Skip to main content
NCBI home page
Journal of Studies on Alcohol and Drugs logoLink to Journal of Studies on Alcohol and Drugs
. 2020 Jun 12;81(3):372–383. doi: 10.15288/jsad.2020.81.372

Effects of Age and Acute Moderate Alcohol Consumption on Electrophysiological Indices of Attention

Christian C Garcia a,b,c,*, Ben Lewis a,b,c, Jeff Boissoneault c,d, Sara Jo Nixon a,b,c
PMCID: PMC7299192  PMID: 32527389

Abstract

Objective:

Despite increased attention to risks and benefits associated with moderate drinking lifestyles among aging adults, relatively few empirical studies focus on acute alcohol effects in older drinkers. Using electroencephalographic indices of early attention modulation (P1 and N1) and later stimulus processing (P3), we investigated whether acute alcohol consumption at socially relevant doses differentially influences neurocognitive performance in older, relative to younger, moderate drinkers.

Method:

Younger (25–35 years; n = 97) and older (55–70 years; n = 87) healthy drinkers were randomly assigned to receive one of three alcohol doses (placebo,.04 g/dl, or .065 g/dl target breath alcohol concentrations). Repeated-measures analysis of variance examined the effects of age, alcohol dose concentration, and their potential interaction on P1/P3 amplitudes and N1 latency during completion of a directed attend/ignore task.

Results:

Age-specific effects on P1amplitudes varied by instruction set, with alcohol-associated decreases in amplitude among older drinkers in response to task-relevant stimuli and increases to irrelevant stimuli, F(2, 141) = 2.70, p =.07, ηp2 =.04. In contrast, N1 analyses demonstrated alcohol-related latency reductions among older, relative to younger, adults, F(2, 83) = 3.42, p =.04. Although no Age × Alcohol interactions were detected for P3, main effects indicated dose-dependent amplitude reductions for relevant stimuli, F(2, 144) = 5.73, p <.01, ηp2 =.08.

Conclusions:

Our results underscore the impact of acute moderate alcohol consumption on attentional functioning, highlighting age-dependent sensitivity in electrophysiological indices of early attentional processing. Given the import of attentional functioning to quality of life and increases in drinking among a rapidly expanding aging population, these findings have broad public health relevance.

It is well documented that the population of older adults is increasing disproportionately as is the proportion of older adults who indicate that they are current drinkers (Breslow et al., 2017; He et al., 2016). Acute alcohol consumption has been associated with dysregulation in behavioral inhibition, as well as attentional allocation, and is associated with a wide range of behavioral (e.g., accuracy, reaction times) and neurophysiological (e.g., event-related potentials [ERPs]) changes. Despite increased attention to the risks (e.g., Holahan et al., 2017) and benefits (e.g., Ilomaki et al., 2015) of “moderate” drinking lifestyles, relatively few empirical studies focus on the acute effects of alcohol use among older drinkers. Instead, acute administration studies often use intoxicating doses (i.e., ≥.08 g/dl) in young adult samples, restricting our understanding of potential age-related vulnerabilities associated with the consumption of socially relevant doses.

Within this limited literature, our laboratory has demonstrated age-dependent behavioral (Boissoneault et al., 2014; Gilbertson et al., 2010; Lewis et al., 2019; Price et al., 2018) and neurophysiological (Boissoneault et al., 2016; Lewis et al., 2013) responses to low/moderate doses, not attributable to differential pharmacokinetics. However, to date, these investigations have not focused on early attentional processes.

Examinations of cognitive change throughout the adult life span suggest age-related decrements in attention modulation including enhancement of attention to relevant stimuli and suppression of attention to irrelevant information (Bollinger et al., 2010; Zanto & Gazzaley, 2009). Decrements in top-down control following moderate alcohol consumption are also well established (e.g., Breitmeier et al., 2007; de Wit et al., 2000; Dougherty et al., 2008; Fillmore, 2007; Fillmore & Vogel-Sprott, 1999; Friedman et al., 2011; Oscar-Berman & Marinkovi, 2007; Reed et al., 2012). However, it is unknown whether age- and alcohol-associated perturbations in attentional processing interact, resulting in unappreciated susceptibility among older social drinkers.

Although a comprehensive review of cognitive aging is beyond the scope of the current work, here we focus on aspects of neurocognitive function and their neurophysiological correlates. More specifically, using electroencephalographic indices of selective attention (P1 and N1) and later stimulus processing (P3), the current study investigates the potential interactive effects of age and acute moderate alcohol consumption.To disambiguate the role of enhancement and suppression processes, we used a directed attend/ignore task developed by Gazzaley and his colleagues (2008), wherein face stimuli were either task-relevant (to be remembered) or task-irrelevant (to be ignored), depending on instructional set.

Based on current evidence (e.g., Barceló et al., 2000; Hillyard &Anllo-Vento, 1998; Pfefferbaum et al., 1984; Salthouse et al., 1996; Sur & Sinha, 2009), we hypothesized that across electroencephalographic components, both alcohol and increasing age would alter responses to relevant (i.e., deficits in enhancement) and irrelevant (i.e., deficits in suppression) stimuli. Investigations by Gazzaley and colleagues (2008) found age effects in attention to both relevant and irrelevant stimuli as evidenced by alterations in P1/P3 amplitudes and N1 latencies. Given our previous investigations (e.g., Boissoneault et al., 2014; Lewis et al., 2013), we also anticipated that alcohol would differentially affect these processes in older individuals. Furthermore, based on evidence from aging (Gazzaley et al., 2008) and alcohol (Weafer & Fillmore, 2016) literatures, we that posited suppression processes (i.e., suppressing attention to irrelevant stimuli) would be affected to a greater degree than would enhancement processes.

