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 thalamic involvement in the generation of the alpha rhythm is being under-valued when looking at the LORETA images of alpha current source generators. The alpha power may come from the sources that LORETA identifies, but the thalamus is intimately involved in alpha rhythm generation, and this is not part of the LORETA image of the sources.

The polarization within the thalamus sets the base frequency of the alpha, but the cortical rhythm requires a complex multi-layer feedback loop from the thalamus to the cortex, and back to the thalamus. Without the cortex, there is a total disruption of the normal spatio-temporal distribution of the alpha wave’s spike trains within the thalamus, and cortical damage often disturbs coherence due to this mechanism.

The thalamus distributes the alpha posteriorly via specific sensory relays, which have a simple return circuit. Like the white matter relay from the lateral geniculate of the thalamus to the occipital lobe’s primary visual areas, and directly back. This thalamo-cortical-thalamic loop is relatively faster than the loop seen frontally. The frontal return circuitry is not simple, but the descending routes are complex and somewhat circuitous, taking more time, and thus it is common for the frontal lobe’s alpha to be at the slower end of the individual’s alpha frequency range. The frontal lobe has a return path through the striatum.

The five divisions of the frontal-striatal pathways are the motor circuit, the oculomotor circuit (from the frontal eye fields), the dorsolateral prefrontal circuit (cortical gating), lateral orbito-frontal circuit (emotive), and the anterior cingulate circuit (emotional and cognitive flexibility). The striatal-thalamic pathways are divided into two descending pathways which both start from the cortex to the head of the caudate and then the putamen, and then this pathway divides between the globus pallidus and substantia nigra, and then these both go to the thalamus. The thalamo-cortical completion of the circuit projects to both the premotor and motor cortex directly.

Not all circuits are simple thalamus-to-cortex-to-thalamus “echoic” returns to the original source…

A cortico-thalamo-cortical projection system exists which originates from the primary visual cortex, relayed by the lateral posterior nucleus of the thalamus, projecting to the suprasylvian visual area (which is involved in highest levels of visual integration and comprehension). This finding suggests that the thalamus modulates transmission of cortical signals from one cortical area to another… the coherence or “connectivity” of the cortex is not cortical-cortical, but cortical-thalamo-cortical.

With maturation, the cortex provides a stimulatory effect on the alpha frequency, raising it to a slightly faster frequency tuning through feedback to the thalamus, but the basic frequencies of alpha are generated by the reticular nucleus of the thalamus providing acetylcholine to the thalamic nuclei, and by the underlying polarization within the thalamus, which is effected by the NE levels from the brainstem, and by fluctuating DC field strength levels in the brain. The other effects are the thalamo cortical transmission times, and an effect of the cortical-thalamic processing time for any given pathway…. Longer time needed for frontal than posterior circuits.

Crudely stated: The frequencies of alpha are set in the thalamus, and the spatial and temporal distribution of alpha are controlled by the cortex, with rhythmic “initiation” (phase reset) done by the DC system’s “modulatory” influence on the AC rhythms of the EEG.

The thalamus can provide rhythms in the range from 3 to 16, with the common range of 8-12 representing an adult group’s “average”. Hyperpolarization of the thalamus slows the alpha, and hypopolarization speeds it up until it desynchronizes at about 16 Hz, and becomes a low voltage fast EEG.

The addition of some GABA (an inhibitory neurotransmitter) easily acquired with the addition of some alcohol will slow the alpha back into a rhythmic pattern. This basic mechanism is the reason alpha-theta training works so well on the low voltage fast EEGs seen so commonly in alcohol addiction.

LORETA may show a generator in the precuneus/cuneus area for the occipital alpha component, and the posterior cingulate for the parietal component (when alpha modulators are identified with ICA analysis and then source localized)… but these localizations miss the full beauty of the real mechanism’s complexity and especially the primary importance of the thalamus.

