This may not surprise too many old-time NF practitioners, except that it is now being proven with well done studies in the traditional neuroscience literature!  Neurofeedback can induce changes in brain plasticity!

Jay

First Direct Evidence of Neuroplastic Changes Following Brainwave Training

ScienceDaily (Mar. 12, 2010) — Significant changes in brain plasticity have been observed following alpha brainwave training.

A pioneering collaboration between two laboratories from the University of London has provided the first evidence of neuroplastic changes occurring directly after natural brainwave training. Researchers from Goldsmiths and the Institute of Neurology have demonstrated that half an hour of voluntary control of brain rhythms is sufficient to induce a lasting shift in cortical excitability and intracortical function.

Remarkably, these after-effects are comparable in magnitude to those observed following interventions with artificial forms of brain stimulation involving magnetic or electrical pulses. The novel finding may have important implications for future non-pharmacological therapies of the brain and calls for a serious re-examination and stronger backing of research on neurofeedback, a technique which may be promising tool to modulate cerebral plasticity in a safe, painless, and natural way.

Continued at http://www.sciencedaily.com/releases/2010/03/100310114936.htm

How can we live a fuller and healthier lifestyle as we get older? Perhaps keeping our body and brain engaged can help. That seems to be the case in Japan where the number of centegenarians is greater than 20,000.

THE ART OF AGING:THE LIMITLESS POTENTIAL OF THE BRAIN introduces a number of these “super-seniors” who lead healthy lives at nearly 100-years-old and, through them,searches for the “keys” to living a healthy and vital life regardless of age.

Click here to view the embedded video.

An excellent story regarding the use of Neurofeedback in sports.  The Mind Room utilizes the Thought Technology Procomp Infiniti equipment.

The Boy with the Incredible Brain

This is the breathtaking story of Daniel Tammet. A twenty-something with extraordinary mental abilities, Daniel is one of the world’s few savants. He can do calculations to 100 decimal places in his head, and learn a language in a week. This documentary follows Daniel as he travels to America to meet the scientists who are convinced he may hold the key to unlocking similar abilities in everyone.


Secrets of the Mind

Click here to view the embedded video.

Phantoms in the Brain: V. S. Ramchandran from T.E.D.

This is a 25 minute video of Ramchandran’s talk presented at TED.

Phantoms in the Brain Part 1 Full Documentary


Phantoms in the Brain Part 2

From Taboo. this clip is about a man who wants to get rid of his own leg. Eventually trying to do it at home.

The participants of the International Conference on Behavioral Health and Traumatic Brain Injury will be holding a Congressional Briefing hosted by:

Congressman Bill Pascrell and  Congressman Todd Platts

Co-Chairs, Congressional Brain Injury Task Force presenting recommendations to improve the care of our wounded warriors NOW!

In October of 2008, St Joseph’s Regional Medical Center hosted the International Conference on Behavioral Health and Traumatic Brain Injury. 100 doctors, researchers and scientists from around the globe discussed issues facing our wounded warriors, identified the barriers to treatment and strategized on the improvements for continuum of care. This briefing will present their reccomendations.

The meeting will be held @ the Capitol Visitors Center- Congressional Meeting Room South

RSVP – rsvp@susandavis.com

The ascending reticular activating system stimulates the diffuse thalamic projection system and sets the general arousal level of the brain. With an increase in the CNS arousal level, there is an increase in the mean frequency of alpha and a decreased slowing. With decreases in arousal there is a slowing of the alpha, as well as eventually an increase in diffusely distributed slowing ( a mixture of diffuse lower voltage delta and theta, usually with a weak vertex prominence in linked ear montages).

When this diffuse slowing with slower alpha is seen, a vertex or central sensory-motor strip beta training will slightly speed up the alpha and decrease the slowing seen. A frontal beta minima seen in the data may respond to a more anterior placement for the beta training.

This increased beta training should correspond to a brain stem shift in RAS activation with increased norepinephrine level’s stimulating effect and results in increasing vigilance.

When this slowing and alpha pattern is seen, but with alpha intruding frontally (occasionally with less of the slowing) , the protocol should include some parietal “high alpha”, defined as 11-16 Hz in the classical EEG literature, but usually in NT, this is from 10 or 11 to 14 Hz.

This parietal high alpha training shifts the alpha mean frequency higher, decreasing the diffuse projection system’s frontal alpha and increasing the posteriorly distributed specific projection system alpha (though at a slightly faster frequency distribution). Often it is the slower alpha frequencies that are intruding frontally, and they are reduced with this shift.

Focal slowing

Focal slowing that is not an artifact (such as a pulse, electrode, electrodermal, eye movement or other artifactual source of slowing) should be evaluated by an electroencephalographer or neurologist.

The focal slowing may be from a tumor, ischemia, stroke, trauma, inflamation or other medical condition. The etiology should be identified prior to any intervention or consideration of NT.

Should the etiology be known, generally the reduction of the slowing and enhancement of faster activity improves the brain function following NT. Slowing is reported in specific learning disabilities and sensory processing problems (Chabot et al., SSNR Aspen,1997)

Faster alpha variants, not low voltage

The alpha frequencies may be faster than usual, sometimes corresponding with anxiousness or hypervigilance. These situations can present with complaints about attentional problems, with the hypervigilance acting as a source of increased distraction, but with the process differing from more usual ADD/ADHD presentations.

The faster alpha often has increased EMG associated with it. Patients with these findings seem to respond with paradoxical increased anxiety if EMG relaxation is tried without first addressing the hypervigilance of the faster alpha. The ‘letting down of the guard’ is anxiety producing.

Training the slower end of the normal alpha band parietally seems to have a strong positive effect on these individuals, with SMR training used by some therapists. If the EMG remains, the subsequent relaxation therapy seems to work without the increased anxiety following the NF intervention.

Frontal lobe disturbances

The frontal lobes inhibit distractions and inappropriate impulsively motivated behavior, control affective mood states and attentional states. The frontal lobes also set the motor strips tone via inhibitory control loops involving subcortical structures. The frontal lobe has general regulatory control over the entire rest of the brain.

In attentional and affective disorders as well as motor dyscontrol such as hyperkinetic disturbances the locus of the dysfunction is commonly the frontal lobe.

Work recently done on ADD/ADHD and affective disorders shows a variety of frontal disturbances seen with the qEEG. These varieties include slowing in theta, alpha intrusion and even excess beta (Chabot et al, 1998 SSNR, Austin). Non- systematically replicated research showed the qEEG to predict the response of the patient to medications. The theta types responded to stimulants, with the frontal alpha, especially when the mean frequency of alpha was slowed, responding to amphetamines. Other researchers show some of the alpha types to respond to SSRI type antidepressants (such as the OCD responsive type in work by L. Prichep and the depressives with frontal alpha in work by S. Suffin and Emory).

In NT, the qEEG may be used to adjust the intervention, with frontal theta responding to beta protocols with suppression of the excessive theta. The alpha frontal types respond less well to frontal alpha downtraining than to posterior high alpha training with concurrent frontal beta training. The frontal beta type seeming to respond to normal frequency alpha training posteriorly and frontal beta suppression if the beta is still excessive (with any slowing noted suppressed as well). The frontal beta excess type is often a difficult patient in my experience.

