Alcohol reduces EEG coupling


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Alcohol reduces EEG coupling

  1. 1. ALCOHOLISM: CLINICAL AND EXPERIMENTAL RESEARCH Vol. **, No. * ** 2013 Alcohol Reduces Cross-Frequency Theta-Phase GammaAmplitude Coupling in Resting Electroencephalography Jaewon Lee and Kyongsik Yun Background: The electrophysiological inhibitory mechanism of cognitive control for alcohol remains largely unknown. The purpose of the study was to compare electroencephalogram (EEG) power spectra and cross-frequency phase–amplitude coupling (CFPAC) at rest and during a simple subtraction task after acute alcohol ingestion. Methods: Twenty-one healthy subjects participated in this study. Two experiments were performed 1 week apart, and the order of the experiments was randomly assigned to each subject. During the experiments, each subject was provided with orange juice containing alcohol or orange juice only. We recorded EEG activity and analyzed power spectra and CFPAC data. Results: The results showed prominent theta-phase gamma-amplitude coupling at the frontal and parietal electrodes at rest. This effect was significantly reduced after alcohol ingestion. Conclusions: Our findings suggest that theta-phase gamma-amplitude coupling is deficiently synchronized at rest after alcohol ingestion. Therefore, cross-frequency coupling could be a useful tool for studying the effects of alcohol on the brain and investigating alcohol addiction. Key Words: Cross-Frequency Phase–Amplitude Coupling, Theta-Phase Gamma-Amplitude Coupling, Alcohol, Electroencephalography, Inhibitory Effect. A LCOHOL ABUSE HAS been closely linked with various health and socioeconomic problems, including crimes, aggression, and suicides (Hagg ard-Grann et al., 2006; Hughes et al., 2007). Alcohol causes a decrease in cognitive function, including impaired inhibitory control and decreased cognitive and motivational conflict processing (Abroms et al., 2006; Field et al., 2010; Fillmore and VogelSprott, 2000; Fillmore et al., 2005). Previously, event-related potentials (ERP) and spectral analysis of electroencephalograms (EEGs) have been used to investigate the electrophysiological effects of alcohol (Curtin and Fairchild, 2003; Porjesz and Rangaswamy, 2007). The reduction in N1 and P3 ERP components (Bierley et al., 1980; Oscar-Berman, 1987) and a decrease in fast frequency bands are well-known electrophysiological responses to alcohol (Bauer, 2001). From the Neuropsychiatry Research Laboratory (JL), Gongju National Hospital, Chungnam, South Korea; Addiction Brain Center (JL), Eulji Addiction Institute, Gangnam Eulji Hospital, Seoul, South Korea; Computation and Neural Systems (KY), California Institute of Technology, Pasadena, California; Division of Biology (KY), California Institute of Technology, Pasadena, California; and Ybrain Research Institute (KY), Seoul, South Korea. Received for publication March 14, 2013; accepted September 20, 2013. Reprint requests: Jaewon Lee, MD, PhD, Addiction Brain Center, Gangnam Eulji Hospital, 59 Nonhyun-dong, Gangnam-gu, Seoul, South Korea; Tel.: +82-2-3438-1126; Fax: +82-2-3438-1001; E-mail: Both authors are contributed equally. Copyright © 2013 by the Research Society on Alcoholism. DOI: 10.1111/acer.12310 Alcohol Clin Exp Res, Vol **, No *, 2013: pp 1–7 It has been suggested that alcohol may impair the balance between control-related and reward-related functional brain networks by decreasing the executive attention and cognitive control processing of the frontal cortex (Curtin and Fairchild, 2003; Ridderinkhof et al., 2004). However, the electrophysiological inhibitory action mechanism of cognitive control for alcohol has not been fully studied. To investigate the details of this mechanism, an optimal tool may be analysis of cross-frequency coupling (Jensen and Colgin, 2007). The cross-frequency coupling between slow and fast oscillations allows for the determination of complex frontal– subcortical interaction (Buzsaki and Draguhn, 2004; Steriade, 2001) and has been examined