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The association between implicit alcohol attitudes and drinking behavior is moderated
by baseline activation in the lateral prefrontal cortex
Supplementary Materials
Resting EEG and alcohol consumption – Supplementary Materials
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Methods and Materials – Detailed Information
Participants.
Eighty-nine participants (Mage = 23.6 years, SDage = 2.8, range: 19-33 years, 54
females) participated in the study. The majority (92%) were university students of various
disciplines, the remaining participants were in training, employed or self-employed. All were
right-handed, with normal or corrected-to-normal vision, and reported no current or past
neurological, psychiatric or medical illnesses, nor alcohol or drug abuse. Eighty-five
participants (96%) reported German as their mother tongue with the remainder reporting to
speak German fluently. None of the participants correctly guessed the hypotheses.
EEG recording session.
After obtaining written informed consent, participants were seated comfortably in a
dimly lit, quiet room with intercom connection to the experimenter. They were instructed that
EEG recording was to be done during resting with open and closed eyes. The protocol
consisted of 20-seconds eyes open followed by 40-seconds eyes closed, repeated five times
(such a protocol guarantees minimal fluctuations in participants' vigilance state). Data
analysis is based on the 200-seconds eyes closed condition (Jann, Koenig, Dierks, Boesch, &
Federspiel, 2010).
Behavioral assessment session.
Implicit alcohol attitudes. A Single Category Implicit Association Test (SC-IAT;
Bluemke & Friese, 2008; Karpinski & Steinman, 2006) was used to measure implicit alcohol
attitudes. In a SC-IAT, stimuli of three different categories are presented one by one on a
computer screen. Participants sort these stimuli into their respective categories by pressing
one of two response keys while response latencies are collected. They were encouraged to
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react as fast as possible while committing as few errors as possible. Category labels were
pleasant, unpleasant, and alcohol and were always visible at the top of the screen. Ten
positive and ten negative words and pictures taken from the International Affective Picture
System (Lang, Bradley, & Cuthbert, 2005) served as evaluative stimuli. Stimuli representing
the target category alcohol were ten words and pictures of alcoholic drinks (e.g., beer, wine,
vodka). The SC-IAT consisted of three blocks. The task started with a practice block in which
participants sorted only evaluative stimuli into their respective categories. In the first critical
combined block, the target category alcohol shared the right response key with the attribute
category pleasant. In the second critical combined block, the target category alcohol shared
the left response key with the attribute category pleasant unpleasant. Each combined block
contained 70 trials in a predetermined random order. Block order was held constant across
participants, because the primary interest of this study was on individual differences and not
on mean SC-IAT effects (Egloff & Schmukle, 2002). SC-IAT scores were calculated using
the D-asis algorithm (Greenwald, Nosek, & Banaji, 2003) such that more positive values
indicated more positive implicit attitudes toward alcohol. The mean error rate was 5.31%.
Internal consistency based on five mutually exclusive subsets of trials was 0.73.
Alcohol use. Alcohol use during the previous week was measured with a
questionnaire based on the timeline follow-back method, a frequently used, reliable and valid
indicator of drinking behavior (Sobell & Sobell, 1990, 1995). For example, Sobell and
colleagues report test-retest reliabilities of .90 or more over a 90-day interval for a sample of
moderate drinkers (Sobell, Sobell, Leo, & Cancilla, 1988). Additional detailed reports on
validity can be found in Sobell and Sobell (2000). The method has also been used extensively
in the context of research on implicit cognition and alcohol (e.g., Houben & Wiers, 2008;
Wiers, van Woerden, Smulders, & de Jong, 2002). In the present study, participants were
asked to recall the number and type of alcoholic drinks consumed over the past seven days.
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300 ml beer, 100 ml wine, and 20 ml hard liquor were coded as one alcoholic drink. The sum
of consumed alcoholic drinks across all seven days served as the dependent measure.
Alcohol-related problems. We assessed alcohol-related problems as a potential
covariate. The Alcohol Use Disorders Identification Test (AUDIT) is a widely used 10-item
screening instrument for alcohol problems with scores ranging from 0 to 40 (Reinert & Allen,
2007; Saunders, Aasland, Babor, Delafuente, & Grant, 1993). A score of 8 serves as a cut-off
for hazardous drinking, but some researchers suggest that this cut-off criterion needs to be
lowered (Reinert & Allen, 2007). In the present sample, the mean AUDIT score was 7.04 (SD
= 5.05; range 0-22). Reliability was α = .82.
Smoking behavior. Participants indicated in an open response field how many
cigarettes they smoked on average during one week. Participants reported smoking on
average 7.81 cigarettes per week, but there was large variability between participants (SD =
25.41; range 0-130) with 70 participants (79%) indicating to not smoke at all.
EEG recording and raw data processing.
