EEG Spectral Changes in Treatment Naïve Active Alcoholics

G. Fein, Ph.D and J. Allen, B.A

Neurobehavioral Research, Inc., Corte Madera, CA

Address reprint request and correspondence to: Dr. George Fein, Neurobehavioral Research, Inc., 201 Tamal Vista Boulevard, Corte Madera, CA 94925, Tel: 415.927.7676, Fax: 415.924.2903, Email:george@nbresearch.com

Abstract

Background

The present study examines the EEG spectra of actively drinking treatment naïve alcoholics (TxNA).

Methods

EEGs were gathered on 51 TxNA’s and age and sex-matched controls during eyes-closed conditions. Participants were excluded for lifetime diagnoses of psychiatric or substance abuse disorders. Power for the theta to high beta bands was examined across midline electrodes.

Results

The TxNA sample exhibited a nexus of disinhibited traits associated with the vulnerability to alcoholism, and had developed alcohol dependence, but no other diagnosable psychiatric or substance abuse disorders. The TxNA subjects evidenced higher power for all EEG bands compared to controls. The magnitude and anterior-posterior extent of the group differences varied across bands. Within the TxNA group, EEG power was negatively correlated with average and peak alcohol drinking duration and average and peak alcohol dose.

Conclusions

Increased EEG power across the theta to high beta bands distinguishes TxNAs without comorbid diagnoses from controls. These effects varied across bands in their magnitude and spatial extent, suggesting that there are different effects for the different EEG spectral generators. We hypothesize the increased power in these individuals is a trait difference associated with the inherited nexus of disinhibited traits and its manifestation in alcoholism.

Based on the strong negative correlations with alcohol use variables, we speculate that decreases in EEG power are a morbid effect of long-term alcohol abuse. We acknowledge that this hypothesized effect of alcohol abuse on EEG power is opposite to the increased EEG power we hypothesize is associated with alcoholism and its inherited nexus of disinhibited traits. An implication of this model is that with continuing alcohol abuse, the increased EEG power in TxNAs will eventually be overpowered by the effects of long-term severe alcohol abuse. This model predicts that in very long-term alcoholics EEG power would be equal to or lower than that of age and sex comparable controls.

Keywords: Resting EEG, power spectra, alcoholism, treatment naïve, aging

INTRODUCTION

Given the EEG’s high heritability and its dramatic response to alcohol intoxication, the EEG has been studied extensively as a trait marker for the genetic vulnerability to alcoholism. Many studies have reported increased slow alpha activity as a response to ethanol ingestion in both men and women (Cohen et al., 1993; Ehlers et al., 1989; Lukas et al., 1986; Lukas et al., 1989), and some have revealed changes in theta, and fast alpha activity as well (Ehlers et al., 1989; Lukas et al., 1986; Volavka et al., 1985). The background EEG is highly heritable (e.g., (Van Baal et al., 1996), as is alcoholism (Begleiter and Porjesz, 1999; Foroud et al., 1998; Foroud et al., 2000; Reich et al., 1998). Moreover, the heritability of the EEG recorded post alcohol administration is even higher than that recorded under resting conditions (Propping, 1977; Sorbel et al., 1996). In alcohol challenge studies, Ehlers and Shuckit reported elevated beta in FHP (family history positive) vs. FHN (family history negative) men 90 minutes post ethanol (Ehlers and Schuckit, 1990) and a decrease in fast alpha post-ethanol in FHN, but not FHP subjects (Ehlers and Schuckit, 1991).

Several studies have examined EEG power as a trait marker for alcoholism, comparing individuals at high vs. low risk for developing alcoholism, with varying results. In a recent study, Rangaswamy et al. found increased beta power in FHP individuals (Rangaswamy et al., 2004). Pollock et al. (Pollock et al., 1995) reported increased beta power in older FHP subjects compared to age- and gender-matched controls. Ehlers and Shuckit found elevated baseline fast alpha in FHP subjects (Ehlers and Schuckit, 1991). In contrast, Finn and Justus found that the offspring of alcoholics showed reduced alpha power and elevated beta power compared to FHN controls (Finn and Justus, 1999). Finally, Cohen et al. found no alpha or beta EEG power differences between FHP vs. FHN samples (Cohen et al., 1991).

Compared to the studies of high-risk samples, there have been relatively few studies of alcoholic samples. Rangaswamy et al. found increased theta power in alcoholics (Rangaswamy et al., 2003), as well as increased low beta power in male alcoholics, and increased mid beta power in female alcoholics (Rangaswamy et al., 2002). Pollock et al. examined the EEG spectra (delta through beta), and found increased theta amplitude for recovered alcoholics, but no differences for any other band (Pollock et al., 1992). These EEG spectral studies included large numbers of participants with comorbid substance abuse disorders, antisocial personality disorder, and depression, all factors independently associated with abnormal EEG power (Bauer and Hesselbrock, 1993; Costa and Bauer, 1997; Knott et al., 2001; Newton et al., 2003; Petersen et al., 1982).

