Research suggests that mindfulness-practices may aid smoking cessation. Yet, the neural mechanisms underlying the effects of mindfulness-practices on smoking are unclear. Response inhibition is a main deficit in addiction, is associated with relapse, and could therefore be a candidate target for mindfulness-based practices. The current study hence investigated the effects of a brief mindfulness-practice on response inhibition in smokers using behavioral and electroencephalography (EEG) measures. Fifty participants (33 females, mean age 20 years old) underwent a protocol of cigarette exposure to induce craving (cue-exposure) and were then randomly assigned to a group receiving mindfulness-instructions or control-instructions (for 15 minutes approximately). Immediately after this, they performed a smoking Go/NoGo task, while their brain activity was recorded. At the behavioral level, no group differences were observed. However, EEG analyses revealed a decrease in P3 amplitude during NoGo vs. Go trials in the mindfulness versus control group. The lower P3 amplitude might indicate less-effortful response inhibition after the mindfulness-practice, and suggest that enhanced response inhibition underlies observed positive effects of mindfulness on smoking behavior.
Citation: Andreu CI, Cosmelli D, Slagter HA, Franken IHA (2018) Effects of a brief mindfulness-meditation intervention on neural measures of response inhibition in cigarette smokers. PLoS ONE 13(1): e0191661. https://doi.org/10.1371/journal.pone.0191661
Editor: Antonio Verdejo-García, Monash University, AUSTRALIA
Received: August 17, 2017; Accepted: January 9, 2018; Published: January 25, 2018
Copyright: © 2018 Andreu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All data files are available from Zenodo data repository, DOI https://doi.org/10.5281/zenodo.1044036.
Funding: This work was supported by National Committee of Science and Technology of Chile (National PhD Grant 21140175 to C.I.A.); Fund for Innovation and Competitiveness (FIC) of the Chilean Ministry of Economy, Development and Tourism, through the Millennium Scientific Initiative, Grant [IS 130005—MIDAP to C.I.A. and D.C.]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Cigarette smoking is the largest preventable cause of death in the world and the costs associated with it correspond to more than $300 billion a year in the United States [1, 2]. Although smokers realize that smoking carries health risks, quitting smoking is notoriously hard and may require several attempts . Some recent studies suggest that mindfulness-based practices are as or more effective in treatment for smoking cessation than conventional behavioral treatments such as Freedom from Smoking treatment, cognitive-behavioral approaches or 12-step programs [4–11].
Mindfulness practices involve attending to present-moment experiences, with non-judgmental and non-reactive awareness to internal and external stimuli . Various studies have reported positive results for the efficacy of mindfulness programs in substance use and relapse prevention (Mindfulness-based relapse prevention, MBRP) [13–17], including studies using randomized-controlled trials with several weeks of mindfulness training [4, 9, 18, 19]. Reduced cigarette craving and withdrawal symptoms have also been observed after shorter mindfulness-based interventions [11, 20]. For example, Bowen and Marlatt used a brief 15 minute cue-exposure paradigm with mindfulness-based instruction and found a reduction in the number of cigarettes smoked 7 days after the intervention, as compared to a control group . This finding suggests that mindfulness instruction can quickly alter smoking behavior.
Although there are theoretical proposals regarding the tentative psychological and neurobiological mechanisms underlying the effects of mindfulness training on addiction [21–24], few studies have so far empirically examined the neurocognitive effects of mindfulness-based practices on addiction [25, 26]. It has been hypothesized that mindfulness-based exercises modulate both bottom-up and top-down processes among individuals with substance use disorders . Evidence for bottom-up modulation includes, for examples reduced stress reactivity after mindfulness training in alcohol and/or cocaine abusers  and decreased reactivity to smoking cues using a mindfulness-instruction in smokers as evidenced by decreased craving-related activity in the anterior cingulate cortex (ACC) . There is also evidence for top-down changes, e.g., in inhibitory control, performance monitoring, and decision-making, related to mindfulness [28–34]. Notably, inhibitory control is disrupted in addictive behaviors, with several models of addiction highlighting its role in the development and maintenance of addiction [35–40]. This raises the possibility that mindfulness may affect addictive behaviors by improving response inhibition. Yet, how mindfulness may do so it still very much unclear. The present study aimed to investigate the neural mechanisms underlying the effect of a brief mindfulness-based practice on response inhibition in cigarette smokers.
In recent years, interest in mindfulness meditation as a practice to train cognitive functions has grown exponentially. Accumulating evidence suggests that meditation practice can modulate attention and executive functions [28, 32, 41–47] as well as emotional reactivity or regulation skills [41, 48–56]. Recent research also suggests that meditation may enhance cognitive control. Experienced meditators and novices, who had 16 weeks of mindfulness training, displayed better performance on Stroop interference tasks [29, 45, 47] and response inhibition tasks [28, 57]. Improvements in conflict monitoring using the Attention Network Test (ANT)  have also been found in experienced meditators  after a week-long meditation retreat  and after just 5 days of meditation training in novices . Functional magnetic resonance imaging (fMRI) studies have shown that ACC and prefrontal activity is modulated by meditation, with experienced meditators exhibiting increased activation and stronger connectivity in and between these areas [61–64].
Although these studies are indicative of enhanced cognitive control in meditators, effects of meditation on neurobiological measures of response inhibition have not been studied yet in the context of addiction. Given that response inhibition is a core function impaired in addictions and a main mechanism through which mindfulness practices may act to decrease smoking addiction or prevent relapse, it is essential to determine if mindfulness-based practices can indeed modulate behavioral and/or neural indices of response inhibition.
Response inhibition has been measured using Go/NoGo tasks with behavioral measures (omission errors in Go trials; commission errors in NoGo trials; reaction times, RTs) . Two event-related potentials (ERPs) have been used as neural indices of response inhibition, the N2 and P3, as both are associated with larger amplitudes in NoGo trials compared to Go trials and thus reflect changes in brain activity needed to inhibit responses in a Go/NoGo task . The N2 is a negative wave emerging 200–300 ms after stimulation and is thought to represent top-down mechanisms necessary to inhibit an automatic response and detect conflict during early stages of inhibition [65–68]. On the other hand, the P3 is a positive wave that emerges 300–500 ms after stimulus onset and is thought to reflect a later stage of the inhibitory process, related to the inhibition of the motor system itself [65, 69–72]. Deficits in response inhibition indices (behavioral or neural) have been described in several substance-dependent patient populations, including cigarette smokers [39, 40]. The specific effect on response inhibition varies between different types of addictions, but the most consistent finding is lower N2 amplitudes in addicted populations relative to controls, accompanied by reduced task performance or increased error rates in a Go/NoGo task . In a clear example, Luijten et al. found reduced N2 amplitudes and diminished accuracy on a smoking Go/NoGo task in smokers compared to controls .