EEG was continuously recorded with a BioSemi Active-Two amplifier system
(Biosemi Inc., Amsterdam, The Netherlands) using 64 active Ag–AgCl active electrodes
mounted in an elastic cap and placed according to the international 10/10 system. During the
recordings, the signals were referenced to CMS (common mode sense), while DRL (driven
right leg) served as ground. Data were recorded with a sampling rate of 512 Hz (24 bit
precision; bandwidth: 0.1-100 Hz). Horizontal and vertical eye movements were recorded
with electrodes at the left and right outer canthi and left infraorbital. Independent component
analysis (ICA) was applied, and ICA components that clearly accounted for vertical and
horizontal eye movements were removed from the EEG without topographic distortion. In
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addition to the rejection of sweeps where any channel exceeded the amplitude of ±100 μV,
the data were visually inspected to reject remaining artifacts, using a moving, nonoverlapping 2-second window. For each participant, channels exhibiting substantial noise
were interpolated using a 3D spherical spline interpolation procedure. The EEG data were
then recomputed against the average reference. On average, there were 85.1 ± 17.1 2-second
epochs available per participant. Fast Fourier Transformation (using a square window) was
applied to each epoch and channel to compute the spectra with 0.5 Hz resolution. For each
participant, the spectra for each channel were averaged over all epochs. Power spectra were
integrated for the following seven independent frequency bands (Kubicki, Herrmann, Fichte,
& Freund, 1979): delta (1.5-6 Hz), theta (6.5-8 Hz), alpha1 (8.5-10 Hz), alpha2 (10.5-12 Hz),
beta1 (12.5-18 Hz), beta2 (18.5-21 Hz), and beta3 (21.5-30 Hz). Following a reviewer’s
suggestion, we checked the internal consistency of alpha1 resting EEG activity. The observed
reliability alpha coefficients (Cronbach, 1951) at electrode Cz were .94 for delta, .95 for theta,
.97 for alpha1, .96 for alpha2, .96 for beta1, .94 for beta2, and .97 for beta3. This
demonstrates that on average 96% of observed variance in the EEG data was accounted for by
true variation in brain activity.
Intracortical source localization analysis.
Standardized low-resolution brain electromagnetic tomography (sLORETA; PascualMarqui, 2002) was used to calculate the intracortical electrical sources that generated the
scalp-recorded activity in each of the seven frequency bands. sLORETA is one of the most
widely used source localization techniques to analyze EEG produced on the scalp. This
technique solves the inverse problem without assuming an a priori number of underlying
sources and computes electric neural activity as standardized current density (unit: amperes
per square meter, A/m2). The sLORETA solution space consisted of 6239 voxels (voxel size:
5 x 5 x 5 mm) and was restricted to cortical gray matter, as defined by the digitized Montreal
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Neurological Institute (MNI) probability atlas. The sLORETA method has received
considerable validation from studies combining EEG and magnetoencephalogram (MEG)
source localizations performed in conjunction with other localization methods including
functional Magnetic Resonance Imaging (fMRI, Mobascher et al., 2009; Olbrich et al., 2009)
and Positron Emission Tomography (PET, Laxton et al., 2010). Further, this method has been
validated with experimental data for which the true generators are known from invasive,
implanted depth electrodes (Zumsteg, Friedman, Wieser, & Wennberg, 2006; Zumsteg,
Lozano, Wieser, & Wennberg, 2006).
To reduce confounds that have no regional specificity, such as total power intersubject variability, a global normalization of the whole-brain sLORETA images was carried
out. Before statistical analyses, the sLORETA images were log-transformed.
Statistical analyses.
The goal of the present study was to investigate whether the association of implicit
alcohol attitudes on drinking behavior is moderated by neural baseline activity. Accordingly,
as a first step, a whole-brain voxel-wise moderation analysis approach was applied to identify
brain regions whose baseline activity moderate the association of implicit alcohol attitudes on
drinking behavior, separately for each EEG frequency band. To minimize Type I errors, only
activity clusters of more than 10 contiguous voxels (1.25 cm3) exceeding corrected p < .05
were considered significant. A non-parametric randomization approach corrected for multiple
testing (Nichols & Holmes, 2002). This approach was used to estimate empirical probability
distribution and the corresponding corrected (for multiple comparisons) critical probability
thresholds. After having identified clusters of potential neural moderators, current density was
averaged across all voxels within a cluster, thus yielding one value per participant. Next,
alcohol use was regressed on implicit alcohol attitudes, baseline activity in the identified
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cluster, and their two-way interaction. To arrive at the correct beta weights, all variables were
z-standardized before the multiple regression analysis (Aiken & West, 1991).
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References
Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions.
Newbury Park, CA: Sage.
Bluemke, M., & Friese, M. (2008). Reliability and validity of the Single-Target IAT (STIAT): Assessing automatic affect towards multiple attitude objects. European Journal
of Social Psychology, 38, 977-997.
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16,
297-334.
