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Nicotine addiction 

Nicotine addiction
Chapter:
Nicotine addiction
Author(s):

David J. Nutt

and Liam J. Nestor

DOI:
10.1093/med/9780198797746.003.0011
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Key points

  • Nicotine is a stimulant that is the main psychoactive element of cigarettes.

  • Nicotine is an agonist at nicotinic acetylcholine receptors in the brain.

  • Nicotine in smoked tobacco is addictive.

  • Nicotine causes disturbances to reward and cognitive processing.

  • Cigarette smoking presents considerable health risks.

  • Cigarette smoking induces high costs on healthcare resources.

  • Smoking cessation medications and safer nicotine products (e.g. e-cigs) are now available to treat nicotine addiction.

11.1 Nicotine

The number of cigarette smokers worldwide is estimated at 1.3 billion, although there has been an observed decline in the prevalence of smoking in the developed world. Despite some success in reversing the tobacco epidemic in the developed world, tobacco use continues to increase in less developed countries where insufficient resources and education concerning tobacco use lead to its widespread consumption. Furthermore, it has been estimated, based on current trends of tobacco use, that 70% of the global deaths from tobacco consumption by 2025 will have occurred in developing nations. Despite these statistics and the well-documented health consequences it is extraordinary that people continue to consume tobacco. This continued use in the face of adversity is a sad, but powerful testimony to the effects of nicotine dependence, clearly demonstrating its addictive properties. While the majority of cigarette smokers endorse the desire to quit, reported abstinence rates after 12 months are in the modest region of 5–17%, with the vast majority relapsing to smoking within a week of cessation. Because cigarette smoking presents a considerable health risk and induces high costs on healthcare resources there is a pressing need to understand, in greater detail, the effects of nicotine addiction in the brain, and how changes in functioning may contribute to its continued use.

11.1.1 Nicotine effects

Nicotine exerts its effects within the brain by acting at nicotinic acetylcholine receptors (nAChRs), to mimic the effects of the natural neurotransmitter acetylcholine (ACh). These receptors control cation-selective, ligand-gated channels. nAChRs are pentameric combinations of 12 genetically distinct homologous subunits (α‎2–α‎10 and β‎2–β‎4), with each nAChR assumed to be hetero-oligomeric (i.e. composed from various combinations of α‎ and β‎ subunits). Early nicotine binding studies identified two distinct nicotine binding sites in the form of the high affinity α‎4β‎2 and the low affinity α‎7 receptor subtypes, which have subsequently provided evidence concerning structural and functional changes related to nicotine use in the brain (see Figure 11.1).

Figure 11.1 Neuronal nicotinic acetylcholine receptors (nAChRs) are widely distributed in different brain regions that include the ventral tegmental area (VTA), nucleus accumbens (NAc), hippocampus, prefrontal cortex (PFC), and amygdala. Activation of nAChRs in these brain areas significantly contributes to the rewarding effects of nicotine. GABAergic (red), glutamatergic (green), and dopaminergic (blue) connections between these structures constitute a major neural circuitry underlying addictive disorders, which includes nicotine addiction.

Figure 11.1 Neuronal nicotinic acetylcholine receptors (nAChRs) are widely distributed in different brain regions that include the ventral tegmental area (VTA), nucleus accumbens (NAc), hippocampus, prefrontal cortex (PFC), and amygdala. Activation of nAChRs in these brain areas significantly contributes to the rewarding effects of nicotine. GABAergic (red), glutamatergic (green), and dopaminergic (blue) connections between these structures constitute a major neural circuitry underlying addictive disorders, which includes nicotine addiction.

Reproduced from Frontiers in Molecular Neuroscience, 5, 83, Feduccia, A. A., Chatterjee, S., and Bartlett, S. E., Neuronal nicotinic acetylcholine receptors: neuroplastic changes underlying alcohol and nicotine addictions. © 2012 Feduccia, Chatterjee, and Bartlett. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/3.0/), which permits use, distribution, and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics, etc.