Frontal beta minima can be seen in frontal disturbances and seem to respond well the NF intervention. Areas commonly seen are F7 in attentional problems, F8 in impulsivity and F3 or Fp1 in depression. The locus or even presence of a minima is difficult to predict behaviorally, as other disturbances of function may yield the same behaviors.

Right frontal training and frontal symmetry

The commonly held belief that in NF the right frontal lobe should be avoided needs to be explored and understood before discarding it.

Frontal alpha and beta interhemispheric ratios seem to correspond well with the perceptual style of the subject. Right hemispheric dominant (more beta and less alpha than the left) subjects have a “glass is half empty” perception and a lower mood state, or more depression.

NF with the frontal lobes needs to keep the dominance on the left, or to establish such a dominance to avoid deteriorating mood states and perceptual styles in the client.

This has led some to avoid the right frontal training, or frontal training in general. This would be a drastically limiting elimination of potentially efficacious NF intervention on brain function, given the importance in brain pathophysiology and functional regulation of the frontal lobes.

If care is taken to measure and assure the desired lateral symmetry, it is my experience that the frontal lobe training on either side may be done without significant difficulty. Without this knowledge and care applied to protocol considerations, it could be a large source of client dissatisfaction.

Spindling excessive beta

This pattern has been reported to be associated with ‘cortical irritability’, viral or toxic encephalopathies and in epilepsy. It has a classically defined higher voltage beta occasionally even exceeding 20 microvolts. This abnormal beta is seen in waxing and waning spindles over the effected cortex. This pattern is seen in less than 10% of the ADD/ADHD and affective disordered population, but when seen, it is an important finding.

I have seen this excessive spindling beta in areas associated with pre-epileptic auras. In one case it was seen occipitally during visual auras and in another fronto-temporally with auras effecting the client more subtly as a smell or even a remembrance.

This pattern responds very badly to any beta training, exacerbating the symptom complex. Beta training is strongly contraindicated.

Beta suppression directly in the area of concern has shown good clinical response. The band of frequencies to be suppressed should be selected based on individual profiles, not by standard bands. I have seen broader bands like14-22 Hz or bands as narrow as 14-16 Hz in excess, with higher 20-30 Hz beta occasionaly involved as well, with many variations.

The customizing of these interventions would be very difficult, if not impossible without the qEEG to provide location, distribution and frequency range information to the NF practitioner.

Areas without significant activity
seen in any EEG band

The EEG can be seen with areas of decreased amplitude, not just a beta minima, an area of general amplitude minima. This phenomenon is seen well in mapping and has been reported in areas of cortical dysfunction.

The EEG requires the generation of alternating currents for any voltages to be measured in the EEG. This requires the activity of neurons in an area with the increased blood flow and glucose metabolism associated with these cellular processes. The flowing of blood in the brain is regulated by the concentration of bicarbonate ion measured as PCO2, a metabolic by-product of the burning of glucose and the generation of energy within the Krebbs cycle. The ADP/ATP cycle within the mitochondria is the site of this electro-chemical interplay.

In careful work done by Pribram, the time frame for these events has been studied, with the slower DC activity preceding the cellular action potential. This DC system has been used since the 1970’s in Europe in NF, showing that the shift to electro-positivity can even stop an epileptiform discharge from occurring (N. Birbaumer).

This phenomenon also can be seen in qEEG as an area of decreased voltages in all bands, progressing from beta through alpha to the slower frequencies. The area is effectively shut down and is not functioning. When the brain’s electropositivity increases enough, the AC activity seen in the EEG is inhibited.

These areas have been observed frontally in attentional and affective disorders (Gunkelman, SSNR1997), and are reported in observations of individuals who have been brain washed and have given the locus of control over to others (personal communication with Brownback, Mason and associates).

Training these frequency “dead areas” is something newer in the field, but it seems to respond to beta training, suppressing any slower frequencies that may be still present. Beta is correlated highly with PET measurements of metabolic activity (I.A. Cook, A. Leuchter et al, 1998 UCLA)

Generally low magnitudes

The occurance or a low voltage EEG is considered a normal variant when it is a low voltage fast EEG. When the low voltages appear slow however, it is a diffuse and non-specific abnormality. The difference between the two patterns is somewhat more qualitative than quantitative. The morphologic presentation differs more significantly than the magnitude differences in the quantitative analysis.

The magnitudes are all low, and in relative terms the power will look slowed in both cases, though the faster morphologic pattern is a normal variant. When a low voltage slow pattern is seen and is confirmed not to be drowsing, it should be evaluated for metabolic, toxic or other diffuffuse encephalopathies such as degenerative or post hypoxic etiologies.

The low voltage slow type is reported in dementias as an early EEG change. This seems to respond to high alpha training from 10 or 11 to 14 Hz. This is the same EEG effect as nootropic medications (smart drugs) will provide.

The low voltage fast type usually corresponds with anxious, nervous and hypervigilant individuals. Though not pathognomonic, it is commonly seen in alcoholism and alcohol free members of families of alcoholics with a strong family history.

Interestingly, in research on this pattern, it is shown to respond to alcohol by suddenly having alpha that is well formed. The alpha will slow and rhythmic slower activity will increase if higher doses are given. The state is reported in euphoric terms by the research subject. This euphoria is also reported associated with alpha induced by opiates.

The low voltage fast pattern responds well to alpha training with a normal alpha distribution of 8-12 or 9-11 Hz. The learning curve for this is well established, having a fifth order curve fit. The phases of the experience are well defined by the curve.

There is an initial increase in alpha due to the habituation to the clinical setting, with subsequent decreases in alpha during the active attempts at controling the alpha. These phases are followed by the release of the active attempts and a return to the habituated level. This is followed by passive volitional attempts and the eventual acquisition of voluntary control seen as the exponential increase at the end of training.

The subsequent alpha/theta training is commonly used by neurotherapists in these cases when there is a concurrent addiction or intense life stress or trauma by history.

Temporal lobe alpha

When alpha is seen in the temporal lobe, it can be from a variety of causes, indicating that a more complex neurofeedback protocol response may be needed.

The alpha from old head trauma is usually a faster alpha variant, adjusted for the individuals alpha ‘tuning’. This is seen over a year or 2 from the time of the trauma, after the acute healing and swelling have long dissipated. It replaces the slowing which may be seen initially.

Temporal alpha may also be an effect seen in response to a decrease in ipsilateral frontal lobe activity. The decrease in uncinate fasciculus or inferior longitudinal fasciculus stimulation from the frontal lobe allows the temporal lobe to be idle. This usually will be seen with one of the frontal lobe patterns discussed earlier.

The temporal idling may be cleared up with the direct frontal work discussed earlier, but may require lower band beta training directly on the temporal site.

Strong alpha at T5 or T6 can contaminate the ear references, yielding a false image of frontal alpha in the qEEG. The ears, having alpha present and the frontal lobes without alpha are compared in the differential amplifier. The amplifier will show alpha in the frontal channel falsely, which has to be controlled for by using a variety of montages in reviewing the data. The frontal alpha will not be seen with sequential or non-contaminated reference montages, such as Cz or in more sophisticated equipment the ‘common average’ or even the ‘Hjorth’ montage may be used to give a more pure look at the data.

The temporal lobes seem sensitive to excessively fast beta training, with 14 Hz training, 12-15 Hz, 14-16 Hz or other lower band beta used more commonly than a higher frequency intervention due to this sensitivity. In my experience, these bands seem to be best adjusted based on clinical response, not to any obvious spectral loss in the CSA or amplitude mapping.

