1 Department of Psychiatry, University of Vienna and 2 SMZ Süd, Vienna, Austria and 3 Department of Psychiatry, University of Würzburg, Germany
* Author to whom correspondence should be addressed at: Department of Psychiatry, University of Vienna, Währinger-Gürtel 1820, A-1090 Vienna, Austria. Tel.: +43 1 40400 3722; Fax: +43 1 40 25 909; E-mail: gerda.saletu-zyhlarz{at}akh-wien.ac.at
(Received 4 November 2003; first review notified 13 November 2003; in revised form 23 December 2003; accepted 23 December 2003)
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ABSTRACT |
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INTRODUCTION |
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Various attempts have been made to predict relapse and identify antecedents, focusing on variables like employment instability, residential or marital status, sex, age, ethnicity and family history (Polich et al., 1981; Rounsaville et al., 1987
; Miller et al., 1992
), previous treatment history (Glenn and Parsons, 1991
), stressful life events and negative mood states (Bromet and Moos, 1977
; Finney et al., 1980
; Marlatt and Gordon, 1985
), depression and other coexistent psychiatric diagnoses (O'Leary et al., 1979
; Rounsaville et al., 1987
; Parsons et al., 1990
; Glenn and Parsons, 1991
), childhood attention deficit disorder symptomatology (Parsons et al., 1990
, Glenn and Parsons, 1991
), psychological trait disturbances and alcohol-related social difficulties (Jin et al., 1998
), neuropsychological performance and psychosocial maladjustment (Glenn and Parsons, 1991
), drug expectancies (Cummings et al., 1980
; Brown, 1985
), self-efficacy (Solomon and Annis, 1990
), social support resources (Finney et al., 1980
), reduced dopamine receptor function (Heinz et al., 1995
), age of onset of alcoholism and cerebrospinal fluid serotonin metabolites (George et al., 1999
) and subjective craving (Roelofs and Dikkenberg, 1987
). Event-related potentials (ERP) were also applied to predict relapse, showing an overall prediction rate of 63%, and of 71% if depressive symptomatology and psychosocial maladjustment were considered (Glenn et al., 1993
). Changes in rapid eye movement (REM) sleep revealed a prediction rate of 7682% (Gillin et al., 1994
). Miller et al. (1996)
identified clients' coping resources (85% hit rate) and disease model beliefs as being most powerful in predicting relapse.
Prior EEG-mapping studies of detoxified alcohol-dependent patients, as compared with normal controls, showed an increase in absolute and relative beta power and a decrease in alpha and delta/theta power (Saletu, 1996, 1997
), which is in agreement with earlier reports of low-voltage fast EEG patterns, as often encountered by visual EEG inspection (Kiloh et al., 1981
; Niedermeyer and Lopes da Silva, 1982
). As slow activities are considered to be inhibitory, alpha activity an expression of normal brain functioning and fast beta activities excitatory, the low-voltage fast desynchronized patterns may be interpreted as hyperarousal of the CNS.
Beta activity has also been used to predict relapse in dependent alcoholics. Bauer (1994, 2001)
found high-frequency beta activity to significantly distinguish relapse-prone patients from abstinence-prone ones. Winterer et al. (1998)
predicted relapse in chronic alcoholics by means of quantitative EEG (Q-EEG). He was able to correctly classify 8385% of the patients, outperforming most earlier attempts at predicting the relapse rate on the basis of clinical evaluations.
The aim of this study, which was conducted within a larger investigation (Wiesbeck et al., 2001), was to compare the EEG maps of drug-free, detoxified alcoholics with those of normal controls, and to describe differences between patients relapsing or abstaining during 6 months of relapse prevention therapy pharmacologically supported by either flupentixol decanoate 10 mg or placebo i.m. every 2 weeks. Furthermore, baseline differences between relapsing and abstaining alcoholics were obtained in order to evaluate the predictive properties of EEG mapping as described by Winterer et al. (1998)
and Bauer (1994
, 2001)
.