Method

Study design

The study used a 2 (age group: older, 55–70 vs. younger, 25–35) × 3 (alcohol dose; placebo; targeted breath alcohol concentration [BrAC] of .04 g/dl or .065 g/dl) double-blind, placebo-controlled factorial design. Age ranges were constrained to maximize identification of potential differences between age groups while minimizing confounds associated with age-related cognitive decline or differences in other drinking patterns (e.g., binge drinking for younger adults).

Participants

Participants were recruited through print, radio ads, flyers, and word of mouth within the university and surrounding community. Interested individuals called the laboratory and were informed of general criteria including (a) being within one of the age groups, (b) having a minimum of 12 years of education, (c) being a nonsmoker, (d) having recent experience consuming alcohol, (e) being in good physical health, and (f) having no history of problems with alcohol or other substances. Participants provided written informed consent before data collection and were compensated for their time. The University of Florida Institutional Review Boarda pproved all procedures.

Screening

Eligible participants were scheduled for a comprehensive screening session. The screening packet gathered demographic information including history of current and lifetime use of alcohol, and other illicit substances using independent substance use questionnaires. Current levels of depressive symptomatology (on the Beck Depression Inventory-II [BDIII; Beck et al., 1996] for the younger group and the Geriatric Depression Scale [GDS; Yesavage et al., 1982–1983] for the older group) and state anxiety (on the State–Trait Anxiety Inventory; Spielberger, 1983) were also obtained. Greater than moderate depression (BDI-II > 20 for younger; GDS > 11 for older) was exclusionary. Older participants also completed the Mini-Mental State Examination (Folstein et al., 1975) and the Hopkins Verbal Learning Test (Benedict et al., 1998) and were required to produce a score greater than 27 on the MiniMental State Examination and greater than 15 for total recall on the Verbal Learning Test to qualify, ensuring that their cognitive performance would not affect data interpretation.

Over-the-counter and prescription medication use is common in older populations (Breslow et al., 2015). To facilitate recruitment and increase generalizability, we included individuals taking over-the-counter or prescription medications if use was stable (≥3 months) and not contraindicated for use with alcohol. Consistent with expectations, previous work from our group found that 61% of older adults reported use of more over-the-counter and prescription medications versus 25% of the younger adults (Boissoneault et al., 2014). The most reported medications included birth control (∼55% of younger women), non-opioid analgesics (∼18% of older adults), and cholesterol medication (∼15.8% of older adults).

Participants continuing to meet inclusion criteria then completed the computerized Diagnostic Interview Schedule (Robins et al., 1995). The computerized Diagnostic Interview Schedule was used to evaluate probabilistic Axis I symptomatology based on the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (American Psychiatric Association, 1994), which was current at the time of data collection. Participants were subsequently excluded if they received a probabilistic diagnosis of a current or lifetime psychiatric disorder, likely to confound cognitive performance and/or data interpretation (e.g., psychotic disorders, substance use disorders, current major depression) or if they reported serious medical conditions associated with neurocognitive change (e.g., stroke, untreated hypertension).

Following these procedures, 97 younger (48 women) and 87 older (44 women) participants met inclusion criteria and completed the laboratory phase of the study. All 184 participants produced negative urine drug screens (i.e., tetrahydrocannabinol [THC], cocaine, benzodiazepines, morphine, and methamphetamine), and BrACs of zero (.000 g/dl) before beverage administration. Women of childbearing potential underwent urine pregnancy screening before participation. Pregnant and breastfeeding women were excluded from the laboratory phase of the study.

Before laboratory participation, participants were instructed to (a) abstain from alcohol consumption for at least 24 hours, (b) sustain a 4-hour fast, (c) limit caffeine consumption to a single cup of coffee or caffeinated beverage the morning of the study to prevent caffeine withdrawal symptoms, and (d) avoid sedating with over-the-counter sinus (e.g., allergy) medication the morning of testing.

Alcohol administration

A light breakfast (∼220 kcal) was provided before beverage administration. Participants were then randomly assigned within each age group to receive a beverage dosed to achieve peak BrAC of .0 (placebo), .04, or .065 g/dl. Individual alcohol concentrations were calculated using a modified version of the Widmark equation (Watson et al., 1981), which accounted for age, sex, and body weight. All drinks, including placebo, were misted with a negligible amount of alcohol to enhance placebo effectiveness. Consistent with previous studies, beverages were prepared with 200-proof (100%) medical-grade alcohol in 355 ml of sugar-free, caffeine-free lemon-lime soda and administered as two separate drinks (Boissoneault et al., 2014; Fillmore et al., 2000; Lewis et al., 2016; Sklar et al., 2014). BrAC was measured throughout the study (approximately 10, 25, 60, and 75 minutes after beverage administration; Intoxilyzer, Model 400; CMI, Inc., Owensboro, KY). Task administration occurred approximately 25 minutes after beverage administration.

Following initial consumption, participants were administered a third “booster” beverage of equal volume. If participant BrAC was below 50% of their targeted peak BrAC, the additional beverage contained half of the original alcohol dose. If BrAC was at least 50% of the target or a participant was in the placebo group, the beverage did not contain alcohol. Seven participants received active booster doses: two in the younger .04 g/dl group, four in the older .04 g/dl group, and one in the older .065 g/dl group.

Subjective intoxication and placebo efficacy

Following alcohol administration but before task initiation, participants reported their self-perceived level of intoxication using a 10-point numeric rating scale ranging from 1 (not at all intoxicated) to 10 (most intoxicated imaginable). On study completion, participants reported whether they believed they received a placebo or active dose. To ensure participant safety and the integrity of blinded procedures, research personnel transported all participants to and from the laboratory.