Egloff, B., & Schmukle, S. C. (2002). Predictive validity of an Implicit Association Test for
assessing anxiety. Journal of Personality and Social Psychology, 83, 1441-1455.
Greenwald, A. G., Nosek, B. A., & Banaji, M. R. (2003). Understanding and using the
Implicit Association Test I: An improved scoring algorithm. Journal of Personality
and Social Psychology, 85, 197-216.
Houben, K., & Wiers, R. W. (2008). Implicitly positive about alcohol? Implicit positive
associations predict drinking behavior. Addictive Behaviors, 33, 979-986.
Jann, K., Koenig, T., Dierks, T., Boesch, C., & Federspiel, A. (2010). Association of
individual resting state EEG alpha frequency and cerebral blood flow. NeuroImage, 51,
365-372.
Karpinski, A., & Steinman, R. B. (2006). The single category implicit association test as a
measure of implicit social cognition. Journal of Personality and Social Psychology, 91,
16-32.
Kubicki, S., Herrmann, W. M., Fichte, K., & Freund, G. (1979). Reflections on the topics:
EEG frequency bands and regulation of vigilance. Pharmakopsychiatr
Neuropsychopharmakol, 12, 237-245.
Resting EEG and alcohol consumption – Supplementary Materials
9
Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (2005). International affective picture system
(IAPS): Affective ratings of pictures and instruction manual Technical Report A-6.
Gainesville, FL: University of Florida.
Laxton, A. W., Tang-Wai, D. F., McAndrews, M. P., Zumsteg, D., Wennberg, R., Keren, R.,
et al. (2010). A phase I trial of deep brain stimulation of memory circuits in
Alzheimer's disease. Annals of Neurology, 68, 521-534.
Mobascher, A., Brinkmeyer, J., Warbrick, T., Musso, F., Wittsack, H. J., Stoermer, R., et al.
(2009). Fluctuations in electrodermal activity reveal variations in single trial brain
responses to painful laser stimuli - A fMRI/EEG study. NeuroImage, 44, 1081-1092.
Nichols, T. E., & Holmes, A. P. (2002). Nonparametric permutation tests for functional
neuroimaging: A primer with examples. Human Brain Mapping, 15, 1-25.
Olbrich, S., Mulert, C., Karch, S., Trenner, M., Leicht, G., Pogarell, O., et al. (2009). EEGvigilance and BOLD effect during simultaneous EEG/fMRI measurement.
NeuroImage, 45, 319-332.
Pascual-Marqui, R. D. (2002). Standardized low-resolution brain electromagnetic tomography
(sLORETA): technical details. Methods Find Exp Clin Pharmacol, 24 Suppl D, 5-12.
Reinert, D. F., & Allen, J. P. (2007). The Alcohol Use Disorders Identification Test: An
update of research findings. Alcoholism-Clinical and Experimental Research, 31, 185199.
Saunders, J. B., Aasland, O. G., Babor, T. F., Delafuente, J. R., & Grant, M. (1993).
Development of the Alcohol-Use Disorders Identification Test (AUDIT) - WHO
collaborative project on early detection of persons with harmful alcohol consumptionII. Addiction, 88, 791-804.
Sobell, L. C., & Sobell, M. B. (1990). Self-report issues in alcohol-abuse: State of the art and
future directions. Behavioral Assessment, 12, 77-90.
Resting EEG and alcohol consumption – Supplementary Materials 10
Sobell, L. C., & Sobell, M. B. (1995). Alcohol consumption measures. In J. P. Allen & M.
Columbus (Eds.), Assessing alcohol problems: A guide for clinicians and researchers
(pp. 55-73). Rockville, MD: National Institute on Alcohol Abuse and Alcoholism.
Sobell, L. C., & Sobell, M. B. (2000). Alcohol timeline followback (TLFB). In A. P.
Association (Ed.), Handbook of Psychiatric Measures (pp. 477-479). Washington,
DC: American Psychiatric Association.
Sobell, L. C., Sobell, M. B., Leo, G. I., & Cancilla, A. (1988). Reliability of a timeline
method: Assessing normal drinkers reports of recent drinking and a comparative
evaluation across several populations. British Journal of Addiction, 83, 393-402.
Wiers, R. W., van Woerden, N., Smulders, F. T., & de Jong, P. J. (2002). Implicit and explicit
alcohol-related cognitions in heavy and light drinkers. Journal of Abnormal
Psychology, 111, 648-658.
Zumsteg, D., Friedman, A., Wieser, H. G., & Wennberg, R. A. (2006). Propagation of
interictal discharges in temporal lobe epilepsy: Correlation of spatiotemporal mapping
with intracranial foramen ovale electrode recordings. Clinical Neurophysiology, 117,
2615-2626.
Zumsteg, D., Lozano, A. M., Wieser, H. G., & Wennberg, R. A. (2006). Cortical activation
with deep brain stimulation of the anterior thalamus for epilepsy. Clinical
Neurophysiology, 117, 192-207.