Arch Toxicol (2014) 88:475–488 DOI 10.1007/s00204-013-1109-2
ORGAN TOXICITY AND MECHANISMS
Effects of binge drinking on action cascading processes: an EEG study Ann-Kathrin Stock • Meinolf Blaszkewicz Christian Beste
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Received: 28 May 2013 / Accepted: 23 July 2013 / Published online: 8 August 2013 Ó Springer-Verlag Berlin Heidelberg 2013
Abstract High-dosage alcohol intoxication (i.e., binge drinking in humans) is an increasingly prevalent problem. Despite the well-known long-term consequences, the acute effects of high-dosage alcohol intoxication on cognitive control processes have not been investigated with respect to neurophysiological changes in humans. We provide insights into the effects of high-dosage ethanol intoxication on action control functions in humans on the basis of neurophysiological (EEG) data. Action control processes were examined in a stop–change task. Based on a detailed analysis of behavioral and electrophysiological data, we demonstrate a specific modulation of action cascading processes. Opposed to commonly held views, high-dosage ethanol intoxication (0.9–1.13 %) exerts highly specific effects on cognitive subprocesses mediating action control. If action control processes are performed in succession, intoxicated and non-intoxicated participants perform equally well. However, action control processes become compromised during high-dosage ethanol intoxication, when different response options require processing resources in parallel. Under high-dose ethanol intoxication, subjects are not able to prioritize different response options. We could demonstrate that the effects were of high Electronic supplementary material The online version of this article (doi:10.1007/s00204-013-1109-2) contains supplementary material, which is available to authorized users. A.-K. Stock (&) C. Beste Department of Biopsychology, Institute for Cognitive Neuroscience, Ruhr-University Bochum, Universita¨tsstrasse 150, 44780 Bochum, Germany e-mail:
[email protected] M. Blaszkewicz Leibniz Research Centre for Working Environment and Human Factors, IfADo, Dortmund, Germany
effect sizes (g2 = 0.702) and rely more on response selection deficits than on deficits in attentional processing. The changes in response selection processes are mediated via the anterior cingulate cortex. The specificity of the observed effects may be due to a differential involvement of dopaminergic and GABAergic processes in action control and attentional selection processes. Keywords Alcohol Response control Wavelet analysis Source localization P3
Introduction Alcohol (ethanol) is a common drug of abuse, and neurological long-term effects of alcohol abuse including information processing have been extensively described (for review: Sinforiani et al. 2011; van Holst and Schilt 2011; Welch 2011). Even for drinking habits that do not meet the criteria of substance abuse, an impairment of cognitive functions over time has been repeatedly demonstrated (e.g., Montgomery et al. 2012). Yet, binge drinking remains a widespread and common drinking pattern among young adults, especially in Western cultures (Jernigan 2001). A large amount of studies have examined the effect of alcohol dependence on executive functions like inhibition and stopping of responses (e.g., Claus et al. 2013; Oddy and Barry 2009; Saunders et al. 2008). Studies of acute alcohol effects showed impaired functioning even at low doses (usually between 0.5 and 0.8 %) (Dougherty et al. 2008; Field et al. 2010; Schweizer et al. 2004; Fillmore and Van Selst 2002). The studied intoxication levels are far lower than peak values usually experienced during binge drinking (Crabbe et al. 2011). Hence, the effects of highdose alcohol on human behavior are hardly understood.
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Moreover, existing studies do not examine the effects of high-dose ethanol intoxication with respect to the underlying neuronal processes. In the current study, we examine the effects of highdosage ethanol intoxication on action cascading processes from a systems’ neuroscience perspective by applying EEG. Action cascading processes have been shown to depend on fronto-striatal networks (Humphries et al. 2006; Bar-Gad et al. 2003; Redgrave et al. 1999; Beste et al. 2009, 2012; Ravizza et al. 2012; Cameron et al. 2010; Willemssen et al. 2011). Within these networks, GABAergic medium spiny neurons (MSNs) play an important role in action selection (Plenz 2003). Moreover, dopaminergic signaling contributes to the selection of actions (e.g., Frank et al. 2009). In vitro and animal studies have demonstrated that high doses of ethanol result in an upregulation of the dopaminergic system (e.g., Rice et al. 2012; Clapp et al. 2008; Crabbe et al. 2011), affecting the ventral tegmental area (Melis et al. 2009; Tateno and Robinson 2011), the substantia nigra (Tateno and Robinson 2011) and the striatum (Urban et al. 2010, study in humans). Aside from dopamine, GABAergic neurotransmission has also been repeatedly demonstrated to increase under the influence of ethanol (e.g., Clapp et al. 2008; Crabbe et al. 2011; Kumar et al. 2009; Kelm et al. 2011; Roberto and Siggins 2006; Siggins et al. 2005; Faingold et al. 1998; Tateno and Robinson 2011). Based on these findings, it is very likely that action cascading processes are subject to ethanolinduced cognitive changes. Several lines of evidence suggest that action cascading can be performed with a varying degree of overlapping between the cascaded actions (Miller et al. 2009; Verbruggen et al. 2008; Oberauer and Kliegl 2004; Sommer et al. 2001; for review: Wiu and Liu 2008). According to computational models, fronto-striatal loops constitute a ‘‘winner-takes-all network’’ (e.g., Leblois et al. 2006; Plenz 2003; Bar-Gad et al. 2003). When this network works properly, responses are selected on a step-by-step basis where competing actions are discarded until a single ‘‘winner’’ response is left to be carried out. When the integrity of this network is compromised, different response options may be represented in parallel. As a consequence, these competing response options may mutually inhibit each other and thus slow the execution of the cascaded actions. Given that fronto-striatal functions are compromised during acute ethanol intoxication, action cascading processes should be performed in a more overlapping (more parallel) manner as compared to a nonintoxicated state without ethanol intoxication. Electrophysiological studies on choice reaction tasks and on tasks requiring a cascaded execution of choicedependent actions have shown that the process between
