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An Introduction to EEG Phenotypes (Part 3)

brainmap_orginal_3Part 1 of this series provided a basic introduction to and a list of the 11 candidate electroencephalography (EEG) phenotypes, while Part 2 covered 5 of 11 EEG phenotypes. Part 3 details the remaining 6 EEG phenotypes with some final thoughts and a wrap up of this popular series. As detailed beforehand, the phenotypes and their implications for neurofeedback mostly draw from the writings of Johnston, Gunkelman, & Lunt (2005), Gunkelman (2006), and Arns, Gunkelman, Breteler, & Spronk (prepublication). All references to scalp locations are based on the International 10/20 System. Fisher & Cordova (2006) provide an excellent descriptive overview of the International 10/20 System complete with magnetic resonance imaging (MRI) pictures of corresponding physical locations.

Here are the remaining 6 candidate EEG phenotypes and their implications for neurotherapy in no particular order:

“Excess Temporal Lobe Alpha” (TLA) classification is used when evidence exists that the temporal lobe generates abnormal amounts of alpha. TLA suggests frontal lobe disengagement which results in temporal lobe idling (Johnston, Gunkelman, & Lunt, 2005). It is important to accurately determine the source of the alpha and to rule out alpha contamination from posterior regions. Alpha contamination occurs when occipital and parietal alpha spreads into EEG sensors placed in the temporal lobe region, creating the illusion of temporal lobe and frontal lobe alpha (due to ear reference contamination) (Hammond & Gunkelman, 2001). Consequently, one must carefully review the raw EEG and QEEG to accurately determine the origins of the alpha activity to ensure accurate assignment of TLA EEG phenotype. Neurotherapy guidelines include inhibiting the excessive alpha and increased low frequency beta (13-16 Hz) to help activate the under aroused region. Agitation can result when beta is over-enforced and thus requires careful symptom monitoring. Additionally, frontal lobe neurotherapy may also help correct under activation of the temporal regions (Johnston, Gunkelman, & Lunt, 2005).

Paroxysmal events, such as spikes, sharp waves, and spike and wave complexes are appropriately labeled “Epileptiform” (Johnstone, Gunkelman, & Lunt, 2005). Spikes and sharp waves are short lived electrical bursts that occur in less than 70 milliseconds or between 70 and 200 milliseconds, respectively (Hughs, 1994). Spike and Wave complexes begin with a sharp wave as described above and are immediately followed by 1 or more slow waves. The 3-second spike and wave complex is one of the most common (Hughs, 1994). Possible neurologist referral can result dependent on a number of factors, particularly the presence of behavioral manifestations (i.e., seizures or other characteristic symptoms) of the epileptiform activity.

“Faster Alpha Variants, Not Low Voltage” (FAV) occur with the presence of 12Hz or higher alpha activity in the posterior regions (Johnstone, Gunkelman, & Lunt, 2005). The normal adult dominant background frequency most often consists of 10Hz activity in the occipital, and sometimes, the parietal regions (Fisch, 1999). Alpha variants with activity above 11 Hz are believed to represent anxiety and hypervigilance. Neurotherapy recommendations include rewarding 8-11 Hz in the posterior regions, particularly at Pz, and Alpha/Theta therapy (Peniston & Kulkosky, 1989).

An EEG recording with a sinusoidal beta is classified as “Spindling Excessive Beta” (SEB) (Johnstone, Gunkelman, & Lunt, 2005). Spindling beta frequently occurs in midline to frontal regions, particularly at Cz. Spindling beta activity is usually defined as beta activity with a rhythmic morphology that exceeds 20 microvolts (Johnstone, Gunkelman, & Lunt, 2005). Psychotropic medications, particularly benzodiazepines, will produce large amounts excessive beta (Rowan & Tolunksy, 2003). Spindling beta represents a nonspecific indicator of cortical dysfunction or encephalopathy (Johnstone, Gunkelman, & Lunt, 2005) when determined not to be a medication effect. Beta spindles often correlate with an overall anxious demeanor characterized by frequent and intense ruminative thinking. Inhibit-only neurofeedback protocols based on customized frequency bands in the affected areas represents an effective first line treatment option, and high frequency training is strongly contraindicated (Johnstone, Gunkelman, & Lunt, 2005).

“Generally Low Magnitudes, Fast or Slow” (GLM) is an overall low voltage pattern that is frequently defined as little or no activity above 20 microvolts within the entire EEG record (Stern, 2005). Some believe that the low voltage fast pattern represents a normal variant (Johnstone, Gunkelman, & Lunt, 2005; Stern, 2005) in many cases. Others have found that GLM correlates with increased risk for substance abuse (Coutin-Churchman, Moreno, Anez, & Vergara, 2006). GLM of the slow variety that are not related to drowsiness often have an encephalopathic origin (Johnstone, Gunkelman, & Lunt, 2005). Johnstone, Gunkelman, & Lunt (2005) find that a low voltage fast pattern responds well to faster frequency enhancement and to posterior alpha rewards. However, beta enhancement in this group may increase anxiety and is thus contraindicated. Low voltage fast patterns may be seen in anxiety prone individuals (Stern, 2005).

