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Original Article |

Responsiveness to Drug Cues and Natural Rewards in Opiate Addiction:  Associations With Later Heroin Use FREE

Dan I. Lubman, MB ChB, PhD, FRANZCP; Murat Yücel, PhD, MAPS; Jonathan W. L. Kettle, PhD, MAPS; Antonietta Scaffidi, BSc; Trudi MacKenzie, BSc; Julian G. Simmons, BSc; Nicholas B. Allen, PhD, MAPS
[+] Author Affiliations

Author Affiliations: ORYGEN Research Centre (Drs Lubman, Yücel, Kettle, and Allen; Mss Scaffidi and MacKenzie; and Mr Simmons), and Melbourne Neuropsychiatry Centre (Dr Yücel), Department of Psychiatry; and Department of Psychology (Mr Simmons and Dr Allen), University of Melbourne, Melbourne, Australia.


Arch Gen Psychiatry. 2009;66(2):205-212. doi:10.1001/archgenpsychiatry.2008.522.
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Published online

Context  Although drug cues reliably activate the brain's reward system, studies rarely examine how the processing of drug stimuli compares with natural reinforcers or relates to clinical outcomes.

Objectives  To determine hedonic responses to natural and drug reinforcers in long-term heroin users and to examine the utility of these responses in predicting future heroin use.

Design  Prospective design examining experiential, expressive, reflex modulation, and cortical/attentional responses to opiate-related and affective stimuli. The opiate-dependent group was reassessed a median of 6 months after testing to determine their level of heroin use during the intervening period.

Setting  Community drug and alcohol services and a clinical research facility.

Participants  Thirty-three opiate-dependent individuals (mean age, 31.6 years) with stabilized opiate-substitution pharmacotherapy and 19 sex- and age-matched healthy non–drug users (mean age, 30 years).

Main Outcome Measures  Self-ratings, facial electromyography, startle-elicited postauricular reflex, and event-related potentials combined with measures of heroin use at baseline and follow-up.

Results  Relative to the control group, the opiate-dependent group rated pleasant pictures as less arousing and showed increased corrugator activity, less postauricular potentiation, and decreased startle-elicited P300 attenuation while viewing pleasant pictures. The opiate-dependent group rated the drug-related pictures as more pleasant and arousing, and demonstrated greater startle-elicited P300 attenuation while viewing them. Although a startle-elicited P300 amplitude response to pleasant (relative to drug-related) pictures significantly predicted regular (at least weekly) heroin use at follow-up, subjective valence ratings of pleasant pictures remained the superior predictor of use after controlling for baseline craving and heroin use.

Conclusions  Heroin users demonstrated reduced responsiveness to natural reinforcers across a range of psychophysiological measures. Subjective rating of pleasant pictures robustly predicted future heroin use. Our findings highlight the importance of targeting anhedonic symptoms within clinical treatment settings.

Figures in this Article

Clinical observations suggest that anhedonia (inability or failure to experience pleasure1) is a common characteristic in drug addiction.24 However, research investigating this phenomenon in drug users has been limited, despite evidence that it persists with prolonged abstinence.5 Research efforts that focus on understanding the neurobiological and psychological processes that underpin anhedonia in drug addiction, and its association with ongoing drug-seeking or relapse, are likely to provide important insights into improving current treatment approaches as well as reducing the substantial personal and community costs of addiction.

Like other abused drugs, opiates are powerfully reinforcing owing to their potent ability to activate the brain's reward system.6 However, after repeated, prolonged exposure, brain reward thresholds become chronically elevated and do not appear to return to baseline levels with abstinence.7,8 Koob and Le Moal9,10 have proposed that this dysfunction within the brain's reward system is at the core of the addictive process and is underpinned by hedonic allostasis (chronic deviation of the brain's reward “set point”) and the development of negative affective states. In line with this notion, anhedonia is common in drug-using populations,24 particularly during periods of acute and long-term withdrawal.11 Such findings are consistent with the notion that drug users develop increased reward thresholds to natural reinforcers following long-term drug exposure and may find it difficult to replace drug-taking behaviors with other, less-harmful rewarding activities.

An alternate model proposes that, with repeated drug use, cues associated with drug-taking acquire incentive value through sensitization of the brain's reward system; as the number of paired stimuli-drug presentations increase, the incentive value of these stimuli intensifies, making them increasingly “wanted.”12 With ongoing use, drug cues acquire excessive incentive salience, “wanting” transforms into drug craving, and drug cues become potent perpetuators of further drug-taking despite awareness of associated adverse consequences. In support of this model, drug cues can selectively capture attentional resources1315 as well as reliably activate key regions within the brain's reward system.1620 Recent studies have demonstrated that attentional bias to drug cues also predicts relapse after clinical treatment.21,22 However, such research predominantly focuses on responses to drug-related stimuli alone and rarely examines how these findings compare with the processing of natural reinforcers, despite evidence for associated anhedonia.

Recently, Lubman et al23 demonstrated that event-related potential responses to drug-related stimuli were greater than those elicited by affective and neutral stimuli in long-term heroin addicts. While these results support the notion of an appetitive bias toward drug cues, the heroin users also lacked the typical enhancement of event-related potential responses to nondrug affective stimuli, consistent with inhibited responding to natural rewards (ie, anhedonia). Few other studies examining affective processing in addiction have been conducted, with most research focused on the brain's responses to drug stimuli (ie, incentive salience) rather than their relationship with natural reinforcers (ie, hedonic allostasis). Research investigating both phenomena is particularly important in determining whether these responses represent autonomous or interdependent processes and in determining their relevance to clinical outcomes.

We used an affective picture-viewing paradigm to comprehensively examine hedonic responses to natural and drug-related reinforcers in a sample of heroin users whose addiction was stabilized with methadone or buprenorphine. However, even with stabilization on opiate pharmacotherapy, prospective studies reveal that many heroin users continue to use illicit heroin while undergoing treatment, though typically at substantially lower levels.2426 Although some of the factors that predict ongoing use of illicit opiates during substitution therapy have been identified (eg, treatment length,27 higher doses of pharmacotherapy28), the role of hedonic processes has not been examined. Thus, we followed up our sample to explore the role of hedonic responses in predicting future heroin use.

In this study, negatively valent (unpleasant) pictures were also presented to determine the specificity of responses across affective stimuli. Psychophysiological responses that were examined included electromyographic (EMG) measurements of expressive facial responses (in the corrugator supercilii and zygomaticus major),29 the startle-elicited postauricular reflex (an appetitive reflex that is potentiated during pleasant stimuli and inhibited during aversive stimuli30), and the startle-elicited event-related potential (a measure of the attentional resources allocated to processing stimuli, indexed by the degree to which cortical processing of the startle stimulus is inhibited29,31). When combined, these measures provide a comprehensive, multimethod description of experiential (self-report), expressive (facial EMG), reflex modulation (postauricular reflex), and cortical/attentional (startle-elicited event-related potential) responses to rewarding stimuli.