Directed attend/ignore–working memory task

After consuming the booster beverage, participants completed the directed attend/ignore task (Figure 1; Gazzaley et al., 2005). Each trial presented two faces and two natural scene stimuli (all grayscale) in pseudo-random order. All stimuli were 225 pixels wide and 300 pixels tall (14 cm × 18 cm). Face stimuli consisted of White male and female posers displaying neutral expressions. Sex and age of face stimuli were held constant within each trial. Participants were instructed to either “remember faces and ignore scenes” (“relevant” face condition) or “ignore faces and remember scenes” (“irrelevant” face condition). A third (control) condition required participants to passively view cue stimuli and respond to the direction of an arrow at the end of each trial. Cue stimuli were presented for 800 ms followed by a 200-ms fixation “+” (interstimulus interval). As described below, electrophysiological indices were time locked to the presentation of face stimuli, not scenes, used as cues. Following a 9-second delay, a probe image was presented, and participants responded via button press with the index finger of their dominant hand to indicate whether the probe image was present in the preceding cue set (50/50 probability). Probe stimuli were consistent with instructional set (e.g., all probes were faces in the “remember-face” condition). Participants were presented with 20 trials in each of the three instructional sets (60 trials total).

Figure 1.

Figure 1.

Open in a new tab

Directed attend/ignore working memory task: Exemplar of stimulus type and task timing. Each trial presented two faces and two natural scene stimuli in counterbalanced order. Of particular relevance, cue stimuli were presented for 800 ms followed by a 200-ms fixation “+” (interstimulus interval). Following a 9-second delay, a probe image was presented, and participants responded via button to indicate whether the probe image was present in the preceding cue set (50/50 probability). Probe stimuli were consistent with instructional set (e.g., all probes were faces in the remember-face condition). Participants completed 20 trials in each of the three instructional sets (60 trials total)

Electrophysiological methods

All procedures were conducted in a sound-attenuated, electrically shielded booth (Eckel Industries of Canada Ltd., Morrisburg, Ontario, Canada). Before beverage administration, participants were fitted with a 64-channel array elastic cap arranged in an expanded International 10/20 System configuration (Electro-Cap International, Eaton, OH). A midforehead ground was used. Linked earlobes were used as reference. Single electrodes were place above and below the outer canthus of the left eye to detect blinks and eye movements. Movement artifacts during data collection were minimized by use of a chin rest attached to the top of the testing table. Impedances were maintained at or below 10 kOhms. The task was presented on a 17-inch LCD monitor approximately 70 cm from participants. An additional computer using NeuroScan 4.4 Acquire software (Compumedics USA, Charlotte, NC) recorded continuous electroencephalography (EEG). E-Prime software was used for task presentation (Psychology Software Tools, Inc., Sharpsburg, PA).

Signals were amplified and digitized at 1,000 Hz with a 24-bit resolution. Online bandpass filtering was set between 0.1 and 100 Hz. The EEGLAB toolbox (Delorme & Makeig, 2004) and ERPLAB plugin (Lopez-Calderon & Luck, 2014) within MATLAB 2015b (The MathWorks, Inc., Natick, MA) were used for data cleaning and ERP analysis. EEG data were segmented into epochs from 200 ms before stimulus onset (-200-0 baseline corrected) to 800 ms poststimulus. Artifacts (e.g., blinks, eye movements, and general discontinuities) were removed using the ADJUST algorithm, an automated artifact reduction toolbox based on independent component analysis (Mognon et al., 2011) and voltage threshold of ±75 µV. Participants producing fewer than 25% acceptable epochs within an instructional set were omitted from that analysis.

Measurement windows for P1 mean amplitude and N1 peak latency components were derived by estimating groupspecific peaks within standard measurement windows (e.g., 50–150 ms for P1; Gomez Gonzalez et al., 1994) and then applying a more conservative range of ±25 ms from the earliest and latest peaks. Resulting ranges were 107–181 ms for P1 and 157–211 ms for N1. P3 mean amplitude was analyzed between 300 and 500 ms. Given data indicating their sensitivity to attention modulation in visual paradigms, electrodes O2, P6, and Pz were used for consideration of P1, N1, and P3, respectively (Gazzaley et al., 2008; Luck, 2014).

Analysis strategy

Demographic, affective, & drinking measures.

SAS Version 9.4 (SAS Institute, Inc., Cary, NC) was used for all analyses. Analysis of variance was used to assess effects of age, dose, and their interaction in a 2 × 3 factorial design. The design was expanded for electrophysiological analyses to include the three instruction sets as repeated measures (RMs). Where RM interactions were observed, analyses were decomposed to examine age and alcohol effects within the instruction set. No differences were observed in preliminary analyses of sex; thus, sex was omitted from further analyses.

Enhancement and suppression.

To aid interpretation of change in electrophysiology across instructional sets, difference measures were derived for enhancement (relevant – passive) and suppression (passive – irrelevant) (consistent with Gazzaley et al., 2008).

Results

Demographic, affective, & drinking measures

Descriptive statistics are presented in Table 1. Consistent with recruitment rates and regional population estimates, participants were of White (82.1%), African American (4.4%), or “other” (4.4%) race/ethnic identity; 9.2% were Hispanic. No differences were detected between dose groups. Although older individuals reported less education, F(1, 180) = 5.66, p = .02, ηp2 = .03, and greater state anxiety, F(1, 181) = 11.63, p < .01, ηp2 = .06, effects were small and all groups appeared well educated and minimally distressed. As expected, younger individuals reported greater maximal alcohol consumption in a single 24-hour period, F(1, 183) = 38.63, p < .01, ηp2 = .18.

Table 1.

