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. Author manuscript; available in PMC: 2024 Jun 1.
Published in final edited form as: J Psychiatr Res. 2023 May 4;162:161–169. doi: 10.1016/j.jpsychires.2023.05.037

Effects of Binge Drinking and Depression on Cognitive-Control Processes During an Emotional Go/No-Go Task in Emerging Adults

Kelsey E Magee 1,*, Rachel McClaine 2, Valerie Laurianti 2, Arin M Connell 2
PMCID: PMC10291491  NIHMSID: NIHMS1900913  PMID: 37163808

Abstract

Introduction:

The goal of the current study was to examine differences in neurocognitive processes across groups marked by binge drinking and depression to identify patterns of cognitive and affective processing impairments.

Methods:

Undergraduate students (N = 104; 64% female) were recruited based on self-reported symptoms of depression and alcohol use. They completed an emotional Go/No-Go task while undergoing EEG. Mean amplitudes for N2 and P3 components were examined with 2 (Depressed/Non-depressed) X 2 (Binge/Non-binge drinkers) X 4 (Happy/Sad/Angry/Calm) X 3 (Left/Middle/Right) X 2 (Go/No-Go) repeated measures ANOVAs.

Results:

There were significant Trial Type X Valence X Depression X Binge Drinking interactions for N2 (F(3, 80) = 6.62, p < .01) and P3 (F(3, 80) = 4.65, p < .01) components. There was a significant Valence X Depression X Binge Drinking interaction for response bias (F(3, 65) = 3.11, p < .05).

Limitations:

The source of our sample may be a limitation, as all participants were university students, potentially making the results less generalizable. Further, we cannot be certain that social desirability did not interfere with honest reporting of alcohol use in this population.

Conclusions:

Differences in early inhibitory control were observed across emotions based on trial type among depressed non-binge drinkers, and these differences were attenuated in the presence of binge drinking. Further, the effects of depression on later inhibitory control were specific to non-binge drinkers. Results help to clarify the nature of underlying patterns of neurocognitive and affective risk processes that could be targeted by prevention and intervention programs.

Keywords: Binge drinking, depression, cognitive control, emotional processing, EEG, Go/No-Go

Introduction

Binge drinking and depression are highly prevalent in late adolescence and emerging adulthood, particularly among the college-aged population. Almost 55% of college students aged 18-22 report drinking alcohol in the past month, and about 37% of them engaged in binge drinking during that same time frame (SAMHSA, 2019). Binge drinking, defined as a pattern of drinking that brings blood alcohol concentration levels to 0.08 g/dL after about 4 drinks for women and 5 drinks for men, increases during adolescence (SAMHSA, 2019). Moving away from home and going to college are related to escalating alcohol-use behaviors, in part because the college environment encourages heavy drinking (Merrill & Carey, 2016). Moreover, for some, early initiation of heavy drinking may escalate into adulthood and set the stage for lifelong difficulties (Aiken et al., 2018).

In addition to alcohol use, depression is highly prevalent during adolescence, and by age 18, up to 25% of adolescents will have experienced a major depressive episode (Richardson et al., 2014). It is common for college students to develop depression during the time of transition and adjustment to highly demanding academic landscapes (Michael et al., 2006). Depression in this age-range interferes with typical growth and development, educational achievement, and interpersonal relationships (McLeod et al., 2016). Further, depressed adolescents are at greater risk of suicide, substance abuse, early pregnancy, low educational attainment, and poor longterm physical health outcomes (Richardson et al., 2014).

There are several theories that may explain the high rate of co-occurrence between alcohol abuse and depression. For instance, the “cumulative failure” model suggests that alcohol abuse may cause impairment in social and educational functioning, which undermines positive self-perceptions and heightens risk for depression (Brown & Harris, 1989). Specifically, longitudinal studies of at-risk individuals showed that alcohol use (and conduct problems, more broadly) were related to social failures, such as peer rejection and academic failures, resulting in escalating depression (Patterson, 1986; Patterson & Stoolmiller, 1991; Patterson et al., 1992; Danzo et al., 2017). In contrast, the self-medication model highlights that depression is often a precursor of alcohol use and alcohol use disorders, as alcohol abuse may be motivated by the desire to reduce distress and negative affect associated with depression (Hussong et al., 2011). Over time, such drinking may escalate, as it is reinforced through the pharmacological and situational consequences of alcohol use (Danzo et al., 2021).

Neurocognitive deficits related to inhibitory control and emotional processing are associated with both problematic alcohol use (Rangaswamy & Porjesz, 2015) and depression (Srivastava et al., 2010). There are growing literatures examining neurocognitive underpinnings of both depression and alcohol abuse via electroencephalograph (EEG)/Event-Related Potential (ERP) methods, but these literatures have generally examined each domain in isolation. These studies have yielded mixed results and have also largely ignored the co-occurrence of binge drinking and depression. Of the studies that have examined co-occurrence, many have focused on early adolescent or clinical samples, and have not examined the impact of co-occurring depression and alcohol abuse during the transition from adolescence to emerging adulthood.

