antisaccades elicited by visual and acoustic cues – an

Antisaccades elicited by visual and acoustic cues –

an investigation of children with and without attention deficit hyperactivity disorder

Dissertation

zur Erlangung des akademischen Grades

des Doktors der Naturwissenschaften (Dr. rer. nat.)

an der Universität Konstanz

Mathematisch-Naturwissenschaftliche Sektion

Fachbereich Psychologie

vorgelegt von Johanna Goepel

Konstanz, 2010

Tag der mündlichen Prüfung: 29.03.2011

1. Referentin: Prof. Dr. Brigitte Rockstroh

2. Referentin: Dr. Isabella Paul - Jordanov

http://nbn-resolving.de/urn:nbn:de:bsz:352-126473

Acknowledgment

-i-

ACKNOWLEDGMENT

Thank you for supporting and accompaning me through

the maze dissertation.

Contents

-ii-

CONTENTS

ACKNOWLEDGMENT..................................... ........................................................................................ I

CONTENTS............................................................................................................................................. II

ABBREVIATIONS...................................... ............................................................................................ III

CONDUCTED STUDIES & OWN RESEARCH CONTRIBUTIONS..... ..................................................V

ZUSAMMENFASSUNG .................................... .....................................................................................VI

SUMMARY ...........................................................................................................................................VIII

I. GENERAL INTRODUCTION ............................ ............................................................................... - 1 -

1.1 ATTENTION DEFICIT HYPERACTIVITY DISORDER.............................................................................. - 1 - 1.1.1 Phenotype and diagnostics................................................................................................ - 1 - 1.1.2 Epidemiology ..................................................................................................................... - 2 - 1.1.3 Aetiology ............................................................................................................................ - 3 - 1.1.4 ADHD and (central) auditory processing disorder ............................................................. - 6 -

1.2 SACCADES ................................................................................................................................ - 10 - 1.2.1 Influences on saccades ................................................................................................... - 13 - 1.2.2 Models of saccade generation: Race model & LATER model ........................................ - 17 - 1.2.3 Neurophysiology of saccades.......................................................................................... - 18 - 1.2.4 Saccades & ADHD .......................................................................................................... - 21 -

1.3 CONCLUSION............................................................................................................................. - 23 -

II. THREE ANTISACCADE STUDIES ...................... ........................................................................ - 26 -

2.1 PRO- AND ANTISACCADES IN CHILDREN ELICITED BY VISUAL AND ACOUSTIC CUES – DOES MODALITY MATTER?......................................................................................................................................... - 26 -

2.1.1 Abstract............................................................................................................................ - 26 - 2.1.2 Introduction ...................................................................................................................... - 26 - 2.1.3 Methods ........................................................................................................................... - 29 - 2.1.4 Results ............................................................................................................................. - 32 - 2.1.5 Discussion........................................................................................................................ - 35 - 2.1.6 Conclusion ....................................................................................................................... - 37 -

2.2 MEDIO-FRONTAL AND ANTERIOR TEMPORAL ABNORMALITIES IN CHILDREN WITH ATTENTION DEFICIT HYPERACTIVITY DISORDER (ADHD) DURING AN ACOUSTIC ANTISACCADE TASK AS REVEALED BY ELECTRO-CORTICAL SOURCE RECONSTRUCTION .............................................................................................. - 39 -

2.2.1 Abstract............................................................................................................................ - 39 - 2.2.2 Introduction ...................................................................................................................... - 39 - 2.2.3 Methods ........................................................................................................................... - 41 - 2.2.4 Results ............................................................................................................................. - 46 - 2.2.5 Discussion........................................................................................................................ - 48 - 2.2.6 Conclusion ....................................................................................................................... - 52 -

2.3 BRAIN ACTIVATION DIFFERENCES DURING THE GENERATION OF VISUALLY AND ACOUSTICALLY GUIDED ANTISACCADES BETWEEN CHILDREN WITH AND WITHOUT ATTENTION DEFICIT HYPERACTIVITY DISORDER (ADHD).......................................................................................................................................... - 53 -

2.3.1 Abstract............................................................................................................................ - 53 - 2.3.2 Introduction ...................................................................................................................... - 53 - 2.3.3 Methods ........................................................................................................................... - 56 - 2.3.4 Results ............................................................................................................................. - 60 - 2.3.5 Discussion........................................................................................................................ - 68 -

III. GENERAL DISCUSSION............................ ................................................................................. - 76 -

LIST OF TABLES AND FIGURES......................... ........................................................................... - 86 -

REFERENCES .................................................................................................................................. - 88 -

Abbreviations

-iii-

ABBREVIATIONS

ACC Anterior cingulate cortex

ADHD Attention deficit hyperactivity disorder

ADHS Aufmerksamkeitsdefizit-/Hyperaktivitätsstörung

ANOVA Analysis of variance

approx. Approximately

BESA Brain electrical analysis software

(C)APD (Central) auditory processing disorder

ca Circa

CAPT Continuous Attention Performance Test

C-DT Choice-Delay tasks

cm Centimetre

CPM Coloured Progressive Matrices

CPT Continuous Performance Test

dB Dezibel

df Degrees of freedom

DPFC Dorsolateral prefrontal cortex

DSM IV Diagnostic and Statistical Manual of Mental Disorders IV

e.g. For example (Latin: exempli gratia)

ed. Edition

Eds. Editors

EEG Electroencephalography, Electroencephalogram, Elektroenzephalografie

ERN Error related negativity

ERP Event - related potential

et al. And others (Latin: et alii)

etc. Et cetera

FD Frequency discrimination

FEF Frontal eye fields

FM Frequency modulation

fMRI Functional Magnetic Resonance Imaging

Hz Hertz (1/sec)

i.e. That is (Latin: id est)

ICD 10 International Classification of Diseases 10

kOhm Kiloohm

Abbreviations

-iv-

LATER Model Linear Approach to Threshold with Ergodic Rate Model

MFC Medio-frontal cortex

mm Millimetre

MPH Methylphenidate

ms Milliseconds

nAm Nano ampermeter

P300 Positive ERP-component at approximately 300 ms

PEF Parietal eye fields

PN Processing negativity

PPC Posterior parietal cortex

RD Reading disability

RT Reaction time

SC Superior colliculus

SD Standard deviation

sec Seconds

SEF Supplementary eye fields

SPECT Single Photon Emission Computed Tomography

SRT Saccadic reaction time

SST Stop-Signal Test

STC Supplemental temporal cortex

TAC Temporal anterior cortex

TPC Temporal parietal cortex

vs. Versus

WMA World Medical Association

µs Micro seconds

µV Micro volt

Conducted studies & own research contributions

-v-

CONDUCTED STUDIES & OWN RESEARCH CONTRIBUTIONS

The studies presented in this work were supported by a number of colleagues. Listed

below are the three different studies and my own research contributions.

Study I:

Pro- and antisaccades in children elicited by visual and acoustic cues – does modality

matter?

Authors: Johanna Goepel1, Stefanie Biehl2, Johanna Kissler1 and Isabella Paul-

Jordanov1

Submitted to BMC Pediatrics

Own Contributions: Research on the theoretical background, running the Eye Tracker

experiments, data analyses, and drafting the manuscript.

Study II:

Medio-frontal and anterior temporal abnormalities in children with attention deficit

hyperactivity disorder (ADHD) during an acoustic antisaccade task as revealed by

electro-cortical source reconstruction

Authors: Johanna Goepel1, Johanna Kissler1, Brigitte Rockstroh1 and Isabella Paul-

Jordanov1

Published in BMC Psychiatry

Own Contributions: Research on the theoretical background, running the EEG-

experiments, data analyses, and drafting the manuscript.

Study III:

Brain activation differences during the generation of visually and acoustically guided

antisaccades between children with and without attention deficit hyperactivity disorder

(ADHD)

Authors: Johanna Goepel1, Johanna Kissler1, Brigitte Rockstroh1 and Isabella Paul-

Jordanov1

Submitted to ADHD Attention Deficit and Hyperactivity Disorders

Own Contributions: Research on the theoretical background, planning the study,

development and programming of the basic design, running the EEG-experiments, data

analyses, and drafting the manuscript.

1 Department of Clinical Psychology, University of Konstanz, 78457 Konstanz 2 Clinic for Psychiatry and Psychotherapy, University of Würzburg, 97080 Würzburg

Zusammenfassung

-vi-

ZUSAMMENFASSUNG

Impulsivität und damit unzureichende Inhibitionskontrolle ist eine der

Kernsymptome der Aufmerksamkeitsdefizit-/Hyperaktivitätsstörung (ADHS) – eine der

häufigsten chronischen psychiatrischen Störungen im Kindes- und Jugendalter. Eine

Möglichkeit inhibitorische Mechanismen zu untersuchen ist die Antisakkadenaufgabe –

eine Aufgabe, bei der ein Proband aufgefordert wird eine Sakkade in Richtung eines

plötzlich erscheinenden Reizes (Prosakkade) zu unterdrücken und stattdessen eine

willentliche Sakkade gleicher Größe auf die gegenüberliegende Seite zu generieren.

Ziel der vorliegenden Doktorarbeit war es zu erforschen, ob Kinder mit ADHS

nicht nur in der Inhibition auf plötzlich erscheinende visuelle, sondern auch auf plötzlich

erscheinende akustische Stimuli eingeschränkt sind, um eine Grundlage für bessere

Differentialdiagnostik zu legen.

