Abnormal EEG synchronisation in heavily drinking students

Clinical Neurophysiology 115 (2004) 2048–2055 www.elsevier.com/locate/clinph

Abnormal EEG synchronisation in heavily drinking students Eveline A. de Bruina,*, Suzanne Bijla, Cornelis J. Stamb, Koen B.E. Bo¨ckera, J. Leon Kenemansa,c, Marinus N. Verbatena a

Department of Psychopharmacology, Faculty of Pharmaceutical Sciences, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Sorbonnelaan 16, NL-3584 CA Utrecht, The Netherlands b Department of Clinical Neurophysiology, VU University Medical Centre, Amsterdam, The Netherlands c Department of Psychonomics, Faculty of Social Sciences, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands Accepted 11 April 2004 Available online 18 May 2004

Abstract Objective: In alcoholics, grey and white brain matter is damaged. In addition, functional brain connectivity as measured by EEG coherence is abnormal. We investigated whether heavily drinking students, although drinking for a shorter period than alcoholics, already show differences in functional connectivity compared to light-drinking controls. Methods: EEG was recorded in 11 light and 11 heavy male student drinkers during eyes closed, and eyes closed plus mental rehearsal of pictures. Functional connectivity was assessed with the Synchronisation Likelihood method. Results: Heavily drinking students had more synchronisation in the theta (4– 8 Hz) and gamma (30– 45 Hz) band than lightly drinking students during eyes closed, both with and without a mental-rehearsal task. Conclusions: Heavy student drinkers have increases in EEG synchronisation that are indicative of changes in hippocampal – neocortical connectivity. Significance: Heavy student drinkers show differences in functional connectivity as compared to their lightly drinking counterparts, even though they have a relatively short drinking history. q 2004 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. Keywords: Brain; Alcohol; EEG; Social drinking; Synchronisation; Theta; Gamma; Students

1. Introduction Drinking alcohol is common among university college students (e.g. Kuo et al., 2002; Wechsler et al., 2000). A certain proportion of these students consists of heavy drinkers, who sometimes even meet the criteria for alcohol dependence and/or abuse (Knight et al., 2002). Heavy drinking affects cognitive functioning, not only during intoxication (Koelega, 1995), but also after blood alcohol concentrations have returned to zero (Verster et al., 2003; Wiese et al., 2000). A review of several studies suggests that, in adults, consumption of 5 or more alcoholic drinks on 5 –7 days per week affects cognitive functioning on a longterm basis (Parsons, 1998; Parsons and Nixon, 1998). The question thus arises: is the long-term deleterious effect of heavy social drinking on brain functioning already * Corresponding author. Tel.: þ31-30-253-7768; fax: þ 31-30-253-7900. E-mail address: [email protected] (E.A. de Bruin).

discernible in young heavy student drinkers, even though their drinking history is relatively short? Alcoholics have reduced cortical grey (Fein et al., 2002; Jernigan et al., 1991; Pfefferbaum et al., 1992), and white matter volumes (Hommer et al., 2001; O’Neill et al., 2001; Pfefferbaum et al., 1992) when compared to light drinkers. In addition, alcoholics have a smaller corpus callosum (Estruch et al., 1997; Hommer et al., 1996, 2001; Pfefferbaum et al., 1996), and show disruptions of the integrity of white matter tracts in the corpus callosum on the micro-structural level (Pfefferbaum and Sullivan, 2002; Pfefferbaum et al., 2000). Subcortical structures such as the hippocampus are also damaged in patients with alcoholism (Agartz et al., 1999; De Bellis et al., 2000; Laakso et al., 2000; Sullivan et al., 1995). White and grey matter damage suggests that functional connectivity between distinct brain regions may be disturbed in alcoholics. Functional connectivity can be assessed by determining synchronisation between electrical activities in

1388-2457/$30.00 q 2004 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.clinph.2004.04.010

