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Drug and Alcohol Dependence 133 (2013) 852– 856

Contents lists available at ScienceDirect

Drug and Alcohol Dependence

j ourna l ho me p age: www.elsev ier .com/ locate /drugalcdep

hort communication

ffects of tolcapone on working memory and brain activity inbstinent smokers: A proof-of-concept study

ebecca L. Asharea,∗, E. Paul Wileytoa,b, Kosha Ruparel c, Patricia M. Goelza,yan D. Hopsonc, Jeffrey N. Valdezc, Ruben C. Gurc, James Lougheadc, Caryn Lermana

Center for Interdisciplinary Research on Nicotine Addiction, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USADepartment of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, 19104, USABrain Behavior Laboratory, Neuropsychiatry Department, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA

r t i c l e i n f o

rticle history:eceived 5 June 2013eceived in revised form 12 August 2013ccepted 6 September 2013vailable online 14 September 2013

eywords:mokingicotineOMTolcaponeMRI

orking memory

a b s t r a c t

Background: Dopamine levels in the prefrontal cortex (PFC) are thought to play an important role in cog-nitive function and nicotine dependence. The catechol-O-methyltransferase (COMT) inhibitor tolcapone,an FDA-approved treatment for Parkinson’s disease, increases prefrontal dopamine levels, with cognitivebenefits that may vary by COMT genotype. We tested whether tolcapone alters working memory-relatedbrain activity and performance in abstinent smokers.Methods: In this double-blind crossover study, 20 smokers completed 8 days of treatment with tolcaponeand placebo. In both medication periods, smokers completed blood oxygen level-dependent (BOLD) fMRIscans while performing a working memory N-back task after 24 h of abstinence. Smokers were genotypedprospectively for the COMT val158met polymorphism for exploratory analysis.Results: Compared to placebo, tolcapone modestly improved accuracy (p = 0.017) and enhanced sup-pression of activation in the ventromedial prefrontal cortex (vmPFC) (p = 0.002). There were no effectsof medication in other a priori regions of interest (dorsolateral PFC, dorsal cingulate/medial prefrontal

cortex, or posterior cingulate cortex). Exploratory analyses suggested that tolcapone led to a decreasein BOLD signal in several regions among smokers with val/val genotypes, but increased or remainedunchanged among met allele carriers. Tolcapone did not attenuate craving, mood, or withdrawal symp-toms compared to placebo.Conclusions: Data from this proof-of-concept study do not provide strong support for further evaluationof COMT inhibitors as smoking cessation aids.

. Introduction

Cognitive deficits are commonly reported during nicotine with-rawal (Hendricks et al., 2006; Hughes, 2007) and have beenssociated with smoking relapse (Patterson et al., 2010). Dopamineevels in prefrontal cortex (PFC), regulated in part by the catechol--methyltransferase (COMT) enzyme, play a role in cognitive

unction and nicotine dependence (Goldberg and Weinberger,004; Nestler, 2005). The functional val158met (rs#4680) poly-orphism in the COMT gene results in increased enzyme activity

nd decreased PFC dopamine for the val allele (Chen et al., 2004;achman et al., 1996; Lotta et al., 1995). Evidence suggests thatmokers with val/val genotypes exhibit greater abstinence-induced

∗ Corresponding author at: Center for Interdisciplinary Research on Nicotineddiction, 3535 Market Street, Suite 4100, Philadelphia, PA 19104, USA.el.: +1 215 746 5789; fax: +1 215 746 7140.

E-mail address: rlashare@mail.med.upenn.edu (R.L. Ashare).

376-8716/$ – see front matter © 2013 Elsevier Ireland Ltd. All rights reserved.ttp://dx.doi.org/10.1016/j.drugalcdep.2013.09.003

© 2013 Elsevier Ireland Ltd. All rights reserved.

decrements in working memory-related PFC activity and in perfor-mance compared with met allele carriers (Loughead et al., 2009).The COMT inhibitor tolcapone, an FDA-approved treatment forParkinson’s disease, improves working memory and processingefficiency in PFC among healthy controls (Apud et al., 2007); how-ever, some data suggest that cognitive benefits of tolcapone maybe specific to persons with val/val genotypes (Apud et al., 2007;Farrell et al., 2012; Giakoumaki et al., 2008). Lastly, COMT genotypehas been associated with quitting success in smokers; however,studies to date have produced mixed results (Colilla et al., 2005;Munafo et al., 2008; Omidvar et al., 2009).

