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Effects of the α4β2 Partial Agonist Varenicline on Brain Activity and Working Memory in Abstinent Smokers
Effects of the ␣42 Partial Agonist Varenicline on Brain Activity and Working Memory in Abstinent Smokers James Loughead, Riju Ray, E. Paul Wileyto, Kosha Ruparel, Paul Sanborn, Steven Siegel, Ruben C. Gur, and Caryn Lerman Background: Cognitive alterations are a core symptom of nicotine withdrawal, contributing to smoking relapse. In rodents and humans, cognitive deficits can be reversed by treatment with the ␣42 nicotinic receptor partial agonist varenicline. This neuroimaging study examined the neural mechanisms that underlie these effects. Methods: Twenty-two smokers completed 13 days of varenicline and placebo treatment in a double-blind crossover study with two functional magnetic resonance imaging sessions: after 3 days of abstinence while on varenicline and after 3 days of abstinence while on placebo (counterbalanced randomized order, 2-week washout). Blood oxygenation level-dependent (BOLD) data were acquired during performance of a visual N-back working memory task. Results: In a region of interest analysis, significant effects of treatment on mean percent signal change (varenicline ⬎ placebo) were observed in the dorsal anterior cingulate/medial frontal cortex, left dorsolateral prefrontal cortex, and right dorsolateral prefrontal cortex. In a cross-region model, there was a significant interaction of treatment by memory load, indicating significant increases in BOLD signal for varenicline versus placebo at the 2-back and 3-back levels but not the 1-back level. Varenicline improved performance (correct response time) in highly dependent smokers with no effect among less dependent smokers. In highly dependent smokers, faster correct response time was associated with increased BOLD signal. Conclusions: This study provides novel evidence that the ␣42 partial agonist varenicline increases working memory-related brain activity after 3 days of nicotine abstinence, particularly at high levels of task difficulty, with associated improvements in cognitive performance among highly dependent smokers. Key Words: Addiction, cognition, fMRI, nicotine, varenicline, withdrawal
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icotine produces a biological dependence that makes quitting difficult, underscoring the importance of developing improved medications (1). One approach to accelerate progress in medication development for complex conditions such as nicotine dependence is to isolate core neurobehavioral symptoms and examine effects of efficacious pharmacotherapies on specific targets (2,3). We investigated the effects of the smoking cessation medication varenicline on brain activity and working memory during nicotine withdrawal. Abstinence from smoking produces a range of withdrawal symptoms, including impaired attention and working memory (4 –7). Nicotine re-exposure reverses these cognitive impairments in rodents (8) and human smokers (9), suggesting that relapse may occur to ameliorate these symptoms. Cognitive impairment is commonly reported by abstinent smokers (10,11), and postcessation cognitive performance predicts relapse (12). Nicotinic acetylcholine receptors (nAChRs) play a key role in cognition. Nicotinic acetylcholine receptors are pentameric ligand gated ion channels with ␣ (␣2–␣10) and  (2–4) subunits. High affinity ␣42 nAChRs are widely distributed in the From the Brain Behavior Laboratory (JL, KR, RCG), Department of Psychiatry, University of Pennsylvania; Transdisciplinary Tobacco Use Research Center (RR, EPW, PS, SS, CL), Department of Psychiatry and Abramson Cancer Center, University of Pennsylvania; and Philadelphia Veterans Administration Medical Center (RCG), Philadelphia, Pennsylvania. Address correspondence to Caryn Lerman, Ph.D., Transdisciplinary Tobacco Use Research Center, Department of Psychiatry and Abramson Cancer Center, University of Pennsylvania, 3535 Market Street, Philadelphia, PA 19104; E-mail: [email protected]. Received Jul 27, 2009; revised Jan 11, 2010; accepted Jan 13, 2010.
0006-3223/10/$36.00 doi:10.1016/j.biopsych.2010.01.016
brain with moderate to high levels of expression in the prefrontal cortex (PFC), cingulate cortex, thalamus, ventral tegmental area, and striatum (13–15). Preclinical studies suggest that both ␣42 and the lower affinity ␣7 nAChRs are critical for cognition, although there is regional variability in these effects (16 –18). The ␣42 nAChRs are expressed on dopamine cell bodies and nerve terminals of gamma-aminobutyric acid (GABA) neurons, while cell bodies of GABA neurons and glutamate terminals express the low affinity ␣7 subtype (14,19). Nicotine has direct effects on dopamine release and indirect effects via nAChR mediated glutamate and GABAergic signaling (20). Although mechanisms other than dopamine release mediate nicotine’s effects, neuroimaging studies support a role for dopamine in the positive reinforcing effects of smoking (21) and provide indirect evidence that dopaminergic mechanisms may underlie nicotine’s effects on cognition (22). Like nicotine, the smoking cessation medication varenicline binds to ␣42 nAChRs (23) and with lower affinity to ␣7 nAChRs (24). As a partial agonist, varenicline stimulates moderate levels of dopamine release (23) and reproduces behavioral effects of nicotine in some rodent models of cognition (25). Of particular relevance, varenicline reverses withdrawal-related impairments in learning and memory in rodents (26) and in abstinent smokers (27). The neurobiological mechanisms that underlie varenicline’s effects on cognition have yet to be fully elucidated. To examine neural mechanisms that underlie varenicline’s effects on withdrawal-related cognitive impairment, we used a double-blind crossover study of short-term varenicline (vs. placebo) treatment. Following 3 days of abstinence, subjects underwent blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) scans while performing an N-back working memory task (27). Based on prior work (22,28), functionally relevant frontal systems were selected as a priori regions of interest, specifically, bilateral dorsolateral prefrontal BIOL PSYCHIATRY 2010;67:715–721 © 2010 Society of Biological Psychiatry
716 BIOL PSYCHIATRY 2010;67:715–721 cortex (DLPFC) and dorsal anterior cingulate/medial frontal cortex (MF/CG). These regions also exhibit moderate levels of 2-containing nAChR availability during abstinence from smoking (13). We hypothesized that varenicline would increase activation in DLPFC and MF/CG relative to placebo, particularly at high levels of task difficulty. Based on prior evidence that nicotine dependence severity modulates brain activity (29,30) and cognitive performance (31), we also explored treatment effects as a function of dependence level.
Methods and Materials Participants Treatment-seeking smokers were enrolled using media advertisements from November 2007 to June 2008 at the University of Pennsylvania. Eligible smokers were ages 18 to 65 and smoked ⱖ10 cigarettes/day for ⱖ6 months. Persons with a history of DSM-IV Axis I psychiatric or substance disorders (except nicotine) and those taking psychotropic medications (e.g., monoamine oxidase inhibitors, benzodiazepines, antidepressants, antipsychotics) were excluded. Figure S1 in Supplement 1 illustrates the flow of participants. Of the 25 participants who completed study requirements, 3 were excluded for measurement artifact (2 had ⬎6 mm relative motion and 1 had a mean temporal signal-to-noise ratio ⬎3 SD below the group mean). In the final sample of 22, 10 received varenicline in the first period and 12 received placebo in the first period. Forty-five percent of participants were female, 77% were Caucasian, 45% completed college, and the average age was 41 years (SD ⫽ 13). The average baseline smoking rate was 18.5 (SD ⫽ 5.3) and the average score on the Fagerstrom Test for Nicotine Dependence (FTND) (32) was 4.5 (SD ⫽ 1.7). These characteristics are representative of treatment-seeking smokers in recent clinical studies (33). There were no differences in baseline variables between the full sample of 25 and the analyzed sample of 22. Design and Procedures This within-subject double-blind crossover study included BOLD fMRI sessions during two 13-day medication periods: 1) after 3 days of abstinence while on varenicline, and 2) after 3 days of abstinence while on placebo (with a 2-week washout). Order of medications was determined using a computer-generated randomization scheme implemented by a programmer. Blood oxygen level-dependent fMRI data were acquired during performance of a visual N-back working memory task. All procedures were approved by the University of Pennsylvania Institutional Review Board. Participants completed a physical examination including a urine drug screen, breath alcohol test, and pregnancy test. The presence of psychiatric or substance abuse disorders was assessed using the Mini International Neuropsychiatric Interview (34). The Wechsler Adult Intelligence Scale-Revised estimated IQ test was administered; individuals with low or borderline intelligence (⬍90 score) were excluded. Eligible participants completed the 6-item Fagerstrom Test for Nicotine Dependence (32). Subgroups were classified based on scores of 0 to 5 (low dependence) versus 6 to 10 (moderate to high dependence), consistent with prior clinical studies (35) and with selection criteria (ⱖ6 on FTND) in neuroimaging studies of dependent smokers (30). Participants completed the 13-day medication periods, as in the paradigm used in our prior study of varenicline effects (27). Varenicline and placebo were supplied by Pfizer, provided in www.sobp.org/journal
J. Loughead et al. blinded blister packs. Varenicline was administered according to standard guidelines: days 1 to 3 (.5 mg once a day), days 4 to 7 (.5 mg twice a day), and days 8 to 13 (1 mg twice a day). Average daily smoking rates during the smoking as usual period (days 1–9) were 16.1 (SD ⫽ 5.5) and 15.0 (SD ⫽ 5.9) during the placebo and varenicline periods, respectively. On day 9 of the medication run-up, participants received a 15-minute counseling session to prepare for the mandatory abstinence period, which was presented as a “practice” quit attempt (27). Participants received a monetary incentive ($25/day) to stay abstinent. Abstinence was verified daily during the mandatory abstinence period using carbon monoxide (CO) (ⱕ10 ppm) (36). The average CO level at baseline was 26.0 (SD ⫽ 9.0). The average CO levels for days 11, 12, and 13 during the placebo period were 2.4 (SD ⫽ 1.4), 2.7 (SD ⫽ 2.1), and 3.1 (SD ⫽ 2.9), respectively; during the varenicline period, the values were 2.1 (SD ⫽ 1.1), 2.2 (SD ⫽ 1.4), and 2.4 (SD ⫽ .95), respectively. One participant had a CO level of 12 ppm on day 13 of the placebo period. This participant was interviewed and assured adherence to abstinence requirements; because environmental sources of CO can result in CO elevations (36), we did not exclude this participant. Participants took their final dose of medication on the morning of day 13 and completed a BOLD fMRI session while performing a visual N-back working memory task (37). The N-back task involved presentation of complex geometric figures (fractals) for 500 msec, followed by fixation stimulus for 2500 msec 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 that two trials back, and in the 3-back condition, they responded if it was identical to three trials back. Each condition consisted of 20 trials (60-sec block) and each class (0-back, 1-back, 2-back, 3-back) was repeated three times. A target-foil ratio of 1:2 (i.e., 33% targets) was maintained in all blocks and visual instructions (9 sec) preceded each block, alerting the participant to the upcoming condition. The task began with a 48-second baseline rest period (fixation point on blank screen) of which the first 24 seconds were discarded to ensure the magnetic resonance imaging signal reached steady state. Additional 24-second baseline rest periods occurred at the middle and end of the acquisition. Total task duration was 924 seconds (308 time points). Equivalent N-back tasks with unique stimuli were used for the two sessions (varenicline, placebo) and version order was counterbalanced. Following completion of the study, participants were offered free smoking cessation treatment. Image Acquisition Blood oxygen level-dependent fMRI was acquired with a Siemens Trio 3 T (Erlangen, Germany) system using a wholebrain, single-shot gradient echo planar sequence with the following parameters: repetition time/echo time ⫽ 3000/30 msec, field of view ⫽ 220 mm, matrix ⫽ 64 ⫻ 64, slice thickness/gap ⫽ 3/0 mm, 40 slices, effective voxel resolution of 3 ⫻ 3 ⫻ 3 mm. Radio frequency transmission used a quadrature body coil and reception used an 8-channel head coil. Echo planar acquisitions included online geometric distortion correction that addresses nonlinear deformation of echo planar images due to main magnetic field inhomogeneity. The sequences are based on those developed by Zaitsev et al. (38) and implement the point spread function mapping method (39), which is acquired with a
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Figure 1. Visual N-back working memory task. (A) Colored regions represent functionally defined ROI masks identified using a whole-brain repeatedmeasures ANOVA with a voxel threshold of p ⬍ .001 and cluster probability of p ⬍ .05. Brain rendering was performed with CARET (http://www.nitrc.org/ projects/caret/) (65). (B) Mean percent signal change (N-back minus 0-back) for the 1-back, 2-back, and 3-back contrasts calculated from a priori ROIs. Main effects of treatment (varenicline, placebo) were observed in all three ROIs, significant at p ⬍ .05. ANOVA, analysis of variance; DLPFC, dorsolateral prefrontal cortex; MF/CG, dorsal anterior cingulate/medial frontal cortex; ROI, region of interest; BOLD, blood oxygenation level-dependent.