The aim of the current study was to examine the cognitive processing of emotional stimuli via the assessment of N200 (N2) and P300 (P3) ERP components measured via EEG during an emotional Go/No-Go task in relation to binge drinking and symptoms of depression in late adolescents and emerging adults. The Go N2 appears to reflect top-down attention towards emotions and may reflect the extent to which attentional control is required (Zhang & Lu, 2012), while the No-Go N2 is thought to reflect attentional control and conflict monitoring processes (Folstein & Van Petten, 2008). The Go P3 appears to be related to motivated attention (Zhang & Lu, 2012), while the No-Go P3 appears to reflect inhibitory motor control processes (Smith et al., 2008). We examined emotional stimuli because emotion dysregulation is central to theories of depression (Bylsma et al., 2008) and patterns of binge drinking (Lannoy et al., 2021), in which alcohol may be used to regulate emotional responding (Connell et al., 2015).

Electrophysiological Correlates of Binge Drinking

Emerging psychophysiological evidence suggests that binge drinking predicts impairments similar to those long observed in relation to chronic alcoholism, including a common finding of attenuation in both early attentional responses associated with N2 and later P3 responses. For instance, in one study, alcoholism predicted attenuated N2 amplitudes, suggesting alcohol-linked cognitive control impairments (Rangaswamy & Porjesz, 2015). In regard to later processing, reduced P3 amplitudes have been observed in binge drinking college students, relative to both daily drinkers and non-drinkers (Maurage et al., 2012). Such reduced P3 amplitudes likely reflect a pattern of neural disinhibition and difficulty with self-regulation (Iacono & Malone, 2011).

Conversely, several studies have found that binge drinking predicts heightened N2 (Crego et al., 2009; Smith et al., 2015) and P3 amplitudes (Almeida-Antunes, 2021). For instance, two studies have found enhanced N2 amplitudes in binge drinkers (Crego et al., 2009; Smith et al., 2015), suggesting the need for additional cognitive resources to match their peers’ task performance. Additionally, several studies found enhanced P3 amplitudes in binge drinkers specifically in response to alcohol related images (Petit et al., 2012; 2013), consistent with theories supporting increased attentional engagement to alcohol cues. Finally, two studies found heightened P3 responses during a working memory task (results occurred despite no behavioral differences across binge and non-binge groups), suggesting a compensatory increase in attentional engagement needed to show equivalent behavioral performance relative to non-drinkers (Crego et al., 2009; Lopez-Caneda et al., 2013). These P3 amplitude increases were larger at one-year follow-up (Lopez-Caneda et al., 2013), suggesting that worsening difficulties may require greater compensation over time.

Electrophysiological Correlates of Depression

Emotion dysregulation is a core feature of depression, which may be associated with deficits in the ability to down-regulate negative emotions, such as sadness, or difficulty up-regulating and maintaining positive emotions (Silk et al., 2003). Theories regarding the nature of emotion dysregulation in depression include a positive attenuation hypothesis, proposing diminished responsivity to positive emotional stimuli, and a negative attenuation hypothesis, proposing heightened emotional reactivity to negative emotional stimuli as a result of high negative mood (Rottenberg et al., 2005). Results across ERP studies have been somewhat mixed but tend to support a third hypothesis of emotion context insensitivity (ECI), which suggests that depressed mood is associated with difficulty responding to emotional content, including a tendency to respond with non-discriminated emotional reactions (Rottenberg et al., 2005). Specifically, depressed mood prompts withdrawal and broad reductions in motivated activity, which encompass reduced activity to both novel positive and novel negative emotional stimuli.

Several ERP studies of depressed adults using oddball and Go/No-Go tasks have found significant reductions in P3 amplitudes relative to non-depressed adults, indicating deficits in processing of emotional stimuli (Bruder et al., 2012). These results are similar to results found in a sample of people with alcohol use disorder (Maurage et al., 2012). Additionally, depression has been associated with attenuated No-Go N2 amplitudes (e.g. Kaiser et al., 2003), and with attenuated N2 amplitudes to negative versus positive trials in emotional Go/No-Go tasks (Krompinger & Simons, 2011).

In contrast, several studies have found that depressed participants did not display differences in reactions to neutral versus emotional faces, supporting for the ECI hypothesis. For example, Foti and colleagues (2010) found no difference between early ERP components to neutral and threatening faces in depressed individuals in comparison to non-depressed participants. Similarly, Kayser & Tenke (2010) found that non-depressed control participants showed enhanced late P3 amplitudes to unpleasant faces compared with neutral faces, whereas depressed individuals did not show these late P3 differences.

