefrontal cognitive control system that

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represents goals and that, as we've seen, can regulate reward antici- pation, is compromised in users (Goldstein and Volkow, 2002).

Supporting evidence for the hypothesis that prefrontal control systems are central to successful abstinence comes from studies that have investigated pre-quit neurobiological predictors of treatment outcome and the neurobiological changes that emerge with contin- ued abstinence. With regard to pre-quit prediction, longitudinal stud- ies have identified attentional biases to drugs and drug-related cues as particularly good indicators of treatment outcome. Cognitive tasks that measure the extent to which performance is impaired by ir- relevant but distracting drug-related stimuli have been shown to pre- dict relapse better than other standard dependence measures such as self-reports of dependence or drug use histories (Cox et al., 2002; Waters et al., 2003). Similarly, scores on self-report measures of im- pulsivity have been shown to predict poorer treatment outcome (Moeller et al., 2001; Patkar et al., 2004).

During abstinence, prefrontally-mediated cognitive control might be important for suppressing drug-seeking behaviors and drug crav- ings. The veracity and utility of subjectively-reported drug cravings as predictors of behavior are frequently called into question and in- deed a number of studies have shown that subjective reports of crav- ing are poorer predictors of subsequent abstinence than are cognitive and neuroimaging measures (Grusser et al., 2004; Kosten et al., 2006). Indeed, it may be the case that an even better predictor of treatment outcome would be the degree to which a user can exert control over their drug urges and not the magnitude of the urges themselves. The relevant neuroimaging literature, though small, does identify prefrontal systems, among other regions, as effective predictors of treatment outcome. Paulus and colleagues showed that activation levels in the prefrontal, temporal and posterior cingulate regions early in abstinence predicted subsequent relapse for meth- amphetamine users (Paulus et al., 2005). Grusser et al. (2004) reported that relapse in alcoholics could be predicted by pre- treatment activity in response to alcohol-related stimuli in the puta- men, anterior cingulate and medial prefrontal cortex. Brewer et al. (2008) identified cognitive control prefrontal regions in addition to other subcortical and posterior cingulate regions as being the best predictors of treatment outcome in a treatment-receiving sample of cocaine users.

Experimental evidence that cognitive control impacts on relapse comes from those few studies that have attempted to train control. Neuroimaging studies show that cognitive control can be improved with practice and, for example, the functional brain differences be- tween good and poor inhibitors are the same regions that change with practice (Kelly et al., 2006). An intensive working memory train- ing program in stimulant abusers reduced delayed discounting, that is, the steepness with which a delayed reward lose subjective value (Bickel et al., 2010). A recent study showed that practicing self- control (i.e., small acts of impulse control such as avoiding sweets, which were practiced over 2 weeks before quitting) significantly im- proved abstinence rates in cigarette smokers; 27% of participants assigned to the self-control training condition relative to 12% of par- ticipants in a control condition were still abstinent 1 month after quitting (Muraven, 2010).

Interventional studies assessing changes in brain function that fol- low users during recovery are, unfortunately, few in number due, pre- sumably, to the high costs associated with high attrition and low abstinence success rates. An alternative cross-sectional approach that we have employed compares current users and non-users to former users to reveal the characteristics of those who have managed to attain and maintain abstinence. Although few in number, these studies are showing a striking difference in activation patterns. The prefrontal hypoactivity that characterizes current users (Hester et al., 2004; Kaufman et al., 2003) is not observed and, instead, former users show prefrontal hyperactivity. For example, Connolly et al. (2011) report that both short-term abstinent cocaine users (1–5 weeks of abstinence)

and long-term abstinent users (40–102 weeks of abstinence) relative to drug-naïve controls show elevated activity in the right inferior frontal gyrus, dorsolateral prefrontal cortex and in the dorsal anterior cingulate. That is, the brain regions involved in inhibitory control and in monitoring behavior (i.e., activated when errors in performance are made) are more active in former users compared to drug-naïve controls.