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stimulus evaluation and responding is reflected by the event-related (parietal) P3 potential (Verleger et al. 2005; Falkenstein et al. 1994a, b; Sigman and Dehaene 2008; Brisson and Jolicoeur 2007; Polich 2007). These processes have recently been shown to predict the degree of overlap of task goal activation during action cascading (Mu¨ckschel et al. 2013). Also, it was demonstrated that these P3 processes originate in the anterior cingulate cortex (ACC) (Mu¨ckschel et al. 2013). Based on these findings, an acute ethanol intoxication should affect ACC activity and the P3 processes based thereon. Furthermore, the P3 is a slow oscillating ERP (e.g., Demiralp et al. 2001; Bas¸ ar et al. 2001; Polich 2007). Slow oscillations in the delta and theta frequency range have been shown to be involved in response monitoring (e.g., Yordanova et al. 2004). Dovetailing with this, ethanol has been shown to alter neural synchronization processes (e.g., Faingold et al. 1998), which are known to determine the efficacy of response control processes (e.g., Beste et al. 2010). With respect to the mechanisms described above, we expect that phase locking as a measure for neural synchronization processes (e.g., Roach and Mathalon 2008) of P3 processes may be decreased in the state of acute ethanol intoxication. Aside from response control processes, attentional processes have also been shown to influence the performance in dual- or multitasking situations (e.g., Brisson and Jolicoeur 2007). In line with what is suggested in different studies (e.g., Heinz et al. 2011), these processes might also be affected by acute ethanol intoxication. However, attentional selection processes are only indirectly influenced by GABAergic or dopaminergic neural transmission (e.g., Sarter et al. 2006). Instead, they heavily rely upon glutamatergic and acetylcholinergic neural transmission (Turchi and Sarter 2001). Given this marked difference in neurochemical modulation, attentional selection processes are probably not as profoundly mediated by the effects of ethanol intoxication. Even though there is a possibility that attentional selection processes are reduced in their efficacy due to ethanol intoxication, the magnitude of this change ought to be smaller than changes in processes mediating stimulus processing and response execution.
Materials and methods Participants Twenty (n = 20) healthy subjects (11 females) aged 20–30 years (23.6 ± 2.8) were recruited. Participants were right-handed as assessed with the Edinburgh Handedness Inventory (Oldfield 1971), having a mean score of 0.89 (range 0.4–1.0). Scores of additionally conducted
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psychological tests can be found in Online Resource 1. All participants had no history of neurological and psychiatric disorders and showed no signs of physical health impairments. Women were required to use oral contraceptives and to select their appointments so that they were under the acute effect of the contraceptives on both experimental sessions. To further reduce the risk of severe physical consequences, only participants reporting at least one heavy drinking episode within the past 12 months were included. To exclude participants with risky drinking habits, all participants had AUDIT (alcohol use disorders identification test, Babor et al. 2001) scores between 1 and 15 (indicating low to moderate risk of alcohol addiction/ abuse as well as a small likelihood of pronounced homeostatic alcohol tolerance). Additionally, we excluded applicants who claimed to drink more than six (women) or eight (men) units of alcohol more than 3 times a month and/or reported alcohol-related memory impairments at this frequency. The study was approved by the Ethics Committee of the Medical Faculty of the Ruhr-University of Bochum (Reg.-Nr. 4490-12) and was conducted in accordance with the declaration of Helsinki. Experimental design and alcohol administration The study was conducted using a within-subject design with all subjects participating twice. The two sessions (one non-intoxicated, one intoxicated) were conducted with a delay of 7 days. The design was counterbalanced for sex and drinking order. All participants were asked to discontinue the use of caffeine, nicotine, etc., 4 h before the start of both experimental sessions and to stop eating at least 3 h prior to the intoxication session in order to present with a rather empty stomach. Aiming at a maximal possible blood alcohol concentration (BAC) of 1.5 % (mg/g) and an approx. mean BAC of 1.2 % (assuming an absorption deficit of 20 %), we used a version of the equation by Widmark (1932) and Watson et al. (1980) to calculate an individual amount of vodka (40 % alcohol by volume) for each participant (see Online Resource 2). The vodka was mixed with an equal amount of orange juice. Irrespective of the individual amount, participants were asked to consume their drink within 30 min. Afterward, participants were required to wait for 30 more minutes for the ethanol to start exerting its effects. During these 60 min, participants watched three ‘‘Big Bang Theory’’ episodes, which had been chosen for their lack of extensive discourses on drinking and party-related topics (e.g., Friedman et al. 2007; Read and Curtin 2007) and helped prevent negative mood swings and differential effects of the engagement in conversations with the experimenters.