E.A. de Bruin et al. / Clinical Neurophysiology 115 (2004) 2048–2055

different brain areas (Horwitz, 2003). One way to estimate this synchronisation is to determine the coherence between EEG signals at different electrode sites. In neurodegenerative disease states, such as Alzheimer’s disease, for example, decreased EEG coherence in the theta, alpha, and beta band indicates altered functional brain connectivity in these patients (e.g. Besthorn et al., 1994; Comi et al., 1998; Locatelli et al., 1998). Contrary to patients with Alzheimer’s disease, alcoholics mainly show an increase in coherence. Kaplan et al. (1985) found a diffuse increase of delta coherence and an increase in fast-beta coherence at the left temporal (F7 – T5) and right occipital (T6 –O2) electrode pairs. Michael et al. (1993) confirmed the finding of increased delta coherence (at F3 –F4) and increased fast-beta coherence (at F3 – F4 and C3 – C4), and additionally reported an increase in theta, alpha, and slow beta coherence at the central electrode pair (C3 – C4). In the latter study, increases in coherence contrasted with decreases in coherence in the alpha, slow beta, and fast beta band at parietal sites (P3 – P4). Finally, Winterer et al. (2003) also reported increased fast-alpha coherence at F7 –O1, and increased slow-beta coherence at F8 –O2, F7 –O1, F8 – T6 and F3 – F4 in alcoholics. Coherence analyses only take linear components of the EEG signals into account. However, the non-linear components of the EEG signals are crucial to the interpretation of the EEG in terms of functional connectivity (Pritchard and Duke, 1995; Pritchard et al., 1995; Stam et al., 2003). Synchronisation Likelihood, a novel method to describe EEG synchronisation, estimates both linear and non-linear interdependencies between a signal from one channel and those from all other channels. This results in a value for each channel that reflects the average synchronisation between that particular channel and all others (Stam and Van Dijk, 2002). Synchronisation Likelihood purely reflects coupling, and is insensitive to changes in signal amplitude that are not related to changes in synchronisation. Conversely, Synchronisation Likelihood can detect changes in synchronisation in the absence of amplitude changes (Stam and De Bruin, 2004). Furthermore, Synchronisation Likelihood is less susceptible to statistical unreliability, as the synchronisation estimates are averaged over many more data points than in a standard coherence analysis (Stam and Van Dijk, 2002). Synchronisation Likelihood has previously been used to detect changes in EEG synchronisation during a working memory task (Stam et al., 2002a) and a semantic processing task (Micheloyannis et al., 2003) in healthy volunteers, and to measure magnetoencephalographic differences between Alzheimer patients and healthy older control subjects (Stam et al., 2002b). In the latter study, patients with Alzheimer’s disease had lower synchronisation in the upper alpha, upper beta, and gamma band than healthy control subjects. In the present study, we used Synchronisation Likelihood to assess whether heavy drinkers have

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differences in EEG synchronisation that may be related to long-term effects of heavy alcohol consumption on functional brain connectivity as compared to light-drinking controls. Neuropsychological studies (e.g. Ciesielski et al., 1995; Dao-Castellana et al., 1998; Ratti et al., 1999; see Moselhy et al. (2001) for a review) as well as volumetric studies (Estruch et al., 1997; Jernigan et al., 1991; Pfefferbaum et al., 1992, 1997) suggest that brain damage in alcoholdependent individuals is predominant in the frontal brain areas. However, other hypotheses on brain damage in alcoholics also exist, such as a selective disruption of the right side of the brain (Ellis and Oscar-Berman, 1989), or diffuse brain damage (Lishman et al., 1987). Thus, possible differences in functional brain connectivity between light and heavy social drinkers may vary across brain regions. Heavy student drinkers are more likely to be smokers than light student drinkers (Novak et al., 2003). Nicotine has been linked with protective (Picciotto and Zoli, 2002) but also with damaging effects on the brain (Brody et al., 2004). Animal studies suggest that the pharmacological effects of alcohol interact with those of nicotine (Schoedel and Tyndale, 2003; Tizabi et al., 2003). To account for possible interactive effects of alcohol and nicotine on the brain, smoking habits were included in the statistical analyses. In the present study, we compared the functional brain connectivity of light and heavy student drinkers to explore long-term effects of heavy alcohol use on brain functioning in students. Multi-channel EEG was measured at rest with eyes closed. Furthermore, to investigate the hypothesis that alcohol use may particularly affect the frontal brain areas, we also recorded the EEG during mental rehearsal of a set of pictures in order to activate these brain areas. The combined use of multiple channels and a frontal activation task allowed us to evaluate whether a possible change in functional connectivity in heavy drinkers was predominant in the frontal brain areas, as has been proposed for alcoholics. Alternatively, the data might be supportive for the right-hemisphere or diffuse-brain-damage hypothesis.

