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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
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).
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.
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
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
and the phase of the lower time series (SI ¼ 1 n ei ½/lt À /ut ; 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 imag½fðtÞ ). The SI value is a complex number, and
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
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).
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.
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 of ﬁrst drink
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
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.
Fig. 3. Power-spectrum analysis. The data are reported as the
mean Æ the standard error of the mean. CON, control condition; DRK,
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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.
ALCOHOL EEG CROSS-FREQUENCY COUPLING
the power spectra, DGC, or during the integer subtraction
task (corrected p 0.05).
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
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
LEE AND YUN
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.
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
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