This effect has also been seen in cigarette smokers who were ab- stinent for 2 years (Nestor et al., 2011a, 2011b). Using the same Go/ NoGo task as described above for the former cocaine users, current smokers showed reduced activity relative to never-smokers in the dorsolateral prefrontal cortex and in the anterior cingulate. However, the former smokers revealed greater inhibition- and error-related ac- tivation in the anterior cingulate relative to both the current smokers and never-smokers (Nestor et al., 2011a, 2011b). These findings are supported by brain structural analyses. Returning to the former co- caine users, Connolly et al. (submitted for publication) showed in- creased gray matter volumes in former users in the anterior cingulate, insula and fronto-parietal cortex. Notably, these structural effects were observed in regions that are typically implicated in cog- nitive control processes which, combined with the activation results, indicates that increased prefrontal functioning is characteristic of suc- cessful abstinence. As both the nicotine and cocaine abstinence stud- ies were cross-sectional in design, it is not possible to determine if the heightened activity or increased gray matter volumes of the former users reflect a pre-existing trait that might have facilitated abstinence or if it arose during the abstinence period in response, perhaps, to the frequent needs to exercise control over behavior. The investigation of gray matter volume revealed numerous regions in which volume cor- related with the duration of abstinence, which, although certainly not definitive, is the pattern that one might predict if the effect arose as a consequence of abstinence. A longitudinal study would resolve these ambiguities but, for now, either possibility supports the hypothesis that heightened prefrontally-mediated cognitive control is character- istic of successful abstinence.

Conclusion

To return to the initial assertion, that recovery processes may be distinct from disease processes, this initial review has focused on the role of the prefrontal cortex in recovery. The frontal lobes have been shown to regulate striatal reward-related processes, to be among the regions that predict treatment outcome, and to show ele- vated functioning in those who have succeeded in maintaining absti- nence. Financial incentives affect their activity levels and lead us to speculate that the efficacy of the incentivizing behavior is mediated through the frontal lobes and the influence they exert in guiding be- havior. One interesting corollary hypothesis to emerge is that the prefrontally-mediated control enabling abstinence can co-exist with the prolonged vulnerability to relapse that is characteristic of addic- tion assuming that the latter is represented elsewhere (e.g., subcortically-mediated incentive salience for drugs). Interestingly, non-human primate models demonstrate that dopamine functioning in the striatum can also recover with abstinence (Beveridge et al., 2009) thus a more in depth knowledge of the brain changes that occur with abstinence, what psychological processes they reflect and what their relative time-courses might be are all important ques- tions for understanding recovery.

In addition, the emphasis being placed on the cognitive processes involved in exerting control during abstinence should not be taken to mean that these processes are not also relevant in the development of addiction. For example, decreased orbitofrontal activity during re- sponse inhibition has recently been shown to be observed in adoles- cents with a very low exposure to alcohol (1–4 lifetime drinks) compared to alcohol-naïve adolescents (Whelan et al., 2012). This result suggests that compromised inhibitory control may have

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preceded drinking and may indeed be a risk factor for early drinking. Thus, there may be an overlap in the role of cognitive control in both risk for use and recovery from use. However, the studies described above showing “supernormal” levels of activity and gray matter in ab- stinent users relative to drug-naïve controls suggest that recovery may be a distinct process insofar as it draws upon these control sys- tems to achieve abstinence and, in this important regard, abstinence is more than a reversion to the pre-addiction state of the individual. Consequently, we believe that investigations of the neurobiology of recovery may yield important new insights into human addiction.

Conflict of interest

The authors declare that there are no conflicts of interest.

References

Aron, A.R., Fletcher, P.C., Bullmore, E.T., Sahakian, B.J., Robbins, T.W., 2003. Stop-signal inhibition disrupted by damage to right inferior frontal gyrus in humans. Nat. Neu- rosci. 6, 115–116.