Finn et al. (Finn et al., 2000) reported that social deviance proneness and excitement/pleasure seeking account for a significant portion of the relationship between a positive family history of alcoholism and later alcohol abuse. Current theories propose that disinhibition is a fundamental mediator of the inherited predisposition toward alcohol dependency (Begleiter and Porjesz, 1999; Cloninger, 1987; Sher et al., 1991; Tartar et al., 1985). It has been proposed that behavioral phenomena such as psychopathy, antisocial and impulsive traits, and alcoholism, should be viewed as variable expressions of a generalized disinhibitory complex (Gorenstein and Newman, 1980). Several studies have reported that EEG power in externalizing disorder samples is similar to that seen in FHP samples. Excessive theta activity has been associated with a number of indicators of disinhibited personality, such as antisocial personality (Mednick et al., 1981), attention-deficit/hyperactivity disorder (Barry et al., 2003), borderline personality disorder (Russ et al., 1999), and criminality (Petersen et al., 1982; Raine et al., 1990). Excessive theta activity is thought to indicate cortical underarousal and has been associated with measures of low autonomic arousal (Raine et al., 1990). Some theorize that excessive theta reflects delayed cortical maturation and poor behavioral control that often leads to disinhibited behavioral syndromes such as antisocial personality and substance abuse (Ishikawa and Raine, 2002). Alpha power has been reported to be increased in persons with extroverted personality traits (Wall et al., 1990).

The current study examines eyes-closed resting EEG power in treatment naïve actively drinking alcoholics (TxNA) compared to age- and gender-matched controls. This study excludes participants with lifetime diagnoses of comorbid psychiatric or substance abuse disorders. Participants were currently drinking, met current DSM-IV-R criteria for alcohol dependence, and had never sought treatment for alcoholism; in fact none of the TxNA participants self identified as alcoholics. This sample is more representative of alcoholic dependent individuals in the general population than are treated samples. We have shown that they come from a different population than treated samples, with less severe drinking histories in the first four to five years after meeting criteria for heavy drinking (Fein and Landman, in press). During this period, long-term abstinent alcoholic men and women drank an average of 210 and 134 drinks per month while TxNA men and women drank an average of 165 and 98 drinks per month. In the current study, we examine the TxNA sample’s EEG spectra, and its association with age and drinking variables.

METHODS

Participants

All participants were recruited from respondents to postings, mailings, newspaper ads, ads on an Internet site, and referrals from other participants. The study involved a sample of treatment naïve, actively drinking, alcohol dependent (TxNA) individuals, and a control sample (C) matched on a one-to-one basis on gender and age with the TxNA sample. The TxNA group was recruited by advertising for ‘heavy social drinkers’ or ‘men and women who have a high tolerance for alcohol’. None of the TxNA participants labeled themselves alcoholics, and we never used the word alcoholism in referring to these participants, either in our advertisements or in their assessment procedures.

The TxNA group (n= 51) was comprised of 20 women and 31 men between the ages of 19 and 50 (mean = 31.9, SD = 8.0). Table 1 presents subject demographics, alcoholism family history measures, and alcohol use variables and a measure of the number of symptoms of externalizing disorders and two personality measures of deviance proneness, the CPI (California Psychological Inventory Socialization Scale (Gough, 1994)), and MMPI-2 Pd (Minnesota Multiphasic Personality Inventory 2 Psychopathic Deviance Scale (Hathaway, 1989)).

Table 1 Characteristics of Participant Groups

The inclusion criteria for the TxNA group was that they meet lifetime DSM-IV-R (American Psychiatric Association, 2000) criteria for alcohol dependence, that they were currently drinking, and that they have never sought treatment for alcoholism. DSM-IV criteria for alcohol dependence were assessed from an initial phone interview with the subjects. Participants were asked a series of questions taken from the DSM-IV-R criteria for alcohol abuse and dependence. If a subject answered “yes” to three or more of these questions at any time in the same twelve-month period, he/she met criteria for alcohol dependence. Similar questions were asked for all other drugs used more than experimentally to exclude individuals who met criteria for abuse or dependence on other drugs. Inclusion criteria for the C group was a lifetime drinking average of less than 30 alcohol containing drinks per month, and never having exceeded 60 drinks per month (a standard drink was defined as 12oz. beer, 1.5 oz. liquor, or 5 oz. of wine).