The release of ACh onto nAChRs throughout the brain takes place through a series of cholinergic forebrain projections, which include the nucleus basalis of Meynert (NBM); the diagonal band nucleus; nucleus ansa lenticularis, and part of the magnocellular preoptic nucleus. Approximately two-thirds of the NBM neurons projecting to the cortex are cholinergic, with ~30% GABAergic, which preferentially connect with GABAergic interneurons. nAChRs have been shown to modulate presynaptic glutamate release with further observations of nicotine-induced GABA modulation in multiple brain regions such as the ventral tegmental area (VTA), thalamus, cerebral cortex, and hippocampus. There are also a number of neurotransmitters, which interfere with cortical ACh release, such as dopamine (DA) acting at the NBM, serotonin modulating neuronal activity at the NBM and the release of ACh in the cortex, and norepinephrine, acting as a tonic inhibitor of cortical ACh release. Therefore, the neuroanatomical distribution of nAChRs is likely to contribute to the long-term effects of nicotine administration during chronic smoking—particularly on neuronal networks which contribute to reward processing and cognitive control.

Nicotine, like other stimulant drugs of abuse, activates the mesocorticolimbic DA system. The ventral striatum (VS) has been implicated in the development of nicotine addiction due to its role in processing the hedonic effects of nicotine. Studies using rats, for example, have demonstrated that nicotine stimulates DA neurons in the midbrain VTA, resulting in increased burst firing at the shell of the NAcc. As described in Chapter 4, there are discrete hedonic ‘hotspots’ for substance ‘liking’, one of which is in the shell of the NAcc. Animals will learn to self-administer dopaminergic drugs (e.g. amphetamine, cocaine) into the shell of the NAcc. Likewise, nicotine induces DA release in humans. One of the most seminal studies to demonstrate this was conducted by Brody and colleagues (2004). This study showed that in smokers, DA release was significantly increased in striatal regions of the brain following cigarette smoking, compared to smokers who were not allowed to smoke. Significantly, this study also showed that the magnitude of DA release in those who smoked was significantly associated with changes in smoking urges, indicating that nicotine-induced DA release attenuates craving. There is also evidence to suggest that genetic variance within the DA system may explain a significant proportion of the interindividual variability in smoking-induced DA release, suggesting a genetic predisposition to the rewarding effects of nicotine. Smokers with genes associated with low resting DA tone, for example, elicit greater smoking-induced (i.e. phasic) DA release compared with smokers with alternate genotypes (Brody et al. 2006b). These results regarding the effects of nicotine-induced DA release (and particularly in attenuation of craving) support the development of medications for nicotine dependence that modulate DA tone to prevent relapse (see 11.4 Treatments).

Most studies concerning the reinforcing effects of nicotine, however, only use acute administration procedures. Because smoking is a chronic form of behaviour that most likely leads to long-term adaptations in the brain, knowledge concerning changes related to its prolonged use is essential for developing better treatments—particularly treatments that can promote abstinence during the early phases of withdrawal. Indeed, animal studies have documented the long-term effects of nicotine, with research showing a paradoxical up-regulation of α‎4β‎2 nAChRs in the brain, perhaps indicating a form of sensitization of the ACh system. Interestingly, striatal DA release is modulated by α‎4β‎2 nAChRs in rats, with research showing that chronic nicotine treatment significantly increases α‎4β‎2 nAChRs in the VS of monkeys, with a corresponding increase in α‎4β‎2 evoked DA release at this region. Given that increased DA neurotransmission in the striatum is also related to self-reported craving in humans, an upregulation of α‎4β‎2 nAChRs in striatal regions may be a neurochemical marker for DA-induced craving in nicotine addiction.

