Alcoholic beverage pictures task design and stimuli samples.
Functional magnetic resonance imaging (fMRI) results during alcoholic beverage picture trials relative to nonalcoholic beverage picture trials. Orange indicates where teens with alcohol use disorder (n = 15) had more response than control subjects (n = 15) to alcoholic beverage pictures. Blue shows where controls had more response to alcoholic beverage pictures (group P<.05; clusters, >515 µL). Numbers refer to axial slice positions. The fMRI results are displayed on averaged anatomic brain maps.
Blood oxygen level–dependent (BOLD) response signal contrast in the right precuneus/posterior cingulate region during exposure to alcoholic beverage pictures relative to nonalcoholic beverage pictures plotted as a function of drinks consumed per month for adolescents with alcohol use disorder (n = 15; r = 0.76 [P<.001]).
Tapert SF, Cheung EH, Brown GG, Frank LR, Paulus MP, Schweinsburg AD, Meloy MJ, Brown SA. Neural Response to Alcohol Stimuli in Adolescents With Alcohol Use Disorder. Arch Gen Psychiatry. 2003;60(7):727-735. doi:10.1001/archpsyc.60.7.727
Cue reactivity studies in alcohol-dependent adults have shown atypical physiological, cognitive, and neural responses to alcohol-related stimuli that differ from the responses of light drinkers. Cue reactivity and its neural substrates are unclear in youth. We hypothesized that teens with alcohol use disorder would show greater brain response than nonabusing teens to alcohol images relative to neutral beverage images in limbic and frontal brain regions.
We tested the hypotheses in a cross-sectional functional magnetic resonance imaging study. Adolescents aged 14 to 17 were recruited from local high schools. Teens with alcohol use disorders (n = 15) and demographically similar infrequent drinkers (n = 15) met strict exclusion criteria (no left-handedness or neurological, other psychiatric, or other substance use disorders). Diagnoses were determined by means of structured and semistructured clinical interviews. Subjects were shown pictures of alcoholic and nonalcoholic beverage advertisements during blood oxygen level–dependent functional magnetic resonance imaging. Self-reports of craving were obtained before and after cue exposure.
Teens with alcohol use disorders showed substantially greater brain activation to alcoholic beverage pictures than control youths, predominantly in the left anterior, limbic, and visual system areas (P<.05; cluster threshold, 515 µL). The degree of brain response to the alcohol pictures was highest in youths who consumed more drinks per month and reported greater desires to drink.
These results confirm previous studies by demonstrating an association between the urge to drink alcohol and blood oxygen use in areas of the brain previously linked to reward, desire, positive affect, and episodic recall. This study extends this relationship to adolescents with relatively brief drinking histories using visual alcohol stimuli, and suggests a neural basis for response to alcohol advertisements in youths with drinking problems.