Studie I – eine Eye Tracker Pilotstudie – untersuchte Kontrollkinder mit einer

randomisierten Anti-/Prosakkadenaufgabe und deckte vergleichbare

Inhibitionsleistungen in der visuellen und akustischen Bedingung auf: mehr Fehler in der

Anti- als in der Prosakkadenbedingung. Zusätzlich wurden modalitätsabhängige

Unterschiede gefunden: Der „Greif-Reflex“ war für die akustisch hervorgerufenen

Sakkaden schwächer und sie schienen weniger stark durch Impulsivität beeinflussbar,

da ihre Latenzen länger waren, was wiederum in weniger Antisakkadenfehlern

resultierte.

Während Studie II wurde das gleiche Paradigma bei Kindern mit und ohne ADHS

in einem Elektroenzephalografie (EEG) Experiment getestet. Auf der Verhaltensebene

wurden keine Gruppenunterschiede gefunden, jedoch wurden bei der 23-Quellen-Modell

Analyse Gruppenunterschiede in den akustisch ausgelösten und richtig ausgeführten

Antisakkaden sichtbar: Kinder mit ADHS zeigten im anterioren temporalen Lappen und

im medio-frontalen Cortex (für die Inhibition wichtige Strukturen) mehr Aktivität um

insgesamt die gleichen Verhaltensleistungen wie Kinder ohne ADHS zu erzeugen.

Möglichweise kann diese erhöhte Aktivität als ein kompensatorischer Mechanismus

gesehen werden.

In Studie III wurden Kinder mit und ohne ADHS während einer geblockten

Sakkadenaufgabe in einem EEG Experiment getestet. In der visuellen Bedingung waren

Kinder mit ADHS auf der Verhaltensebene (mehr Fehler und verlängerte Latenzen in der

Antisakkadenbedingung) als auch auf der physiologischen Ebene eingeschränkt. Die

Befunde weisen auf eine frontale Unteraktivität und ein parietal-cerebellares

kompensatorisches Netzwerk bei ADHS hin. Während des akustischen Experimentes

Zusammenfassung

-vii-

schienen Kinder mit ADHS größere Schwierigkeiten bei der Generierung von Sakkaden

zu haben, nicht aber bei der Inhibition.

Letztendlich ist anzunehmen, dass Kinder mit ADHS durchaus unterschiedlich

bezüglich der Inhibitonskontrolle eingeschränkt sind. Dies scheint von der

Aufgabenkomplexität und nicht von der Stimulus Modalität per se abzuhängen.

Summary

-viii-

SUMMARY

Impulsivity and with it deficient inhibition control is one of the core symptoms of

attention deficit hyperactivity disorder (ADHD) – one of the most prevalent chronic

psychiatric disorders in childhood and adolescence. One possibility to investigate

inhibitory mechanisms is the antisaccade task – a task, in which a subject is required to

suppress a saccade towards a suddenly appearing cue (prosaccade) and to generate a

voluntary saccade of equal size towards the opposite direction instead.

Aim of the present thesis was to investigate if children with ADHD are constricted

in their inhibition of not only suddenly arising visual but also suddenly arising acoustic

cues in order to establish a basis for a better differential diagnostic.

Study I – an eye tracker pilot study – investigated control children in a random

anti-/prosaccade task and revealed similar inhibition performance in visual and acoustic

conditions: more errors in the anti- compared to the prosaccade condition. Additionally,

modality dependant differences were found: the “grasp-reflex” was weaker for

acoustically elicited saccades and they seemed less prone to be influenced by

impulsivity as their latency was longer which in turn resulted in fewer antisaccade errors.

During Study II the same paradigm was tested with children with and without

ADHD in an Electroencephalography (EEG) experiment. On the behavioural level no

group differences were found but in a 23-source-model analysis group differences were

observable in acoustically elicited and correct performed antisaccades: children with

ADHD showed more activity in the anterior temporal lobe and medio-frontal cortex

(structures important for inhibition) to achieve the same behavioural output than children

without ADHD. The heightened activation could possibly be seen as a compensatory

mechanism.

In Study III children with and without ADHD were compared during a blocked

saccade task in an EEG experiment. In the visual condition children with ADHD were

impaired on the behavioural level (more errors and elongated latency in the antisaccade

condition) as well as on the physiological level. The findings suggest a frontal

hypoactivation and a parietal-cerebellar compensatory network in ADHD. During the

acoustic experiment children with ADHD seemed to have greater difficulties generating

saccades but not inhibiting.

Finally, it is to assume that children with ADHD are probably impaired quite

differently concerning inhibition control. This seems to depend on task complexity and

not on cue modality per se.

General introduction

- 1 -

I. GENERAL INTRODUCTION

1.1 Attention deficit hyperactivity disorder

1.1.1 Phenotype and diagnostics

Attention deficit hyperactivity disorder (ADHD) with a worldwide prevalence in

children of about 5-10% (Biederman & Faraone, 2005; Faraone, Sergeant, Gillberg, &

Biederman, 2003; Polanczyk, de Lima, Horta, Biederman, & Rohde, 2007; Scahill &

Schwab-Stone, 2000) is one of the most frequent chronic psychiatric disorders in

childhood and adolescence with substantial lifelong implications on social und personally

functioning, academic performance and quality of life in general (Danckaerts, et al.,

2010; Harpin, 2005). In Germany with a prevalence of 7.7%, boys are affected to a

greater extent than girls with 1.8% (Huss, Holling, Kurth, & Schlack, 2008). The

male/female ratio of 3:1 in the community constitutes an under-identification of girls with

ADHD because they run a lower risk to develop a disruptive behaviour disorder – a

disorder which increases the referral rate (Biederman, 2005). Prevalence estimates

predictably vary according to the methodology (including diagnostic criteria, source of

information etc.) of the studies (Faraone, et al., 2003; Polanczyk, et al., 2007).

Core deficits of ADHD are cross-situational impairments in attention

(distractibility), impulse control (impulsivity) and activity (hyperactivity). The Diagnostic

and Statistical Manual of Mental Disorders IV (DSM IV; American Psychiatric

Association, 2000) and the International Classification of Diseases 10 (ICD 10; World

Health Organization, 1993) distinguish between three different disorder types: a

predominantly hyperactive type (codes 314.01 and F90.1), a predominantly inattentive

type (codes 314.00 and F98.8) and a combined type (codes 314.01 and F90.0). A

common diagnostic criterion of all three subtypes is that core deficits have to be

persistent in more than one setting (e.g. school and home) over six months and have to

develop before the age of seven. For a diagnostic in addition to a multitude of parents

and teacher behaviour questionnaires (e.g. Conners Scale, Diagnose-Checklists ADHD;

Döpfner, Görtz-Dorten, Lehmkuhl, Breuer, & Goletz, 2008; Lauth & Schlottke, 2002),

several neuropsychological measurements are applied in which children with ADHD

perform worse (Nichols & Waschbusch, 2004). Patients with ADHD show deficits in tests

of executive functions (Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005) especially

while performing tests of inhibition (Nigg, 2001). Frequently used tests are the

Continuous Performance Test (CPT, a Go/Nogo paradigm requiring response inhibition

for specific stimulus combinations; Rosvold, Mirsky, Sarason, Bransome, & Beck, 1956)

and the Stop-Signal Test (SST, where primary response has to be made following a

visual stimulus unless there is a signal indicating to stop the response for one trial;


- 2 -

Logan, Cowan, & Davis, 1984). In such tests children with ADHD show higher error rates

and elongated reaction times (RTs) as well as altered brain activities (e.g. Fallgatter, et

al., 2004; Liotti, Pliszka, Perez, Kothmann, & Woldorff, 2005; Overtoom, et al., 2002;

Pliszka, Liotti, & Woldorff, 2000; Russell Schachar & Logan, 1990; Schachar, Mota,

Logan, Tannock, & Klim, 2000; Tamm, Menon, Ringel, & Reiss, 2004). Additionally,

children with ADHD select the shorter delay in a higher rate compared to control children

in Choice-Delay Tasks (C-DT, children have to choose between a large delayed reward

and a smaller immediate reward; e.g. Marco, et al., 2009). These findings support the

hypothesis that one underlying aetiological factor in ADHD might be a behavioural

disinhibition, a dysfunctional executive system and a delay aversion.

1.1.2 Epidemiology

ADHD which develops in early childhood is a chronical disorder (Harpin, 2005)

which persists in 4-78% (Biederman, Petty, Evans, Small, & Faraone, 2010; Mannuzza,

Klein, Bessler, Malloy, & LaPadula, 1998) of children into adulthood.

In infancy more than half of the children have problems related to sleep and

eating. In addition, children with ADHD have gross and fine motor or hearing problems

(Thompson, et al., 2004). As a consequence of lacking social functions, these children

have fewer friends and fewer outside activities. Children with ADHD often are made the

scapegoat resulting in low self-esteem, aggression, low empathy, sadness, isolation, etc.

up to depression (Spencer, Biederman, & Mick, 2007; Thompson, et al., 2004). Thus,

children with ADHD suffer from additional psychiatric disorders to a high degree (Angold,

Costello, & Erkanli, 1999). Comorbid diagnoses include oppositional defiant disorders,

conduct disorders, mood disorders (unipolar and bipolar), anxiety disorders, tic disorders,

substance use disorders and learning disorders (Biederman & Faraone, 2005;

Biederman, et al., 2010; Scahill & Schwab-Stone, 2000; Spencer, et al., 2007;

Thompson, et al., 2004).