Studies in human smokers have shown that smoking saturates α‎4β‎2 nAChRs throughout the brain in a dose-dependent manner. Brody and colleagues (2006a), for example, reported that smoking just one to two puffs of a cigarette resulted in 50% occupancy of α‎4β‎2 nAChRs for three hours after smoking, and that smoking a full cigarette (or more) resulted in more than 88% receptor occupancy—accompanied by a significant reduction in cigarette craving. This suggests that α‎4β‎2 nAChRs play a pivotal role in the effects of cigarette craving in nicotine addiction. Interestingly, Brody and colleagues (2013) has also reported that lesser α‎4β‎2 upregulation in human nicotine dependence is associated with a greater likelihood of smoking cessation. Therefore, nicotine induces α‎4β‎2 nAChRs adaptations in the brain which are likely to be implicated in disordered reward processing in nicotine addiction, which likely provokes smoking relapse during withdrawal.

11.2 Reward

Disturbances in reward-related brain functioning for non-drug incentives have revealed adaptations in neural circuitry that likely contribute to ongoing smoking behaviour in humans. The reward deficiency syndrome (RDS), for example, views addiction as a deficit in DA motivational circuitry for non-drug incentives such that only substances, such as nicotine, are able to normalize DA. There is evidence to suggest that chronic smokers have DA deficits in the striatum, similar to other addiction populations. Fehr and colleagues (2008), for example, showed that smokers, compared to never smokers, had reduced DA D2/D3 receptor numbers in parts of the striatum, appearing to concur with an RDS view of nicotine addiction. Indeed, these deficits in striatal DA integrity in nicotine addiction are likely to translate into disturbances in reward processing and motivation, particularly during acute abstinence—an effect that provokes relapse. As described in Chapter 4, reward is a central component for driving incentive-based learning, eliciting appropriate responses to stimuli, and the development of goal-directed behaviours. Substances of addiction are conceived to ‘hijack’ brain circuitry involved in reward by diminishing the motivation value of non-drug rewards while inflating the reward and motivational value of drugs, such as nicotine. This is particularly evident during abstinence in brain regions such as the striatum when deficits in DA functioning are exacerbated by withdrawal, provoking a shift in the bias towards drug incentives. Indeed, Sweitzer and colleagues have elegantly shown that in smokers in early nicotine abstinence (withdrawal), cues that predict potential nicotine (cigarette puffs) elicit greater fMRI activity in striatal and mesocortical brain regions as compared with cues that predict potential monetary rewards. The opposite is observed, however, when smokers are satiated (see Figure 11.2). The results of this study provided the first direct evidence of a dissociated effect of smoking versus non-smoking rewards as a function of nicotine abstinence. This appears to suggest an important neural pathway that may bias the choice to smoke in the presence of alternative, non-drug reinforcement—a substrate involved in smoking relapse. In conclusion, the current, but still limited, literature appears to suggests that chronic smokers (heavily dependent on nicotine) have deficits in striatal DA integrity and reduced striatal non-drug reward-related neural activity, leading one to surmise that there may be nicotine addiction-induced alterations in the neural correlates of reward and motivational processing. These alterations may provoke relapse during early nicotine withdrawal as only nicotine is capable of normalizing DA in these brain regions.

Figure 11.2 Striatal and prefrontal activation associated with a reward anticipation trial type (money or smoking) X condition (abstinent or non-abstinent) interaction. The right caudate head: 6, 4, 2; F = 23.95, 169 voxels; left caudate head: −4, 6, −2; F = 20.18, 121 voxels; and medial prefrontal cortex: 2, 40, 12; F = 20.60, 194 voxels showed the interaction effect. There was greater activation in these regions during abstinence in response to cues that predicted future cigarette rewards, but greater activation during nonabstinence (satiety) in response to cues that predicted future monetary rewards.

Figure 11.2 Striatal and prefrontal activation associated with a reward anticipation trial type (money or smoking) X condition (abstinent or non-abstinent) interaction. The right caudate head: 6, 4, 2; F = 23.95, 169 voxels; left caudate head: −4, 6, −2; F = 20.18, 121 voxels; and medial prefrontal cortex: 2, 40, 12; F = 20.60, 194 voxels showed the interaction effect. There was greater activation in these regions during abstinence in response to cues that predicted future cigarette rewards, but greater activation during nonabstinence (satiety) in response to cues that predicted future monetary rewards.