Descriptive measures

graphic file with name jsad.2020.81.372tbl1.jpg

Variable Younger (age 25–35 years)
 Older (age 55–70 years)
Placebo (n = 35) M (SD) .04 g/dl (n = 30) M (SD) .065 g/dl (n = 32) M (SD) Placebo (n = 30) M (SD) .04 g/dl (n = 28) M (SD) .065 g/dl (n = 29) M (SD)
Age, in years 27.40 (2.26) 28.13 (3.15) 27.34 (2.47) 62.00 (4.19) 61.25 (4.34) 62.34 (5.07)
Education, in years 16.74 (1.20) 16.87 (1.04) 16.41 (1.13) 16.04 (1.79) 15.96 (1.60) 16.50 (1.71)
Anxiety symptomsa 37.00 (3.99) 37.33 (4.25) 38.34 (4.84) 39.77 (5.18) 39.96 (7.29) 41.04 (6.09)
Depressive symptomsb,c 0.83 (1.95) 1.21 (2.70) 1.50 (2.72) 1.73 (1.95) 1.43 (2.08) 1.24 (1.64)
Standard drinks/day 0.55 (0.38) 0.58 (0.40) 0.63 (0.47) 0.87 (0.68) 0.57 (0.45) 0.60 (0.60)
Maximum number of drinks in 24 hours 6.40 (2.97) 6.02 (2.83) 5.92 (2.28) 3.72 (2.08) 4.07 (2.33) 3.73 (2.10)
Open in a new tab
a

State–Trait Anxiety Inventory (Spielberger, 1983);

b

Beck Depression Inventory, 2nd ed. (Beck et al., 1996);

c

Geriatric Depression Scale (Yesavage et al., 1982–1983).

Breath alcohol concentrations

BrACs are depicted in Figure 2. As expected, BrACs differed between all doses, F(2, 182) = 513.32, p < .01, ηp2 = .85. No age effect or interaction was noted.

Figure 2.

Figure 2.

Open in a new tab

Breath alcohol concentration (BrAC) measures throughout the study. BrACs were measured throughout the study session. We were particularly interested in BrAC at 25 minutes, as it was collected immediately before the directed attend/ignore working memory task and best reflected peak BrACs. Significant effects of dose, F(2, 182) = 513.32, p < .01, ηp2 =.85, but not age, were detected. As expected, analyses revealed differences between placebo and both active doses (ts < 18, ps < .01, ds > 4), as well as differences between the active doses (t =12.57, p < .01, d = 1.87).

Subjective intoxication and placebo efficacy

Approximately 45% of younger and 68% of older participants in the placebo condition believed that they consumed alcohol, a difference that failed to achieve significance (χ2 = 3.07, p = .08). A dose effect was observed for ratings of intoxication, F(2, 178) = 42.12, p < .01, ηp2 = .33, with significantly greater intoxication endorsed at each ascending dose (ts > 2.93, ps < .01).

Electrophysiology

Group-specific grand average waveforms are shown in Figure 3. Summary of electrophysiological results descried below are presented in Table 2.

Figure 3.

Figure 3.

Figure 3.

Open in a new tab

Grand average waveforms by age group and dose for P1 (3A), N1 (3B), and P3 (3C). Figure 3A—Grand average waveforms by age group and dose for P1 mean amplitude (107–181 ms following stimulus onset) at electrode O2. Figure 3B—Grand average waveforms by age group and dose for N1 peak latency (157–211 ms following stimulus onset) at electrode P6. Figure 3C—Grand average waveforms by age group and dose for P3 mean amplitude (300–500 ms following stimulus onset) at electrode Pz.

Table 2.

Summary of electrophysiological results

graphic file with name jsad.2020.81.372tbl2.jpg

Variable Main effects
 Two-way interactions
 Three-way interaction
Age group Alcohol dose Instruction set Age Group × Alcohol Dose Age Group × Instruction Set Alcohol Dose × Instruction Set Age Group × Alcohol Dose × Instruction Set
P1 mean amp. F(1,136) = 1.47 F(2,136) = 0.09 F(2,272) = 0.40 F(1,136) = 1.93 F(2,272) = 0.12 F(4,272) = 0.29 F(4,272) = 2.47
p = .23 p = .92 p = .67 p = .15 p = .89 p = .88 p = .05
N1 peak lat. F(1,90) = 0.09 F(2,90) = 0.29 F(2,180) = 0.46 F(2,90) = 4.30 F(2,180) = 1.19 F(4,180) = 1.27 F(4,180) = 1.22
p = .77 p = .75 p = .64 p = .02 p = .31 p = .28 p = .31
P3 mean amp. F(1,139) = 0.04 F(2,139) = 2.80 F(2,278) = 19.87 F(2,139) = 0.09 F(2,278) = 0.03 F(4,278) = 2.66 F(4,278) = 0.79
p = .85 p = .06 p < .0001 p = .92 p = .97 p = .03 p = .54
Open in a new tab

Notes: Amp. = amplitude; lat. = latency.

P1 mean amplitude.

Decomposition of the three-way interaction, F(4, 272) = 2.47, p = .05, revealed effects in both relevant and irrelevant conditions (Table 2). Analysis of the relevant condition revealed an Age × Dose interaction, F(2, 141) = 2.88, p = .06, ηp2 = .04. This interaction appeared driven by an age-contingent divergence in amplitudes at .065 g/dl, with lower amplitudes among older adults (t = 2.38, p = .02; d = 0.71; Figure 4A), relative to younger at the .065 dose. In contrast, there were no age group differences under active alcohol concentrations for irrelevant face stimuli, F(2, 141) = 2.70, p = .07, ηp2 = .04. Instead, lower amplitudes were noted among adults in the older group than the younger group at placebo (t = 2.21, p = .03; d = 0.48; Figure 4B). Comparison of enhancement and suppression indices revealed no significant differences.

Figure 4.

Figure 4.

Open in a new tab

P1 mean amplitude: Dose effects by age group for relevant (4A) and irrelevant (4B) stimuli. Figure 4A—Relevant face condition: An interaction was observed in responses to relevant face stimuli, F(2, 141) = 2.88, p = .06, ηp2 = .04, at the .065 g/dl dose, amplitudes among older participants appeared diminished, whereas those among younger adults appeared enhanced (t = 2.38, p = .02; d = 0.71). Figure 4B—Irrelevant face condition: The interaction detected for irrelevant face stimuli, F(2, 141) = 2.70, p = .07, ηp2 = .04, reflected alcohol-associated increases in amplitude among older individuals and reduced amplitudes among younger (t = 2.21, p = .03; d = 0.48) at the .04 g/dl dose.

