Brain indices of nicotine's effects on attentional bias to smoking and ...

Nicotine & Tobacco Research Volume 9, Number 3 (March 2007) 351–363

Brain indices of nicotine’s effects on attentional bias to smoking and emotional pictures and to task-relevant targets David G. Gilbert, Chihiro Sugai, Yantao Zuo, Norka E. Rabinovich, F. Joseph McClernon, Brett Froeliger Received 14 October 2005; accepted 16 January 2006

Aversive and smoking-related stimuli are related to smoking urges and relapse and can be potent distractors of selective attention. It has been suggested that the beneficial effect of nicotine replacement therapy may be mediated partly by the ability of nicotine to reduce distraction by such stimuli and thereby to facilitate attention to taskrelevant stimuli. The present study tested the hypothesis that nicotine reduces distraction by aversive and smokingrelated stimuli as indexed by the parietal P3b brain response to a task-relevant target digit. We assessed the effect of nicotine on distraction by emotionally negative, positive, neutral, and smoking-related pictures immediately preceding target digits during a rapid visual information processing task in 16 smokers in a double-blind, counterbalanced, within-subjects design. The study included two experimental sessions. After overnight smoking deprivation (12+ hr), active nicotine patches were applied to participants during one of the sessions and placebo patches were applied during the other session. Nicotine enhanced P3b responses associated with target digits immediately subsequent to negative emotional pictures bilaterally and subsequent to smoking-related pictures only in the right hemisphere. No effects of nicotine were observed for P3bs subsequent to positive and neutral distractor pictures. Another measure of attention, contingent negative variation amplitude in anticipation of the target digits also was increased by nicotine, especially in the left hemisphere and at posterior sites. Together, these findings suggest that nicotine reduces the distraction by emotionally negative and smoking-related stimuli and promotes attention to task-related stimuli by modulating somewhat lateralized and task-specific neural networks.

Introduction Though nicotine replacement therapy (NRT) reduces tobacco-abstinence-related negative affect and craving (Shiffman, Khayrallah, & Nowak, 2000), little is known about the brain and psychological mechanisms that mediate these effects of nicotine (Gilbert, 1995, 1997; Kalman, 2002; Kassel, Stroud, & Paronis, 2003; McClernon & Gilbert, 2004). Understanding these basic mechanisms is important because craving and reactivity to smoking cues and

David G. Gilbert, Ph.D., Chihiro Sugai, B.A., Yantao Zuo, Ph.D., Norka E. Rabinovich, B.A., F. Joseph McClernon, Ph.D., Brett Froeliger, M.A., Department of Psychology, Southern Illinois University, Carbondale, IL. Correspondence: David G. Gilbert, Department of Psychology, Southern Illinois University, Mailcode 6502, Carbondale, IL 629016502, USA. Tel: +1 (618) 453-3558; Fax: +1 (618) 453-3563; E-mail: [email protected]

emotional stimuli have been implicated in smoking motivation and relapse (Niaura et al., 1988; Payne, Smith, Adams, & Diefenbach, 2006; Shiffman, 1982). The high relapse rate among individuals attempting to quit smoking is often attributed to environmental and internal stimuli that grab attention and trigger urges to smoke (Tiffany, 1995) or to negative emotional states that increase the desire to smoke (Baker, Piper, McCarthy, Majeskie, & Fiore, 2004; Eysenck, 1980). The handful of studies that have assessed the effects of NRT on brain responses to smoking-related and emotional cues support the view that such investigations can provide useful insights into the effects of NRT on craving and affect (reviewed by McClernon & Gilbert, 2004, and partially summarized in the following paragraphs). Gilbert, Sugai, Zuo et al. (2004) noted that NRT may decrease both craving and negative affect associated with smoking abstinence by one of two

ISSN 1462-2203 print/ISSN 1469-994X online # 2007 Society for Research on Nicotine and Tobacco DOI: 10.1080/14622200701188810

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NICOTINE’S EFFECTS ON ATTENTIONAL BIAS

possible means: (a) By a common set of mechanisms, such as attentional and associative biasing away from negative and smoking-related stimuli and toward positive and reward-related stimuli (Gilbert, Izetelny, Heaslett, Skerly, & Rabinovich, 2002; Gilbert, Sugai, Zuo et al., 2004; Mogg, Millar, & Bradley, 2000) or (b) by partially or wholly independent mechanisms that may reduce craving and negative affect. Consistent with the common-mechanisms hypothesis, evidence from the small number of studies in this area suggests that nicotine’s effects on abstinencerelated affect and craving may be mediated partly by its ability to modulate underlying attentional processes (reviewed by Gilbert, 1995; and Kassel et al., 2003). It has been hypothesized that nicotine promotes attentional control by acting as a chemical filter (Knott, 1978) or as a gating mechanism (Domino & Kishimoto, 2002), and that it modulates affect by biasing attention and associative processes toward positive stimuli and away from negative stimuli (Gilbert, 1995). Preliminary evidence supports the view that the effects of NRT on craving and affect are mediated by attentional processes (Gilbert et al., 2002; Gilbert, Hammersley, Rabinovich, Izetelny, & Small, 2004; Gilbert, Sugai, Rabinovich, & Froeliger, 2005). Two influential models suggest that nicotine modulates attentional bias to smoking-related and affect-related cues. One of these models is the incentive sensitization model promoted by Robinson and Berridge (1993); the other is the situation6trait adaptive response (STAR) model (Gilbert, 1995). In their description of the incentive sensitization model and its relationship to negative affect and stress, Robinson and Berridge (1993, p. 277) suggest that drugs (including nicotine) that increase mesotelencephalic dopamine have two possible effects. First, such drugs may acutely increase attentional bias to threat-related, drug-related, and reward-related stimuli during the period immediately after drug administration. The ‘‘second possibility is that moderate levels of dopamine activation, such as that produced by natural incentives and stressors … always makes [sic] stimuli attractively salient, whereas even higher levels of dopamine activation makes [sic] stimuli frightening’’ (p. 277). The first possibility makes no predictions about the acute effects of nicotine (or other dopaminergic agonists) on the relative balance of attentional biasing to threat-related relative to reward-related stimuli and thus does not predict that nicotine’s effects on affect are mediated by changes in attentional bias. The second possibility suggests that nicotine should increase negative affect in highly stressful situations to the degree that attention to stressful stimuli increases negative affect. In contrast, the STAR model predicts that nicotine increases attentional