To test the effects of a mindfulness-based practice on response inhibition in an addiction context, using EEG, we investigated the effects of a previously developed brief mindfulness intervention  on behavioral and neural responses on a Go/NoGo task in cigarette smokers . We expected augmented response inhibition indices in smokers after the brief mindfulness-based intervention as compared to smokers after a control intervention. More specifically, we expected lower error rates (particularly commission errors) in the mindfulness compared to control group, accompanied with increased N2/P3 amplitudes. In addition, we expected these behavioral and/or electrophysiological effects to be particularly pronounced in trials with smoking-related compared to neutral pictures. Finally, we expected higher post-intervention craving in the control group than in the mindfulness group.
Fifty cigarette-smokers participated in this study. Subjects had to be 18 years or older, smoke daily, be interested in cutting down or quitting smoking, and not be currently enrolled in a treatment or program. Exclusion criteria included current abuse of a substance other than nicotine, a current diagnosis of a physical or psychiatric illness and previous experience with mindfulness meditation. Eligible participants were randomly assigned to the mindfulness or control group. 25 smokers in the mindfulness group (17 females, Mean Age [MA] = 20.00, Standard Deviation [SD] = 1.72) and 25 smokers in the control group (16 females, MA = 20.6, SD = 1.75) completed the experiment. The mean age (t (48) = -1.2, p = 0.23) and gender ratio (chi-square = 0.09, p = 0.76) of both groups did not differ. Participants were undergraduate students, who received course credits for their participation. The local ethics committee approved the protocol of the study (number 2015–7) and participants provided written informed consent.
Participants were instructed to abstain from smoking for at least two hours before the experiment to reduce the acute effects of nicotine on ERP amplitudes . After arrival, participants approved participation by signing an informed consent and completed the questionnaires. Participants were then seated in a light and sound-attenuated room. Electrodes were applied and the experiment was explained. Following a previous study , participants then completed a cue-exposure protocol and received either mindfulness-based or control coping instructions via audio recordings. As in the previous study, the cue-exposure protocol was delivered in four stages: open a pack of cigarettes, place a cigarette on the table in front of you, place the cigarette in your mouth and, finally, bring a lighter to the cigarette without igniting the cigarette. These instructions were the same for the mindfulness and control groups. Instructions to cope with cue-induced smoking-related thoughts or cravings that arose were different, however. Control participants were asked to use any techniques that they would naturally use to cope with urges or one they had used in the past. At the end of the cue-exposure protocol, control participants reported that they used distraction techniques to cope with smoking urges, allowing us to verify that they did not employ a mindfulness-related technique. The mindfulness group received instructions to accept feelings, sensations, or thoughts in a mindful, nonjudgmental way. They were also given instructions for “urge-surfing”, a technique often included in MBRP treatment for substance use [8, 10]. The duration of the audio sets was the same for both groups, approximately 15 minutes. Then, a modified smoking Go/NoGo task was performed while EEG and behavioral data were collected (see Fig 1 for a schematic illustration of the procedure). Additionally, participants performed an Eriksen-Flanker task after the Go/NoGo task (results not reported here).
All participants completed a questionnaire concerning demographic data (i.e. age, gender, level of education), the amount of cigarettes smoked per day and the Fagerstrom Test for Nicotine Dependence (FTND) [74, 75]. Participants also completed the brief version of the Questionnaire of Smoking Urges (QSU-brief) [76, 77] to assess subjective craving for a cigarette at three different time points of the experiment: before and after the intervention (pre, post) and at the end of the experiment (final).
Smoking Go/NoGo task
A previously developed smoking-related Go/NoGo task  was modified for this study. A series of smoking or neutral pictures were presented. Each picture was displayed for 200 ms and had a blue or yellow frame. Frame color indicated whether a stimulus was a Go or NoGo trial. Each stimulus was followed by a black screen for a randomly varying duration between 1000 ms and 1500 ms. Participants were instructed to respond to the pictures in Go trials by pressing a button with the right index finger as fast as possible, and to withhold their response in NoGo trials. About 25% of all trials were NoGo trials and the proportion of smoking and non-smoking pictures in the task was equal. At most, four Go and two NoGo trials were presented consecutively. Participants completed the task in two blocks of 240 trials each, one with smoking pictures and one with neutral pictures. Block order was randomized and in the middle of the task an additional block with 18 emotionally positive pictures was used to washout possible carry-over effects (EmoMadrid affective picture database, http://www.uam.es/CEACO/EmoMadrid.htm) , but EEG was not analyzed during the additional block. Participants were given the opportunity to take a short break four times during the task. Before the start of the task, participants performed 23 practice trials, involving additional neutral pictures. Total task duration was about 15 minutes.