reference scan before the acquisition of echo planar data. After BOLD fMRI, 5-minute magnetization-prepared, rapid acquisition gradient echo T1-weighted image (repetition time 1620 msec, echo time 3.87 msec, field of view 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. Analysis Sample size was determined using Power and Sample Size (PASS) (NCSS Software, Kaysville, Utah). A sample size of 22 provided power of .80 to detect a drug effect of .60 (one-sample two-sided test, alpha ⫽ .05). Blood oxygen level-dependent time series data were analyzed with FEAT (fMRI Expert Analysis Tool) Version 5.92, part of FSL (FMRIB’s [Oxford Centre for Functional MRI of the Brain] Software Library) 4.0, using standard image analysis procedures including brain extraction, slice time correction, motion correction, high-pass filtering (138 sec), spatial smoothing (6 mm full-width at half maximum, isotropic), and mean-based intensity normalization. The median functional and anatomical volumes were coregistered, and the anatomical image was transformed into standard space (2 mm3 T1 Montreal Neurological Institute template). Resulting transformation parameters were later applied to statistical images and the images were resampled (2 mm3) before group level analyses. The primary outcome was mean percent BOLD signal change. Subject-level statistical analyses were carried out using FILM (FMRIB’s [Oxford Centre for Functional MRI of the Brain] Improved Linear Model) with local autocorrelation correction (40). Four condition events (0-back, 1-back, 2-back, 3-back) were modeled using a canonical hemodynamic response function.
The instruction period and motion correction parameters were included as nuisance covariates and the rest condition (fixation point) was treated as the unmodeled baseline. To characterize the session (drug, placebo) by memory load (1 ⫺ 0 back, 2 ⫺ 0 back, 3 ⫺ 0 back) effects, mean percent signal change was extracted from a priori regions of interest (ROIs) in the DLPFC (right and left) and MF/CG. Mean percent signal change values were exported for analysis using procedures described below. Region of interest masks (Figure 1A) were functionally defined using the main effect of working memory load identified using a whole-brain repeated-measures analysis of variance (ANOVA). Analysis of variance results were further examined as part of an exploratory analysis (see below). Regions of interest were defined using a voxel threshold of p ⬍ .001 and cluster probability of p ⬍ .05 (41). These procedures produced welldefined clusters in the right DLPFC (4400 mm3) and left DLPFC (6584 mm3) and the MF/CG (10,184 mm3). Anatomic assignment was based on the peak Z score within the cluster using the Talairach Daemon database (http://www.talairach.org) confirmed with visual inspection and coordinates reported by Owen et al. (28). The percentage signal change in each of the ROIs was analyzed using repeated-measures ANOVA in a mixed-models regression framework. We accounted for repeated measures with subject-level Gaussian random effects and estimated effects using maximum likelihood techniques. The BOLD signal models included terms for the main effects of session (drug vs. placebo), memory load (1 ⫺ 0 back, 2 ⫺ 0 back, 3 ⫺ 0 back), and relevant covariates (sex, nicotine dependence level [FTND], education). Treatment order was tested in each model as a main effect and treatment modifier. Main effects and individual terms were tested using the Z score (two-sided). Interactions of treatment and www.sobp.org/journal
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categorical predictors (e.g., nicotine dependence level, memory load) were tested using either the likelihood ratio or Wald 2, and interactions were retained in the model at p ⫽ .05. Finally, a cross-region model was tested to extend our repeated-measures analysis to all three ROIs. The cross-ROI model included indicators for region (left DLPFC, right DLPFC, MF/CG), while effects of treatment, memory load, treatment by load interactions, and covariates were estimated using common parameters across regions. We tested for interactions of these covariates and predictors with the ROI variable. The effect of treatment condition on task performance (correct response time, N-back minus 0-back) was examined with models as described above. Models included terms for the main effects of treatment period (drug vs. placebo), memory load (0-back, 1-back, 2-back, 3-back), and covariates, and we tested for interactions of nicotine dependence severity with treatment. To examine the relationship of BOLD signal to correct response time, we added response time difference (1 ⫺ 0 back, 2 ⫺ 0 back, 3 ⫺ 0 back) to the cross-region BOLD model described above and tested interactions of response time with nicotine dependence severity.
Results
Region of Interest Analysis The region of interest analysis of the mean percent signal change (Figure 1B) showed main effects of treatment (varenicline, placebo) in the MF/CG ( ⫽ .053, 95% confidence interval [CI] ⫽ .007–.099, Z ⫽ 2.27, p ⫽ .023), left DLPFC ( ⫽ .074, 95% CI ⫽ .021–.127, Z ⫽ 2.73, p ⫽ .006), and right DLPFC ( ⫽ .064, 95% CI ⫽ .004 –.123, Z ⫽ 2.10, p ⫽ .036). There was a positive main effect of memory load on BOLD signal in the MF/CG [2-Back ⫽ .183, 3-Back ⫽ .213, Wald 2(2) ⫽ 64.38, p ⬍ .0001], left DLPFC [2-Back ⫽ .189, 3-Back ⫽ .194, Wald 2(2) ⫽ 44.89, p ⬍ .0001], and right DLPFC [2-Back ⫽ .212, 3-Back ⫽ .284, Wald 2(2) ⫽ 63.15, p ⬍ .0001]. The treatment ⫻ load interaction was not significant in the individual ROI models (p’s ⬎ .05). In the cross-region model, there was a significant interaction of treatment by memory load [Wald 2(2) ⫽ 10.45, p ⫽ .005], indicating significant increase in BOLD signal for varenicline compared with placebo at the 2-back ( ⫽ .068, 95% CI ⫽ .012–.124, Z ⫽ 2.40, p ⫽ .016) and 3-back ( ⫽ .126, 95% CI ⫽ .071–.181, Z ⫽ 4.44, p ⬍ .0001) levels but not at the 1-back level. None of the covariates, including treatment order, or covariate by treatment interactions approached significance in these models (p’s ⬎ .10). The ROI by treatment interactions were not significant and effects of treatment, memory load, and the interaction appeared uniform across ROIs (interactions with ROI were not retained in the model). The relationship of BOLD signal to correct response time was tested in the cross-region model and was found to vary with nicotine dependence severity. Correct response time was negatively associated with BOLD signal difference (N-back minus 0-back) for participants with high dependence ( ⫽ ⫺.23 [units: 1/sec], p ⫽ .034) and positively related to BOLD signal for low dependence subjects ( ⫽ .50, p ⬍ .0001). This interaction (FTND by response time) was significant [Wald 2(1) ⫽ 30.07, p ⬍ .0001]. The model parameters were similar across ROIs when tested for interaction by region [LR 2(12) ⫽ 9.63, p ⫽ .65].
Behavioral Data Median correct response time increased significantly as memory load increased [Wald 2(3) ⫽ 86.4, p ⬍ .0001] (Table 1). The treatment effect on correct response time varied depending on nicotine dependence severity. Varenicline (vs. placebo) was associated with significantly lower (faster) correct response time in highly dependent participants ( ⫽ ⫺69.33, p ⫽ .014), but treatment had no effect on performance for participants with low dependence ( ⫽ 2.68, p ⫽ .90); the interaction was significant [LR 2(1) ⫽ 4.43, p ⫽ .03]. These were no drug effects or drug by dependence effects on performance accuracy (number of true positives). One participant reported four instances of severe fatigue during varenicline treatment and one instance during placebo. One participant reported severe gastrointestinal disturbance, and another reported severe increased appetite in the placebo phase.
Exploratory Whole-Brain Analysis Significant whole-brain main effects for session (drug vs. placebo) and memory load (0-back, 1-back, 2-back, 3-back) were detected but no interaction was observed (Figure S2 and Table S1 in Supplement 1). Examination of percent signal change (Figure S2B in Supplement 1) for the session main effect (drug vs. placebo) revealed that the placebo condition was generally associated with increased BOLD signal (activation) and the drug condition with decreased BOLD signal (deactivation). This pattern was most striking in the right superior frontal gyrus and left inferior frontal gyrus. However, the opposite pattern (drug activation and placebo deactivation) was observed in the left inferior temporal gyrus and left middle frontal gyrus. The pattern of activation observed for the memory load main effect is consistent with studies using the N-back paradigm (28).
Exploratory Whole-Brain Analysis In an exploratory whole-brain voxel-wise analysis, parameter estimates were entered into a mixed-effects, within-subject session (drug vs. placebo) by memory load (0-back, 1-back, 2-back, 3-back) whole-brain ANOVA. Resulting Z (Gaussianised F) statistic images were thresholded at p ⬍ .05, corrected (familywise error Z ⬎ 4.60). For clarity, only clusters of greater than 50 contiguous voxels are reported. Mean scaled beta coefficients (% BOLD signal change) from each cluster were extracted for graphic examination and further statistical testing.