We hypothesized that (1) opiate-dependent individuals would demonstrate reduced reward responsiveness to natural reinforcers and increased reward responsiveness to drug cues compared with nondrug rewards; and (2) owing to difficulties in replacing drug use with other rewarding activities, addicted individuals who demonstrated reduced reward responsiveness to natural reinforcers, as well as increased reward responsiveness to drug cues (compared with nondrug rewards), would be at the greatest risk of ongoing heroin use (weekly or more frequent use) during the medium-term.

SUBJECTS

Participants included 33 opiate-dependent individuals (recruited from local drug and alcohol services and pharmacies [via advertisement posters and flyers]) and 19 healthy volunteers (recruited through community advertising). Opiate-dependent subjects were required to (1) have a current IQ higher than 70; (2) be stable on their methadone/buprenorphine dosage for at least 2 months; and (3) have no current comorbid mental health disorder. None of the subjects had a history of significant head injury, neurological disease, electroconvulsive therapy, impaired thyroid function, or steroid use. Healthy controls had no history of psychiatric illness or substance misuse. Opiate-dependent subjects were required to abstain from using illicit heroin for at least 24 hours before testing. All subjects gave written informed consent to participate in the study, which was approved by local research and ethics committees.

CLINICAL ASSESSMENT

All subjects were screened using the patient edition of the Structured Clinical Interview for DSM-IV Axis I Disorders32 to ensure that they did not have a current anxiety, mood, or psychotic disorder. Diagnoses of DSM-IV opiate dependence were also established by structured interview.32 While opiates were the primary drug of abuse (current and lifetime) for all clinical participants, other substances were also commonly used, including tobacco (75.8% with daily use), alcohol (21.3% with at least weekly use), cannabis (30.3% with at least weekly use), and benzodiazepines (18.2% with at least weekly use). Thirty percent reported that they also used illicit heroin on at least a weekly basis. Depressive symptoms were assessed using the Beck Depression Inventory,33 while current mood state was assessed using the Positive and Negative Affect Schedule.34

The opiate users reported minimal levels of opiate withdrawal (as indexed by the Short Opiate Withdrawal Scale35) at the time of testing (Table 1) and reported experiencing a moderate degree of problems with psychological dependence (as indexed by the Severity of Dependence Scale36). There were no significant differences between opiate-dependent subjects and healthy volunteers with respect to sex or age. The opiate users did report a significantly higher level of depressive symptoms (t50 = 4.61, P < .001), though current mood ratings (ie, positive and negative affect) between the groups did not differ significantly. The opiate-dependent group reported a significant increase in craving scores from pre– to post–picture viewing (Wilcoxon signed rank, t33 = 4.002; P < .001, 2-tailed).

Table Graphic Jump LocationTable 1. Demographic and Basic Group Characteristics at Baseline and Follow-up

The opiate group was reassessed a median of 6 months after initial assessment (range, 3.9-8 months) to determine their level of heroin use during the intervening period. Participants were grouped by frequency of weekly heroin use, as this has been previously used as a key outcome variable in studies of opiate pharmacotherapy.26 Of the 33 opiate-dependent participants assessed at baseline, 12 reported using heroin at least once a week (36.4%) (ie, regular use) throughout the follow-up period, while 19 were using heroin less than once a week or not at all at follow-up (57.6%). Participants' use of other drugs did not change during the follow-up period, though increased alcohol use was reported (33.4% with at least weekly use). Two participants (6.1%) could not be contacted at follow-up to determine frequency of heroin use and were therefore only included in baseline analyses.

STIMULI
Materials

Affective slides included 30 opiate-related pictures (eg, drug paraphernalia, heroin preparation, and injection), 30 unpleasant pictures (eg, distressed individuals, snakes, mutilation; mean [standard deviation (SD)], valence score: 2.9 [1.6]; arousal score: 5.6 [2.2]; range, 1-9,37 30 pleasant pictures (eg, erotic nudes [changed depending on the participant's sex], food, action sports; valence, male: 7.3 [1.5]; valence, female: 7.1 [1.7]; arousal, male: 6.2 [2.1]; arousal, female: 5.7 [2.2]37), and 30 neutral pictures (eg, household and inanimate objects; valence: 5.0 [1.2]; arousal: 2.0 [1.2]37). The pleasant, neutral, and unpleasant pictures were selected from the International Affective Picture System,37 whereas the opiate-related pictures were taken from media libraries and were matched for social content. Three examples of the 4 slide categories were randomly allocated to 10 experimental blocks. Picture presentation was randomized within each block, which was composed to form 2 different picture orders, counterbalanced between participants within groups.

Procedure

After the clinical interview, participants viewed 120 pictures, with accompanying startle probes delivered binaurally through headphones. The acoustic startle probe was a 50-millisecond, 100-dB burst of white noise with instantaneous rise time, presented either 2500 or 3500 milliseconds after picture onset for 96 of the 120 trials, balanced across picture types. Two startle probes were delivered before experimentation to facilitate startle habituation. Pictures were displayed for 4 seconds, followed by a 6-second interstimulus interval. Participants were instructed to view each picture and to ignore startle probes. After this, participants rated the pictures using a computerized version of the Self-Assessment Manikin (SAM).38 Participants rated pictures on a continuum (range, 1-20) of 2 dimensions: valence (unpleasant vs pleasant) and arousal (calm vs aroused).

PHYSIOLOGICAL DATA COLLECTION AND PROCESSING

Physiological signals were recorded using a Grass Model 12 Neurodata (Grass Technologies, West Warwick, Rhode Island) acquisition system. Version 11.0 of the VPM software package39 was used to control the timing and presentation of stimuli and to collect and store the physiological data. Data processing was conducted using VPMANLOG39 and Neuroscan, version 4.3 (Compumedics USA, Charlotte, North Carolina). Facial EMG signals were amplified and filtered for 30- to 1000-Hz activity (half-amplitude cutoffs). Facial EMG signals were sampled at 1000 Hz from 2 seconds before picture onset to the end of each picture presentation period. Activity at each facial muscle site was integrated separately (full-wave rectified), converted to microvolts, and filtered using a digital finite impulse response (50-Hz, 24 dB per roll-off 0-phase–shift low-pass filter in Neuroscan). Facial reactivity was computed as the mean EMG activity between 0 to 2 seconds after picture onset minus the mean of a 2-second prepicture baseline. The postauricular reflex was recorded, processed, and scored according to the procedures outlined by Benning and colleagues.30

Electroencephalograms (EEG) were recorded from 3 scalp sites (Fz, Cz, Pz) based on the international 10-20 system. Vertical and horizontal electrooculography was also performed. The EEG and electrooculography signals were amplified by 10 000, with high- and low-pass filters set to 0.1 Hz and 30 Hz, respectively. Data were collected at 1000 Hz from 2 seconds before picture onset (baseline period) until picture offset. The EEG data epochs were extracted between 1 second before and 1.25 seconds after probe onset, sampled at 100 Hz, converted to microvolts, and analyzed using Neuroscan, version 4.3. All channels were baseline-corrected to the mean of their 150-millisecond prestimulus period. For each probe presentation, saturated EEG and electro-oculography trials were excluded from additional analyses. An algorithm correcting for eye-movement artifacts40 using the vertical electro-oculogram channel data was then applied within participants, channels, and trials. Event-related potential waveforms were then obtained by averaging the EEG signal within each individual at each scalp site for each picture category. Only the startle-elicited P300 (or P3) component was analyzed, given that it is most sensitive to the affective qualities of foreground stimuli and is understood to reflect attentional allocation.31 To calculate the peak P3 amplitude, it was necessary to define earlier components so that the appropriate latency window could be identified on each waveform (ie, N1 [64-192 milliseconds], P2 [N1 latency until 272 milliseconds], N2 [P2 latency until 336 milliseconds], and P3 [N2 latency until 504 milliseconds]).31