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MATERIALS AND METHODS |
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EEG investigations
A 3-min vigilance-controlled EEG (V-EEG) and a 4-min resting EEG (R-EEG) were recorded at baseline and in a subsample (abstaining patients only) after 6 months of treatment by means of a 21-channel Nihon Kohden 4321-G polygraph (time constant: 0.3 s, high frequency response: 35 Hz; frequency range: 0.535 Hz; amplification: approximately 20 000 times; maximal noise level: 2 µV peak to peak) with the subjects lying relaxed with eyes closed in an electrically shielded room. During V-EEG recordings, the technician kept the subjects alert. As soon as drowsiness patterns appeared in the record, the subjects were aroused by auditory stimuli (tapping). The present paper will discuss V-EEG data only. Electrodes were attached to the scalp according to the international 10/20 system. A vertical electro-oculogram (EOG) was recorded from an electrode at mid-forehead to the average of one electrode below the left eye and one electrode below the right eye. A horizontal EOG was recorded from the outer canthi. EEG recordings from 19 leads (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1 and O2 to averaged mastoids) as well as two EOG recordings were digitized on-line by a 12-bit Burr Brown analogue-digital converter within a Hewlett-Packard Vebra system with a sampling frequency of 102.4 Hz. Spectral analysis was performed for 5-s epochs (512 sample points) with a frequency resolution of 0.2 Hz (Saletu et al., 1987; Anderer et al., 1987
; Anderer et al., 1992
), using the fast Fourier transform technique in floating-point arithmetic to maintain precision.
Artefact-free 5-s epochs were selected after minimizing ocular artefacts by means of an automatic artefact identification method as described by Anderer et al. (1987, 1992)
. The mean spectral curves contained data from 1.3 to 35 Hz quantified into 36 EEG variables: total power (TP); absolute and relative power (AP and RP, respectively) in 12 different frequency bands (1.33.57.510.5131620253035; 1.37.51335 Hz); the dominant frequency (DF, in Hz), AP and RP of the DF; further the centre-of-gravity frequencies (centroids, C) and their standard deviations of the combined delta and theta, alpha and beta bands as well as of the total activity (T). Nineteen single values obtained from the 10/20 electrode set were mapped onto a numerical matrix, 64 x 64. Each interpolated value was based on the cubic distance from the values at the four nearest electrodes.
To display the difference in the distribution of the 36 EEG variables significance probability mapping (SPM) was used (Bartels and Subach, 1976; Duffy et al., 1981
; Saletu et al., 1987
).
Statistical analysis
Statistical analysis was based on the concept of descriptive data analysis as proposed by Abt (1988). All inter-group differences were tested descriptively. Normal distribution was tested by means of the KolmogorovSmirnov test. If in no cases the null hypothesis of normal distributions was rejected at alpha = 0.10, a two-sample t-test for inter-group comparison was used, if the assumption of normal distribution was violated, the non-parametric Wilcoxon Test and the MannWhitney U-test were used. To display inter-group differences in the distribution of the 36 EEG variables, significance probability mapping was used (Duffy et al., 1981
; Saletu et al., 1987
; Anderer et al., 1992
).
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RESULTS |
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DISCUSSION |
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However, different views exist concerning the aetiology of these EEG changes in alcoholic patients. On the one hand, aberrations in brain function, especially in frontal regions, might contribute to the development of alcoholism (Gabrielli et al., 1982; Propping, 1983
; Deckel et al., 1995
; Harden, 1995
; Saletu, 1996
, 1997
; Winterer et al., 1998
; Bauer, 2001
). This is supported by various findings: beta activity was found to be related to the interaction of the two pre-morbid factors of childhood conduct disorder and paternal alcoholism (Bauer, 2001
). Reduced absolute and relative alpha power over occipital and frontal regions and increased relative beta power were observed to characterize the EEG of subjects with a positive family history of alcoholism (Finn and Justus, 1999
). A paradoxical frontal cortical EEG response pattern to threat (more alpha suppression but less beta increase) (Finn et al., 2000
) as well as excessive high-frequency beta activity (Bauer and Hesselbrock, 1993
) were found in subjects with a positive family history of alcoholism and aggressive traits. A significantly higher cue-reactivity consisting of higher ERP amplitudes elicited by alcohol-related pictures than by those not related to alcohol was found in heavy social drinkers. This finding is in line with the results obtained in alcohol-dependent patients (Herrmann et al., 2001
). Increased beta power was observed in the relatives of alcoholics (Pollock et al., 1995
). Furthermore, rats drinking large amounts of alcohol showed decreased P300 and N1 ERP amplitudes in the amygdala and greater cortical EEG power and thus differed markedly from those drinking hardly any alcohol (Slawecki et al., 2000
). The results obtained in this group were similar to those in alcohol-preferring rats, which may be related to the effect of neuropeptide Y (Ehlers et al., 1999
). The hypothesis of a heightened central nervous arousal increasing the risk of relapse and reflected by an increase in beta activity was also substantiated by a corresponding elevation in cardiac output (Bauer, 1994
).