The post traumatic faster alpha seems to respond to coherence training, seemingly reconnecting the functional relationships. This requires the use of qEEG coherence measurements, as without this, the training sites and bands would not be evaluated or selectable based on any objective criteria.

A note on coherence and phase

The cortex is full of neural connections, all electrically active, though not all seen with the EEG. The EEG is measuring summations of radially generated action potentials from pyramidal cells, not the laterally oriented cortical-cortical tracts.

The raw EEG is blind to nearly 2/3 of the electrical activity of the cortex. Much of which is seen by the Magnetoencephalography (MEG), a measure of the “magnetic” activity of the brain (actually lateral current flow of intracellular activity). The MEG is however blind to the extracellular potentials arranged radially which comprise the EEG.

The longitudinal myelinated fiber tracts are the high speed web of the cortex, not the organized subcortical radiations of the thalamo-cortical systems or other subcortical-cortical projection pathways. This network of local arcuate, longitudinal, fronto-temporal (uncinate) and interhemispheric collosal tracts are invisible to the raw EEG.

The activity of this invisible network, however, can be inferred from the coherence and phase relationships between areas “hooked up” by the network. This spatial distribution, “connectivity” reflection of the subsurface activity is obviously quite complex. The covariance of power at two sites (coherence) or their covariance in time (phase) give measure within the EEG to activities within this invisible network.

The view of the raw coherence and phase data yields little to the non-expert. When compared to an age and sex matched normative database, displayed as a Z-score, the magnitude and direction of variance, including the significance of the variance become much more evident.

There are two types of phase relationships seen in qEEG, conducted phase differences and propogated phase differences. The propogated phase is seen where a focal phase reversal indicates the source of an EEG phenomenon. The conductive phase is simply the time delay due to propogation along neural pathways.

The phase measurement reflects the correlation of covariance in time of activity at two sites. This temporal relationship can be slowed by damage to tracts due to demyelinating, structural or toxic/metabolic influences. The phase relationship may be faster with increased nerve conduction velocity, or by volume conduction through fluids and as a field effect.

The simultaneous projection to various cortical locations of synchronized volleys from thalamo-cortical radiations seen during thalamic activity paced by the ventral-medial or reticular nucleus is a “highly connected” state, with high phase synchrony. This unique synchronized state is predictive of meditative expertise in Zen meditators (Gevins et al.). It involves progressively generalizing increased phase synchrony in alpha, followed by increases in theta synchrony (possibly representing slowed alpha).

Similar findings have been seen in Yogic meditators, though with differences in habituation to repetitive sensory stimulation. The Yogic meditators habituated faster than normal and the Zen meditators failed to habituate. Interestingly, this reflects the philosophical views of the two meditative techniques. The Zen meditation aspires to novelty of experience while the Yogic traditions emphasize the imaginary nature of the external reality.

The phase relationship is measured as difference in the degree of arc of tangents of two waveforms, measured at a point in time. This is reported as degrees of phase shift from 1-180 degrees, or time synchrony of the waveforms, reported in milliseconds.

Often confused with phase is the EEG measure of coherence. The confusion comes from the use of the term “coherence” as an adjective descriptor for phase. This misuse of a technical term as a descriptor in the same field of interest is problematic. The term “phase coherence” should be eliminated and replaced by “phase synchrony” or, most properly, by specifying a phase relationship from 0-180 degrees of phase shift or by giving the Z-score deviation from normal. Please retain the use of coherence for it’s technically proper role within this field.

Coherence is the cross correlation of the power (or amplitude) of activity at two sites. Sites that covary highly are presumably processing related cortical/subcortical volleys or have a high “connectivity”. This is reported as a value from 1 to 0 (on some older equipment, as a value from 100% to 0% coherence).

The cortical-cortical long fiber connections compete with the shorter connections. As Bob Thatcher says, “The close siblings can speak to each other, but not to their distant cousin at the same time.”. This is called the two compartmental model of coherence.

There is a third compartment I believe, the subcortical-cortical compartment, which would explain the observation of high coherence locally having decreased connections locally. The coherence is from the subcortical source being connected to both sites, not the cortical-cortical compartment’s connectivity.

Coherence is most easily viewed as morphological similarity, with correction for absolute magnitude and is irrespective of the time synchronization. When two waveforms are shifted in time until maximal coherence values are attained, the time base shift is the phase delay of the waveforms.

Structural damage produces reliable, consistent patterns of change in phase and coherence patterning in the qEEG, though functional influences account for variable findings. This variance makes interpretation of the data subject to having to rely only on strong patterns of variance, not isolated findings.

The proper conservative interpretation of these data must also be viewed in the context of the entire constellation of findings from the rest of the patients EEG, qEEG and clinical presentation.

When training changes in coherence or phase using neurofeedback, over-training (shifting beyond a normal relationship range) needs to be avoided. This requires merely setting a proper normalized goal or value as a training target.

There is currently what I believe to be an inappropriate practice in coherence NT. This is the use if a single channel EEG machine used sequentially (old term bipolar) with the assumption that this will feed back coherence. This assumption suggests that increased highly coherent activity cancels, thus decreasing the amplitude of the channel. Thus training amplitude could train coherence.

This is a grossly false model of coherence feedback. This model assumes the phase of the activity to be synchronous. It also assumes that the synchronous activity has amplitude equivalence. In reality the coherence calculation equally weights amplitudes, correcting for asymmetries, the amplifier does not. The calculation of coherence is not time locked, measuring covariance independent of the time locked nature of the amplifiers response.

I believe we are not yet in the presence of enough information about the clinical impacts and needed protocol controls to make full use of the clinical application of these types of training. The clinical application of these techniques is highly fruitful ground, needing systematic clinically valid research and protocol development.

More fully conclusive research is needed based on the ongoing application of this training before rules for intervention are presented.

I have nothing against the careful clinical use of coherence or phase training. When the more “traditional” neurofeedback applications fail, these phase and coherence based interventions should be empirically tried. With more trials using the proper measures, design and controls, advances will be made.

The departing caveat

Now that I have completed this codification of some of the interventions seen currently in qEEG based NT, I can say without question that this will be a source of future embarrassment. To capture a snapshot during a field’s rapid development stage is a guaranteed red face in the future. If you don’t believe this, look at your own childhood photographs!

So, please be gentle with your ridicule, and don’t mistake this art to be a science… not just yet.

The P300 component of the ERP, occurring 300–600 ms post-stimulus, is the most widely used ERP in psychiatry and other clinical applications (Polich et al. 1994; Polich and Herbst 2000; Pritchard 1981, 1986; Pritchard et al. 2004). The amplitude of the P300 reflects the allocation of attentional resources, while the latency is considered to reflect stimulus evaluation and classification time (Katayama and Polich 1998; Polich and Herbst 2000). The P300 is usually obtained in an oddball paradigm, wherein two stimuli are presented in a random order, one of them frequent (standard) and another one rare (target) (Polich 1990). A modification of the oddball task has been used where a third, also rare stimulus (distracter), is presented along with standard and target stimuli. It was reported that these infrequent distracters elicit a frontocentral P300, so called P3a, whereas the rare targets elicit a parietal P300, so called P3b (Katayama and Polich 1996, 1998). The P3a is recorded at the anterior scalp locations and has been interpreted as reflecting frontal lobe activity (Gaeta et al. 2003; Knight 1984). Though the P300 response in general is thought to represent ‘‘context updating/closure,’’ in a three-stimuli oddball task the P3a is interpreted as ‘‘orienting,’’ and the P3b is viewed as an index of the ability to maintain sustained attention to target (Na¨a¨ta¨nen 1990). The anterior P3a indexes the contextual salience of the rare stimuli, whereas the posterior P3b is indexing task-relevance of the stimuli (Gaeta et al. 2003).