STATISTICAL ANALYSIS

The multivariate statistic Wilks λ test was used for repeated-measures effects to protect against violations of sphericity. Some participants were excluded from SAM analyses (n = 2) owing to equipment failure, and from postauricular reflex analyses (n = 3) and startle-elicited event-related potential analyses (n = 1) owing to excessively noisy EMG/EEG channels. An additional 8 clinical (26%) and 6 nonclinical (33%) participants were excluded from postauricular analyses because they did not exhibit a scorable postauricular reflex, as determined by visual inspection of averaged waveforms. The percentage of nonresponders was not significantly related to group status (n = 49; χ21 = 0.316, P = .57). Previous physiological research, including our own, has indicated that between 20% and 40% of participants with normal hearing do not exhibit a scorable postauricular reflex.41,42 We report startle-elicited P300 amplitude at the Pz scalp location, consistent with past research.31

Data analysis was divided into 2 phases. Baseline group differences in SAM ratings and psychophysiological variables were analyzed using 2-way, mixed-factor analyses of variance (picture type [4] × group [2]). Prediction of regular (at least weekly) heroin use at follow-up was then analyzed using logistic regression models. Missing data were excluded on a pairwise basis (ie, the maximum number of participants with complete data for the variables involved in each analysis was included in that analysis). Measures of baseline processing of pleasant compared with neutral, drug-related compared with neutral, and pleasant compared with drug-related pictures were calculated by examining residuals generated from univariate regression analyses in which the control variable (eg, responses during neutral pictures) was regressed onto the response variable (eg, response during pleasant pictures). In the subsequent logistic regressions predicting outcome, baseline treatment group (methadone or buprenorphine) was entered as the first block, followed by indices of affective stimuli processing. Following this, 2 sets of more stringent analyses were conducted for variables that did significantly predict outcome. Given that 24 of 33 participants (72.7%) reported using heroin at baseline (in addition to opiate pharmacotherapy) and that there was a trend for the group that used heroin weekly or more often at follow-up to report greater baseline craving scores (P < .1), these regressions were run again with both amount of heroin used at baseline and baseline craving scores entered separately as covariates in the first block. This allowed examination of whether picture processing and response variables that significantly predicted outcomes represented epiphenomena of baseline heroin use or baseline craving scores or whether they were distinct phenomena.

BASELINE GROUP DIFFERENCES
Self-Report

Valence ratings differed according to group. As shown in Figure 1, both groups exhibited more positive valence ratings for pleasant compared with neutral pictures and for neutral compared with unpleasant pictures. However, the opiate-dependent group rated the drug-related pictures as significantly more pleasant, relative to neutral and pleasant pictures, than did the control group. This pattern of results was identical for arousal ratings. Whereas the control group rated the pleasant pictures as more arousing than the drug-related pictures, the opiate-dependent group rated the drug-related pictures as significantly more arousing than the pleasant pictures. Table 2 presents the mixed-factor analysis of variance results (picture [4] × group [2]) for self-report ratings and psychophysiological variables.

Place holder to copy figure label and caption
Figure 1.

Self-Assessment Manikin (SAM) valence (A) and arousal (B) ratings and corrugator supercilii (C) and zygomaticus major (D) electromyogram reactivity by picture types within baseline groups. Error bars represent 1 SEM.

Graphic Jump Location
Table Graphic Jump LocationTable 2. Mixed-Factor ANOVA Results for SAM Ratings and Psychophysiological Dependent Variables
Facial Expression

As shown in Figure 1, both groups exhibited similarly enhanced corrugator reactivity during unpleasant pictures. However, the opiate-dependent group displayed significantly greater corrugator activity during pleasant compared with drug-related–picture viewing than did the control group, indicating a lack of the typical pattern of facial reactivity to pleasant stimuli (corrugator relaxation). Both groups also exhibited the normal pattern of greater zygomatic activity during pleasant pictures (Figure 1). However, the opiate-dependent group, in contrast to controls, exhibited significantly greater zygomatic reactivity to unpleasant compared with neutral pictures. Thus, though the control group exclusively responded with increased zygomatic activity to the pleasant pictures, the opiate-dependent group demonstrated a lack of differential reactivity in this usually appetitive response, responding to both pleasant and unpleasant stimuli in a similar fashion.

Reflex Modulation

Consistent with previous findings, the postauricular reflex was significantly greater for pleasant compared with unpleasant pictures in the control group. However, the opiate-dependent group failed to show this effect, consistent with an inhibited response to pleasant stimuli (Figure 2).

Place holder to copy figure label and caption
Figure 2.

Postauricular reflex (A, control group [n = 12]; B, opiate-dependent group [n = 23]) and startle-elicited event-related potential (ERP) amplitude (C, control group [n = 19]; D, opiate-dependent group [n = 32]) by picture type within baseline groups.

Graphic Jump Location
Event-Related Potentials

Startle-elicited P300 amplitude across groups was significantly attenuated for unpleasant, pleasant, and drug-related pictures relative to neutral pictures, suggesting that these affective and drug-related pictures captured more attentional resources, thereby inhibiting the cortical response to the startle probe. However, the opiate-dependent group displayed significantly less P300 attenuation in response to pleasant relative to neutral pictures compared with the control group, suggesting that they did not attend as strongly to these pictures. Also, the control group displayed significantly larger P300 amplitudes during pleasant pictures than during drug-related pictures, whereas the opiate-dependent group displayed the opposite pattern, suggesting that the heroin users had a greater attentional bias toward drug-related stimuli compared with pleasant stimuli.

In summary, baseline group differences were prominent for indices of the processing of and response to pleasant and drug-related pictures. Relative to the control group, and consistent with an anhedonic response, the opiate-dependent group rated the pleasant pictures as less arousing, responded with increased corrugator activity, lacked postauricular potentiation, and had decreased startle-elicited P300 attenuation. Consistent with enhanced processing of drug cues, the opiate-dependent group also rated the drug-related pictures as more pleasant and arousing and demonstrated greater startle-elicited P300 attenuation.

Given the between-group differences in depressive symptoms, we investigated whether Beck Depression Inventory scores were related to these discrepancies in affective responding. There was no significant relationship between the Beck Depression Inventory scores and response to pleasant stimuli compared with neutral, unpleasant, or drug-related stimuli (all r <.183, >.17, 1-tailed), both within and across groups.

