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

Congressman Bill Pascrell and  Congressman Todd Platts

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

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

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

RSVP – rsvp@susandavis.com

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.

Damage is seldom restricted to merely being exclusively either white or gray matter, and mixed findings are seen commonly.  There are studies showing the correlation of quantitative EEG findings with quantitative MRI findings that are instructive in identifying the nature of the effect on the EEG of the different types of damage.

The EEG changes following brain injury are spectrally different between white and gray matter damage, which helps when evaluating the nature of the damage with the EEG.  The white matter is a high speed relay system that innervates the cortex, both with primary sensory input relayed from the thalamus, and with cortical-cortical input via various fasciculi.

When the cortex has decreased innervation, delta content emerges, according to the IFCN’s position paper on the basic mechanisms of cerebral rhythmic EEG**.  Thus, traumatic brain injury resulting in white matter damage is associated with slower spectral increases in the areas cortically where decreased innervation is present.  These slow spectral increases are seen primarily as delta, and may also be seen as a slower band including theta, especially with larger increases in the slow spectra.

White matter also carries signals across the cortex, and from the cortex through subcortical structures to other cortical locations, resulting in the neural network’s “connectivity”.    There has been a small case series showing that in some direct frontal injuries, there is a decrease in correlation from the left to the right frontal lobe, seen as decreased spectral correlation, also referred to as co-modulation (M.B. Sterman and D. Kaiser’s SKIL software).  This is identical to the changes seen with damage to the anterior portions of the corpus callosum following surgery.  This data was presented by Dr. Sterman, and published by the Journal of Neurotherapy as a technical paper describing their co-modulation metric.

Coherence changes may also be seen with head injury, with both hypercoherence and Hypocoherence reported, depending on the nature of the specific case’s damage.  Isolated areas may become hypercoherent due to the lack of input, though separated areas will be hypocoherent due to the damage to their connective network.

Damage may be seen in gray matter, which is highly “plastic”, unlike white matter, where damage persists.  The neural plasticity allows for regeneration of the cortical gray matter following injury, so the spectral changes associated with gray matter damage may change over time, from the more acute stages, through a transition phase into a static phase, which may allow for re-integration into functional relationships with neural network activity.

The immediate changes seen spectrally with gray matter injury is a decrease in the function of the thalmo-cortical neural network activity, seen spectrally as decreased alpha and beta, as well as decreased gamma in the affected gray matter.  These changes last for the period of the healing, commonly seen across a period from 6 months to a year.

As the gray matter heals, but is not integrated into the neural network function, the idling rhythm in alpha may return and even be seen as an excessive value in database comparisons, since the cortical area is not “working”.  The beta and gamma remain low during this phase, since they are not seen at normal levels in the idled cortical areas.  Beta is generated in local gray matter network activity, and gamma is seen in functionally bound and active networks only.

Once the neural network of the local gray matter is re-integrated into the functional processing, the alpha will then be reduced, and the faster activity seen associated with local function will also be seen as returning to more normal levels.  This may not happen spontaneously, and may require specific interventions, such as neurofeedback, physical therapy, and/or various cognitive-behavioral interventions.

The work of Dr. Kirtley Thornton showed that the gamma and beta remain low, even when the alpha return has occurred.  These faster patterns returned following successful clinical therapy to re-integrate the neural tissue into the functional neural network of the cortical gray matter and white matter.

Some software provides multivariate discriminant analysis, differentiating normal controls from mild traumatic brain injured clients.  These were collected retrospectively, with clients in a specific state of the dynamically changing gray matter’s plasticity, within a 9 month range in one product that is commercially available.  Their prospective use clinically, like all other classification systems, provide false positive and false negative results (type 1 and type 2 errors).

When used indiscriminately, discriminants provide a significant “red herring” problem clinically.  They are not appropriately used as a screening test for individuals, but rather they are only appropriate when used to answer a specific clinical question: “Has my client who has had an actual head injury actually suffered a brain injury?”

I personally do not find them useful clinically, since they do not provide a full evaluation of a client’s brain’s specific injury, and have an unacceptable false negative rate in know head injured clients.  The dismissal of clinically significant findings by the relatively blind use of a head trauma discriminant would tell 20%-30% of those who have had a real brain injury that they are “normal”.  This is not acceptable in the real world when a better clinical judgment would be provided by a careful analysis of the EEG and qEEG by experts in this specific application area.  We have also found a 50% false positive rate when applied to a general clinical population (though this is not the intended use of the discriminant).

The neurological professional groups are divided on the use of traumatic brain injury discriminant classification, with the American Academy of Neurology (AAN) refusing to accept discriminant use clinically, but the 1994 position paper of AMEEGA, published in EEG and Clinical Neurophysiology, provides for the clinical acceptability of the technique in the hands of experts.  ECNS (EEG and Clinical Neuroscience Society)  has reiterated the AMEEGA position paper, and the AAN position paper has had specific responses to it from those who use discriminants.

I find the detailed evaluation of the client’s EEG and qEEG, and an understanding of the dynamics of the brain’s response to trauma, provide a superior working understanding of the client’s specific injury.  This is far superior compared with a simplistic sorting into classifications of “normal” or “TBI” by software that admittedly misses 20% of the actual cases of brain injury and 50% of other clinical cases would be classified positive for brain injury in the absence of any history of head injury in an open clinical series.

Therapeutic intervention is not specified by TBI discriminants, nor is it reasonably possible to customize a therapeutic approach using discriminants due to their sensitivity to artifact.  By contrast, the EEG and qEEG data can be used for both understanding the brain injury, as well as to help the clinician customize a therapeutic approach to the specific neural network areas injured traumatically.

**   M. Steriade, P. Gloor, R.R. Llinas, F.H. Lopes da Silva, and M.M. Mesulam (1990)
Report of IFCN Committee on Basic Mechanisms:  Basic mechanisms of Cerebral Rhythmic Activities,
Electroencephalography and Clinical Neurophysiology; 1990, 76: 481-508

References:

Nuwer, M, et al, Routine and Quantitative EEG in Mild Traumatic Brain Injury; Clinical Neurophysiology, 116 (2005) 2001-2025

Thatcher, R.W., Camacho, M,, Salazar, A, Linden, C., Biver, C. and Clarke, L.: Quantitative MRI of Gray-White Matter Distribution in Traumatic Brain Injury. Journal of Neurotrauma, Volume 14, No. 1, 1-14, 1997

Thatcher, R.W., Moore, N, John, E.R., et al.: QEEG and Traumatic Brain Injury: Rebuttal of the American Academy of Neurology 1997. A Report by the EEG and Clinical Neuroscience Society, Clinical Electroencephalography, 30(3): 94-98, 1999