bias to positive (reward-related) stimuli more than it does to threatening and punishment-related stimuli, and decreases attentional bias to threatening stimuli when attentional alternatives have positive associations. The STAR model also hypothesizes that decreases in attentional bias to negative stimuli reduce negative affect when attentional alternatives are available and when conditioned stimuli are predictive of moderate or mild intensity and are not predictive of immediate severe threat. Simply stated, the STAR model sees nicotine as priming attention toward positive and away from negative stimuli. The attentional biasing effects of nicotine hypothesized by the STAR model reflect the information processing nature of this model. The effects of nicotine are seen as moderated by interactions of the certain classes of stimuli (situations) interacting with smoking-related personality and genetic traits (Gilbert, 1995). The STAR model’s lateralized neural networks hypothesis suggests that nicotine modulates information processing, affect, and craving by priming activation of left-frontal-brain dominant controlled processing, approach mechanisms, and positive-affect-related associative networks (Gilbert, 1995, 1997). The lateralized neural networks hypothesis is supported by evidence of lateralized neurotransmitter systems, effects of nicotine on cognitive performance, and studies of nicotine on electroencephalogram (EEG) topography (reviewed by Gilbert, Izetelny et al., 2005). However, even though the brain mechanisms by which such attentional biasing effects may be mediated have been proposed by the STAR model, little evidence directly addresses the question of when and how nicotine modulates attentional processes and distraction by negative-affect-related and smoking-related cues. Nonetheless, a growing body of literature indicates that specific patterns of brain activity occur in response to affect-related and smoking-related cues and in response to nicotine. Several studies provide clues as to how nicotine may modulate brain processes mediating attentional bias and reactivity to negative-affect-related and smoking-related cues. McClernon, Hiott, Huettel, and Rose (2005) found that smoking abstinence did not alter brain responses to smoking cues, but that abstinence-induced increases in craving were highly correlated with abstinence-induced increases in brain activity, particularly in frontal brain regions. Using functional magnetic resonance imaging (fMRI), Due, Huettel, Hall, and Rubin (2002) found that in smokers, but not in nonsmokers, smoking images elicited a different pattern of mesolimbic and visuospatial (parietal and right fusiform gyrus) neural activation than did neutral (nonsmoking) images and that this pattern was similar to those found by others to be activated by other types of drug-related (in drug users) and emotional stimuli.

NICOTINE & TOBACCO RESEARCH

Drug cues typically activate brain circuits associated with responses to biologically important cues, including emotion-inducing and novel stimuli related to incentive motivation (Garavan et al., 2000; Kilts et al., 2001; Volkow, Fowler, Wang, & Swanson, 2004). This similarity of brain responses to smoking-related cues with responses to emotional cues suggests that NRT may modulate affect and craving by the same neural networks and psychological processes. However, few drug reactivity studies have simultaneously assessed the effects of drugs on distractibility by drug-related cues and distraction by emotional cues. We are not aware of any brain imaging studies of distraction using smoking-related stimuli; and apparently only two studies (McDonough & Warren, 2001; Warren & McDonough, 1999) have assessed the effects of smoking-related stimuli on event-related potential (ERP) indices of brain activity. These two ERP studies were able to identify rapidly occurring (within 300 ms of cue onset) brain changes that differentiated smoking-related from neutral cues that occurred more rapidly than the temporal resolution of currently available brain imaging measures other than electrocortical (EEG and ERP) and electromagnetic measures of brain activity. ERPs can also identify rapidly occurring differences in brain responses resulting from emotional, as opposed to neutral, stimuli (Keil et al., 2002), and recent studies indicate that an ERP brain voltage occurring at parietal sites approximately 300 ms after a target event (i.e., the P3 or P3b) is a useful index of attentional allocation. Of special relevance to the present experiment, Morita, Morita, Yamamoto, Waseda, and Maeda (2001) found that parietal cortical P3b amplitude to auditory oddball (infrequently occurring target) stimuli was attenuated by the simultaneous presentation of emotionally positive and negative distractor pictures relative to emotionally neutral pictures. Morita et al. (2001) interpreted these findings as indicating that these emotional pictures were more distracting than the neutral pictures. This interpretation is consistent with Polich’s suggestion (1991) that parietal P3b amplitude reflects attention to target stimuli and that distraction from the primary task (target detection) leads to reduced P3b amplitude. More recently, Comerchero and Polich (1998) noted that distractors (stimuli to which an individual is not supposed to pay attention) produce enhanced positive-going potentials (P3a potentials) at frontal brain sites that occur approximately 300 ms after distractor onset. The effects of emotional versus neutral distractors on P3b amplitude are paralleled by modulatory effects of emotional and other task parameters on the effects of nicotine on brain activity (McClernon & Gilbert, 2004). During relaxing conditions, nicotine