PREDICTION OF REGULAR HEROIN USE AT FOLLOW-UP

Self-Assessment Manikin valence ratings of drug-related pictures (compared with neutral pictures: odds ratio [OR], 5.09; 95% confidence interval [CI], 1.06-24.5; P = .04), pleasant pictures (compared with neutral pictures: OR, 0.18; 95% CI, 0.04-0.79; P = .02; compared with drug-related pictures: OR, 0.18; 95% CI, 0.04-0.86; P = .03), and SAM arousal ratings of pleasant pictures (compared with drug-related pictures: OR, 0.26; 95% CI, 0.09-0.80; P = .02) all significantly predicted regular heroin use at follow-up above the constant and baseline treatment model (eTable). For psychophysiological variables, only the startle-elicited P300 amplitude to pleasant pictures (relative to drug-related pictures) significantly predicted regular heroin use at follow-up (OR, 4.35; 95% CI, 1.15-16.40; P = .03). The model of SAM valence ratings of pleasant pictures (relative to neutral pictures) accounted for the most variance (44%) and had the greatest sensitivity (83.3%) and specificity (82.4%), suggesting it is the superior model. Figure 3 displays the associations between all 5 standardized SAM and startle-elicited P300 predictors and status of heroin use at follow-up.

Place holder to copy figure label and caption
Figure 3.

Scatterplot of standardized residual values for significant baseline predictors of regular heroin use at follow-up. For each variable, a residual score was calculated that reflected the relative response between 2 classes of stimuli. This relative response score is plotted separately for each group (ie, weekly or more frequent heroin use at follow-up [clear circles] vs less than weekly heroin use [black triangles]), within each variable, thereby depicting group differences in their relative response to either drug vs pleasant, drug vs neutral, or pleasant vs neutral stimuli.

Graphic Jump Location

When baseline heroin use was entered in the first block, only the SAM valence ratings of pleasant relative to neutral pictures (OR, 0.095; 95% CI, 0.01-0.75; P = .03) and pleasant relative to drug-related pictures (OR, 0.081; 95% CI, 0.01-0.91; P = .04) remained statistically significant predictors (eTable). The model of SAM valence ratings of pleasant (relative to drug-related) pictures accounted for marginally more variance (78.2%) and had greater specificity (94.1%), suggesting it is the superior model.

When baseline craving scores were entered into the first block, SAM valence ratings of drug-related pictures (compared with neutral pictures: OR, 8.63; 95% CI, 1.29-57.86; P = .03), pleasant pictures (compared with neutral pictures: OR, 0.37; 95% CI, 0.00-0.53; P = .02; compared with drug-related pictures: OR, 0.051; 95% CI, 0.00-0.68; P = .03), and arousal ratings of pleasant pictures (compared with drug-related pictures: OR, 0.33; 95% CI, 0.12-0.97; P = .04) also remained statistically significant predictors of heroin use at follow-up. The model of SAM valence ratings of pleasant relative to neutral pictures accounted for the most variance (67.3%) and had the greatest OR, sensitivity (75%), and specificity (88.2%).

Given the variable follow-up period, we conducted a number of analyses to ensure that frequency of heroin use at follow-up was not a direct consequence of later reassessment. First, using logistic regression, we found that a continuous measure of time to follow-up in months did not predict using heroin weekly or more often or even using heroin at all vs not at all in the follow-up period (> .39 for ORs <1.450). Second, using time to follow-up in the first step of a logistic regression, we demonstrated that all of the original 5 predictor models remained significant (P<.05) with regard to the step 2 increment, whole model at step 2, and OR for the valence and arousal ratings. The OR for time to follow-up was not significant in either step 1 or 2. Finally, arousal and valence ratings still predicted relapse after accounting for time to follow-up.

In this study we used a multimethod approach to examine hedonic responses to natural reinforcers and drug cues in opiate addiction as well as their relationship with clinical outcomes. Across a range of response measures (ie, self-report, expressive, reflex modulation, and cortical/attentional measures), we consistently found altered processing of drug-related and pleasant pictures in opiate-dependent individuals relative to controls. In response to normally pleasant pictures (ie, pictures of natural reinforcers), the opiate-dependent group had lower self-reported arousal, increased corrugator activity, an absence of postauricular potentiation, and decreased startle-elicited P300 attenuation. The opiate-dependent group also rated the drug-related pictures as more pleasant and arousing and demonstrated greater startle-elicited P300 attenuation. Furthermore, SAM valence ratings of pleasant pictures consistently predicted regular heroin use (at least once a week) at follow-up, even after controlling for baseline craving score and heroin use.

The consistent inhibition of normal responses to pleasant pictures (as opposed to a general attenuation of responses to all affective stimuli) in the opiate-dependent group is an important finding, especially because such indices also strongly predicted ongoing regular heroin use. These data contrast with the large body of normative psychophysiological and electrophysiological research on emotional processing, demonstrating that emotionally arousing stimuli reliably elicit a variety of physiological responses.29,43 The results support animal evidence of increased reward thresholds in drug addiction and are consistent with Koob and Le Moal’s9,10 concept of allostasis as well as previous research examining subjective reports of anhedonia in addicted populations.25,11

In addition, the drug cue findings are consistent with previous behavioral studies of attentional processing in addiction, including studies44 of both methadone maintained13 and recently detoxified heroin addicts.45 Such results support the notion that drug cues have implicit motivational salience in addicted users and capture attentional resources. Indeed, enhanced event-related potential responses to drug-related stimuli have been reported across a range of addicted populations,14,15,4650 suggesting that drug cues capture processing resources and influence behavior. While few other addiction studies have included a non–drug-related emotionally salient class of stimuli (eg, sexual imagery, highly aversive images) in their study design, our results support the notion that the hedonic balance between drug cues and natural reinforcers is abnormal in heroin users, with drug cues capturing relatively more attentional and hedonic resources than natural rewards.

However, one outstanding issue is if reduced sensitivity to natural reinforcers is a consequence of long-term drug exposure or opiate-substitution pharmacotherapy (as in the allostasis model), if it represents a vulnerability to addictive disorders, or if it is a combination of both. Franken et al51 recently reported that individuals who engage in high-risk recreational activities (skydivers) report more anhedonic symptoms than those who prefer low-risk activities (rowers). While the authors suggested that frequent exposure to “natural highs” may induce an allostatic state and subsequent anhedonia, an alternative explanation may be that individuals with relatively lower premorbid responsiveness to natural reinforcers (ie, decreased activation of reward circuitry) may be driven to experiment with high-risk, intensely hedonic experiences (such as skydiving or drug use). Indeed, while previous imaging studies in long-term heroin users reveal long-lasting decreases in dopamine D2 receptors within the striatum (suggesting downregulation of the dopaminergic reward system with regular drug use),52,53 more recent evidence suggests that low levels of D2 receptors may increase vulnerability to drug use.54,55

It is important to consider a number of limitations when reviewing our data. The period of reassessment for the opiate group was variable, reflecting some of the difficulties associated with conducting prospective studies in people with heroin dependence. Nevertheless, our analyses indicated that this variable was not related to heroin use at follow-up and did not affect the predictive association between reduced reward responsiveness at baseline and subsequent heroin use. An additional confounder is that the subject groups were not matched on depressive symptomatology. However, it is important to note that no participant met criteria for a current mood disorder; mood ratings (ie, positive affect, negative affect) on the day of testing did not differ significantly between the groups; and exploratory analyses indicated that depressive symptoms did not correlate with the response to pleasant stimuli relative to other classes of stimuli. Nevertheless, high rates of depressive symptoms are commonly reported in addicted populations, including those being treated with substitution pharmacotherapy.56,57

Another limitation is the relatively small sample of heroin users, recruited primarily through advertisements, which raises issues of generalizability to the wider heroin-using population. While the sample size is consistent with other published psychophysiological investigations, our findings require replication in a larger sample, including those who are long-term abstinent. No biological assays were conducted on the day of testing to confirm recent abstinence from use of heroin or other illicit drugs. Nevertheless, participants reported that they had not used any other drugs in the past 24 hours; none appeared to be intoxicated; and all maintained their concentration for the entire experiment. Furthermore, while no biological assay was conducted to confirm heroin use during the follow-up period, previous research has noted the excellent concordance between self-reported use and urine drug testing results in heroin users engaged in treatment.58 Finally, it is important to note that a large number of analyses were conducted, and though we recognize that the risk of type 1 error is a factor in our study, we believe that these risks are outweighed by the benefits of fully describing the patterns observed in this unique data set, so that effects of interest can be subjected to further experimental scrutiny.