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Breath alcohol and blood alcohol analyses Breath alcohol concentration was measured using the ‘‘Alcotest 3000’’ analyzer following the instructions of the producer (Dra¨gerwerk, Lu¨beck, Germany). Breath alcohol concentration was determined at the beginning of the nonintoxicated session. During the exposure (‘‘intoxication’’) session, both breath and alcohol concentration were determined at three time points (before beverage administration, directly before and directly after the experimental task). Blood samples were collected using a venous catheter which had been placed at the beginning of the experiment and a 7.5-ml Sarstedt K-EDTA monovettes (Sarstedt, 51582 Nu¨mbrecht, Germany). After collection, the blood sample was subdivided into two 1-ml aliquots, which were pipetted into 20-ml crimp top vials with PTFE-coated butyl rubber stoppers and aluminum crimp caps. To avoid contamination, the vials and especially the stoppers were heated in a drying cupboard at 100 °C for more than 24 h before use. Until analysis, the samples were stored in a deep freezer (-18 °C). The concentrations of ethanol in the whole blood samples were determined using capillary gas chromatography-headspace technique and a flame ionization detector (FID). For this purpose, the blood samples were warmed to 50 °C in the airtight crimp top vials. After distribution of the ethanol between the liquid and vapor phases has reached equilibrium, an aliquot of the headspace is withdrawn and analyzed by gas chromatography-FID. Calibration curves are obtained by analyzing blood samples to which known quantities of ethanol have been added. The limit of detection (LOD) is 0.02 g/l (DFG, Angerer and Gu¨ndel 1996). Experimental setting and task At the beginning of both appointments (hence prior to beverage administration in the intoxicated session), participants completed a task exercise until they understood the task and felt competent to perform it during the experiment. During the experiment, participants were seated 57 cm from a 17-inch CRT computer monitor. Responses were recorded using four custom-made buttons placed in front of the subjects. For stimulus presentation and response recording, Presentation (version 14.9. by Neurobehavioral Systems, Inc.) was used. The task employed in this study was introduced by Verbruggen et al. (2008) and aims to examine task goal activation processes in action cascading. The setup of the task is shown in Fig. 1 and is identical to the task used in a previous study conducted by our group, which examines the psychophysiological mechanisms underlying action cascading (Mu¨ckschel et al. 2013):
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Fig. 1 Schematic illustration of the stop–change paradigm. GO1 trials end after the first response to the GO1 stimulus (bold). In contrast, SC trials end after the first response to the CHANGE signal (bold). The stop-signal delay (SSD) between the onset of the GO1 stimulus and the STOP signal was adjusted using a staircase procedure described in the ‘‘Materials and methods’’ section. The
stimulus onset asynchrony (SOA) between the onset of the STOP and CHANGE stimuli was set to either 0 or 300 ms. As indicated in the upper right corner, the three CHANGE stimuli were associated with one of the three reference lines (see ‘‘Materials and methods’’ section for details)
The experiment consisted of 864 trials (divided into 6 blocks) and took the participants approx. 30 min to finish. Throughout every trial, a rectangle (20 996 mm) containing four vertically aligned circles (8 mm diameter) and three horizontal reference lines (line thickness 1 mm, width 8 mm) separating the circles were presented on the screen. At the start of each trial, all lines were white and the four circles were filled black. After 250 ms, one of the circles was filled white thus becoming the GO1 target stimulus. The experiment consisted of two conditions: In the GO1 condition (67 % of all trials), the participants’ response was expected to indicate whether this filled white circle (target) was located above or below the middle reference line. Responses were given by pressing the outer right key with the right middle finger (‘‘above’’ judgment) or by pressing the inner right key with the right index finger (‘‘below’’ judgment). All stimuli remained visible until the participant either responded or 2,500 ms had elapsed. In case of reaction times (RTs) longer than 1,000 ms, the German word ‘‘Schneller!’’ (translating to ‘‘Faster!’’) was presented above the box until the participant responded and thereby ended the trial. The remaining 33 % of trials were stop–change (SC) trials. Like the GO1 condition, the SC condition started with the presentation of a white GO1 stimulus. After a variable ‘‘stop-signal delay’’ (SSD), a STOP signal (a red rectangle replacing the usual white frame; depicted gray in Fig. 1) was presented, putting an end to the GO1 trial. This
STOP signal remained on the screen until the end of the trial and requested the participant to try to inhibit the righthand response to the GO1 stimulus whenever possible. The SSD was initially set to 450 ms and adapted to the participants’ performance by means of a ‘‘staircase procedure’’ (see: Verbruggen et al. 2008), yielding a 50 % probability of successfully inhibited GO1 responses. Irrespective of the inhibition performance, every STOP signal was combined with one of three possible CHANGE stimuli. The CHANGE stimulus was a 100-ms sine tone presented via headphones at 75-dB SPL and could be either high (1,300 Hz), medium (900 Hz) or low (500 Hz). It assigned a new reference line in relation to which the GO2 stimulus (the previous GO1 white target circle that had remained on the screen during the whole trial) had to be judged. While the high tone implemented the highest of the three lines as the new reference, the medium tone coded for the middle line and the low tone coded for the lowest line (see Fig. 1). All three reference lines were in effect equally often. The required GO2 response had to be performed with the left hand. If the target circle was located above the newly assigned reference line, an outer left key press (left middle finger) was required, and if target circle was located below the newly assigned reference line, a left inner key press (left index finger) was required. In half of the SC trials, there was a stop–change delay (SCD) with a stimulus onset asynchrony (SOA) of 300 ms between the STOP and the CHANGE signals (‘‘SCD300’’ condition), while in the
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other half of SC trials, there two stimuli were presented simultaneously (SOA of 0 ms, ‘‘SCD0’’ condition). In case of RTs longer than 2,000 ms, the German word ‘‘Schneller!’’ (translating to ‘‘Faster!’’) was presented above the box until the participant responded to end the trial. After each SC trial, the staircase algorithm adjusted the SSD that would be used in the subsequent SC trial (Logan and Cowan 1984). In case of a completely correct SC trial (no response to GO1 stimulus, no response before the GO2 stimulus in SCD300 conditions and a correct left-hand response to the GO2 stimulus), the SSD was adjusted by adding 50 ms to the SSD of the evaluated trial. In case of an erroneous SC trial (if any of the above criteria were not met), the SSD was adjusted by subtracting 50 ms from the SSD of the evaluated trial. Limiting this procedure, SSD values were set not to deceed a value of 50 ms and not to exceed a value of 1,000 ms. During the intertrial interval (ITI, fixed duration of 900 ms), a fixation cross was presented in the center of the screen. Participants were instructed to respond as fast and accurately as possible. All conditions were presented in a randomized order. Therefore, it is very unlikely that there are preparatory effects in the motor system biasing the results. Estimation of action cascading The paradigm introduces two different SCDs based on which we calculated a slope value for the GO2 RTs using the equation: slope ¼