All participants were given a computerized psychiatric diagnostic evaluation (Computerized Diagnostic Interview Schedule (Robins, 1998)) and psychological assessments. Separate lifetime use data was gathered for alcohol and all drugs used more than experimentally (using the timeline follow-back methodology of the Lifetime Drinking History Questionnaire (Skinner and Sheu, 1982; Sobell and Sobell, 1992)). Participants also had their medical history reviewed, had a blood draw to test liver function, and completed the Family Drinking History Questionnaire, based on the Family Tree Questionnaire by Mann et al., (Mann et al., 1985). We derived two measures from the Family Drinking History Questionnaire: the number of first degree relatives that were identified by the participant as problem drinkers, and the proportion of first degree relatives that were identified as problem drinkers. Post-alcohol withdrawal hyper-excitability (PAWH) was implemented partway through the study, after which it was administered to all TxNA subjects (n=28). PAWH was measured using a self-report questionnaire where subjects estimated (on a 0 to10 point scale) the frequency and distress caused by physical and psychological symptoms experienced during alcohol withdrawal. For the frequency estimate, a 0 meant never, 1 corresponded to 10 % of the times one ceased drinking, up to a 10 which indicated the symptom was experienced 100% of the time one ceased drinking. For the degree of distress caused by the presence of the symptom, a 0 meant not at all distressing, a score of 5 meant somewhat distressing, and a 10 meant “unbearable.” The symptoms were compiled from the Diagnostic Interview Schedule (DIS) (Robins, 1998), the alcohol dependence scale (Skinner and Allen, 1982), and SSAGA interviews (Bucholz et al., 1994). We computed the average frequency and intensity over eight symptoms that measure PAWH: i) shakes (hands tremble, shake inside); ii) feel tense, nervous or anxious; iii) feel fidgety or restless; iv) have trouble concentrating v) heart pound or beat rapidly; vi) feel hypersensitive to stimuli (e.g. light, sound, touch); vii) have difficulty sleeping; and viii) have memory problems.

Exclusion criteria for both groups were: 1) history or presence of an Axis I diagnosis on the DIS, 2) history of stroke, diabetes, or hypertension that required medical intervention, 3) significant history of head trauma or cranial surgery, 4) clinical or laboratory evidence of active hepatic disease, 5) Wernickes-Korsakoff syndrome, 6) a history of drug dependence other then caffeine or nicotine, or 7) current substance abuse other then alcohol (aside from caffeine and nicotine). As noted above, substance abuse and dependence were determined from the phone interviews where follow-up questions were asked for all drugs (other than caffeine or nicotine) where the subject acknowledged more than experimental use.

Each subject was informed as to the nature of the study and procedures and signed a consent form prior to their participation. Participants were to complete a total of four sessions that included clinical, neuropsychological, electrophysiological and neuroimaging assessments. All participants were to abstain from drinking for 24 hours prior to each lab visit, and a Breathalyzer was administered before each session. No participants in the current study had positive Breathalyzer results (>.000) on any of their study sessions. Other drugs of abuse were not tested for. For the purposes of this study, we are examining only the data during the eyes closed resting portion of the EEG session, which took place on the third visit. All participants who completed a session were paid for the session and any travel expenses. Participants also received a completion bonus if they completed all four sessions of the study.

EEG Recording and Artifact Reduction

As noted above, participants were given a Breathalyzer upon arrival at the EEG lab; a 0.000 Breathalyzer result was required to continue the session. Participants were seated comfortably in a sound attenuated room. The computer screen, used in presenting stimuli for other EEG/ERP experiments, was turned off. The participants were asked to relax with their eyes closed for five minutes. Over the course of the study, two EEG acquisition systems were used, a 40-channel system (n = 87) and a 64-channel (n = 15). Only the midline electrodes, which were common to both systems, were examined for this study. Reference was the right ear for all recordings, and ground was 4 cm above the nasion for 40-channel caps and 8 cm above the nasion for 64-channel caps. EEG data was acquired using the NuAmps (NuAmp, Neuroscan, Inc.) single-ended 40 channel amplifier and Scan 4.2 Acquisition Software (Neurosoft, Inc.) for the 40-channel recordings. The NuAmps amplifier had a fixed range of ±130 μV sampled with a 22 bit A/D converter where the least significant bit was 0.062 μV. For the 64-channel recordings, EEG data was acquired using the SynAmps2 (SynAmps2, Neuroscan, Inc.) amplifier and Scan 4.3 Acquisition Software (Neurosoft, Inc.). The SynAmps2 amplifier had a fixed range of ± 333 μV sampled with a 24 bit A/D converter where the least significant bit was 0.019 μV. Electrode impedances were maintained below 10 kΩ. The sampling rate was 250 samples per second, and activity was recorded for 5 minutes. Data from control subjects whose data was collected using the different amplifier systems (NuAmps, SynAmps2) were examined, and revealed no differences associated with the different acquisition amplifiers. Vertical eye movements were recorded by electrodes above and below the left eye for later reduction of ocular artifact.