using EEGs (Canolty et al., 2006; Jensen and Colgin, 2007; Palva et al., 2005; Schack et al., 2002b). Recent studies on the human neocortex have demonstrated that the power of fast oscillations (30 to 150 Hz) is modulated by the phase of slow oscillations (1 to 8 Hz) (Canolty et al., 2006; Jensen and Colgin, 2007). From a theoretical perspective, there are several ways in which cross-frequency interactions might occur, including amplitude–amplitude, phase–amplitude, and phase–phase interactions. In particular, the cross-frequency phase–amplitude coupling (CFPAC) reported by Canolty and colleagues (2006) is of interest among clinical psychiatrists because of its potential as a neurophysiological measure of frontal–subcortical interaction (Jensen and Colgin, 2007). The thetaphase gamma-amplitude coupling has been particularly known for indicating various cognitive feedback processes in short-term memory (Park et al., 2011, 2013; Schack et al., 2002a), visual perception (Demiralp et al., 2007), 1
  2. 2. LEE AND YUN 2 association learning (Tort et al., 2009), and decision-making tasks (Cohen et al., 2008). Moreover, CFPAC has also been related with an inhibitory cognitive control network of GABAergic interneurons (Wulff et al., 2009). The purpose of our study was to investigate EEG power spectra and CFPAC at rest and during a simple subtraction task after acute alcohol ingestion. We hypothesized that acute alcohol ingestion would decrease CFPAC when compared with normal control conditions. MATERIALS AND METHODS Subjects Twenty-one healthy participants (9 women, mean age of 35.5 Æ 7.9 years, Edinburgh laterality quotient of 98.4 Æ 5.1) were recruited using an online advertisement in Gongju, South Korea. Participants were interviewed by experienced psychiatrists and excluded if they have any history of psychiatric or neurological disorders. None of the participants were taking any medications, and they had at least 12 years of education (14.4 Æ 1.9 years). This study was approved by the Institutional Review Board of the Gongju National Hospital (Gongju, South Korea) and was in accordance with the Declaration of Helsinki (World Medical Association: Ethical Principles for Medical Research Involving Human Subjects, 1964). All participants provided written informed consent after receiving a detailed explanation of the experimental procedures. The use of alcohol was carefully screened using a digital breath alcohol concentration (BAC) calculator (Alcoscan AL9000; Sentech, Seoul, South Korea) at the experimental location. All subjects with a 0% BAC and a history of less than 5 glasses of alcoholic beverages consumed per week (85 g) were allowed to participate in the experiment. The mean alcohol content of 1 glass of any alcoholic beverages is 17 g (Kerr et al., 2005). Experimental Procedures Each subject was first asked to fill out self-report questionnaires for the Beck Depression Inventory (BDI) (Sharp and Lipsky, 2002), Obsessive Compulsive Drinking Scale (OCDS) (Anton et al., 1995) and Barrett Impulsiveness Scale (BIS) (Stanford et al., 2009) to supplementary investigate whether subjects were within the normal range of behavior. We used the 8 visual analog scales (VAS) for alcohol craving. Subjects then participated in 2 experiments. Experiment 1 provided each subject with 500 ml orange juice that contained 0.7 g/kg alcohol. Experiment 2 provided each subject with only orange juice. The order of experiments 1 and 2 was randomly assigned to subjects for counterbalancing, and the interval between the experiments was at least 7 days. The participants were asked to drink slowly for 30 minutes. Each subject was seated in a comfortable chair in a dimly light room and instructed to relax and keep his or her movements to a minimum. EEGs were recorded for approximately 60 to 90 minutes after beverage consumption. We performed 2 EEG sessions, including 3 minutes with eyes closed at rest and integer subtraction of 7 from 1,000 for 3 minutes. As a general rule, previous studies suggest that at least 120 seconds should