In Part 2 you see how Noah parents learn there are alternatives to Ritalin and other drugs that may be given to their child. Learn about how Neurofeedback and EEG Brain Mapping may be able to help without the use of dangerous pharmaceutical drugs.

Dr. Linden is a Clinical Psychologist and Nationally Certified in Neurofeedback and Biofeedback. He is the director of The Attention Learning Center, which has offices located in San Juan Capistrano, Irvine and Carlsbad, California.

Dr. Linden is a regular contributor to the Journal of Neurotherapy and has been a speaker in many seminars and conferences related to ADD/ADHD and neurotherapy.

It’ll be the first Neuroscience Multidisciplinary Meeting hosted by the Brainmech Foundation in Spain after the last conference held in Holland in 2007. This is a unique oppurtunity for professionals to learn today what investigators and scientists on neuroscience are preparing for the future.

It’ll be the meeting point for Psychiatrists, Psychologists, Neurologists and Pediatricians that will have the chance to learn from the authors about the last investigations and researches on the human brain, new methods of diagnosis, new diagnosis criteria on mental disorders proposed for the DSM-V, neurobiologist database of the ADHD, bipolar disorder, as well as the new treatments and therapy for neurological illness and psychiatric malfunctions.

The purpose of this challenge is to promote a more integrative and innovative approach to Brain (EEG) – Body (Heart Rate) analysis. Brain Resource is sponsoring the challenge with the winner to receive $5,000USD.

The Challenge

Take 20 EEG and Heart Rate recordings from children diagnosed with ADHD and 20 recordings from a control population, and develop an analysis method that demonstrates any new insight relevant to ADHD using the data. The insight may have a basic science or applied clinical perspective.

Each dataset was recorded during a Go/NoGo paradigm and contains EEG, Heart Rate, respiration and Sweat Rate (skin conductance) channels, as well as stimulus and response information. The data sets are sourced from the Brain Resource International Database via BRAINnet.

BRAINnet is a network of scientists from around the world. Its members have access to a large body of data, including genomic information, elemental brain and body function measures, structural and functional MRI, cognitive and medical history data. These data are from healthy people, and those experiencing a range of different brain-related illnesses. Data are provided freely from the Brain Resource International Database for Independent research and for scientific publication, without requiring contribution of data. For more information see BRAINnet.net.

Participants in the challenge will retain all IP rights in their work and may freely use the data for non-commercial research and scientific publication after the competition has closed.

Entries

To obtain the data and program required for the BRAINnet challenge, please visit http://www.brainnet.net and follow the links from the homepage.

Alternatively you may contact us at competition@BRAINnet.net
You will be given access to the following data and software program to do the processing:

1. 20 files from children diagnosed with ADHD.
2. 20 files from control subjects with the same age range.
3. A Java-based program, Jeda, which is needed to extract the data and which should be used for performing the analysis (see http://www.brain-dynamics.net/jeda/).
4. 22 datasets used for demonstrating the power and flexibility of the Jeda program.

Each entry should consist of an account of your method, and should comprise up to 800 words and up to 5 figures. Entries will be accepted from groups as well as individuals.

Judging

Judging will be based on:
Demonstrating the most innovative or pragmatic analysis of the EEG – Heart Rate supplied data.

Analysis in any one modality (such as EEG or Heart Rate alone) will be considered, but preference will be given to analyses which combine EEG and Heart Rate measures.

Dr Evian Gordon (CEO Brain Resource) will judge the winner. His decision is final.

Closing Date

December 31, 2009. The name of the winner of the challenge will be published in the next edition of Clinical EEG & Neuroscience Journal.

Neurotherapy using slow cortical potentials also shows promise in the treatment of epilepsy (Kotchoubey et al., 2001; Birbaumer et al., 1981; Sterman, 2000). Neurotherapy has also been used for ADD/ADHD (Monastra, Monastra, & George, 2002) depression (Rosenfeld, 1997), anxiety (Vanathy, Sharma, & Kumar, 1998), fibromyalgia (Donaldson, 2002), and for cognitive enhancement (Budzynski, 2000; Klimesch, et al.). Commonly reported success rates of 60 to 90% are reported  (Wright & Gunkelman, 1998).