Co-Occurrence of Binge Drinking and Depression

Although the co-occurrence of alcohol use and depression is common in adolescence and young adulthood, little is known about the effects of co-occurring binge drinking and depression on neuropsychological outcomes (e.g., Hermens et al., 2013) and very few ERP studies have examined whether binge drinking and depression exert independent or interacting effects on cognitive functioning. The interactive effects of depression and alcohol use may differ across stages of processing, with a pattern of greater early attentional engagement for depressed bingedrinkers relative to non-depressed binge drinkers (Connell et al., 2015). Using a flanker-task paradigm, Connell and colleagues (2018) found that the interaction of binge drinking and depressive symptoms was related to the magnitude of early attentional components (N2), with individuals that reported elevated depressive symptoms and a history of binge drinking exhibiting enhanced attentional control processing (e.g., more negative N2 amplitudes). In contrast, at later stages of cognitive and emotional processing, P3 results suggest that the combination of binge drinking and depression is associated with diminished responding (Connell et al., 2015). Taken together, these results suggest that co-occurring binge drinking and depression is marked by early attentional impairments requiring the recruitment of additional cognitive control processes to compensate (Connell et al., 2018), followed by later decrements in processing, consistent with vigilance-avoidance response patterns.

Current Study

The goal of the current study was to examine differences in neurocognitive processes across groups marked by binge drinking and depression to clarify patterns of cognitive and affective processing impairments. We examined N2 and P3 ERP components reflecting attentional control and emotional responding during an emotional Go/No-Go task, a wellvalidated paradigm that yields information regarding affective processing and inhibitory control (Schulz et al., 2007). First, we hypothesized that the group marked by binge drinking and depression would exhibit specific processing alterations for sad and angry stimuli, consistent with models of alcohol abuse being reinforced by facilitating reduced attention specifically to negative stimuli. Specifically, we hypothesized that binge drinking and depressed participants would exhibit enhanced early frontal N2 (F3/Fz/F4) and attenuated midline P3 (Fz/Cz/Pz) amplitudes in response to sad and angry stimuli relative to depressed-only participants, consistent with vigilance-avoidance patterns observed in prior studies of co-occurring binge drinking and depression (e.g., Jonkman et al., 2003; Johnstone et al., 2007; Krompinger & Simmons, 2009; Connell et al., 2015; 2018). Second, in-line with the ECI hypothesis, we predicted that individuals in the depressed only group would exhibit difficulties responding to emotional content, characterized by non-discriminated responding across emotions. Finally, consistent with reward-focused models of co-occurrence, we hypothesized that, whereas depressed-only participants would exhibit diminished processing of positive emotional stimuli, binge drinking and depressed participants would show normal processing of positive stimuli (i.e. equivalent to the non-depressed/non-binge drinking group), suggesting that alcohol may alleviate symptoms of depression (Baskin-Sommers & Foti, 2015).

Methods

Participants and Procedures

Participants were recruited from undergraduate psychology courses based on self-reported symptoms of depression and alcohol use. Participants (N = 655) completed computerbased questionnaires assessing symptoms of depression, alcohol and other drug use, handedness, and emotion regulation. Participants that exhibited either clinically significant alcohol use and/or depressive symptoms or low levels of alcohol use and/or depressive symptoms, were invited to take part in a laboratory visit. We aimed to recruit a sample of 100 participants to complete the lab visit based on an a priori power analysis, with participants balanced across four groups marked by depression and binge drinking. Results of the power analysis indicated that the study had more than 80% power to detect small to medium effect sizes (effect size f > .15) for the within group X between group interactions that are central to our hypotheses. Effect sizes of at least this magnitude were expected based on prior research (e.g., Clayson et al., 2019).

During the lab visit, participants completed questionnaires to re-assess symptoms of alcohol use and depression, followed by the emotional Go/No-Go EEG task. All procedures were approved by the university’s Institutional Review Board.

Self-Report Questionnaires

Depressive Symptoms

Center for Epidemiological Studies Depression Scale (CES-D; Radloff, 1977).

The CESD is a 20-item, self-report measure that includes six scales reflecting the major dimensions of depression: depressed mood, feelings of guilt and worthlessness, feelings of helplessness and hopelessness, psychomotor retardation, loss of appetite, and sleep disturbance (Hunter et al., 2003). Items are rated on a 4-point scale, ranging from 0 = rarely or none of the time to 3 = most or all of the time, and reflect the frequency with which symptoms of depression were experienced in the past week. Items are summed to yield a total score (range = 0 – 60). Participants completed the CES-D both online prior to being selected for a laboratory visit and during the laboratory visit.