Atkinson, R.C., Shiffrin, R.M., 1968. Human memory: a proposed system and its control processes. In: Spence, K.W., Spence, J.T. (Eds.), The Psychology of Learning and Mo- tivation: Advances in Research and Theory. Academic Press, New York, pp. 89–195.

Baddeley, A.D., 2001. Fractionating the central executive. In: Stuss, D., Knight, R.T. (Eds.), Principles of Frontal Lobe Function. Oxford University Press, New York, pp. 246–260.

Baddeley, A.D., 2002. Is working memory still working? Am. Psychol. 7, 85–97. Badre, D., Kayser, A.S., D'Esposito, M., 2010. Frontal cortex and the discovery of abstract

action rules. Neuron 66, 315–326. Ballard, I.C., Murty, V.P., Carter, R.M., MacInnes, J.J., Huettel, S.A., Adcock, R.A., 2011.

Dorsolateral prefrontal cortex drives mesolimbic dopaminergic regions to initiate motivated behavior. J. Neurosci. 31, 10340–10346.

Bechara, A., 2005. Decision making, impulse control and loss of willpower to resist drugs: a neurocognitive perspective. Nat. Neurosci. 8, 1458–1463.

Beck, A., et al., 2009. Ventral striatal activation during reward anticipation correlates with impulsivity in alcoholics. Biol. Psychiatry 66, 734–742.

Berman, K.F., Ostrem, J.L., Randolph, C., et al., 1995. Physiological activation of a cortical network during performance of the Wisconsin card sorting test: a positron emis- sion tomography study. Neuropsychologia 33, 1027–1046.

Beveridge, T.J.R., Smith, H.R., Nader, M.A., Porrino, L.J., 2009. Abstinence from chronic cocaine self-administration alters striatal dopamine systems in rhesus monkeys. Neuropsychopharmacology 34, 1162–1171.

Bickel, W.K., Jones, B.A., Landes, R.D., Christensen, D.R., Jackson, L., Mancino, M., 2010. Hypothetical intertemporal choice and real economic behavior: delay discounting predicts voucher redemptions during contingency-management procedures. Exp. Clin. Psychopharmacol. 18, 546–552.

Bjork, J.M., Smith, A.R., Hommer, D.W., 2008. Striatal sensitivity to reward deliveries and omissions in substance dependent patients. Neuroimage 42, 1609–1621.

Bjork, J.M., Momenan, R., Hommer, D.W., 2009. Delay discounting correlates with pro- portional lateral frontal cortex volumes. Biol. Psychiatry 65, 710–713.

Blum, K., Braverman, E.R., Holder, J.M., et al., 2000. Reward deficiency syndrome: a bio- genetic model for the diagnosis and treatment of impulsive, addictive, and com- pulsive behaviors. J. Psychoactive Drugs 32, 1–112.

Brewer, J.A., Worhunsky, P.D., Carroll, K.M., Rounsaville, B.J., Potenza, M.N., 2008. Pre- treatment brain activation during Stroop task is associated with outcomes in cocaine-dependent patients. Biol. Psychiatry 64, 998–1004.

Buhler, M., Vollstadt-Klein, S., Kobiella, A., et al., 2010. Nicotine dependence is charac- terized by disordered reward processing in a network driving motivation. Biol. Psychiatry 67, 745–752.

Chambers, C.D., Bellgrove, M.A., Stokes, M.G., Henderson, T.R., Garavan, H., Robertson, I.H., Morris, A.P., Mattingley, J.B., 2006. Executive "brake failure" following deacti- vation of human frontal lobe. J Cogn. Neurosci. 18, 444–455.

Chib, V.S., Rangel, A., Shimojo, S., O'Doherty, J.P., 2009. Evidence for a common repre- sentation of decision values for dissimilar goods in human ventromedial prefrontal cortex. J. Neurosci. 29, 12315–12320.

