http://informahealthcare.com/ada ISSN: 0095-2990 (print), 1097-9891 (electronic) Am J Drug Alcohol Abuse, 2014; 40(1): 58–66 ! 2014 Informa Healthcare USA, Inc. DOI: 10.3109/00952990.2013.843005

ORIGINAL ARTICLE

Neurocognitive, psychological and behavioral correlates of binge drinking and use of alcohol with caffeinated beverages in college-aged adults Todd D. Watson, PhD, John F. Sweeney, BA, and Hannah Louis, BA Department of Psychology, Lewis & Clark College, Portland, OR, USA

Abstract

Keywords

Background: We examined event-related potential (ERP), behavioral and psychological correlates of binge drinking and the use of alcohol mixed with caffeinated beverages (AmCBs) in college-aged (18–26 years) adults. Objective: Our objective was to delineate the neurocognitive correlates of different patterns of risky alcohol use in this population. Methods: We collected ERP data while an initial sample of 60 participants completed visual oddball and go/no-go tasks. We also collected self-report data measuring levels of sensation seeking, impulsivity, and drinking-induced disinhibition. In our primary analyses between binge drinker (N ¼ 17) and comparison participants (N ¼ 29), we used analysis of covariance (ANCOVA) to control for monthly marijuana usage and excluded participants who reported using other illicit drugs. As separate, exploratory analyses, we compared participants who reported using AmCBs (n ¼ 14) and those who did not (n ¼ 46), co-varying for monthly marijuana and recreational drug use. Results: We found that binge drinkers and AmCB users reported significantly higher levels of sensation seeking and drinking-induced disinhibition. In addition, we found that binge drinkers exhibited greater P3a/b amplitudes in the oddball task. In contrast, AmCB users exhibited significantly attenuated P3a amplitudes to distracter stimuli in the oddball task. However, we found no statistically significant differences in the amplitudes of P2(00) or N2(00) components between binge drinkers and comparison participants or between AmCB users and nonusers. Conclusions: Overall, these data suggest that binge drinking and AmCB use are associated with P3 alterations, but the specific effects may differ for individuals with different patterns of risky alcohol use.

Alcohol mixed with caffeinated beverages, binge drinking, event-related potentials, P300 History Received 29 May 2013 Revised 9 August 2013 Accepted 4 September 2013 Published online 22 November 2013

Introduction

Address correspondence to Todd D. Watson, PhD, Department of Psychology, 0615 S.W. Palatine Hill Road, Portland, OR 97219, USA. E-mail: [email protected]

the neural correlates of alcoholism (10, 11). It has been repeatedly demonstrated that alcoholics (and those at genetic risk for the disorder) exhibit attenuations of the P3(00) component (10). The P3 represents a complex of related but dissociable components with distinct neural and cognitive correlates (12). The P3b is elicited by infrequent, taskrelevant stimuli, such as targets requiring active evaluation. The P3a, also known as the novelty P3 or no-go P3, is elicited by infrequent, task-irrelevant distracters. The P3b has been associated with cognitive operations such as updating of working memory, while the P3a has been associated with an attentional orienting response to deviant or distracting stimuli (13–15). Both P3b and P3a effects have been reported in alcoholics, and it has been suggested that these attenuations represent deficits in cortical inhibition (10). Few studies have examined whether similar effects occur in otherwise healthy binge drinkers. Initial reports are promising, and have consistently demonstrated that bingers exhibit P3 alterations (16–20), although the exact nature of

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Although specific operational definitions of binge drinking vary, it is typically described as rapid, heavy alcohol consumption followed by periods of abstinence (1). The National Institute on Alcoholism and Alcohol Abuse (NIAAA) defines a binge as a pattern of drinking that brings blood alcohol concentration to 0.08 g%, which is approximately equivalent to consuming 5 drinks (males) or 4 drinks (females) in a 2-h period (2). Binge drinking is particularly prevalent among college-aged adults (3–6) and has been linked to a variety of adverse consequences in this population (7–9), making it a significant public health concern (1). Event-related potentials (ERPs) are noninvasive markers of cortical activity that have been extensively used to delineate

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this effect is not yet clear. Some groups have reported P3 attenuations in binge drinkers (18, 19), but others have reported P3 enhancements (16). Although the P3 is a primary component of interest in studies of binge drinking, earlier components, including the P2(00) and N2(00), may also be important. The N2 is often described as a marker of inhibitory cognitive operations and its amplitude is altered in binge drinkers, though the direction of this effect is unclear (17, 19). The P2 has been associated with early, directed attention (21) when incoming sensory information is matched with information stored in memory (22), and is attenuated in binge drinkers (19). It is possible that there are relationships between these ERP alterations and other psychological traits associated with alcohol misuse, such as sensation seeking and impulsivity. Sensation seeking is often defined as a low tolerance for boredom, need for stimulation, and tendency to take risks (23). Impulsivity is often described as the tendency to respond to stimuli or situations without foresight or planning (24). Higher levels of aspects of sensation seeking and impulsivity have been linked to increased alcohol use and/or problems in college-aged adults (4, 25). Higher levels of sensation seeking and impulsivity and are also associated with attenuated P3 amplitudes in alcoholics or their relatives (26, 27), though it is unclear if these relationships are present in binge drinkers. Another potentially risky pattern of alcohol use common among college-aged adults is the consumption of alcohol combined with caffeinated beverages (28, 29). Consuming alcohol mixed with caffeinated beverages (AmCBs) is associated with reaching higher levels of intoxication (30), engaging in dangerous behaviors, such as driving while intoxicated, and experiencing greater adverse consequences while drinking (29). Individuals consuming AmCBs subjectively feel less intoxicated than individuals drinking alcohol alone, but objective behavioral impairments do not differ between these groups (31, 32). As such, some consider AmCB use a developing public health concern (33, 34). However, relatively few studies have examined the psychological correlates of AmCB use (for one recent example, see (35)), and to our knowledge, no previous studies have specifically examined ERP responses in individuals who consume AmCBs. Using ERPs and self-report measures of sensation seeking, impulsivity, and disinhibited behaviors committed while drinking, the goal of this study was to describe the broader behavioral and psychological context of altered neurophysiological activity associated with binge drinking in college-aged adults. Considering the lack of data on the subject, we also explored the correlates of AmCB use. This could be of particular importance, as AmCB users may represent individuals who are especially likely to engage in risky alcohol use, which may be reflected by both neurocognitive and psychological variables.