DRUG CRAVING is often described as the subjective experience of an intense desire for an addictive substance.1,2 It may occupy cognitive resources and influence substance use decisions,3 but it is difficult to measure objectively. Cue reactivity is an observable correlate of craving4 that has been demonstrated in alcohol-dependent adults5- 7 through physiological changes such as increased heart rate or salivation on exposure to alcohol-related words, pictures, scents, tactile cues, or imaginal stimuli.6,8,9 Alcohol-dependent adults show difficulty shifting attention away from alcohol-related stimuli,10 and, consequently, substance cues can interfere with the deployment of effective coping responses.11,12 Although cue reactivity has not been studied in adolescents, substance-dependent youths show difficulty implementing coping skills when substance cues are salient.13
Knowledge about the neural systems subserving cue reactivity and craving has expanded recently.14 Alcohol-dependent adults given small amounts of alcohol showed increased blood flow in the right caudate nucleus that was positively correlated with self-reports of alcohol craving.15 Left prefrontal16- 19 and bilateral orbitofrontal cortices17,20- 24 have commonly activated in response to substance cues among adults. Some studies have reported bilateral activation changes in the amygdala,16,17,21,22 but more have shown a left and bilateral anterior cingulate response that correlates with craving.16- 18,20- 22,25 The reward-related nucleus accumbens appears responsive to substance cues and is related to craving reports, bilaterally16,25 and on the left,26 although some studies could not precisely localize this small region. In the first functional magnetic resonance imaging (fMRI) study of pictoral alcohol cues, George and colleagues19 reported a thalamic and left prefrontal response to alcoholic relative to nonalcoholic beverage pictures (hereafter referred to as alcohol and nonalcohol pictures, respectively) among alcohol-dependent adults compared with matched control subjects. Our group previously studied brain responses of women aged 18 to 25 years to alcohol- and nonalcohol-related words.26 Alcohol-dependent young women demonstrated significantly more blood oxygen level–dependent (BOLD) response than controls during alcohol word presentation relative to neutral words in the anterior cingulate, left prefrontal cortex, bilateral insular gyri, and subcallosal cortex, which houses the nucleus accumbens. However, group differences were relatively modest.26
To summarize, a sizable literature reports that adults with substance use disorders demonstrate atypical reactions to stimuli that have been conditioned to addictive substances.27 However, the brain regions reported to subserve cue reactivity have been somewhat inconsistent, due in part to differing imaging techniques, stimuli, and populations, as previously reviewed.26 Cue-induced brain changes have not been studied in adolescents, although substance-dependent youths report substantial levels of craving,28 posttreatment relapses among adolescents are associated with exposure to substance-related cues,29 and youths report exposure to alcohol cues through advertising an average of 30 times per month.30 To assess the neural substrates of cue reactivity in youth, we studied adolescents aged 14 to 17 years, used pictures instead of words to more directly elicit cue reactivity, and used personally relevant alcohol stimuli. It was hypothesized that adolescents with alcohol use disorders (AUDs) would exhibit more brain activity in response to alcohol cues relative to teens without drinking problems, particularly in anterior cingulate, prefrontal, orbitofrontal, and subcallosal cortices. In addition, we hypothesized that desires to drink would correlate with levels of activation.
Recruitment flyers were distributed at local high schools. When a teen telephoned in response, a brief interview preliminarily ascertained eligibility, then the legal guardian was asked exclusionary questions. After a description of the study, written informed consent and assent, approved by the University of California–San Diego Institutional Review Board, were obtained from parents and adolescents. The teen was administered a 90-minute detailed screening interview, including the Family History Assessment Module screener,31 to assess family history of substance use and psychiatric diagnoses. We used the Customary Drinking and Drug Use Record32 to assess substance use and abuse/dependence criteria, and the Diagnostic Interview Schedule for Children to assess adolescent psychiatric diagnoses.33 The same measures were administered to the parent by a separate interviewer for corroboration. In cases of discrepancies, additional data were obtained or data were coded to represent the lower level of functioning or presence of problems.
Exclusionary criteria were a history of a DSM-IV psychiatric or substance disorder other than AUD, neurological illness, head trauma with loss of consciousness for longer than 2 minutes, serious medical problems and learning disability; current use of medications that could affect the central nervous system; smoking more than 4 cigarettes per day; significant maternal drinking during pregnancy (≥4 drinks per occasion or ≥7 drinks per week); family history of bipolar I or psychotic disorders; inadequate English skills; sensory problems; left-handedness; and irremovable metal on the body. Because of high comorbidity with substance use disorders,34,35 teens meeting criteria for conduct disorder (n = 2) were not excluded.