Reprinted from Biological Psychiatry, 76, 9, Sweitzer, M. M., Geier, C. F., Joel, D.L., et al., Dissociated effects of anticipating smoking versus monetary reward in the caudate as a function of smoking abstinence, pp. 681–688. Copyright © 2014 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

11.3 Cognition

Chapter 4 introduced the concept of cognitive control and its impairment in addiction. Cognitive control processes include a broad class of mental operations, including planning, response inhibition, and action monitoring, and evidence is continually emerging regarding the contributions of the prefrontal cortex (PFC) as a substrate of cognitive control processes. Smoking relapse partly reflects withdrawal, which includes difficulty concentrating and other problems with cognitive control, and these effects can be reversed by cigarette smoking. Effective everyday mental functioning requires cognitive control, and disturbances to cognition during nicotine abstinence may increase the susceptibility to smoking relapse because the control over smoking urges and behaviour is diminished. The ability to monitor one’s behaviour within a certain environment during acute nicotine abstinence may be essential when there is a need to detect conflicting circumstances and resolve them quickly, in order to prevent relapse. Indeed, neural functioning during nicotine abstinence in brain regions has actually been shown to be increased under conditions of cognitive control, and may reflect compensatory and adaptive processes in order to cope with the effects of withdrawal. Studies have shown an exaggerated pattern of neural activity in brain regions associated with cognitive control, an effect that remits under conditions of nicotine satiety.

Azizian and colleagues (2010), for example, were able to show that, under conditions of cognitive response conflict, overnight (>12 h) nicotine abstinence induced greater anterior cingulate cortex (ACC) activity (see Figure 11.3). The same figure shows that after smoking there was greater activity in the middle frontal gyrus (MFG). Therefore, exaggerated neural activity during acute nicotine withdrawal may be a reflection of compensatory mechanisms by which networks expend excessive energy to support selective cognitive control processes, which is resolved upon smoking. This suggests that smokers demonstrate state-dependent (abstinence vs satiety) alterations in cognition, which are dependent on the functioning of prefrontal brain networks.

Figure 11.3 Showing that smokers had a significantly greater response during the no-smoking compared smoking session in (a) the anterior cingulate cortex (ACC—p <0.02, paired t-test), but a significantly greater response during the smoking compared no-smoking session in (b) the middle frontal gyrus (MFG—p <0.003, paired t-test). The scale represents the colour (from dark to light yellow) of the cluster corresponding to the increasing Z-statistic. The structural image represents the MNI152 average normal brain with corresponding coronal (anterior–posterior) and sagittal (right–left) coordinates.

Figure 11.3 Showing that smokers had a significantly greater response during the no-smoking compared smoking session in (a) the anterior cingulate cortex (ACC—p <0.02, paired t-test), but a significantly greater response during the smoking compared no-smoking session in (b) the middle frontal gyrus (MFG—p <0.003, paired t-test). The scale represents the colour (from dark to light yellow) of the cluster corresponding to the increasing Z-statistic. The structural image represents the MNI152 average normal brain with corresponding coronal (anterior–posterior) and sagittal (right–left) coordinates.

Reprinted by permission from Macmillan Publishers Ltd: Neuropsychopharmacology, 35, 3, Azizian, A., Nestor, L. J., Payer, D. et al., Smoking reduces conflict-related anterior cingulate activity in abstinent cigarette smokers performing a Stroop task, pp. 775–782. Copyright © 2009, Rights managed by Nature Publishing Group.

