T. M. Sokhadze – email: email@example.com
Department of Psychiatry and Behavioral Sciences, University of Louisville School of Medicine, Louisville, KY, USA
R. L. Cannon – email: firstname.lastname@example.org
Department of Psychology, The University of Tennessee, Knoxville, TN 37996, USA
D. L. Trudeau – email: email@example.com
Department of Family and Community Health, School of Health Sciences, University of Minnesota, Minneapolis, MN, USA
Electroencephalographic (EEG) biofeedback has been employed in substance use disorder (SUD) over the last three decades. The SUD is a complex series of disorders with frequent comorbidities and EEG abnormalities of several types. EEG biofeedback has been employed in conjunction with other therapies and may be useful in enhancing certain outcomes of therapy. Based on published clinical studies and employing efficacy criteria adapted by the Association for Applied Psychophysiology and Biofeedback and the International Society for Neurofeedback and Research, alpha theta training—either alone for alcoholism or in combination with beta training for stimulant and mixed substance abuse and combined with residential treatment programs, is probably efficacious. Considerations of further research design taking these factors into account are discussed and descriptions of contemporary research are given.
Substance use disorders (SUD) include disorders related to the taking of a drug of abuse (including alcohol), and represent the most common psychiatric conditions (APA 2000) resulting in serious impairments in cognition and behavior. Acute and chronic drug abuse results in significant alteration of the brain activity detectable with quantitative electroencephalography (qEEG) methods. The treatment of addictive disorders by electroencephalographic (EEG) biofeedback (or neurofeedback, as it is often called) was first popularized by the work of Eugene Peniston (Peniston and Kulkosky 1989, 1990, 1991) and became popularly known as the Peniston Protocol. This approach employed independent auditory feedback of two slow brain wave frequencies, alpha (8–13 Hz) and theta (4–8 Hz) in an eyes closed condition to produce a hypnagogic state. The patient was taught prior to neurofeedback to use what amounts to success imagery (beingsober, refusing offers of alcohol, living confidently, and happy) as they drifted down into an alpha-theta state. Repeated sessions reportedly resulted in long-term abstinence and changes in personality testing. Because the method seemed to work well for alcoholics, it has been tried in subjects with cannabis dependence and stimulant dependence—but with limited success until the work of Scott and Kaiser (Scott and Kaiser 1998; Scott et al. 2002, 2005). They described treating stimulant abusing subjects with attention-deficit type EEG biofeedback protocols, followed by the Peniston Protocol, with substantial improvement in program retention and long-term abstinence rates. This approach has become known widely as the Scott–Kaiser modification (of the Peniston Protocol).
This ‘‘white paper’’ on EEG biofeedback for SUD will offer an assessment of efficacy according to the guidelines jointly established by the Association for Applied Psychophysiology and Biofeedback (AAPB) and the International Society for Neurofeedback and Research (ISNR). Assessing the efficacy of neurofeedback for SUD involves several considerations. The first of these involves difficulties assessing the efficacy of any treatment method for SUD. Outcome benchmarks (i.e., total abstinence, improved function and quality of life) and time points of outcome (i.e., one year, two years post treatment) are not clearly established.
Outcome assessment for treatment of SUD in itself is a complex topic well beyond the scope of this article. Because different drugs of abuse are associated with different patterns of EEG abnormality, as will be discussed in detail in this article, it is difficult to assign broad-brush EEG biofeedback solutions to SUD as a whole. Any statements of efficacy will need to describe specific EEG biofeedback protocols for specific substances of abuse. Furthermore substance abuse is often mixed substance type and comorbid conditions are common and vary from subject to subject, as will also be borne out in this article. As of yet there are no gold standard medication or other treatments for the various types of SUD and efficacy of any SUD treatment method likely falls into the ‘‘possibly effective’’ to ‘‘probably effective’’ range according to the efficacy guidelines jointly established by the AAPB and ISNR. Finally, all of the studies of EEG biofeedback in SUD to date employ EEG biofeedback as an add on to cognitive behavioral or twelve step treatment regimes, so any statements of efficacy would have to acknowledge that EEG biofeedback is not a stand alone treatment for SUD.
