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. Author manuscript; available in PMC: 2019 Jun 1.
Published in final edited form as: Exp Clin Psychopharmacol. 2019 Jan 10;27(3):247–256. doi: 10.1037/pha0000251

Effects of Multisensory Stop Signals on Alcohol-induced Disinhibition in Adults with ADHD

Alexandra R D’Agostino a, Michael J Wesley b, Jaime Brown a, Mark T Fillmore a
PMCID: PMC6538486  NIHMSID: NIHMS1030104  PMID: 30628812

Abstract

Multisensory environments facilitate behavioral functioning in humans. The “redundant signal effect” (RSE) refers to the observation that individuals respond more quickly to stimuli when information is presented as multisensory, redundant stimuli (e.g., aurally and visually), rather than as a single stimulus presented to either modality alone. RSE appears to be due to specialized multisensory neurons in the superior colliculus and association cortex that allow intersensory co-activation between the visual and auditory channels. Our studies show that the disinhibiting effects of alcohol are attenuated when stop signals are multisensory (e.g., visual+auditory stop signals) versus unisensory signals (Roberts, Monem, & Fillmore, 2016). The present study expanded on this research to test the degree to which multisensory stop signals could also attenuate the disinhibiting effects of alcohol in those with Attention Deficit Hyperactivity Disorder (ADHD), a clinical population characterized by poor impulse control. The study compared young adults with ADHD (n = 22) to healthy controls (n = 22) and examined the acute impairing effect of alcohol on response inhibition to stop signals that were presented as a unisensory (visual) stimulus or a multisensory (visual + auditory) stimulus. For controls, results showed that alcohol impaired response inhibition to unisensory stop signals but not to multisensory stop signals. Alcohol impaired response inhibition of those with ADHD regardless of whether stop signals were unisensory or multisensory. The failure of multisensory stimuli to attenuate alcohol impairment in those with ADHD highlights a specific vulnerability that could account for heightened sensitivity to the disruptive effects of alcohol.

Keywords: disinhibition, ADHD, alcohol, multisensory

Alcohol is well known for its disinhibiting effects on behavior. Laboratory studies have shown that alcohol reliably increases failures to inhibit responses to stop-signals in a dose-dependent manner (Fillmore, 2003; Marczinski & Fillmore, 2003). Alcohol impairs inhibitory control by slowing information processing to disrupt late stages of stimulus-response selection, and this is evident through cognitive-process models and analyses of event-related potentials (Bartholow et al., 2003; Fillmore & Van Selst, 2002; Lukas, Mendelson, Kouri, Bolduc, & Amass, 1990; Moskowitz & Depry, 1968). The degree to which alcohol impairs cognitive processing can depend on characteristics of the stimuli being processed. It is well known that alcohol impairment intensifies as a function of task complexity. As greater processing is required to complete a task, drinkers tend to be more impaired, even at doses where no impairment is present for simpler tasks (Maylor, Rabbitt, James, & Kerr, 1992).

Processing and improvement of performance may be facilitated by other characteristics of stimuli. For example, people tend to respond more quickly when environmental signals are delivered redundantly to more than one sensory modality (Diederich & Colonius, 2004; Forster, Cavina-Pratesi, Aglioti, & Berlucchi, 2002; Gondan, Götze, & Greenlee, 2010). This phenomenon, referred to as the “redundant signal effect” (RSE), has been recognized for some time (Todd, 1912). Studies of the RSE typically require participants to perform a choice response task with three conditions: one in which participants respond to a visual cue (e.g., an X or O), another where participants respond to an auditory cue (e.g., a high or low tone), and one condition where both stimuli are presented simultaneously (Sinnett, Soto-Faraco, & Spence, 2008). Both in terms of the speed and accuracy of responses, performance in the simultaneous, redundant signal condition is superior to performance in both unisensory conditions.

