The two images below show the referential and sequential montage display of an active right temporal-parietal spike and slow wave focus, seen in a child clinically diagnosed with an attachment disorder. There was no history of convulsion, nor any suspicion of the actual underlying pathophysiological basis for the behavioral presentation.

The focus cortical area is normally involved in comprehension of facial expression and body language, as well as the prosodic (emotive) aspects of language. Any disturbance in that cortical area’s function generally has social contextual implications for behavior due to “prosodic blindness”. (see: Van Bloem, L. QEEG in Children with Reactive Attachment Disorder,

Journal of Neurotherapy, 4(4), 2001.

The implications for treatment option with this pathophysiological source for the behavioral presentation which could really only be discovered through the EEG are enormous. The use of an anticonvulsant or an approach with one of the proven efficacious applications of Neurofeedback in treating epilepsy can be used to target the underlying cause, rather than trying to effect some symptomatic control with antipsychotic or antidepressant medications so commonly used in these situations of severe attachment disorder. (see a review of SMR applied to epilepsy by Dr. M. Barry Sterman, Professor Emeritus, UCLA, from 2000 in Clinical Electroencephalography’s special edition on Neurofeedback)

In these images the referential focus is seen associated with the largest waveform, though in the sequential data the 180 degree phase reversal points to the focus.

EEG & Epilepsy - Referential Montage Display

EEG & Epilepsy - Sequential Montage Display

In modern neuroscience centers, the EEG is still the tool of choice in evaluation of convulsive epilepsy, as well as some other non-convulsive forms, such as staring episodes seen in “absence epilepsy” typically as a 3/second spike and wave dominant anteriorly, or temporal lobe epilepsy, which is seen as a “notched” slow wave discharge fronto-temporally.

The EEG can now be processed through algorithms, such as spike dipole localization software, to identify the “seizure focus” cortically, or spectral averaging to look for changes in the underlying EEG rhythmicity due to the disorder.

One of the difficulty with the two later categories is that they are not always identified as forms of epilepsy, and thus can be mis-diagnosed based on behavior alone as some other disorders, including ADD/ADHD in absence epilepsy “spells” where the attentional process is disturbed by the discharge taking segments of time out of the cognitive streaming of perception, or from discharge in sensory areas.  These segments being removed do not have any conscious awareness of the event for the person experiencing the blips missing from their cognitive process, and they will have trouble tracking on-going events, like driving or listening to a speech or lecture.  Imagine missing a few here and there, to tens of seconds from your awareness, and see if you don’t have “attentional deficits”.

The other major areas of misdiagnosis are of a “schizophrenic” or “psychotic” nature.  This occurs when the discharges are frontal or temporal and disturbing local cortical function, and may be seen as a range of presentations from hallucinations or emotional outbursts of rage, or even “fits of laughter” in “Gelastic seizures”.   Temporal Lobe Epilepsy (TLE) is a particularly difficult one to properly diagnose in the absence of the EEG.

The importance of these missed-diagnoses can be quite severe, with the use of medications to treat the symptoms often being contra-indicated by the epilepsy.  One example of this is TLE that is assumed to be psychosis, since antipsychotic medications lower the seizure threshold, and make the person worse, which can then be responded to with more antipsychotics, spiraling the person into a progressively worsened condition.  The use of stimulants in epilepsy is a controversial area, as the effect of stimulants for inattention in known and treated epileptics may be acceptable, though throwing a stimulant at an undiagnosed epileptic can have severe negative consequences.

The real issue is that IF YOU DO NOT LOOK, YOU WILL NOT SEE… and in epilepsy, looking requires the EEG, as the gold standard.

In surgical approaches, the EEG is used to identify whether there are multiple foci, which generally will preclude a good outcome (you remove the brain tissue and the seizures do not change).

I will post some images of the WIDE variety of morphologic presentation that epilepsy can take, so that some understanding of the task of the Electroencephalographer and Epileptologist can be better appreciated by those who think it is straight-forward.

Thanks for your attention to these obscure issues.