During adolescence symptoms of inattention decline at a very modest rate,

whereas those of hyperactivity and impulsivity remit much more abruptly (Biederman,

Mick, & Faraone, 2000). However, when coming into the teenage years new problems

can arise. As a result patients with ADHD run a higher risk for academic failure

(Biederman, et al., 2004), criminal conduct (Fletcher & Wolfe, 2009), risky sexual

behaviour associated with unwanted pregnancy (Flory, Molina, Pelham, Gnagy, & Smith,

2006), high-risk driveability (Barkley, Murphy, Dupaul, & Bush, 2002) and further

additional psychiatric problems, such as antisocial personality disorder and nonalcohol

substance abuse (Mannuzza, et al., 1998). However, there is a wide range of teenagers


- 3 -

that have the ability to adjust their behaviour. Therefore, the persistence of ADHD is not

associated with a uniform dysfunctional outcome, but instead leads to a range of

emotional, educational and social progression that can be partially predicted by maternal

psychopathology, lager family size, psychiatric comorbidity and impulsive symptoms

(Biederman, Mick, & Faraone, 1998). Thus, it is obvious that symptoms do not disappear

but rather change in the course of life. The current prevalence of adult ADHD is between

2 and 3% (Steinhausen, 2003). The failure of an appropriate symptom description may

reduce the true prevalence of ADHD in adulthood (Spencer, et al., 2007). Inattention in

adulthood is characterised by a poor self and task management, difficulty in initiating,

completing and changing tasks, trouble with multitasking, procrastination and avoiding of

activities that demand attention. The aimless restlessness in childhood changes in

purposeful restlessness in adulthood like working in more than one job, working long

hours or working in active jobs (Adler, 2008).

1.1.3 Aetiology

To date, there is no monocausal explanation for genesis of ADHD. Rather a

multifactor model is assumed with genetic, biologic, and psychosocial interactions

(Biederman & Faraone, 2005).

Studies have shown that ADHD is transmitted in families. According to family

studies, it seems that parents and siblings of children with ADHD have a two- to eightfold

increase to run a risk for developing ADHD (Biederman, 2005). Sprich and colleagues

(2000) reported that the rate of ADHD was similar in the adoptive relatives (6%) of

adopted children with ADHD and the biological relatives (3%) of non-ADHD control

children. Compared to this rate the percentage of ADHD was higher in the relatives of

biological ADHD probands (18%). Based on 20 twin studies from the United States,

Australia, Scandinavia, and the European Union the heritability is up to 76% (Faraone, et

al., 2005).

On the molecular genetics level, genes responsible for expression of

catecholamines (dopamine, serotonin, and noradrenalin) have been the initial

candidates. This was based (1) on results of imaging studies that comprised brain

structures with dopamine innervations (e.g. the fronto-striatal and the fronto-parietal

circuits, which are believed to be critical for executive functioning and regulation of

behavioural responses such as arousal, attention, and inhibition; Dickstein, Bannon,

Castellanos, & Milham, 2006) and (2) on the fact that stimulant medications act as

dopaminergic and noradrenergic agonists (Pliszka, 2005; Pliszka, McCracken, & Maas,

1996) which influence the anterior (dopamine- and noradrenergic) and posterior


- 4 -

(noradrenergic) attention systems (Himelstein, Newcorn, & Halperin, 2000).

Methylphenidate (MPH), which is until now the primary pharmacological treatment for

ADHD, prevents dopamine reuptake by blocking the dopamine transporters (DAT). DATs

were found in higher density in patients with ADHD (Dougherty, et al., 1999; Krause,

Dresel, Krause, la Fougere, & Ackenheil, 2003) which decreases the level of dopamine

in the synaptic cleft. Recent studies, however, failed to verify the DAT hypothesis (see for

review Swanson, et al., 2007). The gene that encodes the DAT protein – located on

human 10-repeat allele of chromosome 5p15.3 – is associated with ADHD (Friedel, et

al., 2007; Yang, et al., 2007). It occurs in a higher concentration in ADHD samples rather

than in those of the control groups (Swanson, et al., 2000). Finally, DAT is connected to

dopamine receptors. Although the results of studies are inconsistent (Faraone, et al.,

2005) it seems that the 7-repeat allele on chromosome 11p15.5, which coded dopamine

receptors D4, also occurs more frequently in children with ADHD than in controls

(Swanson, et al., 2000).

Swanson and colleagues (2000) pointed out that three changes of the systems

are possible: a sub sensitive dopamine receptor D4 (combined with lesser signal

transfer), a hyper efficient DAT (associated with a hyper dopamine reuptake) or both,

which may result in underactivity of brain regions that are involved in attention and

behaviour.

However, more than the mentioned neurotransmitters and genes are involved in

ADHD (Faraone, et al., 2005). To date, findings of genetic studies in ADHD are still

inconsistent and disappointing (Banaschewski, Becker, Scherag, Franke, & Coghill,

2010).

Neuroimaging studies suggested anatomical abnormalities in ADHD individuals,

consisting in smaller sizes than the normal one of several brain regions: e.g. frontal

cortex (Seidman, Valera, & Makris, 2005), cerebellum (Castellanos, et al., 2002), and

subcortical structures, like the anterior cingulate cortex (ACC) (Seidman, et al., 2006),

caudatus nucleus, globus pallidus and corpus callosum (Seidman, et al., 2005). These

subcortical structures are part of the neural circuits underlying motor control, executive

functions, inhibition of behaviour and the modulation of reward (Biederman, 2005).

Additionally, the volume reductions are related to measures of symptom severity in

ADHD samples (Casey, et al., 1997; Castellanos, et al., 2002).

Furthermore, functional studies referred to a “lazy frontal lobe”: the fronto-striatal

regions of patients with ADHD are hypoperfused, hypometabolic, and functionally

disrupted in comparison to control subjects (Hale, Hariri, & McCracken, 2000).


- 5 -

Therefore, functional imaging studies of ADHD, using Single Photon Emission

Computed Tomography (SPECT) showed a reduced blood flow in the frontal lobe and

the basal ganglia but an increased blood flow to the occipital lobe (Lou, Henriksen, &

Bruhn, 1990).

Elevated theta power (slow wave activity) as well as reduced alpha and beta

power during electrophysical studies are interpreted as cortical underarousal (Barry,

Clarke, & Johnstone, 2003). Niedermeyer (2001) postulated that this frontal hypoarousal

results in a deficient inhibition of the motor cortex followed by an excess of motor activity.

One possibility for increasing beta band activity is the prescription of MPH, which

correlates positively with attention performances (Clarke, et al., 2003; Wienbruch, Paul,

Bauer, & Kivelitz, 2005).

The P300 component, an event - related potential (ERP), is also related to

attention. Besides poorer performance in tests of sustained and/or selective attention

children with ADHD showed an altered P300: smaller amplitudes and longer latencies

compared to controls were found (see for review Tannock, 1998) referring to problems in

signal identification and processing. In healthy adults an increased P300 with longer

latency was observed in Nogo stimuli compared to Go stimuli (Bokura, Yamaguchi, &

Kobayashi, 2001; Fallgatter, Bartsch, & Herrmann, 2002). Children with ADHD showed a

diminished Nogo - P300 in such tasks (Fallgatter, et al., 2004; Paul-Jordanov, Bechtold,

& Gawrilow, 2010) which could be increased with MPH or self-regulation strategies

(Paul-Jordanov, et al., 2010; Paul, et al., 2007).

Furthermore, in children with ADHD a reduced N200 – associated with inhibitory

processes – was found during SSTs (Dimoska, Johnstone, Barry, & Clarke, 2003;

Pliszka, et al., 2000). The error related negativity (ERN) – a negative component

observed following errors – is also reduced in children with ADHD compared to control

children reflecting impaired error monitoring (Liotti, et al., 2005).

Finally, environmental factors such as nicotine, lead or foetal adaptation in

response to stress are discussed to influence the risk for ADHD also in interaction with

genes (see for review Swanson, et al., 2007). Fundamentally, the reciprocal influence

between the behaviour of children and that of parents or of other attachment figures and

with that the developing of a vicious circle is considered in the epidemiology (Harpin,

2005), i.e. the intensity of ADHD symptoms is influenced by the behaviour of the

environment.

Different models of aetiology were assumed in order to summarize the results.

Presently, the following important models are discussed: (1) the Executive Attention

Model postulated by Barkley (1997). In this model, patients with ADHD exhibit deficits in


- 6 -

three sub-processes of inhibition (inhibition of prepotent responses, stopping an ongoing

response and interference control). (2) The Delay Aversion Model of Sonuga-Barke

(1994; 1992), which suggests that children’s inattention, hyperactivity and impulsiveness

are situation specific attempts to minimize delay. Finally, there are ambitions to combine

both models to a dual pathway model. This idea assumes that delay aversion (linked to

the fronto-ventral-striatal reward network) and poor inhibition control (associated with the

fronto-dorsal-striatal system) are independently coexisting characteristics of the

combined type of ADHD (Sonuga-Barke, 2002, 2003). To date, a triple pathway model is

discussed including deficits in the domains inhibitory control, delay aversion and

temporal processing (Sonuga-Barke, Bitsakou, & Thompson, 2010).

