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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 2 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 Resting EEG and alcohol consumption – Supplementary Materials 3 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. Resting EEG and alcohol consumption – Supplementary Materials 4 300 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 Resting EEG and alcohol consumption – Supplementary Materials 5 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 Resting EEG and alcohol consumption – Supplementary Materials 6 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 Resting EEG and alcohol consumption – Supplementary Materials 7 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). Resting EEG and alcohol consumption – Supplementary Materials 8 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. 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