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reliably increases electrocortical activation by enhancing fast-wave activity and the amplitude of certain ERP components (Knott, 1989; Knott, Bosman, Mahoney, Ilivitsky, & Quirt, 1999). However, nicotine’s effects are more complex, localized, and lateralized during the presentation of stressors and emotional stimuli (Gilbert, Robinson, Chamberlin, & Spielberger, 1989; Gilbert, Estes, & Welser, 1997; Gilbert et al., 1999; Gilbert, McClernon et al., 2004; Gilbert, Sugai et al., 2004). For example, using a task that required subjects to focus on affect-inducing pictures, Gilbert, Sugai et al. (2004) found that nicotine patch, relative to placebo patch, produced different effects on brain ERPs to negatively valenced affective pictures, in contrast to positive and neutrally valenced pictures. Nicotine reduced frontal lobe negative-going voltage potentials (processing negativity) in response to negatively valenced pictures but had no such effect on these evoked potentials associated with positively and neutrally valenced pictures. Although these studies of brain activity suggest that NRT may modulate attentional biases and information processing of negatively valenced affective stimuli in a different manner than for positive and neutral stimuli, no studies have compared the effects of NRT on attentional bias to (distraction by) smoking-related stimuli with its effects on bias to emotional stimuli. The STAR model predicts that nicotine enhances attention to positive stimuli and should reduce attention (and produce smaller P3b amplitudes) to targets that follow positive pictures, whereas nicotine’s ability to reduce attention to negative stimuli should, relative to placebo, increase attention to targets and increase P3b amplitude to target digits. The present study was intended to test these hypotheses. Of relevance to the STAR and incentive sensitization models, Powell, Tait, and Lessiter (2002) found that, relative to abstinence, smoking increased attentional bias to both negative and positive words during a modified Stroop colornaming task. Powell, Pickering, Dawkins, West, and Powell (2004) found that nicotine lozenges did not significantly reduce distraction by words of any type (neutral, pleasure-related, aversive, smokingrelated), but nicotine tended (p5.07) to increase distraction by pleasure-related words. In contrast, we (D. G. Gilbert, J. Hammersley, R. Radtke, A. Izetelny, N. E. Rabinovich, & J. I. Huggenvik, manuscript in preparation) found that, relative to placebo patch, nicotine patch reduced Stroop taskassessed distraction by negative words but had no significant effect on distraction by positive, neutral, and smoking-related words. Thus the Powell et al. Stroop studies support the view that nicotine may enhance attentional bias to positive stimuli and possibly negative stimuli, whereas our Stroop studies

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are consistent with the view that nicotine reduces attentional bias to emotionally negative stimuli. The overall pattern of results is made more complex by our eye-gaze tracking studies showing that nicotine decreased attentional bias to negative pictures whereas it enhanced attentional bias to positive pictures (Gilbert et al., 2002; D. G. Gilbert, N. E. Rabinovich, J. Mrnak, H. Riise, L. Adams, & M. DevlescHoward, manuscript in preparation). Additional studies using a diverse set of methodologies are needed to better characterize when and how nicotine biases attention. Nicotine has been found to influence contingent negative variation (CNV), a slow, negative-going brain voltage potential that indexes attentional allocation in anticipation of target stimuli. The CNV increases in anticipation of target and motivationally significant stimuli (Hasenfratz, Michel, Nil, & Ba¨ttig, 1989; O’Connor, 1982). Some studies have demonstrated that nicotine/smoking enhances CNV amplitude in smokers, but inconsistent results also were reported (Cook, Gerkovich, Hoffman, McClernon, & O’Connell, 1996; see Pritchard, Sokhadze, & Houlihan, 2004, for a review). In line with our prediction that nicotine will reduce the distraction of negative and smoking-related stimuli, we additionally hypothesized that nicotine would enhance CNV amplitude as an index of attentional allocation in the present study. We investigated whether nicotine modulates attentional bias to (or distraction caused by) smoking and emotional picture stimuli. We used left and right parietal P3b amplitude to target digits in the rapid visual information processing task (RVIP) as our primary index of attentional allocation. We also assessed the effects of nicotine on the CNV in anticipation of target stimuli as an index of nicotine’s effects on the anticipation of important events. Nicotine was delivered by patch rather than by smoking because we have found that, with the patch application procedures used in the present study, smokers cannot reliably distinguish placebo from active nicotine patches (Gilbert, Izetelny et al., 2005). This finding contrasts with evidence indicating that a large portion of smokers distinguish between highand low-nicotine-delivery cigarettes (Perkins et al., 2004).