Reduction of acute alcohol impairment of inhibition can be facilitated through the effects of redundant signals. Physiologically, brain regions implicated in inhibitory control include the anterior cingulate, dorsolateral prefrontal cortex, insula, and parietal regions (Botvinick, Cohen, & Carter, 2004; Seeley et al., 2007). It has been indicated by neuroimaging studies that alcohol decreases activity in these regions (Anderson et al., 2011; Marinkovic, Rickenbacher, Azma, & Artsy, 2012), which may explain why alcohol impairs inhibitory control. Although the neural processes underlying the RSE are not fully understood, there is evidence that specialized multisensory neurons distributed in key brain regions become active in the presence of multisensory stimuli. Chen and colleagues (2015) reported evidence for multisensory activation in many of the brain regions involved in inhibitory control, including the right anterior insula, dorsal anterior cingulate, and posterior parietal cortices, suggesting that multisensory signals may facilitate response inhibition. Indeed, there is some evidence of enhancement of inhibitory control by multisensory inhibitory signals, such as stop-signal tasks (Cavina-Pratesi, Bricolo, Prior, & Marzi, 2001; Gondan et al., 2010; Gondan, Niederhaus, Rösler, & Röder, 2005). The finding that redundant multisensory signals could enhance neural activation in the same inhibitory control regions where alcohol reduces activation suggests that multisensory signals could also ameliorate the drug’s impairing effects on inhibitory control.

Our group recently tested the possibility that multisensory inhibitory signals can reduce the disinhibiting effects of alcohol in healthy adults (Roberts, Monem, & Fillmore, 2016). Inhibitory control was assessed by a go/no-go task, which included unisensory (visual) and multisensory (visual + aural) inhibitory signals. The task measured participant’s inhibitory control following a dose of alcohol designed to produce a peak blood alcohol concentration (BAC) of 80 mg/100 mL and a placebo. Results showed that alcohol reliably impaired inhibitory alcohol when stop signals were unisensory. However, when multisensory stimuli were used as stop signals, alcohol had no impairing effects on inhibitory control. Participants’ eye movements (saccades) were also measured to determine how multisensory signals affected the speed with which they visually located the visual stop and go targets stimuli on the computer screen. Alcohol slowed saccadic reaction time to targets. However, this slowing effect was reduced by the multisensory signals, suggesting that such multisensory signals might facilitate the speed with which drinkers gather relevant information from the environment to guide their behavior.

The results are important because they indicate that multisensory stimuli can serve as a protective factor against the disruptive effects of alcohol. A logical continuation of this research is to determine the degree to which multisensory inhibitory signals can also strengthen inhibitory control in drinkers with deficient inhibitory control and attentional dysfunction, such as those with Attention Deficit Hyperactivity Disorder (ADHD) (Roberts, Milich, & Fillmore, 2013). Individuals with ADHD display sensory modality differences in deficits of auditory and visual processing (Dionne-Dostie, Paquette, Lassonde, & Gallagher, 2015). Due to varying processing deficits among individuals with ADHD, the use of both visual and auditory modalities has been recommended in testing settings because the presentation of both visual and auditory stimuli results in improvement of attention (Nigg, 2006, Kerns, Eso, & Thomson, 1999). Additionally, individuals with ADHD also have heightened impulsivity and increased risk for alcohol and other drug use (Weafer, Milich, & Fillmore, 2011, Charach, Yeung, Climans, & Lillie, 2011; Lee, Humphreys, Floy, Liu, & Glass, 2011). Longitudinal studies have found that childhood ADHD leads to early onset of alcohol use, which can transition to heavy use in young adulthood, especially accompanied by conduct disorder or delinquency (Molina et al., 2014; Sibley, Kuriyan, Evans, Waxmonsky, & Smith, 2014). In terms of acute reaction to alcohol, individuals with ADHD display heightened disinhibition to alcohol similar to other at-risk groups, such as binge drinkers (Marczinski, Combs, & Fillmore, 2007; Weafer, Fillmore, & Milich, 2009).

The purpose of this study was to test whether multisensory signals could reduce the disinhibiting effects of alcohol in individuals with ADHD by facilitating their attention to inhibitory cues. A group of healthy adults and individuals with ADHD received 0.65 g/kg alcohol and placebo and completed a multisensory cued go/no-go task that measured how multisensory signals affect ability to quickly respond as well as inhibit responses (Roberts et al., 2016). We hypothesized that those with ADHD would display generally poorer response inhibition and heightened disinhibition in response to alcohol compared with controls. With respect to multisensory facilitation, it was predicted that multisensory signals would reduce the disinhibiting effects of alcohol in controls. The primary research question concerned the degree to which multisensory signals would yield a similar reduction in alcohol-induced disinhibition in those with ADHD.