Diagnostic Inventory for Depression (DID; Zimmerman et al., 2004).

The DID is a 38-item self-report measure with three subscales assessing symptoms of major depressive disorder according to Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV; American Psychiatric Association, 1994) criteria, psychosocial impairment, and subjective quality of life over the past week. The DID is scored according to the DSM-IV criterion A of a major depressive episode. Thus, to receive a diagnosis of depression, participants had to endorse either sadness or anhedonia and ≥ 5 DSM-IV symptoms of depression that together result in clinically significant impairment. A subthreshold diagnosis of depression does not require that the symptoms of depression result in clinically significant impairment. Participants completed the DID online prior to being selected for the laboratory visit.

Alcohol Use

Alcohol Use Disorders Identification Test (AUDIT; Saunders et al., 1993).

The AUDIT is a 10-item alcohol screening measure that assess the amount and frequency of alcohol intake, alcohol dependence, and problems related to alcohol consumption (Babor et al., 2001). Each response has a score ranging from 0 (never) to 4 (four or more times a week), yielding a total score that can be used to determine overall engagement in risky alcohol use behavior, as well as subscales reflecting alcohol dependence symptoms, hazardous alcohol use (high frequency/quantity), and harmful alcohol use (injuries, blackouts, etc.). Total scores of 8 or more indicate hazardous and harmful alcohol use (Verhoog et al., 2020).

Other information regarding alcohol abuse were collected using validated measures used in prior prevention trials that examined more detailed reports of alcohol use behavior, including measures of typical drinking volume and frequency, peak drinking volume and duration, changes in drinking behaviors over the past 6 months, age of onset, parental drinking histories, as well as reports of other substances, including tobacco, marijuana, and other drug use (e.g., Dishion et al., 2003; Connell et al., 2007). Participants completed the AUDIT both online prior to being selected for a laboratory visit and during the laboratory visit.

Inclusion Criteria for Group Assignment

Participants with a (1) CES-D score ≥ 16 or a sub-threshold diagnosis of depression on the DID or (2) a diagnosis of depression on the DID were assigned to a group marked by depression. Participants who did not meet criteria for a subthreshold or threshold diagnosis of depression on the DID and had a CES-D score ≤15 were assigned to a non-depressed group. Participants with CES-D scores within 4 points above or below the cut-off were not invited to participate in the lab visit. This range of scores corresponds roughly to 1 standard deviation in the original CES-D norming samples (Radloff, 1977). An equal number of participants with scores above and below the clinical cutoff were selected for the current study.

Participants were assigned to a binge drinking group if they endorsed binge drinking (greater than 5 drinks in a single drinking occasion for males and greater than 4 drinks for females; e.g., Hermens et al., 2013) in the past year or received a score of ≥ 8 on the AUDIT. Participants were assigned to a non-binge drinking group if they did not endorse binge drinking in the past year. This method of group assignment resulted in a total of four groups: binge-drinking/depressed, binge-drinking/non-depressed, non-binge-drinking/depressed, and nonbinge-drinking/non-depressed.

Emotional Go/No-Go Task

The Go/No-Go task (see Supplemental Figure 1 for schematic) required participants to monitor four series of emotional stimuli presented individually in the center of a computer screen and respond as rapidly as possible by pressing a mouse button to target stimuli (Go cues), while withholding responses to non-target stimuli (No-Go cues). The frequency of Go cues creates a prepotent tendency to respond that must then be inhibited for No-Go cues, which provides a measure of the ability to inhibit a prepotent response (Schulz et al., 2007). The Go/No-Go task not only provides a measure of behavioral inhibition, but because the task involves trials with emotional faces, it also provides a measure of emotional modulation of this inhibition (Schulz et al., 2007).

The Superlab© software program (Cedrus Corporation; Haxby, 1993) provided accurate timing for the presentation of stimuli and recording of responses. Stimuli were presented in the center of the screen for 500 ms each. The interstimulus interval (ISI) was pseudorandomized from 1250 to 1750 ms (mean per block = 150 ms) to discourage anticipatory responding. A fixation cross was displayed in the center of the screen during the ISI. Instructions were displayed on the computer screen at the beginning of each block and participants pressed a mouse button when ready to begin. The stimuli for Go and No-Go cues consisted of happy, sad, angry, and calm facial expressions from 24 racially and ethnically diverse individuals (12 female, 12 male) selected from the MacBrain Face Stimulus Set (www.macbrain.org).