Although these models try to explain the underlying deficits in children with ADHD,

deficits in executive functions and delay aversion are neither necessary nor sufficient to

cause the group heterogeneity of ADHD occurrences (Nigg, 2005; Willcutt, et al., 2005).

Thus, it is still an open question which multilevel factors are responsible for the diversity

of ADHD symptoms.

1.1.4 ADHD and (central) auditory processing disorder

In recent years, questions arose concerning the coherence between ADHD and

the (central) auditory processing disorder ((C)APD). Musiek described auditory

processing as "How well the ear talks to the brain and how well the brain understands

what the ear tells it” (Paul-Brown, 2003). Thus APD (ICD-10 code F80.20) – compared to

ADHD a relative “new” disorder – is characterised by disturbed hearing despite of a

normally functioning auditory periphery. Typical symptoms are difficulties in sound

localization and lateralization, auditory discrimination and auditory pattern recognition.

Patients are handicapped in temporal aspects of audition, including temporal integration,

temporal discrimination (e.g. temporal gap detection), temporal ordering, and temporal

masking. Children with APD show a poor auditory performance in competing acoustic

signals (including dichotic listening) and a reduced auditory performance with degraded

acoustic signals (American Speech-Language-Hearing Association, 2005; British Society

of Audiology Steering Group, 2007). These symptoms are related to poor performance in

confusing environments, difficulties in following oral instructions or rapid/degraded

speech and difficulties in background noise. Children with APD appear to be inattentive

and distractible. Most of them have academic difficulties as well as language, reading

and spelling disorders (Bamiou, Musiek, & Luxon, 2001). The prevalence ranges

between 2 and 7% (Bamiou, et al., 2001; Chermak, Hall, & Musiek, 1999) with a 2:1

male/female ratio (Nickisch, et al., 2007) and the aetiology is similar heterogeneous to


- 7 -

those of other developmental disorders (Bamiou, et al., 2001; Dawes, Bishop, Sirimanna,

& Bamiou, 2008; Witton, 2010). The heterogeneity of APD itself is accepted as

evidenced by the appearance of symptom-profiling schemas: the Buffalo Model and the

Bellis/Ferre Model (Dawes & Bishop, 2009). These models describe four and five APD

categories, respectively, but must refine their classification for clinical utility (Jutras, et al.,

2007). Three factors seem to make the diagnostic of APD complicate: (1) other types of

childhood feature similar behaviour patterns, (2) some of the audiological tests fail in

differentiating children with APD from children with other problems because there is only

a behavioural performance required of the children and (3) there is always a likelihood

for an encounter with other confounding processes like the lack of motivation, sustained

attention, cooperation or understanding (Jerger & Musiek, 2000).

However, efforts for testing diagnostic manuals are available. For example, Bellis

and colleagues (2008) investigated the dichotic listening task and found a lager right ear

advantage for children with APD compared to control children, and a reversed

asymmetry for a corresponding visual analogous task (left visual field advantage for

control children and right visual field advantage for children with APD). Parallel

questionnaires were tested (e.g. Meister, von Wedel, & Walger, 2004) but up to now

there is no standard diagnostic test set for APD (Dawes, et al., 2008).

An overlap between symptoms of ADHD and APD is described very well in the

literature (e.g. Cacace & McFarland, 2005b; Dawes & Bishop, 2009; Keller & Tillery,

2002) and a comorbidity of both disorders seems to be the rule rather than the exception.

Riccio and colleagues (1994) found that in 30 children diagnosed with APD, 50% would

also conform to a diagnosis of ADHD based on formal evaluation. The other way around

29-79% ADHD was diagnosed in APD children (see for review Keller & Tillery, 2002).

Additionally, rankings by audiologists and paediatricians of symptoms associated with

APD and ADHD correlated very high (Cacace & McFarland, 2005b; McFarland &

Cacace, 2003).

Both, children with APD and children with ADHD, have difficulties in paying

attention and remembering information presented orally, are easily distracted, have

difficulties in following complex acoustic directions or commands, and show low

academic performance.

Anecdotally children with APD show behaviour patterns associated with ADHD

criteria, including among others short attention spans, impulsivity, distractibility,

daydreaming, hypoactivity or hyperactivity, whereas children with ADHD have difficulty

with specific types of dichotic listening tasks, verbal memory measures, and word finding

or rapid naming tasks (see for review Riccio & Hynd, 1996). For example children with


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ADHD showed poorer speech discrimination in noise than control children (Geffner,

Lucker, & Koch, 1996), interpreted as worse figure-ground capabilities in children with

ADHD. Furthermore, patients perceived speech as comfortable or tolerable at lesser

loudness levels as control children (Lucker, Geffner, & Koch, 1996).

In addition to deviations in the auditory modality on behavioural levels, differences

between children with and without ADHD were also found in brainstem evoked potentials

or ERPs. For example Lahat and colleagues (1995) identified prolonged latencies of

early brainstem auditory evoked potentials in children with ADHD, pointing out an

additional dysfunction of early acoustic stimuli perception. Furthermore, during auditory

selective attention tasks the processing negativity (PN) of the ERPs – generated in the

auditory cortex – seemed to be smaller and more anterior located in children with ADHD

(Kemner, et al., 2004). The P300 frontal activity seemed to be higher whereas a reduced

activity in parietal regions was found compared to control subjects (Johnstone & Barry,

1996). At the same time, the reduced parietal activation was interpreted as attention

deficit and the frontal activity as compensatory mechanism. Additionally, imaging studies

provided a neuroanatomical basis through findings of morphologic and structural

differences in auditory brain areas (see for review Chermak, et al., 1999; Riccio & Hynd,

1996).

The overlap between both disorders could be confirmed by the high comorbidity

rate of learning disabilities in both. Thus, according to Barkley (1991a), approximately

25-40% of children with ADHD suffer from a learning disability. And Sharma and

colleagues (2009) reported that 67% of a sample of children with APD additionally had

language or reading problems, or both. Inversely it seems that children with nonverbal

learning disabilities run a risk to develop APD because a study showed that 61% of the

group of children with a nonverbal learning disorder was diagnosed with APD (Keller,

Tillery, & McFadden, 2006).

The results of these studies refer to an association between attention deficits and

performance on central auditory tests. Thus, three hypotheses arise: (1) APD and ADHD

may be distinct but comorbid, (2) one disorder causes the other or (3) both labels are

compatible and are assigned in dependency of the specialisation of the diagnostician

(Dawes & Bishop, 2009).

As a result, a range of authors (Cacace & McFarland, 2005a; Jerger & Musiek,

2000) challenged to improve the differential diagnosis with tests of multiple sensory

modalities because it has to be expected that APD is frequently comorbid showing

symptoms of other disorders (Witton, 2010). Following the theoretical framework, it was

assumed that children with APD perform poorly on auditory tasks, while children with


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ADHD might perform poorly on auditory and visual tasks (Dawes & Bishop, 2009; Jerger

& Musiek, 2000).

A first step for upgrading the diagnostic tools was the development of the

Continuous Attention Performance Test (CAPT) (Starzacher, Nubel, Grohmann, Gaupp,

& Pfeiffer, 2007) based on CPT test versions with visual and/or acoustic stimuli

(Jonkman, et al., 1997a, 1997b; Keith & Engineer, 1991; Klorman, et al., 1990; Mahone,

Pillion, Hoffman, Hiemenz, & Denckla, 2005; Morgan, Hynd, Riccio, & Hall, 1996; Tillery,

Katz, & Keller, 2000). Riccio and colleagues (1996) showed that it was not possible to

separate children with ADHD or ADHD and APD with an auditory CPT. Mahone and

colleagues (2005) found that children with ADHD performed worse in the auditory CPT

compared to control children - but only within very young children. Jonkam and

colleagues (1997b) revealed in both modality conditions higher error rates and reduced

event-related potentials for children with ADHD compared to control children. Starzacher

(2006) showed in 20 children with APD and 31 control children that children with APD

had a weaker performance compared to control children in the whole CAPT. Additionally,

she pointed out that within the control group no differences were found between visual

and auditory subtests – with reservations that high individual differences in performance

between both subtests exist. Children with APD exhibited a lower attention performance

in the acoustic than in the visual test. But an experiment with another integrated visual

and auditory CPT showed in 68 children with suspected APD that more children had

problems with both attention types than with auditory or visual attention alone.

Additionally, the authors suggested that 30% of the children had a normal auditory

attention and a diagnosis of APD, whereas 8% had a poor auditory attention but no APD

diagnosis (Sharma, et al., 2009). Furthermore, the attempts to differentiate APD children

with and without ADHD using electro physical measures have not been successful (Ptok,

Blachnik, & Schonweiler, 2004). These partly conflicting findings demonstrate that the

strict definition which identifies the deficits of children with APD as being restricted to the

auditory modality does not apply.

In summing up, ADHD (and its subtypes) and APD (including possible subtypes)

are very heterogenic and an overlap between behavioural symptomatology is evident.