Raw data were processed offline using the Edit Program in Scan 4.3 (Neurosoft, Inc.). Data from the first and last minute were discarded and the analysis was performed on the middle three minutes of recordings. Ocular artifacts were removed using the ocular artifact reduction algorithm (ARTCOR) implemented in Scan4.3 (Neuroscan, 2003). Data were then bandpass filtered between 0.5 and 30Hz at 48 dB/Octave. Power spectra was computed using the Scan4.3 AVERAGE procedure which computes a standard power spectrum adapted from the Cooley-Tukey method, on 512 sample epochs (2.044 seconds in duration) using a 10% cosine taper. Average power spectra were then aggregated for six frequency bands: theta (3 to 7.5 Hz), low alpha (7.51 to 10 Hz), high alpha (10.01 to 12 Hz), low beta (12.01 to 16 Hz), mid beta (16.01 to 20 Hz), and high beta (20.01 to 28 Hz). A natural log transformation was applied to the absolute power data to normalize the distributions.

Statistical Analysis

This paper only examines the midline recordings common to all participants (Fz, FCz, Cz, CPz, Pz, Oz). Repeated measures ANOVA was carried out on the log power dependent variables using the General Linear Models procedure in the Statistical Analysis System (SAS Institute, 1990), with age, group and gender as between-subject effects and EEG band and electrode as repeated measures. The association of band power with age and alcohol use variables was analyzed using Spearman correlations. Because alcohol use duration is partially confounded with age (older participants have had a longer life in which to drink), associations of EEG measures with alcohol use duration and with age were examined using partial correlation analysis (i.e., association of EEG measures and age with alcohol use duration partialled out, and association of EEG measures and alcohol use duration with age partialled out).

RESULTS

Group Differences in Demographic and Subject Variables

Table 1 presents the demographic, alcohol use and subject variables for men and women in each group. As noted above, the TxNA group and controls were matched for age and gender with age ranging from 19 to 50 years. The groups were also similar in education. The TxNA group had more first degree relatives who were problem drinkers (F₁,₉₈ = 6.72, p < .02), but this effect was not very large, with group membership accounting for only 6.2% of the variance of the number of first degree relatives who were problem drinkers. As expected, the groups differed on alcohol use measures (group membership accounted for 5.2% of the variation in duration of active drinking, 64.1% of the variance of average lifetime drinking dose, 59.8% of the peak dose variance, and 58.7% of the variance of the drinking dose in the 6 months immediately prior to the study. The TxNA group compared to Controls had a larger number of externalizing symptoms (the sum of Antisocial Personality Disorder and Conduct Disorder symptoms on the DIS (Robins, 1998)), with group membership accounting for 8.5% of the symptom count variance (F₁,₉₈ = 9.96, p<.003). They also showed more evidence of deviance proneness on both the California Psychological Inventory (CPI) socialization scale (Group accounting for 21.7% of the variance (F₁,₉₈ = 27.85, p < .0001) and the MMPI Psychopathic Deviance (PD) scale (Group accounting for 9.5% of the variance (F₁,₉₈ = 10.31, p < .002).

As described above, PAWH was measured using a self-report questionnaire where participants estimated (on a 10 point scale) first, the frequency and then, the distress level of physical and psychological symptoms experienced during alcohol withdrawal. The TxNA’s mean score (± sd) for the frequency of withdrawal symptoms was 2.46 ± 1.6, meaning that, on average they experienced withdrawal symptoms after drinking 24.6% of the time. On the distress level scale (10 point scale), a zero indicated that the withdrawal symptoms bothered the participant “not at all”, a three indicated that the symptoms were “a little bothersome” and five indicated that the symptoms were “somewhat bothersome”. The mean score for distress was 2.91 ± 1.89, indicating that the participants typically found the distress of withdrawal symptoms less then “a little bothersome”. There were no significant associations between EEG power and withdrawal measures.

EEG Power

Analysis of between-group effects (between subject variance)

In the between subjects analysis (power averaged across bands and electrodes), group membership accounted for 4.0 % of the log power variance (F_1,93 = 4.4, p < r =" −.22,">1,93 = 5.1, p <>1,93 = 3.0, p < .09), with men having lower EEG power than women.

Analysis of repeated measures effects

The analysis of repeated measures indicated the well known large differences in power between the EEG bands (accounting for 33.3% of the within-subject across band variance, F_5,465 = 51.43, p < .0001), and across electrode sites (accounting for 6.9% of the within-subject across electrode variance, F_5,465 = 8.34, p < .0001). There were also electrode by group interactions (accounting for 5.6% of the within-subject across electrode variance, F_5,465= 6.75, p < .0005), and band by electrode by group interactions (accounting for 2.5% of the within-subject across bands and electrodes variance, F_25,2325 = 2.63, p < .02); both of these effects indicate that differences in power between the groups varied across bands and electrodes. Figure 1

presents this data. The strongest group differences were observed for low alpha and mid beta, where the TxNA group had higher power at all midline electrode locations except the most frontal (Fz). The TxNA group showed higher power at the central-posterior sites (CPz, Pz, Oz) for high beta. For theta, high alpha, and low beta the TxNA group had higher power at CPz and Oz, with a trend towards higher power at Pz.