Connolly, C.G.F.J., Nierenberg, J., Shpaner, M., Garavan, H., 2011. The neurobiology of cognitive control in successful cocaine abstinence. Drug Alcohol Depend. 121, 45–53.

Connolly, C.G., Foxe, J.J., Nierenberg, J., Shpaner, M., Garavan, H., submitted for publica- tion. Neurocognitive markers associated with cocaine abstinence.

Cox, W.M., Hogan, L.M., Kristian, M.R., Race, J.H., 2002. Alcohol attentional bias as a predictor of alcohol abusers' treatment outcome. Drug Alcohol Depend. 68, 237–243.

D' Esposito, M., Detre, J.A., Alsop, D.C., Shin, R.K., Atlas, S., Grossman, M., 1995. The neural basis of the central executive system of working memory. Nature 378, 279–281.

Dalley, J.W., Fryer, Tim D., Brichard, Laurent, et al., 2007. Nucleus accumbens D2/3 recep- tors predict trait impulsivity and cocaine reinforcement. Science 315, 1267–1270.

Delgado, M.R., Li, J., Schiller, D., Phelps, E.A., 2008. The role of the striatum in aversive learning and aversive prediction errors. Philos. Trans. R. Soc. Lond. B Biol. Sci. 363, 3787–3800.

Depue, B.E., Curran, T., Banich, M.T., 2007. Prefrontal regions orchestrate suppression of emotional memories via a two-phase process. Science 317, 215–219.

Diekhof, E.K., Gruber, O., 2010. When desire collides with reason: functional interac- tions between anteroventral prefrontal cortex and nucleus accumbens underlie the human ability to resist impulsive desires. J. Neurosci. 30, 1488–1493.

Dodds, C.M., Morein-Zamir, S., Robbins, T.W., 2011. Dissociating inhibition, attention, and response control in the frontoparietal network using functional magnetic res- onance imaging. Cereb. Cortex 21, 1155–1165.

Dosenbach, N.U.F., Fair, Damien A., Miezin, Francis M., et al., 2007. Distinct brain net- works for adaptive and stable task control in humans. Proc. Natl. Acad. Sci. U. S. A. 104, 11073–11078.

Dove, A., Pollmann, S., Schubert, T., Wiggins, C.J., von Cramon, D.Y., 2000. Prefrontal cortex activation in task switching: an event-related fMRI study. Brain Res. Cogn. Brain Res. 9, 103–109.

Everitt, B.J., Parkinson, J.A., Olmstead, M.C., Arroyo, M., Robledo, P., Robbins, T.W., 1999. Associative processes in addiction and reward: the role of amygdala–ventral striatal subsystems. In: McGinty, J.F. (Ed.), Advancing from the Ventral Striatum to the Extended Amygdala: Implications for Neuropsychiatry and Drug Abuse: In Honor of Lennart Heimer: Ann N Y Acad Sci, pp. 412–438.

Figner, B., Knoch, D., Johnson, E.J., et al., 2010. Lateral prefrontal cortex and self-control in intertemporal choice. Nat. Neurosci. 13, 538–539.

Garavan, H., 2010. The insula and drug cravings. Brain Struct. Funct. 214, 593–601. Garavan, H., Stout, J.C., 2005. Neurocognitive insights into substance abuse. Trends

Cogn. Sci. 9, 195–201. Garavan, H., Ross, T.J., Stein, E.A., 1999. Right hemispheric dominance of inhibitory control:

an event-related functional MRI study. Proc. Natl. Acad. Sci. U. S. A. 96, 8301–8306. Garavan, H., Hester, R., Murphy, K., Fassbender, C., Kelly, C., 2006. Individual differences

in the functional neuroanatomy of inhibitory control. Brain Res. 1105, 130–142. Goldstein, R.Z., Volkow, N.D., 2002. Drug addiction and its underlying neurobiological

basis: neuroimaging evidence for the involvement of the frontal cortex. Am. J. Psy- chiatry 159, 1642–1652.