N1 peak latency.

The interaction between age group and dose detected, F(2, 83) = 3.42, p = .04, appeared driven by opposing effects of alcohol between younger and older groups (Figure 5). At placebo, older adults displayed longer N1 latencies relative to younger counterparts (t = 1.96, p = .05; d = 0.75). In contrast, at .065 g/dl, their latencies were shorter (t = 2.20, p = .03; d = 0.80) than adults in the younger group.

Figure 5.

Figure 5.

Open in a new tab

N1 peak latency: Dose effects by age group across all conditions. A significant Dose × Age interaction, F(2, 83) = 3.42, p = .04, was observed. At placebo, older adults displayed delayed N1 latencies relative to younger adults (t = 1.96, p = .05; d = 0.75). At the .065 g/dl dose, older adults displayed earlier N1 latencies (t = 2.20, p = .03; d = 0.80).

P3 mean amplitude.

A Dose × Instruction Set interaction was observed, F(4, 278) = 2.66, p = .03 (Table 2). Decomposition revealed alcohol effects only under the relevant condition, F(2, 144) = 5.73, p < .01, ηp2 = .08 (Figure 6A). Post hoc analyses indicated lower P3 amplitudes under both active dose conditions relative to placebo (ts > 2.18, ps < .03; ds > .39). These results were consistent with enhancement analyses, which suggested greater enhancement at placebo, relative to either of the active doses (ts > 3.47, ps < .01; ds > .23) (Figure 6B).

Figure 6.

Figure 6.

Open in a new tab

Dose effects by instructional set for P3 mean amplitude (6A) and derived enhancement measure (6B). Figure 6A—Results revealed an interaction between instruction set and dose, F(4, 278) = 2.66, p = .03. Alcohol reduced P3 amplitude to relevant face stimuli, F(2, 144) = 5.73, p < .01, ηp2 = .08, but not irrelevant or passively viewed faces. Post hoc analyses revealed greater P3 amplitudes under placebo, relative to both the .04 (t = 2.18, p = .03; d = 0.39) and .065 (t = 3.31, p < .01; d = 0.62) g/dl doses. Figure 6B—Enhancement was greater at placebo relative to either the .04 (t = 6.19, p < .01; d = 0.37) or .065 (t = 5.21, p < .01; d = 0.63) g/dl doses. Taken together with the larger difference at the .04 g/dl dose relative to the .065 g/dl dose (t = 3.47, p < .01; d = 0.23), these results suggest a dose-dependent effect on enhancement across age groups.

Discussion

Clarifying the extent to which older adults may be differentially sensitive to acute effects of moderate alcohol consumption is critical given current population trends (Breslow et al., 2017; Center for Behavioral Health Statistics and Quality, 2015). As discussed below, our results provide novel evidence of age-contingent susceptibility to alcohol-associated disruptions early in attentional processing (i.e., P1), although results from N1/P3 components revealed only partial support for our hypotheses.

Age-dependent differences were noted for P1 mean amplitude in the relevant and irrelevant face-viewing conditions. At the moderate dose level (.065 g/dl), older adults had lower P1 amplitudes than younger individuals. Older adults also showed lower P1 amplitudes than those in the younger group in response to irrelevant faces, but in the absence of alcohol consumption. These observations suggest alcohol-associated impairments among older individuals in processes of attention enhancement and suppression. In contrast to our hypothesis, effect size estimates failed to provide evidence for greater effects on suppression processes. At the active doses, younger adults showed larger P1 amplitudes to relevant stimuli and lower amplitudes to irrelevant stimuli, suggesting facilitatory effects. Although these age effects appear consistent with previously observed behavior in this task (Boissoneault et al., 2014; Lewis et al., 2019), we did not explicitly hypothesize facilitation effects given mixed and sometimes contradictory findings in the current literature.

N1 results were particularly surprising; under active doses of alcohol, older adults displayed reduced latencies in opposition to trends observed in younger participants. This relationship persisted independent of task instruction. Both the alcohol-associated trends in younger adults and age differences at placebo are consistent with findings from the alcohol (Sur & Sinha, 2009) and age (Salthouse et al., 1996) literatures. Although the Age × Alcohol relationship is novel, the directionality was surprising and not broadly consistent with other behavioral or electrophysiological evidence among older adults and thus bears replicating (e.g., Boissoneault et al., 2014; Lewis et al., 2013).

P3 amplitude has been widely investigated in studies of both age and alcohol (Lewis et al., 2013; Mullis et al., 1985; Pfefferbaum et al., 1984; Rangaswamy & Porjesz, 2014; Raz et al., 2000). Although our findings did not reproduce evidence of interactive effects observed in other behavioral tasks (Lewis et al., 2013), they are generally consistent with the acute alcohol literature (Rangaswamy & Porjesz, 2014; Wolff et al., 2018) showing dose-dependent reductions in P3 amplitude. Interestingly, however, specific differences were noted for relevant face stimuli that were not observed for irrelevant or passive face stimuli, suggesting that contrary to our hypothesis, alcohol may differentially alter enhancement processes. Consistent with this interpretation, difference measures derived for suppression were equivalent across dose concentrations, whereas measures of enhancement were reduced in a dose-dependent fashion.

