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Brain Responses to Smoking Cues Differ Based on Nicotine Metabolism Rate

  • Mary Falcone
    Affiliations
    Center for Interdisciplinary Research on Nicotine Addiction, Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
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  • Wen Cao
    Affiliations
    Center for Interdisciplinary Research on Nicotine Addiction, Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
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  • Leah Bernardo
    Affiliations
    Center for Interdisciplinary Research on Nicotine Addiction, Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
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  • Rachel F. Tyndale
    Affiliations
    Campbell Family Mental Health Research Institute, University of Toronto, Toronto, Ontario, Canada

    Centre for Addiction and Mental Health, and Departments of Psychiatry and Pharmacology & Toxicology, University of Toronto, Toronto, Ontario, Canada
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  • James Loughead
    Affiliations
    Center for Interdisciplinary Research on Nicotine Addiction, Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
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  • Caryn Lerman
    Correspondence
    Address correspondence to: Caryn Lerman, Ph.D., Center for Interdisciplinary Research on Nicotine Addiction, 3535 Market Street, Suite 4100, Philadelphia, PA 19104.
    Affiliations
    Center for Interdisciplinary Research on Nicotine Addiction, Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
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Published:November 25, 2015DOI:https://doi.org/10.1016/j.biopsych.2015.11.015

      Abstract

      Background

      Inherited differences in the rate of metabolism of nicotine, the addictive chemical in tobacco, affect smoking behavior and quitting success. The nicotine metabolite ratio (3′-hydroxycotinine/cotinine) is a reliable measure of nicotine clearance and a well-validated predictive biomarker of response to pharmacotherapy. To clarify the mechanisms underlying these associations, we investigated the neural responses to smoking cues in normal and slow nicotine metabolizers.

      Methods

      Treatment-seeking smokers (N = 69; 30 slow metabolizers and 39 normal metabolizers) completed a visual cue reactivity task during functional magnetic resonance imaging on two separate occasions: once during smoking satiety and once after 24 hours of smoking abstinence.

      Results

      In whole-brain analysis, normal (compared with slow) metabolizers exhibited heightened abstinence-induced neural responses to smoking cues in the left caudate, left inferior frontal gyrus, and left frontal pole. These effects were more pronounced when extreme groups of slow and normal metabolizers were examined. Greater activation in the left caudate and left frontal pole was associated with abstinence-induced subjective cravings to smoke.

      Conclusions

      Inherited differences in rate of nicotine elimination may drive neural responses to smoking cues during early abstinence, providing a plausible mechanism to explain differences in smoking behaviors and response to cessation treatment. Normal metabolizers may benefit from adjunctive behavioral smoking cessation treatments, such as cue exposure therapy.

      Keywords

      Tobacco dependence is a chronic relapsing disorder affecting approximately one in five adults in the United States, with considerable health consequences (
      Centers for Disease Control
      Smoking attributable mortality, years of potential life lost, and productivity losses.
      ,
      Centers for Disease Control
      Vital signs: Current cigarette smoking among adults aged >/=18 years—United States, 2005-2010.
      ). Inherited differences in the rates of metabolism and resulting clearance of nicotine, the addictive chemical in tobacco, affect smoking behavior and quitting success; slower metabolizers tend to smoke less and have higher quit rates than normal metabolizers (
      • Benowitz N.L.
      • Pomerleau O.F.
      • Pomerleau C.S.
      • Jacob 3rd, P.
      Nicotine metabolite ratio as a predictor of cigarette consumption.
      ,
      • Falcone M.
      • Jepson C.
      • Benowitz N.
      • Bergen A.W.
      • Pinto A.
      • Wileyto E.P.
      • et al.
      Association of the nicotine metabolite ratio and CHRNA5/CHRNA3 polymorphisms with smoking rate among treatment-seeking smokers.
      ,
      • Ho M.K.
      • Mwenifumbo J.C.
      • Al Koudsi N.
      • Okuyemi K.S.
      • Ahluwalia J.S.
      • Benowitz N.L.
      • et al.
      Association of nicotine metabolite ratio and CYP2A6 genotype with smoking cessation treatment in African-American light smokers.
      ,
      • Patterson F.
      • Schnoll R.A.
      • Wileyto E.P.
      • Pinto A.
      • Epstein L.H.
      • Shields P.G.
      • et al.
      Toward personalized therapy for smoking cessation: A randomized placebo-controlled trial of bupropion.
      ). Nicotine is primarily metabolized by the liver enzyme CYP2A6 to cotinine, which itself is metabolized to 3′-hydroxycotinine by the same enzyme. The ratio of 3′-hydroxycotinine to cotinine provides a stable and reliable measure of individual differences in nicotine metabolism rate, referred to as the nicotine metabolite ratio (NMR) (
      • Dempsey D.
      • Tutka P.
      • Jacob 3rd, P.
      • Allen F.
      • Schoedel K.
      • Tyndale R.F.
      • et al.
      Nicotine metabolite ratio as an index of cytochrome P450 2A6 metabolic activity.
      ,
      • St Helen G.
      • Novalen M.
      • Heitjan D.F.
      • Dempsey D.
      • Jacob 3rd, P.
      • Aziziyeh A.
      • et al.
      Reproducibility of the nicotine metabolite ratio in cigarette smokers.
      ,
      • Hamilton D.A.
      • Mahoney M.C.
      • Novalen M.
      • Chenoweth M.J.
      • Heitjan D.F.
      • Lerman C.
      • et al.
      Test-retest reliability and stability of the nicotine metabolite ratio among treatment-seeking smokers.
      ,
      • Tanner J.A.
      • Novalen M.
      • Jatlow P.
      • Huestis M.A.
      • Murphy S.E.
      • Kaprio J.
      • et al.
      Nicotine metabolite ratio (3-hydroxycotinine/cotinine) in plasma and urine by different analytical methods and laboratories: Implications for clinical implementation.
      ). Building on prior trials (
      • Patterson F.
      • Schnoll R.A.
      • Wileyto E.P.
      • Pinto A.
      • Epstein L.H.
      • Shields P.G.
      • et al.
      Toward personalized therapy for smoking cessation: A randomized placebo-controlled trial of bupropion.
      ,
      • Lerman C.
      • Tyndale R.
      • Patterson F.
      • Wileyto E.P.
      • Shields P.G.
      • Pinto A.
      • et al.
      Nicotine metabolite ratio predicts efficacy of transdermal nicotine for smoking cessation.
      ,
      • Schnoll R.A.
      • Patterson F.
      • Wileyto E.P.
      • Tyndale R.F.
      • Benowitz N.
      • Lerman C.
      Nicotine metabolic rate predicts successful smoking cessation with transdermal nicotine: A validation study.
      ), a large multisite, placebo-controlled, randomized clinical trial validated the NMR as a predictive biomarker of the relative efficacy of two widely used smoking cessation medications: the transdermal nicotine patch and varenicline (
      • Lerman C.
      • Schnoll R.A.
      • Hawk Jr, L.W.
      • Cinciripini P.
      • George T.P.
      • Wileyto E.P.
      • et al.
      Use of the nicotine metabolite ratio as a genetically informed biomarker of response to nicotine patch or varenicline for smoking cessation: A randomised, double-blind placebo-controlled trial.
      ).
      Despite the well-documented differences between slow metabolizers and normal metabolizers in response to smoking cessation treatment, the mechanisms underlying these effects are not well understood. Slow metabolizers have been shown to smoke fewer cigarettes per day than normal metabolizers; however, these effects tend to be modest (
      • Benowitz N.L.
      • Pomerleau O.F.
      • Pomerleau C.S.
      • Jacob 3rd, P.
      Nicotine metabolite ratio as a predictor of cigarette consumption.
      ,
      • Falcone M.
      • Jepson C.
      • Benowitz N.
      • Bergen A.W.
      • Pinto A.
      • Wileyto E.P.
      • et al.
      Association of the nicotine metabolite ratio and CHRNA5/CHRNA3 polymorphisms with smoking rate among treatment-seeking smokers.
      ). Associations between the NMR and nicotine dependence, withdrawal symptoms, and craving during cessation are inconsistent (
      • Schnoll R.A.
      • Patterson F.
      • Wileyto E.P.
      • Tyndale R.F.
      • Benowitz N.
      • Lerman C.
      Nicotine metabolic rate predicts successful smoking cessation with transdermal nicotine: A validation study.
      ,
      • Johnstone E.
      • Benowitz N.
      • Cargill A.
      • Jacob R.
      • Hinks L.
      • Day I.
      • et al.
      Determinants of the rate of nicotine metabolism and effects on smoking behavior.
      ,
      • Schnoll R.A.
      • George T.P.
      • Hawk L.
      • Cinciripini P.
      • Wileyto P.
      • Tyndale R.F.
      The relationship between the nicotine metabolite ratio and three self-report measures of nicotine dependence across sex and race.
      ,
      • Rubinstein M.L.
      • Benowitz N.L.
      • Auerback G.M.
      • Moscicki A.B.
      A randomized trial of nicotine nasal spray in adolescent smokers.
      ). It is possible that differences between slow metabolizers and normal metabolizers in smoking cessation are mediated by alterations in nicotinic receptor availability. Normal metabolizers show greater nicotinic receptor availability during early abstinence, an effect that may result from faster clearance of nicotine from the brain, greater receptor upregulation during chronic exposure, or a combination of the two (
      • Dubroff J.G.
      • Doot R.K.
      • Falcone M.
      • Schnoll R.A.
      • Ray R.
      • Tyndale R.F.
      • et al.
      Decreased nicotinic receptor availability in smokers with slow rates of nicotine metabolism.
      ). Differences in fluctuation of nicotine levels and nicotinic receptor availability throughout the day could also increase the rewarding effects of nicotine in normal metabolizers compared with slow metabolizers (
      • Sofuoglu M.
      • Herman A.I.
      • Nadim H.
      • Jatlow P.
      Rapid nicotine clearance is associated with greater reward and heart rate increases from intravenous nicotine.
      ). A neuroimaging study found that compared with slow metabolizers, normal metabolizers exhibit greater neural responses to conditioned smoking cues in brain regions within dopamine-dependent reward circuitry, suggesting a plausible mechanism to explain their lower quit rates (
      • Tang D.W.
      • Hello B.
      • Mroziewicz M.
      • Fellows L.K.
      • Tyndale R.F.
      • Dagher A.
      Genetic variation in CYP2A6 predicts neural reactivity to smoking cues as measured using fMRI.
      ). However, in this prior study, neuroimaging was performed at a single time point when participants were smoking as usual. Because slow metabolizers and normal metabolizers may clear nicotine from the brain at different rates (
      • Dubroff J.G.
      • Doot R.K.
      • Falcone M.
      • Schnoll R.A.
      • Ray R.
      • Tyndale R.F.
      • et al.
      Decreased nicotinic receptor availability in smokers with slow rates of nicotine metabolism.
      ), evaluating smoking cue–elicited brain responses during both abstinence and smoking satiety is necessary to clarify the neurobehavioral mechanisms that may underlie differences in quitting success and therapeutic response.
      We completed a within-subject crossover functional magnetic resonance imaging (fMRI) study to examine brain responses to visual smoking cues (vs. neutral images) in slow metabolizers and normal metabolizers during two sessions: 24-hour abstinence challenge versus smoking satiety. Because of more rapid nicotine elimination in normal metabolizers compared with slow metabolizers, we hypothesized that normal metabolizers would exhibit heightened cue responses in the mesocorticolimbic circuitry during the abstinent condition compared with the smoking condition. Given the clinical relevance of neural responses to smoking cues for quitting success (
      • Janes A.C.
      • Pizzagalli D.A.
      • Richardt S.
      • deB Frederick B.
      • Chuzi S.
      • Pachas G.
      • et al.
      Brain reactivity to smoking cues prior to smoking cessation predicts ability to maintain tobacco abstinence.
      ), these data could inform the design of targeted therapies for smokers with variable nicotine metabolism.