The thalamus gates our perceptions into “perceptual packets”, with the “thalamic gate” being open during the negative half-wave (up-side of the waveform), and less open during the positive half wave (the downward going half). Two stimuli presented within 75 to 100 milliseconds of each other will be “perceptually synchronized”, or though of as being instantaneously simultaneous.

The alpha frequency is the perceptual sampling rate… how many perceptual packets are evaluated per unit time, with a better semantic or declarative memory function seen with faster alpha frequencies. This is from the work on IAF (individual alpha frequency) from Professor Dr. Wolfgang Klimesch’s lab in Salzburg Austria, with significant contributions from Drs Michael Doppelmayr and Simon Hanselmayr.

The databases have difficulty characterizing alpha frequency tuning issues, with many identifying too much power at a slower frequency (like 7 Hz)… although the power values would be healthy and normal if alpha were only faster (like 9 Hz)… the databases seldom tell you it is merely 2 Hz slow. The normal alpha coherence values, if the alpha is slowed, are seen as hypercoherence, although they are perfectly normal for alpha. Databases that rely on predetermined band’s peak frequency may miss a shift if it exceeds their defined band, and this will miss the mean frequency if the peak is good but the band width has less faster content than slower content.

Faster alpha may cause similar issues (too much 12-15 Hz power or 12-15 Hz hypercoherence) when it is not “too much” of either that is really wrong, just alpha being too fast.

Thus when there are tuning issues, databases often have difficulty characterizing the core issue of tuning. When a tuning issue is noted, the coherence and power values may be “off” according to the database, when the real values are not really abnormal, just that they are too slow or too fast.

Theoretically, these issues may prove to be an area where Z-score training may have difficulty, flagging red herrings of power and coherence… though this is an empirical question that will be answered with time and experience

Some people have tried to create 3D creatures which show the spatial
distortions, with the enlarged surface proportionate to the motor expression or sensory sensitivity, with varied results.

See if you can identify the differences between the sensory and motor
version, and also critique the accuracy of the artist’s rendition of these
various versions.

Have fun with the exercise!

Jay

One such teacher was E. Roy John, who most knew as a brain researcher, though he had so much more to offer… did you know he was “blacklisted” as a liberal, or that he worked on the Manhattan Project making Plutonium? Did you know he is considered the grandfather of Cuban Neuroscience? How about his love of boating…

Perhaps a quick look through his obituary will make us all appreciate just a bit more the actual magnitude of our loss from his passing…. And then you can look over the diagram of the brain and see some of the complexity of the models Roy passed on to those who now have to carry on without his wise counsel.

Joyful in having known him, and saddened by our loss,

Jay

In Memory of E. Roy John PhD

Brain Functional Diagram from Roy John PhD

Technical Issues in qEEG Power point Presentation

Continue Reading to see parts 2 and 3.

Part 2 Discusses Z-Scores and Standard Deviations

Following the careful recording of the EEG, the quantitative analysis is begun with the sampling of the data to be used in the analysis by the Fourier transform. The Fourier analysis assumes there are no transients (epileptic discharges, episodic voltage changes etc.) or state changes (light sleep, drug effect, mental task, etc.), so these must be avoided when selecting data for analysis in qEEG for eyes closed resting database comparison. There are some eyes open and task databases available more recently (Hudspeth, Sterman, Duffy etc.)

Transients are an event with a rapid onset and ending, with an increase in amplitude of greater than 50% over the ongoing activity. Epileptiform activity is one common example of this phenomenon. The only time transient discharges may validly be included is when dipole localization or “mapping” of the sources of this activity is the intent, and in this circumstance only significant discharges should be sampled, with the ongoing background treated as the state change and eliminated.

Less rigorous analysis selection standards exist for data not intended for database comparison, such as reading or other task related data. Usually these task data will only be used for gross comparison to the more carefully collected steady state data, looking for gross changes in brain function. When there is an intent to compare to an eyes closed normative database, transients will increased the variability of the dataset, but will be averaged out in the mapping unless persistent or very prominent. These transients will simultaneously alter the dataset’s standard deviation from the norm.