Methods

Participants

Forty-four adult drinkers, 22 adults with ADHD (11 men and 11 women; age = 23.8, SD = 2.1 year) and 22 adults with no history of ADHD (10 men and 12 women; age = 22.7, SD = 2.0 year) participated in this study. Recruiting took place through fliers and online advertising seeking adults (ages 21–29) with and without ADHD for a study of the effects of alcohol. Volunteers were screened via telephone to ensure they were at least 21 years old, had normal or corrected vision and hearing, and consumed alcohol at least once per week. Individuals who reported taking psychotropic medication, other than psychostimulant medication for ADHD, were not invited to participate. Volunteers who reported past or current severe psychiatric diagnoses (e.g., bipolar disorder, schizophrenia) did not participate in this study. Following initial screening, volunteers who met these criteria were contacted via telephone and invited to participate in the study. Urine samples were tested for the presence of metabolites of amphetamine, methamphetamine, barbiturates, benzodiazepines, cocaine, opiates, methadone, phencyclidine, tricyclic antidepressants, and tetrahydrocannabinol (THC) (ICUP Drugscreen; Instant Technologies, Norfolk, VA). Positive urine analysis for any substance other than THC or amphetamine, for the ADHD group, resulted in discontinuation from the study. Participants who reported use of marijuana during the 24 hours preceding the session were discontinued. Urine samples were also tested for pregnancy in female participants (Icon25 Hcg Urine Test; Beckman Coulter, Pasadena, CA). No female volunteers who were pregnant or breastfeeding participated in the research. All participants were required to abstain from alcohol for 24 hours prior to each session and a breathalyzer confirmed a zero BAC at the outset of each session.

To ensure that members of the ADHD group experienced symptomatology severe enough to necessitate medication, only volunteers who were currently prescribed medication for ADHD were invited to participate. Members of the ADHD group reported several different prescriptions, including amphetamine (n = 10), lisdexamfetamine (n = 8), methylphenidate (n = 3), and dextroamphetamine (n = 1). Prescription status was confirmed by the experimenter during the first session. To confirm prescription status, the experimenter verified the participant’s name on their medication bottle and the participant filled out a questionnaire, which asked what type of medication, how often, and at what dose the medication was being taken. Participants were asked to abstain from taking their medication for at least 24 hours prior to each session to ensure that they were unmedicated during the testing sessions. ADHD diagnosis was confirmed by clinical interview and we required that volunteers met symptoms-based criteria on three measures of ADHD symptomatology, including the Conners Adult ADHD Rating Scale—Long Form (CAARS—S:L; Conners, Erhardt, & Sparrow, 1999), the Adult ADHD Self-Report Scale Symptoms Checklist (ASRS-v1.1; Kessler, et al., 2005), and the ADD/H Adolescent Self-Report Scale (ADDRV; Robin & Vandermay, 1996). A similar method of diagnostic confirmation has been successfully used by this research group in other studies (Roberts, Fillmore, & Milich, 2011a, 2011b). Participants completed the 45-item Urgency, Premeditation (lack of), Perseverance (lack of), Sensation Seeking, Positive Urgency, Impulsive Behavior Scale (UPPS-P; Lynam, Smith, Whiteside, & Cyders, 2006) as an additional measure of impulsivity. The UPPS-P served to further validate group classification. Rating scale scores for ADHD symptoms and impulsivity measures are reported in Table 1.

Table 1.

Group Comparisons on ADHD Symptoms and Impulsivity Measures

Group
Control (n = 22) ADHD (n = 22)
Mean SD Mean SD t
Diagnostic
CAARS
 DSM-IA 53.0 9.6 73.1 10.9 −6.5***
 DSM-HI 50.3 9.5 59.4 15.8 −2.3
 DSM-Tot 52.7 10.3 69.6 13.3 −4.7***
AASRS 1.9 1.4 4.1 1.6 −4.9***
ADDRV 9.3 5.9 21.2 5.8 −6.8***
UPPS
 UPPS-Premed 1.9 0.4 2.4 0.6 −3.1**
 UPPS-Urg 2.1 0.6 2.6 0.6 −3.0**
 UPPS-Sens 3.2 0.5 3.2 0.5 0.29
 UPPS-Presev 1.8 0.5 2.5 0.5 −4.6***
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Note. For all comparisons, n = 44, df = 42. CAARS scores are T-scores; DSM-IA is DSM-IV Inattentive Symptoms, DSM-HI is DSM-IV Hyperactive-Impulsive Symptoms; and DSM-Tot is DSM-IV ADHD Symptoms Total. AASRS refers to the score on the first six questions of the Adult ADHD Self-Report Scale. ADDRV refers to the total score on first 18 questions of the ADD/H Adolescent Self-Report Scale. UPPS refers to the scores on the four traits measured by the Urgency, Premeditation (lack of), Perseverance (lack of), Sensation Seeking, Positive Urgency, Impulsive Behavior Scale; UPPS-Premed is lack of premeditation, UPPS-Urg is urgency, UPPS-Sens is sensation seeking, and UPPS-Persev is lack of perseverance.