be used in clinical and pharmacological studies for the evaluation of EEG spectral power (Maltez et al., 2004). After the EEG experiment, BART was performed to investigate any risk-taking behavioral changes after acute alcohol ingestion. Detailed experimental procedure was described in the Fig. 1. EEG Recording and Preprocessing The EEG recording was conducted from the scalp using a SynAmps2 DC-amplifier and a 10/20 layout 64-channel Quik-cap The Balloon Analog Risk-Taking Task The Balloon Analog Risk-Taking Task (BART) was designed to quantify risk-taking behavior in a laboratory setting (Lejuez et al., 2002, 2003). According to the method of Lejuez and colleagues (2002), we programmed the BART using an E-prime 2.0 software toolbox (Psychology Software Tools, Inc., Sharpsburg, PA). Initially, the computer screen showed a small simulated balloon accompanied by a balloon pump. The subjects were informed that the objective of the task was to obtain the largest amount of money possible without popping the balloon. The subjects were not given any information about the probability of the balloon popping. Each successful pump of the balloon with the left button marked “INFLATE” caused a defined amount of money to be placed into a temporary reserve. If the subject popped his or her balloon, he or she lost all of the money in the reserve. At any time during a trial, the subject could click the right button marked “STOP” and transfer all of the money from the temporary reserve to a permanent account. A total of 100 trials (100 balloons) were performed after 10 practice trials for each subject, and the time that the balloon popped was controlled by a predetermined pseudo-random sequence for all of the subjects. The average number of successful pumps before the subject clicked the “STOP” button was used as the BART score. Higher BART scores indicated that the subjects sought more money, despite the risk of popping the balloon. Each subject was seated in front of a computer for the BART. The subjects were informed that they would receive the sum of money that they earned during the task at the end of the experiment. The subjects were also told that if the balloon popped, they would not receive any money in the specific trial. Fig. 1. Experimental procedure.
  3. 3. ALCOHOL EEG CROSS-FREQUENCY COUPLING electrode placement system (Neuroscan Inc., Charlotte, NC). The EEGs were recorded from 19 electrode sites (Fp1, Fp2, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, P7, P3, Pz, P4, P8, O1, and O2) based on a standard international 10/20 system at a sampling rate of 1,000 Hz. We used the linked mastoid for reference and 2 additional bipolar electrodes to measure horizontal and vertical eye movements. The impedance of each electrode was kept below 10 kOhm throughout the EEG recording session. We used Matlab 7.0.1 (MathWorks, Natick, MA) and the EEGLAB toolbox (Delorme and Makeig, 2004) to preprocess and analyze the EEG data. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electricity noise. Independent component analysis was also performed to eliminate eyeblink and muscle artifacts. For the analysis, over 2 minutes of artifact-free EEGs were selected from each 3-minute time period of resting and arithmetic task recordings. These data were analyzed based on visual inspection by clinical psychiatrists and EEG experts. Power-Spectrum Analysis and Frequency Bands Seven frequency bands were defined for further analyses, including delta (1 to 4 Hz), theta (4 to 8 Hz), slow alpha (8 to 10 Hz), fast alpha (10 to 13.5 Hz), beta (13.5 to 30 Hz), slow gamma (30 to 58 Hz), and fast gamma (62 to 80 Hz). For the cross-frequency coupling analyses, slow gamma and fast gamma frequencies were divided into 5 subbands, including 35 Hz gamma (30 to 39 Hz), 45 Hz gamma (40 to 49 Hz), 55 Hz gamma (50 to 58 Hz), 65 Hz gamma (62 to 69 Hz), and 75 Hz gamma (70 to 80 Hz). We investigated the power spectrums of the EEG data for each subject using the short-time Fourier transform, “spectrogram.m” function of the signal processing