2. Methods 2.1. Subjects Thirty-six male students were screened, of which 22 males eventually participated in the study. All participants were treated in accordance with the Declaration of Helsinki and amendments. The subjects were aged between 22 and 27 years, and right-handed as determined with the Edinburgh Handedness Inventory. They had a normal Body Mass Index (, 28 kg/m2), normal hearing, and normal or corrected-to-normal sight. The subjects had no (history of) chronic somatic or neurological disease, head trauma, or psychiatric disease as enquired by a detailed questionnaire from our lab, based on the Diagnostic and Statistical Manual

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of Mental Disorders (DSM-IV) and assessing for the presence of psychopathological symptoms. Other exclusion criteria were: excessive nicotine use (. 60 cigarettes per day), excessive caffeine use (. 10 cups of coffee per day), use of psychoactive medication, alcohol dependence, and first- or second-degree relatives with neurological or psychiatric deficits, including alcoholism. After written and oral explanation of the study, the subjects signed the informed consent. During the two weeks prior to the EEG session, the subjects filled out a diary in which they recorded the number of alcoholic drinks and the type of unit (e.g. glass, can, bottle) on a daily basis. Current alcohol intake was estimated by converting the drinks reported into standard units per week, with a standard drink containing 12 g of alcohol. The subjects were divided into two groups on the basis of their current alcohol intake: light drinkers (, 30 units of alcohol per week; n ¼ 11), and heavy drinkers (. 30 units of alcohol per week; n ¼ 11). The cut-off of 30 units per week was determined on the basis of the meta-analysis by Parsons (1998), which demonstrated that consumption of 5 or more alcoholic drinks on 5 –7 days per week (25 – 35 units or more) affects cognitive functioning on a long-term basis. Lifetime alcohol intake was assessed by a questionnaire in Dutch based on the Lifetime Drinking History interview by Skinner and Sheu (1982) as adapted by Lemmens et al. (1997). It is a general finding that alcohol use is underestimated when measured with a retrospective quantity/ frequency questionnaire (Carlsson et al., 2003). Despite their relatively short drinking history, students are no exception to this rule (Townshend and Duka, 2002). For this reason, we did not rely on the Lifetime Drinking History questionnaire for classification, but focused on the diary for a more realistic estimation of the alcohol consumption. The participants were instructed to abstain from alcohol 24 h before the recording session. Alcohol abstinence was verified with a breath test (Alcotestw, Dra¨ger Medical, Lu¨beck, Germany). The subjects also had to refrain from drinking coffee or tea on the day of testing, and to not smoke during 3 h before the experiment, which was verbally verified at arrival at the testing facility.