This proof-of-concept functional magnetic resonance imaging(fMRI) study used a within-subject cross-over design to exam-ine the effects of short-term treatment with tolcapone vs. placeboon working memory and brain activity following 24 h of absti-

nence. Based on prior work (Falcone et al., 2013; Lougheadet al., 2009), we hypothesized that tolcapone (vs. placebo) wouldimprove working memory and increase BOLD signal change intask-positive regions (bilateral dorsolateral PFC (DLPFC) and medial

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rontal/cingulate gyrus (MF/CG)), and increase suppression of BOLDignal in task-negative regions (posterior cingulate cortex (PCC)nd ventromedial prefrontal cortex (vmPFC)) during smoking absti-ence. All smokers were genotyped prior to study initiation forhe COMT val158met polymorphism to explore whether tolcaponeffects are more pronounced among smokers with val/val geno-ypes.

. Methods

.1. Participants

Eligible smokers between 18 and 65 years old who smoked at least 10igarettes/day for at least 6 months were recruited through mass media. Smokersere prospectively screened based on genotyping for the COMT val158met poly-orphism and we attempted to achieve balanced groups with val/val vs. val/met

r met/met genotypes [rs4680, Assay on Demand (c 25746809 50) from Appliediosystems Inc. (Foster City)]. COMT genotypes were in Hardy–Weinberg equilib-ium (p = 0.67). Exclusion criteria included: currently seeking treatment for smokingessation; history of DSM-IV Axis I disorders (except nicotine dependence); use ofsychotropic or smoking cessation medications; pregnancy; history of brain injury;

eft-handedness; presence of fMRI contraindicated material in the body; low or bor-erline intelligence (<90 score on Shipley’s IQ test); kidney and/or liver disease; usef medications contraindicated for use with tolcapone; and any impairment thatould prevent cognitive task performance. Twenty-eight participants completed

he study and eight were excluded for poor fMRI data quality (n = 5) and low taskccuracy (2SD below the mean (n = 2); more than 30% non-responses (n = 1)).

.2. Procedures

All procedures were approved by the University of Pennsylvania Institutionaleview Board and all participants provided written informed consent (clinicaltri-ls.gov NCT01001520). This study included two BOLD fMRI sessions following 24 hf abstinence (treatment order counterbalanced). Participants completed a phys-cal examination including blood work, a urine drug screen, breath alcohol test,nd pregnancy test. Psychiatric or substance abuse disorders were assessed usinghe Mini International Neuropsychiatric Interview (MINI; Sheehan et al., 1998). Thehipley Institute of Living Scale (Zachary, 2000) and Fagerström Test for Nicotineependence (Heatherton et al., 1991) were also administered.

Tolcapone and matching placebo were provided in blinded blister packs. Tol-apone was administered according to standard guidelines: day 1 (100 mg t.i.d.),ays 2–8 (200 mg t.i.d.), day 9 (200 mg b.i.d.), day 10 (200 mg q.d.), and day 11100 mg q.d.). A dose-tapering regimen was utilized on days 9–11 to reduce adverseffects associated with discontinuation of tolcapone (Apud et al., 2007).

During each medication period, participants completed one monitoring visitday 5) and a scanning session (day 8). On scanning session days, blood samplesere tested for liver function and those with a positive drug screen, a breath alcohol

est > 0.01, or a breath carbon monoxide (CO) test > 9 ppm were excluded. Partici-ants completed measures of withdrawal (MNWS; Hughes et al., 1984), cravingQSU-Brief; Cox et al., 2001), mood (PANAS; Watson et al., 1988), and side effects.ollowing a practice session to familiarize participants with the task, participantsere escorted to the radiology clinic for the fMRI scan. After a 2-week washouteriod during which participants returned to their baseline smoking level, the sec-nd medication period began and followed procedures as described above.

.3. Task design

The N-back task presents complex geometric figures (fractals) for 500 ms, fol-owed by an interstimulus interval of 2500 ms under four conditions: 0-back, 1-back,-back and 3-back. In the 0-back condition, participants respond to a specific fractal;or the 1, 2, and 3-back conditions, participants respond if the current fractal wasdentical to the one “n” before it. No response was required for nontargets. Each con-ition was presented three times in 20-trial blocks (25% targets; 60 s). After the firstet was presented blocks in order of increasing memory load, conditions were pre-ented pseudo-randomly; visual instructions (9 s) preceded each block. The first 24 sf a 48 s baseline rest period (fixation point) was discarded to ensure the MRI sig-al reached steady state. To minimize practice effects, equivalent N-back tasks withnique stimuli and presentation sequence were used for the two sessions (orderounterbalanced).