Method Participants Participants were 8 male and 8 female smokers. Exclusion criteria included smoking fewer than 10 cigarettes/day for the past year, smoking cigarettes with Federal Trade Commission (FTC) nicotine deliveries of less than 0.6 mg/cigarette, reported

history of brain injury, use of psychoactive drugs or medications other than caffeine and alcohol, excessive alcohol use, and noncorrected visual problems. The mean age of participants was 24.19 years (SD54.59, range519–37). Reported mean number of cigarettes smoked per day was 17.44 (SD53.95, range510–22). Nicotine dependence scores as assessed with the Fagerstro¨m Test for Nicotine Dependence (FTND; Heatherton, Kozlowski, Frecker, & Fagerstro¨m, 1991) ranged from 3 to 8, with a mean score of 5.0 (SD51.32). According to the Edinburgh Inventory (Oldfield, 1971) that assesses handedness, participants were primarily right lateralized. Biochemical assessment of nicotine and smoking abstinence To ensure at least 12 hr of smoking abstinence, we measured expired breath carbon monoxide (CO) concentrations during the two orientation sessions and each experimental session with a MiniCO meter (Vitalograph, Lenexa, Kansas). Plasma nicotine and cotinine concentrations were determined from blood samples collected during each experimental session immediately prior to application of the patch and at the conclusion of the experimental session, approximately 4 hr later. Cotinine and nicotine measures were assessed to ensure compliance with abstinence requirements, assess habitual nicotine intake, and provide an index of blood nicotine boost provided by the nicotine patches. Procedures Screening interviews ensured participant selection criteria and the participants’ familiarity with the study. All participants read and signed an institutional review board-approved informed consent. The study included two orientation-practice sessions and two experimental sessions. The task used to assess the P3b amplitude to targets and the CNV in anticipation of these targets was the rapid visual information processing task with central emotional distractors (CED-RVIP). Versions of CED-RVIP tasks were performed by each participant during each of these sessions. During one of the experimental sessions, an active transdermal nicotine patch (14 mg Nicoderm, GlaxoSmithKline) was applied to each subject. A placebo patch identical in appearance to the active patch was applied during the other experimental session. Patches were applied to the upper left arm in a double-blind manner. Participants were instructed not to smoke for the 12 hr preceding each of the two experimental sessions and were given detailed information as to how blood


and CO samples would be used to verify compliance with this abstinence requirement. During the two practice sessions, participants practiced tasks that would be used in subsequent experimental sessions, including the emotional picture-viewing task discussed here. During the second of the two practice sessions, subjects wore an EEG cap and were trained to minimize EEG artifacts (e.g., muscle tension, blinks) during the experimental task. Experimental sessions began between noon and 2:00 P.M. A minimum of 2 days and a maximum of 7 days separated the two experimental sessions. When participants arrived at the laboratory, their breath CO concentration was measured to verify that they had not smoked for the preceding 12 hr. Blood samples also were taken at this time and again at the end of the session. During each experimental session, participants spent approximately 1 hr completing questionnaires and then sat quietly while the EEG electrode cap was applied. The picture image task began approximately 3 hr after administration of the patch, after the subject was given a brief bathroom break and completed two attention-related experimental tasks. CED-RVIP. The CED-RVIP was a modification of the rapid visual information processing tasks (Warburton & Wesnes, 1978). The CED-RVIP task required the participant to observe a sequence of digits presented successively at a rate of 100/min at the center of a computer monitor screen. The participant’s task was to press response keys with the left or right index finger whenever three consecutive odd or three consecutive even digits appeared in the center of the screen. Individuals were instructed to respond as rapidly and accurately as possible. Each task lasted slightly over 12 min and included 96 target sequences. The duration of the picture distractors was brief (600 ms), the same duration as the digit presentations. The pictures were 10610 cm square images presented in the center of a computer color LCD monitor, 1 m from the participant’s eyes. At this distance, the central target digits subtended about 0.34u in height and 0.23u in width. Half of the target sequences (48) contained a distractor picture stimulus presented between the second and third digits, and the other half did not include a distractor. An additional 96 distractor pictures occurred during nontarget sequences. The pictures were much larger than the digits and subtended a visual angle of 6.5u, with the inner edge 1.8u eccentric from the center of the digit centers. Participants earned US$200 for completion of the study and a small performance bonus of 2.5 cents for each correct detection; they lost 2.5 cents for each false-positive button press.

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Picture stimuli. Each of the three picture sets used in the three sessions was unique and included 12 smoking-related, 12 negative, 12 positive, and 12 neutral images. During each session, each picture was presented three times, once during a target sequence and twice during a nontarget sequence. Smokingrelated, negative, positive, and neutral images were selected to be as similar as possible in stimulus parameters (complexity, color, saturation, and brightness) and content. The smoking-related images and neutral control images were selected from the International Smoking Image Series (ISIS) and neutral counterparts (Gilbert & Rabinovich, 1999). The smoking-related images consisted of lit cigarettes and hands holding cigarettes, and individuals of various races and genders smoking. The matched control pictures were neutral images that included individuals without emotional expressions and hands holding objects other than a cigarette, such as a calculator or a stapler, among others. The ISIS and neutral counterparts have been validated in studies showing the smoking images to induce different brain responses than neutral images in smokers (McClernon et al., 2005) but not never-smokers (Due et al., 2002) and to induce craving (Gilbert & Rabinovich, 2005). The emotional images were chosen from the International Affective Picture System (IAPS; Lang, Bradley, & Cuthbert, 1995) and a parallel set of images validated in our laboratory (Gilbert & Rabinovich, 2003). There were 36 images in each category (smoking-related, positive, neutral, and negative). All the pictures were rated by a large group of individuals other than the study participants for valence and arousal using the standard Lang et al. (1995) procedures. The mean valence ratings on the Lang 9-point emotional valence scale were as follows: Negative pictures (M52.91, SD51.41), neutral pictures (M54.90, SD51.14), positive pictures (M57.01, SD51.46), and smoking-related pictures (M55.57, SD51.60). Mean arousal ratings of the pictures on Lang’s 9-point arousal scale were as follows: Negative pictures (M55.62, SD52.27), neutral pictures (M52.64, SD51.89), positive pictures (M54.10, SD52.27), and smoking-related pictures (M53.66, SD52.12). All pictures also were rated for their association with craving for smoking on a 9-point scale. Mean craving ratings were as follows: Negative pictures (M53.46, SD52.66), neutral pictures (M52.87, SD52.25), positive pictures (M52.79, SD51.99), and smoking-related pictures (M55.08, SD52.87). Electrophysiological recording Participants sat in a recliner in a sound-attenuated, dimly illuminated room that was electronically