Table 1. Behavioral Data by Treatment and Memory Load Working Memory Load Level Median Correct Treatment Time (msec)
Treatment
0-Back Mean (SD)
1-Back Mean (SD)
2-Back Mean (SD)
3-Back Mean (SD)
Overall Mean (SD)
High Nicotine Dependence (n ⫽ 7)
Placebo Varenicline Placebo Varenicline
637.32 (115.18) 624.38 (181.91) 540.93 (91.60) 539.69 (72.39)
668.43 (151.39) 586.43 (169.11) 573.89 (134.50) 570.21 (78.10)
914.86 (197.20) 715.92 (141.63) 676.59 (173.92) 671.90 (178.00)
726.06 (141.15) 742.61 (234.34) 726.72 (178.97) 747.06 (204.80)
692.14 (128.57) 637.99 (164.30) 581.82 (105.77) 585.59 (82.53)
Low Nicotine Dependence (n ⫽ 15)
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Discussion This study provides novel evidence that short-term treatment with the ␣42 partial agonist varenicline increases working memory-related brain activity after 3 days of nicotine abstinence. Task-related BOLD signal change in the MF/CG and bilateral DLPFC was significantly greater during varenicline treatment, compared with placebo, particularly at higher levels of task difficulty. Effects of varenicline on cognitive performance were dependent on nicotine dependence severity; treatment improved correct response time in smokers with higher dependence, and in this group, faster response time was associated with increased BOLD signal in these regions. Improvements in response time did not extend to the most demanding working memory load (3-back), however. While this study is the first to examine the functional neuroanatomical correlates of varenicline’s cognitive effects, our results can be considered in light of studies of N-back task-related brain activity during abstinence from smoking and following nicotine delivery. Our findings differ from two studies showing increased task-related activity in the PFC during abstinence relative to smoking (7) or nicotine delivery (42). However, our results do converge with those of Xu et al. (7) with respect to the relationship between memory load and BOLD signal in the abstinent versus smoking or varenicline conditions. In the prior study (7), the expected increase in BOLD signal with increased task difficulty was not observed during abstinence, whereas a positive relationship between task load and activation was found during the smoking condition. This is broadly consistent with our finding of a flattening of activation from the 2-back to the 3-back levels during abstinence, which was ameliorated by varenicline. In our study, BOLD fMRI was acquired after 3 days of abstinence using a nonverbal N-back task, whereas the prior studies assessed brain activity after 12 hours of abstinence using a verbal N-back paradigm. Differences in study design and task stimuli may account for differences in activation patterns (28). Indeed, in our prior fMRI study using the same nonverbal N-back paradigm, abstinence from smoking was associated with reduced BOLD signal in DLPFC and MF/CG among smokers homozygous for the valine allele of the catechol-O-methyltransferase valine158methionine polymorphism (22), which has been linked with nicotine dependence (43,44). Moreover, both studies observed the greatest abstinence-related reductions in working memory-related activity at the highest level of task difficulty. Taken together, our prior study (22) and the current study suggest that abstinence from smoking may reduce engagement of the working memory system at high levels of task difficulty, an effect that is reversed by smoking and varenicline. Although the beneficial effects of varenicline on cognitive performance were weaker than those observed in a larger behavioral study (27), treatment effects on performance were modulated by pretreatment level of nicotine dependence. More dependent smokers had slower response times than less dependent smokers during the placebo period, whereas performance during the varenicline session appeared more similar in the two groups. A recent study linked dependence severity with poorer performance on attention-demanding tasks, independent of abstinence (31). Other studies suggest that nicotine dependence level can modulate BOLD signal in regions important in visual attention during exposure to smoking-related cues (29,45,46). Although we did not find a main effect of nicotine dependence on N-back task-related BOLD signal, dependence severity modulated the relationship between BOLD signal and correct re-
BIOL PSYCHIATRY 2010;67:715–721 719 sponse time. Specifically, increased BOLD signal across regions (DLPFC, MF/CG) was associated with faster performance in more dependent smokers and with slower performance in less dependent smokers. This finding fits with the hypothesis that the “window” of PFC activation optimal for working memory varies depending on genetic and environmental influences (47,48). However, our findings related to nicotine dependence require replication due to the smaller sample sizes in the subgroup analyses. A neuropharmacological mechanism for the observed effects of varenicline can be proposed. Both varenicline and nicotine bind with high affinity to ␣42 nAChRs (23), supporting a common neural mechanism for cognitive effects. However, ␣42 nAChRs are rapidly desensitized (49), suggesting that lower affinity ␣7 nAChRs also play a critical role (24). Both genetic and pharmacological manipulations in rodent models support the view that ␣42 and ␣7 nAChRs are critical for cognition and learning (18,50). Thus, activation of both ␣42 and ␣7 nAChRs is likely to contribute to the cognitive-enhancing properties of varenicline, similar to other nicotinic agonists (17,51,52). Activation of ␣42 and ␣7 nAChRs increases dopamine release in regions important in cognition (53), suggesting a neurochemical mechanism for varenicline’s effects on DLPFC and MF/CG activity. The critical role of PFC dopamine circuits in working memory has been established in animal models (54), as well as in human genetic studies (55). For example, mice overexpressing the human catechol-O-methyltransferase valine polymorphism exhibit altered PFC dopamine signaling and impairments in working memory (56), consistent with cognitive deficits observed in human valine allele carriers (22,55). Future neuroimaging studies of varenicline treatment using radioligands with specificity for nAChR subtypes or dopamine receptors are required to clarify the neurochemical processes underlying varenicline in human smokers. An exploratory whole-brain analysis was also performed to identify potentially relevant sites of drug action beyond the a priori regions in the ROI analysis. Abstinence (placebo) was generally associated with increased BOLD signal, which was attenuated by varenicline. Moreover, varenicline-induced deactivations were observed in multiple regions, including left inferior frontal gyrus, bilateral amygdala, and anterior and posterior cingulate. This finding is consistent with prior fMRI studies of nicotine’s effects on attentional performance (57,58) and has been interpreted as a downregulation by nicotine of neural activity in task-independent regions that, in turn, enhances processing efficiency (59). Thus, varenicline-induced increases in neural activity in task-dependent regions (i.e., bilateral DLPFC, MF/CG) and decreases in task independent regions may both be relevant to drug effects on cognitive performance. Our study has strengths and limitations. Strengths include the within-subject design and the 3-day abstinence challenge with assessment of cognitive effects at the time point when withdrawal symptoms are greatest (60). The use of treatment-seeking smokers may also increase sensitivity of this paradigm to medication effects (61,62). One limitation is that CO assessments are relatively imprecise for assessing smoking abstinence. In addition, while the current study was powered to detect treatment effects on BOLD signal, analyses of effects on performance and the modulating role of dependence severity must be replicated due to the small sample sizes in subgroup analysis. This is important because, although faster response time was associated with increased BOLD signal, this effect was not seen at the memory load (3-back) that showed the greatest drug effects. www.sobp.org/journal
720 BIOL PSYCHIATRY 2010;67:715–721 Larger sample sizes are also needed to identify the role of genetic variants that may modify effects of abstinence and nicotinic compounds on cognitive task-related brain activity (22,63). This study provides the first data on varenicline’s effects on working memory-related brain activity during nicotine abstinence, extending prior work documenting varenicline’s cognitive-enhancing effects (27). Cognitive performance deficits are associated with an increased risk for smoking relapse (64,12) and are an important target of nicotine dependence medication development efforts (1). Neuroimaging assessments, together with genetic approaches, can further delineate brain systems that contribute to cognitive deficits and other pathological symptoms of nicotine dependence. If successful, this process will establish neuroimaging as a surrogate marker for treatment response and a tool for screening promising medications.
Support for this study was provided by P50 CA143187, P30 NS045839, the Pennsylvania Department of Health, and a grant from Pfizer, Inc. The industry sponsor had no role in study design or data analysis. The Department of Health specifically disclaims responsibility for any analyses, interpretations, or conclusions. We thank Drs. Julie Blendy and Thomas Gould for valuable feedback on the manuscript. Dr. Lerman has been a consultant and/or received grant support from the following companies that develop and/or market smoking cessation medications: Astra Zeneca, Glaxo SmithKline, Novartis, and Pfizer. Drs. Loughead and Gur have received grant support from Astra Zeneca and Pfizer. Dr. Siegel has been a consultant and/or received grant support from NuPathe, Pfizer, Eli Lilly, and Astra Zeneca that is unrelated to smoking cessation. Dr. Ray, Dr. Wileyto, Ms. Ruparel, and Mr. Sanborn report no biomedical financial interests or potential conflicts of interest. ClinicalTrials.gov: Neural Substrates of Varenicline’s (Chantix®) Efficacy for Smoking Cessation; http://www.clinicaltrials.gov/ct2/show/study/NCT00602927; NCT00602927. Supplementary material cited in this article is available online. 1. Lerman C, LeSage MG, Perkins KA, O’Malley SS, Siegel SJ, Benowitz NL, Corrigall WA (2007): Translational research in medication development for nicotine dependence. Nat Rev Drug Discov 6:746 –762. 2. Conn PJ, Roth BL (2008): Opportunities and challenges of psychiatric drug discovery: Roles for scientists in academic, industry, and government settings. Neuropsychopharmacology 33:2048 –2060. 3. Hyman SE, Fenton WS (2003): Medicine. What are the right targets for psychopharmacology? Science 299:350 –351. 4. Evans DE, Drobes DJ (2009): Nicotine self-medication of cognitive-attentional processing. Addict Biol 14:32– 42. 5. Jacobsen LK, Krystal JH, Mencl WE, Westerveld M, Frost SJ, Pugh KR (2005): Effects of smoking and smoking abstinence on cognition in adolescent tobacco smokers. Biol Psychiatry 57:56 – 66. 6. Mendrek A, Monterosso J, Simon SL, Jarvik M, Brody A, Olmstead R, et al. (2006): Working memory in cigarette smokers: Comparison to nonsmokers and effects of abstinence. Addict Behav 31:833– 844. 7. Xu J, Mendrek A, Cohen MS, Monterosso J, Rodriguez P, Simon SL, et al. (2005): Brain activity in cigarette smokers performing a working memory task: Effect of smoking abstinence. Biol Psychiatry 58:143–150. 8. Davis JA, James JR, Siegel SJ, Gould TJ (2005): Withdrawal from chronic nicotine administration impairs contextual fear conditioning in C57BL/6 mice. J Neurosci 25:8708 – 8713. 9. Myers CS, Taylor RC, Moolchan ET, Heishman SJ (2008): Dose-related enhancement of mood and cognition in smokers administered nicotine nasal spray. Neuropsychopharmacology 33:588 –598.