**

p < .01,

***

p < .001

Materials and Measures

Multisensory Go/No-Go Task.

This task, used in the study by Roberts et al. (2016), examined the ability of participants to inhibit prepotent responses to stop signals that were presented either as a visual stimulus or as a multisensory stimulus, comprised of a visual and auditory signal. The task required individuals to locate the go or no-go target, which appeared in a different location than where the cue was presented. Figure 1 illustrates a trial sequence on the task. Participants were presented with a cue in the middle of the screen, which is followed by the presentation of a target in 1 of 8 possible locations. Participants were instructed to respond to these targets by either pressing a button (forward slash key) in response to a go target (the word “go”) or inhibit a response when presented with a no-go target (the word “no”). Each trial consisted of the following events: (i) presentation of a fixation point for 800 ms; (ii) a preresponse cue that was displayed for 1 of 6 stimulus onset asynchronies preceding the target (SOAs = 100, 200, 300, 400, 500, and 600 ms); (iii) a go or no-go target visible at 1 of 8 locations around the center of the screen which remained visible until either a response was made or 1,000 ms elapsed; and (iv) a 700 ms intertrial interval.

Figure 1.

Figure 1.

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Schematic of a go-cue trial in the multisensory go/no-go task. Following the fixation point (panel A), a go-cue is presented (panel B). A green square serves as a go-cue, signally that a go target is likely to appear. In this example, a no-go target is then presented (panels C1 and C2). In panel C1, the no-go target is a visual only (unisensory) and is displayed in the central left region. In panel C2, the no-go target is presented in the upper right and paired with the no-go (i.e., 125 Hz) tone (multisensory).

The preresponse cue was either green or blue and signaled the probability that a go or no-go target would be displayed. Green squares preceded the go target on 80% of the trials and preceded the no-go target on 20% of the trials. Blue squares preceded the no-go target on 80% of the trials and preceded the go target on 20% of the trials. Green and blue squares functioned as go and no-go cues, respectively. Presentation of the go-cue increases response preparation, making it more difficult to inhibit a response when the no-go target unexpectedly appears. The disinhibiting effects of alcohol are most evident in this cue condition (Marczinski and Fillmore, 2003). The random SOAs (100, 200, 300, 400, 500, and 600 ms) between the cues prevented participants from anticipating the exact onset of the targets.

During half of the trials, targets were presented as multisensory signals by presenting an auditory tone in conjunction with the visual “go” and “no” target stimuli. During multisensory target trials, tones were presented concurrently with the go or no-go targets. Go targets were presented with a 1,000 Hz (high) tone and no-go targets were presented with a 125 Hz (low) tone. Participants were told that some trials may include tones, but they were not given specific instruction about the purpose of the tones. Participants were told to locate the visual target before making the appropriate response.

A test consisted of 200 trials that presented all possible cue-target combinations for both visual and multisensory trials and required 15 minutes to complete. Half of the trials presented visual targets and on the other half of the trials multisensory signals were presented. The target was presented in each possible radial position at least one time for each cue-target combination for both visual and multisensory trials. SOAs were distributed evenly across the different cue-target conditions in both visual and multisensory trials. The trial order was pseudo-random to avoid clustering of visual or multisensory trials. To encourage quick and accurate responding, feedback was presented to the participant during the intertrial interval by displaying the words correct or incorrect and their response time in milliseconds.

The task was operated using E-prime software on a PC (Schneider, Eschman, & Zuccolotto, 2002). Participants’ eye movements during each trial were measured to assess the speed with which they oriented their attention to the visual target when it was presented. A Tobii T120 Eye Tracking Monitor (Tobii Technology, Stockholm, Sweden) equipped with dual embedded cameras was used to track eye movements. Participants were seated with their heads approximately 60 cm in front of the computer with a free range of head and neck motion. Gaze locations were sampled at 120 Hz, and fixations were defined as gazes with standard deviations <0.5° of visual angle for durations of 90 ms or longer. All sampled eye locations during a fixation were averaged to determine the location of that fixation. By tracking eye movements, we were able to quantify how quickly participants attended to response targets once they were presented.