Participants with AUD (n = 15) met current DSM-IV criteria for alcohol abuse or dependence, and normal controls (n = 15) had very limited experience with alcohol or other drugs. Each group contained 6 girls and 9 boys with an average age of 16 years, mostly from upper middle-class families (Table 1). Based on neuropsychological tests administered the day of scanning, participants in both groups were above average intellectually. Youths with AUD typically drank 6 drinks each weekend night and had met criteria for alcohol abuse (n = 7) or dependence (n = 8) for 1 to 2 years. Youths with AUD reported higher levels of depressed mood and nervousness before scanning than controls, although all were in the reference range except 1 participant with AUD (Beck Depression Inventory score, 19; Spielberger Anxiety T score, 64). After excluding this participant, groups had similar Beck Depression Inventory scores. Subjects with AUD still showed more anxiety, although within the normal range (Spielberger Anxiety T score, 44).
Teens and parents were administered a structured clinical interview36 by separate psychometrists covering demographic, medical, academic, family, and social functioning information. Parents were asked about the teen's developmental history and familial socioeconomic status37 and administered the Child Behavior Checklist.38
Substance involvement and use disorder diagnoses were assessed with the Customary Drinking and Drug Use Record,32 which collects lifetime and past 3-month information on alcohol, nicotine, and other drug use and assesses DSM-IV abuse and dependence criteria,39,40 withdrawal symptoms, and other negative consequences of substance use. Strong psychometric properties have been demonstrated in adolescents.32,41 The Timeline Follow-back42 provided substance use patterns for the previous 30 days. All participants submitted urine samples for drug toxicologic screening.
A 2-hour neuropsychological test battery was administered by a trained psychometrist (A.D.S.). The battery covered attention, working memory, learning and memory, and executive, visuospatial, and language functioning. General intellect was estimated with the Wechsler Intelligence Scale for Children-III Vocabulary subtest,43 which correlates highly with full-scale IQ.43,44
The Beck Depression Inventory45 and state scale of the Spielberger State-Trait Anxiety Inventory46 assessed mood at the time of scanning. The Stanford Sleepiness Scale measured alertness immediately before and after scanning with self-reported ratings (1 indicates alert; 7, almost asleep).47
Alcohol craving was evaluated immediately before and after scanning with a 100-mm visual analog scale for rating the urge to drink48 and the Desires for Alcohol Questionnaire (DAQ).49 The DAQ yields the following 3 factors: reinforcing effects, strong desires to drink, and mild desires to drink.
Several days before scanning, participants were asked for their preferred alcoholic and nonalcoholic beverage brands and what brands they had consumed in the past year to guide individualized beverage picture selection. Participants were asked to abstain from alcohol and other drugs for at least 48 hours before imaging. The most recent drinking reported was 72 hours before scanning, and no withdrawal symptoms were reported or evident in any participant the day of scanning. All imaging sessions occurred on Thursdays from 8 to 10 PM to maximize recovery from weekend binge drinking and maintain consistent circadian influence across subjects. Once subjects arrived for the single assessment session, Breathalyzer (Intoximeter, Inc, St Louis, Mo) and urine samples were collected for drug toxicologic screening and, for girls, pregnancy screening. No participant had a measurable breath alcohol concentration, and only 1 participant, who had disclosed recent marijuana use (3 days before), had positive findings in a urine sample.
Teens underwent assessment by a trained bachelor-level psychometrist of the same sex (E.H.C. or A.D.S.). Throughout scheduling and the assessment session, adolescents were told about the imaging procedures and the importance of keeping as still as possible during scanning. This was boldasized again just before scanner entry by the MRI technologist. After lying in the scanner, a soft cloth was placed on the participant's forehead, which was then taped to the head coil to minimize head motion, and a response box was placed in the subject's right hand.