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J. Loughead et al. 10. Hughes JR (2007): Measurement of the effects of abstinence from tobacco: A qualitative review. Psychol Addict Behav 21:127–137. 11. Ward MM, Swan GE, Jack LM (2001): Self-reported abstinence effects in the first month after smoking cessation. Addict Behav 26:311–327. 12. Patterson F, Jepson C, Loughead J, Perkins K, Strasser AA, Siegel S, et al. (2010): Working memory deficits predict short-term smoking resumption following brief abstinence. Drug Alcohol Depend 106:61– 64. 13. Cosgrove KP, Batis J, Bois F, Maciejewski PK, Esterlis I, Kloczynski T, et al. (2009): Beta2-nicotinic acetylcholine receptor availability during acute and prolonged abstinence from tobacco smoking. Arch Gen Psychiatry 66:666 – 676. 14. Gotti C, Zoli M, Clementi F (2006): Brain nicotinic acetylcholine receptors: Native subtypes and their relevance. Trends Pharmacol Sci 27:482– 491. 15. Sarter M, Parikh V, Howe WM (2009): nAChR agonist-induced cognition enhancement: Integration of cognitive and neuronal mechanisms. Biochem Pharmacol 78:658 – 667. 16. Cannady R, Weir R, Wee B, Gotschlich E, Kolia N, Lau E, et al. (2009): Nicotinic antagonist effects in the mediodorsal thalamic nucleus: Regional heterogeneity of nicotinic receptor involvement in cognitive function. Biochem Pharmacol 78:788 –794. 17. Levin ED, McClernon FJ, Rezvani AH (2006): Nicotinic effects on cognitive function: Behavioral characterization, pharmacological specification, and anatomic localization. Psychopharmacology (Berl) 184:523– 539. 18. Kenney JW, Gould TJ (2008): Modulation of hippocampus-dependent learning and synaptic plasticity by nicotine. Mol Neurobiol 38:101–121. 19. Mansvelder HD, McGehee DS (2000): Long-term potentiation of excitatory inputs to brain reward areas by nicotine. Neuron 27:349 –357. 20. Livingstone PD, Wonnacott S (2009): Nicotinic acetylcholine receptors and the ascending dopamine pathways. Biochem Pharmacol 78:744 – 755. 21. Brody AL, Mandelkern MA, Olmstead RE, Allen-Martinez Z, Scheibal D, Abrams AL, et al. (2009): Ventral striatal dopamine release in response to smoking a regular vs a denicotinized cigarette. Neuropsychopharmacology 34:282–289. 22. Loughead J, Wileyto EP, Valdez JN, Sanborn P, Tang K, Strasser AA, et al. (2009): Effect of abstinence challenge on brain function and cognition in smokers differs by COMT genotype. Mol Psychiatry 14:820 – 826. 23. Rollema H, Chambers LK, Coe JW, Glowa J, Hurst RS, Lebel LA, et al. (2007): Pharmacological profile of the alpha4beta2 nicotinic acetylcholine receptor partial agonist Varenicline, an effective smoking cessation aid. Neuropharmacology 52:985–994. 24. Mihalak KB, Carroll FI, Luetje CW (2006): Varenicline is a partial agonist at alpha4beta2 and a full agonist at alpha7 neuronal nicotinic receptors. Mol Pharmacol 70:801– 805. 25. Rollema H, Hajos M, Seymour PA, Kozak R, Majchrzak MJ, Guanowsky V, et al. (2009): Preclinical pharmacology of the alpha4beta2 nAChR partial agonist varenicline related to effects on reward, mood and cognition. Biochem Pharmacol 78:813– 824. 26. Raybuck JD, Portugal GS, Lerman C, Gould TJ (2008): Varenicline ameliorates nicotine withdrawal-induced learning deficits in C57BL/6 mice. Behav Neurosci 122:1166 –1171. 27. Patterson F, Jepson C, Strasser AA, Loughead J, Perkins KA, Gur RC, et al. (2009): Varenicline improves mood and cognition during smoking abstinence. Biol Psychiatry 65:144 –149. 28. Owen AM, McMillan KM, Laird AR, Bullmore E (2005): N-back working memory paradigm: A meta-analysis of normative functional neuroimaging studies. Hum Brain Mapp 25:46 –59. 29. McClernon FJ, Kozink RV, Rose JE (2008): Individual differences in nicotine dependence, withdrawal symptoms, and sex predict transient fMRI-BOLD responses to smoking cues. Neuropsychopharmacology 33: 2148 –2157. 30. Rose JE, Behm FM, Salley AN, Bates JE, Coleman RE, Hawk TC, Turkington TG (2007): Regional brain activity correlates of nicotine dependence. Neuropsychopharmacology 32:2441–2452. 31. Azizian A, Monterosso JR, Brody AL, Simon SL, London ED (2008): Severity of nicotine dependence moderates performance on perceptualmotor tests of attention. Nicotine Tob Res 10:599 – 606. 32. Heatherton TF, Kozlowski LT, Frecker RC, Fagerstrom KO (1991): The Fagerstrom Test for Nicotine Dependence: A revision of the Fagerstrom Tolerance Questionnaire. Br J Addict 86:1119 –1127.
BIOL PSYCHIATRY 2010;67:715–721 721
J. Loughead et al. 33. Schnoll RA, Patterson F, Wileyto EP, Tyndale RF, Benowitz N, Lerman C (2009): Nicotine metabolic rate predicts successful smoking cessation with transdermal nicotine: A validation study. Pharmacol Biochem Behav 92:6 –11. 34. Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, et al. (1998): 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 59(suppl 20):22–33; quiz 34 –57. 35. Lerman C, Kaufmann V, Rukstalis M, Patterson F, Perkins K, AudrainMcGovern J, Benowitz N (2004): Individualizing nicotine replacement therapy for the treatment of tobacco dependence: A randomized trial. Ann Intern Med 140:426 – 433. 36. SRNT Subcommittee on Biochemical Verification (2002): Biochemical verification of tobacco use and cessation. Nicotine Tob Res 4:149 –159. 37. Ragland JD, Turetsky BI, Gur RC, Gunning-Dixon F, Turner T, Schroeder L, et al. (2002): Working memory for complex figures: An fMRI comparison of letter and fractal n-back tasks. Neuropsychology 16:370 –379. 38. Zaitsev M, Hennig J, Speck O (2003) Automated onlnie EPI distortion correction for fMRI applications [abstract]. In: Proceedings of the 11th Annual Meeting of the International Society for Magnetic Resonance in Medicine, Toronto, ON, Canada, July 10 –16, 2003, 1042. 39. Zeng H, Constable RT (2002): Image distortion correction in EPI: Comparison of field mapping with point spread function mapping. Magn Reson Med 48:137–146. 40. Woolrich MW, Ripley BD, Brady M, Smith SM (2001): Temporal autocorrelation in univariate linear modeling of FMRI data. Neuroimage 14: 1370 –1386. 41. Worsley KJ (2001): Statistical analysis of activation images. In: Jezzard P, Matthews PM, Smith SM, editors. Functional MRI. An Introduction to Methods. Oxford, England: Oxford University Press, 251–270. 42. Ernst M, Matochik JA, Heishman SJ, Van Horn JD, Jons PH, Henningfield JE, London ED (2001): Effect of nicotine on brain activation during performance of a working memory task. Proc Natl Acad Sci U S A 98: 4728 – 4733. 43. Beuten J, Payne TJ, Ma JZ, Li MD (2006): Significant association of catechol-O-methyltransferase (COMT) haplotypes with nicotine dependence in male and female smokers of two ethnic populations. Neuropsychopharmacology 31:675– 684. 44. Colilla S, Lerman C, Shields PG, Jepson C, Rukstalis M, Berlin J, et al. (2005): Association of catechol-O-methyltransferase with smoking cessation in two independent studies of women. Pharmacogenet Genomics 15:393–398. 45. Smolka MN, Buhler M, Klein S, Zimmermann U, Mann K, Heinz A, Braus DF (2006): Severity of nicotine dependence modulates cue-induced brain activity in regions involved in motor preparation and imagery. Psychopharmacology (Berl) 184:577–588. 46. Yalachkov Y, Kaiser J, Naumer MJ (2009): Brain regions related to tool use and action knowledge reflect nicotine dependence. J Neurosci 29: 4922– 4929. 47. Goldberg TE, Weinberger DR (2004): Genes and the parsing of cognitive processes. Trends Cogn Sci 8:325–335. 48. Williams GV, Castner SA (2006): Under the curve: Critical issues for elucidating D1 receptor function in working memory. Neuroscience 139: 263–276. 49. Gentry CL, Wilkins LH Jr, Lukas RJ (2003): Effects of prolonged nicotinic ligand exposure on function of heterologously expressed, human alpha4beta2- and
50.
51.
52.
53.
54.
55. 56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
alpha4beta4-nicotinic acetylcholine receptors. J Pharmacol Exp Ther 304: 206–216. Levin ED, Petro A, Rezvani AH, Pollard N, Christopher NC, Strauss M, et al. (2009): Nicotinic alpha7- or beta2-containing receptor knockout: Effects on radial-arm maze learning and long-term nicotine consumption in mice. Behav Brain Res 196:207–213. Cincotta SL, Yorek MS, Moschak TM, Lewis SR, Rodefer JS (2008): Selective nicotinic acetylcholine receptor agonists: Potential therapies for neuropsychiatric disorders with cognitive dysfunction. Curr Opin Investig Drugs 9:47–56. Poorthuis RB, Goriounova NA, Couey JJ, Mansvelder HD (2009): Nicotinic actions on neuronal networks for cognition: General principles and long-term consequences. Biochem Pharmacol 78:668 – 676. Livingstone PD, Srinivasan J, Kew JN, Dawson LA, Gotti C, Moretti M, et al. (2009): Alpha7 and non-alpha7 nicotinic acetylcholine receptors modulate dopamine release in vitro and in vivo in the rat prefrontal cortex. Eur J Neurosci 29:539 –550. Castner SA, Goldman-Rakic PS, Williams GV (2004): Animal models of working memory: Insights for targeting cognitive dysfunction in schizophrenia. Psychopharmacology (Berl) 174:111–125. Savitz J, Solms M, Ramesar R (2006): The molecular genetics of cognition: Dopamine, COMT and BDNF. Genes Brain Behav 5:311–328. Papaleo F, Crawley JN, Song J, Lipska BK, Pickel J, Weinberger DR, Chen J (2008): Genetic dissection of the role of catechol-O-methyltransferase in cognition and stress reactivity in mice. J Neurosci 28:8709 – 8723. Hahn B, Ross TJ, Wolkenberg FA, Shakleya DM, Huestis MA, Stein EA (2009): Performance effects of nicotine during selective attention, divided attention, and simple stimulus detection: An fMRI study. Cereb Cortex 19:1990 –2000. Thiel CM, Fink GR (2007): Visual and auditory alertness: Modality-specific and supramodal neural mechanisms and their modulation by nicotine. J Neurophysiol 97:2758 –2768. Hahn B, Ross TJ, Yang Y, Kim I, Huestis MA, Stein EA (2007): Nicotine enhances visuospatial attention by deactivating areas of the resting brain default network. J Neurosci 27:3477–3489. Teneggi V, Tiffany ST, Squassante L, Milleri S, Ziviani L, Bye A (2002): Smokers deprived of cigarettes for 72 h: Effect of nicotine patches on craving and withdrawal. Psychopharmacology (Berl) 164:177–187. Perkins KA, Stitzer M, Lerman C (2006): Medication screening for smoking cessation: A proposal for new methodologies. Psychopharmacology (Berl) 184:628 – 636. Perkins KA, Lerman C, Stitzer M, Fonte CA, Briski JL, Scott JA, Chengappa KN (2008): Development of procedures for early screening of smoking cessation medications in humans. Clin Pharmacol Ther 84:216 –221. Jacobsen LK, Pugh KR, Mencl WE, Gelernter J (2006): C957T polymorphism of the dopamine D2 receptor gene modulates the effect of nicotine on working memory performance and cortical processing efficiency. Psychopharmacology (Berl) 188:530 –540. Moss TG, Sacco KA, Allen TM, Weinberger AH, Vessicchio JC, George TP (2009): Prefrontal cognitive dysfunction is associated with tobacco dependence treatment failure in smokers with schizophrenia. Drug Alcohol Depend 104:94 –99. Van Essen DC, Dickson J, Harwell J, Hanlon D, Anderson CH, Drury HA (2001): An integrated software system for surface-based analyses of cerebral cortex. J Am Med Inform Assoc 8:443– 459.