Decreased activity in executive brain networks has also been suggested as a marker of relapse. Loughead and colleagues (2015), for example, showed that abstinence-induced decreases in the dorsolateral prefrontal cortex (DLPFC—see Chapter 4) and reduced suppression of the posterior cingulate cortex (PCC) during a working memory task were able to predict smoking relapse, effects that were better than standard clinical variables in predicting treatment outcome (see Figure 11.4). This suggests that during nicotine abstinence, the ability to activate the DLPFC in situations requiring cognitive control may protect against subsequent relapse. Therefore, the failure to develop, or a loss of previously developed cognitive control in nicotine addiction may affect the ability to restrain cigarette smoking during situations requiring a high degree of cognitive control and self-monitoring. To this end, inferior top-down cognitive control neural processing may be an important factor in provoking smoking relapse as a result of increased, overriding bottom-up neural functioning that biases the choice to reinitiate cigarette smoking.

Figure 11.4 Whole-brain condition (abstinence challenge, smoking satiety) by group (relapse, quit) ANOVA interaction effect. (B MF/CG and right DLPFC clusters (see above) show comparable activation under the smoking satiety condition in both groups. There is greater activation in the left DLPFC in the smoking satiety condition for the relapse group (vs quit). Under abstinence, challenge signal in all clusters decreases for the relapse group and increases for the quit group. Abbreviations: MF/CG: medial frontal/cingulate gyrus; DLPFC: dorsolateral prefrontal cortex.

Figure 11.4 Whole-brain condition (abstinence challenge, smoking satiety) by group (relapse, quit) ANOVA interaction effect. (B MF/CG and right DLPFC clusters (see above) show comparable activation under the smoking satiety condition in both groups. There is greater activation in the left DLPFC in the smoking satiety condition for the relapse group (vs quit). Under abstinence, challenge signal in all clusters decreases for the relapse group and increases for the quit group. Abbreviations: MF/CG: medial frontal/cingulate gyrus; DLPFC: dorsolateral prefrontal cortex.

Reprinted by permission from Macmillan Publishers Ltd: Neuropsychopharmacology, 40, 6, Loughead, J., Wileyto, E. P., Ruparel, K., et al., Working memory-related neural activity predicts future smoking relapse, pp. 1311–1320. Copyright © 2015, Rights Managed by Nature Publishing Group.

11.4 Treatments

As supported with empirical evidence, nicotine addiction presents a serious risk to health because of disturbances in brain functioning underlying key behavioural processes—disturbances that promote continued nicotine use. Therefore, treatments that target disruptions to the neural substrates of these processes are likely to improve treatment outcome in nicotine addiction. One such treatment that has been developed for nicotine addiction is varenicline (Chantix). Varenicline is a partial agonist at α‎4β‎2 nAChRs (see Chapter 6), and so mimics the effects of nicotine, but to a lesser extent, and also blocks the effects of smoking. Varenicline has been shown to be one of the leading medications for promoting long-term smoking cessation. Evidence suggests that this effect is most likely due to it reducing neural responses to smoking cues, concomitant with attenuated craving.

Franklin and colleagues (2011), for example, were able to show that three weeks of varenicline treatment (compared with placebo) significantly reduced cigarette craving, together with activity in the orbitofrontal cortex (OFC) in response to smoking cues. The OFC was introduced in Chapter 4 in the context of motivation and drive—the goal-directed pursuit of rewards. The OFC is connected with brain regions involved in DA-dependent reinforcement, such as the NAcc, and receives direct DA projections from the midbrain VTA. Importantly, the OFC is involved in the attribution of salience to reinforcing stimuli, and is an established neural correlate that responds to drug cues. Therefore, varenicline’s clinical efficacy may surround its attenuating effect on the reward-evaluating processes of the OFC in response to smoking cues. Brandon and colleagues (2011) also reported that varenicline, compared with placebo, reduced tonic and cue-provoked craving, as well as the expected value of cigarettes, the time spent smoking, and self-reported reward (i.e. satisfaction) from cigarette smoking (see Figure 11.5).