      Methods and Materials

      Participants

      Participants were treatment-seeking smokers 18–65 years old who reported smoking ≥10 cigarettes per day (CPD) for ≥6 months and were recruited through media advertisements. Exclusion criteria were current use of nicotine products other than cigarettes (e.g., chewing tobacco, snuff, e-cigarettes, or smoking cessation products); pregnancy, planned pregnancy, or breastfeeding; history of DSM-IV Axis I psychiatric or substance disorders except nicotine dependence (assessed by the Mini-International Neuropsychiatric Interview) (
      • Sheehan D.V.
      • Lecrubier Y.
      • Sheehan K.H.
      • Amorim P.
      • Janavs J.
      • Weiller E.
      • et al.
      The Mini-International Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10.
      ); use of psychotropic medications; history of brain injury; left-handedness; material in the body contraindicating fMRI; low or borderline intelligence (<90 score on Shipley’s IQ test) (
      • Zachary R.
      Shipley Institute of Living Scale: Revised Manual.
      ); and any impairment that would prevent task performance.

      Procedures

      Screening

      All procedures were approved by the University of Pennsylvania Institutional Review Board and carried out in accordance with the Declaration of Helsinki. All participants provided written informed consent and completed a urine drug screen and breath alcohol test; women completed a urine pregnancy test. Eligible participants completed the Fagerström Test for Nicotine Dependence (FTND) (
      • Heatherton T.F.
      • Kozlowski L.T.
      • Frecker R.C.
      • Fagerström K.O.
      The Fagerström Test for Nicotine Dependence: A revision of the Fagerström Tolerance Questionnaire.
      ) and provided a saliva sample for NMR determination (
      • Dempsey D.
      • Tutka P.
      • Jacob 3rd, P.
      • Allen F.
      • Schoedel K.
      • Tyndale R.F.
      • et al.
      Nicotine metabolite ratio as an index of cytochrome P450 2A6 metabolic activity.
      ,
      • St Helen G.
      • Novalen M.
      • Heitjan D.F.
      • Dempsey D.
      • Jacob 3rd, P.
      • Aziziyeh A.
      • et al.
      Reproducibility of the nicotine metabolite ratio in cigarette smokers.
      ).

      NMR Determination

      Concentrations of cotinine and 3′-hydroxycotinine in saliva samples taken during baseline smoking were determined by liquid chromatography–tandem mass spectrometry, and the NMR was calculated for each participant (
      • Dempsey D.
      • Tutka P.
      • Jacob 3rd, P.
      • Allen F.
      • Schoedel K.
      • Tyndale R.F.
      • et al.
      Nicotine metabolite ratio as an index of cytochrome P450 2A6 metabolic activity.
      ). Clinical trial data demonstrated differences in quit rates and medication response between slow metabolizers and normal metabolizers using a plasma NMR cut point of ≤.31 for inclusion as slow metabolizers (
      • Lerman C.
      • Schnoll R.A.
      • Hawk Jr, L.W.
      • Cinciripini P.
      • George T.P.
      • Wileyto E.P.
      • et al.
      Use of the nicotine metabolite ratio as a genetically informed biomarker of response to nicotine patch or varenicline for smoking cessation: A randomised, double-blind placebo-controlled trial.
      ). Plasma NMR and saliva NMR are highly correlated; based on previously published regression coefficients (
      • St Helen G.
      • Novalen M.
      • Heitjan D.F.
      • Dempsey D.
      • Jacob 3rd, P.
      • Aziziyeh A.
      • et al.
      Reproducibility of the nicotine metabolite ratio in cigarette smokers.
      ), a .31 plasma cut-point corresponds to a saliva cut-point of .22. To verify this, we obtained plasma NMR values from a subset (n = 32) of participants and used regression modeling to calculate a value for saliva NMR equivalent to a plasma NMR of .31 in this sample. Our modeling indicated a value of .21 in saliva, which is similar to the value obtained using the published model. Based on this regression model, we divided participants into slow metabolizers (saliva NMR ≤ .21) and normal metabolizers (saliva NMR > .21).