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N
icotine produces a biological dependence that makes quitting difficult, underscoring the importance of developing improved medications (1). One approach to accelerate progress in medication development for complex conditions such as nicotine dependence is to isolate core neurobehavioral symptoms and examine effects of efficacious pharmacotherapies on specific targets (2,3). We investigated the effects of the smoking cessation medication varenicline on brain activity and working memory during nicotine withdrawal. Abstinence from smoking produces a range of withdrawal symptoms, including impaired attention and working memory (4 –7). Nicotine re-exposure reverses these cognitive impairments in rodents (8) and human smokers (9), suggesting that relapse may occur to ameliorate these symptoms. Cognitive impairment is commonly reported by abstinent smokers (10,11), and postcessation cognitive performance predicts relapse (12). Nicotinic acetylcholine receptors (nAChRs) play a key role in cognition. Nicotinic acetylcholine receptors are pentameric ligand gated ion channels with ␣ (␣2–␣10) and  (2–4) subunits. High affinity ␣42 nAChRs are widely distributed in the From the Brain Behavior Laboratory (JL, KR, RCG), Department of Psychiatry, University of Pennsylvania; Transdisciplinary Tobacco Use Research Center (RR, EPW, PS, SS, CL), Department of Psychiatry and Abramson Cancer Center, University of Pennsylvania; and Philadelphia Veterans Administration Medical Center (RCG), Philadelphia, Pennsylvania. Address correspondence to Caryn Lerman, Ph.D., Transdisciplinary Tobacco Use Research Center, Department of Psychiatry and Abramson Cancer Center, University of Pennsylvania, 3535 Market Street, Philadelphia, PA 19104; E-mail: [email protected]. Received Jul 27, 2009; revised Jan 11, 2010; accepted Jan 13, 2010.
0006-3223/10/$36.00 doi:10.1016/j.biopsych.2010.01.016
brain with moderate to high levels of expression in the prefrontal cortex (PFC), cingulate cortex, thalamus, ventral tegmental area, and striatum (13–15). Preclinical studies suggest that both ␣42 and the lower affinity ␣7 nAChRs are critical for cognition, although there is regional variability in these effects (16 –18). The ␣42 nAChRs are expressed on dopamine cell bodies and nerve terminals of gamma-aminobutyric acid (GABA) neurons, while cell bodies of GABA neurons and glutamate terminals express the low affinity ␣7 subtype (14,19). Nicotine has direct effects on dopamine release and indirect effects via nAChR mediated glutamate and GABAergic signaling (20). Although mechanisms other than dopamine release mediate nicotine’s effects, neuroimaging studies support a role for dopamine in the positive reinforcing effects of smoking (21) and provide indirect evidence that dopaminergic mechanisms may underlie nicotine’s effects on cognition (22). Like nicotine, the smoking cessation medication varenicline binds to ␣42 nAChRs (23) and with lower affinity to ␣7 nAChRs (24). As a partial agonist, varenicline stimulates moderate levels of dopamine release (23) and reproduces behavioral effects of nicotine in some rodent models of cognition (25). Of particular relevance, varenicline reverses withdrawal-related impairments in learning and memory in rodents (26) and in abstinent smokers (27). The neurobiological mechanisms that underlie varenicline’s effects on cognition have yet to be fully elucidated. To examine neural mechanisms that underlie varenicline’s effects on withdrawal-related cognitive impairment, we used a double-blind crossover study of short-term varenicline (vs. placebo) treatment. Following 3 days of abstinence, subjects underwent blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) scans while performing an N-back working memory task (27). Based on prior work (22,28), functionally relevant frontal systems were selected as a priori regions of interest, specifically, bilateral dorsolateral prefrontal BIOL PSYCHIATRY 2010;67:715–721 © 2010 Society of Biological Psychiatry
716 BIOL PSYCHIATRY 2010;67:715–721 cortex (DLPFC) and dorsal anterior cingulate/medial frontal cortex (MF/CG). These regions also exhibit moderate levels of 2-containing nAChR availability during abstinence from smoking (13). We hypothesized that varenicline would increase activation in DLPFC and MF/CG relative to placebo, particularly at high levels of task difficulty. Based on prior evidence that nicotine dependence severity modulates brain activity (29,30) and cognitive performance (31), we also explored treatment effects as a function of dependence level.
Methods and Materials Participants Treatment-seeking smokers were enrolled using media advertisements from November 2007 to June 2008 at the University of Pennsylvania. Eligible smokers were ages 18 to 65 and smoked ⱖ10 cigarettes/day for ⱖ6 months. Persons with a history of DSM-IV Axis I psychiatric or substance disorders (except nicotine) and those taking psychotropic medications (e.g., monoamine oxidase inhibitors, benzodiazepines, antidepressants, antipsychotics) were excluded. Figure S1 in Supplement 1 illustrates the flow of participants. Of the 25 participants who completed study requirements, 3 were excluded for measurement artifact (2 had ⬎6 mm relative motion and 1 had a mean temporal signal-to-noise ratio ⬎3 SD below the group mean). In the final sample of 22, 10 received varenicline in the first period and 12 received placebo in the first period. Forty-five percent of participants were female, 77% were Caucasian, 45% completed college, and the average age was 41 years (SD ⫽ 13). The average baseline smoking rate was 18.5 (SD ⫽ 5.3) and the average score on the Fagerstrom Test for Nicotine Dependence (FTND) (32) was 4.5 (SD ⫽ 1.7). These characteristics are representative of treatment-seeking smokers in recent clinical studies (33). There were no differences in baseline variables between the full sample of 25 and the analyzed sample of 22. Design and Procedures This within-subject double-blind crossover study included BOLD fMRI sessions during two 13-day medication periods: 1) after 3 days of abstinence while on varenicline, and 2) after 3 days of abstinence while on placebo (with a 2-week washout). Order of medications was determined using a computer-generated randomization scheme implemented by a programmer. Blood oxygen level-dependent fMRI data were acquired during performance of a visual N-back working memory task. All procedures were approved by the University of Pennsylvania Institutional Review Board. Participants completed a physical examination including a urine drug screen, breath alcohol test, and pregnancy test. The presence of psychiatric or substance abuse disorders was assessed using the Mini International Neuropsychiatric Interview (34). The Wechsler Adult Intelligence Scale-Revised estimated IQ test was administered; individuals with low or borderline intelligence (⬍90 score) were excluded. Eligible participants completed the 6-item Fagerstrom Test for Nicotine Dependence (32). Subgroups were classified based on scores of 0 to 5 (low dependence) versus 6 to 10 (moderate to high dependence), consistent with prior clinical studies (35) and with selection criteria (ⱖ6 on FTND) in neuroimaging studies of dependent smokers (30). Participants completed the 13-day medication periods, as in the paradigm used in our prior study of varenicline effects (27). Varenicline and placebo were supplied by Pfizer, provided in www.sobp.org/journal
J. Loughead et al. blinded blister packs. Varenicline was administered according to standard guidelines: days 1 to 3 (.5 mg once a day), days 4 to 7 (.5 mg twice a day), and days 8 to 13 (1 mg twice a day). Average daily smoking rates during the smoking as usual period (days 1–9) were 16.1 (SD ⫽ 5.5) and 15.0 (SD ⫽ 5.9) during the placebo and varenicline periods, respectively. On day 9 of the medication run-up, participants received a 15-minute counseling session to prepare for the mandatory abstinence period, which was presented as a “practice” quit attempt (27). Participants received a monetary incentive ($25/day) to stay abstinent. Abstinence was verified daily during the mandatory abstinence period using carbon monoxide (CO) (ⱕ10 ppm) (36). The average CO level at baseline was 26.0 (SD ⫽ 9.0). The average CO levels for days 11, 12, and 13 during the placebo period were 2.4 (SD ⫽ 1.4), 2.7 (SD ⫽ 2.1), and 3.1 (SD ⫽ 2.9), respectively; during the varenicline period, the values were 2.1 (SD ⫽ 1.1), 2.2 (SD ⫽ 1.4), and 2.4 (SD ⫽ .95), respectively. One participant had a CO level of 12 ppm on day 13 of the placebo period. This participant was interviewed and assured adherence to abstinence requirements; because environmental sources of CO can result in CO elevations (36), we did not exclude this participant. Participants took their final dose of medication on the morning of day 13 and completed a BOLD fMRI session while performing a visual N-back working memory task (37). The N-back task involved presentation of complex geometric figures (fractals) for 500 msec, followed by fixation stimulus for 2500 msec 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 that two trials back, and in the 3-back condition, they responded if it was identical to three trials back. Each condition consisted of 20 trials (60-sec block) and each class (0-back, 1-back, 2-back, 3-back) was repeated three times. A target-foil ratio of 1:2 (i.e., 33% targets) was maintained in all blocks and visual instructions (9 sec) preceded each block, alerting the participant to the upcoming condition. The task began with a 48-second baseline rest period (fixation point on blank screen) of which the first 24 seconds were discarded to ensure the magnetic resonance imaging signal reached steady state. Additional 24-second baseline rest periods occurred at the middle and end of the acquisition. Total task duration was 924 seconds (308 time points). Equivalent N-back tasks with unique stimuli were used for the two sessions (varenicline, placebo) and version order was counterbalanced. Following completion of the study, participants were offered free smoking cessation treatment. Image Acquisition Blood oxygen level-dependent fMRI was acquired with a Siemens Trio 3 T (Erlangen, Germany) system using a wholebrain, single-shot gradient echo planar sequence with the following parameters: repetition time/echo time ⫽ 3000/30 msec, field of view ⫽ 220 mm, matrix ⫽ 64 ⫻ 64, slice thickness/gap ⫽ 3/0 mm, 40 slices, effective voxel resolution of 3 ⫻ 3 ⫻ 3 mm. Radio frequency transmission used a quadrature body coil and reception used an 8-channel head coil. Echo planar acquisitions included online geometric distortion correction that addresses nonlinear deformation of echo planar images due to main magnetic field inhomogeneity. The sequences are based on those developed by Zaitsev et al. (38) and implement the point spread function mapping method (39), which is acquired with a
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Figure 1. Visual N-back working memory task. (A) Colored regions represent functionally defined ROI masks identified using a whole-brain repeatedmeasures ANOVA with a voxel threshold of p ⬍ .001 and cluster probability of p ⬍ .05. Brain rendering was performed with CARET (http://www.nitrc.org/ projects/caret/) (65). (B) Mean percent signal change (N-back minus 0-back) for the 1-back, 2-back, and 3-back contrasts calculated from a priori ROIs. Main effects of treatment (varenicline, placebo) were observed in all three ROIs, significant at p ⬍ .05. ANOVA, analysis of variance; DLPFC, dorsolateral prefrontal cortex; MF/CG, dorsal anterior cingulate/medial frontal cortex; ROI, region of interest; BOLD, blood oxygenation level-dependent.