(Stam et al., 2002a). Next, they were instructed to close their eyes and mentally rehearse the pictures that had been presented (eyes-closed-plus-mental-rehearsal condition). After one minute, they were asked to open their eyes and verbally recall as many pictures as possible. 2.3. EEG recording and analysis The EEG was recorded from 62 tin electrodes placed according to the international 10 – 10 system with the left mastoid as a reference (QuikCap, Neurosoft, El Paso, TX, USA). Vertical and horizontal EOG were recorded bipolarly to monitor eye-movement artifacts. A maximum electrode impedance of 10 kV was allowed. Filters were set at 0.15 and 70 Hz, and signals were digitised at a rate of 500 Hz and a gain of 2500. SynAmps amplifiers with 16 bit A/D resolution (0.033 mV/bit), 10 MOhm input impedance, and 100 dB common mode rejection were used for acquisition with Neuroscan software version 4.1 (Neurosoft, El Paso, TX, USA). For every subject, two consecutive epochs of 4096 samples each were selected at the start of each condition, corresponding to 16.4 s of EEG. Synchronisation Likelihood (SL) was calculated in the following frequency bands: delta: 0.5 – 4 Hz, theta: 4– 8 Hz, alpha: 8 – 12 Hz, slow beta: 12– 20 Hz, fast beta: 20– 30 Hz, and gamma: 30– 45 Hz, using an average reference. In the SL estimation, two dynamic systems, X and Y, are considered. These systems are represented by the vectors Xi and Yi in their respective state spaces, which are obtained from the time series by time-delay embedding. SL is the possibility (likelihood) that, when system X is in a particular state at two different times i and j, system Y will be in

2.2. Procedure On arrival at the lab, subjects were prepared for EEG recording. Next, they were seated in a comfortable chair in a sound-attenuated and electrically shielded testing chamber with a monitor in front of them at a distance of 1 m. The EEG was recorded continuously during two conditions: eyes closed, and eyes closed plus mental rehearsal of pictures. In the eyes-closed condition, subjects were instructed to keep their eyes closed and to remain in a relaxed and awake state. After this, the subjects were asked to open their eyes, and were presented with 12 pictures of common neutral animate and inanimate objects (e.g. a car, a book, a butterfly) that were presented simultaneously on the monitor for 10 s

Fig. 1. Division of electrodes into 6 areas.

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Table 1 Demographics Variable (mean ^ SD)

Light drinkers ðn ¼ 11Þ

Heavy drinkers ðn ¼ 11Þ

t

Age (yr) Current alcohol intake (no. of units per week)a Lifetime alcohol intake (kg)b Duration of drinking history (yr)b Daily cigarette consumption (no. of cigarettes)

24.1 ^ 1.63 20.5 ^ 6.44 71.6 ^ 20.22 10.3 ^ 2.15 1.2 ^ 2.07

24.1 ^ 1.78 53.5 ^ 11.46 152.7 ^ 120.68 9.9 ^ 2.59 8.2 ^ 8.69

t20 t20 t19 t19 t20

a b

P ¼ 0:03 ¼ 8:33 ¼ 2:10 ¼ 0:33 ¼ 2:61

0.997 ,0.001 0.049 0.748 0.017

A standard unit contains 12 g of alcohol in the Netherlands. Age of first drink was unknown for one light drinker.

the same state at those times. ‘Being in the same state’ is operationalised by computing the distance between the vectors Xi and Xj . SL is then averaged over all times i (here: all 4096 samples per epoch, minus 1), resulting in a description of the resemblance of a particular time series to another time series in a particular epoch. To avoid the difficulties of handling 62 £ 62 synchronisation estimates, SL was averaged over all combinations of a particular time series with other time series. In this way, the SL of a particular channel reflects the similarity of the signal from that particular channel to the signals from all other channels (see Stam and Van Dijk (2002), and Stam et al. (2002a, 2003) for more information about SL). 2.4. Statistical analysis Group differences in demographics and memory performance were analysed with t tests for independent samples. SL was averaged over the two epochs, and over 6 areas (see Fig. 1), and analysed per frequency band with a repeated-measures analysis of covariance (ANCOVA). This ANCOVA had Group as between-subjects factor (two levels: light and heavy), and Condition (two levels: eyesclosed versus eyes-closed-plus-mental-rehearsal) and Area (6 levels: frontal, central, parietal, occipital, left-temporal, and right-temporal) as within-subjects factors. The number of cigarettes smoked daily was added as a covariate. Degrees of freedom were Greenhouse –Geisser-corrected. Statistical analyses were performed with SPSS 11.0.1 for Windows.