EEG recording and processing
The EEG was recorded using Biosemi Active-Two amplifier system (Biosemi, Amsterdam, the Netherlands) from 33 scalp sites (following the 10–20 International system with one additional electrode at FCz) with Ag/AgCl electrodes mounted in an elastic cap. Six additional electrodes were attached to left and right mastoids, the two outer canthi of both eyes (HEOG), and infraorbital and supraorbital regions of the eye (VEOG). BrainVision Analyzer2 (Brain products GmbH, Munich, Germany) was used to process the data offline. All signals were digitized with a sampling rate of 512 Hz and a 24-bit A/D conversion with a bandpass of 0–134 Hz. Data were referenced offline to average mastoids. Offline, EEG and EOG activity was bandpass filtered between 0.1–30 Hz (phase shift-free Butterworth filter; 24 dB/octave slope). Data were next segmented in epochs of 1 s (200 ms before and 800 ms after stimulus onset). Ocular correction was applied using the Gratton and Coles algorithm , and epochs with an EEG signal exceeding ±100 mV were excluded from the average. The mean 200 ms pre-stimulus period served as a baseline. After baseline correction, average ERP waves were calculated for artifact-free trials at each scalp site separately for the four conditions of the smoking Go/NoGo task (all correct Go neutral, Go smoking, NoGo neutral, NoGo smoking trials). The N2 was defined as the mean value in the 200–300 ms time interval after stimulus onset, and was studied at a cluster of frontocentral electrodes, including Fz, FC1, FC2, FCz and Cz . The P3 was defined as the mean value in the 300–450 ms time window after stimulus onset, and was studied at a cluster of central electrodes including FCz, Cz, C3, C4 . One participant of the mindfulness group was excluded from the N2/P3 analyses because less than 20 artifact-free ERP epochs in at least one of the conditions remained after preprocessing, which is considered too few to obtain a reliable N2 and P3 . The mean number of analyzable Go and NoGo epochs for the N2 and P3 components was 161.2 (SD = 26.07) and 43.9 (SD = 7.55) for neutral pictures and 161.9 (SD = 20.09) and 42.1 (SD = 6.92) for smoking pictures, respectively. The mean number of available error-epochs did not differ between groups for Go trials, t (47) = -0.51, p = 0.61 and for NoGo trials, t (47) = -0.64, p = 0.52.
Repeated Measure ANOVAs (RM-ANOVAs) (with Greenhouse–Geisser adjusted p-values when appropriate) were used to analyze error rates and reaction time data of the Go/NoGo task, as well as the ERPs. The between-subjects factor in all RM-ANOVAs was Group (Mindfulness versus Control). The RM-ANOVAs examining behavioral effects, two within-subject factors were included: Inhibition (Go versus NoGo) and Picture (smoking versus neutral). The RM-ANOVAs examining ERP effects included a third within-subject factor Electrodes (Fz, FC1, FC2, FCz and Cz for N2; FCz, Cz, C3 and C4 for P3). Additionally, a 2x3 (Group x Time) RM-ANOVA was performed to analyze changes in the QSU-brief in time, and a t-test to compare the final-pre difference score in the QSU-brief between groups.
The number of daily cigarettes, the FTND score and the QSU-brief score per time point are presented in Table 1. There was no difference between groups in the number of daily smoked cigarettes, t(48) = 0.914, p = 0.37. Also, there was no difference between groups on the level of nicotine dependence measured by the FTND, t(48) = 0.4, p = 0.69, where both groups showed low levels of dependence. There was no significant change in the QSU-brief score measured by time, as no main group or time effect, nor group x time interaction was found (all p values > 0.11). Interestingly, a trend towards significance was found when the difference of the final QSU score minus pre QSU score was post hoc compared between groups, t(47) = -1.948, p = 0.057, with an increase in the QSU-brief score only in the control group, suggestive of increased smoking-urges in the control, but not the mindfulness group (Table 1). Fig 2 shows the QSU results for control and mindfulness groups for the three measured times and the final minus the pre score.
This figure displays smoking urge scores for the control group (black) and mindfulness group (gray) separately for the pre, post and final time measurements, as well as the final minus pre (final–pre) difference. The control group shows increased smoking urges over time, contrary to the mindfulness group.
Error rates and reaction times on the smoking Go/NoGo task are presented in Table 1. As expected, participants generally made more errors in NoGo trials compared to Go trials, as indicated by a significant main inhibition effect, F(1,48) = 221.3, p<0.001. Yet, contrary to our prediction, the brief mindfulness intervention was not associated with improved performance on the task, as there was no difference in error rate between groups, F(1,48) = 1.635, p = 0.21. Additionally, there was no difference between groups in error rates between Go and NoGo trials, as no Group x Inhibition interaction was found, F(1,48) = 1.15, p = 0.29. No main or interaction effects for Picture were found for error rates. Regarding reaction times in Go trials, both groups responded equally fast, as no main Group effect was found (F(1,48) = 0.098, p = 0.75). Also, there was no significant main effect for Picture, nor a Group x Picture interaction (all ps > 0.15).
Effects on ERP
Fig 3 shows the grand average ERP waveforms for neutral and smoking-related pictures separately for both groups at electrodes Fz and Cz. As expected, the N2 amplitude in NoGo trials was larger than in Go trials, as evidenced by a robust main effect of Inhibition, F(1, 47) = 13.18, p = 0.001. Yet, the two groups did not differ in N2 amplitude, as reflected in non-significant main Group and Group x Inhibition interaction effects (all p values > 0.6). N2 amplitudes were larger for neutral pictures than for smoking-related pictures, with a main Picture effect, F(1,47) = 7.28, p = 0.01. This N2 amplitude difference was not modulated by group or trial type (NoGo vs Go), as no significant Group x Picture interaction, nor Picture x Inhibition interaction was observed (all p values > 0.3). The main effect of Electrode was significant, F(4,188) = 48.91, p<0.001, reflecting a larger N2 at Fz. The Electrode x Inhibition interaction effect was significant as well, F(4,188) = 4.39, p = 0.013.
Shown are grand-average stimulus-locked ERP waveforms for neutral (left) and smoking pictures (right) at Fz (Panel A) and Cz (Panel B), separately for correct Go and NoGo trials and the mindfulness and control group. Scalp voltage maps are shown in Panel C for mean amplitude for 300–450 ms. As can be seen, the mindfulness group displayed a reduced NoGo P3 compared to the control group.
As to the P3, as expected, there was a main effect of Inhibition on P3 amplitude (F(1, 47) = 44.84, p<0.001), with larger P3 amplitudes during NoGo trials than during Go trials. Notably, this inhibition effect was different between groups, as expressed by a Group x Inhibition effect, F(1,47) = 5.34, p = 0.025, reflecting smaller NoGo P3 amplitudes in the mindfulness group (Fig 3). The main Group effect was not significant, F(1,47) = 1.05, p = 0.3. P3 amplitudes were larger for smoking pictures than for neutral pictures, with a main Picture effect, F(1,47) = 21.16, p<0.001. There was no difference between groups in the smoking-related P3 effect, as no significant Group x Picture interaction, nor Group x Picture x Inhibition interaction was observed, nor did response inhibition modulate this effect, as reflected by a non-significant Picture x Inhibition interaction, (all p values > 0.5). The main effect of Electrode was significant, F(3,141) = 10.33, p<0.001, indicative of a maximal P3 at electrodes C3 and Cz. The Electrode x Inhibition interaction was also significant, F(3,141) = 57.2, p<0.001.