      Scan Day Procedures

      The neuroimaging experiment used a within-subject design with two blood oxygen level–dependent (BOLD) fMRI sessions scheduled 1–3 weeks apart in counterbalanced order: 1) smoking satiety and 2) 24-hour abstinence. Subjects were instructed to refrain from alcohol or other drugs for at least 24 hours before the session. Subjects with a positive drug screen, a breath alcohol test >.01, or a breath carbon monoxide test >9 ppm (abstinent session only) were excluded. For the smoking condition, participants smoked a single cigarette about 1 hour before cue exposure.

      Image Acquisition

      The BOLD fMRI was acquired with a MAGNETOM Trio 3-Tesla system (Siemens, Erlangen, Germany) using a whole-brain, single-shot gradient-echo echo planar sequence with the following parameters: repetition time/echo time = 3000/30 ms, field of view = 220 mm, matrix = 64 × 64, slice thickness/gap = 3.4/0 mm, 48 slices, effective voxel resolution of 3.4 × 3.4 × 3.4 mm. Radiofrequency transmission used a quadrature body coil and reception used a 32-channel head coil. Before BOLD fMRI, 5-minute magnetization prepared rapid acquisition gradient-echo T1-weighted imaging (repetition time 1620 ms, echo time 3.87 ms, field of view 50 mm, matrix 192 × 256, effective voxel resolution of 1 × 1 × 1 mm) was acquired for anatomic overlays of functional data and to aid spatial normalization to standard atlas space.

      Cue Reactivity Task

      The cue reactivity task consisted of exposure to color pictures of smoking-related and neutral images in a pseudorandom, event-related design. Smoking-related images were pictures of people smoking or smoking-related objects, such as cigarettes or ashtrays. Neutral images (control condition) were pictures of people engaged in everyday tasks or unrelated objects, such as pencils. Neutral and smoking images were matched for visual features such as size, shape, and luminosity; images of people were balanced for gender. Each image was presented for 500 ms followed by a blank screen with a fixation point; the interstimulus interval was 1.5–13.5 seconds (mean 3.47 seconds). The total task time was ~8 minutes. A two-item subjective craving questionnaire was administered at three time points during the scan: immediately after structural and resting scans (~15 minutes into BOLD scanning), immediately before the cue task (~50 minutes into BOLD scanning) and immediately after the cue task (
      • Wang Z.
      • Faith M.
      • Patterson F.
      • Tang K.
      • Kerrin K.
      • Wileyto E.P.
      • et al.
      Neural substrates of abstinence-induced cigarette cravings in chronic smokers.
      ). During the time between the structural and resting scans and the cue task, participants were scanned while completing tasks assessing cognitive function, including working memory, attention, and response inhibition; data from these tasks were reported elsewhere (
      • Falcone M.
      • Wileyto E.P.
      • Ruparel K.
      • Gerraty R.T.
      • LaPrate L.
      • Detre J.A.
      • et al.
      Age-related differences in working memory deficits during nicotine withdrawal.
      ,
      • Loughead J.
      • Wileyto E.P.
      • Ruparel K.
      • Falcone M.
      • Hopson R.
      • Gur R.
      • et al.
      Working memory-related neural activity predicts future smoking relapse.
      ). Participants were asked to rate the degree of craving for and withdrawal from cigarettes they were currently experiencing on a scale from 0 (“not at all”) to 10 (“extremely”). Because scores on the craving questionnaire did not differ by time point within each session, a summary score was created for each session by averaging responses across all three time points.

      Image Preprocessing

      The BOLD time series data were preprocessed and analyzed by standard procedures using fMRI Expert Analysis Tool (version 6.00) from FSL (FMRIB's Software Library; Oxford, United Kingdom). Single subject preprocessing included skull stripping using the FSL Brain Extraction Tool (
      • Smith S.M.
      Fast robust automated brain extraction.
      ), slice time correction, motion correction to the median image using MCFLIRT (FSL) (
      • Jenkinson M.
      • Smith S.
      A global optimisation method for robust affine registration of brain images.
      ), high-pass temporal filtering (100 seconds), spatial smoothing using a Gaussian kernel (6 mm full width at half maximum, isotropic) and mean-based intensity normalization of all volumes with the same multiplicative factor. The median functional volume was coregistered to the anatomic T1-weighted structural volume and transformed into a standard anatomic space (T1 Montreal Neurological Institute template) using FLIRT and FNIRT (FSL) (
      • Jenkinson M.
      • Smith S.
      A global optimisation method for robust affine registration of brain images.
      ,
      • Jenkinson M.
      • Bannister P.
      • Brady M.
      • Smith S.
      Improved optimization for the robust and accurate linear registration and motion correction of brain images.
      ). Transformation parameters were later applied to statistical maps for group-level analyses.

      Image Quality Assessment

      The BOLD image quality was assessed using the following metrics: temporal signal-to-noise ratio (tSNR), subject motion, and global signal spike rate. Voxelwise tSNR was computed for all brain voxels by dividing the mean time course voxel amplitude by its SD. Overall imaging session tSNR was computed as the average tSNR over all brain voxels. Subject motion was computed using the motion parameter estimations returned by the MCFLIRT routine. The six motion parameters at each time point were converted to a time course measure of the relative root mean square voxel displacement (
      • Jenkinson M.
      • Bannister P.
      • Brady M.
      • Smith S.
      Improved optimization for the robust and accurate linear registration and motion correction of brain images.
      ). Finally, the temporal average of this time course displacement signal was used to represent overall subject motion for the session. This metric is termed the mean relative displacement and is expressed in millimeters. Quality assessment metrics were plotted for visual inspection, and subjects with notable tSNR (>2 SD from the mean) or mean relative displacement (>3 SD) were flagged for further evaluation. Visual inspection revealed significant motion artifact in ventricles and cortical surface in five subjects (mean relative motion >.25 mm), three subjects showed significant signal loss in frontal regions, and three subjects had low tSNR not associated with motion. In total, 11 participants (of 80) were excluded from further analysis.

      Whole-Brain Image Analysis

      Subject-level statistical analyses used FILM (FMRIB's Improved Linear Model) with local autocorrelation correction (
      • Woolrich M.W.
      • Ripley B.D.
      • Brady M.
      • Smith S.M.
      Temporal autocorrelation in univariate linear modeling of FMRI data.
      ). Smoking cue and neutral image events were modeled using a canonical hemodynamic response function and its temporal derivative. There were 24 motion correction parameters included as nuisance covariates, and the rest periods (fixation point) were treated as the baseline. Our contrast of interest was smoking cue minus neutral image. Image analysis was completed for each individual in subject space, and resulting contrast maps were spatially normalized as described earlier.
      A repeated measures condition (smoking, abstinent) by NMR group (slow, normal metabolizers) whole-brain analysis of variance was used to identify regions sensitive to NMR by condition interaction effects. Main effects were familywise error corrected at p ≤ .05, and the interaction effect was cluster corrected at Z ≥ 1.9, cluster probability p ≤ .05 (
      • Friston K.J.
      • Worsley K.J.
      • Frackowiak R.S.
      • Mazziotta J.C.
      • Evans A.C.
      Assessing the significance of focal activations using their spatial extent.
      ). From the corrected interaction image, each participant’s mean percent signal change was calculated for the cue minus neutral contrast in each cluster and exported for statistical analysis.