Fig. 2. Current alcohol intake in light and heavy student drinkers in standard units containing 12 g of alcohol.

3. Results For a summary of the demographics, see Table 1. See Figs. 2 and 3 for an illustration of the Group differences in current alcohol intake and daily cigarette consumption. Group differences in SL are described in Table 2, and depicted in Fig. 4. 3.1. Demographics Current alcohol intake was higher in the heavy drinkers (range 39.9 – 75.5 units) than in the light drinkers

Fig. 3. Daily cigarette consumption in light and heavy student drinkers.

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Table 2 Group differences in synchronisation likelihood df a

Group Group £ condition Group £ area Group £ condition £ area a

1, 19 1, 19 5, 95 5, 95

0.5–4 Hz

4–8 Hz

8–12 Hz

F

P

F

P

F

0.44 0.59 1.08 1.17

0.517 0.451 0.357 0.325

5.01 1.21 2.67 0.37

0.037 0.285 0.056 0.754

0.31 0.67 0.88 1.45

12 –20 Hz

20–30 Hz

30–45 Hz

P

F

P

F

P

F

P

0.585 0.423 0.414 0.244

0.44 0.94 0.92 0.37

0.514 0.344 0.438 0.687

0.10 1.68 0.93 1.48

0.756 0.211 0.446 0.220

5.82 0.02 0.79 0.80

0.026 0.891 0.502 0.506

Uncorrected degrees of freedom.

(range 7 –27.75 units; see Fig. 2). Although the groups did not differ in the duration of their drinking history (range 6 –14 yr), lifetime alcohol intake of the heavy drinkers (range 55.0 –463.3 kg) was higher than that of the light drinkers (range 45.9– 98.6 kg). Lifetime alcohol intake slightly overlapped between the groups, as the subjects were classified on the basis of the diary. The number of smokers was comparable in the light and heavy group (4 and 7, respectively; one-sided Fisher’s Exact Probability Test n.s.). Heavy drinkers smoked more cigarettes per day (range 0 –25) than light drinkers (range 0 –5; see Fig. 3). The number of cigarettes smoked daily correlated significantly with current alcohol intake (r22 ¼ 0:535; P ¼ 0:010), but not with lifetime alcohol intake (r21 ¼ 20:133; n.s.) or duration of drinking history (r21 ¼ 20:125; n.s.). 3.2. Memory performance Performance data of one light drinker (current alcohol intake 19.7 units per week, lifetime alcohol intake 90.8 kg in 12 years) were lost. The remaining 10 light drinkers remembered on average 6.8 ^ 2.0 items (range 4– 10) correctly from the 12 pictures presented, and the heavy drinkers remembered on average 6.4 ^ 1.8 items (range 3 –9; F1;18 ¼ 1:42; n.s.).

band: F1;19 ¼ 0:02; n.s.; however, the effect size h2p , 0:20; and observed power , 0.50).

4. Discussion The present study showed that functional connectivity in the brain, as assessed by EEG synchronisation during eyes closed, differs between light and heavy student drinkers. Heavy drinkers had abnormally increased synchronisation in the theta (4 – 8 Hz) and gamma (30 – 45 Hz) band. The increases in synchronisation were similar across brain areas, and did not differ between the mental-rehearsal or no-task condition. Heavy student drinkers had increased theta synchronisation as compared to light drinkers during eyes closed, both with and without mental rehearsal. Synchronisation in the theta band is related to memory processes (Kahana et al., 2001; Klimesch, 1999). The augmented theta synchronisation in heavily drinking students resembles the increase in theta coherence in alcoholics versus control subjects found by Michael et al. (1993). It is also in accordance with the low-frequency coherence increase in patients with Alzheimer’s disease reported by Locatelli et al. (1998), although decreases in theta coherence are