This article is divided into several sections. In the first section after ‘‘Introduction,’’ we review SUD prevalence and describe qEEG changes typical for the most widespread drugs of abuse (alcohol, marijuana, heroin, cocaine, and methamphetamine). The second section describes treatment studies employing EEG biofeedback in SUD. Studies that have used the Peniston Protocol are described first, along with critical commentaries of these studies. In the second part of this section, a description of the Scott– Kaiser modification is given, along with some discussion of a rationale for why this approach may be more successful with stimulant abusers. This section also describes some current research. The third section assesses efficacy of the Peniston Protocol and the Scott–Kaiser modification. The fourth section takes a look at the clinical implications of comorbidities in neurobiofeedback treatment of alcohol and drug abuse. The fifth section discusses the clinical implications of standard cognitive-behavioral therapies in SUD treatment and reviews the rationale for the application of qEEG-guided neurofeedback intervention in SUD in conjunction with these therapies. The final section summarizes findings in qEEG and neurofeedback in SUD and additionally proposes further directions for clinical research in this area.
This article represents an update of earlier reviews (Trudeau 2000, 2005a, b) of EEG biofeedback for addictive disorders extended with a review on qEEG in SUD. This review is presented as one of a series of papers in both The Journal of Neurotherapy and The Journal of Applied Psychophysiology & Biofeedback describing and reviewing biofeedback applications for adult populations. No attempt will be made to review the fields of qEEG and neurobiofeedback generally (see current reviews by Hammond 2006; Kaiser 2006), or the field of addictive disorders generally, although some references will be made to specifics the authors feel are pertinent to a discussion of emerging concepts of qEEG as a sensitive tool for the brain function assessment in SUD, and EEG biofeedback as a treatment approach for SUD.
SUD Prevalence and qEEG Changes
Drug addiction can be described as a mental disorder with idiosyncratic behavioral, cognitive, and psychosocial features. The SUD commonly referred to as ‘‘drug addiction’’ is characterized by physiological dependence accompanied by the withdrawal syndrome on discontinuance of the drug use, psychological dependence with craving, the pathological motivational state that leads to the active drug-seeking behavior, and tolerance, expressed in the escalation of the dose needed to achieve a desired euphoric state. Drug addiction is a chronic, relapsing mental disease that results from the prolonged effects of drugs on the brain (Dackis and O’Brain 2001; Volkow et al. 2003, 2004). Drug addiction can take control of the brain and behavior by activating and reinforcing behavioral patterns that are excessively directed to compulsive drug use (Di Chiara 1999; Gerdeman et al. 2003).
From the 11 classes of substances listed in the DSM-IV we will discuss in our review only alcohol, cannabis (marijuana), heroin, and such psychostimulants as cocaine and methamphetamine. Addiction leads to behavioral, cognitive, and social adverse outcomes that incur substantial costs to society. In 2002, it was estimated from the Substance Abuse and Mental Health Service Administration (SAMHSA 2004) that 22 million Americans have a substance abuse or dependence disorder, and 2 million of them were current cocaine users (Vocci and Ling 2005). In 2005, there were 2.4 million persons who were current cocaine users, which is more than in 2004 (SAMHSA 2006). The number of current crack users increased from 467,000 in 2004 to 682,000 in 2005. According to the 2004 revised National Survey on Drug Use and Health, nearly 12 million Americans have tried methamphetamine, and 583,000 of them are chronic methamphetamine users (SAMHSA 2004). In 2005, an estimated 22.2 million persons aged 12 or older were classified with substance dependence or abuse in the past year (9.1% of the population aged 12 or older). Of these, 3.3 million were classified with dependence on or abuse of both alcohol and illicit drugs, 3.6 million were dependent on or abused illicit drugs but not alcohol, and 15.4 million were dependent on or abused alcohol but not illicit drugs. There were 18.7 million persons classified with dependence on or abuse of alcohol in 2005 (7.7%). The specific illicit drugs that had the highest levels of past year dependence or abuse in 2005 were marijuana, followed by cocaine and pain relievers. Of the 6.8 million persons aged 12 or older classified with dependence on or abuse of illicit drugs, 4.1 million were dependent on or abused marijuana in 2005. This number represents 1.7% of the total population aged 12 or older and 59.9% of all those classified with illicit drug dependence or abuse. Marijuana was the most commonly used illicit drug (14.6 million past month users). In 2005, it was used by 74.2% of current illicit drug users. Among current illicit drug users, 54.5% used only marijuana, 19.6% used marijuana and another illicit drug, and the remaining 25.8% used only an illicit drug other than marijuana in the past month (SAMHSA 2006).