Goldstein, R.Z., et al., 2007. Is decreased prefrontal cortical sensitivity to monetary re- ward associated with impaired motivation and self-control in cocaine addiction? Am. J. Psychiatry 164, 43–51.

Grusser, S.M., et al., 2004. Cue-induced activation of the striatum and medial prefrontal cortex is associated with subsequent relapse in abstinent alcoholics. Psychophar- macology (Berl) 175, 296–302.

Hampshire, A., Chamberlain, S.R., Monti, M.M., Duncan, J., Owen, A.M., 2010. The role of the right inferior frontal gyrus: inhibition and attentional control. Neuroimage 50, 1313–1319.

Hester, R., Garavan, H., 2009. Neural mechanisms underlying drug-related cue distrac- tion in active cocaine users. Pharmacol. Biochem. Behav. 93, 270–277.

Hester, R., Murphy, K., Garavan, H., 2004. Beyond common resources: the cortical basis for resolving task interference. Neuroimage 23, 202–212.

Higgins, S.T., Sigmon, S.C., Heil, S.H., 2011. Contingency management in the treatment of substance use disorders: trends in the literature, In: Ruiz, P., Strain, E. (Eds.), Lowinson and Ruiz's Substance Abuse: a Comprehensive Textbook, 5th edition. Lippincott Williams & Wilkins, Hagerstown, MD, pp. 603–621.

Jia, Z., Worhunsky, P.D., Carroll, K.M., et al., 2011. An initial study of neural responses to monetary incentives as related to treatment outcome in cocaine dependence. Biol. Psychiatry 70, 553–560.

Kaufman, J.N., Ross, T.J., Stein, E.A., Garavan, H., 2003. Cingulate hypoactivity in cocaine users during a GO–NOGO task as revealed by event-related functional magnetic resonance imaging. J. Neurosci. 23, 7839–7843.

Kelly, A.M.C., Hester, R., Foxe, J.J., Shpaner, M., Garavan, H., 2006. Flexible cognitive con- trol: effects of individual differences and brief practice on a complex cognitive task. Neuroimage 31, 866–886.

Kim, H., Shimojo, S., O'Doherty, J.P., 2011. Overlapping responses for the expectation of juice and money rewards in human ventromedial prefrontal cortex. Cereb. Cortex 21, 769–776.

Knutson, B., Bossaerts, P., 2007. Neural Antecedents of Financial Decisions. J. Neurosci. 27, 8174–8177.

Knutson, B., Adams, C.M., Fong, G.W., Hommer, D., 2001. Anticipation of increasing monetary reward selectively recruits nucleus accumbens. J. Neurosci. 21, RC159.

Koechlin, E., Hyafil, A., 2007. Anterior prefrontal function and the limits of human decision-making. Science 318, 594–598.

Koob, G., 2000. Drug addiction. Neurobiol. Dis. 7, 543–545. Koob, G.F., Le Moal, M., 2005. Plasticity of reward neurocircuitry and the ‘dark side’ of

drug addiction. Nat. Neurosci. 8, 1442–1444. Koob, G.F., Sanna, P.P., Bloom, F.E., 1998. Neuroscience of addiction. Neuron 21,

467–476. Kosten, T.R., Scanley, B.E., Tucker, K.A., et al., 2006. Cue-induced brain activity changes and

relapse in cocaine-dependent patients. Neuropsychopharmacology 31, 644–650. Kuebler, A., Dixon, V., Garavan, H., 2006. Automaticity and re-establishment of execu-

tive control — an fMRI study. J. Cogn. Neurosci. 18, 1331–1342. Lhermitte, F., 1986. Human autonomy and the frontal lobes. Part II. patient behavior in

complex and social situations: the environmental dependency syndrome. Ann. Neurol. 19, 335–343.