Jay

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

We are honored to welcome several high-profile speakers, including:

• Personalized Medicine in the Age of Technology - Vilayanur S. Ramachandran, MD, PhD; Director of the Center for Brain and Cognition and Professor with the Psychology Department and Neurosciences Program at the University of California, San Diego, and Adjunct Professor of Biology at the Salk Institute

• Regeneration and Stress at Work: Strategies for Improved Employee Health - Tores Theorell, MD, PhD; Professor Emeritus at the University of Stockholm, Sweden

• An Overview of Mind Body Healing - C. Norman Shealy, MD, PhD; founder of the American Holistic Medical Association, and past president of the International Society for the Study of Subtle Energies and Energy Medicine

• Neurotherapy in the Treatment of Traumatic Brain Injury: A Physiological Hypothesis – Paul Rapp, PhD; Professor in the Department of Military and Emergency Medicine at the Uniformed Services University of the Health Sciences

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

I suggested at the time that all the emotion was merely an example of someone yelling “the sky is falling”, like Chicken Little. There was no real problem, just lots of crying out and hand wringing.

I requested in an open international forum for anyone to send me a sample of the problem, and none could be produced. I suspected there was no real problem, as the sample issue was concerning a 500 sample/second device having a time skew… though this was in comparison to a database collected on a 100 sample per second device, with the waveforms interpolated from these samples.

It was highly suspect from my technical perspective when this issue was raised, and it was even more suspect when nobody could produce actual data showing the coherence or phase issue.

Testing now has shown that the old style Mitsar, with the non-simultaneous sampling is identical in performance to the new style amplifier that has simultaneous sampling, and thus no skewing error is possible in the newest amp.  There is also an intermediate style amplifier tested, which is one of the smaller amps, but with a more current sampling design.

The data clearly show that there is no difference in coherence between these devices.

The Mitsar amplifier also has been tested with the new BranMaster  Discovery amplifier, and it also was shown to have indenticle coherence findings with the Mitsar amplifier.

Clearly there is no real issue.

Data rules…. the experimental details are below.

Jay

We performed the following experiment.

We took three different Mitsar amplifiers:

1. Mitsar-EEG-201 – old model of amplifiers with relatively large time shift between channels (1.75 ms maximum) 2. Mitsar-EEG-201M – new model of amplifiers with relatively small time shift between channels (470 microsecond maximum) 3. Mitsar-EEG-202 – 32-channels amplifiers with zero time shift between channels.

Than we take Electro-Cap and put it on the head of one subject. We used linked ears referent for EEG recording.

We sequentially connect Electro-Cap and referents to three different amplifiers and perform independent recording of EEG in eyes closed condition. The duration of recording was longer than 300 seconds.

When we reconnect Electro-Cap and referents from one amplifier to another we do not touch to electrodes on the head and ears.

The total time of out experiment was approximately 20 minutes. This means the functional state of subject remain relatively stable.

Than we remontage the EEG to average referent (very important for time  shift influence measurements), compute the coherence for all three EEG recordings using the same processing parameters and compare them. The duration of time interval for processing was the same for these EEG recording and was equal to 300 seconds.

We do not find any dramatic differences in coherence corresponding to different amplifiers. The small fluctuations can be explaned by amplifiers noise and non-stationarity of EEG.

Mitsar Calibration

The Mitsar system is calibrated at the manufacturer. There is no need to recalibrate the amplifier unless there is a serious problem from damage. The calibration button is so the user can run a test calibration signal to demonstrate that the channels are in fact correct and equal. If these were ever not correct (or equal) then the manufacturer would recalibrate the hardware device. It is blocked so that a user cannot accidentally mess the calibrations up. So in summary the calibration button in the software is pressed and then the button to observe EEG is pressed. Then the test calibration signal is generated and can be recorded. This is a good idea if the case is a medical or legal evaluation so that when the data is presented as evidence there is validation that a microvolt equals a microvolt.

All of these new tools will require clinical validation prior to being able to be considered standard techniques within our field’s armamentarium of efficacious techniques and clinical applications. All of these techniques offer great hope at this time with preliminary results, but careful clinical outcome studies remain to be performed.

In this brief note I will discuss Z-score feedback. This promising technique offers to set normative boundaries around the mean of many features of the EEG, and allow feedback to be controlled by these parameters. This obviously offers great hope to clinical outliers, as their Z-score divergence should be related to their pathology. One difficulty is that database Z-scores also show divergence when an adaptive or counter-balancing feature is used to cope with an abnormal finding. A crutch is not a normal finding, but you can’t walk without it if you have a broken leg.

This suggests that the selection of which Z-score features to include as feedback contingencies and which to “ignore” will become an important feature in clinical decision making using these new tools. Training away an adaptive coping mechanism is not a proper NF Z-score targeting choice.