Fatal poisoning, which include overdoses (ODs) on illicit drugs, alcohol, and medications, is the leading cause of injury death for individuals age 35–44 and the third leading cause of injury death overall, trailing motor vehicle accidents and firearm-related deaths (CDC 2004). Heroin-related ODs have increased at an alarming rate in portions of the US and other countries (Darke and Hall 2003; Landen et al. 2003), and OD has surpassed HIV infection as the primary cause of death for heroin users. Not surprisingly, heroin is frequently associated with opioid-related ODs, both as a single drug and in combination with other substances (CDC 2004).
Many patients seeking treatment for addiction have multiple drug dependencies and psychiatric comorbidities (Volkow and Li 2005). Information from epidemiological surveys indicates that drug addiction is a common phenomenon and is associated with significant effects on both morbidity and mortality. Large individual and societal costs of drug abuse make research and treatment of drug addiction imperative (French et al. 2000; Mark et al. 2001). Recently through intensive clinical neurophysiological research and biological psychiatric studies many specific components of cognitive, emotional, and behavioral deficits typical for SUD have been identified and investigated. However, the practical values of these cognitive neuroscience and applied psychophysiology-based treatment (e.g., neurofeedback) findings depend on a further integration of these methodological approaches.
qEEG in Substance Use Disorders
EEG in Alcoholism
EEG alterations have been described extensively in alcoholic patients (Porjesz and Begleiter 1998), but any attempt at drawing a common picture from qEEG data is difficult due to significant methodological differences, such as different definitions of frequency bands, different filtering methodology, number of channels, reference choice, etc. However, most reports of alcoholic patients agree in describing alterations mainly within the beta (Bauer 1997, 2001a; Costa and Bauer 1997; Rangaswamy et al. 2002, 2004) and/or alpha bands (Finn and Justus 1999).
The qEEG and LORETA 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 et al. 2002), which is in agreement with earlier reports of low-voltage fast EEG patterns, as often encountered by visual EEG inspection (Niedermeyer and Lopes da Silva 1982). As slow activities are considered to be inhibitory, alpha activity may be viewed as an expression of normal brain functioning and fast beta activities as excitatory, the low-voltage fast desynchronized patterns may be interpreted as hyperarousal of the central nervous system (CNS) (Saletu-Zyhlarz et al. 2004). The investigations by Bauer (2001a) and Winterer et al. (1998) showed 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.
The EEG maps of alcohol-dependent patients differ significantly from those of normal controls and patients suffering from other mental disorders and might be useful for diagnostic purposes (Pollock et al. 1992; Saletu et al. 2002; Saletu-Zyhlarz et al. 2004). Decreased power in slow bands in alcoholic patients may be an indicator of brain atrophy and chronic brain damage, while an increase in the beta band may be related to various factors such as medication use, family history of alcoholism, and/or hallucinations, suggesting a state of cortical hyperexcitability (Coutin-Churchman et al. 2006).
Abnormalities in resting EEG are often associated with a predisposition to development of alcoholism. Subjects with a family history of alcoholism were found to have reduced relative and absolute alpha power in occipital and frontal regions and increased relative beta in both regions compared with subjects with a negative family history of alcoholism. These results suggest that resting EEG alpha abnormalities are associated with risk for alcoholism, although their etiological significance is unclear (Finn and Justus 1999).
Alcohol-dependent individuals have different synchronization of brain activity than light drinkers as reflected by differences in resting EEG coherence (Kaplan et al. 1985, 1988; Michael et al. 1993; Winterer et al. 2003a) and power (e.g., Bauer 2001a b; Enoch et al. 2002; Rangaswamy et al. 2002; Saletu-Zyhlarz et al. 2004). Most differences in EEG coherence and power are found in the alpha and beta bands. Non-alcoholdependent relatives of alcohol-dependent individuals also have EEG differences in alpha and beta coherence (Michael et al. 1993) and power (Bauer and Hesselbrock 2002; Finn and Justus 1999; Rangaswamy et al. 2002, 2004) as compared to subjects without alcohol-dependent relatives. This indicates that differences in functional brain activity as measured with qEEG in alcohol-dependent patients not only relate to the impact of long-term alcohol intake, but possibly also to genetic factors related to alcohol dependence.