Materials and methods

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initial restrictions as to a family history of alcoholism, prescription or illicit drug use or cigarette smoking. Participants were recruited through flyers posted on the Lewis & Clark College campus and all but one participant had at least one year of college education. This study was approved by the Human Subjects Research Committee and all participants gave written informed consent and received $20 compensation. Participants did not self-report using alcohol on the day of the study, though this was not confirmed by toxicology screens. Participants’ patterns of alcohol consumption were assessed using the Alcohol Use Questionnaire (36), which was modified to include additional questions assessing AmCB consumption. As suggested by the NIAAA, we defined a binge episode as the consumption of 5 drinks (males) or 4 drinks (females) in a 2-h period (2). We categorized any participant who reported 2 or more binge episodes in the previous 6 months as binge drinkers and categorized the remaining participants as the comparison group. To maximize the power of our exploration of the ERP and behavioral correlates of AmCB use, we separately defined users as individuals with any reported consumption of premixed/ prepackaged or hand-mixed AmCBs (any beverage containing both alcohol and caffeine) in the previous 6 months and defined the remaining participants as nonusers. ERP paradigms/procedure We recorded electroencephalograph (EEG) data while participants performed visual oddball and go/no-go tasks, which are commonly used to study the P3 (13). Task order was counterbalanced across participants. The tasks were presented using EEVOKE software (Advanced Neuro Technology, Enschede, the Netherlands). Participants were comfortably seated in a darkened, quiet room. Tasks were presented on a 20-inch LCD monitor (centered at eye level). Participants responded via button press using a handheld controller, and were instructed to remain as still as possible and to minimize their eye-blinks to reduce artifacts. Participants completed a 15-trial practice session before each task. Oddball task Participants viewed a series of individual stimuli consisting of frequent Standard (p ¼ 0.7; large blue circle), infrequent Target (p ¼ 0.125; small blue circle) and two infrequent Distracter stimuli: a black square (p ¼ 0.125) and a green triangle (p ¼ 0.05). This paradigm allowed investigation of both the P3b component (elicited by Targets) and P3a component (elicited by Distracters). Participants completed two blocks of 300 stimuli presented in a pseudo-random fashion. Stimuli were presented for 500 ms with an interstimulus interval (ISI) of 1300 (100) ms. To control for motor effects across stimulus types, participants were required to respond to Targets by pressing one button and all other stimuli by pressing another.

Participants Healthy college-aged (18–26 years) adults with no reported history of major psychiatric or neurological issues and normal or corrected-to-normal vision participated in the study. As our goal was to recruit a broad community sample, there were no

Go/no-go Participants viewed a series of frequent (p ¼ 0.7) go and infrequent (p ¼ 0.3) no-go stimuli. Participants were required to respond with a button press only to go stimuli. Stimuli

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consisted of 2 equilateral blue triangles (upright and inverted). Participants completed 2 blocks of 200 stimuli presented in a pseudo-random fashion, and the go stimulus (upright or inverted triangle) was counterbalanced across blocks. Stimuli were presented for 500 ms, with an ISI of 1300 (100) ms. EEG/ERP recording/analysis EEG data were collected with 32-channel WaveGuard electrode caps equipped with sintered Ag/AgCl electrodes and were recorded/analyzed with ASA software (Advanced Neuro Technology, Enschede, the Netherlands). Electrode impedances did not exceed 10 k . A fronto-central electrode was used as a ground and an average reference was used for all recordings. Data were continuously sampled at 512 Hz with a gain of 500 and a notch filter set to exclude 60-Hz noise, and were filtered with a 0.2- to 24-Hz bandpass offline. Data were epoched from 100 ms prior to stimulus onset to 800 ms after presentation, and were baseline-corrected to the pre-stimulus interval. To eliminate artifacts, epochs with EEG voltages exceeding 75 mV were excluded using an automated, computerized routine, followed by manual visual inspection of the data. Individual ERP averages were created for each stimulus type in the two tasks. For the oddball paradigm, averages were also created collapsing across the two Distracter types. ERP averages with fewer than 10 artifact-free trials were excluded from further analysis. Grand-average waveforms were created for all participants and then separately for binge drinkers and comparison participants. Based on these grand averages, P2 amplitude was defined as the highest positive data point in a window of 150–230 ms, N2 amplitude was defined as the lowest negative data point in a window of 230–310 ms and P3 amplitude was defined as the highest positive data point in a window of 310– 550 ms. Peak amplitudes (in mV) were calculated relative to the pre-stimulus baseline with an automated, computerized routine. To minimize the number of statistical comparisons, we focused on P2 and N2 amplitudes at electrode Cz and on P3 amplitude at electrodes Pz and Cz. These electrodes were selected based on visual inspection of the data and also because these sites represent a typical maxima for P3 (13) and N2 components (37). Self-report measures Alcohol Use Questionnaire (AUQ) (36) The AUQ is a self-report questionnaire measuring the quantity and pattern of alcohol use. Modifications made to assess the quantity and pattern of AmCB use replicated the AUQ questions on alcohol use. State-Trait Anxiety Inventory (STAI) (38) The STAI is a 40-item scale designed to measure participants’ anxiety in specific situations as well as their anxiety as a general trait. Higher scores on the scale indicate greater levels of state and trait anxiety.