In this EEG study, we used a previously-developed brief mindfulness-based protocol for cigarette smoking  to examine effects of mindfulness on (neural makers of) response inhibition, an important cognitive control process hypothesized to be targeted by such practices. To this end, participants performed a smoking Go/NoGo paradigm  while we recorded their brain activity after a mindfulness-based or a control intervention. Contrary to our prediction, the brief mindfulness intervention was not associated with changes in behavioral response inhibition. Yet, notably, at the neural level, compared to the control group, the mindfulness group showed a reduced NoGo P3, though task performance was the same between groups. Although the explanation of this finding is not straightforward, the lower NoGo P3 in the mindfulness group may reflect reduced effort to reach a similar level of performance as the control participants [78, 81–83]. Together, these results may suggest that response inhibition is an important neurobiological mechanism targeted by mindfulness-based practices to cope with cigarette smoking.
The absence of behavioral improvements in response inhibition may be explained by the short duration of the mindfulness intervention or other factors, such as that the task used may not be sensitive to changes by brief mindfulness interventions. Fifteen minutes may not be long enough to behaviorally influence response inhibition measures in naïve participants using a Go/NoGo task. Indeed, previous studies using brief mindfulness practices (around 15 minutes) did not observe behavioral differences using a Flanker task . In contrast, Bowen and Marlatt using a similar 15 minute cue-exposure paradigm with mindfulness-based instructions reported a reduction in the number of cigarettes smoked 7 days after the intervention, as compared to a control intervention . As we did not include a follow-up measure, it is unclear if the mindfulness intervention may have also been associated with delayed positive effects in the current study. Also, the Go/NoGo task assesses inhibition of habitual responses, which may not be modifiable by short interventions. Other measures of inhibition such as the Stroop task may be more sensitive to changes in inhibitory control induced by short interventions. Future studies are necessary to establish this. Indeed, several studies have found effects of longer mindfulness interventions on Stroop measures [45, 47, 62]. Other studies have shown that mindfulness interventions may be effective in reducing executive deficits in substance abusers  and may enhance brain functions associated with self-control capacities in smokers . Interestingly, using a brief mindfulness meditation may be an efficient strategy to foster self-control under conditions of low resources . However, more studies evaluating the effects of brief mindfulness interventions and the mechanisms underlying effects are certainly needed.
In our study, there was no effect of picture content (smoking vs. neutral) on task performance. Luijten et al. previously also did not find an effect of picture content on performance accuracy in a similar smoking Go/NoGo task . The relatively low level of cigarette dependence of the participants in the current study could explain the absence of an effect of smoking pictures on response inhibition. Interestingly, a trend for a difference between groups was observed in the final-before difference score, reflecting a selective increase in self-reported craving only for the control group (Table 1). Albeit speculative, this may suggest (but note, only trend level) that the mindfulness group did not display the expected increase in craving. A recent meta-analysis of randomized controlled trials of mindfulness treatments for substance misuse revealed significant moderate to large effects of mindfulness treatments on craving . Hence, it should be studied further whether a brief mindfulness intervention can also reduce craving in smokers.
Task performance is of prime importance in the interpretation of neural activation measured by fMRI or EEG. Some studies have found an increase in the activation of response-inhibition related areas or in the amplitude of NoGo P3 in clinical populations, such as children with attention-deficit/hyperactivity disorder or substance abusers [39, 78, 81, 83], while showing performance comparable to that of a control group. This result is often interpreted as more effortful or a less efficient response inhibition. Notably, in this study, we obtained the opposite result, namely equal task performance accompanied by a decrease in the amplitude of NoGo P3 in the mindfulness group. Albeit speculative, this could be interpreted as less effortful response inhibition in smokers who participated in the mindfulness exercise.
Interestingly, mindfulness only modulated the P3, but not the N2 component, suggesting that the effect of this brief mindfulness-based practice may be limited to the actual inhibitory process. Using a Stroop task, increased parietal N2 amplitudes have been described in older adults after an 8-week mindfulness intervention  and in naïve participants after a 16-week mindfulness intervention . In light of our results, it may be possible that longer interventions are needed to modulate processes reflected in the N2 component. Of further note, Moore et al. also found a decrease in P3 amplitude after the 16-week mindfulness intervention . A decreased P3 amplitude is also in line with previous studies showing that meditation practice leads to more effective brain resource allocation  and improved efficiency (reduced fMRI activation) in sustained attention and impulse-control related brain areas , as well as a reduced Pe amplitude—a P3-like component related to error awareness [33, 89]. Yet, the effect of the mindfulness practice on P3 amplitude was observed regardless of the nature of the picture (smoking or neutral), indicating that mindfulness may have modulated response inhibition more generally.
One limitation of this study is that we did not measure changes in mindfulness states during the experiment, rendering it unclear if the intervention specifically modified the mindfulness state during the experiment. Additionally, because of the present study design without a baseline measure, we cannot discard pre-existing differences between groups. Participants were randomized into one of the two groups and recent guidelines argue that in this case, there is no need for baseline difference testing . Moreover, it must be kept in mind that all smokers in this study were young smokers with a relatively low level of dependence, so generalization to other categories of smokers is limited. Their low level of dependence may have also prevented us from observing stronger effects of mindfulness on smoking-related processes. Additionally, we used a brief mindfulness-meditation, and future studies using longer mindfulness interventions, such as the 8-week MBRP program, are required to determine longer-term effects of mindfulness on smoking behavior.
We would like to thank Christiaan J.C. Tieman for his support with task design and programing; Dr. Maartje Luijten for access to and use of previously developed smoking tasks and Dr. Sarah Bowen for access to and use of previously developed mindfulness intervention materials. We would also like to thank two anonymous reviewers for their helpful comments.