Taken together, our analyses provide novel evidence of age-contingent susceptibility to alcohol-associated disruptions early in attentional processing, specifically among older adults. Further, they facilitate interpretation of our earlier work, which notes similar susceptibility in behavioral performance using the current task (see Boissoneault et al., 2014). These results suggest disruptions in early attentional processes (e.g., reflected by P1) and may provide an underlying mechanism by which alcohol consumption, even at moderate doses, exacerbates neurocognitive differences in older individuals. The paucity of literature examining these effects challenges generalizability; whether early attentional functions underlie age-contingent susceptibility across a broader range of cognitive abilities remains an important question.

Limitations

Although the study offers novel findings, several limitations should be noted. (a) Relative to the general community, participants were particularly healthy. This is unsurprising given the nature of community-based sampling and our exclusionary criteria. Although inclusion of individuals with medical/psychiatric conditions could improve generalizability, their exclusion facilitates interpretation of effects in older individuals. Nevertheless, susceptibility to alcohol effects was noted among older participants, suggesting that greater vulnerabilities may persist within the larger community. (b) The sample was predominantly White. Although this may constrain generalization across other racial/ethnic groups, the proportion is largely consistent with previous recruitment rates and regional racial/ethnic distributions (≥78% White). (c) Only moderate drinkers were included, limiting conclusions to similar drinking samples. Although average consumption did not differ by age, controlling for more subtle patterns of consumption (e.g., maximal quantity) was impractical but may have contributed to the observed effects. (d) The current work used a between-subjects design. Within-subject designs may have greater statistical power, but they also incur disadvantages in terms of differential attrition and nonlinear carry-over effects. That said, planned studies address this concern with hybrid designs providing baseline (non-alcohol) data for participants. (e) The task has limited real-world application. We recognize that particular task demands have little apparent association with real-world behaviors but argue that the neurobehavioral processes interrogated are common across many real-world contexts. Future work should consider a more diverse test battery.

Conclusion

Given electrophysiological evidence of disrupted attentional processes in normal aging (Pfefferbaum et al., 1984; Salthouse et al., 1996) and following alcohol administration (Sur & Sinha, 2009), we anticipated that their interaction would differentially affect attentional processes in healthy older adults. Our results underscore the impact of acute moderate alcohol consumption on attentional functioning, specifically highlighting age-specific sensitivity in early electrophysiological indices. These alterations may be important considering the import of attention to health and quality of life at both the individual and public levels.

Acknowledgments

The authors acknowledge the study participants’ willingness to participate and generosity with their time. Adam Gazzaley, M.D., Ph.D., provided stimuli and parameters for the working memory task.

Footnotes

Research reported in this publication was supported by National Institute on Alcohol Abuse and Alcoholism Award Number R01AA019802 (to Sara Jo Nixon, principal investigator). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