      Statistical Analysis

      Descriptive statistics were obtained for all variables. To examine baseline differences in demographic variables and smoking behavior by NMR group, t tests and χ2 tests were used. As an exploratory analysis, mean percent BOLD signal change was extracted from each cluster identified in the whole-brain analysis of variance and modeled using regression with subject-level random effects and maximum likelihood techniques (xtreg; StataCorp LP, College Station, Texas) with linear mixed effects models that included terms for main and interacting effects of condition (abstinent, smoking) and NMR group (slow, normal metabolizers), controlling for nicotine dependence (FTND score). Smoking quantity (CPD) was also examined as a possible covariate; results from these models were essentially the same as those controlling for nicotine dependence. To examine brain-behavior relationships, similar linear regression models were used to estimate main and interacting effects of condition (abstinent vs. smoking satiety) and craving scores on percent BOLD signal change in each cluster identified in the whole-brain analysis; NMR group and nicotine dependence (FTND score) were included as covariates. We also examined main and interacting effects of NMR group and condition (controlling for nicotine dependence or CPD or both) on percent BOLD signal change in the clusters identified in the whole-brain analysis using a subset of participants consisting only of the fastest (top quartile; n = 17) and slowest (bottom quartile; n = 18) metabolizers.

      Results

      Participants

      The analysis included 69 participants. Of these, 31 (44.9%) were women, 39 (56.5%) were African American, and 30 (43.5%) were slow metabolizers. The mean age was 42.5 years (SD 13.0), the mean CPD was 16.3 (SD 5.0), and the mean FTND score was 5.0 (SD 1.7). Exhaled carbon monoxide was significantly lower at the abstinent session (mean 3.8 ppm, SD 2.3 ppm) compared with the smoking as usual session (mean 27.1 ppm, SD 13.5 ppm, p < .0001), indicating compliance with the abstinence requirement. Table 1 displays demographics and behavioral results by NMR group. There were no differences between slow metabolizers and normal metabolizers for age, sex, or race; however, slow metabolizers were less nicotine dependent (p = .032) and smoked fewer CPD (p = .048), consistent with some prior reports (
      • Benowitz N.L.
      • Pomerleau O.F.
      • Pomerleau C.S.
      • Jacob 3rd, P.
      Nicotine metabolite ratio as a predictor of cigarette consumption.
      ,
      • Falcone M.
      • Jepson C.
      • Benowitz N.
      • Bergen A.W.
      • Pinto A.
      • Wileyto E.P.
      • et al.
      Association of the nicotine metabolite ratio and CHRNA5/CHRNA3 polymorphisms with smoking rate among treatment-seeking smokers.
      ). Slow metabolizers reported significantly less craving in both conditions compared with normal metabolizers (p = .001) (Supplemental Table S1), but changes in craving between the smoking session and the abstinence session were not different between slow and normal metabolizers (p > .5).
      Table 1Demographics and Smoking Behavior by Nicotine Metabolite Ratio Group
      MeasureSlow Metabolizers (n = 30)Normal Metabolizers (n = 39)All (N = 69)
      Female Sex11 (36.7%)20 (51.2%)31 (45%)
      Age (Years)42.0 (14.0)42.8 (12.4)42.5 (13.0)
      Ethnic Origin
       African American21 (70%)18 (46%)39 (69%)
       Caucasian7 (23%)19 (49%)26 (38%)
       Other2 (6%)1 (2.5%)3 (4%)
       Not reported0 (0%)1 (2.5%)1 (1%)
      Postsecondary Education22 (73%)26 (67%)48 (70%)
      FTND Score4.5 (1.5)5.3 (1.7)
      p < .05 for normal metabolizers vs. slow metabolizers.
      5.0 (1.7)
      CPD15.0 (4.8)17.4 (5.0)
      p < .05 for normal metabolizers vs. slow metabolizers.
      16.3 (5.0)
      Data are n (%) or mean (SD).
      CPD, cigarettes per day; FTND, Fagerström Test for Nicotine Dependence.
      a p < .05 for normal metabolizers vs. slow metabolizers.

      Whole-Brain Analysis of NMR by Condition

      Whole-brain analysis of the cue minus neutral contrast revealed significant activation (familywise error corrected at p ≤ .05) in regions consistent with other fMRI-based cue reactivity studies (Supplemental Table S2 and Supplemental Figure S1) (
      • McClernon F.J.
      • Kozink R.V.
      • Lutz A.M.
      • Rose J.E.
      24-h smoking abstinence potentiates fMRI-BOLD activation to smoking cues in cerebral cortex and dorsal striatum.
      ,
      • David S.P.
      • Munafo M.R.
      • Johansen-Berg H.
      • Mackillop J.
      • Sweet L.H.
      • Cohen R.A.
      • et al.
      Effects of acute nicotine abstinence on cue-elicited ventral striatum/nucleus accumbens activation in female cigarette smokers: A functional magnetic resonance imaging study.
      ,
      • McBride D.
      • Barrett S.P.
      • Kelly J.T.
      • Aw A.
      • Dagher A.
      Effects of expectancy and abstinence on the neural response to smoking cues in cigarette smokers: An fMRI study.
      ,
      • Engelmann J.M.
      • Versace F.
      • Robinson J.D.
      • Minnix J.A.
      • Lam C.Y.
      • Cui Y.
      • et al.
      Neural substrates of smoking cue reactivity: A meta-analysis of fMRI studies.
      ). There were no regions that displayed greater activation in response to neutral images compared with smoking cues.
      Our primary hypothesis was tested using a whole-brain condition (abstinent, smoking) by NMR group (slow vs. normal) analysis of variance. Significant interaction effects were observed in three regions: left inferior frontal gyrus (IFG), left frontal pole (FP), and left caudate (Table 2). In all three clusters, normal metabolizers demonstrated greater neural responses to smoking cues (vs. neutral stimuli) during abstinence compared with the smoking condition, whereas slow metabolizers demonstrated slightly reduced responses during abstinence (Figure 1). An exploratory analysis of extracted percent signal change was performed, and these interactions remained significant after controlling for FTND score or CPD (left IFG β = .18, 95% confidence interval [CI] = .08–.28, p = .001; left FP β = .29, 95% CI = .12–.46, p = .001; left caudate β = .24, 95% CI = .11–.38, p < .001). Neither FTND score nor CPD was significantly associated with neural responses to smoking cues in these regions. To understand these interactions better, we examined paired abstinence versus smoking contrasts separately for smoking cues and neutral images. In normal metabolizers, abstinence increased neural responses to smoking cues and decreased responses to neutral cues. In slow metabolizers, abstinence decreased neural responses to smoking cues and had no effect on responses to neutral cues.
      Table 2Session (Smoking, Abstinent) by Group (Slow, Normal Metabolizers) Whole-Brain Interaction Results
      Region
      Significant clusters Z ≥ 1.96 and cluster probability p < .05.
      Hem
      Cerebral hemisphere.
      p ValueSizeZ-MAX
      Z-MAX values represent peak activation for cluster.
      X (mm)
      Montreal Neurological Institute coordinates.
      Y (mm)
      Montreal Neurological Institute coordinates.
      Z (mm)
      Montreal Neurological Institute coordinates.
      Frontal PoleLeft<.0015473.68−325022
      Inferior FrontalLeft<.0015023.36−561622
      CaudateLeft<.0012942.91−12200
      a Significant clusters Z ≥ 1.96 and cluster probability p < .05.
      b Cerebral hemisphere.
      c Z-MAX values represent peak activation for cluster.
      d Montreal Neurological Institute coordinates.
      Figure thumbnail gr1
      Figure 1Whole-brain nicotine metabolite ratio by session results. Whole-brain session (abstinent, satiety) by group (slow, normal metabolizers) voxelwise analysis of variance showing interaction effect in blood oxygen level–dependent (BOLD) signal change (cue minus neutral contrast). Clusters are corrected for multiple comparisons using Z ≥ 1.96 and probability of spatial extent p < .05 for the interaction effect. L, left.