The Go/No-Go task consisted of four 192-s blocks. Each block contained 96 stimuli, of which 72 (75%) were Go cues and 24 (25%) were No-Go cues, resulting in a total of 288 Go cues and 96 No-Go cues. Trial order was randomized within blocks, and the order of faces within each block was fully randomized. There were four blocks: Happy No-Go, Sad No-Go, Angry No-Go and Calm No-Go. The order of the blocks was counterbalanced across participants, so that across the sample, each block type was equally likely to appear in every position within the task. This arrangement adds a set-shifting component to two of the blocks, whereby subjects must stop responding to stimuli that were targets in the previous blocks and begin responding to stimuli that were previously non-targets.

EEG Data Acquisition

Continuous EEG data were recorded from 11 scalp sites (F3, Fz, F4, C3, Cz, C4, P3, Pz, P4, O1, and O2) and the right-ear with a Biopac MP 150 system, with a common-ground sensor at FCz. EEG data were recorded using tin electrodes in an Electro-Cap International head-cap following the international 10/20 placement system. A left-ear reference was used during recording, and data were re-referenced offline to the average of the right and left ears. Vertical electrooculogram data were recorded with sensors above and below the right eye, and blink artifacts were removed via independent component analyses (Delorme et al., 2007). Electrode impedances were below 5 kΩ. Data were recorded using a sampling rate of 500 Hz and gain of 2000.

EEG data were bandpass filtered with cutoffs of .1 – 30 Hz, corrected for artifacts, averaged with artifacts removed, and corrected for baseline differences using a series of algorithms built into the MATLAB-based packages, ERP-Lab (www.erpinfo.or/erplab), and EEGlab (Delorme & Makeig, 2004). Continuous data were epoched for the three affective picture categories across a 200ms baseline and 1000ms picture viewing window. Trials were excluded if EEG exceeded ±75 μV.

Analytic Plan

Preliminary analyses examined possible differences across groups marked by binge drinking and depression in terms of demographic factors, gender, levels of depression and alcohol use. Based upon visual inspection of the final ERP grand average and comparison with prior studies (Connell et al., 2015), we used 350-400ms and 450-500ms as the time windows to calculate average amplitudes for frontal N2 (F3/Fz/F4) and midline P3 (Fz/Cz/Pz) ERP components, respectively. Amplitude distributions were examined for possible outliers (+/− 2 SDs from the mean within each component) prior to analysis. Trials were excluded if EEG exceeded ± 75 μV. Out of the 104 participants that completed the lab visit, data from four participants were excluded from the current ERP analyses because they had more than two thirds artifact-rejected trials for more than one trial-type.

In SPSS version 27.0, mean amplitudes for these components were examined with 2 (Depression: Depressed/Non-depressed) X 2 (Binge Drinking: Binge/Non-binge drinkers) X 4 (Valence: Happy/Sad/Angry/Calm) X 3 (Laterality: Left/Middle/Right) X 2 (Trial Type: Go/No-Go) repeated measures ANOVAs. ANOVAs used Huynh-Feldt epsilon for nonspehericity and follow up contrasts used Bonferroni adjustments to correct for multiple comparisons. Gender, race, and year in school were included as covariates in all analyses. However, since these covariates were not primary variables of interest in our hypotheses, main or interactive effects for these covariates were not reported.

Repeated measures ANOVAs were calculated to examine the joint effects of depression and binge drinking on response bias, as indicated by the signal detection measures d’ and β, which were calculated using the following formulas: d’ = z(H) − z(FA) and β = −[z(H) + z(FA)]/2 where z(H) and z(FA) represent the transformation of the hit (e.g., correct Go trials) and false alarm (e.g., commission error) rates to z-scores. The variable d’ represents a measure of the perceptual sensitivity to different stimulus conditions that is independent from respondent biases. In contrast, β is a measure of these response biases and reflects the minimum level of internal certainty needed to that a particular stimulus was present. Thus, β provided a measure of bias towards happy and sad facial expressions in the emotional Go/No-Go task, with lower values indicating greater bias.

Results

Descriptive Statistics

Descriptive statistics are shown in Table 1. There were significant differences in mean CES-D scores across the depressed and non-depressed groups, F(1, 94) = 310.63, p < .001, and there were significant differences across binge drinking and non-binge drinking groups with respect to the maximum number of drinks per episode in the past year, F(1, 84) = 91.09, p < .001. The number of binge drinkers was not significantly different across depressed and non-depressed groups and the average number of drinks per week did not differ across depression groups. There were no significant differences across groups with respect to gender, racial minority status, or handedness.

Table 1.