State changes include typically sleep stages collected for an eyes closed awake database comparison. Stage 1 sleep is a subtle drowsy state, where people recorded will usually deny being drowsy when alerted. The alpha is beginning to wax and wane, with subtle increases in theta and occasionally slow rolling eye movements and a decreased EMG tonus. This is just prior to stage 2, where an object being held will slip from grasp, alerting the client, with most realizing the drowsing is present in stage 2. Many people doing a repetitive task or automatic pilot type task will be in stage 1 without being aware of time passing. This is a dangerous situation if response to change is needed, as there are delays in reaction time associated with this state.

The problem with stage 1 (drowsing) being added to a dataset is that it is a mixing of states, violating the FFT assumptions and making database comparison validity more than merely suspect. The inclusion invalidates comparison.

The task of artifacting is to sample enough data to provide reliable maps while maintaining the validity of the sampling, not taking state changes or transients. The amount of data acquired should provide highly repeatable or reliable mapping. The time required to achieve these results is different for each frequency.

Beta becomes reliable in the first 30 to 45 seconds, with alpha following at 60-90 seconds. The intermittent nature of theta makes it the least easily established reliability, with 120-180 seconds required. Delta is reliable at about 120 seconds. Our lab tries to get 120 seconds of data when possible.

Reliability may be established another way, with split-half replication. This actually looks at the lability within the sampled data by looking for the invariant similarity of the repeatable parts of the two data sets, with the more variable parts looked at with less confidence.

The time span or length of the epochs selected determines the sensitivity to the slower frequencies. The Fourier transform has to have a completed waveform in the data epoch sampling for the frequency to be detected and quantified. A 1/2 Hz low frequency sensitivity is thus achieved only when 2 second epoch lengths or longer are sampled. There is a downside to too long an epoch. It increases the likelihood of including state changes and transients, though when clean state stable data exists in long episodes, it should be sampled.

Some equipment has preset epoch lengths and interactions between the sampling rates and the epoch length, which is problematic in sampling data flexibly. The epochs in some equipment are not able to be adjusted in time to ’slide’ past artifacts, making the data artifacting such that it is difficult to sample the clean data in a record with intermittents like eye movement or other transients. Careful selection of the equipment should precede entry into this field.

The artifacting is mostly concerned with eliminating the more common artifacts of eye movement and EMG, as well as movement or electrode artifacts. This cleaning of the data is part of the art of doing good qEEGs, though the science of adequate sampling and the assumptions of the Fourier must be kept in mind.

New artifacts introduced by the digital processing.

The digital recording and processing of the raw analog waveform of the EEG should be understood in technical detail to properly interpret the resultant maps and numerical tables of findings.

The EEG is digitally converted from the analog data by an analog-to-digital or A-D converter and a resultant digital dataset is derived. The digitizing sampling rate and the bit length of the computer data will determine the resolution of the resultant image and tabular datasets. The faster the sampling rate, the faster the frequency that can be resolved, with a minimal sampling frequency defined by the Nyquist principle as 2 times the frequency being resolved.

Proper reproduction of the EEG for visual perspective requires a more conservative sampling rate than the 2:1 Nyquist ratio. This greater than 2:1 ratio must be set by the individual’s preferences. Few would choose less than 128 samples per second, most would prefer 500/second to 1000/second. (The manufacturer’s with set buffer sizes for the epochs, like the Lexicor, will however need to look at the impact in loss of lower frequency sensitivity. For this situation 128 to 256 is the highest reasonable choice.)

The channel sampling should be simultaneous, to avoid remontaging error or slew. If not simultaneous, a faster sampling rates will reduce this error (as will burst mode sampling) reducing the phase or time base error to a minimum.

The bit length of the computer “word” processed by the CPU effects the amplitude resolution in qEEG, irrespective the sampling rate. The longer the bit length, the better the resolution ( a bit length of 12 is acceptable, but 16 is preferred. Older units will have 8 bit processing and are not fully adequate without further scaling adjustments).