The scanning protocol consisted of high-resolution structural imaging (inversion-recovery-prepared, T1-weighted, sagittally acquired 3-dimensional spiral fast spin echo50; 16 interleaves; echo-train length, 8; repetition time, 2000 milliseconds; inversion time, 700 milliseconds; echo spacing, 15.6 milliseconds; echo time, 15.6 milliseconds; field of view, 240 mm; in-plane resolution, 0.9375 × 0.9375 mm; through-plane resolution, 1.328 mm; 128 continuous slices; acquisition time, 8 minutes 36 seconds) and axially-acquired T2*-weighted spiral gradient recall echo imaging (repetition time, 3000 milliseconds; echo time, 40 milliseconds; flip angle, 90°; field of view, 240 mm; 20-21 axial slices covering the whole brain; slice thickness, 7 mm; reconstructed in-plane resolution, 1.875 × 1.875 mm; 138 repetitions; acquisition time, 6 minutes 54 seconds). Spiral imaging was used because it helps reduce the effects of motion on time series acquisitions.51,52
During fMRI data collection, an alcohol pictures task was administered. This task sequentially presented 20 alcohol and 20 nonalcohol pictures matched by color, visual complexity, and presence of people. The images were selected from a bank of more than 200 advertisements scanned from youth-oriented popular magazines (eg, Rolling Stone, Spin, Sports Illustrated, and Cosmopolitan) or downloaded from the Internet. A personalized set of images was selected for each adolescent, on the basis of his or her alcoholic and nonalcoholic beverage preferences and experiences, to ensure familiarity with the stimuli. To maintain focus on the task without drawing attention to picture content, instructions consisted of the following: "Press 1 if the picture contains a person; press 2 if there is no person" (30% in each condition contained a person). The task alternated between 30-second blocks of each condition (10 trials per block; 2500-millisecond stimulus presentation and 500-second interstimulus interval), with fixation periods at the beginning, middle, and end (Figure 1 shows task design and stimulus samples).
Data were processed and analyzed with the Analysis of Functional NeuroImages package.53 First, we applied to the time series data a 3-dimensional motion-correction algorithm that aligned each volume in the time series to a selected base volume54 and estimated 3 rotational and 3 displacement parameters for each participant. To determine whether bulk motion differed between groups, each subject's absolute mean for each of the 6 motion parameters across the time series was compared in 1-way analyses of variance (ANOVAs). Controls required significantly more motion correction for 2 parameters (roll, 0.05 vs 0.03 mm [P = .02]; left displacement, 0.04 vs 0.02 mm [P =.05]) and showed more variability in motion during the time series for 2 parameters (roll SD, 0.05 vs 0.04 [P = .04]; yaw SD, 0.08 vs 0.05 [P = .02]), although the magnitude of these differences was quite small. To estimate task-correlated motion, the 6 parameters were correlated with the task reference vector across the time series for each subject. The median correlations for the AUD group were −0.069, 0.122, −0.011, −0.006, −0.067, and −0.037; for controls, −0.054, 0.059, 0.003, 0.042, −0.135, and −0.032 for roll, pitch, and yaw rotations and superior, left, and posterior displacements, respectively. Task-correlated values were compared between groups using Spearman correlations (P>.18 for all).
Next, the time series data were correlated with a set of 7 task reference vectors. These consisted of 1 seed reference function representing the alternating conditions during the time course of the task (depicted in Figure 1), and the same reference vector shifted in 1-second increments 6 times forward to account for delays in hemodynamic response,55 while covarying for linear trends and the estimated degree of motion (to control for spin history effects). Only the reference vector producing the highest correlation with the time course data was used, yielding fit coefficients for every subject in each voxel representing the correspondence between the observed and hypothesized signal. Each participant's imaging results were transformed into standard56 coordinates, and the functional map was resampled into isotropic voxels (3.5 mm3). We applied a spatial smoothing Gaussian filter (full width at half maximum, 3.5 mm) to manage individual variability in gyral structure. To test hypotheses, an independent-samples t test (α = .05, 2-tailed) compared groups on BOLD response contrast across the time series, essentially testing a group × condition interaction. Urge scores were correlated with BOLD response contrast for each voxel. Type I error was controlled by requiring that voxels surpass the specified α and compose clusters of more than 515 µL,57,58 yielding a voxelwise P of .00002 and a clusterwise P of .032.