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Sensation Seeking Scale-Form V (SSS-V) (39) The SSS-V is a 40-item, forced-choice questionnaire measuring different aspects of sensation-seeking behavior. Separate scores are obtained for disinhibition, (e.g. engaging in disinhibited social behaviors), experience seeking (e.g. hallucinatory drug use), thrill/adventure seeking (e.g. desire to engage in risky sporting activities) and susceptibility to boredom, as well as a total score. Higher scores indicate greater levels of sensation-seeking behaviors. Participants with more than one unanswered question were excluded from analyses of SSS-V results. UPPS-P Impulsive Behavior Scale (UPPS-P) (40, 41) The UPPS-P is a 59-item, forced-choice scale measuring multiple personality dimensions related to impulsivity. Separate scores are obtained for a lack of premeditation, lack of perseveration, sensation seeking and the tendency toward impulsivity when experiencing positive emotions (positive urgency) and negative emotions (negative urgency). Higher scores indicate greater levels of impulsivity. We calculated participants’ average for each scale. Drinking-Induced Disinhibition Scale (DIDS) (42) The DIDS is a 9-item forced-choice scale assessing levels of disinhibited behaviors/experiences that are directly related to participants’ alcohol consumption. Separate scores are obtained for euphoria/social disinhibition, drinking-induced dysphoria and sexual disinhibition. Participants who reported no alcohol use in the past 6 months were excluded from analyses of DIDS results. Other measures We measured basic demographic information (age, sex, years of education), daily cigarettes smoked, monthly frequency of marijuana use, and monthly frequency of other recreational drug use (defined as any illicit drug other than marijuana). Participants also completed the Balloon Analogue Risk Task (43), but these data are not reported here.

Results Binge drinkers vs. comparison group Sixty individuals (32 females; mean age [SD] ¼ 20.83 [1.84] years) participated in the study. Twenty-seven participants met our criteria for binge drinking (15 females). Table 1 displays the results for analyses of demographic and substance use data. Binge drinkers and comparison participants did not significantly differ in terms of reported cigarette smoking or marijuana use, but binge drinkers trended toward higher levels of other recreational drug use (p ¼ 0.06). Previous work has suggested that recreational drug use is associated with P3 alterations (44). Therefore, to control for this potential confound, we excluded individuals who reported recreational drug use (n ¼ 14, binge drinker ¼ 8) from statistical comparisons between the two groups and included monthly marijuana usage as a covariate in all analyses. We also explored the possibility that participants’ sex may interact with the neurocognitive correlates of binge drinking. Self-report, P2

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Table 1. Demographic and self-report data for initial sample (n ¼ 60) of comparison participants and binge drinkers.

Variable Age Years education # binge episodes (6 months) Cigarettes per day Marijuana per month Other drugs per month

Comparison (n ¼ 33) Mean (SD 20.85 14.64 0.09 0.82 5.45 0.12

Binge drinkers (n ¼ 27) Mean (SD)

(2.08) (1.41) (0.29) (2.34) (10.51) (0.42)

20.82 14.78 16.48 0.59 8.89 1.19

(1.55) (1.22) (23.91) (1.65) (18.34) (3.20)

p Value

ðh2p Þ

0.95 0.68 50.001 0.67 0.37 0.06

50.01 50.01 0.21 50.01 0.01 0.06

SD, standard deviation; ðh2p Þ, partial eta-square. Table 2. Descriptive statistics for self-report measures for male and female comparison participants and binge drinkers included in statistical analyses. Comparison (N ¼ 29)

Binge drinkers (N ¼ 17)

Variable

Male Mean (SE)*

Female Mean (SE)*

Male Mean (SE)*

Female Mean (SE)*

STAI State Trait

N ¼ 14 34.59 (3.30) 36.23 (2.41)

N ¼ 15 40.98 (3.08) 38.97 (2.34)

N¼6 42.04 (4.53) 44.87 (3.73)

N ¼ 10 34.43 (3.78) 35.70 (2.86)

SSS-V Disinhibition Boredom Thrill seeking Experience seeking Total score

N ¼ 11 4.58 (0.63) 2.80 (0.62) 7.43 (0.77) 6.36 (0.52) 21.17 (1.88)

N ¼ 13 5.12 (0.59) 2.90 (0.57) 7.02 (0.71) 7.21 (0.48) 22.54 (1.74)

N¼7 6.92 (0.80) 3.47 (0.78) 8.29 (0.97) 6.66 (0.66) 25.33 (2.37)

N ¼ 10 6.04 (0.66) 2.33 (0.65) 7.01 (0.81) 6.67 (0.55) 22.05 (1.97)