On the other hand, the literature also implies that the EEG changes occurring under conditions of abstinence might indicate either a normalization of the EEG as well as of neuropathological, neurophysiological and neuropsychological measures (Bennett et al., 1956; Page and Linden, 1974
; Carlen et al., 1978
; Fleming and Guthrie, 1980
; Muuronen et al., 1989
) or withdrawal (Coger et al., 1978
). In our study, after 6 months of treatment abstaining patients showed an increase in slow activity, a decrease in fast alpha and slow beta, an acceleration of the delta/theta centroid and a deceleration of the alpha centroid as compared with baseline. These changes are opposite to the differences between patients and normals and represent a normalization of brain function.
Also, other mechanisms of an altered EEG in alcoholic patients have been described. Persistent changes might reflect chronic intoxication. Pollock et al. (1992) found an increased uniformity of theta amplitudes in bilateral anterior and posterior brain regions. Neuroradiological and neuropsychological findings support the hypothesis of a higher susceptibility of the anterior brain to alcohol-related brain damage (Ratti et al., 1999
). Also the effects of ALDH2 genetic variations were observed and subjects expressing the ALDH2*2 allele showed less slow alpha response and periods of decreased slow alpha activity after the ingestion of alcohol, which was explained directly by the higher blood acetaldehyde levels and indirectly by changes of peripheral parameters as heart rate and facial skin temperature (Wall et al., 1993
; Nishimura et al., 2001
).
The normalization of brain function was more pronounced in patients on flupentixol decanoate than on placebo, which reflects the typical neuroleptic effect. However, flupentixol decanoate was shown to have adverse effects on both relapse rates and the cumulative abstinence duration, contradicting the original hypothesis of the larger study and leading to the question whether flupentixol might even be able to induce craving for alcohol. It has been suggested that different types of alcohol-dependent patients might show different degrees of susceptibility to the adverse effects of flupentixol (Lesch and Walter, 1996; Wiesbeck et al., 2001
; Walter et al., 2001
).
Nevertheless, investigations of the predictive properties of the quantitative EEG regarding therapeutic outcome revealed that the baseline EEG of relapsing patients differed highly significantly from that of normal controls, reflecting CNS hyperarousal, whereas in abstaining patients the baseline differences to normal controls were minimal, suggesting only minor hyperarousal. Furthermore, the differences between abstaining and relapsing patients reached the level of significance regardless of the treatment (with the number of patients receiving placebo or flupentixol not being significantly different). These findings confirm the investigations by Bauer (1994, 2001)
and Winterer et al. (1998)
and show a worse prognosis for the patient group with a more pronounced frontal CNS hyperarousal. It may be hypothesized that these hyperaroused relapsing patients require more CNS sedation than abstaining ones, which indeed was seen in the group treated with flupentixol decanoate. However, as our post-drug EEG sample was very small and comprised only abstainers, as the results of the large clinical study (Wiesbeck et al., 2001
) showed opposite findings (more relapsers in the group treated with flupentixol decanoate than with placebo), and last but not least as different subtypes of alcoholics have to be taken into account, further neurophysiological studies in larger subgroups seem to be necessary.
Our findings imply that future investigations of EEG measures in alcoholics concerning aetiopathological explanations as well as therapeutic strategies will have to adopt a more differentiated view of alcohol dependence. EEG maps of alcohol-dependent patients differ significantly from those of normal controls and patients suffering from other mental disorders and thus EEG mapping may be used for diagnostic purposes (Saletu et al., 2002b). Thus, EEG mapping may also be utilized as an objective measure for predicting relapse in chronic alcoholism, and for choosing the optimal drug for a certain patient, as according to a keylock principle the drug of choice should induce changes opposite to those produced by the disease, thereby normalizing brain function (Saletu et al., 2002a
,b
).
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ACKNOWLEDGEMENTS |
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