Lim, S.-L., O'Doherty, J.P., Rangel, A., 2011. The decision value computations in the vmPFC and striatum use a relative value code that is guided by visual attention. J. Neurosci. 31, 13214–13223.

Luria, A.R., Pribram, K.H., 1973. The frontal lobes and the regulation of behavior. In: EAP Oxford (Ed.), Psychophysiology of the Frontal Lobes, p. 332.

McClure, S.M., Laibson, D.I., Loewenstein, G., Cohen, J.D., 2004. Separate neural systems value immediate and delayed monetary rewards. Science 306, 503–507.

Moeller, F.G., Dougherty, D.M., Barratt, E.S., Schmitz, J.M., Swann, A.C., Grabowski, J., 2001. The impact of impulsivity on cocaine use and retention in treatment. J. Subst. Abuse Treat. 21, 193–198.

S23H. Garavan, K. Weierstall / Preventive Medicine 55 (2012) S17–S23

Monchi, O., Petrides, M., Petre, V., Worsley, K., Dagher, A., 2001. Wisconsin card sorting revisited: distinct neural circuits participating in different stages of the task identified by event-related functional magnetic resonance imaging. J. Neurosci. 21, 7733–7741.

Muraven, M., 2010. Practicing self-control lowers the risk of smoking lapse. Psychol. Addict. Behav. 24, 446–452.

Nestler, E.J., 2005. Is there a common molecular pathway for addiction? Nat. Neurosci. 8, 1445–1449.

Nestor, L., Hester, R., Garavan, H., 2010. Increased ventral striatal BOLD activity during non-drug reward anticipation in cannabis users. Neuroimage 49, 1133–1143.

Nestor, L.J., Ghahremani, D.G., Monterosso, J., London, E.D., 2011a. Prefrontal hypo- activation during cognitive control in early abstinent methamphetamine- dependent subjects. Psychiatry Res. 194, 287–295.

Nestor, L., McCabe, E., Jones, J., Clancy, L., Garavan, H., 2011b. Differences in “bottom- up” and “top-down” neural activity in current and former cigarette smokers: evi- dence for neural substrates which may promote nicotine abstinence through in- creased cognitive control. Neuroimage 56, 2258–2275.

Newman, S.D., Carpenter, P.A., Varma, S., Just, M.A., 2003. Frontal and parietal participation in problem solving in the Tower of London: fMRI and computational modeling of planning and high-level perception. Neuropsychologia 41, 1668–1682.

Norman, D.A., Shallice, T., 1985. Attention to action: willed and automatic control of behavior. In: Davidson, R.J., Schwartz, G.E., Shapiro, D. (Eds.), Consciousness and Self-Regulation. : Advances in Research and Theory, vol. 4. Plenum Press, New York, pp. 2–18.

Olds, J., 1977. Drives and Reinforcements Behavioral Studies of Hypothalamic Func- tions, 1st edition. Raven Press.

Owen, A.M., Evans, A.C., Petrides, M., 1996. Evidence for a two-stage model of spatial working memory processing within the lateral frontal cortex: a positron emission tomography study. Cereb. Cortex 6, 31–38.

Patkar, A., Mannelli, P., Certa, K., et al., 2004. Relationship of serum prolactin with severity of drug use and treatment outcome in cocaine dependence. Psychopharmacology (Berl) 176, 74–81.

Paulus, M.P., Tapert, S.F., Schuckit, M.A., 2005. Neural activation patterns of methamphetamine-dependent subjects during decision making predict relapse. Arch. Gen. Psychiatry 62, 761–768.

Peters, J., et al., 2011. Lower ventral striatal activation during reward anticipation in ad- olescent smokers. Am. J. Psychiatry 168, 540–549.

