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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EEG based Personalized Medicine in ADHD

Neurophysiological studies in ADHD have shown a relatively uniform picture with regards to EEG – QEEG data (based on group data). Most studies find excess slow brain activity (theta) (Hermens et al., 2004; Mann et al., 1992; Chabot and Serfontein, 1996; Clarke et al., 1998, 2001; Lazzaro et al., 1998, 1999) and a decreased fast brain activity (beta) (Hermens et al., 2004; Clarke et al., 1998; Mann et al., 1992; Lazzaro et al., 1998, 1999). Theta EEG activity is often associated with an “inattentive” or a dreamy state, and beta activity is often seen when the brain is very busy with for instance solving a cognitive task. Figure 1 shows an example of this based on the data of the Brain Resource International Brain Database of 275 patients with ADHD. In this example the increased theta and decreased beta can be clearly seen, with a frontal localization.

group data
Theta                              Absolute Beta                     Relative Beta

Figure 1: This figure shows the average brain activity (quantitative EEG – QEEG) of 275 children with ADHD, compared to a control group. On the left the increased theta EEG activity (p<.0001) can be seen, in the middle the absolute beta EEG activity (p<.0001) and on the left the decreased relative beta EEG activity (p<.0001). This deviant brain activity has a fronto-central localization. This pattern is found in almost all ADHD studies.

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Introduction to Phenotypes

Identifying subtypes of specific disorders is an attractive exercise, as it expands our understanding of the individual’s response to therapy, but it remains attached to the approach based on the Diagnostic and Statistical Manual of Mental Disorders (DSM), which is rooted in behavior and frequently does not predict therapeutic response by any individual within the DSM grouping. Phenotypes are an intermediate step between genetics and behavior. These proposed electroencephalography (EEG) phenotypes are semistable states of neurophysiological function. The author proposes a framework allowing one to describe much of the observed EEG variance with a small number of phenotypical categories. These groupings cut across the DSM categories, and unlike the DSM, the phenotypes predict the individual’s response to therapy, for neurofeedback as well as for medication.

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