A robust finding in ERP studies on alcoholism is that alcoholics as well as individuals at high risk to develop alcoholism have been shown to have a low P300 amplitude in various task paradigms (Cohen et al. 2002; Hada et al. 2000; Porjesz et al. 2005; Porjesz and Begleiter 1998). Kouri et al. (1996) examined the P300 component in patients who were dually dependent on cocaine and heroin. The results showed no P300 amplitude differences between the patients and healthy non-drug-dependent volunteers when patients presented for detoxification. However, after the course of detoxification, the P300 amplitude was significantly smaller in the cocaine-and heroin-dependent group than in the non-dependent control group. In a study by Bauer (2001b) the P300 did not differentiate among patients characterized by histories of either cocaine, or cocaine and alcohol, or heroin dependence. Across all the patient groups, the P300 was significantly reduced in amplitude relative to the P300 ERPs recorded from individuals with no history of alcohol or drug dependence. This study also demonstrated that continued abstinence from heroin and from cocaine and alcohol is also associated with a trend toward normalization of the P300. In a recent study of Papageorgiou et al. (2004) the P300 component was evaluated during the anticipatory period of a short memory task in 20 patients characterized by a past history of heroin dependence (6 months abstinence), in 18 current heroin users and in 20 matched healthy subjects. Abstinent heroin addicts exhibited a significant reduction of the P300 amplitude at the central frontal region, relative to the other two groups.

The results of early work examining the effect of cannabis use and THC administration on visual and auditory ERPs have been inconclusive (Rodin et al. 1970; Roth et al. 1973). Later studies of Patrick et al. (1995, 1997) could not find P300 latency differences in audio and visual oddball tasks between THC users without psychiatric problems and controls. Although THC users displayed reduced auditory and visual P300 amplitudes in this study, when age differences between THC users and controls were removed, all significant P300 amplitude differences were removed as well.

Acute and chronic use of cocaine exerts neuropharmacological effects on amplitude and latency of both anterior and posterior P300 ERP components (Biggins et al. 1997; Fein et al. 1996; Herning et al. 1994a; Kouri et al. 1996; Polich 1990). Longer P300 (P3b) latency without abnormalities in amplitude was reported in several studies on cocaine withdrawal (Herning et al. 1994a; Lukas 1993). Noldy and Carlen (1997) demonstrated effects of cocaine withdrawal on the latency of the P300 in an auditory oddball task. In cocaine-dependent patients, P3a amplitude decrements over frontal areas are persistent even after long periods of abstinence (Bauer 1997). The latency of the P3a was delayed and the amplitude was reduced to novel non-targets in cocaine and alcohol-dependent subjects compared to controls (Biggins et al. 1997; Hada et al. 2000)in auditory and visual three-stimuli oddball tasks.

Several studies have investigated ERP changes associated with methamphetamine abuse and dependence. The P300 component of the auditory ERP was reported to show a prolonged latency in the oddball task in methamphetamine dependent subjects with a history of psychosis, compared to normal controls (Iwanami et al. 1994, 1998). In particular, the patients with methamphetamine dependence showed reduced P3a amplitude in the reading task and delayed P3b latency with normal P3b amplitude in the auditory oddball task. This was interpreted as indicating a prolonged central noradrenergic dysfunction due to earlier methamphetamine use.

In most ERP studies the P300 did not differentiate among patients characterized by histories of either cocaine, or cocaine and alcohol, or heroin dependence. Across all the patient groups, the P300 was significantly reduced in amplitude relative to P300 ERPs recorded from individuals with no history of alcohol or drug dependence. The latency of the frontal and parietal P300 was reported to be delayed, and the amplitude was reduced to novel non-targets in cocaine and alcohol-dependent subjects compared to controls in auditory and visual three-stimuli oddball tasks. Continued abstinence from heroin, cocaine, and alcohol was shown to be associated with a trend toward P300 normalization. Several studies have investigated ERP changes associated with methamphetamine abuse and dependence. In general, chronic psychoactive substance abuse and drug dependence are associated with delayed and attenuated cognitive ERP in auditory and visual oddball tasks.

qEEG and ERP Abnormalities in Addiction: Psychopharmacological Effects or Trait Markers?

Whether qEEG alterations and P300 decrements found in most of SUD are only a coincident ‘‘marker’’ of vulnerability or make a direct etiologic contribution to risk for substance dependence is still unknown (Bauer and Hesselbrok 2001; Carlson et al. 2002; O’Connor et al. 1994; Polich et al. 1994; Porjesz and Begleiter 1998). The P300 reduction and abnormal qEEG patterns are seen in mental disorders that often are comorbid with substance abuse, such as conduct disorder (Bauer and Hesselbrock 1999, 2001), ADHD (Bauer 1997; O’Connor et al. 1994), and bipolar or major affective disorder (Friedman and Squires-Wheeler 1994). Reduced P300 amplitude related to prefrontal brain dysfunction may suggest that a deficit in inhibitory control is an underlying mechanism shared by different psychopathologies (Bauer and Hesselbrock 1999; Clark et al. 1999; Tarter et al. 2003). According to Bauer (2002), certain ERP and qEEG abnormalities and impaired functioning on complex cognitive tests in patients formerly dependent on cocaine might not be proximately caused by drug use per se but be more related to comorbid alcohol use or another psychiatric condition. Taken together, the findings converge on the conclusion that there exists an inherited predisposition for an externalizing psychopathology that includes ADHD, conduct disorder, and substance abuse. PTSD seems to heighten the risk for addiction as well. Thus, the reviewed findings support the hypothesis that addicted subjects may manifest a P300 amplitude reduction and qEEG abnormalities as a trait reflecting the CNS disinhibition, which may be a predisposing factor for addiction liability, resistance to drug habit extinction, and relapse vulnerability.

Heritability and Neurotransmitter Considerations in Substance Use Disorders

There has been a consistent drift in addiction research between the psychosocial, cognitive and behavioral aspects of addiction and the biological and genetic emphasis. In much of the present data relating to genetics and animal models (Blum et al. 2006; Porjesz et al. 2005; Ryabinin and Weitemier 2006; Samochowiec et al. 2006), studies suggest that a genetic predisposition for SUD is an accepted concept. Much of the genetic research addresses the influence of alleles thought responsible in coding for genes that express phenotypic neurotransmitter production and distribution; mainly involving endorphins, dopamine and serotonin. These neurotransmitters, dopamine in particular, are also suspect in other appetitive and mood disorders and psychopathologies, of particular note, Reward Deprivation Syndrome (RDS). RDS is described as a dysfunction in the Brain Reward Cascade and proposes that abnormal craving behavior is a consequence of defects in the DRD2 and D1, D3, D4 and D5 dopaminergic receptor genes (Blum et al. 2006).