Thus, there is a high possibility for misdiagnosis and comorbidity or a causal conjunction

(Cacace & McFarland, 2005b). Identifying an objective instrument for modality specific

perceptual malfunction would help to clarify criteria and diagnostic (Cacace & McFarland,

2005b). Thus, more research in this domain is necessary to upgrade diagnostic and

treatment in order to help people with the described disturbances.


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1.2 Saccades

Saccades are quick, simultaneous movements of both eyes in the same direction

serving as mechanism to new readjust our gaze over and over again to the particular

scene of interest to position this target on the fovea for a high definition vision (Leigh &

Zee, 1999). Thus, the function of voluntary saccades in primates is directly related to the

existence of a fovea. Saccades include both voluntary and involuntary changes of

fixation, the quick phase of vestibular and optokinetic nystagmus and the rapid eye

movements during sleep.

According to Leigh and Zee’s hierarchy classification (1999, 2006) there are nine

saccade classes from higher to lower order: (1) volitional saccades, (2) predictive,

anticipatory saccades, (3) memory-guided saccades, (4) antisaccades, (5) saccades to

command, (6) reflexives saccades, (7) express saccades, (8) spontaneous saccades

and (9) quick phases. In the context of inhibition investigation, two kind of saccades are

important for research: (i) reflexive saccades (called prosaccades) are saccades

triggered by sudden onset (visual, acoustic or tactile) stimuli and (ii) antisaccades which

are saccades generated towards the mirror image location of a sudden appearance of a

target (Hallett, 1978). Antisaccades as a task of response inhibition consists of two

processes: (1) the capacity to suppress a prepotent response before or after its initiation

and (2) the protection of this response delay and the goal-directed behaviour from the

interference of competing processes (Barkley, 1991a). The understanding of the

cognitive processes that underlie pro- and antisaccades – also described as exo- and

endogenous saccades – is of fundamental importance because there is considerable

evidence suggesting that antisaccade in contrast to prosaccades performance is mostly

impaired in psychiatric patients (Everling & Fischer, 1998; Hutton & Ettinger, 2006;

Karatekin, 2007; Rommelse, Van der Stigchel, & Sergeant, 2008). In the laboratory,

saccades are elicited by instructing the participants to look away from a central fixation

point towards (prosaccades) – which usually serve as control task – or to look to the

mirror image (antisaccades) of a sudden onset periphery target.

In doing so, different parameters are used to characterise saccades (see figure 1):


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Figure 1: The parameters of eye movement

Saccades amplitude: The amplitude is the size of the saccade, usually measured

in degrees of arc. The amplitude determines the saccade accuracy. This is commonly

denoted by the term "gain". The gain is the ratio of the actual saccade amplitude and the

desired saccade amplitude. The degree of dysmetria is usually relatively small (i.e. 10-

20% of the amplitude of the saccade; Leigh & Zee, 2006). Also, there is the

differentiation between hypometria (gains of <1) – i.e. the undershooting of saccades –

and hypermetria (gains of >1) – i.e. the overshooting of saccades.

Saccadic velocity: Saccades are the fastest movements produced by the human

body. After reaction initiation, velocity usually increases to a peak and then either

declines slightly or oscillates around the target velocity. This peak velocity can be used to

determine a value for the gain parameter (peak velocity/target velocity). It is usually

approximately equal to the velocity of the stimuli. The peak angular speed of the eye

during a saccade achieves 400 to 800°/sec (Irving, Steinbach, Lillakas, Babu, &

Hutchings, 2006; Leigh & Zee, 1999; Sparks, 2002).

Saccadic acceleration: The rate of change of the eye velocity is called

acceleration. Maximal acceleration of visual triggered saccades is 20000°/sec 2

(Haarmeier, in press).

Saccadic duration: Saccadic duration is the time taken to complete the saccade.

This is most easily measured from the velocity profile. Most saccades are completed

within 100ms (Leigh & Zee, 2006).

Saccadic latency: The latency is the processing time needed between stimulus

and response onset (conventionally when eye speed exceeds some threshold, such as


- 12 -

30°/sec; Leigh & Zee, 2006). Saccadic latencies can be viewed as decision time because

this time is more than a pure signal transmission. The more saccades made, the less

time there is for fixations so it has to be determined whether the costs of a saccade are

justified. Saccades beginning -300 to +80ms relative to the onset of the cue are defined

as anticipatory responses (Klein, 2001). Reactions with latencies from 81 to 130ms post

stimuli are defined as express saccades – considered to be the most reflexive type of

eye movement – whereas regular saccades latencies are generated between 131 and

700ms (Klein, 2001; Klein, Foerster, Hartnegg, & Fischer, 2005). The typical latency of a

“reflexive” saccade in adults is around 200ms, and the variability around this average is

large. Antisaccades are associated with a higher effort and therefore antisaccades

latencies are typically elongated about 70-80ms in contrast to prosaccades (Everling &

Fischer, 1998; Forbes & Klein, 1996; Ford, Goltz, Brown, & Everling, 2005; Klein,

Raschke & Brandenbusch, 2003; Munoz, Broughton, Goldring, & Armstrong, 1998)

whereas the latency of erroneous antisaccades are generally in the range of standard

prosaccades (Evdokimidis, et al., 2002). The latency is highly dependant upon the nature

of the stimulus (Leigh & Zee, 1999). Modality and temporal properties of cue

presentation and their influence on saccade performance are explained in detail in

section 1.2.1.

Saccadic error rate: During the saccade task, errors occur when a prosaccade is

generated instead of an antisaccade or vice versa. Error rates during antisaccade tasks

reflect the ability to inhibit a response. Errors are usually followed by corrective saccades

to the suitable direction, indicating that the instruction was understood but the reflexive

response could not be suppressed. Thus, in contrast to prosaccades, higher error rates

were found in the antisaccade condition (Klein, et al., 2003; Munoz, et al., 1998). In

healthy adults, the antisaccade error rate is typically around 20% (Evdokimidis, et al.,

2002; Hutton, 2008; Leigh & Zee, 2006) – but the range both across participants and

studies depends to a major extent on the different task parameters.

Relationship between saccades parameters: The negative correlation between

percentage of error and mean latency is well established (Evdokimidis, et al., 2002;

Smyrnis, et al., 2002): the faster the response the higher the error rate. The relationships

between saccade amplitude, duration and velocity are specified, so that it is possible to

use them to characterise an observed eye movement as a normal or deviant saccade.

Thus, there is a linear relationship between saccadic amplitude and peak velocity; called

main sequence (Leigh & Zee, 1999): the bigger the gaze jump, the greater the maximum

speed. Similarly, a linear relation between amplitude and duration is characteristically for


- 13 -

saccades: a saccade of 30° evinces saccade duration of circa 100ms (Leigh & Zee,

1999).

1.2.1 Influences on saccades

There are several cognitive factors (Hutton, 2008), task manipulation parameters

and at least individual properties that may influence the execution of saccades. In the

following paragraph a short overview of the relevant factors is given.

Gap/overlap effects: One of the most common manipulations in saccade tasks is

the relationship between offset of the central fixation stimulus and the onset of the

peripheral target stimulus. In “step” trials the fixation offset conforms to the onset of the

target. Whereas during “gap” trials the fixation offset precedes the target onset. And

during “overlap” trials the target onset takes place while the fixation stimulus is still

visible. The duration for both gap and overlap periods is typically 200ms. A large number

of studies supports the basic finding that the prosaccade latency is reduced in gap trials

and increased in overlap trials compared to step trials (Leigh & Zee, 2006; Saslow,

1967). The same effect was found in the antisaccade condition even though to a reduced

extent (Forbes & Klein, 1996; Klein, et al., 2003; Reuter-Lorenz, Oonk, Barnes, &

Hughes, 1995). Additionally, the delay of the stimulus onset interferes with the error rate:

during a gap condition more errors are generated (Klein, 2001; Klein & Foerster, 2001;

Klein, et al., 2003). These findings are explained by assuming that the disappearance of

the fixation stimulus in gap trials allows attention disengagement before the target arises

(resulting in faster saccade latencies and higher error rates), whereas during overlap

trials visual attention is engaged and saccades are inhibited (resulting in slower

latencies; Leigh & Zee, 2006). But the gap effect is more than a “fixation release”

component. It seems to involve an additional warning component which was found in

experiments with warning signals (Reuter-Lorenz, et al., 1995): fixation offset itself can

speed performance by increasing response readiness.

Eccentricity: Studies in the visual domain show that both the number of directional

errors to visual targets and the saccadic reaction time (SRT) rise with increasing stimulus

eccentricity (Jay & Sparks, 1990; Yao & Peck, 1997; Zambarbieri, Beltrami, & Versino,

1995). Additionally, hypometria is usually more prominent for saccades directed toward

the periphery whereas in normal individuals hypermetria – i.e. the overshooting of

saccades – is observed when the saccade is directed towards the centre (Leigh & Zee,

1999).


- 14 -

Modality: Eye movements can be elicited by both visual and acoustic stimuli.