Drinking Habits.

Participants’ drinking habits were assessed using the Timeline Follow-Back (TLFB; Sobell & Sobell, 1992), which assessed daily drinking patterns over the past 3 months, and the Personal Drinking Habits Questionnaire (PDHQ; Vogel-Sprott, 1992), which provided information regarding participants’ alcohol consumption. For the TLFB, four measures of drinking habits were obtained: (i) total number of drinking days (drinking days), (ii) total number of drinks consumed (total drinks), (iii) total number of days characterized by subjective drunkenness (drunk days), and (iv) total number of days in which binge drinking occurred (binge days). Binge drinking days were determined by estimating participants BACs on each day according to the participants weight, the reported number of drinks they consumed, and the amount of time they spent drinking using anthropometric based BAC estimation formulae that assume an average clearance rate of 15 mg/100 ml per hour (Watson, Watson, & Batt, 1981). For the PDHQ, participants recorded both history of alcohol use (number of months of regular drinking), as well as information regarding current, typical drinking habits, including (i) frequency (the typical number of drinking occasions per week), (ii) quantity (the number of standard alcoholic drinks [e.g., 1.5 oz of liquor] typically consumed per occasion), and (iii) duration (time span in hours of a typical drinking occasion). Participants also completed the Alcohol Use Disorders Identification Test (AUDIT; Babor, Kranzler, & Lauerman, 1989). The AUDIT is a screening instrument that was used to assess the occurrence and severity of alcohol-related problems. The 10-item, self-report questionnaire covers patterns of drinking, dependence, and other negative consequences of drinking over the past year and has a total score range from 0 (no alcohol-related problems) to 40 (most severe alcohol-related problems).

Procedure

Volunteers responding to advertisements for this study underwent an intake screening by telephone. They were told that the purpose of the study was to examine the effects of alcohol on performance of computer tasks. They then made appointments to come to the laboratory for 3 sessions, including 1 familiarization and 2 dose-challenge sessions. The dose-challenge sessions were separated by an average of 4 days for the ADHD group and 7 days for the control group. Participants were instructed to fast for 4 hours prior to each dose-challenge session. They were also instructed to abstain from consuming alcohol or using other psychoactive drugs, including ADHD medication, during the 24 hours preceding each session.

Familiarization Session.

All participants completed a familiarization session during which they became acquainted with laboratory procedures, completed questionnaires, provided informed consent for participation, completed the Kaufman Brief Intelligence Test (Kaufman & Kaufman, 2004), and performed a training version of the multisensory go/no-go task. Volunteers who did not meet criteria for participation in the study were paid $10 and discontinued.

Dose-Challenge Sessions.

Participants were tested under 0.65 g/kg alcohol and placebo. Participants were blinded to dose, and dose order was counterbalanced across the 2 test sessions. Sessions were separated by no less than 1 day and no more than 1 week. Alcohol doses were calculated on the basis of body weight and administered as absolute alcohol mixed with 3 parts carbonated soda. A peak BAC of 80 mg/100 ml is produced by the 0.65 g/kg dose approximately 65 minutes post administration (Fillmore, Marczinski, & Bowman, 2005; Roberts et al., 2013). The placebo dose consisted of an equal volume of carbonated soda mix matching the total volume of the 0.65 g/kg alcohol dose. A small amount (3 ml) of alcohol was floated on the surface of the beverage, and it was sprayed with an alcohol mist that resembled condensation and provided a strong alcoholic scent as the beverage was consumed. All drinks were consumed within 6 minutes.

Participants performed the multisensory go/no-go task 30 minutes after dose administration. BAC levels were recorded throughout the session at 28, 45, 52, 65, and 72 minutes following dose administration for both the 0.0 and 0.65 g/kg dose. BACs were determined from expired air samples measured by an Intoxilyzer Model 400 (CMI, Inc., Owensboro, KY). Following testing, participants remained in a lounge area until their BACs reached 20 mg/100 ml or below. Participants received a meal and were allowed to watch movies and relax. Transportation home was provided if necessary. Participants were paid $80 for completion. Participants were also debriefed upon completion of the final session.