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connected to the observation and control room where the recording equipment was located. An EEG was recorded from the scalp using a commercial cap (Neurosoft, Inc.) with 29 Ag/AgCl electrodes arranged in the standard Jasper (1958) 10/20 configuration. Linked Ag/AgCl electrodes attached to left and right ear lobes served as the reference, and electrodes at the lateral canthi and above and below the left eye served to monitor eye movement artifacts. EEG signals were obtained using Neuroscan SynAmps and Scan 4.1 software. Sampling was at a rate of 500 Hz/channel with a DC high-pass and 125 Hz low-pass settings (down 48 dB/octave). The color picture stimuli were presented on an LCD flatscreen color computer monitor with Neuroscan STIM software that interfaced with the Neuroscan SynAmps EEG acquisition system. Electrophysiological processing and reduction For the P3b data, single-trial EEG data from the 21400 ms pre-target digit to 1,200 ms post-target digit epochs were extracted to exclude trials with eyeblink or substantial eye-movement or electromyographic artifacts. Data were digitally refiltered with a bandpass of 0.1–30 Hz (half-amplitude cutoffs, down 48 dB/octave). Because P3b responses are typically greatest at parietal sites, P3b amplitude in response to target digits was defined using latency windows of 250–650 ms at left and right parietal sites (P3 and P4, respectively). For P3b analyses, the artifact-free (artifact-attenuated and rejected) data were epoched and averaged separately for each of the picture type6nicotine (placebo vs. nicotine) conditions. All ERP epochs were baseline corrected using baselines from 21400 to 21200 ms preceding the target digit (2200 to 0 ms preceding the second digit in the target sequence). This period was chosen as baseline because it is largely independent of responses to a preceding target sequence and anticipation of a target digit. Each of these combined sets of P3b averages was based on a maximum of 12 and a minimum of 7 epochs, depending on the number deleted because of artifacts. P3a amplitudes to pictures during target sequences were measured at left and right frontal sites (F3 and F4) and used a response latency window of 250–650 ms. For the CNV analyses, artifact-free data were averaged separately for nicotine versus placebo patch conditions only during the nondistractor target sequences. Consistent with the work of Hasenfratz et al. (1989) and Michel and Ba¨ttig (1989), we defined CNV amplitude as the mean value from 100 ms before the onset of the target digit to 100 ms after the target digit. The CNV voltage negativity that built up following the presentation of two successive odd or even digits served as a ‘‘warning stimulus’’ that the

upcoming digit could be a target. CNV averages were based on a maximum of 46 and a minimum of 29 epochs. These ERP epochs were baseline corrected using baselines from 2800 to 2600 ms preceding the target digit (2200 to 0 ms preceding the second digit in the target sequence). To provide a contrast condition for the CNV, mean voltage amplitude from 100 ms before the onset of the digit to 100 ms after a third digit were calculated in nontarget sequences. The nontarget sequences for analysis included only trials free of immediately preceding distracters, target sequences, or two previous successive odd or even digits. To match the median epoch count of CNV analysis in the target condition, we included 38 randomly selected artifact-free epochs from nontarget sequences for averaging. More detailed descriptions of the participants and biochemical procedures to assess nicotine, breath CO, and EEG acquisition and artifact removal are provided in Gilbert, Sugai et al. (2004). Data analyses The effect of nicotine on P3b amplitude was assessed using an analysis of variance (ANOVA) with the following within-subjects factors: Nicotine (active nicotine vs. placebo patch)6picture type (smokingrelated vs. positive vs. negative vs. neutral)6hemisphere (right vs. left). We assessed the effects of nicotine on CNV amplitude in anticipation of target digits during nondistractor target sequences. These effects were assessed at left and right (respectively) frontal (F3, F4), central (C3, C4), and parietal (P3, P4) sites using an ANOVA with the following withinsubjects factors: Nicotine (active nicotine vs. placebo patch)6hemisphere (right vs. left)6anterior-posterior (frontal vs. central vs. parietal). All ANOVA probability values were calculated based on the Greenhouse and Geisser (1959) correction for sphericity of repeated measures. Noncorrected degrees of freedom are reported. Follow-up analyses were performed on significant interactions. Behavioral data (button presses in response to target digits) were not analyzed because an equipment malfunction resulted in loss of most of these data.