Neurofeedback is an emerging neurosciencebased clinical application based on the general principles of biofeedback or cybernetics. The Neurofeedback process involves training and learning self regulation of brain activity. Understanding the underlying principles of this process allows the therapist to provide referrals or treatment to their clients with some added understanding, and provides clients with  a framework for understanding the neurofeedback process. The following short paper will provide a quick review of the brain’s function, and the underlying process involved in neurofeedback, a technique  that will allow the client to better regulate and operate their brain.

The brain controls its own blood supply through the dilation and constriction of the blood vessels, and the blood flow is directed to areas that are more active through this self-regulation. The blood supply’s flow, along with the utilization of the oxygen and glucose the blood carries is measured as “perfusion,” a measure that is clearly seen in some of the modern imaging techniques, such as Positron Emission  Tomography (PET) and SPECT technology. Though these techniques are invasive, requiring the injection of small amounts of very short half-life radioactive materials, they do give good resolution of the perfusion due to the emission of the positrons, which are emitted from where the brain utilizes the oxygen and burns the glucose carried by the blood flow.

A research project performed at UCLA’s Neuropsychiatric Institute (Cook, O’Hara, Uijtdehaage, Mandelkern, & Leuchter, 1998) showed that the brain’s electrical activity, or electroencephalogram (EEG), had specific correlates of the brain’s perfusion. This is useful in that the EEG is capable of showing when the perfusion is low, such as  seen frontally in ADD/ADHD. In these situations, the EEG shows a resting or idling rhythm of alpha (8–13 Hz) and/or theta (4–7 Hz), frequency patterns in the EEG that have rhythmic waveforms.
(For a general review of electroencephalography see Niedermeyer & Lopes Da Silva, 1999).

In ADD/ADHD a study of over 400 participants using a neurometric approach (see Prichep & John, 1992; Prichep et al., 1993) showed that there were generally findings of excess alpha and/or theta in the frontal lobes (Chabot & Serfontein, 1996), which corresponded to the frontal hypoperfusion seen in ADD/ADHD with the PET or SPECT perfusion studies. The frontal lobes are executive areas in the  brain, which control attention, emotions (affect) and impulsivity, as well as regulate (inhibit) the motor areas of the brain. The Chabot study (1996) also showed that the EEG could be used to differentiate those with ADD/ADHD from normal clients, as well as differentiating ADD/ADHD from those participants with a learning disability (LD). The LD population was shown to have a slower pattern, with excess activity in the delta frequencies (1–3.5Hz) over the central and parietal lobes (posteriorly at the crown of the head). These areas are responsible for integrating raw sensory stimuli into perceptually interpretable activity.

More recently, a comparison of children and adults seen in a single neurofeedback practice specializing in ADD/ADHD was performed (Gurnee, 2000). This study showed that unlike the children’s study, which showed theta to be the dominant pattern, the adults had an alpha dominance, likely due to maturational changes that have increased the frequencies. In the children the excess theta group is over 50% of the cases, with the adult group showing excess theta to only comprise about 25% of the incidence.

The qEEG data may also be used to select specific medications if a pharmacotherapeutic approach is preferred. The qEEG pattern of frontal theta responds better to stimulants such as methylphenidate (Ritalin), whereas the frontal alpha type responds better to antidepressants. If a specific statistical measure called ‘coherence’ is deviant (too high or too low), the participant may require an anticonvulsant (Suffin & Emory, 1995). These patterns also may coexist such that an individual may require two or more types of medication.

Physicians generally use behavioral indicators in choosing psychoactive medication. However, it was clear from the work of Suffin and Emory that neurophysiological profiles can be used to guide prescription, even in populations of patients with similar behavioral disturbance (e.g., attentional or affective disorders as defined by DSM). Most physicians generally find the proper medication the old fashioned way . . . by trial and error. The “best guess” medication selection method requires more doctor office visits, medication trials, and has the possibility of significant side effects. All this is generally avoided with the more objective qEEG based method, which is based on the person’s physiology, not the behavior. It is not difficult to see why this is the case. The medication treats the physiology, hoping to affect the behavior. The measurement of the physiological indicators should logically be more related to the proper medication choice, since this is what is actually being treated.