The epochs selected during artifacting will all have an abrupt start and stop, without a zero voltage point at each end of all the epochs. This sudden voltage is seen by the FFT (fast Fourier transform) of the computer analysis as a square wave at the start and stop of each epoch. The resultant output of the Fourier is that all frequencies were present at that point to “reconstruct” the square wave. This is termed “leakage artifact”, or “Gibb’s artifact”.

The result of the Gibb’s artifact is that if a 10 Hz waveform was put into the FFT, a spectral plot of the output would have a rise of the baseline where all frequencies were used to reconstruct these abrupt starts and stops of the epochs. There would be a spectral peaking at 10 Hz, with a tapered response and a broadened base to the frequency plot.

To correct for the leakage or Gibb’s artifact, a “windowing” filter is used. The result of this windowing is the return to the baseline of the generally elevated spectral plot mentioned previously. There is a residual broadening of the idealized spectral peak at 10 Hz. This residual artifact of the broadening of the spectrum following the windowing is “smearing” artifact.

The windowing used in qEEG is usually a Hanning window. This filter slowly ramps up the start and ramps down at the end, to avoid the apparent square wave the FFT sees. Other windowing techniques are triangular, Blackman, Hamming, Meyer’s , with the lack of windowing occasionally referred to as a “rectangular window”. A full discussion of these details, contrasting the different styles of windowing is outside the scope of this chapter.

“Aliasing” is an artifact caused by a frequency source near the sampling rate (and above the nyquist sampling rate) so that a beat frequency is created as an alias of the source frequency/sampling rate interaction. Aliasing filters are used to control for this artifact in all modern devices.

Database issues

The datasets derived from the artifacted EEG are the starting point for the comparison of these data points to the databases used in qEEG analysis.

The databases are the means and standard deviations used to establish the significance probabilities for the observed measurements. These will be represented as Z-Scores, roughly these may be seen as the standard
deviations from the normative database of the data points in the dataset. They are calculated as the patient mean minus the database mean, divided by the standard deviation of the population.

Z-scores are reported in tabular form and may be mapped. Significance probability mapping ( a term derived by Frank Duffy, M.D. of Harvard’s Children’s Hospital) allows the interpreting individual to view the spatial distribution and extent of deviation in an easily discernible display. 1.96 Z-score deviation is equal to 2 standard deviations, and 3.08 Z-score deviation is three standard deviations.

Normative databases are constructed with highly screened normal individuals with an age range establishing the limits of the database. The database is constructed controlling for socio-economic and other demographic influences. Importantly, the databases must be established with different norms for male and female, to account for the significantly different neurophysiologic structures and rates of development of the male and female brains.

The database used should be selected to match the end use and population to be seen. The age range may be critical for those seeing children or the elderly. For others, the presence of multivariate stepwise discriminants used in determining the likelihood of membership in one of two or more clinical groupings will be critical in the selection.

For others, the presence of an eyes open database for use in clinical eyes open work, or the task related data will be critical.

The database from E.Roy John, Ph.D. of NYU’s Brain Reasearch Laboratory contains more than univariate measures, with multivariate parametric statistical evaluations of normal and many clinical subgroups. This has stepwise discriminant analysis associated with it. Discriminants for head trauma are also available from Thatcher. The Duffy database is used, though is not is commercial distribution now that the Nicholet BEAM instrumentation does not carry it. Sterman has an age limited performance based database available, with new databases such as Hudspeth’s coming into availability recently as well.

In using the discriminants, if available in a database, care should be exercised to assure the applicability of the discriminant to the client being evaluated. The client must fit the conditions that were set for the construction of the discriminant.

If a discriminant is set up to decide the likelihood of being a member of group A or group B, a member of another group, C, will be classified as A or B, not properly identified as another type, type C. This weakness of discriminants must be controlled in the selection and use of the discriminant, not after it has been performed.

Displays

The displays in the qEEG report will be presented as a progressive analysis of the data. The displays may be sorted into those that are closer to and farther from the raw EEG, being farther from the EEG as more statistical manipulations are performed. The farther from the raw data one goes, the easier it is to mistakenly interpret artifacts as real or misinterpret relational data.