Fig. 1 Displays group differences in EEG power for each band at each midline electrode location. For presentation purposes the inverse of log (power) has been used to show the results as power on a natural log scale. *, **, ***: p < .05, p < (more ...)

There were band by age interactions, electrode by age interactions, and group by band by electrode by age interactions (all F_5,465 > 4.88, p < .002) indicating that the correlations with age differ across groups, bands, and electrodes. In order to better understand this data, we computed age correlations for each group at each electrode within each band. Table 2. presents these associations. In the controls, there were only a few age associations. For high alpha, there was a negative association at Fz, as well as trends for negative associations at FCz and Oz. For low beta, a positive association with age was observed at CPz, with a trend at Cz. Similarly, positive associations between age and power were observed at these same electrode locations for mid and high beta.

Table 2 Association of EEG Power with Age and Lifetime Drinking Duration

Within the TxNA group, the age associations were consistently negative, and showed strong patterns across electrodes within specific bands. Strong negative correlations with age were observed at all midline electrode sites for theta, high alpha, and low beta power. For mid-beta power, negative associations were observed only at Oz, and for low alpha and mid beta power only a trend for a negative correlation at Oz was observed.

Since age may potentially be confounded with lifetime drinking duration (older participants may have had a longer time to drink), we examined the association between lifetime drinking duration and power measures in the TxNA group. There were strong negative associations between power and lifetime drinking duration for theta, high alpha, and low beta at all electrode sites, and for low alpha power and mid beta power at Oz, with a trend for high beta at Oz (see Table 2). Within the TxNA group, we next examined the associations between age and power with lifetime drinking duration partialled out. These partial correlations were close to zero (see Table 2). Because negative associations with age were not seen in controls, the simplest explanation for this pattern of results is that these negative associations in the TxNA group of age with EEG power are a consequence of the negative association of abusive drinking with power.

In a search for additional evidence supporting this hypothesis, we examined the association between lifetime drinking dose (drinks/month) and the power measures within the TxNA group. Table 3 presents these associations. There were strong negative associations at all electrode sites of the alcohol dose variables with low beta, mid beta, and high beta, as well as negative associations with low alpha power at Fz, FCz, Cz, and CPz, with a trend for an association at Pz. Negative associations were also evident for high alpha power at the frontal electrodes, with a trend for a negative association for theta power at Pz and Oz. These negative associations between alcohol dose and power measures is consistent with the hypothesis that the negative associations of power measures with age and with lifetime duration of drinking in the TxNA group are a consequence of abusive drinking rather than of age per se.

Table 3 EEG Power Associations with Lifetime Drinking Dose (Average Drinks/Month)

The few associations observed between power and lifetime drinking dose in the controls were more sporadic, weaker, and positive rather then negative. These positive associations and trends were seen for theta power at CPz, low alpha power at Cz, CPz, Pz, and Oz, and mid beta power at Oz. It is of interest that in the controls, the effects of moderate or light drinking may have the opposite effect to that seen in the alcohol dependent sample, with alcohol use actually increasing EEG power.

In the TxNA group, the measures of alcohol use over the six months prior to study were very highly correlated with the average lifetime dose measures (r = 0.93). For this reason, we did not examine the associations of recent alcohol dose with EEG measures since the results would have been entirely redundant with the results for average dose. Finally, we found no associations of the power measures with either of the family history of alcoholism measures (number of first degree relatives with alcohol problems and percent of first degree relatives with alcohol problems) all r’s < |.22|, p > 0.12.

DISCUSSION

Central Findings

The central finding in this study was that TxNA alcoholics evidence higher power than controls across the theta to high beta bands, with the magnitude and anterior-posterior extent of these effects varying across bands. The largest and most widespread effects were for the low alpha and mid beta bands, where the effects were present for all electrodes posterior to Fz. For the other bands, the effects did not extend as anteriorly and were of smaller magnitude. These differences in the effects across bands indicate that the effects are not a simple global increase in EEG power, but rather are specific and different effects for the various bands. Given that we asked all subjects to abstain from alcohol for 24 hours prior to the EEG session, no subjects had positive breathalyzer tests on the day of their EEG study, and that on average our TxNA subjects reported experiencing withdrawal symptoms only about one quarter of the time, we believe it is highly unlikely that their EEG results reflect the effects of post alcohol withdrawal hyperexcitability.

In the introduction, we reviewed the literature showing that there is a nexus of disinhibitory traits, deviance proneness, externalizing symptoms, and a positive family history for alcoholism that often appear together and are strongly associated with alcoholism and other substance abuse. In its more severe manifestations, this nexus is represented in alcoholics with comorbid psychiatric, other substance abuse, and antisocial diagnoses. In addition, a relatively common set of EEG characteristics has been associated with the various aspects of this nexus.