Both alcohol dependence (Schuckit and Smith 1996) and EEG patterns (Van Beijsterveldt and Van Baal 2002) are highly heritable. In addition, some genes coding for GABA receptors in the brain, which mediate the effects of alcohol, are related to certain EEG patterns (Porjesz et al. 2005; Winterer et al. 2003b). Moreover, some GABA-receptor genes that are related to EEG patterns are also associated with the risk to develop alcohol dependence. These associations again suggest that genetic factors play a major role in the EEG differences associated with alcohol dependence.
The EEG coherence analysis is a technique that investigates the pairwise correlations of power spectra obtained from different electrodes. It measures the functional interaction between cortical areas in different frequency bands. A high level of coherence between two EEG signals indicates a co-activation of neuronal populations and provides information on functional coupling between these areas (Franken et al. 2004). De Bruin et al. (2004, 2006) investigated the pure effects of alcohol intake on synchronization of brain activity, while minimizing the confounding influence of genetic factors related to alcohol dependence. They showed that heavily drinking students with a negative family history had stronger EEG synchronization at theta and gamma frequencies than lightly drinking students with a negative family history. This study suggests that, in students, heavy alcohol intake has an impact on functional brain activity, even in the absence of genetic factors related to alcohol dependence.
The findings of studies on the effects of alcohol dependence on EEG coherence can be summarized as follows: Kaplan et al. (1985) reported lower frontal alpha and slow-beta coherence in alcohol-dependent males and females. Michael et al. (1993) found higher central alpha and slow-beta coherence, but lower parietal alpha and slow-beta coherence in males with alcohol dependence. Winterer et al. (2003a, b) described higher left-temporal alpha and slow-beta coherence and higher slow-beta coherence at right-temporal and frontal electrode pairs in alcohol-dependent males and females. De Bruin et al (2006) showed that moderate-to-heavy alcohol consumption is associated with differences in synchronization of brain activity during rest and mental rehearsal. Heavy drinkers displayed a loss of hemispheric asymmetry of EEG synchronization in the alpha and slow-beta band. Moderately and heavily drinking males additionally showed lower fast-beta band synchronization.
Therefore, qEEG alterations have been described extensively in alcoholics. Most EEG reports in alcoholic patients agree in describing alterations mainly within the beta and alpha bands. Patients with a more pronounced frontal hyperarousal have worse prognosis. Decreased power in slow bands in alcoholic patients may be an indicator of chronic brain damage, while increase in beta band may be related to various factors suggesting cortical hyperexcitability. Abnormalities in resting EEG are highly heritable traits and are often associated with a predisposition to alcoholism development. The studies on the effects of alcohol dependence on EEG coherence can be summarized as lower frontal alpha and slow-beta coherence in alcohol-dependent patients with some topographical coherence abnormality differences between alcohol-dependent males and females.
EEG in Marijuana Abuse
Several lines of evidence suggest that cannabis (marijuana, tetrahydrocannabinol—THC) may alter functionality of the prefrontal cortex and thereby elicit impairments across several domains of complex cognitive function (Egerton et al. 2006). Several studies in both humans and animals have shown that cannabinoid exposure results in alterations in prefrontal cortical activity (Block et al. 2002; O’Leary et al. 2002; Whitlow et al. 2002), providing evidence that cannabinoid administration may affect the functionality of this brain area. Despite the fact that a number of transient physiological, perceptual and cognitive effects are known to accompany acute chronic marijuana (THC) exposure in humans, persistent qEEG effects in humans resulting from continuing exposure to this drug have been difficult to demonstrate (Wert and Raulin 1986). In early reviews of EEG and ERP studies of acute and chronic THC exposure in humans (Struve et al. 1989, 1994), it was reported that significant associations between chronic exposure and clinically abnormal EEG patterns had not been demonstrated and that attempts to use visual EEG analyses to detect transient acute THC exposure induced EEG alterations failed to demonstrate consistent THC–EEG effects across studies.
Quantitative methods of analyzing EEG spectra from single posterior scalp derivations began to be applied to studies of acute THC exposure. These early studies reported that acute THC exposure produced transient increases in either posterior alpha power, decreases in mean alpha frequency or increases in alpha synchrony (Fink et al. 1976; Struve et al. 1989; Tassinari et al. 1976; Volavka et al. 1971, 1973). These studies found that THC produced a transient dose-dependent rapid onset: (1) increase in relative power (amount, abundance) of alpha; (2) decrease in alpha frequency; and (3) decrease in relative power of beta as measured from posterior scalp electrodes.