.4. Image acquisition

Data were acquired on a 3T Siemens TIM TRIO scanner (Erlangen, Germany). T1-weighted whole-brain structural image was acquired for registration

o a standard brain atlas (MPRAGE, TR = 1810 ms, TE = 3.51 ms, TI = 1100 ms,OV = 240 mm × 180 mm, matrix = 256 × 192, resolution = 0.9 mm × 0.9 mm,lices = 160, flip angle = 9◦ , effective voxel resolution of 1 mm × 1 mm × 1 mm).OLD fMRI images were acquired using a whole-brain, single-shot gradient-echoGE) echoplanar sequence (TR/TE = 2000/30 ms, FOV = 240 mm, matrix = 64 × 64,

pendence 133 (2013) 852– 856 853

flip angle = 90◦ , slices = 33, slice thickness/gap = 3.0 mm/0 mm and effective voxelresolution = 3.4 × 3.4 × 3.4).

2.5. Image preprocessing

BOLD fMRI time series data were preprocessed and analyzed using standard pro-cedures using fMRI Expert Analysis Tool (FEAT version 5.98) of FSL (FMRIB’s SoftwareLibrary, Oxford, UK). Single subject preprocessing included nonbrain removal usingBET (Smith, 2002), slice time correction, motion correction to the median imageusing MCFLIRT (Jenkinson et al., 2002), high pass temporal filtering (100 s), spatialsmoothing using a Gaussian kernel (6 mm full-width at half-maximum, isotropic)and mean-based intensity normalization of all volumes using the same multi-plicative factor. The median functional volume was coregistered to the anatomicalT1-weighted structural volume and then transformed into standard anatomicalspace (T1 MNI template) using FLIRT (Jenkinson et al., 2002; Jenkinson and Smith,2001). Transformation parameters were later applied to all statistical contrast mapsfor group-level analyses.

2.6. Image quality assessment

All images were carefully examined for artifacts, acquisition problems andpreprocessing errors. Image quality assessment procedures assessed temporalsignal-to-noise ratio (tSNR) of both smoking and abstinence sessions for poor qualitydata. To assess excessive head motion, mean relative volume-to-volume displace-ment for each session was also evaluated. Subjects with mean tSNR < 26 (equivalentto 2SD below the mean) and/or mean relative motion > 0.3 were excluded from theanalysis. Based on the above criteria, 5 subjects were excluded.

2.7. Subject time-series analysis

Subject-level statistical analyses were carried out voxelwise using FILM (FMRIB’sImproved General Linear Model) with local autocorrelation correction (Woolrichet al., 2001). Four condition events (0-back, 1-back, 2-back, and 3-back) weremodeled using a canonical hemodynamic response function. The instruction period(Jenkinson et al., 2002; Smith, 2002) and six motion correction parameters wereincluded as nuisance covariates and the three rest periods (fixation point) weretreated as the baseline. Image analysis was completed for each individual in subjectspace, and resulting contrast maps were spatially normalized as described above.

2.8. Region of interest (ROI) image analysis

To characterize the group (val/val vs. met/met) by treatment (placebo, tol-capone) effects, mean percent signal change was extracted from a priori regions ofinterest (ROIs) in task-positive (right and left DLPFC and MF/CG) and task-negativeregions (vmPFC and PCC). ROI masks were functionally defined from an independentsample (n = 63) studied with the identical N-back task under comparable abstinenceconditions (Falcone et al., 2013). ROI masks were then registered into native sub-ject space using methods described above. Finally, mean percent signal change wascalculated per subject for the four load conditions separately for each ROI. Thesevalues were exported for further analysis using standard statistical software andprocedures described below.

2.9. Data analysis

BOLD signal change was examined using random effects maximum likelihoodregression (Stata Corporation, College Station, TX, USA). Models included terms forthe main effects of treatment (tolcapone vs. placebo), back level (0, 1, 2, and 3), treat-ment order, and relevant covariates (genotype, race, age, sex, Shipley IQ score, andFTND score). Because interactions of treatment with back level were not significantit was included as a covariate. Behavioral performance (accuracy and reaction time)was tested as described above. Exploratory models tested the genotype x treatmentinteraction. Because the five a priori ROIs are highly correlated (r ≈ 0.80), alpha forBOLD models was adjusted to p = 0.014 (Sankoh et al., 1997). Alpha remained 0.05 forthe performance models. Correlations between BOLD signal and behavioral perfor-mance were examined using Pearson’s correlations, separately by treatment period.Finally, we tested for practice effects on working memory performance and BOLDsignal change by conducting models comparing the first to the second treatmentperiod, irrespective of treatment condition.

