The article “Medication Prediction with Electroencephalography Phenotypes and Biomarkers” in the current issue of the journal Biofeedback offers evidence that quantitative EEG assessment can refine the selection of medications by detecting brain patterns. Author Jay Gunkelman uses the example of a 7-year-old child diagnosed with ADHD.
Following current clinical standards, a patient would be diagnosed with ADHD based on behavioral observations as described in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V). Although this approach defines a problem, it does not predict effective therapy.
Typically, the next step would be to determine which medication would successfully treat the behavior. For ADHD, the options include stimulants, anticonvulsants, antidepressants, and channel blockers—a diverse range of pharmaceutical choices for a single diagnostic cluster. Trying one drug, then another, brings the risk of adverse drug events or severe side effects for the child being treated for ADHD.
The author proposes that quantitative EEG patterns can provide a more reliable basis for medication selection than diagnostic category. Individuals with the same symptom-based diagnoses can show differing patterns of brain dysfunction. Using these neurophysiological indicators can lead to a more accurate selection of treatment and eliminate the trial-and-error approach.
This application of quantitative EEG is an evidence-based approach to medicine. This article summarizes the EEG’s neurophysiological indicators for the medication classes indicated for ADHD, based on the author’s 43 years of experience in the field and outcomes from psychiatric practices.
Although the fields of pharmacology and EEG intersected about 50 years ago, the use of EEG is currently outside clinical practice standards in psychiatry. Change is coming, however, as the DSM-V approach is being abandoned in favor of predictors of treatment efficacy.
Full text of the article, “Medication Prediction with Electroencephalography Phenotypes and Biomarkers,” Biofeedback, Vol. 42, No. 2, 2014, is available at http://www.aapb-biofeedback.com/doi/full/10.5298/1081-5937-42.2.03.
Biofeedback is published four times per year and distributed by the Association for Applied Psychophysiology and Biofeedback. AAPB’s mission is to advance the development, dissemination, and utilization of knowledge about applied psychophysiology and biofeedback to improve health and the quality of life through research, education, and practice. For more information about the association, see www.aapb.org.
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