- 1. Xu X, Bishop EE, Kennedy SM, Simpson SA, Pechacek TF. Annual healthcare spending attributable to cigarette smoking: an update. Am J Prev Med. 2015;48(3):326–33. pmid:25498551; PubMed Central PMCID: PMCPMC4603661.
- 2. Services USDoHaH. The Health Consequences of Smoking-50 Years of Progress: A Report of the Surgeon General. Atlanta: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health. Reports of the Surgeon General2014.
- 3. Mottillo S, Filion KB, Belisle P, Joseph L, Gervais A, O'Loughlin J, et al. Behavioural interventions for smoking cessation: a meta-analysis of randomized controlled trials. Eur Heart J. 2009;30(6):718–30. pmid:19109354.
- 4. Brewer JA, Mallik S, Babuscio TA, Nich C, Johnson HE, Deleone CM, et al. Mindfulness training for smoking cessation: results from a randomized controlled trial. Drug and alcohol dependence. 2011;119(1–2):72–80. pmid:21723049; PubMed Central PMCID: PMCPMC3191261.
- 5. Schuman-Olivier Z, Hoeppner BB, Evins AE, Brewer JA. Finding the right match: mindfulness training may potentiate the therapeutic effect of nonjudgment of inner experience on smoking cessation. Subst Use Misuse. 2014;49(5):586–94. pmid:24611853; PubMed Central PMCID: PMCPMC4096689.
- 6. Garrison KA, Pal P, Rojiani R, Dallery J, O'Malley SS, Brewer JA. A randomized controlled trial of smartphone-based mindfulness training for smoking cessation: a study protocol. BMC Psychiatry. 2015;15:83. pmid:25884648; PubMed Central PMCID: PMCPMC4414369.
- 7. Elwafi HM, Witkiewitz K, Mallik S, Thornhill TAt, Brewer JA. Mindfulness training for smoking cessation: moderation of the relationship between craving and cigarette use. Drug and alcohol dependence. 2013;130(1–3):222–9. pmid:23265088; PubMed Central PMCID: PMCPMC3619004.
- 8. Witkiewitz K, Bowen S, Douglas H, Hsu SH. Mindfulness-based relapse prevention for substance craving. Addictive behaviors. 2013;38(2):1563–71. pmid:22534451; PubMed Central PMCID: PMCPMC3408809.
- 9. Bowen S, Witkiewitz K, Clifasefi SL, Grow J, Chawla N, Hsu SH, et al. Relative efficacy of mindfulness-based relapse prevention, standard relapse prevention, and treatment as usual for substance use disorders: a randomized clinical trial. JAMA Psychiatry. 2014;71(5):547–56. pmid:24647726; PubMed Central PMCID: PMCPMC4489711.
- 10. Bowen S, Marlatt A. Surfing the urge: brief mindfulness-based intervention for college student smokers. Psychol Addict Behav. 2009;23(4):666–71. pmid:20025372.
- 11. Ruscio AC, Muench C, Brede E, Waters AJ. Effect of Brief Mindfulness Practice on Self-Reported Affect, Craving, and Smoking: A Pilot Randomized Controlled Trial Using Ecological Momentary Assessment. Nicotine & tobacco research: official journal of the Society for Research on Nicotine and Tobacco. 2016;18(1):64–73. pmid:25863520.
- 12. Kabat-Zinn J. Full catastrophe living. New York, NY: Delta Publishing. 1990.
- 13. Bowen S, Chawla N, Collins SE, Witkiewitz K, Hsu S, Grow J, et al. Mindfulness-based relapse prevention for substance use disorders: a pilot efficacy trial. Subst Abus. 2009;30(4):295–305. pmid:19904665; PubMed Central PMCID: PMCPMC3280682.
- 14. Davis JM, Mills DM, Stankevitz KA, Manley AR, Majeskie MR, Smith SS. Pilot randomized trial on mindfulness training for smokers in young adult binge drinkers. BMC Complement Altern Med. 2013;13:215. pmid:24006963; PubMed Central PMCID: PMCPMC3847085.
- 15. Zgierska A, Rabago D, Zuelsdorff M, Coe C, Miller M, Fleming M. Mindfulness meditation for alcohol relapse prevention: a feasibility pilot study. J Addict Med. 2008;2(3):165–73. pmid:21768988; PubMed Central PMCID: PMCPMC4106278.
- 16. Bowen S, Witkiewitz K, Dillworth TM, Marlatt GA. The role of thought suppression in the relationship between mindfulness meditation and alcohol use. Addictive behaviors. 2007;32(10):2324–8. Epub 2007/02/16. pmid:17300875; PubMed Central PMCID: PMC1989113.
- 17. Brewer JA, Sinha R, Chen JA, Michalsen RN, Babuscio TA, Nich C, et al. Mindfulness training and stress reactivity in substance abuse: results from a randomized, controlled stage I pilot study. Subst Abus. 2009;30(4):306–17. pmid:19904666; PubMed Central PMCID: PMCPMC3045038.
- 18. Witkiewitz K, Bowen S. Depression, craving, and substance use following a randomized trial of mindfulness-based relapse prevention. J Consult Clin Psychol. 2010;78(3):362–74. pmid:20515211; PubMed Central PMCID: PMCPMC3280693.
- 19. Imani S, Atef Vahid MK, Gharraee B, Noroozi A, Habibi M, Bowen S. Effectiveness of Mindfulness-Based Group Therapy Compared to the Usual Opioid Dependence Treatment. Iran J Psychiatry. 2015;10(3):175–84. pmid:26877751; PubMed Central PMCID: PMCPMC4749687.
- 20. Cropley M, Ussher M, Charitou E. Acute effects of a guided relaxation routine (body scan) on tobacco withdrawal symptoms and cravings in abstinent smokers. Addiction. 2007;102(6):989–93. pmid:17523994.
- 21. Brewer JA, Elwafi HM, Davis JH. Craving to quit: psychological models and neurobiological mechanisms of mindfulness training as treatment for addictions. Psychol Addict Behav. 2013;27(2):366–79. pmid:22642859; PubMed Central PMCID: PMCPMC3434285.
- 22. Witkiewitz K, Lustyk MK, Bowen S. Retraining the addicted brain: a review of hypothesized neurobiological mechanisms of mindfulness-based relapse prevention. Psychol Addict Behav. 2013;27(2):351–65. pmid:22775773; PubMed Central PMCID: PMCPMC3699602.