We report that heroin users undergoing opiate-substitution therapy demonstrate an inhibited response to natural reinforcers (anhedonia) across a range of response measures and that subjective ratings of pleasant pictures robustly predict regular heroin use at follow-up. Such findings are of clear clinical relevance and highlight the importance of targeting anhedonic symptoms within addiction treatment settings. Finally, our results support previous literature that demonstrated enhanced attentional processing of drug cues in opiate addiction, emphasizing the importance of effectively managing exposure to cues within relapse-prevention training. Future studies will need to examine the specificity of these findings across addictive disorders and the effectiveness of interventions that target improved hedonic responses to alternate reinforcers.

Correspondence: Dan I. Lubman, MB ChB, PhD, FRANZCP, ORYGEN Research Centre, 35 Poplar Rd (Locked Bag 10), Parkville, Victoria 3052, Australia (dan.lubman@mh.org.au).

Submitted for Publication: February 16, 2008; final revision received August 2, 2008; accepted August 5, 2008.

Aurhor Contributions: Dr Lubman had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis and had final responsibility for the decision to submit for publication.

Financial Disclosure: None reported.

Funding/Support: This study was supported by the Colonial Foundation and a University of Melbourne Early Career Researcher Grant (Dr Lubman). Dr Yücel is supported by a National Health and Medical Research Council Clinical Career Development Award (No. 509345).

Additional Information: The eTable is available at http://www.archgenpsychiatry.com.