Response logging failed for 8 AUD group and 6 control participants. Using available data, groups did not differ between task conditions in reaction time (alcohol pictures, 805.76 vs 860.31 milliseconds; neutral pictures, 736.18 vs 840.83 milliseconds for AUD and control groups, respectively) or accuracy (alcohol pictures, 96% both groups; neutral pictures, 98% both groups). The AUD group reported significantly more desires to drink than controls for all 3 DAQ factors before and after scanning. However, no significant increases were found after scanning for either group on the DAQ or the visual analog scale scores. As a result, we averaged prescanning and postscanning DAQ scores for use in analyses.
Throughout the brain, the AUD group showed significantly more BOLD response than controls to alcohol pictures relative to neutral beverage pictures, particularly in the left hemisphere, including frontal and limbic regions (P<.05; effect sizes, 3.67-9.69). This included hypothesized regions (ie, the ventral anterior cingulate, prefrontal cortex, orbital gyrus, and subcallosal cortex) and other areas (eg, the inferior frontal gyrus, paracentral lobule, parahippocampus, amygdala, fusiform gyrus, temporal lobe, hypothalamus, posterior cingulate, precuneus, cuneus, and angular gyrus). In contrast, controls showed more BOLD response to alcohol pictures relative to neutral pictures than the AUD group in 2 right frontal regions (P<.05; Table 2 and Figure 2). This analysis was rerun excluding the AUD participant with higher levels of depressed mood and anxiety. All regions listed in Table 2 remained significant except 2, and the AUD group showed more response to alcohol pictures in the left inferior parietal lobule (Brodmann area 39) than controls. The group × condition interactions were confirmed using the 3-dimensional ANOVA2 program of the Analysis of Functional NeuroImages,53 comparing signals from alcohol and nonalcohol picture conditions relative to fixation for both groups. All regions listed in Table 2 were confirmed, except for the right inferior frontal gyrus and right lateral precuneus.
To understand the main effect of condition, we studied each group separately in single-sample t tests. The AUD group showed greater response to alcohol pictures relative to neutral pictures in 21 regions, whereas they showed increased response to neutral beverages in just 2 regions (P<.05). Controls had more response to alcoholic beverage pictures in 5 locations, yet more response to neutral pictures relative to alcoholic beverage pictures in 16 regions (P<.05). To examine the main effect of group, we contrasted the BOLD response between the alcoholic beverage pictures and fixation conditions. Both groups showed considerably more response to the alcoholic beverage pictures than to the fixation cross, but the AUD group showed a more extensive response contrast. However, this contrast and the contrast between neutral pictures and fixation revealed some BOLD response in the AUD group during fixation blocks, perhaps indicating continued reactivity to alcohol stimuli.
To test the relationship between self-reported desire to drink and brain response, the 3 DAQ factors (reinforcement, strong desires, and mild desires) were examined in regressions to predict the BOLD response contrast between the alcoholic and nonalcoholic beverage pictures for each group. Among those in the AUD group, the reinforcement factor did not predict variability in BOLD response contrast, but the strong desires factor predicted left temporal(β = 16.15) and right thalamic (β = −22.88) responses. The mild desires factor significantly and positively predicted 10 regions of enhanced BOLD response to alcoholic beverage pictures relative to neutral pictures in the AUD group (Table 3). Among controls, low reinforcement scores predicted more BOLD response contrast in the right posterior cingulate and temporal regions, and the strong and mild desires factors were unrelated to the BOLD response. To confirm these findings, we extracted each participant's signal intensity values from the regions that differed between groups (Table 2) and correlated these values with drinking and craving scores. For controls, only the following correlations were found: left temporal/fusiform BOLD signal contrast correlated with drinks consumed per month (r =0.56 [P = .03]), and left parahippocampal/amygdalar signal correlated with mild desires to drink (r =−0.55 [P = .03]). For the AUD group, 4 regions correlated positively with drinks per month (left inferior frontal, r = 0.51 [P = .04]; left paracentral lobule/dorsal cingulate, r = 0.59 [P = .02]; right precuneus/cuneus, r = 0.64[P = .01]; and right precuneus/posterior cingulate, r = 0.76 [P = .001]) (Figure 3), and 2 areas correlated negatively with reinforcement scores (right precuneus/cuneus, r =−0.73 [P = .002]; and right inferior frontal, r = −0.53 [P = .04]).