Alpha is expected to decrease by fifty percent (50%) in posterior regions upon opening of the eyes (Fisch, 1999). “Persistent Alpha With Eyes Open” (PAE) is used to describe this failure of alpha to appropriately attenuate with eyes opening (Johnstone, Gunkelman, & Lunt, 2005). The pathophysiology of persistent alpha may be attributable to a reticulo-thalamic dysfunction and resultant persistent states of under arousal. Neurotherapy often targets direct alpha inhibition combined with beta enhancement (Johnstone, Gunkelman, & Lunt, 2005).

A few final comments and opinions about EEG phenotypes: Research of EEG phenotypes remain in the initial phases of development. In fact, much of the work completed so far is based on clinical experience and case studies. Nonetheless, Gunkelman et al.’s proposed phenotypes and phenotype-guided neurofeedback protocols are consistent with widely reported clinical observations. If future research can establish the reliability of these measures and positive treatment outcomes, phenotype-guided neurofeedback may represent an important contribution to the field and provide “standardization,” or at least an excellent starting point, for neurofeedback protocols. Despite the somewhat limited protocol selections hypothesized to treat specific phenotype abnormalities, competent QEEG analysis and interpretation is required to derive the specific frequencies used in treatment. Although phenotypes provide a relatively simple and elegant way to classify the EEG that informs general neurotherapy recommendations, this simplicity might entice neurotherapists into believing that protocols may be used in generic ways. Phenotype analysis does not preclude accurate QEEG interpretation and individual treatment planning. Moreover, a QEEG is required to create the custom bandwidths specified by Johnstone, Gunkelman, & Lunt (2005).

Arns, M., Gunkelman, J., Breteler, M., & Spronk, D. (unpublished manuscript). EEG phenotypes predict treatment outcome to stimulants in children with ADHD.

Coutin-Churchman, P., Moreno, R., Anez, Y., & Vergara, F. (2006). Clinical correlates of quantitative EEG alterations in alcoholic patients. Clinical Neurophysiology, 117, 740- 751.

Demos, J. (2005). Getting Started with Neurofeedback. New York: W. W. Norton & Company.

Fisch, B. (1999). Fisch and Spehlmann’s EEG Primer: Basic Principles of Digital and Analog EEG. San Diego: Elsevier.

Fisher, R., & Cordova, S. (2006) EEG for beginners. In G.L. Krauss & R.S. Fisher (Eds.), The Johns Hopkins Atlas of Digital EEG: An Interactive Training Guide (pp. 11-74). Baltimore: The Johns Hopkins University Press.

Gunkelman, J., (2006). Transcend the DSM using phenotypes. Biofeedback, 34(3), 95-98.

Gunkelman, J., Crocket, C.A., & Cripe, C. (unpublished manuscript). Clinical outcomes in addiction: A large neurofeedback case series.

Hughes, J., (1994). EEG in Clinical Practice. Newton: Butterworth-Heinemann.

Hammond, D.C., & Gunkelman, J. (2001). The art of artifacting. International Society for Neurofeedback and Research.

Johnstone, J., Gunkelman, J., & Lunt, J. (2005). Clinical database development: Characterization of EEG phenotypes. Clinical EEG and Neuroscience, 36(2), 99-107.

Peniston, E., & Kulkosky, P. (1989). Alpha-Theta brainwave training and beta-endorphin levels in alcoholics. Alcoholism: Clinical and Experimental Research, 13(2), 271-279.

Rowan, A.J., & Tolunksy, E. (2003). Primer of EEG: With A Mini-Atlas. Philadelphia: Butterworth Heinemann.

Stern, J. (2005). An Atlas of EEG Patterns. Philadelphia: Lippincott Williams & Wilkins.

Thompson, M., & Thompson, L. (2003). The Neurofeedback Book. Warwick: Association for Applied Psychophysiology And Biofeedback.

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3 Responses to An Introduction to EEG Phenotypes (Part 3)

  1. avatar
    Glyn Blackett December 3, 2011 at 5:41 AM #

    I’m a nutritional therapist and use nutrients e.g. amino acids in an effort to rebalance neurotransmitters. It’s very interesting that you can use EEG phenotype to predict which drugs will be of benefit – so it should be possible to do the same for nutrients. If anyone can give me some pointers in this direction I’d be very pleased to hear from you.

    • avatar
      Christopher Fisher, PhD December 3, 2011 at 1:30 PM #

      I wouldn’t even know where to start to try to determine which EEG phenotypes of nutrients. This will take a whole line of new, carefully conducted and controlled experimental studies – I am not even sure that it can done… probably along the lines of many years (and dollars) of research. Sorry I couldn’t be of more help with this.

      • avatar
        Glyn Blackett (York Biofeedback) December 9, 2011 at 7:32 AM #

        Thanks for the reply Christopher. I take your point about a lot of research being needed and look forward to the time when some of it happens, but in the mean time I wonder if we can make best estimates. If we can predict which medications will be beneficial based on EEG phenotype, and we know the mechanism of medications e.g. dopamine agonist, then we could expect nutrients that are also dopamine agonists (or whatever) would also be helpful. I look forward to hearing your thoughts.

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