Prabhakaran, V., Smith, J.A.L., Desmond, J.E., Glover, G.H., Gabrieli, J.D.E., 1997. Neural substrates of fluid reasoning: an fMRI study of neocortical activation during perfor- mance of the Raven's Progressive Matrices Test. Cogn. Psychol. 33, 43–63.

Reynolds, B., Ortengren, A., Richards, J.B., de Wit, H., 2006. Dimensions of impulsive be- havior: personality and behavioral measures. Pers. Indiv. Differ. 40, 305–315.

Robinson, T.E., Berridge, K.C., 1993. The neural basis of drug craving: an incentive- sensitization theory of addiction. Brain Res. Rev. 18, 247–297.

Robinson, T.E., Berridge, K.C., 2003. Addiction. Annu. Rev. Psychol. 54, 25–53. Salmon, E., VanderLinden, M., Collette, F., et al., 1996. Regional brain activity during

working memory tasks. Brain 119, 1617–1625. Schneider, W., Shiffrin, R.M., 1977. Controlled and automatic human information pro-

cessing: I. Detection, search and attention. Psychol. Rev. 84, 1–66. Schneider, S., et al., 2011. Risk taking and the adolescent reward system: a potential

common link to substance abuse. Am. J. Psychiatry 169, 39–46. Shiffrin, R.M., Schneider, W., 1977. Controlled and automatic human information pro-

cessing: II. Perceptual learning, automatic attending, and general theory. Psychol. Rev. 84, 127–190.

Simoes-Franklin, C., Hester, R., Shpaner, M., Foxe, J.J., Garavan, H., 2010. Executive func- tion and error detection: the effect of motivation on cingulate and ventral striatum activity. Hum. Brain Mapp. 31, 458–469.

Sohn, M.H., Ursu, S., Anderson, J.R., Stenger, V.A., Carter, C.S., 2000. The role of prefron- tal cortex and posterior parietal carter in task switching. Proc. Natl. Acad. Sci. U. S. A. 97, 13448–13453.

Staudinger, M.R., Erk, S., Walter, H., 2011. Dorsolateral prefrontal cortex modulates striatal reward encoding during reappraisal of reward anticipation. Cereb. Cortex 21, 2578–2588.

Stuss, D.T., Benson, D.F., 1987. The frontal lobes and control of cognition and memory. Pages In: Perecman, E. (Ed.), The Frontal Lobes Revisited. IRBN Press, New York, pp. 141–154.

Van Hell, H.H., Vink, M., Ossewaarde, L., Jager, G., Kahn, R.S., Ramsey, N.F., 2010. Chronic effects of cannabis use on the human reward system: an fMRI study. Eur. Neuro- psychopharmacol. 20, 153–163.

Volkow, N.D., Wang, G.J., Fowler, J.S., et al., 1999. Prediction of reinforcing responses to psychostimulants in humans by brain dopamine D-2 receptor levels. Am. J. Psychi- atry 156, 1440–1443.

Volkow, N.D., Fowler, J.S., Wang, G.J., Telang, F., Logan, J., Jayne, M., Ma, Y.M., Pradhan, K., Wong, C., Swanson, J.M., 2010. Cognitive control of drug craving inhibits brain reward regions in cocaine abusers. Neuroimage 49, 2536–2543.

Volkow, N.D., Wang, G.-J., Fowler, J.S., Tomasi, D., Telang, F., 2011. Addiction: Beyond dopamine reward circuitry. Proc. Natl. Acad. Sci. U. S. A. 108, 15037–15042.

Waters, A.J., Shiffman, S., Bradley, B.P., Mogg, K., 2003. Attentional shifts to smoking cues in smokers. Addiction 98, 1409–1417.

Whelan, R., et al., 2012. Adolescent impulsivity phenotypes characterized by distinct brain networks. Nat. Neurosci. Apr 29. [Epub ahead of print].

Wrase, J., et al., 2007. Dysfunction of reward processing correlates with alcohol craving in detoxified alcoholics. Neuroimage 35, 787–794.