The population studied here is unique with regard to this nexus discussed above. All participants met criteria for alcohol dependence (alcoholism), yet they had at most a minimally greater family history for alcoholism than controls. Individuals with comorbid antisocial personality disorder, conduct disorder, depression, anxiety, or other substance abuse disorders were excluded. The TxNA sample came from a population with a history of early abusive drinking (in the first five years immediately after meeting criteria for heavy alcohol consumption) that was 30–40 % less in average and peak dose than treated samples (Fein and Landmann, in press). They had an increased rate of externalizing symptoms and psychological evidence of deviance proneness compared to controls, although these rates were markedly less than those of treated samples (Fein et al., 2004). Our hypothesis is that the population studied is composed of individuals with less severe manifestations of the nexus described above who have gone on to develop alcohol dependence but no other diagnosable psychiatric or substance abuse disorders. We believe our results show that this select population is characterized by increased EEG power across the theta to high beta bands.

While the sample studied (TxNA) was advantageous in that it is more representative of alcoholics in the general population in that it is an untreated sample free of comorbid disorders, there were limitations inherent in studying this sample. We did not examine severe active alcoholics, and although it is possible that our sample represents severe alcoholics in the relatively early stages of their alcoholism, previous examination of this population suggests that this is in fact a different population from alcoholics typically studied. Furthermore, the sample studied reported experiencing relatively minor withdrawal symptoms. Although it is beneficial to be able to show that it is highly unlikely that our results are associated with alcohol withdrawal, our results are silent on the EEG effects of more severe withdrawal that may be present in samples with greater alcoholism severity.

There are other limitations to the current study. In hindsight, we should have assessed for caffeine or nicotine dependence or recent caffeine or nicotine use to determine the degree to which such use or dependence could have influenced our EEG results. Finally, since our TxNA sample is almost by definition in denial about their alcoholism, it is also highly likely that they would be in denial with regard to alcohol problems in their first degree relatives. Their data regarding the family history of alcoholism assessment is highly suspect and may be a gross underreporting of alcohol problems in their extended families. Therefore, the negative findings regarding the association of the EEG power measures with the family history of alcoholism should be discounted.

Our data support the hypothesis that an effect of long-term alcohol abuse is to negatively impact the substrate underlying EEG power. Negative associations between EEG power and alcohol use variables (both dose and duration), suggests that a reduction in EEG power is a morbid effect of accumulating alcohol abuse. We acknowledge that this hypothesized effect of alcohol abuse is opposite to the increased EEG power effect that we hypothesize is associated with the inherited nexus of disinhibited traits that conveys a vulnerability to alcoholism. The current subjects were studied at the relatively early stages of this process, before these morbid effects of chronic alcohol abuse can overpower the trait-related increased EEG power present in this sample of alcoholics.

With continuing alcohol abuse, we would expect to see the trait of increased EEG power in alcoholics overpowered by the effects of long-term severe alcohol abuse. In other words, in longer term and more severe alcoholics, we hypothesize that we would not see the increased EEG power observed in the current study. In the most severe and longest-term alcoholics, we hypothesize that we would see an actual reduction in EEG power. We have been studying a sample of long-term abstinent treated alcoholics in whom we can test these hypotheses.

Footnotes

This work was supported by Grants AA11311 (GF) and AA13659 (GF), both from the National Institute of Alcoholism and Alcohol Abuse. We also express our appreciation to the NRI recruitment and assessment staff, and to each of our volunteer research participants.