3.3. Synchronisation likelihood Heavy drinkers had a higher theta SL than light drinkers (main effect of Group F1;19 ¼ 5:01; P ¼ 0:037; effect size h2p ¼ 0:21; observed power 0.57; see Fig. 4). In addition, heavy drinkers had a higher gamma SL than light drinkers (main effect of Group F1;19 ¼ 5:82; P ¼ 0:026; effect size h2p ¼ 0:23; observed power 0.63; see Fig. 4). In the other frequency bands, no main Group effects or interactions with the factor Group were present; however, the effect size h2p , 0:20; and observed power , 0.50 for these bands. For comparison, relative power in the theta (light drinkers: 9.9%, heavy drinkers: 11.1%) and gamma band (light drinkers: 4.3%, heavy drinkers: 4.1%) did not differ between the groups (theta band: F1;19 ¼ 2:21; n.s.; gamma

Fig. 4. Higher theta and gamma synchronisation in heavily drinking students as compared to lightly drinking students. Stars indicate significant group differences (P , 0:05).

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also reported in this patient population (Adler et al., 2003; Besthorn et al., 1994). Heavy student drinkers also had higher gamma synchronisation than light drinkers. Synchronous oscillations in the gamma band have been associated with higher brain processes such as perception, attention, and learning (Engel and Singer, 2001). The increase in gamma synchronisation in heavy drinkers contrasts with the decrease in gamma synchronisation found in patients with Alzheimer’s disease (Stam et al., 2002b) and schizophrenia (Kissler et al., 2000). To our knowledge, this is the first report about the relationship between long-term alcohol intake and synchronisation in the gamma frequency band. Theta and gamma oscillations are associated with memory formation as subserved by hippocampo-neocortical connections (Buzsa´ki, 1996). The fact that differences in EEG synchronisation were found at both theta and gamma frequencies suggests that heavy student drinkers may have changes in the hippocampus, the cortex, and/or hippocampo-neocortical connections as compared to light drinkers. As stated in the introduction, alcohol dependence is related to both cortical (Hommer et al., 2001; O’Neill et al., 2001; Pfefferbaum et al., 1992) and hippocampal (Agartz et al., 1999; De Bellis et al., 2000; Sullivan et al., 1995) volume deficits. It is not known whether heavy social drinkers have similar brain damage, but memory deficits have been demonstrated repeatedly (Parsons, 1998; Parsons and Nixon, 1998). The increases in theta and gamma synchronisation in heavy drinkers do not necessarily imply better functional connectivity in that group as compared to light drinkers. During seizure activity, for instance, the EEG is extremely synchronised (e.g. Le Van Quyen et al., 1998). Optimal brain functioning requires not only synchronisation, but also desynchronisation of brain processes (Friston, 2000a,b; Stam and De Bruin, 2004). While structural brain research points towards a frontal predominance of brain damage in alcoholics, this spatial differentiation was not found in the present study. The Synchronisation-Likelihood algorithm was set to examine synchronisation between one particular electrode and all others. So, the lack of spatial differences in EEG Synchronisation Likelihood does not preclude that EEG synchronisation measured over the frontal areas may change with increasing alcohol use when other electrode combinations are used. The spectral content of the EEG of non-alcoholic subjects with a close relative who is alcohol-dependent differs from that of non-alcoholics without a family history of alcoholism (Bauer and Hesselbrock, 2002; Ehlers and Schuckit, 1990; Gabrielli et al., 1982). These power differences only partly resemble those found for alcoholics versus controls (Bauer and Hesselbrock, 2002; Ehlers and Schuckit, 1991). As the present study did not include a measurement before the onset of drinking, it could not be verified that the EEG differences between