Later studies of Struve et al. (1998, 1999, 2003) demonstrated and replicated a significant association between chronic marijuana use and topographic qEEG patterns of persistent ‘‘alpha hyperfrontality’’ (i.e., elevations of alpha absolute power, relative power, and interhemispheric coherence over frontal cortex) as well as reductions of alpha mean frequency. These findings from chronic users are consistent with both non-topographic (Hockman et al. 1971; Tassinari et al. 1976; Volavka et al. 1973) and topographic (Lukas et al. 1995; Struve et al. 1994) transient EEG effects of acute THC administration. Therefore, chronic daily THC use was found to be associated with distinct topographic qEEG features. Compared with nonusers, THC users had significant elevations of absolute and relative power, and interhemispheric coherence of alpha activity over the bilateral frontal cortex (referred to as ‘‘alpha hyperfrontality’’). A second finding was that the voltage (not relative power or coherence) of all non-alpha frequency bands was significantly elevated in THC users, although the voltage increase was generalized and not frontally dominant. A third finding involved a widespread decrease in the relative power of delta and beta activity for cannabis users, particularly over the frontal cortical regions. A fourth finding was that interhemispheric coherence of theta and possibly delta activity was also significantly elevated over frontal cortex for marijuana users. Because most studies included daily THC users and non-users drawn from an inpatient psychiatric population, the effects of psychiatric diagnoses or medication were not controlled.
Thus, qEEG studies on acute THC exposure reported a transient dose-dependent increase in relative power of alpha, decrease in alpha frequency, and decrease in relative power of beta at posterior EEG recording sites. Chronic marijuana abuse is known to result in a number of physiological, perceptual and cognitive effects, but persistent qEEG effects from continuing exposure to THC have been difficult to demonstrate. However, recent studies of Struve and his colleagues have demonstrated a significant association between chronic marijuana use and topographic qEEG patterns of persistent elevations of alpha absolute power, relative power, and interhemispheric coherence over frontal cortex, as well as reductions of alpha mean frequency. Another important qEEG finding was the elevated voltage of all non-alpha bands in THC users. A third qEEG finding involved a widespread decrease in the relative power of delta and beta activity over the frontal cortical regions in marijuana users.
EEG in Heroin Addiction
Only a few studies have investigated qEEG changes in heroin addicts. Qualitative changes were observed in more than 70% of heroin addicts in the early abstinence (acute withdrawal) period, and these included low-voltage background activity with diminution of alpha rhythm, an increase in beta activity, and a large amount of low-amplitude delta and theta waves in central regions (Olivennes et al. 1983; Polunina and Davydov 2004). Franken et al. (2004) found that abstinent heroin-dependent subjects have an enhanced fast beta power compared with healthy controls, and this finding is concordant with other EEG studies on alcohol and cocaine abusing subjects (Costa and Bauer 1997; Herning et al. 1994b; Rangaswamy et al. 2004; Roemer et al. 1995). Spectral power and event-related potentials (ERP) in heroin addicts strongly relate to abstinence length (Shufman et al. 1996, Bauer 2001a; Polunina and Davydov 2004). Most studies showed considerable or even complete normalization of EEG spectral power or magnitude of ERP components in heroin ex-addicts who maintained abstinence for at least 3 months (Bauer 2001b, 2002; Costa and Bauer 1997; Papageorgiou et al. 2001; Polunina and Davidov 2004; Shufman et al. 1996).
Some quantitative changes were also reported in methadone-maintenance heroin addicts (Gritz et al. 1975), current heroin addicts, and subjects in heroin abstinence less than 80 days (Shufman et al. 1996). Gritz et al. (1975) demonstrated a significant slowing of occipital alpha rhythm peak frequency in 10 methadone-maintained patients and the same trend in 10 abstinent heroin-addicted subjects. In one study (Polunina and Davydov 2004), slowing of slow alpha (8–10 Hz) mean frequency was significantly related to the amount of heroin taken by these patients daily before withdrawal. The prolongation of ERP component latencies in heroin addicts was also reported (Papageorgiou et al. 2001), and these delays significantly correlated with years of heroin use, rather than with abstinence length in the study of Bauer (1997). Polunina and Davydov (2004) demonstrated frequency shifts in the fast alpha range at the frontal and central recording sites and a slowing of slow alpha mean frequency at the central, temporal, and occipital sites of recording in heroin abusers who used heroin for at least 18 months.