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Stevens  APeschk  ISchwarz  J Implicit learning, executive function and hedonic activity in chronic polydrug abusers, currently abstinent polydrug abusers and controls. Addiction 2007;102 (6) 937- 946
PubMed Link to Article
Altman  JEveritt  BJGlautier  SMarkou  ANutt  DOretti  RPhillips  GDRobbins  TW The biological, social and clinical bases of drug addiction: commentary and debate. Psychopharmacology (Berl) 1996;125 (4) 285- 345
PubMed Link to Article
Kenny  PJPolis  IKoob  GFMarkou  A Low dose cocaine self-administration transiently increases but high dose cocaine persistently decreases brain reward function in rats. Eur J Neurosci 2003;17 (1) 191- 195
PubMed Link to Article
Nazzaro  JMSeeger  TFGardner  EL Morphine differentially affects ventral tegmental and substantia nigra brain reward thresholds. Pharmacol Biochem Behav 1981;14 (3) 325- 331
PubMed Link to Article
Koob  GFLe Moal  M Drug abuse: hedonic homeostatic dysregulation. Science 1997;278 (5335) 52- 58
PubMed Link to Article
Koob  GFLe Moal  M Drug addiction, dysregulation of reward, and allostasis. Neuropsychopharmacology 2001;24 (2) 97- 129
PubMed Link to Article
Heinz  ASchmidt  LGReischies  FM Anhedonia in schizophrenic, depressed, or alcohol-dependent patients: neurobiological correlates. Pharmacopsychiatry 1994;27 ((suppl 1)) 7- 10
PubMed Link to Article
Robinson  TEBerridge  KC The neural basis of drug craving: an incentive-sensitization theory of addiction. Brain Res Brain Res Rev 1993;18 (3) 247- 291
PubMed Link to Article
Lubman  DIPeters  LAMogg  KBradley  BPDeakin  JF Attentional bias for drug cues in opiate dependence. Psychol Med 2000;30 (1) 169- 175
PubMed Link to Article
Franken  IH Drug craving and addiction: integrating psychological and neuropsychopharmacological approaches. Prog Neuropsychopharmacol Biol Psychiatry 2003;27 (4) 563- 579
PubMed Link to Article
Lubman  DIAllen  NBPeters  LADeakin  JF Electrophysiological evidence of the motivational salience of drug cues in opiate addiction. Psychol Med 2007;37 (8) 1203- 1209
PubMed Link to Article
Goldstein  RZVolkow  ND Drug addiction and its underlying neurobiological basis: neuroimaging evidence for the involvement of the frontal cortex. Am J Psychiatry 2002;159 (10) 1642- 1652
PubMed Link to Article
Lubman  DIYücel  MPantelis  C Addiction, a condition of compulsive behaviour? neuroimaging and neuropsychological evidence of inhibitory dysregulation. Addiction 2004;99 (12) 1491- 1502
PubMed Link to Article
Daglish  MRCWeinstein  AMalizia  ALWilson  SMelichar  JKBritten  SBrewer  CLingford-Hughes  AMyles  JSGrasby  PNutt  DJ Changes in regional cerebral blood flow elicited by craving memories in abstinent opiate-dependent subjects. Am J Psychiatry 2001;158 (10) 1680- 1686
PubMed Link to Article
Childress  ARMozley  PDMcElgin  WFitzgerald  JReivich  MO'Brien  CP Limbic activation during cue-induced cocaine craving. Am J Psychiatry 1999;156 (1) 11- 18
PubMed
Wang  GJVolkow  NDFowler  JSCervany  PHitzemann  RJPappas  NRWong  CTFelder  C Regional brain metabolic activation during craving elicited by recall of previous drug experiences. Life Sci 1999;64 (9) 775- 784
PubMed Link to Article
Marissen  MAFranken  IHWaters  AJBlanken  Pvan den Brink  WHendriks  VM Attentional bias predicts heroin relapse following treatment. Addiction 2006;101 (9) 1306- 1312
PubMed Link to Article
Waters  AJShiffman  SSayette  MAPaty  JAGwaltney  CJBalabanis  MH Attentional bias predicts outcome in smoking cessation. Health Psychol 2003;22 (4) 378- 387
PubMed Link to Article
Lubman  DIAllen  NBPeters  LADeakin  JFW Electrophysiological evidence that drug cues have greater salience than other affective stimuli in opiate addiction. J Psychopharmacol 2008;22(8)836- 842
PubMed Link to Article
Gossop  MMarsden  JStewart  DKidd  T The National Treatment Outcome Research Study (NTORS): 4-5 year follow-up results. Addiction 2003;98 (3) 291- 303
PubMed Link to Article
Mattick  RPAli  RWhite  JMO'Brien  SWolk  SDanz  C Buprenorphine versus methadone maintenance therapy: a randomized double-blind trial with 405 opioid-dependent patients. Addiction 2003;98 (4) 441- 452
PubMed Link to Article
Bloor  MMcIntosh  JMcKeganey  NRobertson  M ‘Topping up’ methadone: an analysis of patterns of heroin use among a treatment sample of Scottish drug users [published online ahead of print May 19, 2008]. Public Health 2008;122 (10) 1013- 1019
PubMed Link to Article
Brewer  DDCatalano  RFHaggerty  KGainey  RRFleming  CB A meta-analysis of predictors of continued drug use during and after treatment for opiate addiction. Addiction 1998;93 (1) 73- 92
PubMed Link to Article
Strain  ECBigelow  GELiebson  IAStitzer  ML Moderate- vs high-dose methadone in the treatment of opioid dependence: a randomized trial. JAMA 1999;281 (11) 1000- 1005
PubMed Link to Article
Bradley  MM Emotion and motivation. Cacioppo  JTTassinary  LGBernston  GGHandbook of Psychophysiology. 2nd ed. New York, NY Cambridge University Press2000;602- 642
Benning  SDPatrick  CJLang  AR Emotional modulation of the post-auricular reflex. Psychophysiology 2004;41 (3) 426- 432
PubMed Link to Article
Schupp  HTCuthbert  BNBradley  MMBirbaumer  NLang  PJ Probe P3 and blinks: two measures of affective startle modulation. Psychophysiology 1997;34 (1) 1- 6
PubMed Link to Article
First  MBSpitzer  RLMiriam  GWilliams  JBW Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Patient Edition.  New York, NY Biometrics Research, New York State Psychiatric Institute2001;
Beck  ATWard  CHMendelson  MMock  JErbaugh  J An inventory for measuring depression. Arch Gen Psychiatry 1961;4561- 585
PubMed Link to Article
Watson  DClark  LATellegen  A Development and validation of brief measures of positive and negative affect: The PANAS scales. J Pers Soc Psychol 1988;54 (6) 1063- 1070
PubMed Link to Article
Gossop  M The development of a Short Opiate Withdrawal Scale (SOWS). Addict Behav 1990;15 (5) 487- 490
PubMed Link to Article
Gossop  MDarke  SGriffiths  PHando  JPowis  BHall  WStrang  J The Severity of Dependence Scale (SDS): psychometric properties of the SDS in English and Australian samples of heroin, cocaine and amphetamine users. Addiction 1995;90 (5) 607- 614
PubMed Link to Article
Center for the Study of Attention and Emotion, The International Affective Picture System: Photographic Slides.  Gainesville, FL University of Florida, Centre for Research in Psychophysiology1994;
Hodes  RLCook  EWLang  PJ Individual differences in autonomic response: conditioned association or conditioned fear? Psychophysiology 1985;22 (5) 545- 560
PubMed Link to Article
Cook  EW VPM Reference Manual (Version 11.2).  Birmingham, AL University of Alabama2000;
Semlitsch  HVAnderer  PSchuster  PPresslich  O A solution for reliable and valid reduction of ocular artefacts, applied to the P300 ERP. Psychophysiology 1986;23 (6) 695- 703
PubMed Link to Article
Cody  DTBickford  RG Averaged evoked myogenic responses in normal man. Laryngoscope 1969;79 (3) 400- 416
PubMed Link to Article
Kiang  NYSCrist  AHFrench  MAEdwards  AG Postauricular electrical response to acoustic stimuli in humans. Q Prog Rep 1963; (68) 218- 225
Schupp  HTFlaisch  TStockburger  JJunghofer  M Emotion and attention: event-related brain potential studies. Prog Brain Res 2006;15631- 51
PubMed
Robbins  SJEhrman  RN The role of attentional bias in substance abuse. Behav Cogn Neurosci Rev 2004;3 (4) 243- 260
PubMed Link to Article
Franken  IHKroon  LYWiers  RWJansen  A Selective cognitive processing of drug cues in heroin dependence. J Psychopharmacol 2000;14 (4) 395- 400
PubMed Link to Article
Franken  IHHulstijn  KPStam  CJHendriks  VMvan den Brink  W Two new neurophysiological indices of cocaine craving: evoked brain potentials and cue modulated startle reflex. J Psychopharmacol 2004;18 (4) 544- 552
PubMed Link to Article
Herrmann  MJWeijers  HGWiesbeck  GAAranda  DBoning  JFallgatter  AJ Event-related potentials and cue-reactivity in alcoholism. Alcohol Clin Exp Res 2000;24 (11) 1724- 1729
PubMed Link to Article
Herrmann  MJWeijers  HGWiesbeck  GABoning  JFallgatter  AJ Alcohol cue-reactivity in heavy and light social drinkers as revealed by event-related potentials. Alcohol Alcohol 2001;36 (6) 588- 593
PubMed Link to Article
van de Laar  MCLicht  RFranken  IHHendriks  VM Event-related potentials indicate motivational relevance of cocaine cues in abstinent cocaine addicts. Psychopharmacology (Berl) 2004;177 (1-2) 121- 129
PubMed Link to Article
Warren  CAMcDonough  BE Event-related brain potentials as indicators of smoking cue-reactivity. Clin Neurophysiol 1999;110 (9) 1570- 1584
PubMed Link to Article
Franken  IHZijlstra  CMuris  P Are nonpharmacological induced rewards related to anhedonia? a study among skydivers. Prog Neuropsychopharmacol Biol Psychiatry 2006;30 (2) 297- 300
PubMed Link to Article
Wang  GJVolkow  NDFowler  JSLogan  JAbumrad  NNHitzemann  RJPappas  NSPascani  K Dopamine D2 receptor availability in opiate-dependent subjects before and after naloxone-precipitated withdrawal. Neuropsychopharmacology 1997;16 (2) 174- 182
PubMed Link to Article
Zijlstra  FBooij  Jvan den Brink  WFranken  IH Striatal dopamine D2 receptor binding and dopamine release during cue-elicited craving in recently abstinent opiate-dependent males. Eur Neuropsychopharmacol 2008;18 (4) 262- 270
PubMed Link to Article
Volkow  NDWang  GJFowler  JSLogan  JGatley  SJGifford  AHitzemann  RDing  YSPappas  N Prediction of reinforcing responses to psychostimulants in humans by brain dopamine D2 receptor levels. Am J Psychiatry 1999;156 (9) 1440- 1443
PubMed
Nader  MAMorgan  DGage  HDNader  SHCalhoun  TLBuchheimer  NEhrenkaufer  RMach  RH PET imaging of dopamine D2 receptors during chronic cocaine self-administration in monkeys. Nat Neurosci 2006;9 (8) 1050- 1056
PubMed Link to Article
Abbott  PJWeller  SBWalker  SR Psychiatric disorders of opioid addicts entering treatment: preliminary data. J Addict Dis 1994;13 (3) 1- 11
PubMed Link to Article
Mason  BJKocsis  JHMelia  DKhuri  ETSweeney  JWells  ABorg  LMillman  RBKreek  MJ Psychiatric comorbidity in methadone maintained patients. J Addict Dis 1998;17 (3) 75- 89
PubMed Link to Article
Darke  S Self-report among injecting drug users: a review. Drug Alcohol Depend 1998;51 (3) 253- 263
PubMed Link to Article

Figures

Place holder to copy figure label and caption
Figure 1.