The stimulant medications typically decrease appetite, with weight loss commonly noted, as are sleep problems. Long-term use of stimulants have been known to cause teeth grinding (Bruxism), cardiac rhythm changes, blood pressure increases, weight loss, changes in sleep patterns, anxiety/nervousness and even “psychotic” symptoms (such as hearing voices or other sensory hallucinations). There are also those with medical contraindications for stimulant use, such as heart problems, gastro-intestinal and blood pressure problems and other more rare complications that preclude prescribing them. In these individuals, as in those with complications from taking the medications, the presence of an alternative treatment is essential for proper behavioral adjustment and scholastic achievement. For those individuals uncomfortable with using potent medications, or those with adverse side effects, it is fortunate that a non-medication intervention is available.

The choice of medicating the client requires continued treatment, as it is merely a temporary change, due to the drug’s effects. Neurotherapy is a treatment, which changes the way the brain works, and once the skill is learned, (unlike medication) it appears to be persistent. Follow-up studies show long-term change in the brain’s function following neurotherapy (Monastra, 2003). Both of these methods (medication and neurotherapy) improve the client’s attentional and behavioral states. The choice of which method to use is merely a personal choice. Medications, when used long term, may end up being more expensive than Neurotherapy. Neurotherapy has less likelihood of having side effects than does the medication, but it takes a number of training sessions before the effect is noted and becomes more persistent.

It is no surprise that the brain can learn, but what may surprise some is that the brain changes structurally when it learns. This morphologic change is microscopic, the forming and reinforcing of small connections between a part of a neuron, called “dendrite,” but it is a structural change, nevertheless. This highly changeable connective nature is referred to as “neural plasticity,” based on the original definition of plastic, not as a substance, but as a descriptor of the malleability or change—ability of materials or structures.

The brain has a method of developing and expanding the pathways that are used, and “pruning” the connections that aren’t utilized. This process is most dramatic early in life, but continues throughout life. We are born with about twice as many neurons as are present when we become young adults. The pathways that are more consistently utilized are protected from the pruning process through a mechanism still unknown to science, though the fact of the change is irrefutable.

Another time when this process of plasticity is evident is following damage, such as head injury, or disuse of an area, due to “deafferentation,” such as when hearing is lost. In these situations the surrounding functions may take over an area not utilized, occasionally causing some subjective changes, which may be uncomfortable. One example of this is tinnitus, or ringing in the ears, following loss of hearing; another is “phantom pain” when a limb is severed and is no longer present, but sensations seeming to come from the missing limb are felt. The functions adjacent to these areas in the brain merely intrude into the area and the person misinterprets these new
signals as the older inputs.

These examples are dramatic, but “growththrough-utilization” is the underlying process we want to focus on. This process is how we build additional capacity for the nervous system to do its work. Analogous to exercise building muscle mass, the utilization of the brain builds the mass of the brain’s dendritic connections.

Certain intense negative experiences may change the body’s chemistry, increasing the stress hormone released from the adrenal cortex of the kidneys. This chemical, cortisol, is a healthy response to stress, though with chronic or overly intense stressors, the cortisol has deleterious effects on the brain, specifically attacking a temporal lobe structure, the hippocampus, which has immune receptors. This structure has important non-immune system functions as a memory comparator, required for both comprehension and recall. The implication for this latter process where stress deteriorates the brain’s ability to comprehend and recall has large implications for education. A person with a stressful existence may never reach his or her true potential due to the damaging effects of the stress hormones. If the stress was experienced during pregnancy from the mother’s hormonal balance, the newborn will have a disproportionately intense reaction to stress, causing inordinately large amounts of strain (and thus more cortisol) and ultimately more extensive deterioration of the brain’s capacity.