- 23. Witkiewitz K, Bowen S, Harrop EN, Douglas H, Enkema M, Sedgwick C. Mindfulness-based treatment to prevent addictive behavior relapse: theoretical models and hypothesized mechanisms of change. Subst Use Misuse. 2014;49(5):513–24. pmid:24611847.
- 24. Brewer JA, Bowen S, Smith JT, Marlatt GA, Potenza MN. Mindfulness-based treatments for co-occurring depression and substance use disorders: what can we learn from the brain? Addiction. 2010;105(10):1698–706. pmid:20331548; PubMed Central PMCID: PMCPMC2905496.
- 25. Garland EL, Froeliger B, Howard MO. Neurophysiological evidence for remediation of reward processing deficits in chronic pain and opioid misuse following treatment with Mindfulness-Oriented Recovery Enhancement: exploratory ERP findings from a pilot RCT. J Behav Med. 2015;38(2):327–36. pmid:25385024; PubMed Central PMCID: PMCPMC4355224.
- 26. Tang YY, Tang R, Posner MI. Brief meditation training induces smoking reduction. Proceedings of the National Academy of Sciences of the United States of America. 2013;110(34):13971–5. pmid:23918376; PubMed Central PMCID: PMCPMC3752264.
- 27. Westbrook C, Creswell JD, Tabibnia G, Julson E, Kober H, Tindle HA. Mindful attention reduces neural and self-reported cue-induced craving in smokers. Social cognitive and affective neuroscience. 2013;8(1):73–84. pmid:22114078; PubMed Central PMCID: PMCPMC3541484.
- 28. Zanesco AP, King BG, Maclean KA, Saron CD. Executive control and felt concentrative engagement following intensive meditation training. Frontiers in human neuroscience. 2013;7:566. Epub 2013/09/26. pmid:24065902; PubMed Central PMCID: PMC3776271.
- 29. Teper R, Inzlicht M. Meditation, mindfulness and executive control: the importance of emotional acceptance and brain-based performance monitoring. Social Cognitive Affective Neuroscience. 2013;8(1):85–92. Epub 2012/04/18. pmid:22507824; PubMed Central PMCID: PMC3541488.
- 30. Tang YY, Ma Y, Wang J, Fan Y, Feng S, Lu Q, et al. Short-term meditation training improves attention and self-regulation. Proceedings of the National Academy of Sciences of the United States of America. 2007;104(43):17152–6. pmid:17940025; PubMed Central PMCID: PMC2040428.
- 31. Saunders B, Rodrigo AH, Inzlicht M. Mindful awareness of feelings increases neural performance monitoring. Cognitive, Affective and Behavioral Neuroscience. 2015. pmid:26350627.
- 32. Lutz A, Slagter HA, Dunne JD, Davidson RJ. Attention regulation and monitoring in meditation. Trends in cognitive sciences. 2008;12(4):163–9. Epub 2008/03/11. pmid:18329323; PubMed Central PMCID: PMC2693206.
- 33. Larson MJ, Steffen PR, Primosch M. The impact of a brief mindfulness meditation intervention on cognitive control and error-related performance monitoring. Frontiers in human neuroscience. 2013;7:308. pmid:23847491; PubMed Central PMCID: PMC3705166.
- 34. Chiesa A, Calati R, Serretti A. Does mindfulness training improve cognitive abilities? A systematic review of neuropsychological findings. Clinical psychology review. 2011;31(3):449–64. Epub 2010/12/25. pmid:21183265.
- 35. Volkow ND, Wang GJ, Tomasi D, Baler RD. Unbalanced neuronal circuits in addiction. Curr Opin Neurobiol. 2013;23(4):639–48. pmid:23434063; PubMed Central PMCID: PMCPMC3717294.
- 36. Baler RD, Volkow ND. Drug addiction: the neurobiology of disrupted self-control. Trends Mol Med. 2006;12(12):559–66. pmid:17070107.
- 37. Tang YY, Posner MI, Rothbart MK, Volkow ND. Circuitry of self-control and its role in reducing addiction. Trends in cognitive sciences. 2015. pmid:26235449.
- 38. Goldstein RZ, Volkow ND. Drug addiction and its underlying neurobiological basis: neuroimaging evidence for the involvement of the frontal cortex. The American journal of psychiatry. 2002;159(10):1642–52. pmid:12359667; PubMed Central PMCID: PMCPMC1201373.
- 39. Luijten M, Machielsen MW, Veltman DJ, Hester R, de Haan L, Franken IH. Systematic review of ERP and fMRI studies investigating inhibitory control and error processing in people with substance dependence and behavioural addictions. Journal of Psychiatry and Neuroscience. 2014;39(3):149–69. Epub 2013/12/24. pmid:24359877; PubMed Central PMCID: PMC3997601.
- 40. Luijten M, Littel M, Franken IH. Deficits in inhibitory control in smokers during a Go/NoGo task: an investigation using event-related brain potentials. PLoS One. 2011;6(4):e18898. pmid:21526125.
- 41. Tang YY, Holzel BK, Posner MI. The neuroscience of mindfulness meditation. Nature Reviews Neuroscience. 2015;16(4):213–25. pmid:25783612.
- 42. Brefczynski-Lewis JA, Lutz A, Schaefer HS, Levinson DB, Davidson RJ. Neural correlates of attentional expertise in long-term meditation practitioners. Proceedings of the National Academy of Sciences of the United States of America. 2007;104(27):11483–8. Epub 2007/06/29. pmid:17596341; PubMed Central PMCID: PMC1903340.
- 43. Hasenkamp W, Barsalou LW. Effects of meditation experience on functional connectivity of distributed brain networks. Frontiers in human neuroscience. 2012;6:38. Epub 2012/03/10. pmid:22403536; PubMed Central PMCID: PMC3290768.
- 44. Lutz A, Slagter HA, Rawlings NB, Francis AD, Greischar LL, Davidson RJ. Mental training enhances attentional stability: neural and behavioral evidence. The Journal of Neuroscience. 2009;29(42):13418–27. Epub 2009/10/23. pmid:19846729; PubMed Central PMCID: PMC2789281.
