EEG-based personalized medicine for ADHD and depression

284 Pages·2011·5.79 MB·English

by  Martijn Arns

PERSONALIZED MEDICINE IN ADHD AND
DEPRESSION
A QUEST FOR EEG TREATMENT PREDICTORS

Martijn Arns

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Cover: Mathanje Huisman
Printing: Ipskamp Drukkers, Amsterdam
ISBN: 978-94-6191-098
© 2011, Martijn Arns, Beek-Ubbergen
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PERSONALIZED MEDICINE IN ADHD AND
DEPRESSION:
A QUEST FOR EEG TREATMENT PREDICTORS
EEG gebasseerde personalisatie van behandeling bij Depressie en ADHD.
(met een samenvatting in het Nederlands)

Proefschrift
ter verkrijging van de graad van doctor aan de Universiteit Utrecht op gezag van de rector
magnificus, prof. dr. G.J. van der Zwaan, ingevolge het besluit van het college voor
promoties in het openbaar te verdedigen op

vrijdag 23 december 2011 des middags te 12.45 uur
door

Martijn Wilco Arns
geboren op 4 december 1974 te Apeldoorn

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Promotor: Prof. dr. J.L. Kenemans
Co-promotor: Dr. W.H.I.M. Drinkenburg

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Table of contents

Chapter 1: Introduction

Part 1: Treatment prediction in ADHD
Chapter 2 EEG Phenotypes predict treatment outcome to stimulants in children with
ADHD
Chapter 3 EEG-vigilance and response to stimulants in paediatric patients with Attention
Deficit/Hyperactivity Disorder
Chapter 4 The increase in theta/beta ratio on resting state EEG in boys with attention-
deficit/hyperactivity disorder is mediated by slow alpha peak frequency.
Chapter 5 Efficacy of neurofeedback treatment in ADHD: The effects on inattention,
impulsivity and hyperactivity: A meta-analysis.
Chapter 6 The effects of QEEG-informed neurofeedback in ADHD: An open label pilot
study.

Part 2: Treatment prediction in depression
Chapter 7 An investigation of EEG, genetic and cognitive markers of treatment response
to antidepressant medication in patients with major depressive disorder: a
pilot study
Suppl. 1 EEG Phenotypes in depression
Chapter 8 Repetitive Transcranial Magnetic Stimulation in depression: Protocols,
Mechanisms and New Developments
Chapter 9 Potential differential effects of 9 Hz rTMS and 10Hz rTMS in the treatment of
depression.
Chapter 10 Neurophysiological predictors of non-response to rTMS in depression

Summary and conclusions
References
Summary in Dutch / Samenvatting in het Nederlands
Acknowledgement / Dankwoord
Curriculum Vitae
Publications
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Chapter 1
Introduction

This chapter is adapted from the following book chapter with some updates and modifications:
Arns, M., Gunkelman, J., Olbrich, S., Sander, C., & Hegerl, U. (2010). EEG
vigilance and phenotypes in neuropsychiatry: Implications for intervention. In
R. Coben & J. Evans (Eds.), Neuromodulation and neurofeedback: Techniques
and applications. Elsevier.
© Copyright (2011) with permission from Elsevier

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Introduction
In 1929 Hans Berger reported in an extensive publication his observations on what he
termed ‘das Elektrenkephalogramm’ (Berger, 1929), which would become the seminal
paper highlighting the beginning of research on the human electroencephalogram also
abbreviated as EEG. In his first experiments he recorded the EEG from his son Klaus – among
others – and described extensively the methods he used and what he observed. Figure 1
below demonstrates 2 graphs from that first publication recorded from his son. The bottom
figure represents the rhythm he would eventually call the ‘alpha EEG rhythm’ and the top
figure represents what he would eventually call the ‘beta EEG rhythm’. Since that time much
research has been dedicated to measuring EEG under different conditions as well as
measuring EEG in a variety of disorders ranging from neurological to psychiatric.

Figure 1: The first reports of the human EEG from the first publication from Hans Berger (1929). Both represent
samples of EEG recorded from his son Klaus (16 years old). The bottom figure represents a sample of what he
would later call the ‘alpha rhythm’ (a sinusoidal rhythm of approximately 10 Hz) and the figure above what he
would later call the ‘beta rhythm’ (or a desynchronized EEG with no obvious rhythmicity). The lowest tracing in
both graphs is a generated 10 Hz sine wave, and the middle tracing from the top figure is the ECG. From:
Berger (1929).

In the period between the discovery of EEG in 1929 and the late 1960’s EEG was mainly
inspected visually until digital equipment became available which made it possible to apply
Fourier analysis to EEG data to extract the spectral content – or frequency content - of a
signal. This eventually enabled the field of Quantitative EEG (QEEG) as we know it today. In
the simplest form one speaks of QEEG when the EEG is submitted to spectral analysis
(Niedermeyer & Da Silva, 2004). In this respect some prefer to speak of ‘Normative EEG’ to
emphasize that the EEG should not only be submitted to spectral analysis but also compared
to a control group and/or normative database.
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The group led by Ross Adey at the UCLA Brain Research Institute in the period 1961-1974
pioneered the use of QEEG. They were the first to use digital computers in the analysis of
EEG with the production of brain maps and developed the first normative library of brain
maps. See figure 2 for some photos with the first equipment developed to measure EEG in
outer space and during driving. As part of the Space Biology Laboratory they studied the
effects of outer space and space travel on the brain, to determine whether prolonged space
flight would be possible for the human body. As part of this NASA program Graham and
Dietlein were the first to coin the term Normative EEG (Graham & Dietlein, 1965). In the last
20 years due to the increasing availability of affordable computer equipment with increasing
calculating power, the field of QEEG has expanded even further and has become available
for many practicing clinicians and clinics. Along with this, several different normative
databases have been developed as well and are available to most clinicians.

Figure 2: A photo from 1963 showing the equipment developed by Adey et al. to measure EEG in space. Ross
Adey – who pioneered QEEG – is the person on the right in the top left picture. (Courtesy of the Computer
History Museum)
In this thesis where we speak of QEEG we focus on normative EEG, or QEEG data that are
compared to a control group or normative database. Furthermore, we will restrict our focus
to the application of QEEG to neuropsychiatric conditions, whereas more strictly
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