In general, pronounced desynchronization is characteristic for acute heroin withdrawal, but as it was mentioned above, several studies (Bauer 2001a, 2002; Costa and Bauer 1997; Papageorgiou et al. 2001; Polunina and Davydov 2004; Shufman et al. 1996) showed that spectral power of EEG tends to normalize almost completely after several weeks of abstinence. The most consistent changes in EEG of heroin addicts were reported in alpha and beta frequencies, and included a deficit in alpha activity and an excess of fast beta activity in early heroin abstinence. The latter abnormality appears to reverse considerably when heroin intake is stopped for several months, and therefore it may be viewed as an acute withdrawal effect. The dynamics and characteristics of spectral power changes within the early opiate withdrawal suggest the participation of catecholamine imbalances, especially noradrenaline and perhaps to a lesser degree dopamine, which are widely recognized as a main cause of opiate physical dependency symptoms (Devoto et al. 2002; Maldonado 1997). Acute opiate administration has been shown to increase, while abstinence from chronic opiate use has been shown to decrease extracellular dopamine (DA) in the nucleus accumbens. In contrast, extracellular DA in the prefrontal cortex is not modified by acute opiate use, but is markedly increased during morphine and heroin abstinence syndrome (Devoto et al. 2002). Relationships between theta and beta frequencies shifts and neurotransmitter imbalances characteristic for heroin withdrawal remain unclear.
Withdrawal state in heroin addicts is known to elicit a strong craving for drug, anxiety, nervousness, deficits in inhibitory control, dysphoric motivational state, and intrusive thoughts related to drugs (Franken 2003; Franken et al. 1999, 2004; Stormark et al. 2000). Research on functional connectivity in drug withdrawal states is restricted to a few studies on coherence of the EEG signal in abstinent heroin users (Franken et al. 2004; Fingelkurts et al. (2006a), active heroin abusers (Fingelkurts et al. 2006b), and in abstinent polysubstance abusers (Roemer et al. 1995). In a study on 22 opioid-dependent patients under acute opioid influence, Fingelkurts et al. (2006b) showed that longitudinal opioid exposure impairs cortical local and remote functional connectivity, and found that local connectivity increased, whereas the remote one decreased. These findings were interpreted as specific signs of independent processing in the cortex of chronic heroin addicts. It has been suggested that such independent processes may constitute the candidate mechanism for a well-documented pattern of impairment in addicts that expresses the lack of integration of different cognitive functions for effective problem solving and helps to explain the observed deficits in abstract concept formation, behavioral control, and problems in the regulation of affect and behavior.
Specifically, Fingelkurts et al. (2006b) found that the number and strength of remote functional connections among different cortical areas estimated by the index of EEG synchrony was significantly higher in patients in acute heroin withdrawal than in healthy controls for most categories of functional connections. Although this result was observed in the alpha as well as in the beta frequency bands, it was most prominent for the beta range. In the same patient sub-sample under acute opioid influence the authors (Fingelkurts et al. 2006a) observed the opposite: a significant decrease in the number and strength of remote functional connections, when compared with healthy controls. Thus, the increase of remote synchronicity among cortical areas during the short-term withdrawal period may indicate the selective attentional focus on cues and memories related to drugs while ignoring neutral cues (Franken et al. 2000; Sokhadze et al. 2007). Generally this can explain a narrowing of the behavioral repertoire and compulsive drug seeking in abstinent addicted subjects (Vanderschuren and Everitt 2004). Therefore, the elevated synchrony within the beta frequency band in these studies (Fingelkurts et al. 2006a, b) may reflect a state of CNS activation toward reward-seeking behavior, with this being a prerequisite of relapse among opiate drug dependent patients (Bauer 2001a).
qEEG changes in heroin addicts in the acute withdrawal period have been described as low-voltage background activity with a diminution of alpha rhythm, an increase in beta activity, and a large amount of low-amplitude delta and theta waves in central regions. In general, pronounced desynchronization is characteristic for acute heroin withdrawal, but the spectral power of EEG tends to normalize almost completely after several weeks of abstinence. The most consistent changes in EEG of heroin addicts were reported in the alpha and beta frequencies, and included a deficit in alpha activity and an excess of fast beta activity in early heroin abstinence. The excess of beta appears to reverse considerably when heroin intake is stopped for several months, and therefore it may be viewed as an acute withdrawal effect. Recent studies found that the number and strength of remote functional connections among different cortical areas estimated by the index of EEG synchrony for the beta range was significantly higher in patients in acute heroin withdrawal than in healthy controls for most categories of functional connections.