3. Results

3.1. Descriptive data

Table 1 contains smoking and demographic characteristics for

the full sample. There were no genotype differences on any variable.CO levels were consistent with the abstinence requirement duringplacebo (mean = 3.1 ppm, SD = 1.4) and tolcapone (mean = 3.7 ppm,SD = 2.1).

854 R.L. Ashare et al. / Drug and Alcohol Dependence 133 (2013) 852– 856

Table 1Demographic and smoking characteristics by genotype.

Measure Full sample Val/val (n = 9) Val/met (n = 11) p-Value

Sex, n (%) female 10 (50%) 4 (44%) 6 (55%) 0.65Race, n (%) Caucasian 15 (75%) 5 (56%) 10 (91%) 0.07Age 34 (11.6) 38.4 (13) 30.4 (10) 0.13Nicotine dependence 4.3 (1.6) 4.6 (1.3) 4.1 (1.9) 0.55Cigarettes per day 15.1 (4.0) 14.4 (4.2) 15.6 (3.8) 0.53Number of years smoked 16.8 (13.2) 20.9 (15.2) 13.8 (11.4) 0.26Shipley Institute of Living Scale 108.1 (9.2) 104.3 (7) 111.2 (10) 0.10Order of medication period, n (%) tolcapone first 9 (45%) 3 (33%) 6 (55%) 0.34

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.2. Treatment effects on smoking rate and subjective measures

There were no treatment effects on smoking rate during theun-up period or on craving, withdrawal, mood, or side effectssee Table 2). Common side effects on tolcapone were urine discol-ration (n = 21), irritability (n = 9) and excessive dreaming (n = 9);ll were rated as mild or moderate. There were no significant inter-ctions with genotype.

.3. Treatment effects on working memory and BOLD signalhange

There was a small increase in accuracy during tolcapone com-ared to placebo (p = 0.017; Table 2), but no medication effectn reaction time (p = 0.88). Accuracy decreased and reaction timencreased with increasing memory load (ps < 0.0001).

There were no treatment effects on BOLD signal in right or leftLPFC, MF/CG, or PCC. For the vmPFC, there was greater deacti-ation during tolcapone compared to placebo (p = 0.002; Table 2).OLD signal was not correlated with accuracy or reaction time dur-

ng either treatment period (ps > 0.05).

.4. Exploratory analyses by genotype

A genotype x treatment interaction (p = 0.001) suggested thatolcapone (vs. placebo) improved reaction time in the val/metroup (p = 0.009), whereas the val/val group was slower duringolcapone (vs. placebo) (p = 0.04). For BOLD signal, there wereenotype × treatment interactions in the MF/CG, right DLPFC, andmPFC (ps < 0.01), but not PCC or left DLPFC (ps > 0.10; Table 2). Forhe val/val group, tolcapone (vs. placebo) reduced activation in theask-positive region, MF/CG (p = 0.02) and increased suppressionf activation in the task-negative region, vmPFC (ps < 0.001). Con-ersely, tolcapone (vs. placebo) increased activation in the MF/CGp = 0.03) and right DLPFC (p = 0.01) for the val/met group.

.5. Practice effects on working memory and BOLD signal change

There was no change in accuracy or reaction time from the firsto the second medication period, irrespective of treatment condi-ion (ps > 0.2).

There was a significant decrease in activation in all five ROIsrom the first to the second medication period (ps ≤ 0.001). Thisffect did not vary by genotype (ps > 0.07). We therefore controlledor session (treatment × treatment order interaction term) in ourrimary analyses.

. Discussion

Data from this within-subject proof-of-concept study provideimited evidence for beneficial effects of tolcapone in abstainingmokers. There were no signals for medication effects on smoking

ison; ppm = parts per million.

rate, subjective craving, withdrawal, or mood, using measuresthat have been previously validated as sensitive to effects ofmedications efficacious for smoking cessation (Patterson et al.,2009). There was a very small medication effect on accuracy andno effect on correct reaction time on a working memory task,which previously demonstrated sensitivity to abstinence effects(vs. smoking) and to effects of efficacious medications (Falconeet al., 2013; Loughead et al., 2010). Treatment effects on workingmemory-related brain activity were observed for only one (vmPFC)out of five ROIs. Although it is possible that a larger sample wouldhave yielded greater power to detect a treatment effect, an a prioripower analysis using a moderate effect size observed in our priorstudy (Loughead et al., 2009), suggested that we had 78% powerto detect a main effect of treatment. Therefore, we do not believethat our null findings are the result of low power.