References

  1. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 4th ed. Washington, DC: Author; 1994. [Google Scholar]
  2. Barceló F., Suwazono S., Knight R. T. Prefrontal modulation of visual processing in humans. Nature Neuroscience. 2000;3:399–403. doi: 10.1038/73975. doi:10.1038/73975. [DOI] [PubMed] [Google Scholar]
  3. Beck A. T., Steer R. A., Brown G. K. Beck Depression Inventory. Second Edition. San Antonio, TX: The Psychological Corporation; 1996. [Google Scholar]
  4. Benedict R. H., Schretlen D., Groninger L., Brandt J. Hopkins Verbal Learning Test–Revised: Normative data and analysis of interform and test-retest reliability. The Clinical Neuropsychologist. 1998;12:43–55. doi:10.1076/clin.12.1.43.1726. [Google Scholar]
  5. Boissoneault J., Frazier I., Lewis B., Nixon S. J. Effects of age and acute moderate alcohol administration on electrophysiological correlates of working memory maintenance. Alcoholism: Clinical and Experimental Research. 2016;40:1874–1883. doi: 10.1111/acer.13154. doi:10.1111/acer.13154. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Boissoneault J., Sklar A., Prather R., Nixon S. J. Acute effects of moderate alcohol on psychomotor, set shifting, and working memory function in older and younger social drinkers. Journal of Studies on Alcohol and Drugs. 2014;75:870–879. doi: 10.15288/jsad.2014.75.870. doi:10.15288/jsad.2014.75.870. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Bollinger J., Rubens M. T., Zanto T. P., Gazzaley A. Expectation-driven changes in cortical functional connectivity influence working memory and long-term memory performance. Journal of Neuroscience. 2010;30:14399–14410. doi: 10.1523/JNEUROSCI.1547-10.2010. doi:10.1523/JNEUROSCI.1547-10.2010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Breitmeier D., Seeland-Schulze I., Hecker H., Schneider U. The influence of blood alcohol concentrations of around 0.03% on neuropsychological functions—A double-blind, placebo-controlled investigation. Addiction Biology. 2007;12:183–189. doi: 10.1111/j.1369-1600.2007.00056.x. doi:10.1111/j.1369-1600.2007.00056.x. [DOI] [PubMed] [Google Scholar]
  9. Breslow R. A., Castle I. P., Chen C. M., Graubard B. I. Trends in alcohol consumption among older Americans: National Health Interview Surveys, 1997 to 2014. Alcoholism: Clinical and Experimental Research. 2017;41:976–986. doi: 10.1111/acer.13365. doi:10.1111/acer.13365. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Breslow R. A., Dong C., White A. Prevalence of alcohol interactive prescription medication use among current drinkers: United States, 1999 to 2010. Alcoholism: Clinical and Experimental Research. 2015;39:371–379. doi: 10.1111/acer.12633. doi:10.1111/acer.12633. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Center for Behavioral Health Statistics and Quality . Behavioral health trends in the United States: Results from the 2014 National Survey on Drug Use and Health. HHS Publication No; 2015. SMA 15-4927, NSDUH Series H-50. [Google Scholar]
  12. de Wit H. Laboratory-based assessment of alcohol craving in social drinkers. Addiction. 2000;95(Supplement 2):165–169. doi: 10.1080/09652140050111735. doi:10.1046/j.1360-0443.95.8s2.5.x. [DOI] [PubMed] [Google Scholar]
  13. Delorme A., Makeig S. EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods. 2004;134:9–21. doi: 10.1016/j.jneumeth.2003.10.009. doi:10.1016/j.jneumeth.2003.10.009. [DOI] [PubMed] [Google Scholar]
  14. Dougherty D. M., Marsh-Richard D. M., Hatzis E. S., Nouvion S. O., Mathias C. W. A test of alcohol dose effects on multiple behavioral measures of impulsivity. Drug and Alcohol Dependence. 2008;96:111–120. doi: 10.1016/j.drugalcdep.2008.02.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Fillmore M. T. Acute alcohol-induced impairment of cognitive functions: Past and present findings. International Journal on Disability and Human Development. 2007;6:115–125. doi:10.1515/IJDHD.2007.6.2.115. [Google Scholar]
  16. Fillmore M. T., Dixon M. J., Schweizer T. A. Alcohol affects processing of ignored stimuli in a negative priming paradigm. Journal of Studies on Alcohol. 2000;61:571–578. doi: 10.15288/jsa.2000.61.571. doi:10.15288/jsa.2000.61.571. [DOI] [PubMed] [Google Scholar]
  17. Fillmore M. T., Vogel-Sprott M. An alcohol model of impaired inhibitory control and its treatment in humans. Experimental and Clinical Psychopharmacology. 1999;7:49–55. doi: 10.1037//1064-1297.7.1.49. doi:10.1037/1064-1297.7.1.49. [DOI] [PubMed] [Google Scholar]
  18. Folstein M. F., Folstein S. E., McHugh P. R. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research. 1975;12:189–198. doi: 10.1016/0022-3956(75)90026-6. doi:10.1016/0022-3956(75)90026-6. [DOI] [PubMed] [Google Scholar]
  19. Friedman T. W., Robinson S. R., Yelland G. W. Impaired perceptual judgment at low blood alcohol concentrations. Alcohol. 2011;45:711–718. doi: 10.1016/j.alcohol.2010.10.007. doi:10.1016/j.alcohol.2010.10.007. [DOI] [PubMed] [Google Scholar]
  20. Gazzaley A., Clapp W., Kelley J., McEvoy K., Knight R. T., D’Esposito M. Age-related top-down suppression deficit in the early stages of cortical visual memory processing. Proceedings of the National Academy of Sciences of the United States of America. 2008;105:13122–13126. doi: 10.1073/pnas.0806074105. doi:10.1073/pnas.0806074105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Gazzaley A., Cooney J. W., McEvoy K., Knight R. T., D’Esposito M. Top-down enhancement and suppression of the magnitude and speed of neural activity. Journal of Cognitive Neuroscience. 2005;17:507–517. doi: 10.1162/0898929053279522. doi:10.1162/0898929053279522. [DOI] [PubMed] [Google Scholar]
  22. Gilbertson R., Prather R., Nixon S. J. Acute alcohol administration and placebo effectiveness in older moderate drinkers: Influences on cognitive performance. Journal of Studies on Alcohol and Drugs. 2010;71:345–350. doi: 10.15288/jsad.2010.71.345. doi:10.15288/jsad.2010.71.345. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Gomez Gonzalez C. M., Clark V. P., Fan S., Luck S. J., Hillyard S. A. Sources of attention-sensitive visual event-related potentials. Brain Topography. 1994;7:41–51. doi: 10.1007/BF01184836. doi:10.1007/BF01184836. [DOI] [PubMed] [Google Scholar]
  24. He W., Goodkind D., Kowal P. An aging world: 2015. Vol. P95/16-1. Washington, DC: U.S. Census Bureau; 2016. Retrieved from https://www.census.gov/content/dam/Census/library/publications/2016/demo/p95-16-1.pdf. [Google Scholar]