EEG in Cocaine Addiction
Qualitative and quantitative EEG measures are highly sensitive to the acute and chronic effects of neurointoxication produced by such psychostimulants as cocaine, as well as effects from withdrawal and long-term abstinence from cocaine use (Ehlers et al. 1989). However, some EEG characteristics observed in cocaine addicts are considered to be due to the toxic effects of this drug on the brain, whereas some EEG characteristics in cocaine addicts may also indicate a predisposition toward the development of SUD (Porjesz et al. 2005).
Hans Berger (1937, cited by Gloor 1969; Herning et al. 1985) was the first to study the effects of cocaine on human EEG, reporting an increase in activity in the beta bandwidth. This was replicated in subsequent studies with a larger number of subjects (Alper 1999; Alper et al. 1990, 1998; Costa and Bauer 1997; Herning et al. 1985; Noldy et al. 1994; Prichep et al. 1996, 1999, 2002; Roemer et al. 1995). Beside beta effects, studies have reported an increase in delta activity (Herning et al. 1985) and frontal alpha activity (Herning et al. 1994b), while others have reported an increase in alpha wave EEG associated with bursts of cocaine-induced euphoria (Lukas 1991). More recently, researchers have begun analyzing qEEG profiles of cocaine-dependent patients using the spectral power of each primary bandwidth over the different topographic cortical areas. Excess alpha activity (Alper et al. 1990; Herning et al. 1994b; Lukas 1991; Prichep et al. 1996) and decreased delta activity (Alper et al. 1990; Noldy et al. 1994; Prichep et al. 1996; Roemer et al. 1995) have been reported, while others have reported increased beta power (Herning et al. 1985, 1994b; Noldy et al. 1994) in cocaine-dependent patients, recorded in eyes closed, resting conditions. The qEEG abnormalities, primarily found in anterior cortical regions, were shown to correlate with the amount of prior cocaine use (Herning et al. 1996a; Prichep et al. 1996; Roemer et al. 1995; Venneman et al. 2006). The qEEG has been used more often to characterize the effects of withdrawal in cocaine-dependent patients. Several studies reported that during protracted abstinence from cocaine qEEG effects are featured by long-lasting increases in alpha and beta bands together with reduced activity in delta and theta bands (Alper et al. 1990; Prichep et al. 1996; Roemer et al. 1995).
Recently Reid et al. (2006) investigated qEEG profiles in cocaine-dependent patients in response to an acute, single-blind, self-administered dose of smoked cocaine base (50 mg) versus placebo. Cocaine produced a rapid increase in absolute theta, alpha, and beta power over the prefrontal cortex, lasting up to 25 min after administration of the drug. The increase in theta power was correlated with a positive subjective drug effect (‘‘high’’), and the increase in alpha power was correlated with nervousness. Cocaine also produced a similar increase in delta coherence over the prefrontal cortex, which was correlated with nervousness. Placebo resulted only in a slight increase in alpha power over the prefrontal cortex. These data demonstrate the involvement of the prefrontal cortex in the qEEG response to acute cocaine, and indicate that slow wave qEEG, delta and theta activity are involved in the processes related to experiencing rewarding properties of cocaine.
Prichep et al. (1999, 2002) extended the idea of relating baseline EEG activity to outcome in cocaine-dependent patients in treatment programs. Subjects with cocaine dependence have persistent changes in brain function assessed with qEEG methods, present when evaluated at baseline, 5–14 days after last reported crack cocaine use, and persistent at one and six month follow-up evaluations (Alper 1999; Alper et al. 1990, 1998; Prichep et al. 1996, 2002; Venneman et al. 2006). Several recent studies employing qEEG techniques have already demonstrated an association between the amount of beta activity in the spontaneous EEG and relapse in cocaine abuse (Bauer 1997, 2001a). A decrease in the delta and theta bands of the EEG can be regarded as a specific sign of brain dysfunction.