Blum and colleagues (1990, 1993, 1996) described this syndrome and identified the D2 dopamine receptor gene as a possible candidate for susceptibility to alcoholism in severe alcoholics (Blum et al. 1993) and proposed this gene’s association with dopamine production and distribution may produce a sevenfold increase in the likelihood of developing alcohol use problems (Uhl et al. 1993). This DRD2 dopamine receptor gene and polymorphisms within its genetic coding specific to addiction remain unclear due to its involvement in other disorders; including, obesity (Blum et al. 2006), Tourette’s syndrome (Comings et al. 1991) pathological aggression and violence, PTSD (Comings et al. 1996) and schizoid—avoidant disorder (Chen et al. 2005). SUD were classified as a subtype of RDS and treatment regimens for these disorders have been classified as inadequate (Blum et al. 2007) and research continues in developing possible genetic interventions that may produce dopamine and other neurotransmitter regulation in substance-induced rapid dopamine increase in limbic regions (Blum et al. 2007).

It is clear that heritability plays an important role in addictive disorders, however, to what extent environment, perception and synaptic permanency and plasticity influence the course of genetic adaptation or maladaptive traits requires further investigation. Suggested neuroanatomical substrates involved in SUD implicate mesolimbic and diencephalon regions; including the substantia nigra, reticular formation, medial forebrain bundle, nucleus accumbens, septum pediculum, olfactory tubercule and hippocampus and suggest that any concentration of alcohol exposure to these regions would make alcohol use virtually unavoidable (Myers and Privette 1989).

Studies of EEG Biofeedback in Substance Abuse Treatment

The Peniston Protocol (Alpha-Theta Feedback)

The early studies of Kamiya (e.g., Nowlis and Kamiya 1970) on self-regulation of alpha rhythm elicited substantial interest in the potential clinical applications of alpha biofeedback for SUD treatment. There were reported several uncontrolled case studies and conceptual reviews on alpha EEG training for alcohol (DeGood and Valle 1978; Denney et al. 1991; Jones and Holmes 1976; Passini et al. 1977; Tarbox 1983; Watson et al. 1978) and drug abuse treatment (Brinkman 1978; Goldberg et al. 1976, 1977; Lamontagne et al. 1977; Sim 1976), but the impact of alpha biofeedback training as a SUD therapy was not significant.

The bulk of the literature to date regarding EEG biofeedback of addictive disorders is focused on alpha-theta biofeedback. The technique involves the simultaneous measurement of occipital alpha (8–13 Hz) and theta (4– 8 Hz) and feedback by separate auditory tones for each frequency representing amplitudes greater than pre set thresholds. The subject is encouraged to relax and to increase the amount of time the signal is heard, that is to say, to increase the amount of time that the amplitude of each defined bandwidth exceeds the threshold. A variety of equipment and software has been used to acquire, process, and filter these signals, and there are differences in technique inherent with equipment and software.

Alpha-theta feedback training was first employed and described by Elmer Green and colleagues (Green et al. 1974) at the Menninger Clinic. This method was based on Green’s observations of single lead EEG during meditative states in practiced meditators, during which increased theta amplitude was observed following an initial increased alpha amplitude, then a drop off of alpha amplitude (theta/alpha crossover). When the feedback of the alpha and theta signal was applied to subjects, states of profound relaxation and reverie were reported to occur. The method was seen as useful in augmenting psychotherapy and promoting individual insight. It could be seen as a use of brain wave signal feedback to enable a subject to maintain a particular state of consciousness similar to a meditative or hypnotic relaxed state over a 30-or 40-min feedback session.

Goslinga (1975) gave the first description of the use of alpha-theta feedback in a SUD treatment program. This integrated program started in 1973 at the Topeka VA, and included group and individual therapies. Daily 20-min EEG biofeedback sessions (integrated with EMG biofeedback and temperature control biofeedback) were conducted over 6 weeks, resulting in free, loose associations, heightened sensitivity, and increased suggestibility. Patients discussed their insights and experiences associated with biofeedback in therapy groups several times a week, augmenting expressive psychotherapy. The first published clinical reports of efficacy of alpha-theta training at the Topeka VA were by Twemlow and Bowen (1976), who explored the impact of alpha-theta training on psychodynamic issues in 67 non-psychotic chronic male alcoholics in an inpatient treatment program. In this non-controlled study, they found that ‘‘religiousness’’ as a predictor of ‘‘self-actualization’’ may have increased as a result of imagery experienced in theta states. This was seen as positive to the program goal of augmenting Alcoholics Anonymous as a recovery philosophy. The high suggestibility of the method was acknowledged; ‘‘treatments such as brainwave training, which utilize abstract, ill understood techniques are potential repositories of magical projection and fantasy and would logically be more acceptable to alcoholics who are able to have ‘faith’ (devoutly or moderately religious)’’ (Twemlow and Bowen 1977). In another uncontrolled study at the Topeka VA, 21 alcoholics were reported to exhibit within and across session increases in raw theta amplitudes at occipital areas bilaterally measured by single lead EEG during the course of alpha-theta training, becoming more able to achieve deep states as manifested by EEG (Twemlow et al. 1977). These initial studies advanced the utility of biofeedback induced theta states in promoting insight and attitude change in alcoholics, with the assumptions that biofeedback-induced theta states are associated with heightened awareness and suggestibility, and that this heightened awareness and suggestibility would enhance recovery. Outcome data regarding abstinence were not reported.

In the first reported randomized and controlled study of alcoholics treated with alpha-theta EEG biofeedback, Peniston and Kulkosky (1989) described positive outcome results. Their subjects were inpatients in a VA hospital treatment program, all males with established chronic alcoholism and multiple past failed treatments. Following a temperature biofeedback pre-training phase, Peniston’s experimental subjects (n = 10) completed 15 30-min sessions of eyes closed occipital alpha-theta biofeedback. Compared to a traditionally treated alcoholic control group (n = 10), and nonalcoholic controls (n = 10), alcoholics receiving brainwave biofeedback showed significant increases in percentages of EEG recorded in the alpha and theta rhythms, and increased alpha rhythm amplitudes (single lead measurements at international site O1). The experimentally treated subjects showed reductions in Beck Depression Inventory scores compared to the control groups. Control subjects who received standard treatment alone showed increased levels of circulating beta-endorphin, an index of stress, whereas the EEG biofeedback group did not. Thirteen-month follow-up data indicated significantly more sustained prevention of relapse in alcoholics who completed alpha-theta brainwave training as compared to the control alcoholics, defining successful relapse prevention as ‘‘not using alcohol for more than six contiguous days’’ during the follow-up period. In a further report on the same control and experimental subjects, Peniston and Kulkosky (1990) described substantial changes in personality test results in the experimental group as compared to the controls. The experimental group showed improvement in psychological adjustment on 13 scales of the Millon Clinical Multiaxial Inventory compared to the traditionally treated alcoholics who improved on only two scales and became worse on one scale. On the 16-PF personality inventory, the neurofeedback training group demonstrated improvement on seven scales, compared to only one scale among the traditional treatment group. This small n study employed controls and blind outcome evaluation, with actual outcome figures of 80% positive outcome versus 20% in the traditional treatment control condition at 4-year follow up.