Criterion Variables and Data Analyses

The multisensory go/no-go task measured inhibitory control and response speed to visual and multisensory stimuli. Response inhibition was measured as participants’ failures to inhibit responses to stop targets (i.e., failures of response inhibition). Failure of response inhibition was measured as the proportion (p) of no-go targets in the go-cue condition in which a participant failed to inhibit a response (i.e., p-inhibition failures). Manual RT was defined as the mean time taken to make a response during go target trials. Shorter RTs indicated greater facilitation of response execution. Responses with RTs <100 ms were excluded. These outliers were infrequent, occurring on average <0.25% of the trials for which a response was observed (i.e., <1 trial per test). RTs and p-inhibition failures were calculated separately for unisensory (i.e., visual) and multisensory trials.

Visual fixations were used to determine saccadic RT. Saccadic RT was defined as the number of milliseconds that elapsed between the presentation of the target and the beginning of the first visual fixation at the location of the target.

p-inhibition failures, manual RT, and saccadic RT were each analyzed by 2 (dose: placebo vs. 0.65 g/kg alcohol) by 2 (target condition: unisensory vs. multisensory) by 2 (group: control vs. ADHD) repeated-measures analyses of variance (ANOVAs). A limited number of simple effect tests were performed to test the hypothesis that alcohol-induced disinhibition would be reduced by multisensory stop targets and compare the groups in the magnitude of this effect.

We conducted all analyses of alcohol effects to include sex as a factor. These analyses found no significant effect of sex and did not change the significance level of other main effects or interactions. As such, reported analyses of task performance are collapsed across sex.

Results

Drinking and Demographic Information

Participants’ drinking habits and demographic information are presented in Table 2. The table shows no difference between the two groups regarding level of education or IQ scores. With respect to drinking habits, the TLFB and PDHQ show no differences between the two groups. Both groups drank twice a week on average with a typical quantity per occasion of approximately 4 standard drinks. In addition to moderate alcohol use, some participants reported past month use of nicotine (n = 10), marijuana (n = 13), sedatives (n = 1), stimulants (n = 2), cocaine (n = 3), and club drugs (n = 1). Seven participants had tetrahydrocannabinol positive urine screens during one or more dose-challenge session. No other drug urine screens were positive.

Table 2.

Group Comparisons on Demographic Characteristics and Self-Reported Drinking Habits

Group
Control (n = 22) ADHD (n = 22)
Mean SD Mean SD t
Demographic
Age 23.8 2.1 22.7 2.0 1.7
Sex (% male) 50.0 45.5
Weight (kg) 76.8 15.1 75.8 17.2 0.2
Education 15.5 1.6 15.0 1.6 0.9
IQ: Verbal 102.8 12.8 102.2 7.5 0.2
IQ: Nonverbal 99.2 13.8 103.9 10.9 −1.3
IQ: Composite 101.4 14.0 103.9 9.3 −0.7
Drinking Habits
TLFB
 Drinking Days 30.4 17.6 23.4 13.5 1.5
 Total Drinks 152.0 117.6 113.0 107.8 1.2
 Drunk Days 11.8 9.5 8.8 9.4 1.1
 Binge Days 12.3 14.3 8.9 10.8 0.9
PDHQ
 Frequency 2.5 1.7 1.6 0.9 2.1
 Quantity 3.9 1.8 4.4 1.9 −0.8
AUDIT 8.1 4.2 9.2 4.7 −0.8
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Note. For all comparisons, n = 44, df = 42. Age is reported in years. TLFB refers to variables reported on the Timeline Follow-Back Procedure. PDHQ refers to the Personal Drinking Habits Questionnaire. AUDIT refers to the Alcohol Use Disorders Identification Test.

Blood Alcohol Concentrations

Group differences in BAC under 0.65 g/kg alcohol were examined using a 2 (group) × 4 (time: 25, 45, 65, and 95) mixed-design ANOVA. There was a significant main effect of time, F (3, 126) = 38.23, p < .001, ηp2=.477, owing to the rise and decline of BAC over the time course of the testing session. The mean BACs (mg/100 mL) at 25, 45, 65, and 95 minutes were 68.2 (SD = 21.4), 84.2 (SD = 15.3), 86.4 (SD = 17.3), and 69.6 (SD = 14.0), respectively. There was no main effect of group or group x time interaction, ps > 0.05. No detectable BACs were observed in the placebo condition.