Self-Assessment Manikin (SAM) valence (A) and arousal (B) ratings and corrugator supercilii (C) and zygomaticus major (D) electromyogram reactivity by picture types within baseline groups. Error bars represent 1 SEM.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2.

Postauricular reflex (A, control group [n = 12]; B, opiate-dependent group [n = 23]) and startle-elicited event-related potential (ERP) amplitude (C, control group [n = 19]; D, opiate-dependent group [n = 32]) by picture type within baseline groups.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 3.

Scatterplot of standardized residual values for significant baseline predictors of regular heroin use at follow-up. For each variable, a residual score was calculated that reflected the relative response between 2 classes of stimuli. This relative response score is plotted separately for each group (ie, weekly or more frequent heroin use at follow-up [clear circles] vs less than weekly heroin use [black triangles]), within each variable, thereby depicting group differences in their relative response to either drug vs pleasant, drug vs neutral, or pleasant vs neutral stimuli.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Demographic and Basic Group Characteristics at Baseline and Follow-up
Table Graphic Jump LocationTable 2. Mixed-Factor ANOVA Results for SAM Ratings and Psychophysiological Dependent Variables

References

Ribot  T La psychologie des sentiments.  Paris, France Felix Alcan1896;
Gawin  FHEllinwood  EH  Jr Cocaine and other stimulants: actions, abuse, and treatment. N Engl J Med 1988;318 (18) 1173- 1182
PubMed Link to Article
Miller  NSSummers  GLGold  MS Cocaine dependence: alcohol and other drug dependence and withdrawal characteristics. J Addict Dis 1993;12 (1) 25- 35
PubMed Link to Article
Janiri  LMartinotti  GDario  TReina  DPaparello  FPozzi  GAddolorato  GDi Giannantonio  MDe Risio  S Anhedonia and substance-related symptoms in detoxified substance-dependent subjects: a correlation study. Neuropsychobiology 2005;52 (1) 37- 44
PubMed Link to Article
Stevens  APeschk  ISchwarz  J Implicit learning, executive function and hedonic activity in chronic polydrug abusers, currently abstinent polydrug abusers and controls. Addiction 2007;102 (6) 937- 946
PubMed Link to Article
Altman  JEveritt  BJGlautier  SMarkou  ANutt  DOretti  RPhillips  GDRobbins  TW The biological, social and clinical bases of drug addiction: commentary and debate. Psychopharmacology (Berl) 1996;125 (4) 285- 345
PubMed Link to Article
Kenny  PJPolis  IKoob  GFMarkou  A Low dose cocaine self-administration transiently increases but high dose cocaine persistently decreases brain reward function in rats. Eur J Neurosci 2003;17 (1) 191- 195
PubMed Link to Article
Nazzaro  JMSeeger  TFGardner  EL Morphine differentially affects ventral tegmental and substantia nigra brain reward thresholds. Pharmacol Biochem Behav 1981;14 (3) 325- 331
PubMed Link to Article
Koob  GFLe Moal  M Drug abuse: hedonic homeostatic dysregulation. Science 1997;278 (5335) 52- 58
PubMed Link to Article
Koob  GFLe Moal  M Drug addiction, dysregulation of reward, and allostasis. Neuropsychopharmacology 2001;24 (2) 97- 129
PubMed Link to Article
Heinz  ASchmidt  LGReischies  FM Anhedonia in schizophrenic, depressed, or alcohol-dependent patients: neurobiological correlates. Pharmacopsychiatry 1994;27 ((suppl 1)) 7- 10
PubMed Link to Article
Robinson  TEBerridge  KC The neural basis of drug craving: an incentive-sensitization theory of addiction. Brain Res Brain Res Rev 1993;18 (3) 247- 291
PubMed Link to Article
Lubman  DIPeters  LAMogg  KBradley  BPDeakin  JF Attentional bias for drug cues in opiate dependence. Psychol Med 2000;30 (1) 169- 175
PubMed Link to Article
Franken  IH Drug craving and addiction: integrating psychological and neuropsychopharmacological approaches. Prog Neuropsychopharmacol Biol Psychiatry 2003;27 (4) 563- 579
PubMed Link to Article
Lubman  DIAllen  NBPeters  LADeakin  JF Electrophysiological evidence of the motivational salience of drug cues in opiate addiction. Psychol Med 2007;37 (8) 1203- 1209
PubMed Link to Article
Goldstein  RZVolkow  ND Drug addiction and its underlying neurobiological basis: neuroimaging evidence for the involvement of the frontal cortex. Am J Psychiatry 2002;159 (10) 1642- 1652
PubMed Link to Article
Lubman  DIYücel  MPantelis  C Addiction, a condition of compulsive behaviour? neuroimaging and neuropsychological evidence of inhibitory dysregulation. Addiction 2004;99 (12) 1491- 1502
PubMed Link to Article
Daglish  MRCWeinstein  AMalizia  ALWilson  SMelichar  JKBritten  SBrewer  CLingford-Hughes  AMyles  JSGrasby  PNutt  DJ Changes in regional cerebral blood flow elicited by craving memories in abstinent opiate-dependent subjects. Am J Psychiatry 2001;158 (10) 1680- 1686
PubMed Link to Article
Childress  ARMozley  PDMcElgin  WFitzgerald  JReivich  MO'Brien  CP Limbic activation during cue-induced cocaine craving. Am J Psychiatry 1999;156 (1) 11- 18
PubMed
Wang  GJVolkow  NDFowler  JSCervany  PHitzemann  RJPappas  NRWong  CTFelder  C Regional brain metabolic activation during craving elicited by recall of previous drug experiences. Life Sci 1999;64 (9) 775- 784
PubMed Link to Article
Marissen  MAFranken  IHWaters  AJBlanken  Pvan den Brink  WHendriks  VM Attentional bias predicts heroin relapse following treatment. Addiction 2006;101 (9) 1306- 1312
PubMed Link to Article
Waters  AJShiffman  SSayette  MAPaty  JAGwaltney  CJBalabanis  MH Attentional bias predicts outcome in smoking cessation. Health Psychol 2003;22 (4) 378- 387
PubMed Link to Article
Lubman  DIAllen  NBPeters  LADeakin  JFW Electrophysiological evidence that drug cues have greater salience than other affective stimuli in opiate addiction. J Psychopharmacol 2008;22(8)836- 842
PubMed Link to Article
Gossop  MMarsden  JStewart  DKidd  T The National Treatment Outcome Research Study (NTORS): 4-5 year follow-up results. Addiction 2003;98 (3) 291- 303
PubMed Link to Article
Mattick  RPAli  RWhite  JMO'Brien  SWolk  SDanz  C Buprenorphine versus methadone maintenance therapy: a randomized double-blind trial with 405 opioid-dependent patients. Addiction 2003;98 (4) 441- 452
PubMed Link to Article
Bloor  MMcIntosh  JMcKeganey  NRobertson  M ‘Topping up’ methadone: an analysis of patterns of heroin use among a treatment sample of Scottish drug users [published online ahead of print May 19, 2008]. Public Health 2008;122 (10) 1013- 1019