The protocol described by Peniston at the Fort Lyons VA cited above is similar to that initially employed by Twemlow and colleagues at the Topeka VA and Elmer Green at the Menninger Clinic, with two additions, i.e., (1) temperature training and (2) script. Peniston introduced temperature biofeedback training as a preconditioning relaxation exercise, along with an induction script to be read at the start of each session. This protocol (described as follows) has become known as the ‘‘Peniston Protocol’’ and has become the focus of research in subsequent studies. Subjects are first taught deep relaxation by skin temperature biofeedback for a minimum of five sessions that additionally incorporates autogenic phrases. Peniston also used the criteria of obtaining a temperature of 94F before moving on to EEG biofeedback. Participants then are instructed in EEG biofeedback and in an eyes closed and relaxed condition, receive auditory signals from an EEG apparatus using an international site O1 single electrode. A standard induction script employing suggestions to relax and ‘‘sink down’’ into reverie is read. When alpha (8–12 Hz) brainwaves exceed a preset threshold, a pleasant tone is heard, and by learning to voluntarily produce this tone, the subject becomes progressively relaxed. When theta brainwaves (4–8 Hz) are produced at a sufficiently high amplitude, a second tone is heard, and the subject becomes more relaxed and according to Peniston, enters a hypnagogic state of free reverie and high suggestibility. (Although theta increase and alpha decrease are thought by Peniston to be associated with a deeply relaxed state where hypnagogic reverie is present, this may simply represent drowsiness) (Niedermeyer 1999). Following the session, with the subject in a relaxed and suggestible state, a therapy session is conducted between the subject and therapist where the contents of the imagery experienced is explored and ‘‘abreactive’’ experiences are explored (Peniston and Kulkosky 1989, 1990, 1991).

Saxby and Peniston (1995) reported on 14 chronically alcohol dependent and depressed outpatients using this same protocol of alpha-theta brainwave biofeedback. Following treatment, subjects showed substantial decreases in depression and psychopathology as measured by standard instruments. Twenty-one month follow-up data indicated sustained abstinence from alcohol confirmed by collateral report. These male and female outpatients received 20 40-min sessions of feedback.

Bodenhamer-Davis and Calloway (2004) reported a clinical trial with 16 chemically dependent outpatients, 10 of whom were probationers classified as high risk for re-arrest. Subjects completed an average of 31 alpha-theta biofeedback sessions. Psychometrics demonstrated improvements in personality and mood. Follow-up at 74–98 months indicated 81.3% of the treatment subjects were abstinent. Re-arrest rates and probation revocations for the probation treatment group were lower than those for a probation comparison group (40% vs. 79%).

Fahrion (1995) gave a preliminary report (n = 119) on a large randomized study of alpha-theta training for addiction in the Kansas Prison System using group-training equipment. A report of the completed study (n = 520) (Fahrion 2002) showed little difference between the two groups overall at 2-year outcome. But, when results were analyzed for age, race and drug of choice, neurofeedback emerged as a more efficacious treatment for younger and non-white and non-stimulant abusing participants. Interestingly, this protocol was not effective for cocaine abusers. (Stimulant abusers will be discussed later in this article under the Scott–Kaiser modification of the Peniston protocol.)

The issue of alpha-theta biofeedback in culturally sensitive groups that have not responded to traditional modes of addiction treatment (such as confrontational group therapies) has been considered in an open case series reported by Kelly (1997). This three year follow-up study presented the treatment outcomes of 19 Dine’ (Navajo) clients. Four (21%) participants achieved ‘‘sustained full remission,’’ 12 (63%) achieved ‘‘sustained partial remission,’’ and 3 (16%) remained ‘‘dependent.’’ The majority of participants also showed a significant increase in ‘‘level of functioning’’.

Schneider et al. (1993) used slow cortical potential biofeedback to treat 10 unmedicated alcoholic patients in four neurofeedback sessions after hospitalization. Seven patients participated in a fifth session an average of 4 months later. Six out of these seven patients had not had a relapse at the follow-up. These results are similar to those reported for alpha theta training.

Several other studies using the Peniston protocol and its modifications reported cases with positive clinical effects (Burkett et al. 2003, DeBeus et al. 2002; Fahrion et al. 1992; Finkelberg et al. 1996; Skok et al. 1997). These studies suggest that an applied psychophysiological approach based on an alpha-theta biofeedback protocol is a valuable alternative to conventional substance abuse treatment (Walters 1998). Nevertheless, most of these results were reported at the society meetings, and only few of these studies were published in mainstream peer-reviewed journals other than The Journal of Neurotherapy.

A critical analysis of the Peniston Protocol is discussed at length in the previous reviews (Trudeau 2000, 2005a, b). Several controlled studies of the Peniston protocol for addictions, completed by Lowe (1999), Moore and Trudeau (1998), and Taub and Rosenfeld (1994), suggest that alpha-theta training for addictions may be non-specific in terms of effect when compared to suggestion, sham or controlled treatment, or meditational techniques. By contrast, Egner et al. (2002) showed that alpha-theta training results in an increase of theta/alpha ratios, as compared to a control condition. In an in depth critical analysis that examines inconsistencies reported in the original Peniston papers, Graap and Freides (1998) raise serious issues about the reporting of original samples and procedures in these studies. In their analyses, the results may have been due as much to the intense therapies accompanying the biofeedback as due to the biofeedback itself. The subjects may have been comorbid for a number of conditions, which were not clearly reported, particularly PTSD, which may have been the focus of the treatment. In his reply to these criticisms, Peniston (1998) acknowledges that it ‘‘remains unknown whether the temperature training, the visualizations, the ATBWNT (alpha-theta brain wave neurotherapy), the therapist, the placebo, or the Hawthorne effects are responsible for the beneficial results.’’ The criticism raised above by Graap and Friedes (1998) regarding Peniston’s papers could also be applied to earlier replication studies. Neither Peniston’s studies nor the replication studies provide sufficient detail regarding the specifics of the types of equipment used for alpha-theta feedback, including filtering methods for the EEG signal or other technical information, to permit exact reproduction of the feedback protocols with other equipment. Outcome criteria also vary in the replication studies, with varying measures of abstinence and improvement. An exception to these concerns is the report of Scott et al. (2005), which will be discussed later in greater detail.

It should be noted that psychostimulant (cocaine, methamphetamine) addictions may require approaches and neurofeedback protocols other than alpha/theta training. Persons who are cocaine-dependent are cortically under-aroused during protracted abstinence (Roemer et al. 1995). qEEG changes, such as a decrease in high beta (18–26 Hz) power are typical for withdrawal from cocaine (Noldy et al. 1994). Cocaine abusers who are still taking this drug often show low amounts of delta and excess amounts of alpha and beta activity (Alper 1999; Prichep et al. 1999), whereas chronic methamphetamine abusers usually exhibit excessive delta and theta activity (Newton et al. 2003). Thus, cocaine and methamphetamine users may warrant a different EEG biofeedback protocol, at least at the beginning stages of neurofeedback therapy.

The Scott–Kaiser Modification of the Peniston Protocol

Scott and Kaiser (1998) describe combining a protocol for attentional training (beta and/or SMR augmentation with theta suppression) with the Peniston protocol (alpha-theta training) in a population of subjects with mixed substance abuse, rich in stimulant abusers. The beta protocol is similar to that used in ADHD (Kaiser and Othmer 2000) and was used until measures of attention normalized, and then the standard Peniston protocol without temperature training was applied (Scott et al. 2002). The study group is substantially different than that reported in either the Peniston or replication studies. The rationale is based in part on reports of substantial alteration of qEEG seen in stimulant abusers associated with early treatment failure (Prichep et al. 1996, 2002) likely associated with marked frontal neurotoxicity and alterations in dopamine receptor mechanisms (Alper 1999). Additionally, preexisting ADHD is associated with stimulant preference in adult substance abusers, and is independent of stimulant associated qEEG changes. These findings of chronic EEG abnormality and high incidence of preexisting ADHD in stimulant abusers suggest they may be less able to engage in the hypnagogic and auto-suggestive Peniston protocol (Trudeau et al. 1999). Furthermore, eyes-closed alpha feedback as a starting protocol may be deleterious in stimulant abusers because the most common EEG abnormality in crack cocaine addicts is excess frontal alpha (Prichep et al. 2002).