      Associations of Neural Responses to Subjective Measures

      We examined whether BOLD signal changes for the cue minus neutral contrast in regions identified by the NMR by session interaction were associated with average craving ratings during the scan. There were significant abstinence by craving interaction effects on BOLD signal in the left FP (β = .03, 95% CI = .009–.05, p = .004) and left caudate (β = .02, 95% CI = .0003–.03, p = .046). Increased BOLD signal change in these regions for cue (vs. neutral) was associated with increased craving during abstinence but not during the smoking condition (Supplemental Figure S2). There were no significant interaction effects in the left IFG (p > .2), and the BOLD-craving associations were consistent in both normal metabolizers and slow metabolizers.

      Exploratory Analysis of Extreme Groups of Metabolizers

      There were no differences in age, sex, or race between the slowest (n = 18) and fastest (n = 17) metabolizers (Supplemental Table S3). In this extreme group analysis, the condition by NMR group (slowest vs. fastest metabolizers) interaction effects were more pronounced: in all three brain regions, the slowest metabolizers showed no effect of abstinence on neural responses in the cue minus neutral contrast (p values > .3), whereas the fastest metabolizers showed a significant increase in BOLD response to smoking cues during abstinence compared with smoking (p values < .001) (Figure 2). The condition by craving interaction effect was similar to that observed in the full sample in the left FP (p = .005) but was nonsignificant in the left caudate (p = .07) and left IFG (p > .3).
      Figure thumbnail gr2
      Figure 2Condition by nicotine metabolite ratio group effects in slowest vs. fastest metabolizers. In the exploratory analysis, the slowest metabolizers (n = 18) showed no effect of abstinence on blood oxygen level–dependent (BOLD) response to smoking cues (vs. neutral stimuli) in the regions identified by the whole-brain analysis of variance in the larger sample; however, the fastest metabolizers (n = 17) displayed significant increases in percent BOLD signal change (cue minus neutral) during abstinence compared with smoking satiety. IFG, inferior frontal gyrus.