Prosaccades to acoustically triggered cues are slower (Goldring, Dorris, Corneil,

Ballantyne, & Munoz, 1996; Zambarbieri, Schmid, Magenes, & Prablanc, 1982;

Zambarbieri, Schmid, Prablanc, & Magenes, 1981) and have a lower peak velocity and

longer duration (Jay & Sparks, 1990; Zambarbieri, et al., 1982; Zambarbieri, et al., 1981).

Effects of eccentricity are dissociable for the acoustic versus the visual

experiment: an increasing distance between cue and fixation centre is associated with a

decreasing latency for acoustic stimuli and an increasing latency for visual cues (Frens &

Van Opstal, 1995; Jay & Sparks, 1990; Yao & Peck, 1997; Zambarbieri, 2002;

Zambarbieri, et al., 1995). But the findings are inconsistent. One study related any

effects on both visually and acoustically elicited saccades latencies (Zambarbieri, et al.,

1982) and another on acoustically triggered saccades only (Zambarbieri, et al., 1981). In

these studies, different presentation times were chosen – a hypothetical explanation for

absence of eccentricity influence. Concerning the accuracy, the eccentricity is not of high

importance: the accuracy rises with increasing eccentricity in both visually and

acoustically triggered saccades (Jay & Sparks, 1990; Yao & Peck, 1997). For target

eccentricity of ±20° and ±30°, acoustically trigger ed saccades were less accurate than

visually elicited reactions, but there were no significant differences in accuracy when

stimuli eccentricity was 10° (Yao & Peck, 1997).

Investigations related to the latency in the gap condition reported a reduction in

the gap effect in saccades elicited by acoustic stimuli compared to the effect in visual

conditions (Fendrich, Hughes, & Reuter-Lorenz, 1991; Shafiq, Stuart, Sandbach, Maruff,

& Currie, 1998; Taylor, Klein, & Munoz, 1999; Zambarbieri, 2002). Thus, it seems that

the gap effect is not modality specific. This is consistent with the interpretation that deep

layers of the superior colliculus (SC) appear as a likely candidate for the source of

premotor facilitation given that this structure receives convergent visual and acoustic

input (Jay & Sparks, 1990; Taylor, et al., 1999). Thus, findings from neurophysiological

studies investigating the SC in awake animals suggest that visual and acoustic signals

employ a final common pathway for the generation of saccades (Jay & Sparks, 1987a,

1987b). However, there is at least one critical difference: the superficial layer of the SC

receives direct visual input from the retina via the retinotectal pathway whereas acoustic

signals reach the SC via multisynaptic pathways that include the inferior colliculus.

Studies concerning the control of reflexive saccades elicited by acoustic stimuli

are rare. To date, only two studies investigated auditory antisaccades. One study

compared pro- and antisaccade performance in response to visual and acoustic cues in

healthy young adults and schizophrenia patients (Schooler, Roberts, & Cohen, 2008).


- 15 -

The authors found a higher error rate for acoustically than visually triggered antisaccades

in healthy young adults, but the reverse pattern identifying more errors for visually than

for acoustically elicited antisaccades in schizophrenia patients. The authors attributed the

higher error rate for acoustically evoked antisaccades (as well as prosaccades) in

healthy adults to additional demands of the remapping process between the craniotopic

and the retinotopic system. Schizophrenia patients, by contrast, who generated overall

more errors than the healthy controls, produced relatively fewer errors on the acoustic

than the visual version of the task. This reverse pattern in schizophrenia patients is

assumed to be due to the fact that the remapping process reduces immediate inhibitory

demands on the system, making the stimulus ‘less preemptive’. Therefore, people with

reduced executive system capacities may experience a relative benefit from different

modality cues on tasks requiring response inhibition. The second study compared pro- to

antisaccade performance in response to acoustic cues in healthy adults and patients with

a hemispherectomy (Reuter-Lorenz, Herter, & Guitton, in press). The authors elicited

saccades by acoustic stimuli because of permanent hemianopia, which limits visually

evoked contralesional saccades. Patients generated more direction errors contralesional

than the control participants, whereas the rate of ipsilesional errors was approximately

equivalent to the error rate of the control group. Additionally, patients were slower in

initiating antisaccades than controls. Thus, the authors assumed that a single

hemisphere is not able to suppress reflexive saccades bilaterally but is capable to

generate antisaccades in response to acoustic stimuli. Finally, they hypothesised

alterations in the SC in the intact hemisphere.

Furthermore, Yao and Peck (1997) assumed that the motor coordinate system

used by humans in generating saccades to acoustic targets is identical to that used in

making saccades to visual cues. The only difference may be that the information about

the location of auditory targets must be transformed from craniotopic, i.e. head-related,

into retinocentric coordinates prior accessing the burst generators.

Age: Naturally, age is one of the cardinal factors of influence on saccadic

performance. During adolescence, the brain undergoes specialization that enables the

individual to adapt to their environment. This developmental maturation is related to brain

myelination, which progresses from dorsal to ventral brain regions and supports the

cognitive control of behaviour. Frontal and posterior parietal cortices involved in visually

guided saccades handling, continue to acquire myelin throughout childhood (Gogtay, et

al., 2004). Thus, many studies showed that the saccade latency itself as well as their

variability are relatively high in children (circa 210ms), decrease curvilinear from

childhood to adolescence (circa 170ms; Fischer, Biscaldi, & Gezeck, 1997; Fukushima,


- 16 -

Hatta, & Fukushima, 2000; Irving, et al., 2006; Klein & Foerster, 2001; Klein, et al., 2005;

Munoz, et al., 1998; Salman, et al., 2006) and increases in a linear fashion in later years

(circa 210ms; Abel, Troost, & Dell'Osso, 1983; Fischer, et al., 1997; Klein, Fischer,

Hartnegg, Heiss, & Roth, 2000; Klein, et al., 2005; Munoz, et al., 1998). The change in

saccadic latencies in adolescents may reflect a shorter saccadic processing time as a

function of brain development. Longer saccadic latencies in children and elderly people

may reflect the immaturity and decomposition of several saccadic relevant processes,

respectively. Age also influences the gap effect in regard to latency – i.e. the SRT

reduction under the gap as compared to the overlap condition. This effect becomes

smaller with an increasing age (Klein, 2001).

Along with a decrease in latency there is only a weak relationship between age

and express saccades (Fischer, et al., 1997; Klein, et al., 2005). This lack of age-related

changes in express saccades suggests that the fixation system supported by subcortical

structures matures earlier than the cognitive processes needed for voluntary saccades

(Luna, Velanova, & Geier, 2008).

Findings in the domain of developmental changes of velocity have not been

consistent. A number of studies concluded that the peak velocity is not influenced by age

(Abel, et al., 1983; Fukushima, et al., 2000; Munoz, et al., 1998; Salman, et al., 2006)

while others reported a velocity increase in adolescence (from 446°/sec to 610°/sec) and

a gradual decline with age (345°/sec; Fioravanti, I nchingolo, Pensiero, & Spanio, 1995;

Irving, et al., 2006). Because of differences in task parameters it is difficult to compare

the studies with one another, but Luna and colleagues (2008) concluded that age

appears to have an effect, at least. Hypometria seems to be evident among the youngest

children (Munoz, et al., 1998), but stabilizes in childhood so that age effects are no

longer predominant (Luna, et al., 2008). Thus, no differences in the amplitude between

children and adults (Salman, et al., 2006) or adults and elderly (Abel, et al., 1983) were

found. One study pointed out that duration increased significantly across age groups

(Munoz, et al., 1998).

Finally, development looms large in response inhibition and working memory.

Both aspects develop on a different time course and influence performance in complex

executive tasks (Luna, Garver, Urban, Lazar, & Sweeney, 2004). In addition, there is

evidence that these two processes may be affected differentially in psychiatric disorders

like schizophrenia or ADHD (Ross, Harris, Olincy, & Radant, 2000). Both adult patients

groups are impaired in inhibition control, but only schizophrenic subjects demonstrate an

impaired working memory.


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The error rate during antisaccades tends to decrease from 60 to 13% as a

function of age (Fischer, et al., 1997; Fukushima, et al., 2000; Klein & Foerster, 2001;

Munoz, et al., 1998) and increases to 20% with higher age (Fischer, et al., 1997; Klein, et

al., 2000). Most of the participants correct their errors, indicating that all subjects,

independent of age, have the ability of generating post-inhibition voluntary saccades

(Luna, et al., 2008). During development there is also a reduction of intra-subject

variability (Klein, et al., 2005). At younger ages, a wide distribution of performances is

observed: some individuals mature earlier than others. Furthermore, the error gap effect

decreases from childhood to adulthood (Klein, 2001; Klein & Foerster, 2001). The same

pattern was found for the latency. It decreases curvilinear with age through childhood to

adolescence age (from approx. 350ms to 220ms; Fischer, et al., 1997; Fukushima, et al.,

2000; Irving, et al., 2006; Klein & Foerster, 2001; Klein, et al., 2005; Munoz, et al., 1998;

Salman, et al., 2006) and increases linearly again in later years (approx. 280ms; Abel, et

al., 1983; Fischer, et al., 1997; Klein, et al., 2000; Klein, et al., 2005; Munoz, et al., 1998).

Thus, studies have consistently demonstrated improvement of antisaccade performance

from childhood to adolescence.