Because individuals with family histories of AUD tend to respond abnormally to alcohol and show other neural anomalies,59- 62 we compared brain responses to alcohol pictures between those in the AUD group with family histories that were positive (FHP) and negative (FHN) for AUD. The AUD-group teens with FHP (n = 9; 5 [56%] female) showed more BOLD response contrast between the alcoholic and nonalcoholic beverage pictures than the AUD-group teens with FHN (n = 6; 1 [17%] female), especially in the left posterior cingulate and prefrontal, orbital, and inferior temporal gyrus. Controls with FHP (n = 9; 3 [33%] female) showed more brain response to alcoholic beverage pictures relative to nonalcoholic beverage pictures than controls with FHN(n = 6; 3 [50%] female), particularly in the left paracentral, medial frontal, prefrontal, cuneus, and anterior cingulate areas. However, when comparing the 9 AUD-group teens with FHP and the 9 controls with FHP, the AUD group showed substantially more response to alcoholic beverage pictures throughout the brain. Similarly, the 6 AUD-group teens with FHN showed more brain response to alcohol pictures than the 6 controls with FHN, except in the right frontal pole and left dorsolateral prefrontal cortex.
In adolescence, sex differences in neuromaturation63- 65 influence affective response66 and may relate to cue reactivity. We compared responses with alcohol pictures relative to nonalcohol pictures in boys and girls of the AUD and control groups. In the AUD group, girls (n = 6) showed more brain response than boys (n = 9) to alcohol pictures, particularly in the anterior cingulate and left prefrontal regions, whereas boys showed a strong response to alcohol pictures in the left orbital gyrus and bilateral paracentral gyri. No sex differences were apparent among controls.
Our results supported the hypothesis that adolescents with AUD produce more brain activity in response to alcohol cues than teens without drinking problems. This was specifically supported in the ventral anterior cingulate and subcallosal, prefrontal, orbital, and limbic regions, areas previously associated with reward and drug craving. In addition, we found increased response in posterior regions that may be critical to visual association, episodic recall, appetitive functions, and the formation of associations. In teens with alcohol abuse/dependence, mild desires to drink alcohol were associated with enhanced BOLD response in frontal and visual regions and diminished response in the ventral anterior cingulate.
These results are consistent with previous studies on alcohol and other drug cues. Like many neuroimaging studies of craving, we found an anterior cingulate response to substance cues among problem users.16- 18,20- 22,25,26,67,68 These findings also support studies that found subcallosal cortex/nucleus accumbens,16,25,26 orbitofrontal,17,20- 24 left prefrontal,16- 19,22,26,68 amygdala,17,21 temporal,17,23,67,69 and posterior cingulate16,22,23 responses. The most similar design to ours is the 2001 study by George and colleagues.19 Both studies found increased left prefrontal BOLD response in individuals with AUD during exposure to alcohol relative to nonalcohol pictures. Although our study did not replicate their thalamic activation, the hypothalamus of teens in our AUD group was more responsive to alcohol pictures than to nonalcohol pictures, whereas controls did not show such an effect.
The present study found larger areas of brain activation to alcohol pictures than the study by George et al.19 Several dissimilarities between projects could account for these differences. First, youth tend to exhibit larger areas of BOLD response relative to adults across tasks70,71 owing to regional specialization that develops throughout adolescence. Second, our study used personalized stimuli instead of a standard picture series. Many youths have used only certain alcoholic beverage types, and we wanted to ensure previous exposure to beverage pictures to maximize cue reactivity. Third, our study examined the whole brain, demonstrating group differences in posterior regions. Fourth, the current study repeated images, using 40 images, whereas George et al19 used 56 pictures. This leaves the possibility that recognition might have affected results. Fifth, our pulse sequences collected data spirally in k-space, a technique that is less sensitive to motion artifact.51,52 Finally, the larger sample size resulted in more statistical power to detect subtler effects.