In their initial report, Scott and Kaiser (1998) described substantial improvement in measures of attention and also of personality (similar to those reported by Peniston and Kulkosky 1990). Their experimental subjects underwent an average of 13 SMR-beta (12–18 Hz) neurofeedback training sessions followed by 30 alpha-theta sessions during the first 45 days of treatment. Treatment retention was significantly better in the EEG biofeedback group and was associated with the initial SMR-beta training. A subsequent published paper (Scott et al. 2005) reported on an expanded series of 121 inpatient drug program subjects randomized to condition, followed up at 1 year. Subjects were tested and controlled for the presence of attentional and cognitive deficits, personality states and traits. The experimental group showed normalization of attentional variables following the SMR-Beta portion of the neurofeedback, while the control group showed no improvement. Experimental subjects demonstrated significant changes (p \ .05) beyond the control subjects on 5 of the 10 scales of the MMPI-2. Subjects in the experimental group were also more likely to stay in treatment longer and more likely to complete treatment as compared to the control group. Finally, the one-year sustained abstinence levels were significantly higher for the experimental group as compared to the control group.

The approach of beta training in conjunction with alpha-theta training has been applied successfully in a treatment program aimed at homeless crack cocaine abusers in Houston, as reported by Burkett et al. (2003), with impressive results. Two hundred and seventy (270) male addicts received 30 sessions of a protocol similar to the Scott Kaiser modification. One-year follow-up evaluations of 94 treatment completers indicated that 95.7% of subjects were maintaining a regular residence; 93.6% were employed/in school or training, and 88.3% had no subsequent arrests. Self-report depression scores dropped by 50% and self-report anxiety scores by 66%. Furthermore, 53.2% reported no alcohol or drug use 12 months after biofeedback, and 23.4% used drugs or alcohol only one to three times after their stay. This was a substantial improvement from the expected 30% or less expected recovery in this group. The remaining 23.4% reported using drugs or alcohol more than 20 times over the year. Urinalysis results corroborated self-reports of drug use. The treatment program saw substantial changes in length of stay and completion. After the introduction of the neurofeedback to the mission regimen, length of stay tripled, beginning at 30 days on average and culminating at 100 days after the addition of neurotherapy. In a later study the authors reported follow-up results on 87 subjects after completion of neurofeedback training (Burkett et al. 2005). The follow-up measures of drug screens, length of residence, and self-reported depression scores showed significant improvement. It should be noted that this study had limitations, because neurofeedback was positioned only as an adjunct therapy to all other faith-based treatments for crack cocaine abusing homeless persons enrolled in this residential shelter mission and was an uncontrolled study. Yet the improvement in program retention is impressive and may well be related to the improved outcome.

R. L. Cannon – email: rcannon2@utk.edu
Department of Psychology, The University of Tennessee, Knoxville, TN 37996, USA

D. L. Trudeau – email: trude003@maroon.tc.umn.edu
Department of Family and Community Health, School of Health Sciences, University of Minnesota, Minneapolis, MN, USA

Abstract

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.

Introduction

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.

The Concept of Phenotypes

Prior studies using statistical analysis of electroencephalography (EEG) have documented clusters of EEG/quantitative EEG (QEEG) features within psychiatric populations (John, Prichep, & Almas, 1992). Experience over the past 30-plus years with a large number of clinical EEGs and, more recently, decades worth of clinical and research experience with QEEG, as well as a review of the field’s literature, have shown that a limited set of EEG patterns can characterize the majority of EEG variance.

In the perspective I’d like the reader to consider, these proposed EEG/EEG groupings might be considered as phenotypes, based on genetics. There is an indirect linkage between genetics and behavior, with an intermediate step. This intermediate step is one that is based on the expression of the genetics and other factors and constitutes the bridging between the person’s genetics and behavior: the phenotype. These phenotypic EEG/QEEG divergence patterns constitute reliable indices of brain function and predict response to therapy.

It should be noted that these phenotypic patterns are not isomorphic with the established Diagnostic and Statistical Manual of Mental Disorders (DSM) categories, although the phenotypes have powerful implications for therapeutic interventions, both with medication and with neurofeedback. One phenotype may be seen in a wide variety of DSM groupings, from posttraumatic encephalopathy, to affective and attentional related DSM groupings, to many more. These ideas have been published elsewhere (Johnstone, Gunkelman, & Lunt, 2005), although these concepts seem to have especially important perspective implications for this special issue of Biofeedback focusing on subtypes of specific DSM categories.(1)

The very concept of an EEG pattern’s being a subtype of a specific disorder seems foundationally flawed to me because of the lack of specificity of the pattern for the DSM grouping. The theta-beta ratio being increased for age may be a metric that is sensitive to attention deficit hyperactivity disorder (ADHD), but the same increase in this ratio also may be seen in a wide variety of other clinical presentations as well as in the absence of ADHD, so the lack of specificity remains a problem. I’d like to invite the reader to transcend the DSM’s limited perspective through the use of phenotypes.

Enhancing Neurofeedback Through the Use of Phenotypes

Behaviorally based neurofeedback interventions have been used with great effectiveness in the hands of good clinicians and practitioners, as evidenced by our field’s ever growing efficacy literature. This efficacy literature is based on actual clinical outcome data and provides support for the rapidly growing list of neurofeedback applications that can claim efficacy based on the jointly adopted Association for Applied Psychophysiology and Biofeedback (AAPB)/International Society for Neuronal Regulation (ISNR) efficacy template (La Vaque et al., 2002).

Recently, there have been several good publications detailing the state of the efficacy literature in biofeedback and neurofeedback, including the book by Yucha and Gilbert (2004), Evidence-Based Practice in Biofeedback and Neurofeedback, as well as several white papers on specific disorders, in a series sponsored by the AAPB and ISNR (Monastra et al., 2005; Moss, LaVaque, & Hammond, 2004).

Transcend the DSM Using Phenotypes

The literature on medication response prediction suggests that a phenotypic perspective may help enhance our efficacy (Suffin & Emory, 1995). This was also suggested in the outcome improvement reported by Wright and Gunkelman (1998) when he added the QEEG approach to guide neurofeedback.

The presence of genetically linked EEG patterns provides a solidly data-based set of observations on which to propose an initial list of phenotypic patterns. One EEG pattern with genetic links is the low-voltage fast EEG (Gunkelman, 2001). This low-voltage fast pattern was characterized as a phenotype in a recently published study of phenotypic patterns in alcoholism (Enoch, White, Harris, Rohrbaugh, & Goldman, 2002) and by others who have identified the genetic link to Gene 4’s regulation over gamma-aminobutyric acid receptors (Bierut et al., 2002).