The ability to teach a new response to the situational stressors can change the life course for these individuals, creating a much more favorable outcome. The operant conditioning technique, Neurotherapy, mentioned earlier is one such method of intervention. Similarly to the stress response, there is a relaxation response, which can be trained to counteract these deleterious effects.

The brain’s electrical patterns are a form of behavior, which is subject to behavior modification through “operant conditioning,” a fact discovered in research done in the late 1960s by Dr Barry Sterman, now a professor emeritus at UCLA. The operant conditioning of the EEG was first demonstrated in cats, where placebo effects are assumed to be absent. Sterman’s original work with animals was replicated in humans starting in the 1970s (Sterman, 2000).

Recording and analysis of EEG has been shown to yield reliable results (Fein, Galin, Yingling, Johnstone, & Nelson, 1984). Further, those studies done with control groups have shown the neurotherapy technique to be a robust and valid intervention. Many more studies are of a case series variety, without control groups. Though this latter category is not held in high regard, perhaps this is changing. A recent issue of the New England Journal of Medicine reviews of research design have cast doubt on the need for placebo-controlled designs. Their review has shown that when there is a preponderance of case series reports, the concordance between those results and those of the “gold standard” (double blind placebo-controlled studies) was very high. Many in the field are now arguing against doing a double blind study due to the lack of proper humane treatment of those in the control group (receiving no treatment), an approach which is also now considered unethical by the World Health Organization when known treatments exist. Interestingly, recent work with placebo effect has elucidated brain mechanisms underlying placebo response that were different than those mediating medication response (Leuchter, Cook, Morgan, Witte, & Abrams, 2002).

With the neurotherapy approach, the brain frequencies that are in excess are reduced, and those with a deficit are increased. The technique uses the EEG, amplified from the minute voltages and hooked up with special instruments to control a computer game. The person’s EEG is the “joystick” they use to operate the game. Over a series of sessions the person learns to use the EEG to control the game. The clinician slowly adjusts criteria for reward presented to the individual, and thereby “shapes” the behavior of the participant’s brain into a more normal pattern.

The neurotherapy technique requires time to learn, and varies depending on the initial condition the individual starts with. In general, the more severe the starting condition, the more learning has to occur to correct the state. Simple relaxation may take as few as 10 sessions to learn; although with more severe cases, a longer training course may be needed, such as with generalized anxiety disorder, or panic attacks. The important point is that the learning is internalized so that the benefit of the training persists, and does not require on-going training. This is unlike the use of medications where symptoms typically reappear after discontinuing medication.

The learning curve for EEG has been described in research done at Langley Porter Neuropsychiatric Institute, at the University of California, San Francisco. The research showed that the curve is a fifth-order curve, which contains an initial increase, followed by a dip, a second increase, followed by the exponential increase at the end of the training (Hardt, 1975). This corresponds to the subjective states reported in some individuals. They are initially presented in a slightly anxious state, which gets better when they habituate to the training situation, corresponding to the initial increase in the curve. The individuals then report that they “try hard to relax,” a counterproductive attempt, which corresponds to the dip. They give up trying hard (active volitional attempts), corresponding to the second increase, which is then followed by the learning of the passive volitional state, which is the final exponential increase.

In some cases, the individuals may need more peripheral forms of biofeedback-based intervention, such as muscle relaxation or training of temperature or the electrodermal responses. These will depend upon whether their individual response profile shows their stress in these areas as well. The peripheral training often requires less training time, though the source of the difficulty is universally central, as these modalities are all under the control of the central nervous system. Training peripherally may be all that is required in more mild cases, but in many individuals, the central training is the only method that will have a persistent result. In our clinical experience with these more severe cases there may be symptom substitution without the central intervention. In cases with mild stress, the peripheral intervention is often a complete intervention, though when additional complaints such as attentional problems, depression, or hyperactivity are noted, the central intervention is usually the first choice, to minimize the total number of sessions, by getting directly to the common source
of the problem.