Figure 11.5 Comparison of varenicline (solid line) versus placebo (dotted line) across sessions on (a) tonic craving (Questionnaire of Smoking Urges, total score); (b) cue-provoked craving (ratings in response to smoking images, controlling for neutral images); (c) expected reinforcement value (cigarette choice procedure cross-over value) before the full cigarette; (d) total time smoking (seconds); (e) total number of puffs smoked; and (f) perceived reward (mCEQ: Modified Cigarette Evaluation Questionnaire Satisfaction Scale) following the full cigarette. Error bars indicate standard error. *p < 0.05; **p < 0.01; ***p < 0.001.

Figure 11.5 Comparison of varenicline (solid line) versus placebo (dotted line) across sessions on (a) tonic craving (Questionnaire of Smoking Urges, total score); (b) cue-provoked craving (ratings in response to smoking images, controlling for neutral images); (c) expected reinforcement value (cigarette choice procedure cross-over value) before the full cigarette; (d) total time smoking (seconds); (e) total number of puffs smoked; and (f) perceived reward (mCEQ: Modified Cigarette Evaluation Questionnaire Satisfaction Scale) following the full cigarette. Error bars indicate standard error. *p < 0.05; **p < 0.01; ***p < 0.001.

Reproduced from Psychopharmacology, 218, 2, Brandon, T. H., Drobes, D. J., Unrod, M. et al, Varenicline effects on craving, cue reactivity, and smoking reward, pp. 391–403. Copyright © 2011, Springer-Verlag.

Cognitive disturbances are a core symptom of nicotine withdrawal, and as reported, contribute to smoking relapse. Varenicline has also been shown to increase cognitive control-related brain activity during nicotine abstinence (Loughead et al. 2010). These effects were reported in the dorsal anterior cingulate/medial frontal cortex and DLPFC during a working memory task, particularly at high levels of task difficulty, with associated improvements in cognitive performance among those who were heavily nicotine dependent (see Figure 11.6).

Figure 11.6 Visual N-back working memory task. (a) Coloured regions represent functionally defined ROI masks identified using a whole-brain repeated-measures ANOVA. (b) Mean percent signal change for the 1-back, 2-back, and 3-back conditions from the ROIs. Main effects of treatment (varenicline, placebo) were observed in all three ROIs, significant at p < 0.05; DLPFC, dorsolateral prefrontal cortex; MF/CG, dorsal anterior cingulate/medial frontal cortex; ROI, region of interest; BOLD, blood oxygenation level-dependent.

Figure 11.6 Visual N-back working memory task. (a) Coloured regions represent functionally defined ROI masks identified using a whole-brain repeated-measures ANOVA. (b) Mean percent signal change for the 1-back, 2-back, and 3-back conditions from the ROIs. Main effects of treatment (varenicline, placebo) were observed in all three ROIs, significant at p < 0.05; DLPFC, dorsolateral prefrontal cortex; MF/CG, dorsal anterior cingulate/medial frontal cortex; ROI, region of interest; BOLD, blood oxygenation level-dependent.

Reprinted from Biological Psychiatry, 67, 8, Loughhead, J., Ray, R., Wileyto, E. P. et al., Effects of the alpha4beta2 partial agonist varenicline on brain activity and working memory in abstinent smokers, pp. 715–721. Copyright © 2010 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Bupropion (see Chapter 7) is a DA reuptake inhibitor and a weak releasing agent that increases the ability to quit smoking, due to its attenuation of withdrawal symptoms. Smokers treated with bupropion describe a reduction in nicotine withdrawal symptoms that include negative affect, the urge to smoke (craving), difficulty concentrating, and irritability. Several studies have now reliably replicated the success of bupropion treatment in smokers, suggesting a common mechanism by which this medication facilitates smoking cessation. One mechanism by which buproprion promotes abstinence is through its ability to attenuate craving and its neural correlates. Culbertson and colleagues (2011) showed that bupropion-treated participants reported less craving, together with less activation in the VS, OFC, and ACC when actively resisting craving, compared with placebo-treated participants. This study also reported that reductions in self-reported craving correlated with reduced activation in these brain regions. Interestingly, bupropion also acts as a weak antagonist at nAChRs, decreasing the probability of their activation by nicotine. Therefore, bupropion may also reduce the reinforcing value of smoking should patients relapse during treatment.