References

• American Psychiatric Association. DSM-IV-R: Diagnostic and Statistical Manual of Mental Disorders. 4. American Psychiatric Publishing, Inc; Washington, DC: 2000.
• Barry RJ, Clarke AR, Johnstone SJ. A review of electrophysiology in attention-deficit/hyperactivity disorder: I. Qualitative and quantitative electroencephalography. Clin Neurophysiol. 2003;114(2):171–83. [PubMed]
• Bauer LO, Hesselbrock VM. EEG, autonomic and subjective correlates of the risk for alcoholism. J Stud Alcohol. 1993;54(5):577–89. [PubMed]
• Begleiter H, Porjesz B. What is inherited in the predisposition toward alcoholism? A proposed model. Alcohol Clin Exp Res. 1999;23(7):1125–35. [PubMed]
• Bucholz KK, Cadoret R, Cloninger CR, et al. A new semi-structured psychiatric interview for use in genetic linkage studies: A report on the reliability of the SSAGA. Journal of Studies on Alcohol. 1994;55(2):149–158. [PubMed]
• Cloninger CR. Neurogenetic adaptive mechanisms in alcoholism. Science. 1987;236:410–416. [PubMed]
• Cohen HL, Porjesz B, Begleiter H. EEG characteristics in males at risk for alcoholism. Alcohol Clin Exp Res. 1991;15(5):858–61. [PubMed]
• Cohen HL, Porjesz B, Begleiter H. Ethanol-induced alterations in electroencephalographic activity in adult males. Neuropsychopharmacology. 1993;8(4):365–70. [PubMed]
• Costa L, Bauer L. Quantitative electroencephalographic differences associated with alcohol, cocaine, heroin and dual-substance dependence. Drug and Alcohol Dependence. 1997;46(1–2):87–93. [PubMed]
• Ehlers CL, Schuckit MA. EEG fast frequency activity in the sons of alcoholics. Biol Psychiatry. 1990;27(6):631–41. [PubMed]
• Ehlers CL, Schuckit MA. Evaluation of EEG alpha activity in sons of alcoholics. Neuropsychopharmacology. 1991;4(3):199–205. [PubMed]
• Ehlers CL, Wall TL, Schuckit MA. EEG spectral characteristics following ethanol administration in young men. Electroencephalography and Clinical Neurophysiology. 1989;73(3):179–87. [PubMed]
• Fein G, Landman B. Treated and treatment-naive alcoholics come from different popultation. Alcohol. in press.
• Fein G, Whitlow B, Finn P. Mismatch Negativity: No difference between controls and abstinent alcoholics. Alcoholism, Clinical and Experimental Research. 2004;28(1):137–142.
• Finn PR, Justus A. Reduced EEG alpha power in the male and female offspring of alcoholics. Alcohol Clin Exp Res. 1999;23(2):256–62. [PubMed]
• Finn PR, Sharkansky EJ, Brandt KM, Turcotte N. The effects of familial risk, personality, and expectancies on alcohol use and abuse. J Abnorm Psychol. 2000;109(1):122–33. [PubMed]
• Foroud T, Bucholz KK, Edenberg HJ, Goate A, Neuman RJ, Porjesz B, Koller DL, Rice J, Reich T, Bierut LJ, Cloninger CR, Nurnberger JI, Jr, Li TK, Conneally PM, Tischfield JA, Crowe R, Hesselbrock V, Schuckit M, Begleiter H. Linkage of an alcoholism-related severity phenotype to chromosome 16. Alcohol Clin Exp Res. 1998;22(9):2035–42. [PubMed]
• Foroud T, Edenberg HJ, Goate A, Rice J, Flury L, Koller DL, Bierut LJ, Conneally PM, Nurnberger JI, Bucholz KK, Li TK, Hesselbrock V, Crowe R, Schuckit M, Porjesz B, Begleiter H, Reich T. Alcoholism susceptibility loci: confirmation studies in a replicate sample and further mapping. Alcohol Clin Exp Res. 2000;24(7):933–45. [PubMed]
• Gorenstein EE, Newman JP. Disinhibitory psychopathology: a new perspective and a model for research. Psychol Rev. 1980;87(3):301–15. [PubMed]
• Gough HG. Theory, development, and interpretation of the CPI socialization scale. Psychol Rep. 1994;75(1 Pt 2):651–700. [PubMed]
• Hathaway JMS. MMPI-2: Minnesota Multiphasic Personality Inventory. The University of Minnesota Press; Minneapolis: 1989.
• Ishikawa S, Raine A, editors. Psychophysiological correlates of antisocial behavior: A central control hypothesis. Kluwer Academic Publishers; Dordrecht Netherlands: 2002.
• Knott V, Mahoney C, Kennedy S, Evans K. EEG power, frequency, asymmetry and coherence in male depression. Psychiatry Res. 2001;106(2):123–40. [PubMed]
• Lukas SE, Mendelson JH, Benedikt RA, Jones B. EEG alpha activity increases during transient episodes of ethanol-induced euphoria. Pharmacol Biochem Behav. 1986;25(4):889–95. [PubMed]
• Lukas SE, Mendelson JH, Woods BT, Mello NK, Teoh SK. Topographic distribution of EEG alpha activity during ethanol-induced intoxication in women. J Stud Alcohol. 1989;50(2):176–85. [PubMed]
• Mann RE, Sobell LC, Sobell MB, Pavan D. Reliability of a family tree questionnaire for assessing family history of alcohol problems. Drug Alcohol Depend. 1985;15(1–2):61–7. [PubMed]