These results confirm the findings of a recent study from our group26 in which alcohol-related cues appeared to provoke increased BOLD response in the nucleus accumbens region among college-age women with AUD. However, the magnitude of response in the present study is much larger and involved the visual system, possibly due to the pictoral stimulus modality and younger developmental stage of participants. The role of the visual system in the response of heavy drinkers to pictoral alcohol cues was not predicted, but it merits consideration. Feature-based visual attention may serve as the earliest stage of cortical response to visual stimuli and is linked to substantial BOLD response in visual brain regions.72 Because of personal experiences and affective responses, adolescents with AUD may have attended to a broader array of features in the alcohol images, involving more visual system neurons than were involved for controls. Future studies could examine this hypothesis using eye-movement measures. The response of the AUD group to alcohol pictures in the ventromedial region (Brodmann area 47) corresponds to findings of decision-making studies73,74 that showed the importance of this region for making selections based on reward contingencies.
Several limitations of the current study warrant consideration. First, teens did not report increased craving after the alcohol pictures task, whereas the adults in the study by George et al19 did, suggesting that dynamic craving processes were not captured in the imaging sessions. Teens tend to drink in the absence of adults, so the MRI setting may have been antithetical to typical drinking situations, and youths may be less able to discriminate and report changes in craving than adults. Urge ratings were not collected during scanning, but 5 minutes before and after scanner entry. Second, adolescents with AUD had more extensive histories of other drug and nicotine use than did controls, although sex, ethnicity, and family history were comparable. Third, our use of personalized cues may diminish generalizability and comparability between participants, and it is possible that social aspects of some pictures produced different responses across participants. Within-subject designs are needed to compare the effects of individualized vs standard cues.75 Fourth, although all participants had some familiarity with the stimuli presented, teens with AUD may have had more experience with the alcohol stimuli, so results may represent differential recall effects.
This fMRI study demonstrated that high school youths with alcohol abuse or dependence show widespread and intense brain activation in response to pictures of alcohol advertisements. These results suggest that, not only are liking and remembering alcohol advertisements associated with frequent drinking76 and expecting to drink more,77 but alcohol advertisements may have a strong effect on youths with established heavy drinking patterns as well as those with family histories of AUD, similar to how media depictions of aggression have detrimental effects on children with preexisting aggressive traits.78 For young drinkers, this neural response may indicate that advertisement content has been conditioned with drinking experiences, and may reflect increasing salience of alcohol advertisements as drinking escalates. Fortunately, encouraging youths to evaluate advertisements and critique the intentions of advertisers appears to help counter the negative influences.79,80 For teens in treatment for substance use disorders, research is needed to determine whether cue reactivity can be reduced and whether diminished response to cues predicts treatment success.
Corresponding author: Susan F. Tapert, PhD, Psychology Service (116B), Veterans Affairs San Diego Healthcare System, 3350 La Jolla Village Dr, San Diego, CA 92161 (e-mail: firstname.lastname@example.org).
Submitted for publication August 23, 2002; final revision received February 6, 2003; accepted February 6, 2003.
This study was supported by grants R21 AA12519 and R01 AA13419 from the National Institute on Alcohol Abuse and Alcoholism, Bethesda, Md (Dr Tapert) and a Veterans Affairs Merit Grant (Dr Frank).
This study was presented in part at the annual meeting of the Research Society on Alcoholism, June 25, 2001; Montreal, Quebec.
We thank Lisa Eyler Zorrilla, PhD, Laura Santerre, Carmen Pulido, and Valerie Cestone for their contributions to this research.