- 45. Moore A, Gruber T, Derose J, Malinowski P. Regular, brief mindfulness meditation practice improves electrophysiological markers of attentional control. Frontiers in human neuroscience. 2012;6:18. Epub 2012/03/01. pmid:22363278; PubMed Central PMCID: PMC3277272.
- 46. Slagter HA, Lutz A, Greischar LL, Francis AD, Nieuwenhuis S, Davis JM, et al. Mental training affects distribution of limited brain resources. PLoS biology. 2007;5(6):e138. Epub 2007/05/10. pmid:17488185; PubMed Central PMCID: PMC1865565.
- 47. Moore A, Malinowski P. Meditation, mindfulness and cognitive flexibility. Consciousness and cognition. 2009;18(1):176–86. pmid:19181542.
- 48. Brown KW, Goodman RJ, Inzlicht M. Dispositional mindfulness and the attenuation of neural responses to emotional stimuli. Social Cognitive Affective Neuroscience. 2013;8(1):93–9. Epub 2012/01/19. pmid:22253259; PubMed Central PMCID: PMC3541486.
- 49. Chambers R, Gullone E, Allen NB. Mindful emotion regulation: An integrative review. Clinical psychology review. 2009;29(6):560–72. Epub 2009/07/28. pmid:19632752.
- 50. Desbordes G, Negi LT, Pace TW, Wallace BA, Raison CL, Schwartz EL. Effects of mindful-attention and compassion meditation training on amygdala response to emotional stimuli in an ordinary, non-meditative state. Frontiers in human neuroscience. 2012;6:292. Epub 2012/11/06. pmid:23125828; PubMed Central PMCID: PMC3485650.
- 51. Farb NA, Anderson AK, Segal ZV. The mindful brain and emotion regulation in mood disorders. Canadian Journal of Psychiatry. 2012;57(2):70–7. Epub 2012/02/22. pmid:22340146; PubMed Central PMCID: PMC3303604.
- 52. Klimecki OM, Leiberg S, Lamm C, Singer T. Functional neural plasticity and associated changes in positive affect after compassion training. Cerebral Cortex. 2013;23(7):1552–61. Epub 2012/06/05. pmid:22661409.
- 53. Lutz A, Brefczynski-Lewis J, Johnstone T, Davidson RJ. Regulation of the neural circuitry of emotion by compassion meditation: effects of meditative expertise. PLoS One. 2008;3(3):e1897. Epub 2008/03/28. pmid:18365029; PubMed Central PMCID: PMC2267490.
- 54. Ortner C, Kilner S, Zelazo PD. Mindfulness meditation and reduced emotional interference on a cognitive task. Motivation and Emotion. 2007;31(4):271–83.
- 55. Paul NA, Stanton SJ, Greeson JM, Smoski MJ, Wang L. Psychological and neural mechanisms of trait mindfulness in reducing depression vulnerability. Social Cognitive Affective Neuroscience. 2013;8(1):56–64. Epub 2012/06/22. pmid:22717383; PubMed Central PMCID: PMC3541493.
- 56. Taylor VA, Grant J, Daneault V, Scavone G, Breton E, Roffe-Vidal S, et al. Impact of mindfulness on the neural responses to emotional pictures in experienced and beginner meditators. Neuroimage. 2011;57(4):1524–33. Epub 2011/06/18. pmid:21679770.
- 57. Sahdra BK, MacLean KA, Ferrer E, Shaver PR, Rosenberg EL, Jacobs TL, et al. Enhanced response inhibition during intensive meditation training predicts improvements in self-reported adaptive socioemotional functioning. Emotion. 2011;11(2):299–312. Epub 2011/04/20. pmid:21500899.
- 58. Fan J, McCandliss BD, Sommer T, Raz A, Posner MI. Testing the efficiency and independence of attentional networks. Journal of cognitive neuroscience. 2002;14(3):340–7. pmid:11970796.
- 59. Jha AP, Krompinger J, Baime MJ. Mindfulness training modifies subsystems of attention. Cognitive, Affective and Behavioral Neuroscience. 2007;7(2):109–19. pmid:17672382.
- 60. Elliott JC, Wallace BA, Giesbrecht B. A week-long meditation retreat decouples behavioral measures of the alerting and executive attention networks. Frontiers in human neuroscience. 2014;8:69. pmid:24596550; PubMed Central PMCID: PMC3926190.
- 61. Tang YY, Lu Q, Geng X, Stein EA, Yang Y, Posner MI. Short-term meditation induces white matter changes in the anterior cingulate. Proceedings of the National Academy of Sciences of the United States of America. 2010;107(35):15649–52. pmid:20713717; PubMed Central PMCID: PMC2932577.
- 62. Allen M, Dietz M, Blair KS, van Beek M, Rees G, Vestergaard-Poulsen P, et al. Cognitive-affective neural plasticity following active-controlled mindfulness intervention. Journal of Neuroscience. 2012;32(44):15601–10. Epub 2012/11/02. pmid:23115195.
- 63. Holzel BK, Ott U, Hempel H, Hackl A, Wolf K, Stark R, et al. Differential engagement of anterior cingulate and adjacent medial frontal cortex in adept meditators and non-meditators. Neuroscience letters. 2007;421(1):16–21. Epub 2007/06/06. pmid:17548160.
- 64. Froeliger BE, Garland EL, Modlin LA, McClernon FJ. Neurocognitive correlates of the effects of yoga meditation practice on emotion and cognition: a pilot study. Frontiers in integrative neuroscience. 2012;6:48. pmid:22855674; PubMed Central PMCID: PMC3405281.
- 65. Huster RJ, Enriquez-Geppert S, Lavallee CF, Falkenstein M, Herrmann CS. Electroencephalography of response inhibition tasks: functional networks and cognitive contributions. Int J Psychophysiol. 2013;87(3):217–33. Epub 2012/08/22. pmid:22906815.
- 66. Nieuwenhuis S, Yeung N, van den Wildenberg W, Ridderinkhof KR. Electrophysiological correlates of anterior cingulate function in a go/no-go task: effects of response conflict and trial type frequency. Cognitive, affective & behavioral neuroscience. 2003;3(1):17–26. Epub 2003/06/26. pmid:12822595.