1.2.2 Models of saccade generation: Race model & LATER model

Many authors (Hutton & Ettinger, 2006; Massen, 2004; Munoz & Everling, 2004)

argue that with stimulus onset a “competition” between the exogenously triggered pro-

and the endogenously initiated antisaccade starts. This rivalry is described by the Horse-

Race Model (Logan, et al., 1984) which proposes that the two processes – ongoing and

stop process – race against each other. If the stopping process wins (e.g. reaches some

threshold for activation), the reflexive saccade is cancelled. If the ongoing process wins

the competition, an erroneous prosaccade is made, sometimes followed by a corrective

antisaccade. Two interpretations of error generation have been developed: (1) errors will

occur if processes related to the initiation of the reflexive saccades are inadequately

interrupted, resulting in an increased likelihood of reaching the threshold for saccade

generation (Munoz & Everling, 2004). This describes two processes: an active inhibition

mechanism and an antisaccade generation (Everling & Fischer, 1998; Hutton, 2008). (2)

An alternative interpretation is that antisaccade errors result from a failure of adequate

activation of the correct response (Hutton, 2008; Nieuwenhuis, Broerse, Nielen, & de

Jong, 2004), which requires a single process: the generation of the antisaccade (Hutton,

2008). This view is also supported by Carpenter’s LATER model (Linear Approach to

Threshold with Ergodic Rate model; Cutsuridis, Smyrnis, Evdokimidis, & Perantonis,

2007) of neural decision-making. This model tries to explain the long and surprisingly


- 18 -

variable latencies (approx. 200ms). Consequentially, in effect there has to be only a

delay around 60ms between stimulus and response because humans need 40ms to

transfer a visual signal to the SC and additional 20ms to trigger a saccade in the

brainstem (Carpenter, 1981) – the shortest neural circuit between retina and oculomotor

muscles. Carpenter proposes that the delay reflects an intentional “procrastination” that

allows a more elaborated processing of saccade cues than simply their location. It is a

kind of cost-benefit analysis to ensure that the processing resources are directed

towards the most relevant part of the scene. With the onset of the cue a decision signal

starts to rise linearly at a constant rate (r) from an initial level (S0) until it reaches a

threshold (ST), at which a saccade is initiated. The rate of rise varies randomly from trial

to trial in a Gaussian way (with a mean µ and a variance σ2), establishing the skewed

distribution of latencies (Carpenter & McDonald, 2007). Thus, despite their ubiquity and

apparent lightness, saccadic eye movements involve a wide variety of different cognitive

processes.

1.2.3 Neurophysiology of saccades

Saccadic generation includes a trade-off between “bottom up” signals that

concern basic stimulus properties, e.g. stimulus position and size, and “top down” signals

reflecting the current goal and ambitions of the person (Hutton, 2008). Reflexive

saccades seem to be controlled by subcortical systems while voluntary saccades appear

to be induced by a cortically dominated network (see figure 2).

Information about a visual target of interest enters through the retina and reaches

(1) via retinotectal pathway the SC and (2) via the geniculostrate pathway the visual

cortex. This brain region shows a higher activation in pro- than in antisaccades

(Clementz, et al., 2010; Dyckman, Camchong, Clementz, & McDowell, 2007; McDowell,

Dyckman, Austin, & Clementz, 2008), indicating that in this early state task-dependant

activation modulation is available (McDowell, et al., 2008). The visual cortex (including

striate and extrastriate visual areas) sends target information to the SC and to the

posterior parietal cortex (PPC with the parietal eye fields (PEF)). The PPC is also

connected to the SC and the frontal motor regions (McDowell, et al., 2008). It is primarily

involved in visuo-spatial integration and attention – a process which accompanies

saccade generation – whereas the PEF are directly involved in saccade programming via

the SC (Leigh & Zee, 2006; Pierrot-Deseilligny, et al., 2004). The activity in the PPC is

higher during voluntary than reflexive saccades (Dyckman, et al., 2007; Ettinger, et al.,

2008; Ford, et al., 2005; McDowell, et al., 2008).


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Figure 2: Cortical areas involved in saccade generation and execution. After processing visual information in the visual cortex and after integration of visuo-spatial integration in the PPC, reflexive saccades are triggered by the PEF. In contrast, intentionally saccades are elicited by the FEF. If a saccade is inhibited the DLPFC will play a primary role. Furthermore, the SEF are responsible for the control of motor programming, whereas the CEF inherit a motivational role for areas controlling intentional saccades. ACC: anterior cingulate cortex; CEF: cingulate eye fields; DLPFC: dorsolateral prefrontal cortex; FEF: frontal eye fields; PEF: parietal eye fields; PPC: posterior parietal cortex; SC: superior colliculus; SEF: supplementary eye field Modified to Pierrot-Deseilligny, Milea, & Muri, 2004; Pierrot-Deseilligny, Muri, Nyffeler, & Milea, 2005

The frontal cortex is important for motor control. The frontal eye fields (FEF) are

involved in planning and triggering of voluntary saccades (Reuter, Kaufmann, Bender,

Pinkpank, & Kathmann, 2009). Thus, lesions or transcranial stimulation of the FEF lead

to longer latencies of correct antisaccades and to inaccurate memory guided saccades

(Gaymard, Ploner, Rivaud, Vermersch, & Pierrot-Deseilligny, 1998; Muri, Hess, &

Meienberg, 1991; Nyffeler, et al., 2006). As early as during the instruction phase higher

activity was found in the FEF for voluntary saccades compared to reflexives saccade

tasks (Connolly, Goodale, Menon, & Munoz, 2002; Ford, et al., 2005). In sum, the FEF

influence the variation of RT within individuals and across different saccade tasks

(McDowell, et al., 2008) – probably via the direct connection to the SC. Also, strong

reciprocal connections exist between FEF and supplementary eye fields (SEF). It seems

that elicitation of antisaccades requires the suppression of saccade neurons in the FEF

and SC (Everling, Dorris, & Munoz, 1998; Everling & Munoz, 2000) before stimulus


- 20 -

presentation. This inhibition seems to be caused by the SEF. Movement-related neurons

of the SEF discharge with a higher rate before anti- than prosaccades (Dyckman, et al.,

2007; Ford, et al., 2005; McDowell, et al., 2005). Finally, the dorsolateral prefrontal

cortex (DLPFC) as part of a prefrontal network is involved in the generation of

antisaccades, memory guided saccades and in decisional processes (Pierrot-Deseilligny,

et al., 2004). Several lesion studies showed that the percentage of errors in the

antisaccade task is increased after DLPFC lesion (Pierrot-Deseilligny, et al., 2003;

Pierrot-Deseilligny, Rivaud, Gaymard, & Agid, 1991; Ploner, Gaymard, Rivaud-Pechoux,

& Pierrot-Deseilligny, 2005). Therefore, it is assumed that the DLPFC is involved in

inhibition of reflexive eye movements (Gaymard, Ploner, et al., 1998; Pierrot-Deseilligny,

et al., 2004; Pierrot-Deseilligny, et al., 2005; Pierrot-Deseilligny, et al., 2003; Pierrot-

Deseilligny, et al., 1991; Ploner, et al., 2005). However, recent studies indicated that

DLPFC activation is more likely to reflect greater demands in the activation and

maintenance of task rules or in response selection (Dyckman, et al., 2007; Ettinger, et

al., 2008).

The role of subcortical structures during saccadic control has not yet been

clarified. Studies reporting saccade-related activity showed activation in ACC,

cerebellum, striatum, thalamus and SC. The ACC, including the cingulate eye fields

(CEF), seems to be involved in motivation of intentional saccades via preparation of FEF,

SEF and DLPFC (Gaymard, Rivaud, et al., 1998; Pierrot-Deseilligny, et al., 2003) and in

evaluation of the error response (Brown & Braver, 2005), as higher activation during

erroneous compared to correct antisaccades was found in this region (Ford, et al., 2005;

Polli, et al., 2005). The cerebellum has an influence on saccade steering and stopping –

i.e. determining the accuracy of saccades (Jenkinson & Miall, 2010; Ramat, Leigh, Zee,

& Optican, 2007). The striatum is involved in saccade initiation (Watanabe, Lauwereyns,

& Hikosaka, 2003) and inhibition (Hikosaka, Takikawa, & Kawagoe, 2000). The SC,

besides FEF and SEF, is a structure with projections directly to the reticular formation

(Munoz & Everling, 2004). It is a structure with seven layers of neurons responding to

visual, acoustic and somatosensory stimuli (Sparks, 2002). It receives projections from

FEF, SEF, DLPFC, PPC and the cerebellum (Munoz & Everling, 2004). Some of these

pathways are direct and some continue via basal ganglia (Leigh & Zee, 2006). The

neurons of the SC include saccade neurons, which increase their discharging before and

during the saccade, and fixation neurons, which are inactive during saccades but fire

during visual fixation (Munoz & Everling, 2004). Thus, the SC is basically involved in the

saccade generation. Disinhibition and inhibition of the SC are inherit by the SEF and


- 21 -

additionally by the basal ganglia which are strongly modulated depending on behavioural

context, reflecting working memory, expectation or attention (Hikosaka, et al., 2000).

As mentioned above there are three parallel pathways to the brainstem neurons,

generating saccades: the SC, the FEF and the cerebellum directly influence the saccade

generation in the reticular formation (Munoz & Everling, 2004) where three cranial nerves

(Oculomotor nerve, Trochlear nerve and Abducens nerve) arise, innervating motor

neurons of three extraocular muscles pairs. These muscles are responsible for the final

control of the eye (Sparks, 2002).