Demographic Characteristics

Variable Non-depressed/Non-
binge
(N = 22)		 Non-
depressed/Binge
(N = 24)		 Depressed/Non
binge
(N = 24)		 Depressed/Binge
(N = 34)
Race (N)*
  White 14 17 8 29
  Black 0 0 5 2
  Asian 10 9 10 5
  Other 0 0 2 1
  Missing 1 0 0 0
Ethnicity (N)
  Hispanic 1 3 3 3
Female (N) 12 14 17 23
Age (Years); Mean (SD) 18.7 (.88) 19.9 (1.3) 19.2 (1.6) 19.4 (1.2)
  Range 18-21 18-22 18-24 18-22
Right-handed (N) 20 22 23 30
CES-D; Mean (SD) 7.1 (1.9) 6.3 (2.0) 29.8 (6.4) 27.7 (8.0)
  Range 1-12 4-10 20-42 13-49
AUDIT; Mean (SD) .238 (.625) 2.5 (2.0) .292 (.908) 4.3 (4.2)
  Range 0-2 0-7 0-4 0-17
Number of days consumed alcoholic beverage in past 2 weeks; Mean (SD) .40 (1.0) 2.6 (2.5) .26 (1.1) 2.4 (2.0)
Number of drinks consumed during drinking occasion in past 2 weeks; Mean (SD) .15 (.49) 2.00 (1.8) .14 (.66) 2.9 (2.5)
Consumed ≥ 2 drinks in 1 hour in past 2 weeks (N) 2 13 1 23
Current tobacco use (N) 1 1 0 0
Ever tried marijuana (N) 1 10 3 27
Marijuana use in past year (N) 2 9 4 24
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Among the participants who reported binge drinking in the past year, 38 (71.7%) reported drinking in the past 2 weeks. Conversely, only 6 (14%) non-binge drinkers reported drinking within the past 2 weeks. In the binge drinking group, 60.4% of participants reported drinking 3 or more drinks per drinking episode in the past 2 weeks.

The grand averages of N2 and P3 ERP waveforms for trial type across all participants are shown in Figures 1 and 2 (see supplemental Figure 2 for scalp topographies). Descriptive statistics for N2 frontal components (F3, F4, Fz) across Go and No-Go trials are shown in Supplemental Tables 1 and 2, respectively. Descriptive statistics for P3 components (Fz, Cz, Pz) from midline electrodes are shown in Supplemental Table 3 and 4, respectively. Correlations between N2 and P3 ERPs and CES-D and AUDIT scores are shown in Supplemental Table 5.

Figure 1.

Figure 1.

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Grand averages of N2 event related potentials (ERPs) for Go (top) and No-Go (bottom) trials for frontal electrodes, F3, Fz, F4, across all participants.

Figure 2.

Figure 2.

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P3 event related potentials (ERPs) for Go (left) and No-Go (right) trials for midline electrodes, Fz, Cz, and Pz, across all participants.

N2

There was a significant 4-way Trial Type X Valence X Depression X Binge Drinking interaction, F(3, 80) = 6.35, p < .001, partial η2 = .072 (see Supplemental Table 6 for complete N2 ANOVA results) . When emotions were compared across groups marked by depression and binge drinking, there were significant differences across emotions based on trial type. Among binge drinkers and non-binge drinkers, there were no significant differences across trials for depressed versus non-depressed groups. However, among depressed non-binge drinkers, there were significant differences in N2 amplitudes across Calm (M = −.945, SE = 1.26) versus Angry (M = −2.89, SE = 1.19) Go trials. Further, among depressed non-binge drinkers, there were trend level differences for Calm (M = −.945, SE = 1.26) vs. Sad (M = −2.62, SE = 1.23) and Calm (M = −.945, SE = 1.26) vs. Happy (M = −2.80, SE = 1.17) N2 amplitudes on Go trials (Figure 3).

Figure 3.

Figure 3.

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Mean amplitudes of N2 components for non-depressed binge drinkers across Go trials. There were significant differences (p < .05) in N2 amplitudes across Calm vs. Angry Go trials and trend level differences for Calm vs. Sad and Calm vs. Happy N2 amplitudes.

P3

There was a significant 4-way Trial Type X Valence X Depression X Binge Drinking interaction, F(3, 80) = 4.65, p < .01, η2 = .054 (see Supplemental Table 6 for complete P3 ANOVA results). Follow up analyses suggest that among non-binge drinkers, there were significant differences across depressed versus non-depressed groups on Happy Go trials (Depressed: M = 2.31 μV, SE = 1.07 < Non-depressed: M = 7.39 μV, SE = 1.12), Angry Go trials (Depressed: M = 2.41 μV, SE = 1.09 < Non-depressed: M = 5.91 μV, SE = 1.14) and Sad No-Go trials (Depressed: M = 2.61 μV, SE = 1.27 < Non-depressed: M = 6.59 μV, SE = 1.33; Figure 4). Among binge drinkers, there were no significant differences across trials for depressed versus non-depressed groups (Go trials, Depressed: M = 4.62 μV, SE = .88; Non-depressed: M = 5.28 μV, SE = 1.03; No-Go trials, Depressed: M = 4.72 μV, SE = 1.02; Non-depressed: M = 5.49 μV, SE = 1.18).