Prior to starting work with an individual, and in order to design an appropriate operant conditioning intervention, their existent brain function must be known. Optimally, this would entail a full recording of their EEG, with quantitative analysis and comparison of the individual’s brain activity to an age matched database. These databases are commercially available from a number of sources, and are described in a recent Journal of Neurotherapy special edition (Vol. 7, No. 3 and 4, 2003). Following such a comparison, areas that deviate from normal may be identified, as well as the direction of the deviation from normal. This shows whether an excess or a deficit of any frequency pattern exists, as well as the location of the deviation. Specific individualized patterns of results are used to guide intervention with neurotherapy.

Following this evaluation, an appropriately customized operant training may be designed which optimizes the training time by focusing on the areas of deviation. The training will take time, with 30 to 40 sessions being quite common before a permanent result is established, and even more are required for more severe or complex cases such as Asperger’s/Autism (Thompson & Thompson, 2003). There are commonly reported behavioral changes long before this end point of training is reached, with the early signs of change showing themselves at 5–10 sessions for most individuals, though some have strong changes even after their initial session. It is also common for an individual to not notice the change, as they are occasionally not self-aware, though the changes are easily seen in objective testing and reported by those observing the individual’s behavior.

Commonly reported success rates of 60 to 80% are seen in the scientific literature, with up to 90% reported in qEEG based intervention (Wright and Gunkelman, 1998). Using strict criteria (total remission of complaint) the percentage range from 50 to 60%, with those reporting positive results, though with less stringent measurements of success, such as “feeling like you got a positive benefit,” ranging in the 80 to 90% rate.

Many therapists are not aware of neurofeedback as an application of operant conditioning, being familiar with more easily observable behavioral operant training than the operant training of “internal states.” The neurofeedback literature is most well accepted in the area of operant training of EEG in epilepsy. Well-controlled studies show that the technique can assist in cases where medication alone was shown to be inadequate at controlling the electrical discharges associated with the epilepsy. A review of this application is published in a special edition of Clinical EEG, in the January 2000 issue (Sterman, 2000). Neurotherapy using slow cortical potentials also shows promise in the treatment of epilepsy (see, Kotchoubey et al., 2001; Birbaumer et al.,1981).

Since the EEG in epileptics can be taught to stop the abnormal discharges, leading to the elimination of the behavioral manifestations of the epilepsy, the neurotherapy technique has also been applied in less severe neurological disorders such as ADD/ADHD (Monastra et al., 2002) depression (Rosenfeld, 1997), anxiety (Vanathy et al., 1998), and fibromyalgia (Donaldson, 2002). Budzynski (2000) used neurofeedback to reverse cognitive decline in an elderly population (see also the work of Klimesch et al. for studies of EEG related to memory performance). For recent reviews of neurobehavioral disorders noted to respond to this emerging technologically based operant training technique see Yucha and Gilbert (2004), and Nelson, (2003).

There are two international professional organizations dedicated to the study of this technology and these applications: the Association of Applied Psychophysiology and Biofeedback (AAPB), and the International Society for Neuronal Regulation (ISNR). Both of these societies have annual conferences and often sponsor additional regional workshops. There are also many state and regional organizations, often affiliated with one of these international organizations. ISNR also has international chapters in other countries. Both organizations have web sites (www.aapb.org and www.isnr.org), and both sponsor a professionally published journal with material focused on Neurofeedback: The Journal of Neurotherapy (ISNR), and Applied Psychophysiology and Biofeedback (AAPB).