One area which is not very well discussed in the field of qEEG is how poorly the databases are at characterizing shifts in the frequency “tuning” of the EEG. The NeuroGuide database reports peak frequency, but the calculation is not for the peak, but for a “centroid” which is more related to the Mean than the Peak frequency. Nx-link does not report the peak, but uses a mean frequency calculation. BRC database uses a peak frequency of alpha statistic, but it is constrained to looking within the alpha predefined band.

Databases report “too much” and “not enough” amplitude/magnitude/power, but they do not tell you if this value would be normal at a different frequency tuning. An example is in order to illustrate this important concept. Take a normal amount of 9.5 Hz sinusoidal alpha seen dominant posteriorly, with normal coherence relationships… let’s arbitrarily say there is 50 microvolts of amplitude in the alpha spindles. Now, take this alpha tuning and shift it 2 Hz slower, so 7.5 Hz is the sinusoidal frequency, and what does the database tell you?

Databases will say there is too much 7.5 Hz power, and that it is hypercoherent, since the database does not expect alpha at 7.5 Hz. In reality, the alpha frequency is slow, but the fact that it is 50 microvolts is not too much power for the background, and it really is not hypercoherent, it is just too slow.

Frequency tuning issues are so poorly described in databases that the databases will not do a good job of normalizing the client’s function… dropping the background’s normal power and coherence relationships is not appropriate, but the database would use these values as their contingencies for NF based on the database.

This would suggest that frequency shifted clients may comprise another group that will require special adaptations for Z-score based feedback to be properly applied.

One other area that deserves some discussion is the use of NF in non-medical applications for “peak performance”. By definition, these peak states are not a common occurrence, as they are seen in uniquely gifted athletes, scholars, and business leaders that are not that common in the first place, and then these states are not always seen in these individuals in their average states. These states that are being trained for are not statistically “Mean-oriented” states, but rather exist as a unique pattern of outliers which are not capable of being reported in univariate statistics such as Z-scores. It requires a flexible nervous system to achieve these outlier states, and a resilient nervous system to “return” or “recover” and continue to function “normally”.

These observations suggest that peak performance may not be the best application for Z-score feedback, though this is hypothetical, and requires the validation only achieved with experience over the years.

The database selected also will become an issue, as the NeuroGuide is severely restricted in the frequency range, with the amplifier response stopping at 28Hz, as seen in the FRC curve(Fig 1). It is not possible to do gamma based feedback with Z-scores of the database used does not go to gamma.

Figure 1

What is clear at this time is that Z-score feedback remains experimental until the validation studies are performed, and though it is a promising new application utility, there are areas which deserve special attention even in this early stage of the evaluation of this emerging technique, including coping mechanisms, frequency shifts, peak performance applications, and database limitations.

The vendors who promote Z-score feedback are all adamant that the Z-score feedback does not preclude the need for client evaluation, but rather that is increases the complexity of the evaluation as various features are selected or de-selected for being feedback contingencies to account for client coping mechanisms, and the various frequency shifting issues and other database inadequacies.

Welcome to the New World of high tech clinical application tools, please check your expectation that this will be “quick and easy” at the door. More on LORETA and ICA based neurofeedback later.

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.

Part 1

Part 2

Dr. George H. Green and John LeMay, MFT, have been collaborating in the area of brainwave biofeedback for several years. About a year and a half ago Cerebotix focused on using the brainwaves monitored in biofeedback to move a remote object. After hundreds of hours of development, initial test subjects were able to successfully loft a remote controlled device (BCI – Brain Controlled Interface) in the Cerebotix corporate office. Since that time, countless refinements have been made, and the initial clinical results have been excellent.

In order to control a remote object, brainwaves are measured through five electrodes placed on the head. The resulting brainwave impulses are sent to a computer where they are processed through proprietary Cerebotix algorithms into three live data streams. These data streams are converted into radio frequency signals that are then transmitted to a wireless receiver mounted on a helium-filled Mylar balloon that has been ballasted to be slightly heavier than the surrounding air. As the person’s brainwaves become increasingly organized, the Remote Controlled Object (RCO) will develop enough power to activate a propeller, ascend and start to fly. The device is entirely under the control of the individual’s brainwaves. There are no additional controls in place whatsoever. The RCO is lifting off and flying literally 100% under brain control. This is the first time in history that brain waves have been used successfully to move remote objects.

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.