To avoid these easy and eventually certain mistakes, the full visual interpretation of the EEG must precede any review of the analyzed data. Only then should this be followed by a review of the raw amplitude mapping, in as detailed a frequency display as possible. This may be followed by broad band analysis. The amplitude mapping should be followed by the power and then the relative power analysis. Only after this step-wise evaluation this should absolute or relative power Z score or other database comparisons be done.

Following the spectral evaluation, the statistically extracted measures of symmetry, coherence and phase are evaluated, without being as likely to make a mistake.

The presence of artifact should be expected in clinical work. In clinical work the luxury of prolonged recordings and rejection from a study due to artifacts is not present as it is in academic situations. The progressive step by step evaluation will control for these situations as best they may be.

To err may be human, but it should be controlled and accounted for with methodologic routine to the extent humanly possible.

I see the field of neurofeedback gradually moving more toward the use of qEEG, but it is not required by any stretch of the imagination, much less a standard of practice. I am sometimes misquoted as having said it is unethical to do neurofeedback without a qEEG. It may be less than optimal, in my estimation, but it is certainly not unethical.

The argument has been raised that the qEEG is only a way to bill the client additional charges, draining the vital cash reserves of the clients, with no scientific evidence of a benefit for the use of the qEEG. I agree there is an expense for a qEEG. To routinely perform a qEEG without a demonstrable treatment benefit would be difficult to justify.

There is an increasing body of evidence that there is a positive treatment impact from the use of a qEEG and the resultant customized NF intervention. The initial information coming from those using the technique “feeling” they got some clinical utility from qEEG data. The more persuasive evidence to date is a retrospective evaluation of outcomes in a single practice.

The retrospective research compared 3 years of NF data using a commonly used standard treatment approach to 2 years using the qEEG based customized intervention. A gross summarizing of the paper shows a doubling of the consevatively estimated clinical success, from 30 to 60%. Further, the total treatment benefit (both ’some benefit’ and ‘full’ benefit groups added together) increased from the commonly previously reported 80% increased to 90% now receiving perceived benefit (C. Wright et al., SSNR, Austin 1998).

The cost effectiveness is seen easily if there are a few sessions spent “getting it right” using the clinical guesses to select sites. It only takes a few wasted sessions, not to mention possible adverse reactions, to pay for the proper selection using the qEEG.

I believe the strongest argument for the use of qEEG stems from the reported incidence of non-convulsive frontal and temporal lobe epilepsy comorbid with diagnosis of ADD/ADHD. When I saw 10% quoted in the literature, I was shocked and had some doubts about the reliability of the observation. Following nearly 3 years doing the screening for one ADD/ADHD practice, I saw a similar percentage of undiagnosed or “occult epilepsy”. I now have more faith in the figure.

To use a standard ADD/ADHD intervention with an undiagnosed epileptic may be problematic. The lack of awareness being no excuse (read ‘defense’) if legalities are invoked. The qEEG has a clinical EEG read during its evaluation, allowing for the proper referal or diagnosis of epilepsy (or any other occult condition such as tumor, metabolic or toxic encephalopathy or early dementia).

How do the maps tell you where to intervene?

I once heard qEEG referred to as “electro-phrenology”, a term that conjures up images of ancient times and archaic beliefs about brain function. I sort of like the term, as I think the term speaks to the potential to make simplistic assumptions about intervention, based on colored map “hot” spots, the ‘bumps’ of electro-phrenology.

QEEGers without an appropriately sophisticated model of how the brain works will be tempted to stick the intervening electrodes on areas that ‘light up’ with some color in a map. The area is likely to be an artifact, a normal finding, a normal variant or even the proper area for intervention. It may also be an effect of a distant cause or change in brain regulation.

The time consuming study of the brain’s function, EEG and the quantitative analysis techniques, including artifacts is needed to understand the colorful and informative mappings, tables of values and database comparisons. The careful study of the database selected is also needed to understand its strengths and weaknesses (Thatcher, 1998).