- 67. Falkenstein M. Inhibition, conflict and the Nogo-N2. Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology. 2006;117(8):1638–40. pmid:16798078.
- 68. Kaiser S, Weiss O, Hill H, Markela-Lerenc J, Kiefer M, Weisbrod M. N2 event-related potential correlates of response inhibition in an auditory Go/Nogo task. Int J Psychophysiol. 2006;61(2):279–82. pmid:16298004.
- 69. Dimoska A, Johnstone SJ, Barry RJ. The auditory-evoked N2 and P3 components in the stop-signal task: indices of inhibition, response-conflict or error-detection? Brain and cognition. 2006;62(2):98–112. pmid:16814442.
- 70. Smith JL, Jamadar S, Provost AL, Michie PT. Motor and non-motor inhibition in the Go/NoGo task: an ERP and fMRI study. Int J Psychophysiol. 2013;87(3):244–53. Epub 2012/08/14. pmid:22885679.
- 71. Smith JL, Johnstone SJ, Barry RJ. Movement-related potentials in the Go/NoGo task: the P3 reflects both cognitive and motor inhibition. Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology. 2008;119(3):704–14. Epub 2008/01/01. pmid:18164657.
- 72. Band GP, van Boxtel GJ. Inhibitory motor control in stop paradigms: review and reinterpretation of neural mechanisms. Acta Psychol (Amst). 1999;101(2–3):179–211. pmid:10344185.
- 73. Houlihan ME, Pritchard WS, Robinson JH. Effects of smoking/nicotine on performance and event-related potentials during a short-term memory scanning task. Psychopharmacology (Berl). 2001;156(4):388–96. pmid:11498715.
- 74. Vink JM, Willemsen G, Beem AL, Boomsma DI. The Fagerstrom Test for Nicotine Dependence in a Dutch sample of daily smokers and ex-smokers. Addictive behaviors. 2005;30(3):575–9. pmid:15718074.
- 75. Heatherton TF, Kozlowski LT, Frecker RC, Fagerstrom KO. The Fagerstrom Test for Nicotine Dependence: a revision of the Fagerstrom Tolerance Questionnaire. Br J Addict. 1991;86(9):1119–27. pmid:1932883.
- 76. Cox LS, Tiffany ST, Christen AG. Evaluation of the brief questionnaire of smoking urges (QSU-brief) in laboratory and clinical settings. Nicotine & tobacco research: official journal of the Society for Research on Nicotine and Tobacco. 2001;3(1):7–16. pmid:11260806.
- 77. Littel M, Franken IH, Muris P. Psychometric properties of the brief Questionnaire on Smoking Urges (QSU-Brief) in a Dutch smoker population. Netherlands Journal of Psychology. 2011;66:44–9.
- 78. Lopez-Martin S, Albert J, Fernandez-Jaen A, Carretie L. Emotional response inhibition in children with attention-deficit/hyperactivity disorder: neural and behavioural data. Psychol Med. 2015;45(10):2057–71. pmid:25708692.
- 79. Gratton G, Coles MG, Donchin E. A new method for off-line removal of ocular artifact. Electroencephalography and clinical neurophysiology. 1983;55(4):468–84. pmid:6187540.
- 80. Rietdijk WJ, Franken IH, Thurik AR. Internal consistency of event-related potentials associated with cognitive control: N2/P3 and ERN/Pe. PLoS One. 2014;9(7):e102672. pmid:25033272; PubMed Central PMCID: PMCPMC4102542.
- 81. Tapert SF, Schweinsburg AD, Drummond SP, Paulus MP, Brown SA, Yang TT, et al. Functional MRI of inhibitory processing in abstinent adolescent marijuana users. Psychopharmacology (Berl). 2007;194(2):173–83. pmid:17558500; PubMed Central PMCID: PMCPMC2269705.
- 82. Albert J, Lopez-Martin S, Carretie L. Emotional context modulates response inhibition: neural and behavioral data. Neuroimage. 2010;49(1):914–21. Epub 2009/09/01. pmid:19716425.
- 83. Schulz KP, Fan J, Tang CY, Newcorn JH, Buchsbaum MS, Cheung AM, et al. Response inhibition in adolescents diagnosed with attention deficit hyperactivity disorder during childhood: an event-related FMRI study. The American journal of psychiatry. 2004;161(9):1650–7. pmid:15337656.
- 84. Alfonso JP, Caracuel A, Delgado-Pastor LC, Verdejo-Garcia A. Combined Goal Management Training and Mindfulness meditation improve executive functions and decision-making performance in abstinent polysubstance abusers. Drug and alcohol dependence. 2011;117(1):78–81. pmid:21277705.
- 85. Friese M, Messner C, Schaffner Y. Mindfulness meditation counteracts self-control depletion. Consciousness and cognition. 2012;21(2):1016–22. pmid:22309814.
- 86. Li W, Howard MO, Garland EL, McGovern P, Lazar M. Mindfulness treatment for substance misuse: A systematic review and meta-analysis. J Subst Abuse Treat. 2017;75:62–96. pmid:28153483.
- 87. Malinowski P, Moore AW, Mead BR, Gruber T. Mindful Aging: The Effects of Regular Brief Mindfulness Practice on Electrophysiological Markers of Cognitive and Affective Processing in Older Adults. Mindfulness. 2015:1–17. pmid:28163795
- 88. Kozasa EH, Sato JR, Lacerda SS, Barreiros MA, Radvany J, Russell TA, et al. Meditation training increases brain efficiency in an attention task. Neuroimage. 2012;59(1):745–9. pmid:21763432.
- 89. Ridderinkhof KR, Ramautar JR, Wijnen JG. To P(E) or not to P(E): a P3-like ERP component reflecting the processing of response errors. Psychophysiology. 2009;46(3):531–8. pmid:19226310.
- 90. de Boer MR, Waterlander WE, Kuijper LD, Steenhuis IH, Twisk JW. Testing for baseline differences in randomized controlled trials: an unhealthy research behavior that is hard to eradicate. Int J Behav Nutr Phys Act. 2015;12:4. pmid:25616598; PubMed Central PMCID: PMCPMC4310023.