reference scan before the acquisition of echo planar data. After BOLD fMRI, 5-minute magnetization-prepared, rapid acquisition gradient echo T1-weighted image (repetition time 1620 msec, echo time 3.87 msec, field of view 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. Analysis Sample size was determined using Power and Sample Size (PASS) (NCSS Software, Kaysville, Utah). A sample size of 22 provided power of .80 to detect a drug effect of .60 (one-sample two-sided test, alpha ⫽ .05). Blood oxygen level-dependent time series data were analyzed with FEAT (fMRI Expert Analysis Tool) Version 5.92, part of FSL (FMRIB’s [Oxford Centre for Functional MRI of the Brain] Software Library) 4.0, using standard image analysis procedures including brain extraction, slice time correction, motion correction, high-pass filtering (138 sec), spatial smoothing (6 mm full-width at half maximum, isotropic), and mean-based intensity normalization. The median functional and anatomical volumes were coregistered, and the anatomical image was transformed into standard space (2 mm3 T1 Montreal Neurological Institute template). Resulting transformation parameters were later applied to statistical images and the images were resampled (2 mm3) before group level analyses. The primary outcome was mean percent BOLD signal change. Subject-level statistical analyses were carried out using FILM (FMRIB’s [Oxford Centre for Functional MRI of the Brain] Improved Linear Model) with local autocorrelation correction (40). Four condition events (0-back, 1-back, 2-back, 3-back) were modeled using a canonical hemodynamic response function.
The instruction period and motion correction parameters were included as nuisance covariates and the rest condition (fixation point) was treated as the unmodeled baseline. To characterize the session (drug, placebo) by memory load (1 ⫺ 0 back, 2 ⫺ 0 back, 3 ⫺ 0 back) effects, mean percent signal change was extracted from a priori regions of interest (ROIs) in the DLPFC (right and left) and MF/CG. Mean percent signal change values were exported for analysis using procedures described below. Region of interest masks (Figure 1A) were functionally defined using the main effect of working memory load identified using a whole-brain repeated-measures analysis of variance (ANOVA). Analysis of variance results were further examined as part of an exploratory analysis (see below). Regions of interest were defined using a voxel threshold of p ⬍ .001 and cluster probability of p ⬍ .05 (41). These procedures produced welldefined clusters in the right DLPFC (4400 mm3) and left DLPFC (6584 mm3) and the MF/CG (10,184 mm3). Anatomic assignment was based on the peak Z score within the cluster using the Talairach Daemon database (http://www.talairach.org) confirmed with visual inspection and coordinates reported by Owen et al. (28). The percentage signal change in each of the ROIs was analyzed using repeated-measures ANOVA in a mixed-models regression framework. We accounted for repeated measures with subject-level Gaussian random effects and estimated effects using maximum likelihood techniques. The BOLD signal models included terms for the main effects of session (drug vs. placebo), memory load (1 ⫺ 0 back, 2 ⫺ 0 back, 3 ⫺ 0 back), and relevant covariates (sex, nicotine dependence level [FTND], education). Treatment order was tested in each model as a main effect and treatment modifier. Main effects and individual terms were tested using the Z score (two-sided). Interactions of treatment and www.sobp.org/journal
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categorical predictors (e.g., nicotine dependence level, memory load) were tested using either the likelihood ratio or Wald 2, and interactions were retained in the model at p ⫽ .05. Finally, a cross-region model was tested to extend our repeated-measures analysis to all three ROIs. The cross-ROI model included indicators for region (left DLPFC, right DLPFC, MF/CG), while effects of treatment, memory load, treatment by load interactions, and covariates were estimated using common parameters across regions. We tested for interactions of these covariates and predictors with the ROI variable. The effect of treatment condition on task performance (correct response time, N-back minus 0-back) was examined with models as described above. Models included terms for the main effects of treatment period (drug vs. placebo), memory load (0-back, 1-back, 2-back, 3-back), and covariates, and we tested for interactions of nicotine dependence severity with treatment. To examine the relationship of BOLD signal to correct response time, we added response time difference (1 ⫺ 0 back, 2 ⫺ 0 back, 3 ⫺ 0 back) to the cross-region BOLD model described above and tested interactions of response time with nicotine dependence severity.
Results
Region of Interest Analysis The region of interest analysis of the mean percent signal change (Figure 1B) showed main effects of treatment (varenicline, placebo) in the MF/CG ( ⫽ .053, 95% confidence interval [CI] ⫽ .007–.099, Z ⫽ 2.27, p ⫽ .023), left DLPFC ( ⫽ .074, 95% CI ⫽ .021–.127, Z ⫽ 2.73, p ⫽ .006), and right DLPFC ( ⫽ .064, 95% CI ⫽ .004 –.123, Z ⫽ 2.10, p ⫽ .036). There was a positive main effect of memory load on BOLD signal in the MF/CG [2-Back ⫽ .183, 3-Back ⫽ .213, Wald 2(2) ⫽ 64.38, p ⬍ .0001], left DLPFC [2-Back ⫽ .189, 3-Back ⫽ .194, Wald 2(2) ⫽ 44.89, p ⬍ .0001], and right DLPFC [2-Back ⫽ .212, 3-Back ⫽ .284, Wald 2(2) ⫽ 63.15, p ⬍ .0001]. The treatment ⫻ load interaction was not significant in the individual ROI models (p’s ⬎ .05). In the cross-region model, there was a significant interaction of treatment by memory load [Wald 2(2) ⫽ 10.45, p ⫽ .005], indicating significant increase in BOLD signal for varenicline compared with placebo at the 2-back ( ⫽ .068, 95% CI ⫽ .012–.124, Z ⫽ 2.40, p ⫽ .016) and 3-back ( ⫽ .126, 95% CI ⫽ .071–.181, Z ⫽ 4.44, p ⬍ .0001) levels but not at the 1-back level. None of the covariates, including treatment order, or covariate by treatment interactions approached significance in these models (p’s ⬎ .10). The ROI by treatment interactions were not significant and effects of treatment, memory load, and the interaction appeared uniform across ROIs (interactions with ROI were not retained in the model). The relationship of BOLD signal to correct response time was tested in the cross-region model and was found to vary with nicotine dependence severity. Correct response time was negatively associated with BOLD signal difference (N-back minus 0-back) for participants with high dependence ( ⫽ ⫺.23 [units: 1/sec], p ⫽ .034) and positively related to BOLD signal for low dependence subjects ( ⫽ .50, p ⬍ .0001). This interaction (FTND by response time) was significant [Wald 2(1) ⫽ 30.07, p ⬍ .0001]. The model parameters were similar across ROIs when tested for interaction by region [LR 2(12) ⫽ 9.63, p ⫽ .65].
Behavioral Data Median correct response time increased significantly as memory load increased [Wald 2(3) ⫽ 86.4, p ⬍ .0001] (Table 1). The treatment effect on correct response time varied depending on nicotine dependence severity. Varenicline (vs. placebo) was associated with significantly lower (faster) correct response time in highly dependent participants ( ⫽ ⫺69.33, p ⫽ .014), but treatment had no effect on performance for participants with low dependence ( ⫽ 2.68, p ⫽ .90); the interaction was significant [LR 2(1) ⫽ 4.43, p ⫽ .03]. These were no drug effects or drug by dependence effects on performance accuracy (number of true positives). One participant reported four instances of severe fatigue during varenicline treatment and one instance during placebo. One participant reported severe gastrointestinal disturbance, and another reported severe increased appetite in the placebo phase.
Exploratory Whole-Brain Analysis Significant whole-brain main effects for session (drug vs. placebo) and memory load (0-back, 1-back, 2-back, 3-back) were detected but no interaction was observed (Figure S2 and Table S1 in Supplement 1). Examination of percent signal change (Figure S2B in Supplement 1) for the session main effect (drug vs. placebo) revealed that the placebo condition was generally associated with increased BOLD signal (activation) and the drug condition with decreased BOLD signal (deactivation). This pattern was most striking in the right superior frontal gyrus and left inferior frontal gyrus. However, the opposite pattern (drug activation and placebo deactivation) was observed in the left inferior temporal gyrus and left middle frontal gyrus. The pattern of activation observed for the memory load main effect is consistent with studies using the N-back paradigm (28).
Exploratory Whole-Brain Analysis In an exploratory whole-brain voxel-wise analysis, parameter estimates were entered into a mixed-effects, within-subject session (drug vs. placebo) by memory load (0-back, 1-back, 2-back, 3-back) whole-brain ANOVA. Resulting Z (Gaussianised F) statistic images were thresholded at p ⬍ .05, corrected (familywise error Z ⬎ 4.60). For clarity, only clusters of greater than 50 contiguous voxels are reported. Mean scaled beta coefficients (% BOLD signal change) from each cluster were extracted for graphic examination and further statistical testing.