toolbox in Matlab, 1,000-ms time windows with an 800-ms overlap, and the Hamming window. The outliers that were far from the spectral value distribution of each frequency band were removed using a significance level of 0.05. The absolute powers were averaged together over all of the time windows and each frequency band for further analysis. The Synchronization Index for CFPAC We employed the synchronization index (SI) that was proposed by Cohen (2008) to assess cross-frequency interactions between the low frequency (1 to 13.5 Hz) phase and the power of the gamma (30 to 80 Hz) oscillations. Briefly, the SI is a phase coherence measurement between the phase of the upper (gamma) power time series P and the phase of the lower time series (SI ¼ 1 n ei ½/lt À /ut Š; n, t¼0 n the number of time points; φut, the phase of the upper frequency power time series at time point t; φlt the phase of the lower frequency time series at time point t) (Cohen, 2008). To avoid distortions of the phase value during filtering, we used the 2-way, leastsquared finite impulse response filter (eegfilt.m) that was included in the EEGLAB toolbox (Delorme and Makeig, 2004). In addition, 1,000-ms time windows with an 800-ms overlap and 3-Hz windowing for each 5 gamma subfrequencies were used. The gamma-power time series was extracted as the squared magnitude of f(t), which is the analytic signal obtained from the Hilbert transform (power time series: p(t) = real[f(t)]2 + imag[f(t)]2). The phases of the 2 time series were extracted from the Hilbert transform (phase ¼ arctan imag½fðtފ ). The SI value is a complex number, and real½fðtފ the magnitude (SIm) of this number reflects the extent to which the phases are synchronized (0 = completely desynchronized; 1 = perfectly synchronized). The outliers were removed using the same procedure that was applied to the spectral data. The SIm values were averaged together over all of the time windows and the each 5 3 gamma subfrequencies window for further analyses. We investigated the 4 different CFPAC, including delta-phase gamma-power coupling (DGC), theta-phase gamma-power coupling (TGC), slowalpha-phase gamma-power coupling (SAGC), and fast-alpha-phase gamma-power coupling (FAGC). Statistical Analysis The Matlab 7.0.1 statistical toolbox was used for statistical analyses. All of the values were expressed as the mean and the standard deviation (SD). The paired t-test was used to assess the differences between drinking and control conditions. The problem of multiple comparisons was corrected using a false discovery rate (FDR) control. We applied the Benjamini–Hochberg FDR method using the “mafdr.m” function of the bioinformatics toolbox in Matlab. The statistical significance was defined as p 0.05. RESULTS The demographic data and behavioral test scores showed that all participants are within a normal range of scores and that there was no difference between males and females in any of the tests (p 0.05) (Table 1). After alcohol consumption, the BAC increased to 0.068 Æ 0.016%. The BART and VAS scores showed no differences between alcoholic and nonalcoholic beverage conditions (Fig. 2). Table 1. The Demographic Data Collected Included Scores from the Obsessive Compulsive Drinking Scale (OCDS), Beck Depression Inventory (BDI), Barrett Impulsiveness Scale (BIS), and the Balloon Analog Risk-Taking Task (BART) Male (n = 12) Age (year) Age of first drink (year) OCDS BDI BIS BART Female (n = 9) Total (n = 21) 34.50 Æ 7.44 17.83 Æ 1.99 33.56 Æ 11.00 19.44 Æ 4.80 34.10 Æ 8.89 18.52 Æ 3.47 9.42 7.75 24.50 43.91 Æ Æ Æ Æ 4.19 7.75 4.80 22.53 8.89 4.44 22.56 46.12 Æ Æ Æ Æ 7.13 3.71 7.91 15.66 9.19 6.33 23.67 44.86 Æ Æ Æ Æ 5.48 6.43 6.22 19.46 Fig. 2. Repeated measures of the BAC, risk-taking propensity, and the craving for alcohol were obtained. The data are reported as the mean Æ the standard error of the mean. BAC, breath alcohol concentration; BART, Balloon Analog Risk-Taking Task; VAS, the sum of 8 visual analog scales for alcohol craving; CON, control condition; DRK, drinking condition. **p = 0.0000.