Results Plasma nicotine, cotinine, and verification of abstinence Plasma nicotine and cotinine concentrations were assessed at the onset of the session, prior to patch application, and at the conclusion of the experimental session about 4 hr later. Consistent with overnight smoking abstinence requirements, the mean plasma nicotine concentration was approximately 1.0 ng/ml at


the onset of each of the four experimental sessions. The concentration rose to a mean of approximately 15 ng/ml at the end of the two active nicotine patch experimental sessions, whereas no significant increase in plasma nicotine was observed in the placebo conditions. Baseline cotinine concentrations were consistent with self-reported daily smoking intakes in the range of one pack per day. The plasma nicotine and cotinine concentrations are depicted in Gilbert, Sugai et al. (2004). Effects of nicotine on P3b and CNV amplitudes Study results supported the primary hypothesis that nicotine would reduce distraction, as indexed by P3b, to a greater extent with negative and smoking-related distractors than with positive and emotionally neutral distractors (Figures 1 and 2). Specifically, nicotine enhanced P3b responses associated with target digits immediately subsequent to negative emotional pictures bilaterally and subsequent to smoking-related pictures in the right but not left hemisphere. These effects were shown by a significant nicotine6valence6hemisphere interaction, F(3, 45)53.73, p5.037. Follow-up analysis showed the amplitudes of P3b subsequent to negative pictures were greater in nicotine than in the placebo condition in both the left, F(1, 15)58.83, p5.01, and the right

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hemisphere, F(1, 15)57.01, p5.018. In response to smoking-related pictures, the mean amplitude of P3b in the nicotine relative to the placebo condition was significantly greater in the right than in the left hemisphere, t(15)52.929, p5.01. In contrast, we found no significant differences in P3b amplitudes between nicotine and the placebo condition at either hemisphere or between hemispheres, subsequent to either neutral or positive pictures. The main effect of nicotine on P3b amplitude approached significance, F(1, 15)53.65, p5.075. Nicotine tended to increase P3b amplitude across hemispheres and picture types. Nicotine was associated with a nonsignificant (p5.13) tendency to increase P3b to target digits during nondistractor target sequences. We found no significant effects of nicotine on parietal P3b or frontal P3a responses to the picture distractors themselves during target sequences. The ANOVA conducted on CNV amplitude revealed a significant main effect of nicotine, a significant nicotine6hemisphere interaction, and a significant nicotine6anterior-posterior interaction (Figures 3 and 4). Nicotine appeared to enhance CNV amplitude across all the sites assessed, F(1, 15)512.131, p,.01. The effect of nicotine, relative to placebo, on CNV amplitude was greater in the left hemisphere than in the right, F(1, 15)54.936, p,.05. The enhancing effect of nicotine was also larger at

Figure 1. Mean and standard errors of P3b amplitudes as a function of distractor type and nicotine versus placebo conditions at left (P3) and right (P4) parietal electrode sites.

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Figure 2. Averaged ERP wave forms as a function of distractor type and nicotine versus placebo conditions at left (P3) and right (P4) parietal electrode sites.

central and parietal sites than at frontal sites, t(15)53.278, p,.01. For nontarget sequences without two odd or two even numbers, we found no significant CNV (M5.041 and 20.111 mV, for the nicotine and placebo conditions, respectively). In contrast, mean CNV amplitudes for the target sequences were 21.860 mV and 20.533 mV for the nicotine and placebo conditions, respectively.

Discussion In support of the STAR model and contrary to the incentive sensitization model, the present findings suggest that NRT decreases attention to both smoking-related and negative emotional distractors while enhancing attention to task-relevant stimuli. Nicotine reduced our P3b measure of distraction by emotionally negative pictures in both hemispheres and by smoking-related pictures in the right hemisphere but did not alter distraction resulting from emotionally positive and neutral stimuli. Thus, as discussed below, these findings support the view that to understand the effects of NRT on attention and distraction one must consider the emotionally positive versus negative versus smoking-related

nature of the distractor, as well as the informationprocessing demands of the task itself. The greater right than left hemisphere effects of nicotine on distraction by smoking stimuli, the ability of nicotine to reduce distraction by aversive stimuli, and the greater left hemisphere enhancement effects of nicotine on the CNV are consistent with the STAR model’s lateralized neural networks hypothesis of nicotine’s effects on information processing, affect, and craving (Gilbert, 1995, 1997). Below we first discuss the effects of nicotine on distraction by smoking-related and negative pictures as indexed by P3b amplitude. We then address our CNV findings; the implications of our findings for models of the effects of smoking abstinence and NRT on attention, affect, and urge to smoke; limitations of our study; and future directions for work in this area. Our results support the hypothesis that nicotine withdrawal enhances distraction by emotionally negative and smoking stimuli, but the differences in hemispheric effects imply that the neurobiological mechanisms mediating these effects may differ. P3b amplitudes to a task-relevant target digit were lower when preceded by emotionally negative distractors than by neutral and positive ones in the placebo


a

CNV Mean Amplitude (µV)

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Figure 3. Mean and standard errors of contingent negative variation amplitudes as a function of nicotine versus placebo conditions in the left (F3, C3, P3) and right (F4, C4, P4) hemispheres (a), and at frontal (F3, F4), central (C3, C4), and parietal (P3, P4) electrode sites (b).