GO2 RTSCD0 GO2 RTSCD300 SOA0 SOA300
The slope value was individually calculated for each participant and appointment. It becomes steeper the more SCD0-RT and SCD300-RT differ. The interpretation of the slope value as a measure of response selection processes on a serial–parallel continuum is based on the following rationale: The SOA in the SCD300 condition enforces a serial processing of the STOP- and CHANGE-related processes because the STOP process has usually been finished when the CHANGE stimulus is presented 300 ms later. In contrast to this, the SCD0 condition provides the participants with different possibilities of how to cascade STOP- and CHANGE-associated processes. Most (bottleneck) models suggest that response selection can be done rather serially (cognitive substeps being executed one after another) or rather parallel (cognitive substeps being processed in parallel so that there is a temporal overlap of processes) (Miller et al. 2009, Verbruggen et al. 2008; Wiu and Liu 2008). Because response selection depends on a restricted resource, the processing strategy may differentially affect the GO2 RT in the SCD0 condition. When the
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STOP process has not finished at the time the CHANGE process is initiated (parallel processing), the slope value becomes larger. If it has finished (serial processing), the slope is closer to 0 (Verbruggen et al. 2008).1 Obtaining a mean slope value in between 0 and -1 hence suggests that the initiation of some (but not all) of the GO2 response processes were initiated before the termination of the inhibitory process of stopping the GO1 response. Therefore, the slope of the SOA-RT function is flatter in case of more serial processing than in the case of a more parallel processing mode. EEG recording and analysis EEG data were analyzed as in the previous study by Mu¨ckschel et al. (2013). EEG was recorded from 65 Ag– AgCl electrodes using a standard 10–20 scalp positions against a reference electrode located at FCz. Electrode impedances were kept below 5 kX. After data recording, the sampling rate was down-sampled from 1,000 to 256 Hz. A band-pass filter (IIR filter from 0.5 to 20 Hz at a slope of 48 db/oct) was applied, and a raw data inspection was conducted to remove technical artifacts. Periodically recurring artifacts (pulse artifacts, horizontal and vertical eye movements) were corrected for using an independent component analysis (ICA; Infomax algorithm) which was applied to the unepoched data. Stimulus-locked segments based on the STOP signal were formed. Automated artifact rejection procedures were applied. Rejection criteria included a maximum voltage step of more than 60 lV/ms, a maximal value difference of 150 lV in a 250-ms interval or activity below 1 lV. Artifact rejection was followed by a CSD transformation yielding a reference-free evaluation of the electrophysiological data and helping to identify the electrodes showing the strongest effects. Baseline correction was conducted using the interval from -900 to -700 ms as pre-stimulus baseline (i.e., a baseline set before the occurrence of the GO1 stimulus). The electrodes used for the quantification of the P1, N1 and P3 ERPs,2 were selected in a data-driven manner. Based on the scalp topography maps, the visual P1 and N1 were quantified at 1
One limitation to this interpretation remains: According to Verbruggen et al. (2008), it is impossible to distinguish between the behavioral effects (RT slope values) of a nondeterministic serial processes and parallel processing based on the RT slope value (c.f. Verbruggen et al. 2008 for a detailed discussion on this issue). 2 The (Nogo)-N2 (occurring 200–300 ms after the inhibitory signal, see e.g. van Boxtel et al. 2001; Falkenstein et al. 1999) has frequently been analyzed with respect to inhibitory control processes. In the SC trials, a Nogo-N2 like component is evident in the SCD 300 condition (refer Fig. 4). However, in the SCD 0 condition, this component is not detectable due to the simultaneously occurring change processes. Since the (Nogo)-N2 is thus not quantifiable in all conditions, the (Nogo)-N2 was not included in the analyses.
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electrodes PO7 and PO8 (P1 0 ms till 140 ms; N1 150 till 250), the auditory N1 at C5 and C6 (0 till 500 ms) and the P3 at Cz and Pz (200 till 600 ms). All ERP components (peak amplitude and latency values) were quantified in a peak-to-baseline manner (relative to the pre-stimulus baseline) on the single subject level. Time–frequency decomposition Time–frequency (TF) analysis of the stimulus-locked ERPs was performed by using a continuous wavelet transform (CWT), which applies Morlet wavelets (w) to different frequencies (f) in the time domain: wðt; f Þ ¼ A exp t2 2r2t exp ð2ipftÞ; pffiffiffi 1=2 ; rt is the wavelet t is time, A is set to A ¼ ðrt pÞ pffiffiffiffiffiffiffi duration and i ¼ 1. A ratio of f0/rf = 5.5 was used, where f0 is the central frequency and rf is the width of the Gaussian shape in the frequency domain. The analysis was limited to a frequency range of 0.5–20 Hz with a central frequency at 0.5-Hz intervals. For different f0 values, time and frequency resolutions (wavelet duration and spectral bandwidth; see Tallon-Baudry et al. 1997) can be calculated as 2rt and 2rf, respectively. rt and rf are related by the equation rt = 1/(2prf). The segments used for the wavelet analysis had a length of 4,000 ms; starting 2,000 ms before stimulus (STOP signal) onset and ending 2,000 ms after stimulus onset. This length was chosen to allow for a reliable estimation of the total (induced) power of low-frequency oscillations (e.g., Beste et al. 2010; Stock et al. 2013). Maximal TF power and corresponding peak power latencies were quantified in the same time intervals as used for ERP quantification (around the time point the ERP reached its maximum in the time domain). A time window from 800 to 600 ms prior to the STOP signal was used to estimate background noise. The wavelet power in the time range of interest was measured normalized to the wavelet power at the baseline. To analyze neural synchronization mechanisms, we quantified the waveletdecomposed single-trial data using the phase-locking factor (PLF). The PLF gives an estimate of the reliability of neural synchronization processes in time and frequency across trials (Roach and Mathalon 2008; Beste et al. 2011) and is independent of the signal’s amplitude (Yordanova and Kolev 1998). PLF values vary between 0 and 1 with 1 indicating perfect phase alignment across trials and 0 almost randomly varying phases across trials.