p-inhibition Failures

Figure 2 presents the mean p-inhibition failures following placebo and alcohol in response to multisensory and unisensory signals for the control (left panel) and ADHD group (right panel). For unisensory stop signals, controls displayed increased inhibitory failures in response to alcohol compared with placebo (i.e., disinhibition). However, for multisensory stop signals, alcohol had no disinhibiting effect. By contrast, for those with ADHD, alcohol increased inhibitory failures compared with placebo to both unisensory and multisensory signals. Indeed, Figure 2 shows that the highest degrees of p-inhibition failures under alcohol were displayed by those with ADHD regardless of whether signals were unisensory or multisensory. These data were analyzed by a 2 (dose) ×2 (target condition) ×2 (group) ANOVA. The ANOVA showed a significant main effect of dose, F (1, 42) = 12.28, p = .001, ηp2=.226, and a target condition x group interaction, F (1, 42) = 4.40, p = .042, ηp2=.095. No additional main effects or interactions were significant. For controls, simple effect comparisons tested the hypothesis that alcohol-induced disinhibition was reduced by multisensory versus unisensory stop targets. This hypothesis was supported as alcohol significantly increased inhibitory failures compared with placebo in the unisensory target condition, t (21) = −3.0, p = .007, dz = −0.64, but not in the multisensory target condition, t (21) = −.442, p = .663, dz = −0.10. Indeed, under the active dose, controls made significantly fewer inhibitory failures to multisensory versus unisensory targets, t (21) = 3.0, p = .006, dz = 0.65. For the ADHD group, who displayed similar increases in p-inhibition failures following alcohol regardless of target condition, there were no significant differences in inhibition failures between target conditions following placebo, t (21) = −1.5, p = .143, dz = −0.32, or alcohol, t (21) = −0.662, p = .515, dz = −0.14.

Figure 2.

Figure 2.

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Effects of alcohol dose and target condition on proportion of inhibition failures in the control and ADHD group. Capped vertical lines show SEMs.

Reaction Time

Manual.

Figure 3 presents mean manual RT following placebo and alcohol in response to multisensory and unisensory signals for the control (left panel) and ADHD group (right panel). The figure shows that alcohol slowed reaction time compared with placebo in both groups. Additionally, responses were faster to multisensory go targets versus unisensory go targets for both groups under both placebo and alcohol. The 2 (dose) ×2 (target condition) × 2 (group) ANOVA showed a significant main effects of dose, F (1, 42) = 19.57, p < .001, ηp2=.318, and target condition, F (1, 42) = 88.95, p < .001, ηp2=.679. No interactions were significant, ps > 0.05.

Figure 3.

Figure 3.

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Effects of alcohol dose and target condition on manual reaction time in the control and ADHD group. Capped vertical lines show SEMs.

Saccadic.

Figure 4 plots the mean saccadic RT following placebo and alcohol in response to multisensory and unisensory signals for the control (left panel) and ADHD group (right panel). The figure shows that alcohol slowed saccadic reaction time compared to placebo in both groups. Additionally, responses were faster to multisensory go targets versus unisensory go targets for both groups under both placebo and alcohol. The 2 (dose) × 2 (target condition) ×2 (group) ANOVA showed a significant main effect of dose, F (1, 42) = 69.45, p < .001, ηp2=.623, and target condition, F (1, 42) = 9.68, p = .003, ηp2=.187. No interactions were significant, ps > 0.05.

Figure 4.

Figure 4.

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Effects of alcohol dose and target condition on saccadic reaction time in the control and ADHD group. Capped vertical lines show SEMs.

Discussion

This study used the go/no-go task to examine the ability of multisensory stop signals to attenuate the disinhibiting effects of alcohol in individuals with ADHD and healthy controls. The results were consistent with previous research that has shown that multisensory stop signals can attenuate the disinhibiting effects of alcohol (Roberts et al., 2016). With the controls, alcohol increased inhibitory failures when the stop signal was unisensory, but when the signal was multisensory, response inhibition was not impaired. In the ADHD group, alcohol increased inhibitory failures regardless of whether the stop signal was unisensory or multisensory. With respect to reaction time, alcohol slowed responses to go targets similarly for controls and ADHD subjects and reaction time was faster to multisensory signals in both groups compared to unisensory signals regardless of dose. Saccadic RT to locate the targets showed the same pattern of results. Alcohol slowed saccadic reaction time with RTs faster to multisensory versus unisensory targets regardless of dose.