Figure 4.

Figure 4.

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Mean amplitudes of P3 components for depressed versus non-depresed non-binge drinkers (p < .05).

Response Bias (β) and Perceptual Sensitivity (d’)

There was a significant 3-way Valence X Depression X Binge Drinking interaction for response bias (β; F(3, 65) = 3.11, p < .05; see supplemental Table 7 for complete ANOVA results for behavioral data). Follow-up analyses compared β across emotions for groups marked by binge drinking and depression. Among non-binge drinkers, there was a significant difference in β in depressed individuals for Happy (M = .066, SE = .113) versus Angry (M = .379, SE = .093) faces, and, in non-depressed individuals, for Calm (M = −.001, SE = .101) versus Sad (M = .212, SE = .079) faces. There were no significant interactions for perceptual sensitivity (d’). Correlations between β and d’ and CES-D and AUDIT scores are shown in Supplemental Table 5.

Discussion

The goal of the current study was to clarify the nature of cognitive and affective processing impairments across groups marked by binge drinking and depression during a Go/No-Go EEG task. We hypothesized that (1) consistent with vigilance-avoidance patterns, binge drinking and depressed participants would exhibit enhanced early N2 (F3/Fz/F4) responses and attenuated P3 (Fz/Cz/Pz) amplitudes in response to sad and angry stimuli relative to depressed-only participants, (2) individuals in the depressed only group would exhibit difficulties responding to emotional content, characterized by non-discriminated responding across emotions, in-line with the ECI hypothesis and (3) depressed-only participants would exhibit diminished processing of positive emotional stimuli, but binge drinking and depressed participants would show normal processing of positive stimuli, consistent with reward-focused models of co-occurrence.

Our findings indicate that among depressed non-binge drinkers, greater attentional engagements were required in early stages of processing, as evidence by enhanced N2 amplitudes on Go trials. However, among binge drinkers the effects of depression were attenuated, which suggests that adolescents may use alcohol to regulate affect. In the absence of binge drinking, the effects of depression are also observed during later processing, as indicated by attenuated P3 amplitudes, suggesting that individuals with depression exhibit impairments in the cognitive processing of emotional stimuli. In later processing, the effect of binge drinking was significant only in the absence of depression, consistent with motivational theories of alcohol use. These results account for the interaction of binge drinking and depression in the context of early and late stages of emotional processing and are discussed, in turn.

Early Processing (N2) among Non-Binge Drinkers

Among non-binge drinkers, depression was associated with enhanced (more negative) N2 amplitudes for emotional Go trials versus Calm Go trials, suggesting that depressed non-binge drinkers recruit more attentional resources when required to enact a response for emotional versus Calm faces. This pattern indicates that non-binge drinkers with depression may need to recruit more early attentional resources to emotional faces when required to enact a response, suggesting an emotional interference process leading to compensatory increases in attention required to accurately respond.

Early Processing (N2) among Binge Drinkers

Among binge drinkers, the depression-related differences in N2 amplitudes across emotions were eliminated, suggesting that binge drinking may attenuate some of the emotional response alterations that are otherwise seen in relation to depression in the absence of binge drinking. This pattern of results is generally consistent with motivational models, suggesting that binge drinking may reflect efforts to regulate depression-related affect (Haynes et al., 2008). Therefore, binge drinking may be supported in the context of depression because it may signal efforts to regulate emotional experiences, consistent with the motivational properties of alcohol use in the presence of elevated depressive symptoms (Hermens et al., 2013).

Later Processing (P3) among Non-binge Drinkers

Among non-binge drinkers, depressed individuals exhibited attenuated P3 amplitudes relative to non-depressed individuals for Happy and Angry stimuli on trials requiring an enactment of a response (Go trials), and to Sad stimuli on trials requiring the inhibition of a response (No-Go trials). First, when required to enact a response in the face of approach emotions (e.g., happy and angry stimuli), attenuated P3 amplitudes suggest that participants may have difficulty engaging with approach emotions, consistent with the literature on depression-related processing alterations (Derntl et al., 2011; Trew, 2011). Second, on Sad “No-Go” trials, non-binge drinkers with depression exhibited difficulty recruiting cognitive resources, as indicated by attenuated P3 amplitude, due to the interference of negative emotions associated with depression (Elliott et al., 2002; Dai & Feng, 2011; Joorman & Stanton, 2016; Starr et al., 2019). More broadly, these findings suggest that the effects of depression on later inhibitory control were specific to non-binge drinkers. Thus, individuals with depression exhibit impairments in the cognitive processing of emotional stimuli and supports the emotion context insensitivity (ECI) model of depression where depressed mood significantly influences ongoing responses to the environment and interactions with others (Rottenberg et al., 2005).