sLORETA ERPs source localization was conducted using sLORETA (standardized low-resolution brain electromagnetic
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tomography; Pascual-Marqui 2002; Pascual-Marqui et al. 2002) on the ERPs. sLORETA provides the user with a single linear solution to the inverse problem based on extra-cranial measurements without a localization bias (Marco-Pallare´s et al. 2005; Pascual-Marqui 2002; Pascual-Marqui et al. 2002; Sekihara et al. 2005). sLORETA has been validated in simultaneous EEG/fMRI studies (Vitacco et al. 2002). sLORETA partitions the intracerebral volume in 6,239 voxels at a spatial resolution of 5 mm. For each voxel, the standardized current density is calculated in a realistic head model (Fuchs et al. 2002) using the MNI152 template (Mazziotta et al. 2001). The current study compares voxel-based sLORETA images between groups using the sLORETA built-in voxel-wise randomization tests with 3,000 permutations (based on statistical nonparametric mapping). Voxels with significant differences (p \ .05, corrected for multiple comparisons) between the ethanol intoxicated state and the non-intoxicated state were located in the MNI brain and Brodman areas (BAs). Coordinates in the MNI brain were determined using the sLORETA software (www.unizh.ch/ keyinst/NewLORETA/sLORETA/sLORETA.htm). Statistics Behavioral and electrophysiological data were analyzed using repeated measures ANOVAs comprising the withinsubject factors ‘‘intoxication status (yes/no),’’ the factor SCD (SCD 0 vs. 300) and the factor ‘‘electrode’’ (wherever necessary). Post hoc tests were Bonferroni-corrected whenever necessary. All included variables were normally distributed as tested with Kolmogorov–Smirnov tests (all z \ 0.9; p [ .3).
Results Intoxication data Before the administration of ethanol, all participants presented with a breath and blood alcohol concentration of 0.00 %. When the experiment was started 30 min after the end of the beverage administration, the participants had a mean breath alcohol concentration of 1.2 % (±0.26) and a mean blood alcohol concentration of 1.1 % (±0.23). After the experiment (about 60 min after the end of the beverage administration), mean breath alcohol concentration was 1.2 % (±0.14) and a mean blood alcohol concentration of 1.2 % (±0.20). Neither breath alcohol concentration (paired t test t = -.272, p = .79) nor blood alcohol concentration (paired t test t = -2. 27, p = .07) differed significantly between the second and third measuring point. Hence, we only analyzed the effect of alcohol intoxication,
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but did not include changes in breath or blood alcohol concentration as covariates in further analyses.
status’’ (F(1,19) = 1.95; p [ .2), showing that there was no speed-accuracy trade-off.
Behavioral data
Electrophysiological data
The reaction time (RT) data of the participants are summarized in Online Resource 1. RT data were analyzed in a repeated measures ANOVA including a within-subject factor ‘‘trial (GO1 trials without stop–change signals and GO2 trials on SCD 0 and 300)’’ and a within-subject factor ‘‘intoxication status’’. The mean and standard error of the mean (SEM) are given for descriptive statistics. There was a main effect ‘‘trial’’ (F(1,38) = 24.82; p \ .001; g2 = 0.566). Post hoc tests revealed that RTs were shortest for GO1 trials (566 ± 26), followed by RTs in the SCD 0 (897 ± 54) and SCD 300 (718 ± 46) conditions. All conditions differed from each other (p \ .03). There was an interaction ‘‘trial x intoxication status’’ (F(2,38) = 4.1; p = .024; g2 = 0.177). Paired-samples t tests were run to compare RTs in the intoxicated and non-intoxicated status. For RTs on the GO1 stimulus, there was no difference between the intoxicated (567 ± 33) and the non-intoxicated state (565 ± 25) (p [ .9). However, for RTs on the GO2 stimulus in the SCD 0 condition, RTs were longer in the intoxicated (961 ± 71) than in the non-intoxicated state (834 ± 43) (t = -2.84; df = 19; p = .005). In the SCD 300 condition, RTs were also longer in the intoxicated state (759 ± 75), compared to the non-intoxicated state (678 ± 44); however, this difference failed to reach significance and there was only a trend (t = -1.34; df = 19; p = .08). When analyzing only the SCD 0 and SCD 300 conditions in a repeated measures ANOVA (i.e., excluding RTs in GO1 trials), the observed effects of ethanol in the SCD conditions are substantiated by an interaction ‘‘intoxication status x SCD interval’’ (F(1,19) = 14.44; p = .001; g2 = 0.434). Consequently, the slope of the SCD-RT2 function differed between the intoxicated and the non-intoxicated state (t = -4.11; df = 19; p \ .001) with the slope in the intoxicated state being steeper (-0.72 ± 0.05), compared to the non-intoxicated state (-0.55 ± 0.03). According to the model by Verbruggen et al. (2008), a steeper slope indicates for more parallel execution of goal activation during multitasking (see also: Mu¨ckschel et al. 2013). The mean stop–signal reaction time (SSRT) was longer in the intoxicated (271.8 ms ± 14.1) compared to the non-intoxicated state (235.8 ms ± 14.1) (p \ .001). However, prolongation of RTs in the intoxicated state in the SCD 0 condition may reflect a speed-accuracy tradeoff. Therefore, we examined the rate of correct responses in the SCD 0 and SCD 300 conditions in the intoxicated and non-intoxicated states. The repeated measures ANOVA revealed no interaction ‘‘SCD interval x intoxication