REFERENCES
Birbaumer, N., Elbert, T., Rockstroh, B., et al. (1981). Biofeedback
of event-related slow potentials of the brain. International  Journal of Psychology, 16, 389–415.
Budzynski, T. H. (2000). Reversing age-related cognitive decline:
Use of neurofeedback and audio-visual stimulation. Biofeedback,  28, 19–21.
Chabot, R. J., & Serfontein, G. (1996). Biological Psychiatry, 40, 951–963. Sensitivity and specificity of QEEG in children with attention deficit or specific developmental learning disorders.  Clinical EEG, 27, 26–34.
Cook, I. A., O’Hara, R., Uijtdehaage, S. H., Mandelkern, M., &  Leuchter, A. F. (1998). Assessing the accuracy of topographic  EEG mapping for determining local brain function. Electroencephalography  and Clinical Neurophysiology, 107(6),  408–414.
Donaldson, S. (2002). Society for Neuronal Regulation Annual Meeting, Scottsdale, AZ.
Fein, G., Galin, D., Yingling, C. D., Johnstone, J., & Nelson, M. A.
(1984). EEG spectra in 9–13-year-old boys are stable over 1–3
years. Electroencephalography and Clinical Neurophysiology,
58(6), 517–518.
Gurnee, R. L. (2000). EEG Based Subtypes of Anxiety (GAD)
and Treatment Implications Society for Neuronal Regulation
Annual Meeting.
Hardt, J. V. (1975). The ups and downs of learning alpha feedback.
Proceedings, Biofeedback Research Society, Vol. 6,Monterey,
California, February.
Klimesch, W. (1999). EEG alpha and theta oscillations reflect
cognitive and memory performance: A review and analysis.
Brain Research and Brain Research Review, 29(2–3), 169–195.
Kotchoubey, B., Strehl, U., Uhlmann, C., Holzapfel, S., Konig,
M., Froscher, W., Blankenhorn, V., & Birbaumer, N. (2001).
Modification of slow cortical potentials in patients with refractory
epilepsy: A controlled outcome study. Epilepsia, 42(3),
406–416.
Leuchter, A. F., Cook, I. A., Morgan, M. L., Witte, E. A., &
Abrams, M. (2002). Changes in brain function of depressed
subjects during treatment with placebo. American Journal of
Psychiatry, 159(1), 122–129.
Monastra, V. J., Monastra, D.M., & George, S. (2002). The effects
of stimulant therapy, EEG biofeedback, and parenting style
on the primary symptoms of attention-deficit/hyperactivity
disorder. Applied Psychophysiology and Biofeedback, 27(4),
231–249.
Nelson, L. A. (2003). Neurotherapy and the challenge of empirical
support: A call for a neurotherapy practice research network.
Journal of Neurotherapy, 7(2), 53–67.
Niedermeyer, E., & Lopes Da Silva, F. (Eds). (1999). Electroencephalography:
Basic Principles, Clinical Applications, and
Related Fields (4th ed.). Baltimore: Lippincott, Williams &
Wilkins.
Prichep, L. S., & John, E. R. (1992). QEEG profiles of psychiatric
disorders. Brain Topography, 4(4), 249–257.
Prichep, L. S., Mas, F., Hollander, E., Liebowitz, M., John,
E. R., Almas, M., DeCaria, C. M., & Levine, R. H. (1993).
Quantitative electroencephalographic subtyping of obsessivecompulsive
disorder. Psychiatry Research, 50(1), 25–32.
Rosenfeld, J. P. (1997). EEG biofeedback of frontal alpha asymmetry
in affective disorders. Biofeedback, 25(1), 8–25.
Sterman, M. B. (2000). Basic concepts and clinical findings in the
treatment of seizure disorders with EEG operant conditioning.
Clinical Electroencephalography, 31(1), 45–55.
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.
Vanathy, S., Sharma, P. S. V. N., & Kumar, K. B. (1998). The efficacy
of alpha and theta neurofeedback training in treatment
of generalized anxiety disorder. Indian Journal of Clinical
Psychology, 25(2), 136–143.
Wright, C., & Gunkelman, J. (1998). QEEG evaluation doubles
the rate of clinical success. Series data and case studies.
Abstracts 6th Annual Conference, Society for the Study of
Neuronal Regulation, September 10–13, Austin, TX.
Yucha, C., & Gilbert, C. (2004). Evidence-based practice in
biofeedback and neurofeedback. Association of Applied Psychophysiology
and Biofeedback (www.aapb.org), 2004.