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the groups did not exist before the students started to drink. However, the possible influence of a genetic predisposition for alcoholism was minimised by the exclusion of participants with alcohol-dependent relatives in the first or second degree. Alcoholism in the adult population is heavily associated with other psychiatric syndromes such as anxiety disorder and antisocial-personality disorder (Goodwin and Hamilton, 2003; Kushner et al., 2000; Waldman and Slutske, 2000), which may by themselves affect brain functioning. In the student population, the level of alcohol consumption is unrelated to anxiety (Novak et al., 2003), but strongly positively related to antisocial traits such as novelty seeking and low harm avoidance (Hartzler and Fromme, 2003). Although these antisocial personality characteristics are, according to Hartzler and Fromme, less extreme in the student population, it cannot be ruled out that the present group differences in functional connectivity may in some way be related to group differences in personality profile. Another possible source of confounding is the strong interrelatedness of drinking habits with smoking habits in the student population (Novak et al., 2003). To avoid a selection bias, the subjects were not matched on smoking behaviour. Although the number of smokers did not differ between the groups, the heavy drinkers smoked more cigarettes than the light drinkers. To account for possible effects of smoking habits or nicotine withdrawal on functional brain activity, daily cigarette use was included as a covariate in the statistical analyses. Smoking abstinence at the time of testing was verified verbally, and most students explicitly reported to not have smoked for at least 3 h before the test because of courses. In a future replication study, a measure of carbon monoxide or cotinine levels could provide a more reliable confirmation of nicotine abstinence. Winterer et al. (2002) compared 8 year-abstinent and currently drinking alcoholics with control subjects, and found similar differences in EEG coherence in both patient groups versus the control group. This indicated that acute withdrawal effects were unlikely to be the cause of these differences in functional brain connectivity. Additionally, in the present study, the minimum period of abstinence was at least 24 h, by which time clinical manifestations of acute withdrawal have disappeared in alcoholics (Rubino, 1992). Taking into account that the level of drinking in our students was relatively low as compared to alcoholics, it is not likely that the EEG differences we found between the groups can be attributed to withdrawal effects. The heavy drinkers remembered just as many pictures as the light drinkers. Thus, heavy drinking does not seem to influence immediate memory performance on this test in students at this age. This finding also indicates that neither alcohol nor nicotine withdrawal played a significant role in performance on this memory task. As all subjects remembered between 3 and 10 of the 12 items presented, the absence of differences between the groups cannot be attributed to either floor or ceiling effects. As the increases

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in theta and gamma synchronisation in heavy drinkers are suggestive of differences in the memory-formation system, it may nonetheless be worthwhile to investigate memory performance in student drinkers with a more extensive test battery. The present findings are restricted to male students. Both acute and long-term effects of alcohol differ significantly between males and females (Mumenthaler et al., 1999). As these gender differences cannot simply be corrected for by merely adjusting alcohol consumption measures (Graham et al., 1998), we measured only male students. A study including female students could provide information about the generalisability of the presently reported EEG differences across gender. In addition, the absence of a group difference in EEG synchronisation in the frequency bands other than theta and gamma may be related to the relatively low effect size and observed power in these bands. Therefore, it would be interesting to find out whether the present results can be replicated in larger subject groups. In summary, heavily drinking students have increased EEG synchronisation in the theta and gamma band as compared to lightly drinking students. These increases in synchronisation are suggestive of changes in hippocamponeocortical connectivity. Thus, even though they have a relatively short drinking history, students who drink heavily show differences in functional brain connectivity as compared to their lightly drinking counterparts.

Acknowledgements We are grateful to W.M. Schapendonk and M.G. Veldhuizen for their assistance with data collection, and to Dr P.H.H.H.M. Lemmens for providing us with the Dutch version of the Lifetime Drinking History questionnaire. We would like to thank the two anonymous referees for their helpful comments on an earlier draft of this paper. This study was supported by the Dutch Foundation for Scientific Research (NWO/ZonMw) grant No. 96040000-39.

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