  4. 4. 4 Fig. 3. Power-spectrum analysis. The data are reported as the mean Æ the standard error of the mean. CON, control condition; DRK, drinking condition. LEE AND YUN No differences were found in any of the power spectra results, including delta, theta, slow alpha, and fast alpha, during rest or the integer subtraction task (p 0.05) (Fig. 3). When the subjects were at rest, the CFPAC results show that DGC and TGC were reduced after alcohol ingestion compared with the control condition (DGC p = 0.0181, TGC p = 0.0011). There were no significant changes in CFPAC during the integer subtraction task (p 0.05) (Fig. 4). TGC was further investigated using topographic analysis (Fig. 5). The results show that a decrease in CFPAC was observed in diffuse cortical regions, including the frontal and parietal regions. Specifically, the left frontal (Fp1, F3 at 35 Hz), left parietal (P7 at 45 Hz), and centroparietal areas (Pz at 75 Hz) showed an FDR corrected significance (corrected p 0.05). No significant differences were found in Fig. 4. Cross-frequency phase–amplitude coupling. The data are reported as the mean Æ the standard error of the mean. DGC, delta–gamma coupling; TGC, theta–gamma coupling; SAGC, slow-alpha–gamma coupling; FAGC, fast-alpha–gamma coupling; CON, control condition; DRK, drinking condition. *p = 0.0184, **p = 0.0011. Fig. 5. Topographic map of theta–gamma coupling (TGC). The upper topography denotes the topographical distribution of t-values (paired t-test). The lower topography denotes the topographical distribution of the corresponding p-values. The blue dot indicates the significant channels after adjusting for the false discovery rate (corrected p 0.05). CON, control condition; DRK, drinking condition.
  5. 5. ALCOHOL EEG CROSS-FREQUENCY COUPLING the power spectra, DGC, or during the integer subtraction task (corrected p 0.05). DISCUSSION In the present study, we found a significant decrease in theta-phase gamma-amplitude coupling after the acute ingestion of alcohol when compared with the control. This decrease was found only during the resting state and not during the integer subtraction task. This cross-frequency coupling reduction can be thought of as an inhibitory effect of alcohol on the cerebral cortex (Field et al., 2010; Weafer and Fillmore, 2008). The results are consistent with previous studies that reported an alcohol-induced decrease in cortical activity and functional connectivity (Levin et al., 1998; Meda et al., 2008). Unlike spectral analysis, CFPAC mainly uses the phase information generated from EEG oscillations. The conceptual framework for the difference between the phase and amplitude of EEG oscillations has been outlined in detail by Klimesch and colleagues (2007). Briefly, the phase represents the timing of neuronal activity, whereas the amplitude indicates the extent of task involvement of the relevant neurons in the ongoing EEG (Klimesch et al., 2007). The discrepancy in our findings between the CFPAC and spectral analysis could be explained by this hypothesis regarding neuronal activity. Indeed, this is likely because the CFPAC might serve as a mechanism to transfer information from largescale brain networks to the local cortical processing regions (Jensen and Colgin, 2007). Taken together, our results suggest that the degree of CFPAC may differ across various localized brain areas. Furthermore, the CFPAC measures the amount of timing information from the interacting functional systems across multiple spatiotemporal scales. This is unlike the spectral results, which describe only the amount of excitation of the functional neuronal system. Based on meta-analysis, alcohol-related aggression has been explained by 3 different causes, including physiological disinhibition, expectancy, and indirect cause (Bushman, 1997). Physiological disinhibition suggests that alcohol increases aggression directly by anesthetizing the center of the brain that normally inhibits aggressive behavior. In direct opposition, the expectancy model suggests that alcohol increases aggression not by its pharmacological properties, but by the mere belief that one has consumed alcohol. The indirect cause model states that alcohol increases aggression by causing changes within the person that increases the probability of aggression (e.g., reducing intellectual functioning) (Bushman, 1997). These causes might be related to cognitive executive functioning, which is largely processed by the frontal lobe of the cerebral cortex. According to a study on alcohol-related aggression, executive