Later Processing (P3) among Binge Drinkers

Among binge drinkers, there were no differences in patterns of P3 responses to emotional images in relation to depression, indicating that the pattern of responding among individuals with depressive symptoms depends on the co-occurrence of binge drinking. Depression may be associated with emotional dysregulation due to deficits in the ability to down-regulate negative emotions or difficulty up-regulating and maintaining positive emotions (Silk et al., 2003). Thus, consistent with the self-medication model of alcohol use, individuals with depression may abuse alcohol to alleviate symptoms of depression, contributing to the co-occurrence (Willoughby & Fortner, 2015). Because alcohol abuse alters these potentially maladaptive patterns of depression-related emotional responding, it may ultimately be reinforced (Sher & Grekin, 2007).

Response Bias

Behavioral results were in line with ERP findings, indicating that depressed non-binge drinkers exhibited response bias for Happy versus Angry faces. However, there were no significant differences across groups with respect to behavioral indices of perceptual sensitivity (d’), or ability to identify and correctly respond to emotions. These findings are consistent with literature that suggests that biases in inhibition based on emotional information are related to performance differences on the emotional Go/No-Go task (Schulz et al., 2007; Zhang et al., 2016). Negative affect may lead to a facilitation of processing negative information, consistent with the mood congruency hypothesis of depression (Goeleven et al., 2006; Albert et al., 2010). Thus, it is not surprising that individuals with depression exhibited greater response bias to Happy versus angry faces, since they likely had a bias towards mood-congruent negative stimuli. It is important to consider that these biases across emotions only occurred in the absence of binge drinking, which suggests that alcohol abuse may ameliorate the effects of emotions on individuals with depression (Sher & Grekin, 2007).

Response biases towards negative emotional stimuli could have important implications for depressed adolescents in terms of how they interpret their environment and interact with it (Ridout et al., 2003). In depression, a tendency to attend to negative emotions may be associated with difficulties in understanding others’ emotions, or a deficit in the ability to read signals of interpersonal threat and safety (Tse & Bond, 2004; Kupferberg et al., 2016). As a result, individuals with depression may exhibit increased withdrawal from social interactions and may exhibit increased sensitivity to social rejection, which is particularly salient during adolescence (Kupferberg et al., 2016). As a result, adolescents may use alcohol to cope with problems related to difficult social interactions, consistent with the self-medication reinforcement model of depression (Khantzian, 1997; Hussong et al., 2011).

Limitations and Future Directions

Although a strength of the current study is that binge drinking was broadly defined, capturing a large number of binge drinkers, there are several ways in which binge drinking can be operationalized (e.g., Lannoy et al., 2021). Future research should examine variability in relation to some of these differences in how to define binge drinking among young adults. Relatedly, it has been argued that self-reports of binge drinking may not be representative of actual alcohol intake (Petit et al., 2012). In the current study, strong assurances of confidentially were offered; however, we cannot be certain that social desirability did not interfere with honest reporting of alcohol use. Alcohol use diaries completed on a daily basis could be used in future research to address this limitation (Petit et al., 2012).

The source of our sample may also be a limitation, as all participants were university students, potentially making the results less generalizable. Further, it is important to note that this group marked by depression and binge drinking had a higher proportion of females, however, we controlled for gender to address this limitation. Future studies should examine the effects of a broader range of problems, including antisocial behavior and sensation seeking (Carlson et al., 2010), that often co-occur with depression and binge drinking, as well as the temporal relationship between the neurobiological correlates of risk for binge drinking and depression. Despite these limitations, our findings demonstrate that young adults with depression exhibit impairments in the cognitive processing of emotional stimuli, and in turn, use alcohol as means to regulate negative affect associated with depression.

Supplementary Material

1

Highlights.

  • In early stages of processing, depressed non-binge drinkers required greater attentional engagements, as evidence by enhanced N2 amplitudes on Go trials.

  • In early stages of processing, the effects of depression were attenuated among binge drinkers, suggesting that adolescents may use alcohol to regulate affect.

  • During later stages of processing, in the absence of binge drinking, the effects of depression were observed, as indicated by attenuated P3 amplitudes, suggesting depression-related impairments in the cognitive processing of emotional stimuli, consistent with motivational theories of alcohol use.

Acknowledgements:

We would like to thank the undergraduate students for their participation in this research and our dedicated research team for their efforts.

Funding:

KM was supported by a postdoctoral training grant from the National Institute of Mental Health (T32 MH018951). The funding source had no involvement in the study design, data collection, analysis and preparation of this manuscript.

Footnotes

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Conflict of Interest

The authors have no conflict of interest to declare.

Declarations of interest: None.

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