Visual P1 and N1 The visual P1 and N1 are shown in Fig. 2, separated for the intoxicated and the non-intoxicated group and the SCD conditions. For the visual P1, the repeated measures ANOVA using ‘‘trial (SCD 0 vs. SCD 300),’’ ‘‘intoxication status’’ and ‘‘electrode (P7, P8)’’ as within-subject factors revealed a main effect ‘‘electrode’’ F(1,19) = 12.18; p = .002; g2 = 0.391), showing that the P1 was larger at electrode P8 (11.73 ± 0.61), compared to electrode P7 (8.73 ± 0.55). Moreover, the main effect ‘‘SCD’’ was significant (F(1,19) = 451.28; p \ .001; g2 = 0.391), with the P1 being larger in the SCD 0 (11.55 ± 0.45) compared to the SCD 300 condition (8.18 ± 0.48). Additionally, there was an interaction ‘‘SCD x intoxication status’’ (F(1,19) = 71.70; p \ .001; g2 = 0.791), which is plotted in the middle of Fig. 2. Post hoc tests showed that intoxication status did not change the P1 amplitude in the SCD 300 condition (t = -0.31; df = 19; p [ .4), but in the SCD 0 condition, where the P1 was larger in nonintoxicated state (7.86 ± 0.43) compared to the intoxicated state (6.52 ± 0.86) (t = -3.61; df = 19; p = .001). There were no further main or interaction effects in the amplitudes, and there were generally no latency effects (all F’s \0.81; p [ .3). Since the P1 revealed effects, the N1 was quantified with respect to the P1 amplitude (i.e., the N1 peak-to-peak amplitude was calculated). The N1 ANOVA only revealed a main effect ‘‘interval’’ (F(1,19) = 67.28; p \ .001; g2 = 0.780), showing that the N1 was larger in the SCD 0 (-26.38 ± 0.76) compared to the SCD 300 (-21.69 ± 0.72) condition. All other main or interaction effects were not significant (all F \ 2.1; p [ .15). As with the results in the Mu¨ckschel et al. (2013) study, there was no correlation between amplitude variations in the P1 and N1 and RT variation across SCD conditions (all r \ 0.2; p [ .3). Auditory P1 and N1 The auditory P1 and N1 are given in Fig. 3 separated for the SCD conditions and intoxication status. For the P1, the repeated measures ANOVA revealed a main effect ‘‘interval’’ (F(1,19) = 14.53; p = .001; g2 = 0.433), showing that the P1 was larger in the SCD 0 (7.13 ± 0.27) compared to the SCD 300 condition (5.57 ± 0.31). The main effect ‘‘electrode’’ showed that the P1 was larger at electrode C6 (7.35 ± 0.26) than at C5 (5.39 ± 0.31) (F(1,19) = 32.92; p \ .001; g2 = 0.634). The main effect ‘‘intoxication’’
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Fig. 2 Visual P1 and N1 ERPs at electrodes P7 and P6 along with the scalp topography plots of the P1 (bottom left) and the N1 (bottom right). Red curves denote the ERPs in the intoxicated state, black curves denote the ERPs in the non-intoxicated state. The upper row shows the data in the SCD 0 condition; the bottom row denotes the data in SCD 300 condition. Time point 0 denotes the time point of
STOP signal presentation. The vertical dashed line denotes the time point of CHANGE signal presentation in the SCD 300 condition. The interaction plot in the middle of the figure denotes the interaction ‘‘intoxication state x SCD interval’’ as observed for the visual P1 amplitude (color figure online)
showed that the P1 was larger in non-intoxicated (6.94 ± 0.06) compared to the intoxicated state (5.79 ± 0.36) (F(1,19) = 11.01; p = .004; g2 = 0.367). There was an interaction ‘‘interval x intoxication’’ (F(1,19) = 9.39; p = .005; g2 = 0.343), plotted in the middle of the figure. Post hoc tests revealed that the P1 did not differ between the intoxicated and the non-intoxicated states in the SCD 0 condition (t = -0.64; df = 19; p [ .3), but in the SCD 300 condition where the P1 was greater in the nonintoxicated (8.53 ± 0.14) compared to the intoxicated state (5.84 ± 0.58) (t = 4.11; df = 19; p \ .001). There were no effects in the latency of the P1 (all F \ 1.1; p [ .3). For the N1 (peak-to-peak amplitude), the repeated measures ANOVA revealed a main effect ‘‘electrode’’ (F(1,19) = 17.06; p = .001; g2 = 0.473), showing that the N1 was larger at electrode C6 (-15.85 ± 0.48) compared to C5 (-12.48 ± 0.61). Moreover, there was a main effect ‘‘intoxication’’ (F(1,19) = 143.75; p \ .001; g2 = 0.883),
showing that the auditory N1 was lower in the intoxicated (-10.66 ± 0.65) compared to the non-intoxicated state (-17.67 ± 0.27). There were no further main or interaction effects (all F \ 0.9; p [ .3). Also, for the latencies, no significant effects were obtained (all F \ 1.1; p [ .3). Also here, there was no correlation between amplitude variations in the auditory P1 and N1 and RT variations across the SCD condition (r \ 0.3; p [ .3).