PubMed Link to Article
Brewer  DDCatalano  RFHaggerty  KGainey  RRFleming  CB A meta-analysis of predictors of continued drug use during and after treatment for opiate addiction. Addiction 1998;93 (1) 73- 92
PubMed Link to Article
Strain  ECBigelow  GELiebson  IAStitzer  ML Moderate- vs high-dose methadone in the treatment of opioid dependence: a randomized trial. JAMA 1999;281 (11) 1000- 1005
PubMed Link to Article
Bradley  MM Emotion and motivation. Cacioppo  JTTassinary  LGBernston  GGHandbook of Psychophysiology. 2nd ed. New York, NY Cambridge University Press2000;602- 642
Benning  SDPatrick  CJLang  AR Emotional modulation of the post-auricular reflex. Psychophysiology 2004;41 (3) 426- 432
PubMed Link to Article
Schupp  HTCuthbert  BNBradley  MMBirbaumer  NLang  PJ Probe P3 and blinks: two measures of affective startle modulation. Psychophysiology 1997;34 (1) 1- 6
PubMed Link to Article
First  MBSpitzer  RLMiriam  GWilliams  JBW Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Patient Edition.  New York, NY Biometrics Research, New York State Psychiatric Institute2001;
Beck  ATWard  CHMendelson  MMock  JErbaugh  J An inventory for measuring depression. Arch Gen Psychiatry 1961;4561- 585
PubMed Link to Article
Watson  DClark  LATellegen  A Development and validation of brief measures of positive and negative affect: The PANAS scales. J Pers Soc Psychol 1988;54 (6) 1063- 1070
PubMed Link to Article
Gossop  M The development of a Short Opiate Withdrawal Scale (SOWS). Addict Behav 1990;15 (5) 487- 490
PubMed Link to Article
Gossop  MDarke  SGriffiths  PHando  JPowis  BHall  WStrang  J The Severity of Dependence Scale (SDS): psychometric properties of the SDS in English and Australian samples of heroin, cocaine and amphetamine users. Addiction 1995;90 (5) 607- 614
PubMed Link to Article
Center for the Study of Attention and Emotion, The International Affective Picture System: Photographic Slides.  Gainesville, FL University of Florida, Centre for Research in Psychophysiology1994;
Hodes  RLCook  EWLang  PJ Individual differences in autonomic response: conditioned association or conditioned fear? Psychophysiology 1985;22 (5) 545- 560
PubMed Link to Article
Cook  EW VPM Reference Manual (Version 11.2).  Birmingham, AL University of Alabama2000;
Semlitsch  HVAnderer  PSchuster  PPresslich  O A solution for reliable and valid reduction of ocular artefacts, applied to the P300 ERP. Psychophysiology 1986;23 (6) 695- 703
PubMed Link to Article
Cody  DTBickford  RG Averaged evoked myogenic responses in normal man. Laryngoscope 1969;79 (3) 400- 416
PubMed Link to Article
Kiang  NYSCrist  AHFrench  MAEdwards  AG Postauricular electrical response to acoustic stimuli in humans. Q Prog Rep 1963; (68) 218- 225
Schupp  HTFlaisch  TStockburger  JJunghofer  M Emotion and attention: event-related brain potential studies. Prog Brain Res 2006;15631- 51
PubMed
Robbins  SJEhrman  RN The role of attentional bias in substance abuse. Behav Cogn Neurosci Rev 2004;3 (4) 243- 260
PubMed Link to Article
Franken  IHKroon  LYWiers  RWJansen  A Selective cognitive processing of drug cues in heroin dependence. J Psychopharmacol 2000;14 (4) 395- 400
PubMed Link to Article
Franken  IHHulstijn  KPStam  CJHendriks  VMvan den Brink  W Two new neurophysiological indices of cocaine craving: evoked brain potentials and cue modulated startle reflex. J Psychopharmacol 2004;18 (4) 544- 552
PubMed Link to Article
Herrmann  MJWeijers  HGWiesbeck  GAAranda  DBoning  JFallgatter  AJ Event-related potentials and cue-reactivity in alcoholism. Alcohol Clin Exp Res 2000;24 (11) 1724- 1729
PubMed Link to Article
Herrmann  MJWeijers  HGWiesbeck  GABoning  JFallgatter  AJ Alcohol cue-reactivity in heavy and light social drinkers as revealed by event-related potentials. Alcohol Alcohol 2001;36 (6) 588- 593
PubMed Link to Article
van de Laar  MCLicht  RFranken  IHHendriks  VM Event-related potentials indicate motivational relevance of cocaine cues in abstinent cocaine addicts. Psychopharmacology (Berl) 2004;177 (1-2) 121- 129
PubMed Link to Article
Warren  CAMcDonough  BE Event-related brain potentials as indicators of smoking cue-reactivity. Clin Neurophysiol 1999;110 (9) 1570- 1584
PubMed Link to Article
Franken  IHZijlstra  CMuris  P Are nonpharmacological induced rewards related to anhedonia? a study among skydivers. Prog Neuropsychopharmacol Biol Psychiatry 2006;30 (2) 297- 300
PubMed Link to Article
Wang  GJVolkow  NDFowler  JSLogan  JAbumrad  NNHitzemann  RJPappas  NSPascani  K Dopamine D2 receptor availability in opiate-dependent subjects before and after naloxone-precipitated withdrawal. Neuropsychopharmacology 1997;16 (2) 174- 182
PubMed Link to Article
Zijlstra  FBooij  Jvan den Brink  WFranken  IH Striatal dopamine D2 receptor binding and dopamine release during cue-elicited craving in recently abstinent opiate-dependent males. Eur Neuropsychopharmacol 2008;18 (4) 262- 270
PubMed Link to Article
Volkow  NDWang  GJFowler  JSLogan  JGatley  SJGifford  AHitzemann  RDing  YSPappas  N Prediction of reinforcing responses to psychostimulants in humans by brain dopamine D2 receptor levels. Am J Psychiatry 1999;156 (9) 1440- 1443
PubMed
Nader  MAMorgan  DGage  HDNader  SHCalhoun  TLBuchheimer  NEhrenkaufer  RMach  RH PET imaging of dopamine D2 receptors during chronic cocaine self-administration in monkeys. Nat Neurosci 2006;9 (8) 1050- 1056
PubMed Link to Article
Abbott  PJWeller  SBWalker  SR Psychiatric disorders of opioid addicts entering treatment: preliminary data. J Addict Dis 1994;13 (3) 1- 11
PubMed Link to Article
Mason  BJKocsis  JHMelia  DKhuri  ETSweeney  JWells  ABorg  LMillman  RBKreek  MJ Psychiatric comorbidity in methadone maintained patients. J Addict Dis 1998;17 (3) 75- 89
PubMed Link to Article
Darke  S Self-report among injecting drug users: a review. Drug Alcohol Depend 1998;51 (3) 253- 263
PubMed Link to Article

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Multimedia

Responsiveness to Drug Cues and Natural Rewards in Opiate Addiction: Associations With Later Heroin Use
Arch Gen Psychiatry.2009;66(2):205-212.eTable. Significant Prospective Baseline Predictors of Regular Heroin Use During the Follow-up Period

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