In exploratory analyses, we did observe genotype by treatmentinteractions for some performance and BOLD signal measures. Inthe val/val group, tolcapone (vs. placebo) slowed reaction time,increased suppression of activation in vmPFC, and reduced acti-vation in task-positive regions. Among val/met smokers, tolcapone(vs. placebo) reduced reaction time, had no effect on task-negativeregions, and increased activation in task-positive regions. Never-theless, these preliminary data should be interpreted cautiously.First, the genotype groups were small and without independentreplication data, it is possible that these findings are spurious. Fur-thermore, the direction of effects was opposite to what would bepredicted based on past evidence. Two studies found that tolcaponeimproved working memory only among val/val genotypes (Farrellet al., 2012; Giakoumaki et al., 2008). A third study found thattolcapone improved working memory performance and reducedBOLD signal in the DLPFC, but did not interact with COMT geno-type for most outcomes (Apud et al., 2007). Importantly, unlikethese prior studies, we examined abstaining smokers. Prior workhas attributed differences between COMT genotypes to the invertedU-shaped dopamine hypothesis, which suggests that only opti-mal levels of dopamine improve performance (Arnsten, 2009;Goldman-Rakic et al., 2004). Nicotine withdrawal also alters pre-frontal dopamine levels (Grieder et al., 2012; Zhang et al., 2012),which may partially account for inconsistencies between studies.

These discrepancies are not surprising considering failures toreplicate other findings with COMT val158met variant. For example,there are reported associations of the COMT val allele with deficitsin cognitive performance and task-related brain activity in somestudies (Dennis et al., 2010; Egan et al., 2001; Stokes et al., 2011),but not in others (Barnett et al., 2008; Wardle et al., 2013). Simi-larly, the COMT val allele is associated with smoking in some studies(Munafo et al., 2008), but not others (Mutschler et al., 2013), andthe direction of association varies across the positive studies (Colilla

et al., 2005; Munafo et al., 2008; Omidvar et al., 2009). Genetic stud-ies, particularly imaging studies, have drawbacks that contributeto these inconsistencies including small samples, focus on singlecandidate genes, and in some cases, post hoc genotyping.

R.L. Ashare et al. / Drug and Alcohol De

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pendence 133 (2013) 852– 856 855

There are other limitations of this study. We observed signif-icant decreases in BOLD signal from the first to second sessionin all five ROIs, irrespective of treatment condition. We examinedself-report measures that relate to anxiety (PANAS and WithdrawalScale) by session and no significant difference was found. Practiceeffects were not observed for performance. Reduced BOLD signalfor the second session is consistent with previous reports (Cheinand Schneider, 2005; van Raalten et al., 2008) and attributed totask familiarity that may allow reduced the effort to maintain per-formance during a second exposure to the task. This is an importantmethodological consideration for within-subject neuroimagingstudies of cognitive function. Our study included pre-scan practiceto criteria, alternate task form, and randomization of treatment bysession to minimize this effect. Without a smoking-as-usual base-line session, any effects may not be specific to treatment effects onabstinence. Also, a longer duration of abstinence may have yieldedgreater performance deficits or changes in fMRI BOLD signal. Addi-tionally, this study used non-treatment seeking smokers, whichmay limit the sensitivity for detecting medication effects (Perkinset al., 2008). Nevertheless, the limited effects of tolcapone on crav-ing, withdrawal, and cognitive performance observed in this studydo not support further examination of COMT inhibitors as smokingcessation aids.

Role of funding source

This research was supported by NIH grant R01 DA26849 fromthe National Institutes on Drug Abuse. The NIH had no further rolein study design; in the collection, analysis and interpretation ofdata; in the writing of the report; or in the decision to submit thepaper for publication.

Contributors

Authors CL, JL, and RCG were responsible for the conceptionand design of the study. Authors PMG, KR, JNV, and RDH man-aged participant recruitment, data collection, and implemented theprotocol. Authors RLA, EPW, KR, JNV, and RDH conducted statisti-cal analyses. Author RLA wrote the initial draft of the manuscript.Authors CL and KR contributed to the revision of subsequent drafts.All authors were involved in writing and revising the manuscriptand all have approved the final manuscript.

Conflict of interest

Dr. Lerman has served as a consultant to Pfizer on pharmaco-genetic testing for smoking cessation treatment and has receivedresearch funding from and consulted for AstraZeneca, Targacept,Pfizer, and GlaxoSmithKline, for work unrelated to this manuscript.No other authors have any potential conflict of interests to declare.

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