UPPS-P Negative urgency Lack premeditation Lack perseveration Sensation seeking Positive urgency

N ¼ 14 2.22 (0.19) 2.02 (0.16) 1.85 (0.14) 3.06 (0.17)

N ¼ 15 2.61 (0.18) 1.96 (0.15) 1.79 (0.13) 2.85 (0.16)

N¼7 2.41 (0.27) 1.81 (0.22) 2.16 (0.19) 3.18 (0.24)

N ¼ 10 2.09 (0.23) 1.87 (0.19) 1.81 (0.16) 2.92 (0.20)

DIDS Euphoria Dysphoria Sexual disinhibition

N ¼ 12 12.41 (0.64) 8.18 (1.15) 7.69 (0.98)

N ¼ 10 12.97 (0.73) 4.80 (1.32) 6.64 (1.14)

N¼7 13.44 (0.83) 6.40 (1.51) 10.68 (1.29)

N ¼ 10 13.33 (0.70) 7.18 (1.26) 8.82 (1.08)

*Descriptive statistics adjusted for self-reported monthly instances of marijuana use; SE, standard error; STAI, State-Trait Anxiety Inventory; SSS-V, Sensation Seeking Scale-Form V; UPPS-P, UPPS-P Impulsive Behavior Scale; DIDS, Drinking-Induced Disinhibition Scale.

and N2 data were analyzed using group (binge, comparison)  sex (male, female) analyses of covariance (ANCOVA). P3a/b data were analyzed using group  sex  electrode (Cz, Pz) ANCOVAs ( ¼ 0.05). Effect sizes are reported as partial eta-squares (p2 ). Self-report data Table 2 displays descriptive statistics for self-report data. Significant effects of group revealed that binge drinkers reported higher scores on the disinhibition scale of the SSS-V, F(1, 36) ¼ 5.66, p ¼ 0.2, p2 ¼ 0.14, and the sexual-disinhibition scale of the DIDS, F(1, 33) ¼ 5.18, p ¼ 0.03, p2 ¼ 0.14. There were also significant effects of sex, F(1, 41) ¼ 4.49, p ¼ 0.04, p2 ¼ 0.10, and group, F(1, 41) ¼ 4.97, p ¼ 0.03, p2 ¼ 0.11 on the positive urgency scale of the UPPS-P (male4female, binge drinkers5comparison). There was a significant group  sex interaction on STAI trait anxiety, F(1, 40) ¼ 4.32, p ¼ 0.04, p2 ¼ 0.10. Male binge drinkers trended (p ¼ 0.07) to exhibit higher trait anxiety scores than male comparison participants. See Supplementary Table 1 (available online) for complete ANCOVA results.

ERP measures The grand-average waveforms at electrodes of interest for binge drinker and comparison participants included in statistical analyses are presented Figure 1. Table 3 lists ERP amplitude data for both tasks. Supplementary Figures 1 and 2 (available online) present grand-average waveforms for all participants (including recreational drug users) at 10 electrodes. Supplementary Table 1 (available online) presents complete ANCOVA results. Oddball paradigm There were significant effects of group on P3b amplitude to Targets, F(1, 37) ¼ 5.03, p ¼ 0.03, p2 ¼ 0.12, and P3a amplitude to Distracters, F(1, 37) ¼ 4.43, p ¼ 0.04, p2 ¼ 0.11. Binge drinkers exhibited significantly greater P3a/b amplitudes. These effects trended to interact with sex (p ¼ 0.06, and p ¼ 0.08, respectively, p2 ¼ 0.09 in each case), as P3a/b amplitude differences between binge drinkers and comparison participants were most evident in males. Go/no-go paradigm There was a trend level main effect of group (p ¼ 0.07, p2 ¼ 0.09) and a trend-level group  sex interaction (p ¼ 0.07, p2 ¼ 0.09) on P3a amplitude to no-go

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Am J Drug Alcohol Abuse, 2014; 40(1): 58–66

Figure 1. Grand average ERP waveforms at electrodes Cz and Pz for binge drinker and comparison participants who did not report any recreational drug use.

Table 3. Descriptive statistics for event-related potential amplitudes for male and female binge drinker and comparison participants included in statistical analyses. Comparison (N ¼ 26) Component Oddball paradigm Target P2 at Cz N2 at Cz P3b at Cz P3b at Pz Distracter P2 at Cz N2 at Cz P3a at Cz P3b at Pz Go/no-go paradigm Go P2 at Cz N2 at Cz P3 at Cz P3 at Pz No-go P2 at Cz N2 at Cz P3a at Cz P3a at Pz

Binge drinkers (N ¼ 16)

Male Mean (SE)*

Female Mean (SE)*

Male Mean (SE)*

Female Mean (SE)*

N ¼ 13

N ¼ 13

N¼7

N¼9

3.72 (.98) 3.10 (1.09) 7.78 (1.33) 9.30 (1.22)

5.05 (0.98) 3.39 (1.09) 7.24 (1.33) 12.14 (1.22)

3.46 (1.35) 3.06 (1.49) 12.26 (1.82) 14.36 (1.67)

5.29 (1.18) 2.28 (1.31) 7.75 (1.60) 12.46 (1.47)

3.28 (0.86) 2.15 (0.92) 5.84 (0.87) 5.99 (0.84)

3.93 (1.18) 3.20 (0.92) 6.05 (0.88) 7.10 (0.84)