One of the earliest NF clinicians to use the qEEG to intervene in the 1970’s was Pourier, a Canadian clinician/researcher. He used the Fourier analysis derived compressed spectral array (CSA) to select the ‘deficient’ bands and those in ‘excess’, setting his protocol to act like a bull dozer, chopping off peaks and filling in valleys. His clinical judgements were based on experience, not database comparisons, but he did report positive results clinically.

Hopefully qEEG based NF has advanced since these early days of simplistic assumptions and electro-phrenology. For now, the study of the digital manipulations of the data needs to be put in place.

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.

Whenever a client presents with the question of dementia, all other forms of
dementia need to be ruled out before you are left with the diagnosis of AD.
There are many EEG signatures of various forms of dementia, all of which are
helpful in evaluating a client’s presentation of dementia.

Done by experts in EEG in dementia, the EEG and qEEG may be of substantial
additive value in the differential diagnosis puzzle that all cases of
dementia represent clinically.

One EEG pattern seen in dementia is the presence of periodic triphasic
slowing in the EEG, which is actually diagnostic of subacute sclerosing
panencephalitis (SSPE). SSPE is a “spongiform encephalopathy” where the
brain becomes like “Swiss cheese”, with holes scattered throughout. This
periodic triphasic finding is differentiated from MULTIFOCAL triphasics
which are diagnostic of Crutzfeld-Jacob Syndrome (CJD), which in lay terms
is a form of mad cow disease in humans.

Other helpful EEG signatures include PAFA (periodic anterior fast activity)
which is seen in many with Pick’s disease (a fronto-temporal dementia),
FIRDA (frontal intermittent rhythmic delta activity) or in children OIRDA
(an occipital variant), and also diffuse slowing patterns are all reported
in those with hypoxic/anoxic encephalopathies… as well as the rather
differentiation of multi-infarct dementia (MID) versus AD, where the
difference is seen in coherence, which in AD is seen as anterior posterior
hypocoherence, and from MID, which is seen as fronto-temporal hypocoherence.

This doesn’t speak to more common dementias where non-specific slowing is
seen, like Korsakov’s Syndrome, and Binswanger’s Disease, and some
interesting ones that have more specific EEG signatures, like atypical
frontal and/or temporal Status Epilepticus, which in EEG terms may be seen
as PLEDS (periodic lateralized epileptiform discharges) which is also common
in chronic alcohol related presentation of an “acute dementia”.

The progressive slowing of the alpha peak and increased slower content may
be seen in normo-tensive hydrocephalus, which is a reversible dementia.
This is reversed with the simple surgical placement of a V-P shunt
(ventricular-periteneal) which drains the cerebrospinal fluid accumulation
into the abdominal cavity where it is absorbed.

One other EEG finding that has a virtually diagnostic EEG signature is a
hepatic encephalopathy, where the classic EEG pattern was called a “liver
wave”. This is a triphasic slow wave, with an anterior-to-posterior phase
lead of 100-150 milliseconds. The reason I said “virtually” is that some
with less experience have mistaken triphasic slowing s from anoxia/hypoxia,
and the previously noted periodic and multifocal forms of triphasic slowing,
but this doesn?t have the phase change anterior-posteriorly seen with the
hepatic related findings.

Some point to qEEG measures of “theta” correlating with more severe AD in
the older qEEG literature, but it is really slowed alpha that shows up as
theta in broad band databases… see the work on AD by Brain Resource
Company (BRC)… increased delta with advanced severity, also with decreased
faster activity, and most importantly slowing of the alpha peak which is
more severe with advancing AD.

The EEG is very useful in dementia, but only in the hands of those expert
enough to have seen these patterns in the clinical EEG in full detail.

Jay

Consciousness – A  seven part video series. Part 1.

Continue reading to view parts 2 – 7.

Download the full Power Point Presentation on Consciousness

Part 2

Part 3

Part 4

Part 5

Part 6

Part 7