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P3 The P3 ERP traces are given in Fig. 4a for the SCD 0 and SCD 300 conditions in the intoxicated and the non-intoxicated states. As can be seen in the scalp topography plots, the P3 was maximal around electrode Cz (see also: Mu¨ckschel et al. 2013). Therefore, this electrode was analyzed: The repeated measures ANOVA revealed a main effect ‘‘SCD interval’’
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Fig. 3 Auditory N1 ERPs at electrodes C5 and C6 along with the scalp topography plots of the auditory N1 for the left side of scalp (bottom left) and the right side of the scalp (bottom right). Red curves denote the ERPs in the intoxicated state; black curves denote the ERPs in the non-intoxicated state. The upper row shows the data in
the SCD 0 condition; the bottom row denotes the data in SCD 300 condition. Time point 0 denotes the time point of STOP signal presentation. The vertical dashed line denotes the time point of CHANGE signal presentation in the SCD 300 condition (color figure online)
(F(1,19) = 123.60; p \ .001; g2 = 0.867), showing that the amplitude was larger in the SCD 0 (28.31 ± 1.2) compared to the SCD 300 condition (12.71 ± 1.1). The main effect ‘‘intoxication status’’ revealed that the P3 amplitude was larger in the non-intoxicated (24.79 ± 0.81) compared to the intoxicated state (16.22 ± 1.31) (F(1,19) = 43.81; p \ .001; g2 = 0.698). Importantly, there was an interaction ‘‘SCD interval x intoxication status’’ (F(1,19) = 44.79; p \ .001; g2 = 0.702). This interaction is plotted in Fig. 4a. Post hoc tests revealed that in the SCD 0 condition, the P3 was larger in the non-intoxicated state (37.15 ± 1.2) compared to the intoxicated state (19.47 ± 2.1) (t = 8.07; df = 19; p \ .001). Opposed to this, there was no difference in P3 amplitude in the SCD 300 condition (t = -0.35; df = 19; p [ .3). The sLORETA analysis suggests that the difference in SCD0 P3 amplitudes between the intoxicated and the nonintoxicated state is mediated via anterior cingulate areas (BA32) (see Fig. 4a).
Previous results obtained in a non-intoxicated sample suggested a positive correlation between the above-calculated slope and a comparable slope calculated across P3 amplitudes (c.f. Mu¨ckschel et al. 2013). In the current study, there was a similar correlation in the non-intoxicated state (r = 0.834; R2 = 0.71; p \ .001), but no such correlation was evident in the intoxicated state (r = -0.006; p [ .4) (refer Fig. 4b). As the P3 was predictive of the behavioral performance in the non-intoxicated state, we analyzed the differential effects of ethanol intoxication on processes reflected by the P3 in more detail. For this purpose, the total (induced) wavelet power and the phase-locking factor (PLF) were analyzed. This analysis was restricted to the SCD 0 condition, since only in this condition, differences between the intoxicated and the non-intoxicated state were evident in ERPs and at the behavioral level (RTs). As can be seen in Fig. 4c (left two graphs), total wavelet power at electrode
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Fig. 4 a P3 ERPs at electrode Cz in the SCD 0 (top) and SCD 300 conditions (bottom). Red curves denote the ERPs in the intoxicated state; black curves denote the ERPs in the non-intoxicated state. Time point 0 denotes the time point of STOP signal presentation. The vertical dashed line denotes the time point of CHANGE signal presentation in the SCD 300 condition. The scalp plots at the top of each condition (i.e., SCD 0 and SCD 300) denote the topography in the non-intoxicated state, the plots below the topography in the intoxicated state. The sLORETA analysis within the SCD 0 condition depicts differences between the intoxicated and the non-intoxicated
state in the anterior cingulate cortex (ACC). The interaction plot shows the interaction ‘‘intoxication state 9 SCD interval’’ observed for the P3 amplitude. b Scatterplot denoting the correlation between the slope of the SOA-RT2 function and the slope of the SCD-P3 peak amplitude in the intoxicated (red dots) and non-intoxicated groups (black dots). c Results of the time–frequency analyses in the SCD 0 condition. Left is the total (induced) wavelet power of the nonintoxicated and the intoxicated states; right is the phase-locking factor (PLF) in the non-intoxicated and the intoxicated states (color figure online)
Cz was maximal in the delta frequency band. In the delta frequency band, power was stronger in the non-intoxicated (4.16 ± 0.06) compared to the intoxicated state (3.23 ± 0.13) (t = 22.22; df = 19; p \ .001). As can be seen in Fig. 4c (right two graphs), the PLF in the SCD 0 condition was maximal in delta frequency bands. We compared PLF between the intoxicated and the nonintoxicated state at 3 Hz, which is the central frequency of the delta band. The results show that PLF was higher in the non-intoxicated state (0.42 ± 0.01) compared to the intoxicated state (0.23 ± 0.02) (t = 7.85; df = 19; p \ .001).3
Discussion
3
We run an analysis where we included the factor ‘‘time’’ (first or second session with alcohol intoxication) as a between-subject factor in the ANOVA. There was no main effect ‘‘time’’ and no interaction effect with this factor in the behavioral and the neurophysiological data (all F \ 1.1; p [ .3). The results are therefore unbiased with respect to the testing point.