1Q-Metrx, Inc., Burbank, CA.
2Department of Psychology, University of California, Los
Angeles, CA.
3To whom correspondence should be addressed at Q-Metrx, Inc.,Burbank, CA

Neurofeedback is a treatment where real-time feedback is provided for specific brain activity (most often EEG) in order to learn the brain to suppress or produce specific brain activity. This method was initially discovered for the treatment of Epilepsy and from 1976 investigated further for the treatment of ADHD. This technique has become more popular by clinicians worldwide, and is currently provided for the treatment of several disorders. Critics have often questioned the efficacy of Neurofeedback and whether it can be considered an Evidence Based treatment or not.

In collaboration with researchers from Tübingen University (Germany), Radboud University (Nijmegen, the Netherlands), Brainclinics and EEG Resource Institute a so-called meta-analysis was conducted on all published research about Neurofeedback treatment in ADHD. This meta-analysis included 15 studies and 1194 ADHD patients. Based on this study – which will be published in the July issue of EEG and Clinical Neuroscience – it could be concluded that Neurofeedback can indeed be considered an Evidence-Based treatment for ADHD. The results show that neurofeedback treatment has large and clinically significant effects on Impulsivity and Inattention and a modest improvement of Hyperactivity.

These findings apply to Neurofeedback treatment for ADHD, but do not automatically imply that Neurofeedback can be considered evidence based for any disorder. The efficacy of Neurofeedback has to be assessed separately for each disorder. For example, a meta-analysis of EEG biofeedback in Epilepsy is published in the same issue of EEG and Clinical Neuroscience demonstrating clinical efficacy in the treatment of epilepsy.

Interested clients are advised to make an informed choice regarding Neurofeedback therapists, since there is a large heterogeneity in neurofeedback treatment approaches and clinicians. It is advised to look for psychologists or physicians who are at least a member of a professional organization such as the International Society for  Neurofeedback and Research (ISNR: www.isnr.org) or other professional organizations and who use investigated methods.

Literature Arns, M., de Ridder, S., Strehl, U., Breteler, M. & Coenen, A. Efficacy of Neurofeedback Treatment in ADHD: The effects on Inattention, Impulsivity and Hyperactivity: a Meta-Analysis. EEG and Clinical Neuroscience; 40(3), 180-189.

For children with attention deficit/hyperactivity disorder (ADHD), a reduction of inattention, impulsivity and hyperactivity by neurofeedback (NF) has been reported in several studies. But so far, unspecific training effects have not been adequately controlled for and/or studies do not provide sufficient statistical power. To overcome these methodological shortcomings we evaluated the clinical efficacy of neurofeedback in children with ADHD in a multisite randomised controlled study using a computerised attention skills training as a control condition.

Methods: 102 children with ADHD, aged 8 to 12 years, participated in the study. Children performed either 36 sessions of NF training or a computerised attention skills training within two blocks of about four weeks each (randomised group assignment). The combined NF treatment consisted of one block of theta/beta training and one block of slow cortical potential (SCP) training. Pre-training, intermediate and post-training assessment encompassed several behaviour rating scales (e.g., the German ADHD rating scale, FBB-HKS) completed by parents and teachers. Evaluation (‘placebo’) scales were applied to control for parental expectations and satisfaction with the treatment. Results: For parent and teacher ratings, improvements in the NF group were superior to those of the control group. For the parent-rated FBB-HKS total score (primary outcome measure), the effect size was .60. Comparable effects were obtained for the two NF protocols (theta/beta training, SCP training). Parental attitude towards the treatment did not differ between NF and control group. Conclusions: Superiority of the combined NF training indicates clinical efficacy of NF in children with ADHD. Future studies should further address the specificity of effects and how to optimise the benefit of NF as treatment module for ADHD. Keywords: Neurofeedback, attention deficit/hyperactivity disorder (ADHD), slow cortical potentials (SCPs), theta/beta training, randomised controlled trial (RCT), EEG.

Read the full PDF here.

