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An Introduction to EEG Phenotypes (Part 1)

eegResearchers* recently proposed the existence of numerous EEG phenotypes. EEG phenotypes were derived through Johnston, Gunkelman, & Lunt’s (2005) extensive clinical experience and the observation that similar EEG patterns recur in persons with different psychological disorders. EEG phenotypes are a useful method of EEG pattern categorization that requires visual inspection the raw EEG, and often, the quantitative EEG (QEEG).

Simply stated, EEG phenotypes are clusters of commonly occurring EEG patterns found in the general population that are believed to be the result of underlying genetics. These phenotypes are purported to play an intermediate role between genetics and behavior (Gunkelman, 2006).  In fact, Gunkelman believed that a relatively small number of phenotypes can describe a majority of EEG records in the population.

EEG phenotypes are further described as highly heritable and reliable measures of brain functioning that carry treatment planning implications (Johnstone, Gunkelman, & Lunt, 2005). The phenotypical approach acknowledges the overlap of symptoms in many psychological disorders, and, as opposed to DSM-IV labels, does not require a diagnosis as a precondition for effective treatment. Phenotypes provide classification of the abnormal EEG record only and any EEG record that does not contain the traits of a specific phenotype is often classified as “normal.”

Johnston, Gunkelman, & Lunt (2005) initially reported their observations that persons may respond differently (i.e., more positively or negatively) to psychotropic medications based on their individual phenotypes. An important recent discovery that provides confirmatory evidence of this hypothesis is that specific phenotypes may predict medication response in Attention Deficit/Hyperactivity Disorders in children (Arns, Gunkelman, Breteler, & Spronk, prepublication), as well as in depression (Suffin & Emory, 1995).  These findings present obvious meaningful benefits to physicians and psychiatrists and their patients. Gunkelman and others strongly believe and publicly advocate that phenotypes should play a major role in the future of neurofeedback treatment planning in addition to medication prescription management.

Currently, there are 11 candidate EEG phenotypes that include:

  1. Diffuse Slow Activity, With or Without  Slower  Alpha
  2. Focal  Abnormalities, Not Epileptiform
  3. Mixed Fast and Slow
  4. Frontal Lobe Disturbances
  5. Frontal Asymmetries
  6. Excess Temporal Lobe Alpha
  7. Epileptiform
  8. Faster Alpha Variants, Not Low Voltage
  9. Spindling Excessive Beta
  10. Generally Low Magnitudes, Fast or Slow (also known as “low-voltage fast”)
  11. Persistent Alpha With Eyes Open

In Part 2 of this series, I provide a detailed descriptions of these EEG phenotypes and their implications for neurofeedback treatment planning. My guess is that due to voluminous amount of information that I have to discuss, this series will most likely extend to a Part 3 or Part 4.

4/23/09 Update: Part 2 can now be found here.

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

*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.

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

Suffin, S., & Emory, W.H. (1995). Neurometric subgroups in attentional and affective disorders and their association with pharmacotherapeutic outcome. Clinical Electroencephalography, 26, 76-83.

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