3.20 (0.86) 1.16 (1.26) 8.21 (1.20) 9.59 (1.16)

3.82 (1.03) 2.12 (1.11) 5.92 (1.06) 7.58 (1.02)

N ¼ 11 2.88 (0.82) 1.22 (0.75) 6.40 (0.99) 4.27 (1.03)

N ¼ 11 2.92 (0.82) 1.64 (0.76) 3.82 (0.99) 5.44 (1.03)

N¼7 4.79 (1.03) 1.93 (0.95) 6.82 (1.24) 6.74 (1.29)

N¼9 3.81 (0.90) 0.08 (0.83) 4.28 (1.09) 5.34 (1.13)

2.72 (1.02) 2.80 (1.39) 10.24 (1.46) 6.86 (1.46)

3.96 (1.02) 4.71 (1.39) 10.08 (1.46) 9.20 (1.46)

5.29 (1.28) 2.53 (1.75) 14.84 (1.84) 11.58 (1.84)

5.88 (1.13) 4.59 (1.53) 10.92 (1.61) 8.53 (1.62)

*Descriptive statistics adjusted for self-reported monthly instances of marijuana use; SE, standard error.

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Table 4. Results of analyses of self-report measures for AmCB users and nonusers.

Variable

Nonusers Mean (SE)*

AmCB users Mean (SE)*

p

STAI State Trait

N ¼ 45 36.84 (1.72) 37.24 (1.31)

N ¼ 14 32.29 (3.12) 35.16 (2.37)

0.48 0.45

0.01 0.01

SSS-V Disinhibition Boredom Thrill seeking Experience seeking Total score

N ¼ 41 5.49 (0.34) 2.82 (0.31) 7.22 (0.38) 6.81 (0.25) 22.34 (0.91)

N ¼ 12 6.65 (0.63) 3.62 (0.58) 8.24 (0.71) 7.67 (0.47) 26.18 (1.70)

0.11 0.23 0.22 0.10 0.05

0.05 0.03 0.03 0.05 0.07

UPPS-P Negative urgency Lack premeditation Lack perseveration Sensation seeking Positive urgency

N ¼ 46 2.35 (0.11) 1.99 (0.08) 1.89 (0.07) 2.95 (0.09) 1.72 (0.09)

N ¼ 14 2.52 (0.20) 2.04 (0.15) 2.07 (0.13) 2.94 (0.17) 1.92 (0.16)

0.48 0.80 0.23 0.94 0.30

0.01 50.01 0.03 50.01 0.02

DIDS Euphoria Dysphoria Sexual disinhibition

N ¼ 38 12.59 (0.34) 6.71 (0.67) 9.07 (0.69)

N ¼ 14 14.34 (0.58) 7.50 (1.14) 9.98 (1.17)

0.01 0.55 0.51

0.14 0.01 0.01

p2

*Descriptive statistics adjusted for self-reported monthly instances of marijuana use and recreational drug use; SE, standard error; STAI, State-Trait Anxiety Inventory; SSS-V, Sensation Seeking Scale-Form V; UPPS-P, UPPS-P Impulsive Behavior Scale; DIDS, Drinking-Induced Disinhibition Scale.

Table 5. Descriptive statistics for event-related potential amplitudes for AmCB users and nonusers.

Component Oddball paradigm Target P2 at Cz N2 at Cz P3b at Cz P3b at Pz Distracter P2 at Cz N2 at Cz P3a at Cz P3b at Pz Go/no-go paradigm Go P2 at Cz N2 at Cz P3 at Cz P3 at Pz No-go P2 at Cz N2 at Cz P3a at Cz P3a at Pz

Nonusers Mean (SE)* N ¼ 43

AmCB users Mean (SE)* N ¼ 12

4.46 (0.51) 3.20 (0.58) 8.18 (0.85) 11.89 (0.71)

3.72 (0.98) 3.97 (1.12) 5.27 (1.63) 12.82 (1.37)

3.70 (0.42) 2.21 (0.51) 6.59 (0.49) 7.40 (0.48)

2.47 (0.81) 3.89 (0.97) 4.00 (0.94) 8.17 (0.94)

N ¼ 37 3.58 (0.44) 0.40 (0.49) 5.46 (0.52) 5.22 (0.53)

N ¼ 13 3.13 (0.74) 1.22 (0.83) 3.61 (0.89) 5.64 (0.90)

4.60 (0.55) 3.68 (0.76) 11.47 (0.87) 8.96 (0.80)

3.38 (0.92) 3.85 (1.28) 8.67 (1.49) 9.42 (1.37)

AmCB users vs. nonusers Fourteen of the initial 60 participants (8 females) reported AmCB consumption in the previous 6 months. Eleven of these AmCB users met the study’s criteria for binge drinking. However, users and nonusers did not significantly differ in terms of the number of binge episodes reported in the past 6 months, the number of cigarettes smoked per day, monthly use of marijuana or monthly use of other recreational drugs. Due to the small sample size of AmCB users, we were unable to exclude participants who reported recreational drug use and were unable to explore possible sex  group interactions. However, we included monthly marijuana usage and recreational drug usage as covariates in all analyses comparing AmCB users and nonusers. We used ANCOVA to explore the effect of group (AmCB user, nonuser) on self-report, P2 and N2 data. We analyzed P3a/b data with group  electrode (Cz, Pz) ANCOVAs. Self-report data

*Descriptive statistics adjusted for self-reported monthly instances of marijuana and recreational drug use; SE, standard error.