condition, and nicotine attenuated this effect of emotionally negative distractors in both hemispheres. Nicotine also reduced the P3b index of distraction by smoking pictures, but this effect was limited to the right hemisphere. The right hemisphere limitation of nicotine’s effects is consistent with findings by Due et al. (2002), who found greater right than left parietal fMRI responses to smoking stimuli in smokers than in nonsmokers. Together, this evidence suggests that visual smoking cues activate primarily the posterior attention network in the right hemisphere. However, this conclusion appears to be somewhat inconsistent with the findings of McClernon et al. (2005), who found no effects of smoking on fMRI responses to smoking cues but high correlations of left frontal regions with abstinence-induced craving. The present findings that nicotine enhances P3b following presentation of negative stimuli are consistent with recent behavioral findings indicating that NRT reduces attentional bias to negative pictures in abstaining smokers (Gilbert et al., 2002; Gilbert, Sugai et al., 2005). Our findings that nicotine reduced distraction by smoking-related pictures can be interpreted within this attentional bias framework if it is assumed that smoking-related pictures are

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experienced as negative when the smoker cannot smoke. However, this model does not explain why nicotine reduced the P3 index of distraction in both hemispheres with negative distractors but reduced only right hemisphere P3 with smoking pictures. One possibility is that the smoking pictures were less complex and demanded less cognitive processing capacity than the negative pictures. With salient cigarette images, the smoking pictures contained the least amount of ambiguous information among the four types of stimuli. In contrast, the complexity of the negative pictures may engage a substantial amount of left hemispheric cognitive processing resources. Therefore, negative stimuli may compete with the RVIP task for left hemispheric resources to a greater extent. Nicotine appeared to compensate for this greater distractibility of negative stimuli as indexed by left P3b amplitude. Another possible explanation for the right hemisphere effect of NRT on smoking-related distractors relates to the possibility that smoking-related schema, like some other automatic processes, are relatively more right than left lateralized (reviewed by Gilbert, 1997). Interestingly, whereas initial responses to externally presented smoking-related stimuli may be largely right lateralized, the approach behavior associated with the expectation of imminent smoking is predicted to be associated with relatively greater left than right frontal activation, according to the STAR model (Gilbert, 1997) and related work (Zinser, Fiore, Davidson, & Baker, 1999). The observed effects of nicotine on P3b to target digits in the right hemisphere subsequent to negative stimuli are consistent with evidence indicating that negative stimuli activate predominantly right hemispheric brain regions (Hartikainen, Ogawa, & Knight, 2000; Keil et al., 2002; Keil et al., 2001; Troisi et al., 1999). Combined with our earlier finding that smoking reduces right relative to left parietal EEG activation during the stressful parts of a movie, but not during nonstressful parts (Gilbert et al., 1989), the present results suggest that nicotine shifts somewhat lateralized brain attentional resources away from attention-grabbing emotionally negative stimuli and smoking-related stimuli and enhances attention toward task-related target stimuli. Generally speaking, our findings lend further support to the idea that smoking increases gating of irrelevant but enhances attention to relevant information (Domino & Kishimoto, 2002). The failure of nicotine to attenuate P3b responses immediately subsequent to positive distractors in abstaining smokers could be seen as surprising because recent evidence suggests that nicotine may enhance attentional bias and responding to emotionally positive stimuli (Caggiula et al., 2002, Gilbert et al., 2002; Gilbert, Sugai et al., 2005).

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Figure 4. Averaged contingent negative variation and surrounding ERP waveforms as a function of nicotine versus placebo conditions at left frontal (F3), central (C3), and parietal (P3) and at right frontal (F4), central (C4), and parietal (P4) electrode sites. Time ‘‘0’’ in the middle of the CNV box is the onset of the target digit.

Both the STAR model and the incentive sensitization model hypothesize greater distraction by positive distractors than by emotionally neutral distractors. However, this failure of nicotine to promote the distracting effects of positive picture distractors may be accounted for by the competing tendency of nicotine to enhance attentional focus on the task-related goal of identifying target sequences that were themselves reward related because each detection of a target digit was rewarded by a small monetary reinforcement. Thus, even though nicotine may have enhanced attentional bias to rewardrelated stimuli, the task included two types of such stimuli (distractors and targets), the effects of which may counteract each other. Our finding that nicotine did not attenuate the P3b index of distraction by positive stimuli may be related to the nature of these stimuli. Positive stimuli did not result in a lower P3b amplitude than did neutral stimuli in the placebo condition. Thus, these stimuli did not produce as much of a distracting effect as did negative and smoking-related images. In contrast to the present findings, Morita et al. (2001) found that positive facial drawings reduced parietal cortical P3b amplitude relative to neutral drawings, indicating that the

positive drawings were more distracting than the neutral facial drawings. Perhaps the positive pictures used in the present study failed to produce strong interfering effects, given that we did not include highly arousing images such as sexual stimuli (Schimmack, 2005). It remains to be determined whether nicotine modulates distraction by more arousing and salient emotionally positive pictures. In the present study, an equipment problem resulted in the loss of most of the behavioral response data (button presses to the target digits). However, we did collect behavioral reaction-time data from a larger group of smokers from whom we did not collect EEG data. The RVIP task and other experimental procedures we used in that study (Gilbert, Izetelny et al., 2005) were identical to those used in the present study, with the exception of EEG collection. In that study, we found that nicotine produced highly significant increases in target detection accuracy and decreases in reaction time to target digits. Nicotine increased detection accuracy in the CED-RVIP task (78% and 72% for nicotine and placebo, respectively, p,.001), and the effects of nicotine were moderated by both negative and smoking picture distractors. Consistent with the