The effects of bupropion on smoking cessation may also include improvements in cognitive processing during nicotine abstinence which as discussed, is a potential prognostic marker of relapse. Perkins and colleagues (2013) has shown that, compared with placebo, bupropion during early smoking abstinence significantly improves working memory performance (see Figure 11.7). The DLPFC is particularly involved in the ‘on-line’ processing of information (i.e. working memory), and is in receipt of DA projections from the midbrain VTA. Importantly, DA activity in the DLPFC is involved in the modulation of cognitive control processes, perhaps suggesting that the efficacy of bupropion to improve cognitive processing in nicotine abstinence is due to its dopaminergic effects in this region.

Figure 11.7 Mean±SE of the median response time for correct responses on N-back task of working memory, by memory load, during initial smoking baseline to learn the task and due to placebo or bupropion after overnight tobacco abstinence (N = 24). * p = 0.01 for the difference between bupropion and placebo in responding to 2-back vs 0-back.

Figure 11.7 Mean±SE of the median response time for correct responses on N-back task of working memory, by memory load, during initial smoking baseline to learn the task and due to placebo or bupropion after overnight tobacco abstinence (N = 24). * p = 0.01 for the difference between bupropion and placebo in responding to 2-back vs 0-back.

Reprinted from Drug and Alcohol Dependence, 133, 1, Perkins, K. A., Karelitz, J. L., Jao, N. C., et al., Effects of bupropion on cognitive performance during initial tobacco abstinence, pp. 283–286. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

One other potential benefit of bupropion for smokers is that it also has antidepressant effects—indeed, this was its first medical use. When people smoke tobacco become addicted to the nicotine, there are also chemicals in the burning tobacco that block the enzyme called monoamine oxidase (MOA). This enzyme breaks down DA, serotonin, and noradrenaline, which means smoking can elevate levels of these neurotransmitters. Fowler and colleagues (1996) indeed reported that the brains of living smokers show a 40% decrease in the level of MAO-B compared to non-smokers or former smokers. MAO-B is specifically involved in the breakdown of DA, which as discussed throughout this volume, is a neurotransmitter implicated in reward and motivation. Upon smoking cessation, however, MAO blockade by tobacco is lifted, so the enzyme then resumes its breaking-down activity of these neurotransmitters. Therefore, increased MAO activity in potentially deficient neurotransmitter systems in nicotine addiction, contributes to the low mood many smokers experience when they initially quit. Bupropion, by enhancing DA and noradrenaline, offsets this deficit to some extent. Recently we have seen in many countries the rise of e-cigarettes (e-cigs) or vaping. Here a nicotine solution is heated and inhaled. This provides the brain with nicotine to counteract the deficit seen in tobacco withdrawal. As vaping liquids are much less toxic than burning tobacco (see Nutt et al. 2014), vaping is seen as a powerful means of tobacco harm reduction.

11.5 Conclusion

The majority of cigarette smokers endorse the desire to give up smoking but only a small percentage will ever achieve full nicotine abstinence. Despite their best efforts and expressed preferences, nicotine-dependent individuals often appear incapable of exerting sufficient control over their smoking urges and behaviour. This inability to exert control appears to implicate potential disturbances in reward processing, salience attribution, and ultimately, diminished cognitive control. Importantly, treatments that attenuate disturbances to reward and cognitive networks in the brain, particular during early abstinence, are conferred with the efficacy to promote smoking cessation and protect against relapse.