Table 1. Behavioral Data by Treatment and Memory Load Working Memory Load Level Median Correct Treatment Time (msec)
Treatment
0-Back Mean (SD)
1-Back Mean (SD)
2-Back Mean (SD)
3-Back Mean (SD)
Overall Mean (SD)
High Nicotine Dependence (n ⫽ 7)
Placebo Varenicline Placebo Varenicline
637.32 (115.18) 624.38 (181.91) 540.93 (91.60) 539.69 (72.39)
668.43 (151.39) 586.43 (169.11) 573.89 (134.50) 570.21 (78.10)
914.86 (197.20) 715.92 (141.63) 676.59 (173.92) 671.90 (178.00)
726.06 (141.15) 742.61 (234.34) 726.72 (178.97) 747.06 (204.80)
692.14 (128.57) 637.99 (164.30) 581.82 (105.77) 585.59 (82.53)
Low Nicotine Dependence (n ⫽ 15)
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Discussion This study provides novel evidence that short-term treatment with the ␣42 partial agonist varenicline increases working memory-related brain activity after 3 days of nicotine abstinence. Task-related BOLD signal change in the MF/CG and bilateral DLPFC was significantly greater during varenicline treatment, compared with placebo, particularly at higher levels of task difficulty. Effects of varenicline on cognitive performance were dependent on nicotine dependence severity; treatment improved correct response time in smokers with higher dependence, and in this group, faster response time was associated with increased BOLD signal in these regions. Improvements in response time did not extend to the most demanding working memory load (3-back), however. While this study is the first to examine the functional neuroanatomical correlates of varenicline’s cognitive effects, our results can be considered in light of studies of N-back task-related brain activity during abstinence from smoking and following nicotine delivery. Our findings differ from two studies showing increased task-related activity in the PFC during abstinence relative to smoking (7) or nicotine delivery (42). However, our results do converge with those of Xu et al. (7) with respect to the relationship between memory load and BOLD signal in the abstinent versus smoking or varenicline conditions. In the prior study (7), the expected increase in BOLD signal with increased task difficulty was not observed during abstinence, whereas a positive relationship between task load and activation was found during the smoking condition. This is broadly consistent with our finding of a flattening of activation from the 2-back to the 3-back levels during abstinence, which was ameliorated by varenicline. In our study, BOLD fMRI was acquired after 3 days of abstinence using a nonverbal N-back task, whereas the prior studies assessed brain activity after 12 hours of abstinence using a verbal N-back paradigm. Differences in study design and task stimuli may account for differences in activation patterns (28). Indeed, in our prior fMRI study using the same nonverbal N-back paradigm, abstinence from smoking was associated with reduced BOLD signal in DLPFC and MF/CG among smokers homozygous for the valine allele of the catechol-O-methyltransferase valine158methionine polymorphism (22), which has been linked with nicotine dependence (43,44). Moreover, both studies observed the greatest abstinence-related reductions in working memory-related activity at the highest level of task difficulty. Taken together, our prior study (22) and the current study suggest that abstinence from smoking may reduce engagement of the working memory system at high levels of task difficulty, an effect that is reversed by smoking and varenicline. Although the beneficial effects of varenicline on cognitive performance were weaker than those observed in a larger behavioral study (27), treatment effects on performance were modulated by pretreatment level of nicotine dependence. More dependent smokers had slower response times than less dependent smokers during the placebo period, whereas performance during the varenicline session appeared more similar in the two groups. A recent study linked dependence severity with poorer performance on attention-demanding tasks, independent of abstinence (31). Other studies suggest that nicotine dependence level can modulate BOLD signal in regions important in visual attention during exposure to smoking-related cues (29,45,46). Although we did not find a main effect of nicotine dependence on N-back task-related BOLD signal, dependence severity modulated the relationship between BOLD signal and correct re-
BIOL PSYCHIATRY 2010;67:715–721 719 sponse time. Specifically, increased BOLD signal across regions (DLPFC, MF/CG) was associated with faster performance in more dependent smokers and with slower performance in less dependent smokers. This finding fits with the hypothesis that the “window” of PFC activation optimal for working memory varies depending on genetic and environmental influences (47,48). However, our findings related to nicotine dependence require replication due to the smaller sample sizes in the subgroup analyses. A neuropharmacological mechanism for the observed effects of varenicline can be proposed. Both varenicline and nicotine bind with high affinity to ␣42 nAChRs (23), supporting a common neural mechanism for cognitive effects. However, ␣42 nAChRs are rapidly desensitized (49), suggesting that lower affinity ␣7 nAChRs also play a critical role (24). Both genetic and pharmacological manipulations in rodent models support the view that ␣42 and ␣7 nAChRs are critical for cognition and learning (18,50). Thus, activation of both ␣42 and ␣7 nAChRs is likely to contribute to the cognitive-enhancing properties of varenicline, similar to other nicotinic agonists (17,51,52). Activation of ␣42 and ␣7 nAChRs increases dopamine release in regions important in cognition (53), suggesting a neurochemical mechanism for varenicline’s effects on DLPFC and MF/CG activity. The critical role of PFC dopamine circuits in working memory has been established in animal models (54), as well as in human genetic studies (55). For example, mice overexpressing the human catechol-O-methyltransferase valine polymorphism exhibit altered PFC dopamine signaling and impairments in working memory (56), consistent with cognitive deficits observed in human valine allele carriers (22,55). Future neuroimaging studies of varenicline treatment using radioligands with specificity for nAChR subtypes or dopamine receptors are required to clarify the neurochemical processes underlying varenicline in human smokers. An exploratory whole-brain analysis was also performed to identify potentially relevant sites of drug action beyond the a priori regions in the ROI analysis. Abstinence (placebo) was generally associated with increased BOLD signal, which was attenuated by varenicline. Moreover, varenicline-induced deactivations were observed in multiple regions, including left inferior frontal gyrus, bilateral amygdala, and anterior and posterior cingulate. This finding is consistent with prior fMRI studies of nicotine’s effects on attentional performance (57,58) and has been interpreted as a downregulation by nicotine of neural activity in task-independent regions that, in turn, enhances processing efficiency (59). Thus, varenicline-induced increases in neural activity in task-dependent regions (i.e., bilateral DLPFC, MF/CG) and decreases in task independent regions may both be relevant to drug effects on cognitive performance. Our study has strengths and limitations. Strengths include the within-subject design and the 3-day abstinence challenge with assessment of cognitive effects at the time point when withdrawal symptoms are greatest (60). The use of treatment-seeking smokers may also increase sensitivity of this paradigm to medication effects (61,62). One limitation is that CO assessments are relatively imprecise for assessing smoking abstinence. In addition, while the current study was powered to detect treatment effects on BOLD signal, analyses of effects on performance and the modulating role of dependence severity must be replicated due to the small sample sizes in subgroup analysis. This is important because, although faster response time was associated with increased BOLD signal, this effect was not seen at the memory load (3-back) that showed the greatest drug effects. www.sobp.org/journal
720 BIOL PSYCHIATRY 2010;67:715–721 Larger sample sizes are also needed to identify the role of genetic variants that may modify effects of abstinence and nicotinic compounds on cognitive task-related brain activity (22,63). This study provides the first data on varenicline’s effects on working memory-related brain activity during nicotine abstinence, extending prior work documenting varenicline’s cognitive-enhancing effects (27). Cognitive performance deficits are associated with an increased risk for smoking relapse (64,12) and are an important target of nicotine dependence medication development efforts (1). Neuroimaging assessments, together with genetic approaches, can further delineate brain systems that contribute to cognitive deficits and other pathological symptoms of nicotine dependence. If successful, this process will establish neuroimaging as a surrogate marker for treatment response and a tool for screening promising medications.
Support for this study was provided by P50 CA143187, P30 NS045839, the Pennsylvania Department of Health, and a grant from Pfizer, Inc. The industry sponsor had no role in study design or data analysis. The Department of Health specifically disclaims responsibility for any analyses, interpretations, or conclusions. We thank Drs. Julie Blendy and Thomas Gould for valuable feedback on the manuscript. Dr. Lerman has been a consultant and/or received grant support from the following companies that develop and/or market smoking cessation medications: Astra Zeneca, Glaxo SmithKline, Novartis, and Pfizer. Drs. Loughead and Gur have received grant support from Astra Zeneca and Pfizer. Dr. Siegel has been a consultant and/or received grant support from NuPathe, Pfizer, Eli Lilly, and Astra Zeneca that is unrelated to smoking cessation. Dr. Ray, Dr. Wileyto, Ms. Ruparel, and Mr. Sanborn report no biomedical financial interests or potential conflicts of interest. ClinicalTrials.gov: Neural Substrates of Varenicline’s (Chantix®) Efficacy for Smoking Cessation; http://www.clinicaltrials.gov/ct2/show/study/NCT00602927; NCT00602927. Supplementary material cited in this article is available online. 1. Lerman C, LeSage MG, Perkins KA, O’Malley SS, Siegel SJ, Benowitz NL, Corrigall WA (2007): Translational research in medication development for nicotine dependence. Nat Rev Drug Discov 6:746 –762. 2. Conn PJ, Roth BL (2008): Opportunities and challenges of psychiatric drug discovery: Roles for scientists in academic, industry, and government settings. Neuropsychopharmacology 33:2048 –2060. 3. Hyman SE, Fenton WS (2003): Medicine. What are the right targets for psychopharmacology? Science 299:350 –351. 4. Evans DE, Drobes DJ (2009): Nicotine self-medication of cognitive-attentional processing. Addict Biol 14:32– 42. 5. Jacobsen LK, Krystal JH, Mencl WE, Westerveld M, Frost SJ, Pugh KR (2005): Effects of smoking and smoking abstinence on cognition in adolescent tobacco smokers. Biol Psychiatry 57:56 – 66. 6. Mendrek A, Monterosso J, Simon SL, Jarvik M, Brody A, Olmstead R, et al. (2006): Working memory in cigarette smokers: Comparison to nonsmokers and effects of abstinence. Addict Behav 31:833– 844. 7. Xu J, Mendrek A, Cohen MS, Monterosso J, Rodriguez P, Simon SL, et al. (2005): Brain activity in cigarette smokers performing a working memory task: Effect of smoking abstinence. Biol Psychiatry 58:143–150. 8. Davis JA, James JR, Siegel SJ, Gould TJ (2005): Withdrawal from chronic nicotine administration impairs contextual fear conditioning in C57BL/6 mice. J Neurosci 25:8708 – 8713. 9. Myers CS, Taylor RC, Moolchan ET, Heishman SJ (2008): Dose-related enhancement of mood and cognition in smokers administered nicotine nasal spray. Neuropsychopharmacology 33:588 –598.