interpretation of P3b amplitude to target stimuli following a distractor as being an inverse index of attentional distraction findings, our analyses showed that nicotine, relative to placebo, improved target detections most when pictures were negative (p,.001) or smoking related (p5.024) but had no significant effect when they were positive or neutral. The effects of nicotine on subjects’ performance in the RVIP task fully paralleled our P3b findings and thereby strengthen the argument that the P3b amplitude in the present study reflected an inverse index of distraction. Thus, our P3b results, combined with those of the Gilbert, Sugai et al. (2005) study, are consistent with the view that nicotine-deprived, relative to nondeprived, smokers are more biased to attend to emotionally negative than to positive and neutral stimuli. The tendency in the present study of NRT to enhance left more than right CNV amplitude is consistent with both the lateralized neural networks proposition of the STAR model and with previous findings that have demonstrated greater effects of nicotine on left than right hemisphere CNV in smokers (Michel & Ba¨ttig, 1989) or in subgroups of smokers (Norton, Brown, & Howard, 1992). The STAR model proposes that nicotine enhances brain activation in a task-dependent manner and enhances left activation when tasks require greater left than right hemisphere resources, as we expect the RVIP task does since it involves verbal rehearsal of rapidly presented verbal-like (oddness or evenness of digits) information. Additionally, the greater enhancing effect of NTR on CNV at parietal than at frontal sites indicates that nicotine may affect the posterior mechanisms associated with visual attention, something found in an fMRI study (Lawrence, Ross, & Stein, 2002). Our finding lends further support to the idea that the parietal cortex is particularly important in mediating the effect of nicotine on the orientation and maintenance of attention (e.g., Lawrence et al., 2002; Murphy & Klein, 1998). As suggested by Lawrence et al. (2002), nicotine likely enhances parietal activation by enhancing ascending cholinergic and noradrenergic input to the parietal cortex (Murphy & Klein, 1998) or by enhancing glutamatergic transmission in the region (Marrocco & Davidson, 1998). Most studies of the effects of nicotine and smoking on CNV (reviewed by Pritchard et al., 2004) did not use the RVIP paradigm, did not appear to tap left hemisphere resources, and did not involve long-duration and highly demanding tasks. Thus, it is not surprising that these studies did not consistently show beneficial or lateralized effects of nicotine on the CNV. Finally, although nicotine enhanced P3b amplitude to target digits immediately subsequent to negative and smoking-related pictures, we found no

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effect of nicotine on frontal P3a amplitude to the distractors themselves. This failure of P3a amplitude to be modulated by nicotine is somewhat surprising given that P3a is produced by distractors in tasks similar to the presently used RVIP (Comerchero & Polich, 1998). Possibly the effects of nicotine on responses to negative and smoking-related cues occur in more posterior brain regions as suggested by Lawrence et al. (2002). Limitations of the present investigation should be considered. The sample was relatively small, largely university based, moderately well educated, relatively young, and limited to moderately heavy smokers without excessive alcohol or psychoactive substance abuse. In addition, although attempts were made to equate the negative and positive picture stimuli in as many dimensions as possible, differences in the distractor pictures other than emotional valence and smoking-related stimuli may have accounted for the differential effects of NRT on our P3b index of distraction. For instance, one might argue that the observed differential effects of NRT on P3b are associated with higher arousal levels of negative pictures (M55.62) in comparison with positive pictures (M54.10). Our finding that NRT attenuated P3b amplitude to the target stimuli subsequent to smoking-related pictures (mean arousal53.66) rather than to positive pictures argues against the interpretation that higher arousal levels alone can account for greater effects of NRT on distraction by pictures of difference valences; however, development and use of positive and negative emotional stimuli that are matched more closely along the arousal dimension would be desirable in future studies. One primary reason that positive pictures used in this study were rated lower in arousal than negative ones is that arousing sexual stimuli tend to be gender specific. We excluded such images from our positive picture set so that the same picture set could be used for both male and female subjects. Additional studies characterizing when and how NRT modulates distraction by different types of smoking and emotional stimuli are needed. Such studies should use a range of indices of attention and different primary tasks. A substantial literature (reviewed by Gilbert, 1995; Gilbert et al., 2004; and Pritchard et al., 2004) suggests that individual differences in personality and genetic traits are also likely to moderate the effects of NRT and taskrelated stimuli. Finally, future studies will be enhanced by comparing brain indices of attention and distraction with behavioral performance indices. In summary, our overall pattern of findings supports the lateralized neural networks hypothesis of the STAR model and is inconsistent with the incentive sensitization model. Specially, NRT

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reduced distraction by smoking-related and emotionally negative stimuli during a task that required sustained and rapid visual information processing. The incentive sensitization model predicts that NRT and nicotine would increase rather than decrease distraction by both smoking-related and emotionally negative stimuli. NRT also produced greater left than right hemisphere effects on the CNV during anticipation of target digits. The effects of NRT on our P3b index of distraction may reflect an ability of NRT and nicotine to reduce attentional bias toward negative stimuli and smoking-related stimuli. Perceived attentional and emotional benefits of nicotine may be a primary reason that individuals relapse to smoking under stressful conditions (Kassel et al., 2003). The present findings support the hypothesis (Gilbert, 1995, 1997) that nicotine biases attention away from negative and smoking-related stimuli and thereby reduces craving and negative affect in situations allowing attentional choice. The present findings also are consistent with behavioral finding from a large study (Gilbert, Izetelny et al., 2005) that showed NRT to reduce distraction by smoking-related and negative pictures but to have no effects on distraction by positive and neutral pictures.

Acknowledgments This research was supported in part by National Cancer Institute grant CA81644-02 and by National Institute on Drug Abuse grant DA017837.

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