However, this sign, as well as other qEEG abnormal patterns, can be found in many different psychiatric disorders and none of them can be considered as pathognomonic of any specific mental or neurological disorder. EEG coherence in cocaine addiction was investigated in only one study (Roemer et al. 1995). The authors reported globally reduced interhemispheric coherence in the delta and theta bands, and frontally in the beta band. It should be noted that subjects in this study were cocaine-preferring polysubstance abusers during abstinence and these results can hardly be generalized to crack cocaine-only users or other categories of cocaine-dependent subjects not enrolled in any treatment.
Therefore, acute effects of smoked crack cocaine have been shown to produce a rapid increase in absolute theta, alpha, and beta power over the prefrontal cortex, lasting up to half-an-hour after administration of the drug. The increase in theta power was reported to correlate with a positive subjective drug effect, while the increase in alpha power was reported to correlate with nervousness. qEEG measures are also sensitive to the acute and chronic effects of cocaine, as well as the effects from withdrawal and long-term abstinence from cocaine use. Some EEG characteristics observed in cocaine addicts are considered to be due to the neurotoxic effects, whereas some EEG characteristics in cocaine addicts may also indicate a predisposition toward the development of cocaine addiction. qEEG has been used more often to characterize the effects of withdrawal in cocaine-dependent patients. During protracted abstinence from cocaine qEEG effects are featured by long-lasting increases in alpha and beta bands together with reduced activity in delta and theta bands. Several recent studies employing qEEG techniques have demonstrated an association between the amount of beta activity in the spontaneous EEG and relapse in cocaine abuse.
EEG in Methamphetamine Addiction
Several studies have examined the neurobiological consequences of methamphetamine dependence using qEEG methods (e.g., Newton et al. 2003, 2004). It was found that methamphetamine dependent patients exhibited a significant power increase in the delta and theta bands as compared to non-drug-using controls (Newton et al. 2003). These results are in accordance with other neurocognitive studies (Kalechstein et al. 2003) suggesting that methamphetamine abuse is associated with psychomotor slowing and frontal executive deficits. Within the methamphetamine-dependent subjects, increased theta qEEG power was found to correlate with response time and was accompanied with reduced accuracy (Newton et al. 2004). To our knowledge, qEEG patterns associated with acute withdrawal and recent abstinence in methamphetamine dependence have not yet been sufficiently described. One study reported (Newton et al. 2003) that methamphetamine dependent volunteers with 4 days of abstinence had increased EEG power in the delta and theta but not in the alpha and beta bands. Within the methamphetamine dependent group, a majority of the conventional EEGs were abnormal (64%), compared to 18% in the non-methamphetamine using group.
The qEEG may provide a sensitive neurophysiological outcome measure of methamphetamine abuse-related persistent alterations in neurocognitive functions (Newton et al. 2004). In a study by Simon et al. (2002), when performance of patients with SUD was compared to their matched non-using control groups, both methamphetamine and cocaine abusers were impaired on cognitive measures, but the type and degree of impairments were somewhat different. Some of these differences between methamphetamine and cocaine effects on cognitive functions and electrophysiological alterations can be explained by differential pharmacokinetics of these two drugs, as cocaine is rapidly metabolized with an elimination half-life of several hours, whereas methamphetamine is eliminated more slowly, with an elimination half-life averaging 12 h (Cook et al. 1993; Jeffcoat et al. 1989). Moreover, cocaine differs from methamphetamine in that cocaine inhibits the reuptake of dopamine, serotonin, and norepinephrine, whereas methamphetamine mobilizes and releases these monoamines from storage granules, thus producing rapid and large increases in synaptic concentrations (Simon et al. 2002, 2004). This might be responsible for the discrepancies in observed qEEG manifestations associated with chronic methamphetamine and cocaine abuse.
Only a few studies have examined the qEEG consequences of methamphetamine dependence. They report that methamphetamine dependent patients exhibited a significant power increase in the delta and theta bands as compared to non-drug-using control. The qEEG patterns associated with acute withdrawal and recent abstinence in methamphetamine dependence have not yet been sufficiently described. One study reported that abstinent methamphetamine dependent patients had increased EEG power in the delta and theta but not in the alpha and beta bands. In general, qEEG studies of methamphetamine addiction are in accordance with other neurocognitive studies suggesting that methamphetamine abuse is associated with psychomotor slowing and frontal executive deficits.