Table 4 displays descriptive statistics and results for analyses of self-report data. AmCB users had significantly higher total scores on the SSS-V, F(1, 49) ¼ 3.92, p ¼ 0.05, p2 ¼ 0.07, and significantly higher drinking-induced euphoria scores on the DIDS, F(1, 42) ¼ 6.75, p ¼ 0.01, p2 ¼ 0.14. ERP measures

stimuli. Binge drinkers (particularly males) tended to exhibit enhanced P3a amplitudes at Cz. Similarly, binge drinkers trended (p ¼ 0.06, p2 ¼ 0.10) to exhibit enhanced P2 amplitudes to no-go stimuli. In addition, there was a significant main effect of sex on N2 amplitude to go stimuli, F(1, 33) ¼ 8.18, p ¼ 0.01, p2 ¼ 0.20. Females (regardless of binging status) exhibited enhanced N2s.

Table 5 lists ERP amplitudes for both tasks. Supplementary Table 2 (available online) presents the complete ANCOVA results. Oddball paradigm There was a significant group  electrode interaction on P3a amplitude to Distracters, F(1, 51) ¼ 6.72, p ¼ 0.01, p2 ¼ 0.12. AmCB users exhibited significantly attenuated P3a amplitude at electrode Cz

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(p ¼ 0.02, p2 ¼ 0.10). There was also a trend level (p ¼ 0.08, p2 ¼ 0.06) group  electrode interaction on P3b amplitude to targets. Go/no-go paradigm There were no statistically significant effects.

Discussion We found that binge drinkers reported significantly greater tendencies to engage in disinhibited behaviors, exhibited significantly larger P3a and P3b amplitudes in the oddball task and trended to exhibit enhanced P3a amplitudes to no-go stimuli. Our results are similar to those of Crego and colleagues, who reported enhanced P3b amplitudes in binge drinkers and suggested that these effects may reflect differences in attentional processing or functional alterations of the neural generators of the component (16). Our data extend these findings by demonstrating that these alterations are not limited to the neural correlates of processing task-relevant stimuli that generate the P3b, but are also present when binge drinkers are required to process distracting stimuli that generate the P3a. As the P3a/b components are thought to be mediated by a network of frontal, temporal and parietal regions (13), these data suggest that binge drinkers exhibit widespread differences in cortical function related to attentional processing. Further, our data suggest that these neurocognitive effects appear in context with increases in disinhibited behaviors that may have potentially adverse consequences (e.g. sexual disinhibition while drinking). It is important to note that other groups have reported P3 attenuations, rather than enhancements in binge drinkers. Ehlers and colleagues (18) found that young adults with a history of binge drinking exhibited decreased amplitudes of components in the P3 complex during an affective facial identification task, while Maurage and colleagues (19) found that binge drinkers exhibited attenuations in P2, N2 and P3b amplitude during a facial detection visual oddball task. Thus, while P3 alterations have consistently been reported in this population, the exact nature of this effect remains unclear. This inconsistency may be due to differences in experimental tasks employed in different studies, but could also be partly due to heterogeneity in patterns of alcohol use across samples of binge drinkers, either in terms of the amount of alcohol consumed during binge sessions or other types of drinking behaviors. For example, the quantity of alcohol consumption per occasion as well as frequency of alcohol use may be more important in identifying risky drinking than relying on the 5/4 drinks per 2-h period or 0.08% definition of binge drinking (45). Our exploratory comparisons between AmCB users and nonusers also suggest the possibility that different patterns of alcohol use may be related to distinct alterations in P3 activity. While binge drinkers exhibited enhanced P3a/b activity, AmCB users exhibited attenuated P3a amplitudes at Cz to distracting stimuli. To our knowledge, these are the first data to suggest that AmCB users may exhibit altered neurocognitive responses similar to those that have been reported in alcoholics and their unaffected relatives (10) and some studies of binge drinkers (19). AmCB users also had

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higher overall levels of sensation seeking and drinkinginduced euphoria. This is consistent with reports that common motivations for AmCB use are to be able to drink more, to get drunk faster, and to drink with reduced sedation (46). Indeed, the small sample of users in our study reported that they became intoxicated in 76% of drinking sessions that involved AmCBs (and 50% of the AmCB group reported that they became intoxicated 100% of the time they used these beverages). Our data suggest the possibility that AmCB users may represent a subset of drinkers that are particularly driven to seek intense, novel alcohol-use experiences and may be particularly likely to exhibit attenuated P3 responses. Beyond this, the data suggest that further study of ERP and behavioral correlates of AmCB use could be a useful avenue in delineating the neurocognitive underpinnings of risky patterns of alcohol use in college-aged adults, and may be important in describing the potential health-related consequences of AmCBs. Finally, these data also suggest that interventions designed to reduce problem drinking in young adults should not focus solely on binge drinkers or binging behaviors. It should be noted that this study had several weaknesses. First, although our sample size was large for an ERP study, it was relatively small for delineating behavioral and psychological effects, particularly when controlling for recreational drug use and exploring the effects of sex or AmCB use. Because of this, we did not correct for multiple statistical comparisons to maximize our statistical power. The results should therefore be interpreted cautiously and the relationships we found should be more thoroughly examined in future studies with larger sample sizes. Second, we recruited a broad sample of participants with diverse patterns of alcohol use. However, excluding individuals with a positive family history of alcoholism, excluding heavy binge drinkers who may be alcohol-dependent, characterizing drinking behaviors over the days immediately prior to testing, excluding cigarette smokers, controlling for caffeine use and recruiting control participants with a more homogenous pattern of alcohol use could have added to this study. More importantly, however, refining operational definitions of risky drinking (including examining drinking behaviors as continuous (e.g. overall number of binge episodes) or stepwise (e.g. control vs. AmCB use only vs. binge drinking only vs. binge drinking þ AmCB use), rather than dichotomous variables may help to clarify which, if any, specific ERP alterations occur in individuals who engage in certain drinking behaviors.