  25. Hillyard S. A., Anllo-Vento L. Event-related brain potentials in the study of visual selective attention. Proceedings of the National Academy of Sciences of the United States of America. 1998;95:781–787. doi: 10.1073/pnas.95.3.781. doi:10.1073/pnas.95.3.781. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Holahan C. J., Brennan P. L., Schutte K. K., Holahan C. K., Hixon J. G., Moos R. H. Late-life drinking problems: The predictive roles of drinking level vs. drinking pattern. Journal of Studies on Alcohol and Drugs. 2017;78:435–441. doi: 10.15288/jsad.2017.78.435. doi:10.15288/jsad.2017.78.435. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Ilomaki J., Jokanovic N., Tan E. C., Lonnroos E. Alcohol consumption, dementia and cognitive decline: An overview of systematic reviews. Current Clinical Pharmacology. 2015;10:204–212. doi: 10.2174/157488471003150820145539. doi:10.2174/157488471003150820145539. [DOI] [PubMed] [Google Scholar]
  28. Lewis B., Boissoneault J., Frazier I., Nixon S. J. Effects of age and acute moderate alcohol administration on neurophysiology during simulated driving. Alcoholism: Clinical and Experimental Research. 2016;40:2519–2527. doi: 10.1111/acer.13243. doi:10.1111/acer.13243. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Lewis B., Boissoneault J., Gilbertson R., Prather R., Nixon S. J. Neurophysiological correlates of moderate alcohol consumption in older and younger social drinkers. Alcoholism: Clinical and Experimental Research. 2013;37:941–951. doi: 10.1111/acer.12055. doi:10.1111/acer.12055. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Lewis B., Garcia C. C., Boissoneault J., Price J. L., Nixon S. J. Working memory performance following acute alcohol: Replication and extension of dose by age interactions. Journal of Studies on Alcohol and Drugs. 2019;80:86–95. doi: 10.15288/jsad.2019.80.86. doi:10.15288/jsad.2019.80.86. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Lopez-Calderon J., Luck S. J. ERPLAB: An open-source toolbox for the analysis of event-related potentials. Frontiers in Human Neuroscience. 2014;8:213. doi: 10.3389/fnhum.2014.00213. doi:10.3389/fnhum.2014.00213. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Luck S. J. An introduction to the event-related potential technique. Cambridge, MA: MIT Press; 2014. [Google Scholar]
  33. Mognon A., Jovicich J., Bruzzone L., Buiatti M. ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features. Psychophysiology. 2011;48:229–240. doi: 10.1111/j.1469-8986.2010.01061.x. doi:10.1111/j.1469-8986.2010.01061.x. [DOI] [PubMed] [Google Scholar]
  34. Mullis R. J., Holcomb P. J., Diner B. C., Dykman R. A. The effects of aging on the P3 component of the visual event-related potential. Electroencephalography and Clinical Neurophysiology. 1985;62:141–149. doi: 10.1016/0168-5597(85)90026-7. doi:10.1016/0168-5597(85)90026-7. [DOI] [PubMed] [Google Scholar]
  35. Oscar-Berman M., Marinkovi K. Alcohol: Effects on neurobehavioral functions and the brain. Neuropsychology Review. 2007;17:239–257. doi: 10.1007/s11065-007-9038-6. doi:10.1007/s11065-007-9038-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Pfefferbaum A., Ford J. M., Wenegrat B. G., Roth W. T., Kopell B. S. Clinical application of the P3 component of event-related potentials. I. Normal aging. Electroencephalography and Clinical Neurophysiology. 1984;59:85–103. doi: 10.1016/0168-5597(84)90026-1. doi:10.1016/0168-5597(84)90026-1. [DOI] [PubMed] [Google Scholar]
  37. Price J. L., Lewis B., Boissoneault J., Frazier I. R., Nixon S. J. Effects of acute alcohol and driving complexity in older and younger adults. Psychopharmacology. 2018;235:887–896. doi: 10.1007/s00213-017-4806-3. doi:10.1007/s00213-017-4806-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Rangaswamy M., Porjesz B. Understanding alcohol use disorders with neuroelectrophysiology. Handbook of Clinical Neurology. 2014;125:383–414. doi: 10.1016/B978-0-444-62619-6.00023-9. doi:10.1016/B978-0-444-62619-6.00023-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Raz N. Aging of the brain and its impact on cognitive performance: Integration of structural and functional findings. In: Craik F. I. M., Salthouse T. A., editors. The handbook of aging and cognition. Mahwah, NJ: Lawrence Erlbaum Associates; 2000. pp. 1–90. [Google Scholar]
  40. Reed S. C., Levin F. R., Evans S. M. Alcohol increases impulsivity and abuse liability in heavy drinking women. Experimental and Clinical Psychopharmacology. 2012;20:454–465. doi: 10.1037/a0029087. doi:10.1037/a0029087. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Robins L. N., Cottler L., Bucholz K. K., Compton W. The Diagnostic Interview Schedule. St. Louis, MO: Washington University; 1995. Version IV. [Google Scholar]
  42. Salthouse T. A., Hancock H. E., Meinz E. J., Hambrick D. Z. Interrelations of age, visual acuity, and cognitive functioning. The Journals of Gerontology. Series B, Psychological Sciences. 1996;51B:317–330. doi: 10.1093/geronb/51b.6.p317. doi:10.1093/geronb/51B.6.P317. [DOI] [PubMed] [Google Scholar]
  43. Sklar A. L., Boissoneault J., Fillmore M. T., Nixon S. J. Interactions between age and moderate alcohol effects on simulated driving performance. Psychopharmacology. 2014;231:557–566. doi: 10.1007/s00213-013-3269-4. doi:10.1007/s00213-013-3269-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Spielberger C. D. Manual for State–Trait Anxiety Inventory. Palo Alto, CA: Consulting Psychologists Press; 1983. [Google Scholar]
  45. Sur S., Sinha V. K. Event-related potential: An overview. Industrial Psychiatry Journal. 2009;18:70–73. doi: 10.4103/0972-6748.57865. doi:10.4103/0972-6748.57865. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Watson P. E., Watson I. D., Batt R. D. Prediction of blood alcohol concentrations in human subjects. Updating the Widmark equation. Journal of Studies on Alcohol. 1981;42:547–556. doi: 10.15288/jsa.1981.42.547. doi:10.15288/jsa.1981.42.547. [DOI] [PubMed] [Google Scholar]
  47. Weafer J., Fillmore M. T. Low-dose alcohol effects on measures of inhibitory control, delay discounting, and risk-taking. Current Addiction Reports. 2016;3:75–84. doi:10.1007/s40429-016-0086-y. [Google Scholar]
  48. Wolff N., Gussek P., Stock A. K., Beste C. Effects of high-dose ethanol intoxication and hangover on cognitive flexibility. Addiction Biology. 2018;23:503–514. doi: 10.1111/adb.12470. doi:10.1111/adb.12470. [DOI] [PubMed] [Google Scholar]
  49. Yesavage J. A., Brink T. L., Rose T. L., Lum O., Huang V., Adey M., Leirer V. O. Development and validation of a geriatric depression screening scale: A preliminary report. Journal of Psychiatric Research. 1982–1983;17:37–49. doi: 10.1016/0022-3956(82)90033-4. doi:10.1016/0022-3956(82)90033-4. [DOI] [PubMed] [Google Scholar]
  50. Zanto T. P., Gazzaley A. Neural suppression of irrelevant information underlies optimal working memory performance. Journal of Neuroscience. 2009;29:3059–3066. doi: 10.1523/JNEUROSCI.4621-08.2009. doi:10.1523/JNEUROSCI.4621-08. 2009. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Journal of Studies on Alcohol and Drugs are provided here courtesy of Rutgers University. Center of Alcohol Studies

RESOURCES