Another EEG pattern with genetic links has been identified in some cases of idiopathic epilepsy (Haug et al., 2003). The paroxysmal epileptiform bursts seen in the EEG in these clinical cases may achieve many hundreds of microvolts, occasionally exceeding 400 to 600 ÌV, with spikes and slow components emerging from a relatively normal background EEG. In a survey of genetic factors in epilepsy, Kaneko, Iwasa, and Okada (2002) showed that the most common human genetic epilepsies display a complex pattern of inheritance and that the identities of the specific genes are largely unknown, despite recent advances in the science of genetics. They also show that the genetic markers of certain types of epilepsy have been identified, such as those with neurodegenerative characteristics and a small number of familial idiopathic epilepsies (Haug et al., 2003). A similar pattern of seizures and EEG phenotype is seen in a group of subjects with benign childhood epilepsy with centrotemporal spikes, found in three children with de novo terminal deletions of the long arm of Chromosome 1q. This suggests that this chromosomal location could be a potential site for a candidate gene (Vaughn, Greenwood, Aylsworth, & Tennison, 1996).

Characterizations linking genomic information, intermediate EEG phenotypes, and behavioral manifestation are likely to have important implications for therapeutics. The International Brain Database (Brain Resource Company) includes the EEG, event related potential, neuropsychological test measures, and genetic testing, thus making it unique in allowing an integrative approach that combines neurophysiology, neuroanatomy, cognition, and genetics. The M.I.N.D. Center at the University of California, Davis, uses the phenomic approach in working with pervasive developmental disorder/autism to avoid treating all clients alike in such a heterogeneous population and to help clarify research on clinical therapeutic effects.

Subtypes Versus Phenotypes

Although many studies have shown subtypes within a DSM-identified disorder, such as the work of Prichep et al. (1993) with obsessive-compulsive disorder and that of Chabot, Merkin, Wood, Davenport, and Serfontein (1996) identifying subgroups of ADHD, these subgroups do not have diagnostic specificity because they can be seen in other disorders. These EEG clusters did, however, predict treatment efficacy using medication in both the ADHD and obsessive-compulsive disorder studies. Suffin and Emory’s (1995) article identified frontal theta in attentional problems, similar to the Chabot et al. findings in ADHD, but the same pattern was also identified in affective disorders. The behavioral grouping did not predict the EEG/QEEG pattern, nor did behavior groupings predict the proper pharmaceutical treatment, although the EEG patterns did predict the effective drug intervention in both studies.

The medication intervention, when clinically based on prospectively identifying the phenotype, was the basis for the highly effective intervention in treatment refractory depression in recent pilot work (S. Suffin, personal communication, 2003) at the Sepulveda Veterans Affairs in Los Angeles. The application of these principles was also at the heart of the doubling of the clinical efficacy in a recent study using QEEG to design the neurofeedback intervention for the attention deficit disorder/ADHD population tested, as compared to behaviorally based neurofeedback interventions (Wright & Gunkelman, 1998).

In an updated and abbreviated review of prior work (Johnstone et al., 2005), the Appendix offers proposed phenotypic patterns as well as a listing of the associated neurofeedback interventions. A reading of the original article is advised for more detail, especially with respect to medication response prediction.

Conclusion: An Introductory Framework for EEG/QEEG Phenotypes

It must be remembered that phenotypes may coexist, and the various combinations are too variable to be handled in this limited presentation. This appendix should not be construed as a replacement for professional assistance in designing a neurofeedback intervention, nor in any way can this be used to fully characterize an individual’s EEG/QEEG.

Appendix: Electroencephalography (EEG)/Quantitative EEG Phenotypes

Note

1. The material is adapted from Johnstone, Gunkelman, and Lunt (2005), Clinical database development: Characterization of EEG phenotypes. Clinical EEG and Neuroscience, 2, 99-107, and is used with the permission of Clinical EEG and Neuroscience.

References

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Chabot, R. J., Merkin, H., Wood, L. M., Davenport, T. L., & Serfontein, G. (1996). Sensitivity and specificity of QEEG in children with attention deficit or specific developmental learning disorders. Clinical EEG, 27, 26-34.

Chabot, R. J., & Serfontein, G. (1996). Quantitative electroencephalographic
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Enoch, M. A., White, K. V., Harris, C. R., Rohrbaugh, J. W., & Goldman, D. (2002). The relationship between two intermediate phenotypes for alcoholism: Low voltage alpha EEG and low P300 ERP amplitude. Journal of Studies on Alcohol, 63, 509-517.

Gunkelman, J. (1998). Evaluating the frontal lobes in affective and attentional disorders with QEEG and EP—the electrophysiology of frontal lobe disconnection syndrome: Implications for neurotherapy. Journal of Neurotherapy, 3(1-2).

Gunkelman, J. (2001). Low voltage or absolute power. Journal of Neurotherapy, 5(1-2), 107-110.

Haug, K., Warnstedt, M., Alekov, A. K., Sander, T., Ramirez, A., Poser, B., et al. (2003). Mutations in CLCN2 encoding a voltage-gated chloride channel are associated with idiopathic generalized epilepsies. Nature Genetics, 33, 527.532.

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Johnstone, J., Gunkelman, J., & Lunt, J. (2005). Clinical database development: Characterization of EEG phenotypes. Clinical EEG and Neuroscience, 2, 99-107.

Kaneko, S., Iwasa, H., & Okada, M. (2002). Genetic identifiers of epilepsy. Epilepsia, 43(Suppl. 9), 16-20.

La Vaque, T. J., Hammond, D. C., Trudeau, D., Monastra, V., Perry, P., & Lehrer, P. (2002). Template for developing guidelines for the evaluation of the clinical efficacy of psychophysiological interventions. Applied Psychophysiology and Biofeedback, 27, 273-281.

Monastra, V., Lynn, S., Linden, M., Lubar, J. F., Gruzelier, J., & Vaque,T.J.(2005). Electroencephalographic biofeedback in the treatment of Attention Deficit/Hyperactivity Disorder, Applied Psychophysiology and Biofeedback, 30(2), 95-114.

Moss, D., LaVaque, T. J., & Hammond, D. C. (2004). Introduction to white paper series—Guest editorial. Applied Psychophysiology and Biofeedback, 19, 151-152.

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Prichep, L. S., & John, E. R. (1992). QEEG profiles of psychiatric disorders. Brain Topography, 4, 249-257.

Prichep, L. S., Mas, F., Hollander, E., Liebowitz, M., John, E. R., Almas, M., et al. (1993). Quantitative electroencephalographic subtyping of obsessive-compulsive disorder. Psychiatry Research, 50(1), 25-32.

Suffin, S. C., & Emory, W. H. (1995). Neurometric subgroups in attentional and affective disorders and their association with pharmacotherapeutic outcome. Clinical Electroencephalography, 26, 76-83.

Vaughn, B. V., Greenwood, R. S., Aylsworth, A. S., & Tennison, M. B. (1996). Similarities of EEG and seizures in del(1q) and benign rolandic epilepsy. Pediatric Neurology, 15, 261-264.

Wright, C., & Gunkelman, J. (1998, September). QEEG evaluation doubles the rate of clinical success: Series data and case studies. Abstract presented at the sixth annual conference of the Society for the Study of Neuronal Regulation, Austin, TX.

Yucha, C., & Gilbert, C. (2004). Evidence-based practice in biofeedback and neurofeedback. Wheat Ridge, CO: Association for Applied Psychophysiology and Biofeedback.

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