For the control participants, results were consistent with previous research that has shown that multisensory stop signals can attenuate the disinhibiting effects of alcohol (Roberts et al., 2016). Although it is not entirely clear how multisensory signals protect against alcohol-induced disinhibition, one possible explanation concerns alcohol-induced slowing of information processing speed. Evidence suggests that alcohol impairs behavior by slowing the speed with which drinkers are able to process information (Bartholow et al., 2003; Fillmore & Van Selst, 2002). Presenting multisensory response targets may facilitate the recruitment of additional processing resources, perhaps via activation of multisensory neurons in the superior colliculus involved in saccadic eye movement (Lee, Rohrer, & Sparks, 1988). Saccadic RT to target stimuli was hastened by multisensory signals raising the possibility that multisensory stimuli protected against alcohol impairment by increasing the speed with which drinkers can attend to and process the stimuli, including no-go stimuli that signal response inhibition. However, findings from those with ADHD indicate that hastening of saccadic RT by multisensory stimuli cannot fully explain their attenuating effects on alcohol-induced disinhibition. Although those with ADHD also demonstrated a hastening of their saccadic RT from multisensory signals, these signals had no attenuating effects on the degree to which alcohol impaired their inhibitory control. It is also important to note that for the controls, multisensory signals did not improve inhibitory control in the sober state (i.e., following placebo). This is possibly due to the fact that while sober, controls’ inhibitory control was near optimal, with few inhibitory failures. Therefore, a floor effect could have precluded observing any facilitation of the multisensory signals.

There may be several reasons why multisensory signals failed to attenuate the disinhibiting effect of alcohol in those with ADHD. One possibility is that those with ADHD have an impaired ability to integrate multisensory stop signals that guide behavior. However, those with ADHD showed benefits of multisensory signals in facilitating their reaction time to go stimuli that were comparable to controls. Therefore, any failure of the ADHD group to integrate signals would appear to be specific to signals concerning the inhibition of actions (i.e., stop/no-go signal) rather than signals to execute behavior (go signals). Future studies using functional neuroimaging of these behaviors in those with ADHD in the intoxicated state would be helpful in testing such hypotheses. Any deficit of multisensory integration in those with ADHD could be specific to neural regions involved in the suppression of behavior (e.g., the anterior cingulate insula and dorsolateral prefrontal cortex) (Botvinick et al., 2004).

Regardless of the neural basis, the failure of multisensory stimuli to attenuate alcohol-induced impairment of inhibitory control in these individuals highlights a potential vulnerability of the group that could account for their heightened sensitivity to the behaviorally disruptive effects of alcohol. Previous research by our lab has shown that moderate drinkers with ADHD have heightened sensitivity to the disinhibiting effects of alcohol on cued go/no-go tasks and display less acute tolerance to alcohol, prolonging impairment of inhibitory control compared with controls (Roberts, Fillmore, & Milich, 2012; Weafer et al., 2009; Roberts et al., 2013). Laboratory observations of these impairments could also translate to everyday problems for this population. Outside of the laboratory, cues that signal behavior are often multisensory. It is likely that individuals commonly benefit from redundant multisensory stimuli in the environment to signal whether or not actions should be expressed or withheld. However, those with ADHD might not benefit from this additional information to guide behavior.

The findings of this study should be considered in light of some limitations. First, participants were tested under a single active dose of alcohol. Although this dose was selected for its ability to produce considerable behavioral impairment in adult drinkers (Holloway, 1995), it would be informative to examine how multisensory signals affect alcohol impairment under a range of doses. Second, we did not test participants’ performance in an auditory only condition. It is possible that the presence of auditory response target, rather than the multisensory combination of both visual and aural targets, may account for the differences between target conditions. However, prior research using similar paradigms to study the RSE on response activation shows similar RTs to visual and auditory unisensory targets (Fillmore, 2010). Further, these studies find that multisensory response targets engender comparable improvement in RT over both unisensory conditions.

In conclusion, future research using the model of multisensory facilitation of behavior could be used to better understand current behavioral and pharmacological treatments for impulse control disorders. Those with ADHD in the current study were tested in the unmedicated state. It is possible that their inhibitory control could have benefited from these multisensory signals if they had received their medication treatment (e.g., methylphenidate). Stimulant medications improve inhibitory control (Tannock, Ickowicz, & Schachar, 1995; Schachter, King, Langford, & Moher, 2001; Fillmore, Kelly, & Martin, 2005; Fillmore, Rush, & Hays, 2006). However, these tests have been based on unisensory models in which the stop signal is a single unimodal stimulus. It is unknown how stimulant medications would interact with multisensory signals to inhibit action. These types of treatments could have clinical efficacy through improved integration of multisensory signals that guide behavior. This possibility waits to be explored.

Footnotes

The authors declare no conflict of interest

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