functioning is both a mediator and a moderator of intoxicated aggression (Giancola, 2000). It has been proposed that acute alcohol intoxication disrupts executive functioning and moderates aggression (Giancola, 2000). Acute alcohol consumption is more likely 5 to facilitate aggressive behavior in persons with low, rather than high, executive functioning (Giancola, 2000). Taken together, alcohol-related behavioral problems may be deeply associated with a weakened functioning of cognitive control in the frontal cortex. This is consistent with our results that CFPAC is decreased after acute alcohol ingestion. According to Cole and Schneider (2007), various cortical regions form a functionally connected cognitive control network that shows high interregional correlations within the network during rest and while completing tasks; furthermore, these regions exhibit consistently higher correlations within that network than the rest of the cortex. Cognitive control networks include the anterior cingulate cortex, dorsolateral prefrontal cortex, inferior frontal junction, anterior insular cortex, dorsal premotor cortex, and posterior parietal cortex (Cole and Schneider, 2007). Our EEG results from the integer subtraction task did not differ between alcohol and nonalcohol conditions. We contend that during the task, the cognitive control network may compensate for the inhibitory effect of alcohol to maintain relatively normal CFPAC. Another explanation of the diminished CFPAC during only the resting state is that alcohol may exert a stronger effect on the default-mode network (Broyd et al., 2009; Raichle and Snyder, 2007). It has been suggested that the default-mode network has a putative role in the pathophysiology of mental disorders (Broyd et al., 2009). A previous functional magnetic resonance imaging (fMRI) study showed that alcoholic patients had a significantly lower default-mode network efficiency than normal controls, but no difference was found in the performance of a spatial working memory task between the alcoholic and control groups (Chanraud et al., 2011). This finding indicates that there may exist a fundamental qualitative difference between the resting default-mode network and the task-positive network. This previous finding is consistent with our results in that we did not find any difference between the alcohol and control groups in the EEG or behavioral performance during the integer subtraction task. More complex tasks, such as the Iowa gambling task (Kim et al., 2006), may be necessary to further investigate the complete relationship between the cognitive load and the alcohol TGC inhibitory effect. We should mention a relatively small number of participants (n = 21) in our study. Even though sample sizes tend to be small (n = 15 to 40) in EEG and MRI brain imaging studies (Robins et al., 2009), further investigations with larger sample size are warranted to confirm the alcohol effect on the EEG. In addition, medical problems except psychiatric and neurological disorders were not included in our exclusion criteria. For example, liver cirrhosis can possibly confound EEG signals (Amodio et al., 2001). The CFPAC analysis technique can be applied to diagnose other psychiatric disorders characterized by deficient cortical functional connectivity, such as methamphetamine addiction (Yun et al., 2012a), depression (Anand et al., 2005; Greicius et al., 2007), and schizophrenia (Garrity et al., 2007; Lawrie et al., 2002). Furthermore, the cross-frequency coupling and
  6. 6. LEE AND YUN 6 functional connectivity between-region analyses have the potential to deepen our understanding of the underlying functional mechanisms of various complex cognitive processes, such as social interaction (Yun et al., 2008, 2012b) and learning and memory (Kim et al., 2012). The resting state inhibitory cognitive control effect of alcohol gives an insight into understanding the default-mode network of addicted brain. Our results suggest that the alcohol effects are different between the task-negative and the task-positive brain network. Further investigations are warranted to fully understand the relationship between default-mode network and alcohol addiction. ACKNOWLEDGMENTS The authors thank Sujin Kim, So Yul Kim, Yul Mai Song, and Young Sung Kim (Neuropsychiatry Research Laboratory, Gongju National Hospital, South Korea) for their valuable help for this study. This work was supported by Sinhye Choi Research Fund (2011) of Korean Neuropsychiatric Association. 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