      Discussion

      Normal (vs. slow) metabolizers exhibited significantly heightened neural responses to smoking cues in the left caudate, left FP, and left IFG during abstinence compared with smoking satiety. These differences were more pronounced when the extreme metabolism groups were examined. Increased BOLD signal in the left FP and left caudate predicted increased cravings in both groups of smokers during abstinence.
      Prior preclinical studies suggest that abstinence-induced differences in cholinergic modulation of dopaminergic signaling between slow and normal metabolizers may explain our results. Nicotine binds to neuronal nicotinic acetylcholine receptors to induce striatal dopamine release (
      • Brody A.L.
      • Olmstead R.E.
      • London E.D.
      • Farahi J.
      • Meyer J.H.
      • Grossman P.
      • et al.
      Smoking-induced ventral striatum dopamine release.
      ,
      • Zhang T.
      • Zhang L.
      • Liang Y.
      • Siapas A.G.
      • Zhou F.M.
      • Dani J.A.
      Dopamine signaling differences in the nucleus accumbens and dorsal striatum exploited by nicotine.
      ). Following chronic nicotine exposure, smoking cues themselves induce dopamine release through conditioned associations with nicotine reward (
      • Yasuno F.
      • Ota M.
      • Ando K.
      • Ando T.
      • Maeda J.
      • Ichimiya T.
      • et al.
      Role of ventral striatal dopamine D1 receptor in cigarette craving.
      ,
      • Bassareo V.
      • De Luca M.A.
      • Di Chiara G.
      Differential impact of pavlovian drug conditioned stimuli on in vivo dopamine transmission in the rat accumbens shell and core and in the prefrontal cortex.
      ,
      • Jasinska A.J.
      • Stein E.A.
      • Kaiser J.
      • Naumer M.J.
      • Yalachkov Y.
      Factors modulating neural reactivity to drug cues in addiction: A survey of human neuroimaging studies.
      ). Nicotine abstinence results in a withdrawal syndrome associated with reduced extracellular dopamine concentrations and lower tonic dopaminergic signaling (
      • Zhang L.
      • Dong Y.
      • Doyon W.M.
      • Dani J.A.
      Withdrawal from chronic nicotine exposure alters dopamine signaling dynamics in the nucleus accumbens.
      ). Nicotine metabolism rate is expected to accelerate clearance of nicotine from the brain; consistent with this, normal metabolizers show greater nicotinic receptor availability during early abstinence compared with slow metabolizers (
      • Dubroff J.G.
      • Doot R.K.
      • Falcone M.
      • Schnoll R.A.
      • Ray R.
      • Tyndale R.F.
      • et al.
      Decreased nicotinic receptor availability in smokers with slow rates of nicotine metabolism.
      ). Faster nicotine clearance, reduced cholinergic signaling, and more rapid onset of withdrawal may alter phasic dopamine release in response to smoking cues during abstinence, resulting in stronger neural responses and craving in normal metabolizers compared with slow metabolizers.
      Comparing slow metabolizers with normal metabolizers during abstinence versus smoking revealed novel smoking cue–responsive brain regions as well as expected cue-reactive regions. Although it is impossible to accurately infer specific psychological processes based on the observed activation patterns, the regions identified are plausibly related to cue reactivity based on prior studies. The caudate was previously identified in smoking cue reactivity studies (
      • Engelmann J.M.
      • Versace F.
      • Robinson J.D.
      • Minnix J.A.
      • Lam C.Y.
      • Cui Y.
      • et al.
      Neural substrates of smoking cue reactivity: A meta-analysis of fMRI studies.
      ,
      • McClernon F.J.
      • Hiott F.B.
      • Huettel S.A.
      • Rose J.E.
      Abstinence-induced changes in self-report craving correlate with event-related FMRI responses to smoking cues.
      ,
      • McClernon F.J.
      • Kozink R.V.
      • Rose J.E.
      Individual differences in nicotine dependence, withdrawal symptoms, and sex predict transient fMRI-BOLD responses to smoking cues.
      ). One hypothesis that could be tested in future studies is that greater cue-induced activation of caudate among normal metabolizers during abstinence is associated with altered dopaminergic signaling in response to smoking cues (
      • Yasuno F.
      • Ota M.
      • Ando K.
      • Ando T.
      • Maeda J.
      • Ichimiya T.
      • et al.
      Role of ventral striatal dopamine D1 receptor in cigarette craving.
      ). The left IFG and left FP (approximately Brodmann area 10) were not consistently identified in prior smoking cue reactivity research. However, prior studies showed activation of the left IFG by both emotional inhibition and motor inhibition tasks (
      • Tabibnia G.
      • Creswell J.D.
      • Kraynak T.
      • Westbrook C.
      • Julson E.
      • Tindle H.A.
      Common prefrontal regions activate during self-control of craving, emotion, and motor impulses in smokers.
      ), and increased activity has been observed in this region when smokers are actively resisting craving in response to smoking cues (
      • Hartwell K.J.
      • Johnson K.A.
      • Li X.
      • Myrick H.
      • LeMatty T.
      • George M.S.
      • et al.
      Neural correlates of craving and resisting craving for tobacco in nicotine dependent smokers.
      ). The role of activation in the frontopolar area is incompletely understood, and it is not frequently implicated in cue reactivity. However, this region has been implicated in prospective memory (generating an intention for a future action) (
      • Burgess P.W.
      • Quayle A.
      • Frith C.D.
      Brain regions involved in prospective memory as determined by positron emission tomography.
      ,
      • Burgess P.W.
      • Scott S.K.
      • Frith C.D.
      The role of the rostral frontal cortex (area 10) in prospective memory: A lateral versus medial dissociation.
      ). Further research using probabilistic analyses or data mining approaches could be useful to clarify the involvement of specific cognitive processes underlying nicotine metabolism–based differences in neural cue reactivity (
      • Poldrack R.A.
      Inferring mental states from neuroimaging data: From reverse inference to large-scale decoding.
      ). Our results show that slow metabolizers may experience reduced cue reactivity in all three of these regions during abstinence compared with smoking. The cause for this finding is unclear; however, because slow metabolizers relapse at lower rates than normal metabolizers, it may offer further support for the hypothesis that altered cue responses contribute to differences in relapse rates. Additional research may elucidate the role of cue reactivity in smoking behavior for slow metabolizers of nicotine.
      The regions differentiating responses of slow metabolizers and normal metabolizers differ in part from the regions identified by Tang et al. (
      • Tang D.W.
      • Hello B.
      • Mroziewicz M.
      • Fellows L.K.
      • Tyndale R.F.
      • Dagher A.
      Genetic variation in CYP2A6 predicts neural reactivity to smoking cues as measured using fMRI.
      ), who tested cue reactivity only during satiety. Although these investigators demonstrated that the fastest metabolizers (those in the fourth quartile of NMR) showed greater cue-induced activation in the caudate nucleus, similar to our study, they also identified group differences in the anterior and posterior cingulate cortices, amygdala, hippocampus, and insula. However, smokers in that study were not deprived of cigarettes before scanning during a single session. Results of studies comparing neural cue reactivity during abstinent and smoking states have been inconsistent (
      • McClernon F.J.
      • Kozink R.V.
      • Lutz A.M.
      • Rose J.E.
      24-h smoking abstinence potentiates fMRI-BOLD activation to smoking cues in cerebral cortex and dorsal striatum.
      ,
      • David S.P.
      • Munafo M.R.
      • Johansen-Berg H.
      • Mackillop J.
      • Sweet L.H.
      • Cohen R.A.
      • et al.
      Effects of acute nicotine abstinence on cue-elicited ventral striatum/nucleus accumbens activation in female cigarette smokers: A functional magnetic resonance imaging study.
      ,
      • McBride D.
      • Barrett S.P.
      • Kelly J.T.
      • Aw A.
      • Dagher A.
      Effects of expectancy and abstinence on the neural response to smoking cues in cigarette smokers: An fMRI study.
      ); however, given the effect of individual nicotine metabolism rates on nicotine clearance in the brain, it is important to study metabolism-based differences in cue reactivity during early abstinence versus the smoking state. We chose to use a 24-hour abstinence challenge because most relapse to smoking occurs within the first 24 hours of a quit attempt (
      • Piasecki T.M.
      Relapse to smoking.
      ), and positron emission tomography imaging demonstrated differences in nicotinic receptor availability between slow metabolizers and normal metabolizers after 24 hours of abstinence (
      • Dubroff J.G.
      • Doot R.K.
      • Falcone M.
      • Schnoll R.A.
      • Ray R.
      • Tyndale R.F.
      • et al.
      Decreased nicotinic receptor availability in smokers with slow rates of nicotine metabolism.
      ). However, the precise time course of nicotine clearance from the brain and the emergence of abstinence symptoms for slow metabolizers versus normal metabolizers has not been well established. Future research into these topics may reveal the optimal time for investigating metabolism-based differences in abstinence effects and may highlight periods of time during which each group is most vulnerable to relapse.
      These data provide insight into a plausible explanation for why faster metabolizers of nicotine have increased relapse rates when trying to quit with transdermal nicotine replacement therapy or placebo (
      • Patterson F.
      • Schnoll R.A.
      • Wileyto E.P.
      • Pinto A.
      • Epstein L.H.
      • Shields P.G.
      • et al.
      Toward personalized therapy for smoking cessation: A randomized placebo-controlled trial of bupropion.
      ,
      • Lerman C.
      • Tyndale R.
      • Patterson F.
      • Wileyto E.P.
      • Shields P.G.
      • Pinto A.
      • et al.
      Nicotine metabolite ratio predicts efficacy of transdermal nicotine for smoking cessation.
      ,
      • Schnoll R.A.
      • Patterson F.
      • Wileyto E.P.
      • Tyndale R.F.
      • Benowitz N.
      • Lerman C.
      Nicotine metabolic rate predicts successful smoking cessation with transdermal nicotine: A validation study.
      ,
      • Lerman C.
      • Schnoll R.A.
      • Hawk Jr, L.W.
      • Cinciripini P.
      • George T.P.
      • Wileyto E.P.
      • et al.
      Use of the nicotine metabolite ratio as a genetically informed biomarker of response to nicotine patch or varenicline for smoking cessation: A randomised, double-blind placebo-controlled trial.
      ). Increased neural reactivity to smoking cues has been shown to predict future relapse among smokers who are trying to quit (
      • Janes A.C.
      • Pizzagalli D.A.
      • Richardt S.
      • deB Frederick B.
      • Chuzi S.
      • Pachas G.
      • et al.
      Brain reactivity to smoking cues prior to smoking cessation predicts ability to maintain tobacco abstinence.
      ); it is possible that increased cue reactivity may contribute to increased relapse rates among normal metabolizers. However, varenicline, a partial α4β2 nicotinic acetylcholine receptor agonist, is a highly efficacious medication for normal metabolizers (
      • Lerman C.
      • Schnoll R.A.
      • Hawk Jr, L.W.
      • Cinciripini P.
      • George T.P.
      • Wileyto E.P.
      • et al.
      Use of the nicotine metabolite ratio as a genetically informed biomarker of response to nicotine patch or varenicline for smoking cessation: A randomised, double-blind placebo-controlled trial.
      ). Cue reactivity is thought to reflect conditioned responses in dopamine-rich reward areas (
      • Jasinska A.J.
      • Stein E.A.
      • Kaiser J.
      • Naumer M.J.
      • Yalachkov Y.
      Factors modulating neural reactivity to drug cues in addiction: A survey of human neuroimaging studies.
      ), and varenicline, which increases dopamine concentrations in reward areas, has been shown to decrease brain responses to smoking cues (
      • Franklin T.
      • Wang Z.
      • Suh J.J.
      • Hazan R.
      • Cruz J.
      • Li Y.
      • et al.
      Effects of varenicline on smoking cue-triggered neural and craving responses.
      ). Furthermore, varenicline shows greater affinity than nicotine for α4β2 nicotinic receptors, a property that may allow it to act as an antagonist blocking the reinforcing effects of nicotine from cigarettes smoked during a lapse (
      • Coe J.W.
      • Brooks P.R.
      • Vetelino M.G.
      • Wirtz M.C.
      • Arnold E.P.
      • Huang J.
      • et al.
      Varenicline: An alpha4beta2 nicotinic receptor partial agonist for smoking cessation.
      ,
      • Rollema H.
      • Hajos M.
      • Seymour P.A.
      • Kozak R.
      • Majchrzak M.J.
      • Guanowsky V.
      • et al.
      Preclinical pharmacology of the alpha4beta2 nAChR partial agonist varenicline related to effects on reward, mood and cognition.
      ). It is possible that this antagonist effect may help to break the conditioned association between smoking and reward to reduce cue reactivity, and this effect could be particularly beneficial for normal metabolizers.
      Strengths of our study include a large sample size; the use of a within-subject design, which allowed each participant to serve as his or her own control; and the use of a biochemically verified abstinence challenge to directly compare effects of abstinence versus smoking within slow metabolizers and normal metabolizers. A limitation of our study is that we did not include a treatment condition, and therefore our discussion of mechanisms underlying differences in quit rates must be considered speculative and hypothesis generating.
      In conclusion, we demonstrated an increase in neural responses to smoking cues during abstinence in normal (but not slow) metabolizers of nicotine in regions associated with reward, emotion regulation, and prospective memory. However, further research is necessary to investigate whether neural responses to smoking cues predict clinical outcomes in slow and normal metabolizers. If so, treatments that decrease neural response to smoking cues, such as cue exposure therapy (
      • Vollstädt-Klein S.
      • Loeber S.
      • Kirsch M.
      • Bach P.
      • Richter A.
      • Buhler M.
      • et al.
      Effects of cue-exposure treatment on neural cue reactivity in alcohol dependence: A randomized trial.
      ), may be effective adjunctive therapies for normal metabolizers. In addition, cue reactivity in these brain regions may provide a novel target for development of new cessation treatments in this subgroup of smokers.

      Acknowledgments and Disclosures

      This work was supported by grants from the National Cancer Institute and National Institute on Drug Abuse Grant Nos. P50CA143187 and U01DA020830 (to CL) and Commonwealth of Pennsylvania Department of Health. The funding sources had no role in the study design; the collection, analysis, or interpretation of the data; the writing of the manuscript; or the decision to submit the article for publication. The Commonwealth of Pennsylvania Department of Health disclaims responsibility for any analyses, interpretations, or conclusions.
      We thank Dr. Marcus Munafò and Dr. Emma Mullings for providing the smoking and neutral cue images used in this study and Maria Novalan and Bin Zhoa for assessments of the nicotine metabolite ratio.
      CL has received research funding from Pfizer and has served as a consultant to Gilead Sciences, Inc. The other authors report no biomedical financial interests or potential conflicts of interest.

      Appendix A. Supplementary Materials

      References

        • Centers for Disease Control
        Smoking attributable mortality, years of potential life lost, and productivity losses.
        MMWR Morb Mortal Wkly Rep. 2008; 57: 1226-1228
        • Centers for Disease Control
        Vital signs: Current cigarette smoking among adults aged >/=18 years—United States, 2005-2010.
        MMWR Morb Mortal Wkly Rep. 2011; 60: 1207-1212
        • Benowitz N.L.
        • Pomerleau O.F.
        • Pomerleau C.S.
        • Jacob 3rd, P.
        Nicotine metabolite ratio as a predictor of cigarette consumption.
        Nicotine Tob Res. 2003; 5: 621-624
        • Falcone M.
        • Jepson C.
        • Benowitz N.
        • Bergen A.W.
        • Pinto A.
        • Wileyto E.P.
        • et al.
        Association of the nicotine metabolite ratio and CHRNA5/CHRNA3 polymorphisms with smoking rate among treatment-seeking smokers.
        Nicotine Tob Res. 2011; 13: 498-503
        • Ho M.K.
        • Mwenifumbo J.C.
        • Al Koudsi N.
        • Okuyemi K.S.
        • Ahluwalia J.S.
        • Benowitz N.L.
        • et al.
        Association of nicotine metabolite ratio and CYP2A6 genotype with smoking cessation treatment in African-American light smokers.
        Clin Pharmacol Ther. 2009; 85: 635-643
        • Patterson F.
        • Schnoll R.A.
        • Wileyto E.P.
        • Pinto A.
        • Epstein L.H.
        • Shields P.G.
        • et al.
        Toward personalized therapy for smoking cessation: A randomized placebo-controlled trial of bupropion.
        Clin Pharmacol Ther. 2008; 84: 320-325
        • Dempsey D.
        • Tutka P.
        • Jacob 3rd, P.
        • Allen F.
        • Schoedel K.
        • Tyndale R.F.
        • et al.
        Nicotine metabolite ratio as an index of cytochrome P450 2A6 metabolic activity.
        Clin Pharmacol Ther. 2004; 76: 64-72
        • St Helen G.
        • Novalen M.
        • Heitjan D.F.
        • Dempsey D.
        • Jacob 3rd, P.
        • Aziziyeh A.
        • et al.
        Reproducibility of the nicotine metabolite ratio in cigarette smokers.
        Cancer Epidemiol Biomarkers Prev. 2012; 21: 1105-1114
        • Hamilton D.A.
        • Mahoney M.C.
        • Novalen M.
        • Chenoweth M.J.
        • Heitjan D.F.
        • Lerman C.
        • et al.
        Test-retest reliability and stability of the nicotine metabolite ratio among treatment-seeking smokers.
        Nicotine Tob Res. 2015; 17: 1505-1509
        • Tanner J.A.
        • Novalen M.
        • Jatlow P.
        • Huestis M.A.
        • Murphy S.E.
        • Kaprio J.
        • et al.
        Nicotine metabolite ratio (3-hydroxycotinine/cotinine) in plasma and urine by different analytical methods and laboratories: Implications for clinical implementation.
        Cancer Epidemiol Biomarkers Prev. 2015; 24: 1239-1246
        • Lerman C.
        • Tyndale R.
        • Patterson F.
        • Wileyto E.P.
        • Shields P.G.
        • Pinto A.
        • et al.
        Nicotine metabolite ratio predicts efficacy of transdermal nicotine for smoking cessation.
        Clin Pharmacol Ther. 2006; 79: 600-608
        • Schnoll R.A.
        • Patterson F.
        • Wileyto E.P.
        • Tyndale R.F.
        • Benowitz N.
        • Lerman C.
        Nicotine metabolic rate predicts successful smoking cessation with transdermal nicotine: A validation study.
        Pharmacol Biochem Behav. 2009; 92: 6-11
        • Lerman C.
        • Schnoll R.A.
        • Hawk Jr, L.W.
        • Cinciripini P.
        • George T.P.
        • Wileyto E.P.
        • et al.
        Use of the nicotine metabolite ratio as a genetically informed biomarker of response to nicotine patch or varenicline for smoking cessation: A randomised, double-blind placebo-controlled trial.
        Lancet Respir Med. 2015; 3: 131-138
        • Johnstone E.
        • Benowitz N.
        • Cargill A.
        • Jacob R.
        • Hinks L.
        • Day I.
        • et al.
        Determinants of the rate of nicotine metabolism and effects on smoking behavior.
        Clin Pharmacol Ther. 2006; 80: 319-330
        • Schnoll R.A.
        • George T.P.
        • Hawk L.
        • Cinciripini P.
        • Wileyto P.
        • Tyndale R.F.
        The relationship between the nicotine metabolite ratio and three self-report measures of nicotine dependence across sex and race.
        Psychopharmacology (Berl). 2014; 231: 2515-2523
        • Rubinstein M.L.
        • Benowitz N.L.
        • Auerback G.M.
        • Moscicki A.B.
        A randomized trial of nicotine nasal spray in adolescent smokers.
        Pediatrics. 2008; 122: e595-e600
        • Dubroff J.G.
        • Doot R.K.
        • Falcone M.
        • Schnoll R.A.
        • Ray R.
        • Tyndale R.F.
        • et al.
        Decreased nicotinic receptor availability in smokers with slow rates of nicotine metabolism.
        J Nucl Med. 2015; 56: 1724-1729
        • Sofuoglu M.
        • Herman A.I.
        • Nadim H.
        • Jatlow P.
        Rapid nicotine clearance is associated with greater reward and heart rate increases from intravenous nicotine.
        Neuropsychopharmacology. 2012; 37: 1509-1516
        • Tang D.W.
        • Hello B.
        • Mroziewicz M.
        • Fellows L.K.
        • Tyndale R.F.
        • Dagher A.
        Genetic variation in CYP2A6 predicts neural reactivity to smoking cues as measured using fMRI.
        Neuroimage. 2012; 60: 2136-2143
        • Janes A.C.
        • Pizzagalli D.A.
        • Richardt S.
        • deB Frederick B.
        • Chuzi S.
        • Pachas G.
        • et al.
        Brain reactivity to smoking cues prior to smoking cessation predicts ability to maintain tobacco abstinence.
        Biol Psychiatry. 2010; 67: 722-729
        • Sheehan D.V.
        • Lecrubier Y.
        • Sheehan K.H.
        • Amorim P.
        • Janavs J.
        • Weiller E.
        • et al.
        The Mini-International Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10.
        J Clin Psychiatry. 1998; 59 (quiz 34–57): 22-33
        • Zachary R.
        Shipley Institute of Living Scale: Revised Manual.
        Western Psychological Services, Los Angeles1986
        • Heatherton T.F.
        • Kozlowski L.T.
        • Frecker R.C.
        • Fagerström K.O.
        The Fagerström Test for Nicotine Dependence: A revision of the Fagerström Tolerance Questionnaire.
        Br J Addict. 1991; 86: 1119-1127
        • Wang Z.
        • Faith M.
        • Patterson F.
        • Tang K.
        • Kerrin K.
        • Wileyto E.P.
        • et al.
        Neural substrates of abstinence-induced cigarette cravings in chronic smokers.
        J Neurosci. 2007; 27: 14035-14040
        • Falcone M.
        • Wileyto E.P.
        • Ruparel K.
        • Gerraty R.T.
        • LaPrate L.
        • Detre J.A.
        • et al.
        Age-related differences in working memory deficits during nicotine withdrawal.
        Addict Biol. 2014; 19: 907-917
        • Loughead J.
        • Wileyto E.P.
        • Ruparel K.
        • Falcone M.
        • Hopson R.
        • Gur R.
        • et al.
        Working memory-related neural activity predicts future smoking relapse.
        Neuropsychopharmacology. 2015; 40: 1311-1320
        • Smith S.M.
        Fast robust automated brain extraction.
        Hum Brain Mapp. 2002; 17: 143-155
        • Jenkinson M.
        • Smith S.
        A global optimisation method for robust affine registration of brain images.
        Med Image Anal. 2001; 5: 143-156
        • Jenkinson M.
        • Bannister P.
        • Brady M.
        • Smith S.
        Improved optimization for the robust and accurate linear registration and motion correction of brain images.
        Neuroimage. 2002; 17: 825-841
        • Woolrich M.W.
        • Ripley B.D.
        • Brady M.
        • Smith S.M.
        Temporal autocorrelation in univariate linear modeling of FMRI data.
        Neuroimage. 2001; 14: 1370-1386
        • Friston K.J.
        • Worsley K.J.
        • Frackowiak R.S.
        • Mazziotta J.C.
        • Evans A.C.
        Assessing the significance of focal activations using their spatial extent.
        Hum Brain Mapp. 1994; 1: 210-220
        • McClernon F.J.
        • Kozink R.V.
        • Lutz A.M.
        • Rose J.E.
        24-h smoking abstinence potentiates fMRI-BOLD activation to smoking cues in cerebral cortex and dorsal striatum.
        Psychopharmacology (Berl). 2009; 204: 25-35
        • David S.P.
        • Munafo M.R.
        • Johansen-Berg H.
        • Mackillop J.
        • Sweet L.H.
        • Cohen R.A.
        • et al.
        Effects of acute nicotine abstinence on cue-elicited ventral striatum/nucleus accumbens activation in female cigarette smokers: A functional magnetic resonance imaging study.
        Brain Imaging Behav. 2007; 1: 43-57
        • McBride D.
        • Barrett S.P.
        • Kelly J.T.
        • Aw A.
        • Dagher A.
        Effects of expectancy and abstinence on the neural response to smoking cues in cigarette smokers: An fMRI study.
        Neuropsychopharmacology. 2006; 31: 2728-2738
        • Engelmann J.M.
        • Versace F.
        • Robinson J.D.
        • Minnix J.A.
        • Lam C.Y.
        • Cui Y.
        • et al.
        Neural substrates of smoking cue reactivity: A meta-analysis of fMRI studies.
        Neuroimage. 2012; 60: 252-262
        • Brody A.L.
        • Olmstead R.E.
        • London E.D.
        • Farahi J.
        • Meyer J.H.
        • Grossman P.
        • et al.
        Smoking-induced ventral striatum dopamine release.
        Am J Psychiatry. 2004; 161: 1211-1218
        • Zhang T.
        • Zhang L.
        • Liang Y.
        • Siapas A.G.
        • Zhou F.M.
        • Dani J.A.
        Dopamine signaling differences in the nucleus accumbens and dorsal striatum exploited by nicotine.
        J Neurosci. 2009; 29: 4035-4043
        • Yasuno F.
        • Ota M.
        • Ando K.
        • Ando T.
        • Maeda J.
        • Ichimiya T.
        • et al.
        Role of ventral striatal dopamine D1 receptor in cigarette craving.
        Biol Psychiatry. 2007; 61: 1252-1259
        • Bassareo V.
        • De Luca M.A.
        • Di Chiara G.
        Differential impact of pavlovian drug conditioned stimuli on in vivo dopamine transmission in the rat accumbens shell and core and in the prefrontal cortex.
        Psychopharmacology (Berl). 2007; 191: 689-703
        • Jasinska A.J.
        • Stein E.A.
        • Kaiser J.
        • Naumer M.J.
        • Yalachkov Y.
        Factors modulating neural reactivity to drug cues in addiction: A survey of human neuroimaging studies.
        Neurosci Biobehav Rev. 2014; 38: 1-16
        • Zhang L.
        • Dong Y.
        • Doyon W.M.
        • Dani J.A.
        Withdrawal from chronic nicotine exposure alters dopamine signaling dynamics in the nucleus accumbens.
        Biol Psychiatry. 2012; 71: 184-191
        • McClernon F.J.
        • Hiott F.B.
        • Huettel S.A.
        • Rose J.E.
        Abstinence-induced changes in self-report craving correlate with event-related FMRI responses to smoking cues.
        Neuropsychopharmacology. 2005; 30: 1940-1947
        • McClernon F.J.
        • Kozink R.V.
        • Rose J.E.
        Individual differences in nicotine dependence, withdrawal symptoms, and sex predict transient fMRI-BOLD responses to smoking cues.
        Neuropsychopharmacology. 2008; 33: 2148-2157
        • Tabibnia G.
        • Creswell J.D.
        • Kraynak T.
        • Westbrook C.
        • Julson E.
        • Tindle H.A.
        Common prefrontal regions activate during self-control of craving, emotion, and motor impulses in smokers.
        Clin Psychol Sci. 2014; 2: 611-619
        • Hartwell K.J.
        • Johnson K.A.
        • Li X.
        • Myrick H.
        • LeMatty T.
        • George M.S.
        • et al.
        Neural correlates of craving and resisting craving for tobacco in nicotine dependent smokers.
        Addict Biol. 2011; 16: 654-666
        • Burgess P.W.
        • Quayle A.
        • Frith C.D.
        Brain regions involved in prospective memory as determined by positron emission tomography.
        Neuropsychologia. 2001; 39: 545-555
        • Burgess P.W.
        • Scott S.K.
        • Frith C.D.
        The role of the rostral frontal cortex (area 10) in prospective memory: A lateral versus medial dissociation.
        Neuropsychologia. 2003; 41: 906-918
        • Poldrack R.A.
        Inferring mental states from neuroimaging data: From reverse inference to large-scale decoding.
        Neuron. 2011; 72: 692-697
        • Piasecki T.M.
        Relapse to smoking.
        Clin Psychol Rev. 2006; 26: 196-215
        • Franklin T.
        • Wang Z.
        • Suh J.J.
        • Hazan R.
        • Cruz J.
        • Li Y.
        • et al.
        Effects of varenicline on smoking cue-triggered neural and craving responses.
        Arch Gen Psychiatry. 2011; 68: 516-526
        • Coe J.W.
        • Brooks P.R.
        • Vetelino M.G.
        • Wirtz M.C.
        • Arnold E.P.
        • Huang J.
        • et al.
        Varenicline: An alpha4beta2 nicotinic receptor partial agonist for smoking cessation.
        J Med Chem. 2005; 48: 3474-3477
        • Rollema H.
        • Hajos M.
        • Seymour P.A.
        • Kozak R.
        • Majchrzak M.J.
        • Guanowsky V.
        • et al.
        Preclinical pharmacology of the alpha4beta2 nAChR partial agonist varenicline related to effects on reward, mood and cognition.
        Biochem Pharmacol. 2009; 78: 813-824
        • Vollstädt-Klein S.
        • Loeber S.
        • Kirsch M.
        • Bach P.
        • Richter A.
        • Buhler M.
        • et al.
        Effects of cue-exposure treatment on neural cue reactivity in alcohol dependence: A randomized trial.
        Biol Psychiatry. 2011; 69: 1060-1066