Pharmaco-EEG: A Study of Individualized Medicine in Clinical Practice


Ronald J. Swatzyna, Gerald P. Kozlowski, and Jay D. Tarnow


Pharmaco-electroencephalography (Pharmaco-EEG) studies using clinical EEG and quantitative EEG (qEEG) technologies have existed for more than 4 decades. This is a promising area that could improve psychotropic intervention using neurological data. One of the objectives in our clinical practice has been to collect EEG and quantitative EEG (qEEG) data. In the past 5 years, we have identified a subset of refractory cases (n = 386) found to contain commonalities of a small number of electrophysiological features in the following diagnostic categories: mood, anxiety, autistic spectrum, and attention deficit disorders, Four abnormalities were noted in the majority of medication failure cases and these abnormalities did not appear to significantly align with their diagnoses. Those were the following: encephalopathy, focal slowing, beta spindles, and transient discharges. To analyze the relationship noted, they were tested for association with the assigned diagnoses. Fisher’s exact test and binary logistics regression found very little (6%) association between particular EEG/qEEG abnormalities and diagnoses. Findings from studies of this type suggest that EEG/qEEG provides individualized understanding of pharmacotherapy failures and has the potential to improve medication selection.

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Technology Helps Explain Medication Failure

In almost every area of medicine, doctors can order tests to provide objective physical data to guide their medication selection. However, the practice of psychiatry is most often based on observation, self-report and psychological testing. It appears that we are better at measuring impairment than we are at identifying the source and prescribing an effective medication. Is there a way we can do better?

The director of the National Institute of Mental Health, Tomas Insel, suggests there are many medicines, but they are not working adequately. This is because the symptoms of mental illness are too illusive and are shared by many diagnoses. Insel (2012) says, “It’s much harder to fix something if you don’t know what is going wrong.” Medications are being prescribed to treat a set of symptoms suggestive of a specific disorder without any objective evidence of the cause. Additionally, the practice of polypharmacy has become way too common in children and adolescents.

Pharmaceutical industry advertising promotes adding a medication when the first medication fails to produce the desired results (i.e., adding Abilify to your antidepressant). The message is that when one medication fails, keep adding more in an effort to address the additional symptoms. Each additional medication increases the risk of side effects. It is not uncommon for children to come to us with several medications prescribed. Last month, for example, we saw a 9-year-old female with prescriptions for Olanzapine three times a day, Lithium Carbonate daily and Amphetamine Salts three times a day. Also, a 10-year-old male came to us on Focolin three times a day, Seroquel twice a day, Lexipro daily and Zyprexa daily. If there was a way to determine why a medication failed, would it not be prudent to investigate why? If current technology could help?

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EEG Complexity as a Biomarker for Autism Spectrum Disorder Risk

William Bosl1,2*, Adrienne Tierney3,4, Helen Tager-Flusberg5, Charles Nelson1,4


Background: Complex neurodevelopmental disorders may be characterized by subtle brain function signatures early in life before behavioral symptoms are apparent. Such endophenotypes may be measurable biomarkers for later cognitive impairments. The nonlinear complexity of electroencephalography (EEG) signals is believed to contain information about the architecture of the neural networks in the brain on many scales. Early detection of abnormalities in EEG signals may be an early biomarker for developmental cognitive disorders. The goal of this paper is to demonstrate that the modified multiscale entropy (mMSE) computed on the basis of resting state EEG data can be used as a biomarker of normal brain development and distinguish typically developing children from a group of infants at high risk for autism spectrum disorder (ASD), defined on the basis of an older sibling with ASD.
Methods: Using mMSE as a feature vector, a multiclass support vector machine algorithm was used to classify typically developing and high-risk groups. Classification was computed separately within each age group from 6 to 24 months.

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Technical Details in EEG Diagnosis of Autism

Many have heard experts in the neurofeeback field state vehemently that the “ICA deartifacting ruins the EEG”, and that “remontaging to a Laplacian montage ruins coherence”. There are internet tutorials attempting to support these opinions. This self-publication on-line on a commercial site is not the same as peer review, and many publish bad opinions without an alternative approach even considered.

Rather than engage in the meaningless back and forth of mere opinions, I thought it was better to wait for the decision of the jury.. a jury of our peers inherent to the peer reviews in professional publications seen in the field of neuroscience. I comfortably accept the judgment of the field’s journal’s editors.

Harvard’s famous Electroencephalographer, Frank Duffy M.D. just published a large scale well designed study. Some of the key aspects in the paper are highlighted and discussed below:

“Remaining eye blink and eye movement artifacts, which may be surprisingly prominent even during the eyes closed state, were removed by utilizing the source component technique [42, 43] as implemented in the BESA (BESA GmbH, Freihamer Strasse 18, 82116 Gräfelfing Germany) software package”

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QEEG-guided Neurofeedback: New Brain-based Individualized Evaluation and Treatment for Autism

by James Neubrander, MD, Michael Linden, PHD, Jay Gunkelman, QEEGd, and Cynthia Kerson, PHD

QEEG-guided neurofeedback is based on normalizing dysregulated brain regions that relate to specific clinical presentation. With ASD, this means that the approach is specific to each individual’s QEEG subtype patterns and presentation. The goal of neurofeedback with ASD is to correct amplitude abnormalities and balance brain functioning, while coherence neurofeedback aims to improve the connectivity and plasticity between brain regions. This tailored approach has implications that should not be underestimated. . . . Clinicians, including the authors, have had amazing results with ASD, including significant speech and communication improvements, calmer and less aggressive behavior, increased attention, better eye contact, and improved socialization. Many of our patients have been able to reduce or eliminate their medications after completion of QEEG-guided neurofeedback.

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Coherence Models and artifacts – Prior published findings in Autism are artifactual.

The following link to the article “Movement during brain scans may lead to spurious patterns” contains peer reviewed hard evidence of a clear cut case of poor deartifacting and excessively short recording times combining to create artifactual findings… findings that had high reliability within the data set, but which had results which were determined by artifact (movement). Even bad data can be repeatable.

This paper brings into clear question the commonly taught model of short and long distance connectivity which has been taught as a “cortical-cortical connectivity” issue, when many have pointed to the logical fallacy to this theory seen in the International Federation of Clinical Neurophysiology position paper (Basic Mechanisms of Cerebral Rhythmic Activities) on EEG generators, which showed that cutting cortical-cortical connections did not alter coherence (making the theory false).

I have presented this to the people in the field in an effort to correct the “cortical-cortical connectivity” theory – that has been promoted.

I hope the two compartmental cortical-cortical connectivity theory will fade away, especially as publications like this and the IFCN position paper point in a different direction.


More Reading: Control of Spatiotemporal Coherence of a Thalamic Oscillation by Corticothalamic Feedback Science 1 November 1996:Vol. 274 no. 5288 pp. 771-774 DOI: 10.1126/science.274.5288.771

Movement during brain scans may lead to spurious patterns from Simons Foundation Autism Research Initiative (SFARI)