• Mednick S, Volavka J, Gabrielli W, Itil T, Pollock V. EEG as a predictor of antisocial behavior. Criminality. 1981;19:219–229.
• Neuroscan C. Scan 4.3: Edit 4.3. Vol. 2. Compumedics Neuroscan; El Paso, TX: 2003.
• Newton TF, Cook IA, Kalechstein AD, Duran S, Monroy F, Ling W, Leuchter AF. Quantitative EEG abnormalities in recently abstinent methamphetamine dependent individuals. Clin Neurophysiol. 2003;114(3):410–5. [PubMed]
• Petersen KG, Matousek M, Mednick SA, Volavka J, Pollock V. EEG antecedents of thievery. Acta Psychiatr Scand. 1982;65(5):331–8. [PubMed]
• Pollock VE, Earleywine M, Gabrielli WF. Personality and EEG beta in older adults with alcoholic relatives. Alcohol Clin Exp Res. 1995;19(1):37–43. [PubMed]
• Pollock VE, Schneider LS, Zemansky MF, Gleason RP, Pawluczyk S. Topographic quantitative EEG amplitude in recovered alcoholics. Psychiatry Res. 1992;45(1):25–32. [PubMed]
• Propping P. Genetic control of ethanol action on the central nervous system. An EEG study in twins. Hum Genet. 1977;35(3):309–34. [PubMed]
• Raine A, Venables PH, Williams M. Relationships between central and autonomic measures of arousal at age 15 years and criminality at age 24 years. Arch Gen Psychiatry. 1990;47(11):1003–7. [PubMed]
• Rangaswamy M, Porjesz B, Chorlian DB, Choi K, Jones KA, Wang K, Rohrbaugh J, O’Connor S, Kuperman S, Reich T, Begleiter H. Theta power in the EEG of alcoholics. Alcohol Clin Exp Res. 2003;27(4):607–15. [PubMed]
• Rangaswamy M, Porjesz B, Chorlian DB, Wang K, Jones KA, Bauer LO, Rohrbaugh J, O’Connor SJ, Kuperman S, Reich T, Begleiter H. Beta power in the EEG of alcoholics. Biol Psychiatry. 2002;52(8):831–42. [PubMed]
• Rangaswamy M, Porjesz B, Chorlian DB, Wang K, Jones KA, Kuperman S, Rohrbaugh J, O’Connor SJ, Bauer LO, Reich T, Begleiter H. Resting EEG in offspring of male alcoholics: beta frequencies. Int J Psychophysiol. 2004;51(3):239–51. [PubMed]
• Reich T, Edenberg HJ, Goate A, Williams JT, Rice JP, Van Eerdewegh P, Foroud T, Hesselbrock V, Schuckit MA, Bucholz K, Porjesz B, Li TK, Conneally PM, Nurnberger JI, Jr, Tischfield JA, Crowe RR, Cloninger CR, Wu W, Shears S, Carr K, Crose C, Willig C, Begleiter H. Genome-wide search for genes affecting the risk for alcohol dependence. Am J Med Genet. 1998;81(3):207–15. [PubMed]
• Robins LN, Cottler L, Buckholz K, Compton W. The Diagnostic Interview Schedule for DSM-IV. Washington University School of Medicine; 1998.
• Russ MJ, Campbell SS, Kakuma T, Harrison K, Zanine E. EEG theta activity and pain insensitivity in self-injurious borderline patients. Psychiatry Res. 1999;89(3):201–14. [PubMed]
• SAS Institute. SAS procedures guide: version 6. 3. SAS Institute; Cary, NC: 1990.
• Sher KJ, Walitzer KS, Wood PK, Brent EE. Characteristics of children of alcoholics: putative risk factors, substance use and abuse, and psychopathology. J Abnorm Psychol. 1991;100(4):427–48. [PubMed]
• Skinner HA, Allen BA. Alcohol dependence syndrome: Measurement and validation. Journal of Abnormal Psychology. 1982;91:199–209. [PubMed]
• Skinner HA, Sheu WJ. Reliability of alcohol use indices: The lifetime drinking history and the MAST. Journal of Studies on Alcohol. 1982;43(11):1157–1170. [PubMed]
• Sobell LC, Sobell MB. Timeline Follow-back: A technique for assessing self-reported ethanol consumption. In: Allan J, Litten RZ, editors. Measuring Alcohol Consumption: Psychosocial and Biological Methods. Mumana Press; New Jersey: 1992. pp. 41–72.
• Sorbel J, Morzorati S, O’Connor S, Li TK, Christian JC. Alcohol effects on the heritability of EEG spectral power. Alcoholism, Clinical and Experimental Research. 1996;20(9):1523–7.
• Stoltenberg SF, Mudd SA, Blow FC, Hill EM. Evaluating measures of family history of alcoholism: density versus dichotomy. Addiction. 1998;93:1511–20. [PubMed]
• Tartar RE, Alterman AI, Edwards KL. Vulnerability to alcoholism in men: a behavior-genetic perspective. Journal of Studies on Alcohol. 1985;46(4):329–356. [PubMed]
• Van Baal GC, De Geus EJ, Boomsma DI. Genetic architecture of EEG power spectra in early life. Electroencephalogr Clin Neurophysiol. 1996;98(6):502–14. [PubMed]
• Volavka J, Pollock V, Gabrielli WF, Jr, Mednick SA. The EEG in persons at risk for alcoholism. Recent Dev Alcohol. 1985;3:21–36. [PubMed]
• Wall TL, Schuckit MA, Mungas D, Ehlers CL. EEG alpha activity and personality traits. Alcohol. 1990;7(5):461–4. [PubMed]