References and Further Reading

Azizian A, Nestor LJ, Payer D, et al. (2010). Smoking reduces conflict-related anterior cingulate activity in abstinent cigarette smokers performing a stroop task. Neuropsychopharmacology, 35(3), 775–82. doi:10.1038/npp.2009.186.Find this resource:

Brandon TH, Drobes DJ, Unrod M, et al. (2011). Varenicline effects on craving, cue reactivity, and smoking reward. Psychopharmacology (Berl), 218(2), 391–403.Find this resource:

Brody AL, Olmstead RE, London ED, et al. (2004). Smoking-induced ventral striatum dopamine release. American Journal of Psychiatry, 161(7), 1211–18.Find this resource:

Brody AL, Mandelkern MA, London ED, et al. (2006a). Cigarette smoking saturates brain alpha 4 beta 2 nicotinic acetylcholine receptors. Archives of General Psychiatry, 63(8), 907–15.Find this resource:

Brody AL, Mandelkern MA, Olmstead RE, et al. (2006b). Gene variants of brain dopamine pathways and smoking-induced dopamine release in the ventral caudate/nucleus accumbens. Archives of General Psychiatry, 63(7), 808–16. doi:10.1001/archpsyc.63.7.808.Find this resource:

Brody AL, Mukhin AG, Stephanie S, et al. (2013). Treatment for tobacco dependence: effect on brain nicotinic acetylcholine receptor density. Neuropsychopharmacology, 38(8), 1548–56. doi:10.1038/npp.2013.53.Find this resource:

Culbertson CS, Bramen J, Cohen MS, et al. (2011). Effect of bupropion treatment on brain activation induced by cigarette-related cues in smokers. Archives of General Psychiatry, 68(5), 505–15.Find this resource:

Feduccia AA, Chatterjee S, and Bartlett SE (2012). Neuronal nicotinic acetylcholine receptors: neuroplastic changes underlying alcohol and nicotine addictions. Frontiers in Molecular Neuroscience, 5, 83. doi:10.3389/fnmol.2012.00083.Find this resource:

Fehr C, Yakushev I, Hohmann N, et al. (2008). Association of low striatal dopamine d2 receptor availability with nicotine dependence similar to that seen with other drugs of abuse. American Journal of Psychiatry, 165(4), 507–14.Find this resource:

Fowler JS, Volkow ND, Wang GJ, et al. (1996). Inhibition of monoamine oxidase B in the brains of smokers. Nature, 379(6567), 733–6. doi:10.1038/379733a0.Find this resource:

Franklin T, Wang Z, Suh JJ, et al. (2011). Effects of varenicline on smoking cue-triggered neural and craving responses. Archives of General Psychiatry, 68(5), 516–26.Find this resource:

Loughead J, Wileyto EP, Ruparel K, et al. (2015). Working memory-related neural activity predicts future smoking relapse. Neuropsychopharmacology, 40(6), 1311–20.Find this resource:

Loughead J, Ray R, Wileyto EP, et al. (2010). Effects of the alpha4beta2 partial agonist varenicline on brain activity and working memory in abstinent smokers. Biological Psychiatry, 67(8), 715–21.Find this resource:

Nutt DJ, Phillips LD, Balfour D, et al. (2014). Estimating the Harms of Nicotine-Containing Products Using the MCDA Approach. European Addiction Research, 20(5), 218–25. doi:10.1159/000360220.Find this resource:

Perkins KA, Karelitz JL, Jao NC, et al. (2013). Effects of bupropion on cognitive performance during initial tobacco abstinence. Drug and Alcohol Dependency, 133(1), 283–6.Find this resource:

Rose EJ, Ross TJ, Salmeron BJ, et al. (2011). Chronic exposure to nicotine is associated with reduced reward-related activity in the striatum but not the midbrain. Biological Psychiatry, 71(3), 206–13.Find this resource:

Sweitzer MM, Geier CF, Joel DL, et al. (2014). Dissociated effects of anticipating smoking versus monetary reward in the caudate as a function of smoking abstinence. Biological Psychiatry, 76(9), 681–8. doi:10.1016/j.biopsych.2013.11.013.Find this resource: