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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Mol Psychiatry. Author manuscript; available in PMC 2014 January 21.
Published in final edited form as:
PMCID: PMC3896978

Effect of abstinence challenge on brain function and cognition in smokers differs by COMT genotype

J Loughead,1,2 EP Wileyto,2,3,4 JN Valdez,1 P Sanborn,2,3,4 K Tang,2,3,4 AA Strasser,2,3,4 K Ruparel,1 R Ray,2,3,4 RC Gur,1,2,5 and C Lerman2,3,4


The val allele of the catechol-O-methyltransferase (COMT) val158met polymorphism has been linked with nicotine dependence and with cognitive performance in healthy volunteers. We tested the hypothesis that the val allele is a risk factor for altered brain function and cognition during nicotine abstinence as compared with the normal smoking state. Chronic smokers (n = 33) were genotyped prospectively for the COMT polymorphism for balanced selection of met/met, val/met and val/val groups. A visual N-back working memory task was performed during two separate blood oxygen level-dependent (BOLD) functional magnetic resonance imaging sessions in counterbalanced order: (1) smoking as usual, and (2)≥14 h confirmed abstinence. Significant genotype by session interactions were observed for BOLD signal in right dorsolateral prefrontal cortex (DLPFC; (P = 0.0005), left DLPFC (P = 0.02) and dorsal cingulate/medial prefrontal cortex (P = 0.01) as well as for task reaction time (P = 0.03). Smokers with val/val genotypes were more sensitive to the abstinence challenge than carriers of the met allele, with the greatest effects on BOLD signal and performance speed at the highest working memory load. These data suggest a novel brain–behavior mechanism that may underlie the increased susceptibility to nicotine dependence and smoking relapse associated with the COMT val allele. Exploration of the effects of COMT inhibitors as a possible smoking cessation aid in this group may be warranted.

Keywords: nicotine, addiction, COMT, neuroimaging, genetics


The methylation enzyme catechol-O-methyltransferase (COMT) regulates dopamine levels in prefrontal cortex (PFC).1,2 The COMT gene has G–A transition in exon 3 that results in a substitution of methionine for valine at codon 158 (val158met). The val allele is associated with a 3- to 4-fold increase in enzyme activity and decreased prefrontal dopamine levels.35 Consistent with the role of dopamine in nicotine addiction,6 the COMT val allele is associated with increased susceptibility to nicotine dependence, and greater risk for smoking relapse.79

Converging lines of evidence suggest that this increased relapse risk in val allele carriers may be mediated, in part, by cognitive function. There is evidence that carriers of the COMT val allele exhibit less efficient prefrontal neural signaling, and in some studies, deficits in working memory and executive cognitive function.1016 Other studies have identified cognitive deficits as a core symptom of nicotine withdrawal,1719 and a predictor of smoking relapse.20,21

In an effort to integrate these converging lines of research, we tested the hypothesis that in chronic smokers the COMT val allele is a risk factor for altered brain function and cognitive deficits during abstinence from smoking, as compared with a normal smoking state. Specifically, we predicted that smokers homozygous for the val allele would exhibit greater abstinence-induced changes in activation in frontal regions sensitive to working memory load (that is, bilateral dorsolateral prefrontal cortices (DLPFCs) and dorsal cingulate/medial PFC);22 effects of the abstinence challenge on brain function were not expected for met allele carriers. A mechanistic understanding of the role of the COMT val allele in abstinence-induced cognitive symptoms that promote smoking relapse could facilitate medication development for nicotine dependence. Such information may further establish cognitive performance measures as endophenotypes for nicotine dependence.

Materials and methods


A total of 36 healthy smokers of at least 10 cigarettes per day were recruited by advertisements in newspaper and on radio. Eligible smokers were genotyped prospectively for the COMT val158met polymorphism (rs4680, Assay on Demand (c_25746809_50) from Applied Biosystems Inc., Foster City, CA, USA) to ensure balance in the three genotype groups. To reduce potential bias due to ethnic admixture, all participants were of European ancestry. Persons with a history of or current neurological or Diagnostic and Statistical Manual of Mental Disorders, 4th edition Axis I psychiatric or substance disorders (except nicotine dependence) were excluded. Persons currently taking psychotropic medications (for example, MAO inhibitors, benzodiazepines, antidepressants, antipsychotics) were also excluded. The final sample (n = 33) (three participants were excluded for measurement artifact) included 18 men and 15 women with an average age of 33 years (s.d. = 10.55); met/met = 11, val/met = 12 and val/val = 10.


This neuroimaging experiment used a within-subject design with two blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) sessions occurring 1–3 weeks apart in counterbalanced order: (1) smoking as usual and (2) overnight (≥14 h) abstinent. Both sessions occurred before noon, and subjects were instructed to refrain from alcohol or other drugs for at least 24 h before the session.

Before each session participants completed the Fagerstrom Test for Nicotine Dependence (FTND)23 and provided a carbon monoxide (CO) breath sample. This sample was used to verify abstinence for the abstinent session, and was based on a maximum CO criterion of 15 p.p.m., consistent with previous neuroimaging studies.24 On the smoking as usual day, participants smoked immediately before the scanning session to standardize exposure. There was approximately 45–60 min delay between the last cigarette smoked and the beginning of BOLD imaging. A breath alcohol test was performed before both sessions to confirm compliance with the alcohol restriction ( > .01 cutoff) and participants completed a withdrawal symptom checklist25 and the Questionnaire of Smoking Urges.26 Following these assessments and procedures, participants were escorted to the radiology clinic (about 20 min) for the scanning session.

Task design

Working memory function was assessed using a visual N-back paradigm,27,28 based on the sensitivity of the N-back to nicotine abstinence.29,30 Briefly, the N-back task involved presentation of complex geometric figures (fractals) for 500 ms, followed by an interstimulus interval of 2500 ms under four conditions: 0-back, 1-back, 2-back and 3-back. In the 0-back condition, participants responded with a button press to a specified target fractal. For the 1-back condition, participants responded if the current fractal was identical to the previous one. In the 2-back condition, participants responded if the current fractal was identical to the item presented two trials back and in the 3-back condition if it was identical to three trials back. A response was not required for non-targets. Each condition consisted of a 20-trial block (60 s) and each class of blocks (0-back, 1-back, 2-back, 3-back) was repeated three times. A target-foil ratio of 1:2 (that is, 33% targets) was maintained in all blocks and visual instructions (9 s) preceded each block, alerting the participant of the upcoming condition. The task began with a 48 s baseline rest period (fixation point on blank screen) of which the first 24 s was discarded to ensure the MRI signal reached steady state. Additional 24 s baseline rest periods occurred at the middle and end of the acquisition. Total task duration was 924 s (308 time points). Equivalent N-back tasks with unique stimuli were used for the two sessions (overnight abstinent or smoking as usual) and version order was counterbalanced.

Image acquisition and processing

BOLD fMRI was acquired with a Siemens Trio 3 T (Erlangen, Germany) system using a whole-brain, single-shot gradient-echo (GE) echoplanar sequence with the following parameters: TR/TE = 3000/30 ms, field of view (FOV) = 220 mm, matrix = 64 × 64, slice thickness/gap = 3/0 mm, 40 slices. After BOLD fMRI, 5-min magnetization-prepared, rapid acquisition gradient echo T1-weighted image (TR = 1620 ms, TE = 3.87 ms, FOV = 250 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 a standard atlas space.31

BOLD time series data were analyzed with FEAT Version 5.92, part of FSL 4.0 ( using standard image analysis procedures that included brain extraction, slice time correction, motion correction, high pass filtering (138 s), spatially smoothing (6 mm full-width at half-maximum, isotropic) and mean-based intensity normalization. The median functional and anatomical volumes were co-registered, and the anatomical image was transformed into standard space (T1 MNI template). Transformation parameters were later applied to statistical images for group-level analyses.

Data analysis

Subject-level statistical analyses were carried out using FILM with local autocorrelation correction.32 Four condition events (0-back, 1-back, 2-back and 3-back) were modeled using a canonical hemodynamic response function. The instruction period was included as nuisance covariate and the baseline rest condition (fixation point) was treated as the unmodeled baseline. For each subject, contrast maps testing parametric increase in working memory load (−2, −1, 1, 2) were generated and subsequently carried to second-level group analyses. First, the parametric model was tested for session effects (abstinent > smoking, smoking > abstinent) with separate one tailed, paired t-tests. Then, the overall task activation was characterized by calculating the mean of each subject’s smoking and abstinent sessions and the result was entered into a single-group t-test. Statistic images (Gaussianized T) were corrected for multiple comparisons using GRF-theory-based maximum height thresholding at P≤0.05 (corrected). To characterize the session by genotype effects, mean percent signal change was extracted from voxels (z≥5.1, minimum 50 contiguous voxels) in the DLPFC (right and left DLPFC) and the dorsal cingulate/medial PFC (MF/CG). Anatomic assignment was based on the peak z-score within the group of contiguous voxels using the Talairach Daemon Database33 and confirmed through visual inspection using coordinates reported by Owen et al.22 These data were exported for offline analysis using standard statistical software and procedures described below.

The percent signal change analysis used regression with subject-level random effects, estimated using maximum likelihood techniques (Stata xt-reg; Stata Corporation, College Station, TX, USA) with fixed effects (Gaussian model). The BOLD signal models included terms for the main effects of genotype (val/val vs val/met vs met/met), session (abstinent vs smoking), N-back load and relevant covariates (sex, nicotine dependence score (FTND), age and CO level at the smoking session). CO level at the smoking session was used because there was minimal variability in CO levels for the abstinence session; results did not differ with both CO values included. The interaction was tested using the Wald χ2-test for differences in effects across genotype groups. Task performance (accuracy and reaction time) was examined with models as described above, except that counts were treated as Poisson or binomial outcomes.


Descriptive data

Demographic and smoking history data are presented in Table 1. There were no significant differences by genotype; however, there were fewer women in the val/met group. As a manipulation check, we confirmed that CO levels for the abstinent session (6.06 p.p.m., s.d. = 3.58) were significantly lower than CO levels for the smoking as usual session (24.85 p.p.m., s.d. = 11.93, P < 0.001). The average percent reduction in CO levels between sessions was 74% (range 46–95%). Further, all three genotype groups exhibited significant increases in smoking urges from the smoking to the abstinent session (P < 0.001), whereas only the val/met and val/val groups exhibited significant increases in withdrawal (P < 0.05). The difference scores (abstinence minus smoking) for the three genotype groups were not significantly different (Table 1).

Table 1
Demographic and smoking variables by COMT genotype

COMT genotype associations with task performance

The overall median reaction time differed among the three genotypes (Wald χ2(2) = 7.05, P = 0.03). The val/val group showed significantly faster reaction times in the smoking session, compared to the abstinent session, for the 3-back condition (Figure 1). Accuracy (number of true positives) did not differ across session or genotype (Supplementary Table).

Figure 1
Visual N-back median reaction time difference scores. Median reaction time difference scores (N-back minus 0-back) for all responses show significant differences between the three genotype groups (Wald χ2(2) = 7.05, P = 0.03). Within the val/val ...

COMT genotype associations with BOLD signal

The visual N-back task robustly activated brain regions consistently associated with working memory tasks (Figure 2a; Table 2). Whole-brain voxelwise paired t-tests revealed no effect of session for the parametric model of working memory load when genotype was not considered. A significant genotype by session interaction was observed in the dorsal cingulate/medial PFC (Wald χ2(2) = 9.86, P = 0.01), left DLPFC (Wald χ2(2) = 7.91, P = 0.02) and right DLPFC (Wald χ2(2) = 15.06, P = 0.0005) in the region-of-interest analysis of the mean percent signal change. As shown in Figure 2b, only the val/val group exhibited a significant (P < 10−5) effect of session, with an increased BOLD signal in smoking relative to abstinence.

Figure 2
Visual N-back working memory task activation. (a) Mean activation for abstinent and smoking sessions identified by a parametric model of increased working memory load (P≤0.05, corrected). Brain rendering performed with CARET.49 (b) Mean percent ...
Table 2
N-back task local maxima of mean activation for the parametric model of working memory load


This fMRI study provides novel evidence that smokers who are homozygous for the high activity val allele of the COMT val158met polymorphism are more sensitive to the effects of an abstinence challenge on brain function and working memory. Smokers with the COMT val/val genotype exhibited a relative decrease in BOLD signal in bilateral DLPFC and dorsal cingulate/medial PFC during abstinence (compared to smoking), whereas carriers of the met allele (met/met or val/met) exhibited no such effects. Further, only the val/val group exhibited a significant beneficial effect of smoking (vs abstinence) on overall reaction time on the visual N-back working memory task. These effects on BOLD signal and speed of performance in the val/val group are most pronounced during the 3-back trials. Although a recent meta-analysis has called into question the cognitive effects of this polymorphism,35 our data suggest that cognitive effects may emerge in smokers with val/val genotypes when the working memory system is challenged by abstinence from smoking.

Earlier fMRI studies examining associations of COMT val158met genotype with brain function and cognition have found increased DLPFC BOLD signal in the val/val group; some of these studies found no genotype effect on working memory performance11,13 and others reported poorer performance in the val/val group.36,37 We observed a similar, but nonsignificant trend in the smoking as usual session (Figure 2b), with the val/val group showing greater BOLD signal than the other groups across all memory loads. This observation is in accord with the ‘inefficiency hypothesis’,11 which posits that cognitive performance may require greater effort (and brain activation) in the val allele carriers in an unchallenged state. However, under an abstinence challenge in chronic smokers, a relative decrease in DLPFC BOLD signal during abstinence was observed in the val/val group in our study. Direct comparison of the present findings to previous studies is difficult because smoking status was not reported, and time since last cigarette (or length of abstinence) may have varied between participants who are chronic smokers. Our data suggest that future studies of COMT genotype and cognition should consider controlling for smoking status of the study participants. This may be especially important because the COMT val allele is significantly more prevalent among smokers.38

This study also contributes to a growing literature on the functional neuroanatomy underlying nicotine abstinence effects on cognition, although it is the first abstinence challenge study to incorporate genetic variation. In a prior fMRI study39 using the letter N-back task, BOLD signal in left DLPFC was increased during abstinence relative to smoking, but only at a low working memory load (1-back). Among abstinent smokers, nicotine vs placebo gum decreased right PFC activation; however, decreased activation predicted worse performance,40 a finding consistent with our results. Although our knowledge of the effects of nicotine abstinence on brain function is far from complete, the present data suggest that individual variability in the effects of abstinence is accounted for, in part, by genetic variation in COMT val158 met.

On the basis of prior preclinical and clinical evidence, a neurobiological hypothesis for the observed COMT genotype by abstinence effect can be offered. Rodent and human investigations have established that the COMT gene, and the val158met polymorphism in particular, regulates PFC COMT enzyme activity and presynaptic dopamine levels.3,41 Lower dopamine levels in the PFC are thought to contribute to deficits in cognitive processing among carriers of the COMT val allele.11,13,16 Smoking a single cigarette increases dopamine levels to a greater extent among smokers with COMT val/val genotypes compared to met allele carriers.42 Thus, the effects of smoking state on brain activity and cognitive function observed in the val/val group may be attributable to greater effects of smoking (vs abstinence) on dopaminergic tone.

Nicotine dependence exacts a heavy burden on society, and improved therapies are urgently needed.43 Although carriers of the COMT val allele are less responsive to existing therapies for smoking cessation79 they may be candidates for targeted therapy with COMT inhibitors. For example, in healthy volunteers the COMT inhibitor tolcapone enhances working memory,44 particularly among persons with the COMT val/val genotype.45 The therapeutic potential of COMT inhibitors for smoking cessation warrants further attention.

Strengths of the present study include the within-subject design, which manipulates both smoking status and working memory load, and the prospective genotyping for COMT. However, there are potential limitations as well. First, statistical power was relatively low for detecting associations of genotype with behavioral performance, and effects were found for only one of the behavioral measures examined (overall reaction time). This may be attributable to reduced variability in accuracy measures relative to reaction time measures. A second potential limitation is that some participants may have experienced minor abstinence symptoms during the smoking session, which occurred 45–60 min since the last cigarette. However, CO levels and craving scores between the two sessions were significantly different, suggesting that the abstinence manipulation was highly effective. Finally, recent data suggest that effects of COMT on brain activation and cognitive performance may be modified by genetic variation in the dopamine transporter11,46 and in the dopamine D2 receptor,47 as well as by additional single nucleotide polymorphisms in COMT.15,48 The interacting effects of these genetic variants on abstinence-induced deficits in cognitive function can be examined in future research.

In summary, the present study suggests a novel brain–behavior mechanism through which the COMT val allele may increase susceptibility to smoking relapse. These data underscore the importance of assessing smoking status and standardizing exposure within investigations of COMT and cognitive performance. Given the high prevalence of tobacco use and a population frequency of 0.44 for the COMT val allele in persons of European ancestry,49 translating these findings for medication development could have a significant clinical and public health impact.

Supplementary Material

Suppl 1


This research was supported by grants from the National Cancer Institute and National Institutes on Drug Abuse (P50CA/DA84718), NIH/NINDS Neuroscience Neuroimaging Center (P30 NS045839) and the Pennsylvania Department of Health. The Department of Health disclaims responsibility for any analyses, interpretations or conclusions.


Disclosure/conflict of interest The authors do not have any conflicts of interest pertaining to this research.

Supplementary Information accompanies the paper on the Molecular Psychiatry website (


1. Gogos JA, Morgan M, Luine V, Santha M, Ogawa S, Pfaff D, et al. Catechol-O-methyltransferase-deficient mice exhibit sexually dimorphic changes in catecholamine levels and behavior. Proc Natl Acad Sci USA. 1998;95:9991–9996. [PubMed]
2. Huotari M, Gogos JA, Karayiorgou M, Koponen O, Forsberg M, Raasmaja A, et al. Brain catecholamine metabolism in catechol-O-methyltransferase (COMT)-deficient mice. Eur J Neurosci. 2002;15:246–256. [PubMed]
3. Chen J, Lipska BK, Halim N, Ma QD, Matsumoto M, Melhem S, et al. Functional analysis of genetic variation in catechol-O-methyltransferase (COMT): effects on mRNA, protein, and enzyme activity in postmortem human brain. Am J Hum Genet. 2004;75:807–821. [PubMed]
4. Lachman H, Papolos DF, Saito T, Yu YM, Szumlanski CL, Weinshilboum RM. Human catechol-O-methyltransferase pharmacogenetics: description of a functional polymorphism and its potential application to neuropsychiatric disorders. Pharmacogenetics. 1996;6:243–250. [PubMed]
5. Lotta T, Vidgren J, Tilgmann C, Ulmanen I, Melén K, Julkunen I, et al. Kinetics of human soluble and membrane-bound catechol O-methyltransferase: a revised mechanism and description of the thermolabile variant of the enzyme. Biochemistry. 1995;34:4202–4210. [PubMed]
6. Nestler EJ. Is there a common molecular pathway for addiction? Nat Neurosci. 2005;8:1445–1449. [PubMed]
7. Colilla S, Lerman C, Shields PG, Jepson C, Rukstalis M, Berlin J, et al. Association of catechol-O-methyltransferase with smoking cessation in two independent studies of women. Pharmacogenet Genomics. 2005;15:393–398. [PMC free article] [PubMed]
8. Johnstone EC, Elliot KM, David SP, Murphy MF, Walton RT, Munafò MR. Association of COMT Val108/158Met genotype with smoking cessation in a nicotine replacement therapy randomized trial. Cancer Epidemiol Biomarkers Prev. 2007;16:1065–1069. [PMC free article] [PubMed]
9. Munafo MR, Johnstone EC, Guo B, Murphy MF, Aveyard P. Association of COMT Val108/158Met genotype with smoking cessation. Pharmacogenet Genomics. 2008;18:121–128. [PubMed]
10. Barnett JH, Jones PB, Robbins TW, Muller U. Effects of the catechol-O-methyltransferase Val158Met polymorphism on executive function: a meta-analysis of the Wisconsin Card Sort Test in schizophrenia and healthy controls. Mol Psychiatry. 2007;12:502–509. [PubMed]
11. Bertolino A, Blasi G, Latorre V, Rubino V, Rampino A, Sinibaldi L, et al. Additive effects of genetic variation in dopamine regulating genes on working memory cortical activity in human brain. J Neurosci. 2006;26:3918–3922. [PubMed]
12. Caldu X, Vendrell P, Bartrés-Faz D, Clemente I, Bargalló N, Jurado MA, et al. Impact of the COMT Val108/158 Met and DAT genotypes on prefrontal function in healthy subjects. Neuroimage. 2007;37:1437–1444. [PubMed]
13. Egan MF, Goldberg TE, Kolachana BS, Callicott JH, Mazzanti CM, Straub RE, et al. Effect of COMT Val108/158 Met genotype on frontal lobe function and risk for schizophrenia. Proc Natl Acad Sci USA. 2001;98:6917–6922. [PubMed]
14. McIntosh AM, Baig BJ, Hall J, Job D, Whalley HC, Lymer GK, et al. Relationship of catechol-O-methyltransferase variants to brain structure and function in a population at high risk of psychosis. Biol Psychiatry. 2007;61:1127–1134. [PubMed]
15. Meyer-Lindenberg A, Nichols T, Callicott JH, Ding J, Kolachana B, Buckholtz J, et al. Impact of complex genetic variation in COMT on human brain function. Mol Psychiatry. 2006;11:867–877. 797. [PubMed]
16. Winterer G, Musso F, Vucurevic G, Stoeter P, Konrad A, Seker B, et al. COMT genotype predicts BOLD signal and noise characteristics in prefrontal circuits. Neuroimage. 2006;32:1722–1732. [PubMed]
17. Davis JA, James JR, Siegel SJ, Gould TJ. Withdrawal from chronic nicotine administration impairs contextual fear conditioning in C57BL/6 mice. J Neurosci. 2005;25:8708–8713. [PMC free article] [PubMed]
18. Domier CP, Monterosso JR, Brody AL, Simon SL, Mendrek A, Olmstead R, et al. Effects of cigarette smoking and abstinence on Stroop task performance. Psychopharmacology. 2007;195:1–9. [PMC free article] [PubMed]
19. Semenova S, Stolerman IP, Markou A. Chronic nicotine administration improves attention while nicotine withdrawal induces performance deficits in the 5-choice serial reaction time task in rats. Pharmacol Biochem Behav. 2007;87:360–368. [PMC free article] [PubMed]
20. Krishnan-Sarin S, Reynolds B, Duhig AM, Smith A, Liss T, McFetridge A, et al. Behavioral impulsivity predicts treatment outcome in a smoking cessation program for adolescent smokers. Drug Alcohol Depend. 2007;88:79–82. [PMC free article] [PubMed]
21. Rukstalis M, Jepson C, Patterson F, Lerman C. Increases in hyperactive-impulsive symptoms predict relapse among smokers in nicotine replacement therapy. J Subst Abuse Treat. 2005;28:297–304. [PubMed]
22. Owen AM, McMillan KM, Laird AR, Bullmore E. N-back working memory paradigm: a meta-analysis of normative functional neuroimaging studies. Hum Brain Mapp. 2005;25:46–59. [PubMed]
23. Pomerleau CS, Carton SM, Lutzke ML, Flessland KA, Pomerleau OF. Reliability of the Fagerstrom Tolerance Questionnaire and the Fagerstrom Test for Nicotine Dependence. Addict Behav. 1994;19:33–39. [PubMed]
24. McClernon FJ, Hiott FB, Huettel SA, Rose JE. Abstinence-induced changes in self-report craving correlate with event-related FMRI responses to smoking cues. Neuropsychopharmacology. 2005;30:1940–1947. [PMC free article] [PubMed]
25. Hughes JR, Hatsukami DK, Pickens RW, Krahn D, Malin S, Luknic A. Effect of nicotine on the tobacco withdrawal syndrome. Psychopharmacology. 1984;83:82–87. [PubMed]
26. Tiffany ST, Drobes DJ. The development and initial validation of a questionnaire on smoking urges. Br J Addict. 1991;86:1467–1476. [PubMed]
27. Braver TS, Cohen JD, Nystrom LE, Jonides J, Smith EE, Noll DC. A parametric study of prefrontal cortex involvement in human working memory. Neuroimage. 1997;5:49–62. [PubMed]
28. Ragland JD, Turetsky BI, Gur RC, Gunning-Dixon F, Turner T, Schroeder L, et al. Working memory for complex figures: an fMRI comparison of letter and fractal n-back tasks. Neuropsychology. 2002;16:370–379. [PubMed]
29. Jacobsen LK, Krystal JH, Mencl WE, Westerveld M, Frost SJ, Pugh KR. Effects of smoking and smoking abstinence on cognition in adolescent tobacco smokers. Biol Psychiatry. 2005;57:56–66. [PubMed]
30. Mendrek A, Monterosso J, Simon SL, Jarvik M, Brody A, Olmstead R, et al. Working memory in cigarette smokers: comparison to non-smokers and effects of abstinence. Addict Behav. 2006;31:833–844. [PMC free article] [PubMed]
31. Talairach J, Tournoux P. Co-planer Stereotaxic Atlas of the Human Brain. Thieme Medical Publishers; New York, NY: 1988.
32. Woolrich MW, Ripley BD, Brady M, Smith SM. Temporal autocorrelation in univariate linear modeling of FMRI data. Neuroimage. 2001;14:1370–1386. [PubMed]
33. Lancaster JL, Woldorff MG, Parsons LM, Liotti M, Freitas CS, Rainey L, et al. Automated Talairach atlas labels for functional brain mapping. Hum Brain Mapp. 2000;10:120–131. [PubMed]
34. Van Essen DC, Drury HA, Dickson J, Harwell J, Hanlon D, Anderson CH. An integrated software suite for surface-based analyses of cerebral cortex. J Am Med Inform Assoc. 2001;8:443–459. [PMC free article] [PubMed]
35. Barnett JH, Scoriels L, Munafo MR. Meta-analysis of the cognitive effects of the catechol-O-methyltransferase gene Val158/108Met polymorphism. Biol Psychiatry. 2008;64:137–144. [PubMed]
36. Diaz-Asper CM, Goldberg TE, Kolachana BS, Straub RE, Egan MF, Weinberger DR. Genetic variation in catechol-O-methyltransferase: effects on working memory in schizophrenic patients, their siblings, and healthy controls. Biol Psychiatry. 2008;63:72–79. [PMC free article] [PubMed]
37. Goldberg TE, Egan MF, Gscheidle T, Coppola R, Weickert T, Kolachana BS, et al. Executive subprocesses in working memory: relationship to catechol-O-methyltransferase Val158Met genotype and schizophrenia. Arch Gen Psychiatry. 2003;60:889–896. [PubMed]
38. Beuten J, Payne TJ, Ma JZ, Li MD. Significant association of catechol-O-methyltransferase (COMT) haplotypes with nicotine dependence in male and female smokers of two ethnic populations. Neuropsychopharmacology. 2006;31:675–684. [PubMed]
39. Xu J, Mendrek A, Cohen MS, Monterosso J, Rodriguez P, Simon SL, et al. Brain activity in cigarette smokers performing a working memory task: effect of smoking abstinence. Biol Psychiatry. 2005;58:143–150. [PMC free article] [PubMed]
40. Ernst M, Matochik JA, Heishman SJ, Van Horn JD, Jons PH, Henningfield JE, et al. Effect of nicotine on brain activation during performance of a working memory task. Proc Natl Acad Sci USA. 2001;98:4728–4733. [PubMed]
41. Yavich L, Forsberg MM, Karayiorgou M, Gogos JA, Mannisto PT. Site-specific role of catechol-O-methyltransferase in dopamine overflow within prefrontal cortex and dorsal striatum. J Neurosci. 2007;27:10196–10209. [PubMed]
42. Brody AL, Mandelkern MA, Olmstead RE, Scheibal D, Hahn E, Shiraga S, et al. Gene variants of brain dopamine pathways and smoking-induced dopamine release in the ventral caudate/nucleus accumbens. Arch Gen Psychiatry. 2006;63:808–816. [PMC free article] [PubMed]
43. Lerman C, LeSage MG, Perkins KA, O’Malley SS, Siegel SJ, Benowitz NL, et al. Translational research in medication development for nicotine dependence. Nat Rev Drug Discov. 2007;6:746–762. [PubMed]
44. Gasparini M, Fabrizio E, Bonifati V, Meco G. Cognitive improvement during Tolcapone treatment in Parkinson’s disease. J Neural Transm. 1997;104:887–894. [PubMed]
45. Apud JA, Mattay V, Chen J, Kolachana BS, Callicott JH, Rasetti R, et al. Tolcapone improves cognition and cortical information processing in normal human subjects. Neuropsychopharmacology. 2007;32:1011–1020. [PubMed]
46. Yacubian J, Sommer T, Schroeder K, Gläscher J, Kalisch R, Leuenberger B, et al. Gene–gene interaction associated with neural reward sensitivity. Proc Natl Acad Sci USA. 2007;104:8125–8130. [PubMed]
47. Jacobsen LK, Pugh KR, Mencl WE, Gelernter J. C957T polymorphism of the dopamine D2 receptor gene modulates the effect of nicotine on working memory performance and cortical processing efficiency. Psychopharmacology. 2006;188:530–540. [PubMed]
48. Nackley AG, Shabalina SA, Tchivileva IE, Satterfield K, Korchynskyi O, Makarov SS, et al. Human catechol-O-methyltransferase haplotypes modulate protein expression by altering mRNA secondary structure. Science. 2006;314:1930–1933. [PubMed]
49. Palmatier MA, Kang AM, Kidd KK. Global variation in the frequencies of functionally different catechol-O-methyltransferase alleles. Biol Psychiatry. 1999;46:557–567. [PubMed]