Acknowledgements The authors would like to thank Zenab Amin, PhD, for writing/editorial support, and would like to particularly thank two anonymous reviewers for their comments and suggestions on an earlier version of this manuscript. We also thank and Stacey Patino and Matthew Webb for help with data collection. This research was supported in part by a grant to Lewis & Clark College from the Howard Hughes Medical Institute through the Precollege and Undergraduate Education Program and support from the James F. and Marion L. Miller Foundation.

DOI: 10.3109/00952990.2013.843005

Declaration of interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

References 1. Courtney KE, Polich J. Binge drinking in young adults: data, definitions, and determinants. Psychol Bull 2009;135:142–156. 2. NIAAA. NIAAA Council Approves Definition of Binge Drinking. NIAAA Newsletter 2004;3:1–4. 3. CDC. Vital signs: binge drinking among high school students and adults—United States, 2009. MMWR Morb Mortal Wkly Rep 2010;59:1274–1279. 4. DAlessio M, Baiocco R, Laghi F. The problem of binge drinking among Italian university students: a preliminary investigation. Addict Behav 2006;31:2328–2333. 5. Gill JS. Reported levels of alcohol consumption and binge drinking within the UK undergraduate student population over the last 25 years. Alcohol Alcohol 2002;37:109–120. 6. Wechsler H, Lee JE, Kuo M, Seibring M, Nelson TF, Lee H. Trends in college binge drinking during a period of increased prevention efforts: findings from 4 Harvard School of Public Health College Alcohol Study surveys: 1993–2001. J Am Coll Health 2002;50:203–217. 7. Mundt MP, Zakletskaia LI, Fleming MF. Extreme college drinking and alcohol-related injury risk. Alcohol Clin Exp Res 2009;33:1532–1538. 8. Novik MG, Howard DE, Boekeloo BO. Drinking motivations and experiences of unwanted sexual advances among undergraduate students. J Interpers Violence 2011;26:34–49. 9. Ross LT, Kolars CL, Krahn DD, Gomberg ES, Clark G, Niehaus A. Nonconsensual sexual experiences and alcohol consumption among women entering college. J Interpers Violence 2011;26:399–413. 10. Porjesz B, Rangaswamy M, Kamarajan C, Jones KA, Padmanabhapillai A, Begleiter H. The utility of neurophysiological markers in the study of alcoholism. Clin Neurophysiol 2005;116:993–1018. 11. Ceballos NA, Bauer LO, Houston RJ. Recent EEG and ERP findings in substance abusers. Clin EEG Neurosci 2009;40:122–128. 12. Squires NK, Squires KC, Hillyard SA. Two varieties of longlatency positive waves evoked by unpredictable auditory stimuli in man. Electroencephalogr Clin Neurophysiol 1975;38:387–401. 13. Polich J. Updating P300: an integrative theory of P3a and P3b. Clin Neurophysiol 2007;118:2128–2148. 14. Polich J, Criado JR. Neuropsychology and neuropharmacology of P3a and P3b. Int J Psychophysiol 2006;60:172–185. 15. Soltani M, Knight RT. Neural origins of the P300. Crit Rev Neurobiol 2000;14:199–224. 16. Crego A, Cadaveira F, Parada M, Corral M, Caamano-Isorna F, Rodriguez Holguin S. Increased amplitude of P3 eventrelated potential in young binge drinkers. Alcohol 2012;46:415–425. 17. Crego A, Holguin SR, Parada M, Mota N, Corral M, Cadaveira F. Binge drinking affects attentional and visual working memory processing in young university students. Alcohol Clin Exp Res 2009;33:1870–1879. 18. Ehlers CL, Phillips E, Finnerman G, Gilder D, Lau P, Criado J. P3 components and adolescent binge drinking in Southwest California Indians. Neurotoxicol Teratol 2007;29:153–163. 19. Maurage P, Joassin F, Speth A, Modave J, Philippot P, Campanella S. Cerebral effects of binge drinking: respective influences of global alcohol intake and consumption pattern. Clin Neurophysiol 2012;123:892–901. 20. Maurage P, Pesenti M, Philippot P, Joassin F, Campanella S. Latent deleterious effects of binge drinking over a short period of time revealed only by electrophysiological measures. J Psychiatry Neurosci 2009;34:111–118.