Holger Gevensleben,1 Birgit Holl,3 Bjo¨rn Albrecht,1 Claudia Vogel, Dieter Schlamp,3 Oliver Kratz,2 Petra Studer,2 Aribert Rothenberger, Gunther H. Moll,2 and Hartmut Heinrich2,3 1Child & Adolescent Psychiatry, University of Go¨ttingen, Germany; 2Child & Adolescent Psychiatry, University of Erlangen-Nu¨rnberg, Germany; 3Heckscher-Klinikum, Mu¨nchen, Germany

Theta                              Absolute Beta                     Relative Beta

Figure 1: This figure shows the average brain activity (quantitative EEG – QEEG) of 275 children with ADHD, compared to a control group. On the left the increased theta EEG activity (p<.0001) can be seen, in the middle the absolute beta EEG activity (p<.0001) and on the left the decreased relative beta EEG activity (p<.0001). This deviant brain activity has a fronto-central localization. This pattern is found in almost all ADHD studies.

If one takes a look at the individual data, however, (see figure 2) a completely different picture emerges. In figure 2 the individual data of 36 random ADHD patients is represented drawn from this same Brain Resource International Brain Database. In the table below the following is represented Theta – Red; Alpha – Yellow and Beta – light blue. The quantative EEG data of these children – or QEEG’s – are compared to a normative database of more than 5000 healthy controls enabling an individual comparison. It can clearly be seen that indeed 47% of the ADHD patients have an increased theta activity. However, 5,6% of the patients show a decreased beta activity and 22% show an increased beta activity. The increased beta can be explained by the presence of beta spindles which occur in 20% of the cases of the ADHD children and which demand a different treatment strategy. The inter-individual variability within a behaviorally homogenous group such as ADHD patients thus is quite considerable.

Figure 2: This figure shows the EEG data of 36 random children (4-digit ID codes) with ADHD from the same dataset as in figure 1. This time, however, the individual data are represented. Indeed some children show an increased theta EEG activity (47%), but only 5,6% of the children show a decreased beta EEG activity and 22% show an increased beta EEG activity. Note the contrast between the individual and group data.

Considering the fact that Ritalin does not have a clinically meaningful effect in 20-40% of the ADHD patients (Swanson et al., 1993; Gordon, 2007) is seems plausible that the cause of it is to be found in the interindividual variability of the brain functioning, as shown.

The above mentioned group of ADHD children is part of a large scale clinical investigation, and therefore all of these children were treated with a stimulant such as Ritalin. Consequently we tested the prior mentioned hypothesis. A group of 50 of those children were divided into different groups according to their individual EEG phenotype, as seen in figure 3. It turned out that only the group with frontal slow activity (frontal theta) responded well to treatment with stimulant medication (Methylphenidate: Ritalin), as measured by the improvement on a continuous performance test (CPT), whereas the other groups did not show any improvement on the CPT as a result of medication (Arns et al. 2008).

Comparable studies of ADHD have shown that patients who responded well to stimulant medication such as Ritalin indeed showed excess slow brain activity frontally (Delta en Theta: Clarke et al., 2002; Satterfield et al., 1972; Suffin & Emory, 1995). This is clearly a well identifiable ADHD sub-type that responds well to medication. This knowledge can very well be applied to personalizing ADHD treatment not only with respect to pharmacotherapy but also for EEG Biofeedback or Neurofeedback treatment.

Figure 3: In the figure above the prevalence of the occurrence of the different EEG Phenotypes is shown for a group of ADHD children and a matched control group. It can clearly be seen that ADHD and control group differ particularly on op Frontal Slow, Slow Alpha Peak Frequency, Low Voltage en Frontal Alpha. This demonstrates that there is a great diversity of EEG patterns observed within a group of patients, but also within a group of ‘healthy’ controls. According to the literature these ADHD patients will all respond favourably to different medications, the Frontal Slows will respond best to Ritalin (Arns et al., 2008) and the Frontal Alpha will respond better to an SSRI (Suffin & Emory, 1995).

To Read the full Research report EEG PHENOTYPES PREDICT TREATMENT OUTCOME TO STIMULANTS IN CHILDREN WITH ADHD

For more background information on EEG Based Pesonalized Medicine also see: http://www.brainclinics.com/personalized-medicine