www.sobp.org/journal
J. Loughead et al. 10. Hughes JR (2007): Measurement of the effects of abstinence from tobacco: A qualitative review. Psychol Addict Behav 21:127–137. 11. Ward MM, Swan GE, Jack LM (2001): Self-reported abstinence effects in the first month after smoking cessation. Addict Behav 26:311–327. 12. Patterson F, Jepson C, Loughead J, Perkins K, Strasser AA, Siegel S, et al. (2010): Working memory deficits predict short-term smoking resumption following brief abstinence. Drug Alcohol Depend 106:61– 64. 13. Cosgrove KP, Batis J, Bois F, Maciejewski PK, Esterlis I, Kloczynski T, et al. (2009): Beta2-nicotinic acetylcholine receptor availability during acute and prolonged abstinence from tobacco smoking. Arch Gen Psychiatry 66:666 – 676. 14. Gotti C, Zoli M, Clementi F (2006): Brain nicotinic acetylcholine receptors: Native subtypes and their relevance. Trends Pharmacol Sci 27:482– 491. 15. Sarter M, Parikh V, Howe WM (2009): nAChR agonist-induced cognition enhancement: Integration of cognitive and neuronal mechanisms. Biochem Pharmacol 78:658 – 667. 16. Cannady R, Weir R, Wee B, Gotschlich E, Kolia N, Lau E, et al. (2009): Nicotinic antagonist effects in the mediodorsal thalamic nucleus: Regional heterogeneity of nicotinic receptor involvement in cognitive function. Biochem Pharmacol 78:788 –794. 17. Levin ED, McClernon FJ, Rezvani AH (2006): Nicotinic effects on cognitive function: Behavioral characterization, pharmacological specification, and anatomic localization. Psychopharmacology (Berl) 184:523– 539. 18. Kenney JW, Gould TJ (2008): Modulation of hippocampus-dependent learning and synaptic plasticity by nicotine. Mol Neurobiol 38:101–121. 19. Mansvelder HD, McGehee DS (2000): Long-term potentiation of excitatory inputs to brain reward areas by nicotine. Neuron 27:349 –357. 20. Livingstone PD, Wonnacott S (2009): Nicotinic acetylcholine receptors and the ascending dopamine pathways. Biochem Pharmacol 78:744 – 755. 21. Brody AL, Mandelkern MA, Olmstead RE, Allen-Martinez Z, Scheibal D, Abrams AL, et al. (2009): Ventral striatal dopamine release in response to smoking a regular vs a denicotinized cigarette. Neuropsychopharmacology 34:282–289. 22. Loughead J, Wileyto EP, Valdez JN, Sanborn P, Tang K, Strasser AA, et al. (2009): Effect of abstinence challenge on brain function and cognition in smokers differs by COMT genotype. Mol Psychiatry 14:820 – 826. 23. Rollema H, Chambers LK, Coe JW, Glowa J, Hurst RS, Lebel LA, et al. (2007): Pharmacological profile of the alpha4beta2 nicotinic acetylcholine receptor partial agonist Varenicline, an effective smoking cessation aid. Neuropharmacology 52:985–994. 24. Mihalak KB, Carroll FI, Luetje CW (2006): Varenicline is a partial agonist at alpha4beta2 and a full agonist at alpha7 neuronal nicotinic receptors. Mol Pharmacol 70:801– 805. 25. Rollema H, Hajos M, Seymour PA, Kozak R, Majchrzak MJ, Guanowsky V, et al. (2009): Preclinical pharmacology of the alpha4beta2 nAChR partial agonist varenicline related to effects on reward, mood and cognition. Biochem Pharmacol 78:813– 824. 26. Raybuck JD, Portugal GS, Lerman C, Gould TJ (2008): Varenicline ameliorates nicotine withdrawal-induced learning deficits in C57BL/6 mice. Behav Neurosci 122:1166 –1171. 27. Patterson F, Jepson C, Strasser AA, Loughead J, Perkins KA, Gur RC, et al. (2009): Varenicline improves mood and cognition during smoking abstinence. Biol Psychiatry 65:144 –149. 28. Owen AM, McMillan KM, Laird AR, Bullmore E (2005): N-back working memory paradigm: A meta-analysis of normative functional neuroimaging studies. Hum Brain Mapp 25:46 –59. 29. McClernon FJ, Kozink RV, Rose JE (2008): Individual differences in nicotine dependence, withdrawal symptoms, and sex predict transient fMRI-BOLD responses to smoking cues. Neuropsychopharmacology 33: 2148 –2157. 30. Rose JE, Behm FM, Salley AN, Bates JE, Coleman RE, Hawk TC, Turkington TG (2007): Regional brain activity correlates of nicotine dependence. Neuropsychopharmacology 32:2441–2452. 31. Azizian A, Monterosso JR, Brody AL, Simon SL, London ED (2008): Severity of nicotine dependence moderates performance on perceptualmotor tests of attention. Nicotine Tob Res 10:599 – 606. 32. Heatherton TF, Kozlowski LT, Frecker RC, Fagerstrom KO (1991): The Fagerstrom Test for Nicotine Dependence: A revision of the Fagerstrom Tolerance Questionnaire. Br J Addict 86:1119 –1127.
BIOL PSYCHIATRY 2010;67:715–721 721
J. Loughead et al. 33. Schnoll RA, Patterson F, Wileyto EP, Tyndale RF, Benowitz N, Lerman C (2009): Nicotine metabolic rate predicts successful smoking cessation with transdermal nicotine: A validation study. Pharmacol Biochem Behav 92:6 –11. 34. Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, et al. (1998): 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 59(suppl 20):22–33; quiz 34 –57. 35. Lerman C, Kaufmann V, Rukstalis M, Patterson F, Perkins K, AudrainMcGovern J, Benowitz N (2004): Individualizing nicotine replacement therapy for the treatment of tobacco dependence: A randomized trial. Ann Intern Med 140:426 – 433. 36. SRNT Subcommittee on Biochemical Verification (2002): Biochemical verification of tobacco use and cessation. Nicotine Tob Res 4:149 –159. 37. Ragland JD, Turetsky BI, Gur RC, Gunning-Dixon F, Turner T, Schroeder L, et al. (2002): Working memory for complex figures: An fMRI comparison of letter and fractal n-back tasks. Neuropsychology 16:370 –379. 38. Zaitsev M, Hennig J, Speck O (2003) Automated onlnie EPI distortion correction for fMRI applications [abstract]. In: Proceedings of the 11th Annual Meeting of the International Society for Magnetic Resonance in Medicine, Toronto, ON, Canada, July 10 –16, 2003, 1042. 39. Zeng H, Constable RT (2002): Image distortion correction in EPI: Comparison of field mapping with point spread function mapping. Magn Reson Med 48:137–146. 40. Woolrich MW, Ripley BD, Brady M, Smith SM (2001): Temporal autocorrelation in univariate linear modeling of FMRI data. Neuroimage 14: 1370 –1386. 41. Worsley KJ (2001): Statistical analysis of activation images. In: Jezzard P, Matthews PM, Smith SM, editors. Functional MRI. An Introduction to Methods. Oxford, England: Oxford University Press, 251–270. 42. Ernst M, Matochik JA, Heishman SJ, Van Horn JD, Jons PH, Henningfield JE, London ED (2001): Effect of nicotine on brain activation during performance of a working memory task. Proc Natl Acad Sci U S A 98: 4728 – 4733. 43. Beuten J, Payne TJ, Ma JZ, Li MD (2006): Significant association of catechol-O-methyltransferase (COMT) haplotypes with nicotine dependence in male and female smokers of two ethnic populations. Neuropsychopharmacology 31:675– 684. 44. Colilla S, Lerman C, Shields PG, Jepson C, Rukstalis M, Berlin J, et al. (2005): Association of catechol-O-methyltransferase with smoking cessation in two independent studies of women. Pharmacogenet Genomics 15:393–398. 45. Smolka MN, Buhler M, Klein S, Zimmermann U, Mann K, Heinz A, Braus DF (2006): Severity of nicotine dependence modulates cue-induced brain activity in regions involved in motor preparation and imagery. Psychopharmacology (Berl) 184:577–588. 46. Yalachkov Y, Kaiser J, Naumer MJ (2009): Brain regions related to tool use and action knowledge reflect nicotine dependence. J Neurosci 29: 4922– 4929. 47. Goldberg TE, Weinberger DR (2004): Genes and the parsing of cognitive processes. Trends Cogn Sci 8:325–335. 48. Williams GV, Castner SA (2006): Under the curve: Critical issues for elucidating D1 receptor function in working memory. Neuroscience 139: 263–276. 49. Gentry CL, Wilkins LH Jr, Lukas RJ (2003): Effects of prolonged nicotinic ligand exposure on function of heterologously expressed, human alpha4beta2- and
50.
51.
52.
53.
54.
55. 56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
alpha4beta4-nicotinic acetylcholine receptors. J Pharmacol Exp Ther 304: 206–216. Levin ED, Petro A, Rezvani AH, Pollard N, Christopher NC, Strauss M, et al. (2009): Nicotinic alpha7- or beta2-containing receptor knockout: Effects on radial-arm maze learning and long-term nicotine consumption in mice. Behav Brain Res 196:207–213. Cincotta SL, Yorek MS, Moschak TM, Lewis SR, Rodefer JS (2008): Selective nicotinic acetylcholine receptor agonists: Potential therapies for neuropsychiatric disorders with cognitive dysfunction. Curr Opin Investig Drugs 9:47–56. Poorthuis RB, Goriounova NA, Couey JJ, Mansvelder HD (2009): Nicotinic actions on neuronal networks for cognition: General principles and long-term consequences. Biochem Pharmacol 78:668 – 676. Livingstone PD, Srinivasan J, Kew JN, Dawson LA, Gotti C, Moretti M, et al. (2009): Alpha7 and non-alpha7 nicotinic acetylcholine receptors modulate dopamine release in vitro and in vivo in the rat prefrontal cortex. Eur J Neurosci 29:539 –550. Castner SA, Goldman-Rakic PS, Williams GV (2004): Animal models of working memory: Insights for targeting cognitive dysfunction in schizophrenia. Psychopharmacology (Berl) 174:111–125. Savitz J, Solms M, Ramesar R (2006): The molecular genetics of cognition: Dopamine, COMT and BDNF. Genes Brain Behav 5:311–328. Papaleo F, Crawley JN, Song J, Lipska BK, Pickel J, Weinberger DR, Chen J (2008): Genetic dissection of the role of catechol-O-methyltransferase in cognition and stress reactivity in mice. J Neurosci 28:8709 – 8723. Hahn B, Ross TJ, Wolkenberg FA, Shakleya DM, Huestis MA, Stein EA (2009): Performance effects of nicotine during selective attention, divided attention, and simple stimulus detection: An fMRI study. Cereb Cortex 19:1990 –2000. Thiel CM, Fink GR (2007): Visual and auditory alertness: Modality-specific and supramodal neural mechanisms and their modulation by nicotine. J Neurophysiol 97:2758 –2768. Hahn B, Ross TJ, Yang Y, Kim I, Huestis MA, Stein EA (2007): Nicotine enhances visuospatial attention by deactivating areas of the resting brain default network. J Neurosci 27:3477–3489. Teneggi V, Tiffany ST, Squassante L, Milleri S, Ziviani L, Bye A (2002): Smokers deprived of cigarettes for 72 h: Effect of nicotine patches on craving and withdrawal. Psychopharmacology (Berl) 164:177–187. Perkins KA, Stitzer M, Lerman C (2006): Medication screening for smoking cessation: A proposal for new methodologies. Psychopharmacology (Berl) 184:628 – 636. Perkins KA, Lerman C, Stitzer M, Fonte CA, Briski JL, Scott JA, Chengappa KN (2008): Development of procedures for early screening of smoking cessation medications in humans. Clin Pharmacol Ther 84:216 –221. Jacobsen LK, Pugh KR, Mencl WE, Gelernter J (2006): C957T polymorphism of the dopamine D2 receptor gene modulates the effect of nicotine on working memory performance and cortical processing efficiency. Psychopharmacology (Berl) 188:530 –540. Moss TG, Sacco KA, Allen TM, Weinberger AH, Vessicchio JC, George TP (2009): Prefrontal cognitive dysfunction is associated with tobacco dependence treatment failure in smokers with schizophrenia. Drug Alcohol Depend 104:94 –99. Van Essen DC, Dickson J, Harwell J, Hanlon D, Anderson CH, Drury HA (2001): An integrated software system for surface-based analyses of cerebral cortex. J Am Med Inform Assoc 8:443– 459.
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