Neurocognitive Correlates of Risky Alcohol Use

65

21. Luck SJ, Hillyard SA. Electrophysiological correlates of feature analysis during visual search. Psychophysiology 1994;31:291–308. 22. Freunberger R, Klimesch W, Doppelmayr M, Holler Y. Visual P2 component is related to theta phase-locking. Neurosci Lett 2007;426:181–186. 23. Zuckerman M. The sensation seeking motive. Prog Exp Pers Res 1974;7:79–148. 24. Potenza MN, de Wit H. Control yourself: alcohol and impulsivity. Alcohol Clin Exp Res 2010;34:1303–1305. 25. Magid V, Colder CR. The UPPS impulsive Behavior scale: factor structure and associations with college drinking. Pers Indiv Differ 2007;43:1927–1937. 26. Chen AC, Porjesz B, Rangaswamy M, Kamarajan C, Tang Y, Jones KA, Chorlian DB, et al. Reduced frontal lobe activity in subjects with high impulsivity and alcoholism. Alcohol Clin Exp Res 2007;31:156–165. 27. Ratsma JE, van der Stelt O, Schoffelmeer AN, Westerveld, Boudewijn Gunning W. P3 event-related potential, dopamine D2 receptor A1 allele, and sensation-seeking in adult children of alcoholics. Alcohol Clin Exp Res 2001;25:960–967. 28. Arria AM, Caldeira KM, Kasperski SJ, Vincent KB, Griffiths RR, O’Grady KE. Energy drink consumption and increased risk for alcohol dependence. Alcohol Clin Exp Res 2011;35: 365–375. 29. O’Brien MC, McCoy TP, Rhodes SD, Wagoner A, Wolfson M. Caffeinated cocktails: energy drink consumption, high-risk drinking, and alcohol-related consequences among college students. Acad Emerg Med 2008;15:453–460. 30. Thombs D, Rossheim M, Barnett TE, Weiler RM, Moorhouse MD, Coleman BN. Is there a misplaced focus on AmED? Associations between caffeine mixers and bar patron intoxication. Drug Alcohol Depend 2011;116:31–36. 31. Marczinski CA, Fillmore MT. Clubgoers and their trendy cocktails: implications of mixing caffeine into alcohol on information processing and subjective reports of intoxication. Exp Clin Psychopharmacol 2006;14:450–458. 32. Ferreira SE, de Mello MT, Pompeia S, de Souza-Formigoni MLO. Effects of energy drink ingestion on alcohol intoxication. Alcoholism Clin Exp Res 2006;30:598–605. 33. Attwood AS. Caffeinated alcohol beverages: a public health concern. Alcohol Alcohol 2012;47:370–371. 34. Weldy DL. Risks of alcoholic energy drinks for youth. J Am Board Fam Med 2010;23:555–558. 35. Peacock A, Bruno R, Martin FH. The subjective physiological, psychological, and behavioral risk-taking consequences of alcohol and energy drink co-ingestion. Alcohol Clin Exp Res 2012;36:2008–2015. 36. Mehrabian A, Russell JA. A questionnaire measure of habitual alcohol use. Psychol Rep 1978;43:803–806. 37. Huster RJ, Enriquez-Geppert S, Lavallee CF, Falkenstein M, Herrmann CS. Electroencephalography of response inhibition tasks: functional networks and cognitive contributions. Int J Psychophysiol 2013;87:217–233. 38. Spielberger CD, Gorsuch RL, Lushene. R.E. Manual for the StateTrait Anxiety Inventory. Palo Alto, CA: Consulting Psychologists Press; 1970. 39. Zuckerman M, Eysenck S, Eysenck HJ. Sensation seeking in England and America: cross-cultural, age, and sex comparisons. J Consult Clin Psychol 1978;46:139–149. 40. Cyders MA, Smith GT, Spillane NS, Fischer S, Annus AM, Peterson C. Integration of impulsivity and positive mood to predict risky behavior: development and validation of a measure of positive urgency. Psychol Assess 2007;19:107–118. 41. Whiteside SP, Lynam DR. The Five Factor Model and impulsivity: using a structural model of personality to understand impulsivity. Pers Individ Diff 2001;30:669–689. 42. Leeman RF, Toll BA, Volpicelli JR. The Drinking-Induced Disinhibition Scale (DIDS): a measure of three types of disinhibiting effects. Addict Behav 2007;32:1200–1219. 43. Lejuez CW, Read JP, Kahler CW, Richards JB, Ramsey SE, Stuart GL, Strong DR, Brown RA. Evaluation of a behavioral measure of

66

T. D. Watson et al.

risk taking: the Balloon Analogue Risk Task (BART). J Exp Psychol Appl 2002;8:75–84. 44. Casco C, Forcella M, Beretta G, Grieco A, Campana G. Longterm effects of MDMA (Ecstasy) on the human central nervous system revealed by visual evoked potentials. Addict Biol 2005;10:187–195.

Am J Drug Alcohol Abuse, 2014; 40(1): 58–66

45. Fillmore MT, Jude R. Defining ‘‘binge’’ drinking as five drinks per occasion or drinking to a 0.08% BAC: which is more sensitive to risk? Am J Addict 2011;20:468–475. 46. Marczinski CA. Alcohol mixed with energy drinks: consumption patterns and motivations for use in U.S. college students. Int J Environ Res Public Health 2011;8:3232–3245.

Supplemental content Supplemental Figure 1: Grand average ERP waveforms at central and parietal electrode sites for all binge drinker and comparison participants in the oddball paradigm. Supplemental Figure 2: Grand average ERP waveforms at central and parietal electrode sites for binge drinker and comparison participants in the go/no-go paradigm. Supplemental Table 1: Results of group (binge drinker, comparison)  sex (male, female) analyses of covariance on self-report, P2, and N2 data and group  sex  electrode (Cz, Pz) on P3a/b data. Supplemental Table 2: Results of group (AmCB user, nonuser)  sex (male, female) analyses of covariance on P2 and N2 data and group  sex  electrode (Cz, Pz) on P3a/b data. Supplemental content is available online at informahealthcare.com/ada

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