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We examined the effects of high-dosage ethanol intoxication on action cascading processes using a systems neuroscience approach. The behavioral data show that action cascading processes are rendered more parallel during an acute ethanol intoxication of 1.1–1.2 %, as can be seen in the slope of the SCD-RT2 function, which became steeper (mean value of -0.72) than during the non-intoxicated condition (mean value of -0.55). This effect was due to a prolonging of SDC0 GO2 RTs in the intoxicated condition. No such RT differences were observed in the GO1 and the SCD300 conditions. This means that despite the participants’ profound intoxication level, only the condition with the highest cognitive demands was significantly altered. Based on these results, it can be stated that action cascading processes only become compromised when two to be cascaded actions simultaneously demand access to restricted processing resources. In the SCD0 condition, the
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simultaneous presentation of the STOP and CHANGE signals leaves the participants with the choice of how to cascade the associated cognitive processes. While intoxicated, they seem to be less capable of withholding information for the purpose of serial processing (postponing of CHANGE-associated processes). As a consequence, mutual inhibition of the parallel STOP and CHANGE processes within WTA networks is likely to lead to the significant prolongation of GO2 RTs in the SCD0 condition. Following from this, the RT slope becomes steeper during intoxication. Wherever significant intoxication effects were found, the amplitude was smaller for the intoxicated than for the non-intoxicated condition. On the neurophysiological level, the modulation of the auditory P1 ERP suggests that ethanol intoxication renders the processing of the STOP stimulus less effective while leaving the processing of the CHANGE stimulus rather intact. The finding that the attentional components had a weaker effect on the slope than the P3 component (that reflects processes at the response level) could to some degree be explained by the fact that attentional processes mainly rely on glutamatergic and acetylcholinergic neurotransmission (Turchi and Sarter 2001), which might be influenced differently by the intake of ethanol. Matching the behavioral results, the P3 component displayed intoxication differences in the SCD0 condition (intoxication \ non-intoxication). These ERP differences were due to activation differences within the ACC (area BA32), which is known to play a key role in response monitoring (Botvinick et al. 2004). Further analyses revealed that high-dosage ethanol intoxication compromised neural synchronization processes (i.e., PLF values) in the delta frequency band, which is known to play an important role in response monitoring (Yordanova et al. 2004). The ACC is part of a ‘‘multiple demand’’ (MD) system dealing with multicomponent behavior such as action cascading (Duncan 2010). Using the same task, we recently demonstrated that the temporo-parietal junction (TPJ) as well as the ACC play an important role in action cascading (Mu¨ckschel et al. 2013). Based on these findings, it is likely that the changes in ACC activity observed in the current study alter the function of the MD system as described by Duncan (2010). The finding that ethanol intoxication only alters ACC (but not TPJ) activity strengthens the assumption that despite its diffuse action within the brain, ethanol does not modulate or dampen brain activity in a global fashion. Instead, it seems that behavioral changes during intoxication are due to specific changes within the MD system. Even though the P3 amplitude is usually increased in more parallel processing (Mu¨ckschel et al. 2013), SCD0 P3 amplitude values were smaller during intoxication. It is
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assumed that the P3 component reflects a link between stimulus evaluation and response selection (Verleger 1988, Falkenstein et al. 1994a, b; Verleger et al. 2005; Polich 2007). In this context, our findings do not match the assumption that increased ACC activity might reflect increased demands on action selection processes (e.g., Botvinick et al. 2004). However, it could be argued that rather than depicting the cognitive demand itself, an augmented P3 might instead be an indicator of a successful strategy of dealing with increased cognitive demands. In line with this assumption, an augmented P3 could be interpreted as an indicator of a tactics selection process (Mu¨ckschel et al. 2013; Matsuzaka et al. 2012), which partly fails under the influence of ethanol. There are several possible explanations for how ethanol might lead to this alteration in ACC function. In vitro and animal studies have repeatedly shown that during acute ethanol intoxication, the release of dopamine is upregulated (e.g., Melis et al. 2009; Tateno and Robinson 2011). It has been repeatedly demonstrated that the influence of dopamine follows an inverted U-shaped curve (Seamans and Yang 2004; Goldman-Rakic et al. 2000) where performance is disrupted when the optimal level of dopamine is either exceeded or deceeded. In case of too much dopamine, incoming stimuli no longer elicit an appropriate ‘‘stimulus-dependent tuning’’ (Seamans and Yang 2004), which may provide information for the cascading of actions. Instead, different input stimuli are represented nearly simultaneously without one of them being represented more strongly than the others (Seamans and Yang 2004). Given this failure in ‘‘winner-takes-all’’ modulation, the STOP and CHANGE signals are likely to become represented simultaneously during intoxication. In other words, the ethanol-induced increase in dopamine is likely to entail an increase in the degree of response representation overlapping and parallel processing. Aside from dopamine, ethanol also enhances GABAergic activity via a number of different mechanisms (e.g., Kelm et al. 2011; Tateno and Robinson 2011; Kumar et al. 2009; Faingold et al. 1998). Under normal (non-intoxicated) conditions, WTA models would predict that an increase in GABA results in an improved ability to inhibit concurrent processes (rendering processing more serial, not more parallel) (e.g., Plenz 2003; Bar-Gad et al. 2003). However, our behavioral findings do not match this prediction. We therefore suspect that the effects of a striatal increase in GABA might be counteracted by the dopaminergic modulation of striatal GABAergic MSNs, or dopaminergic modulation of the ACC or a combination of both. In other words, the benefits of ethanol-induced GABA increases may be canceled out by the change in dopaminergic modulation, resulting in an overall shift to more parallel processing.
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In this study, we demonstrated that high-dosage ethanol intoxication of 1.1–1.2 % impairs action cascading processes by shifting the strategy to a more parallel processing mode. As a consequence, performance decreased as the simultaneously represented response options mutually inhibited each other. However, high-dosage ethanol intoxication exerts highly specific effects on cognitive subprocesses mediating action control, a finding that strongly contradicts commonly held views. The results show that high-dosage ethanol intoxication especially affects response selection processes and that attentional selection processes are affected to a much lesser extent. These differences may be due to a differential involvement of dopaminergic and GABAergic processes in response control and attentional selection. Acknowledgments This research was supported by a Grant from the Deutsche Forschungsgemeinschaft (DFG) BE4045/10-1. For the duration of data collection, the breathalyzer ‘‘Alcotest 300’’ was provided by Dra¨ger Safety AG & Co. KGaA.
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