1.2.4 Saccades & ADHD

Eye movement tasks are a unique neuroscientific tool which allows examining the

relationship between brain and behaviour and its development because it is very well

investigated. According to this it builds an important tool to explore the neurobiological

basis of psychiatric illness.

Moreover, this tool is particularly useful when studying children with

developmental disorders for numerous reasons. First, the saccade task is simple and

can be successfully performed on children. Second, the performance is less likely to be

facilitated by verbal or learning strategies like other neurophysiological tests. As a

consequence, the antisaccade task allows to investigate the inhibitory control with

minimal working memory demands except for remembering the instruction (Luna, et al.,

2008). Third, whether conducting with an eye tracker or Electroencephalography (EEG)

these systems are non-invasive. Fourth, the well described neural circuitry underlying the

control of eye movements is based on years of electrophysiological and lesion

experiments in monkeys and humans. Fifth, normal and abnormal eye movement

performances are unique. This allows drawing connections between performance,

development, disease, anatomical sources or drug interference. Sixth, oculomotor

methods – especially tasks with voluntary control of saccades – have proven to be

sensitive for impaired executive function in psychiatric disorders that are believed to have

a neurodevelopmental basis such as schizophrenia, autism and ADHD (Everling &

Fischer, 1998; Karatekin, 2007; Rommelse, et al., 2008; Sweeney, Takarae, Macmillan,

Luna, & Minshew, 2004).

Thus, antisaccade tasks may establish an objective and sensitive measure of

brain (dys-) function in children with and without ADHD and may also provide a basis for

differentiating these two groups.

There is a growing body of literature on saccade experiments comparing children

with ADHD to control children (Karatekin, 2007; Rommelse, et al., 2008). Despite some


- 22 -

inconsistencies, the general finding is altered performance in pro- and antisaccade tasks

in children with ADHD compared to typically developed children. The majority of studies

on prosaccades revealed that children with ADHD compared with control children show

an increased variability of the SRT (Mostofsky, Lasker, Cutting, Denckla, & Zee, 2001;

Mostofsky, Lasker, Singer, Denckla, & Zee, 2001; Munoz, Armstrong, Hampton, &

Moore, 2003). Additionally, some studies revealed longer latencies itself for children with

ADHD (Klein, et al., 2003; Mahone, Mostofsky, Lasker, Zee, & Denckla, 2009;

Mostofsky, Lasker, Singer, et al., 2001; Munoz, et al., 2003). However, some

investigators did not find differences in the latency (Goto, et al., 2010; Hanisch, Radach,

Holtkamp, Herpertz-Dahlmann, & Konrad, 2006; Karatekin, 2006; Loe, Feldman, Yasui,

& Luna, 2009; Mostofsky, Lasker, Singer, et al., 2001; O'Driscoll, et al., 2005; Rothlind,

Posner, & Schaughency, 1991). It is assumed that the latency of prosaccades is more

variable and possibly slower in children with ADHD. This might be an indicator for ADHD

childrens’ difficulty in saccade generation (Rommelse, et al., 2008).

During antisaccade tasks some studies found slower antisaccade latencies (Goto,

et al., 2010; Karatekin, 2006; Klein, et al., 2003; Munoz, et al., 2003) and a higher

variability in children with ADHD compared with control children (Karatekin, Bingham, &

White, 2009). Other studies revealed no group differences in antisaccade latencies

(Karatekin, et al., 2009; O'Driscoll, et al., 2005; Rothlind, et al., 1991). The majority of

studies found elevated error rates in antisaccade tasks (Goto, et al., 2010; Karatekin,

2006; Klein, et al., 2003; Loe, et al., 2009; Mahone, et al., 2009; Mostofsky, Lasker,

Cutting, et al., 2001; Mostofsky, Lasker, Singer, et al., 2001; Munoz, et al., 2003;

O'Driscoll, et al., 2005) and a fewer correction in children with ADHD compared with

control children (Karatekin, et al., 2009; Klein, et al., 2003), whereas no effects were

discovered in a number of other studies (Aman, Roberts, & Pennington, 1998; Hanisch,

et al., 2006; Karatekin, et al., 2009; Rothlind, et al., 1991). The lack of the antisaccade

effect in these studies might be explained by a lack of statistical power and task

parameters (Rommelse, et al., 2008). One study – in which the different tasks are

presented in a mixed way – reported a statistical trend of p=.16 towards more directional

errors in the patient group (Hanisch, et al., 2006). In the second study – with an immense

age range – each participant performed only ten trials in each condition, possibly

resulting in low statistical power (Rothlind, et al., 1991). In a third study a different kind of

antisaccade task was used where an additional detection task was presented (Aman, et

al., 1998).

Finally, it seems that children with ADHD are impaired in inhibition of erroneous

prosaccades. This is in line with clinical evidence demonstrating that patients with ADHD


- 23 -

are challenged by executive functions like response inhibition, vigilance, working

memory, and planning (Willcutt, et al., 2005).

Variables such as development, subtype of ADHD and medication additionally

influence the performance of children with ADHD. Thus, elongated latencies und

increased error rates in children with ADHD could be reduced by medication application

(Klein, Jr Fischer, Fischer, & Hartnegg, 2002; O'Driscoll, et al., 2005). This indicates that

the mechanisms responsible for the deficits in antisaccade performance are related to

the cortical structures influenced by methylphenidate (Rommelse, et al., 2008). Children

with ADHD show not the age-related latency pattern of control children: higher increase

in antisaccade compared to prosaccade latencies (Klein, et al., 2003). Furthermore, the

influence of different ADHD subtypes has not yet been investigated very well. It was

found that children with an ADHD combined subtype but no participants with an ADHD

inattentive subtype generated more errors than control children during antisaccade tasks.

Whereas the proportion of subjects with impaired antisaccade performance was higher in

the combined than in the inattentive subtype (O'Driscoll, et al., 2005). Additionally, it was

shown that there are no differences between the subtypes concerning the error rate

(Loe, et al., 2009; O'Driscoll, et al., 2005). One study pointed out that with training or

reduced working memory demands the performance of adolescents with ADHD can

reach the level of control subjects indicating that elevated SRTs during antisaccades in

this disorder may be related to regulating arousal on a novel task (Karatekin, 2006). It

was also found that participants with ADHD made more premature saccades and fewer

corrective saccades than both, the age-matched and younger groups. This suggests

difficulties with impulsivity and goal neglect or poor working memory for task instructions

(Karatekin, 2006).

1.3 Conclusion

One of the core deficits of ADHD is a disturbed response inhibition, manifested in

the difficulty in delaying responses, in blurting out answers to questions before they have

been completed, in difficulty in awaiting one´s turn in games or in group situations and in

interrupting or intruding on others (American Psychiatric Association, 2000). Inhibitory

deficits were verified in more than one study with responding to visual stimuli. But the

question arises if inhibitory deficits are restricted to the visual modality.

Inhibition of reaction on acoustic cues has a high practical relevance. Thus, a

child on a bike, for example, has to pay his or her full attention to the traffic in the

moment of crossing a road instead to a person calling his or her name. Or else, children

in the classroom having the motivation to listen to the teacher have to inhibit knee-jerk,


- 24 -

focusing on whispering neighbours or siren sounds from outside. Thus, children and, of

course, adults need to be able to inhibit reflexive responses to acoustic stimuli.

It has to question if children with ADHD also impaired in this kind of inhibition

performance. This question immediately suggest itself due to impairments in range of

acoustic tasks (Riccio & Hynd, 1996), the large degree of overlaps between ADHD and

APD (although there are some behaviours more often associated with one disorder than

the other; Chermak, Tucker, & Seikel, 2002; Ptok, Buller, Schwemmle, Bergmann, &

Luerssen, 2006) and the up to 70% high comorbidity rate of learning disabilities (Mayes,

Calhoun, & Crowell, 2000), which enclose deficits in recording, integration, storing and

retrieving of visual as well as acoustic information.

In order to answer the core question whether children with ADHD are impaired in their

inhibitory control of reflexive reactions to visual as well as to acoustic cues and thereby

to promote the needed differential diagnostic, it is important to develop tests with

comparable performance measurements in different modalities. Thus, it is possible to

gain assumptions in which modality systems deficits are prior-ranking.

To enlarge the already existing multimodal diagnostic approach the idea of the

present thesis was to investigate the impulsivity in antisaccade experiments with different

modality inputs. Saccades triggered by visual stimuli are well investigated in children with

and without ADHD (Rommelse, et al., 2008). Some studies investigated also

prosaccades elicited by acoustic stimuli in adults (Fendrich, et al., 1991; Frens & Van

Opstal, 1995; Shafiq, et al., 1998; Yao & Peck, 1997; Zambarbieri, 2002; Zambarbieri, et

al., 1995; Zambarbieri, et al., 1982; Zambarbieri, et al., 1981). However, to date there are

only two studies that investigated antisaccades elicited by acoustic cues in the context of

schizophrenia and hemispherectomy, respectively (Reuter-Lorenz, et al., in press;

Schooler, et al., 2008) and none had investigated the antisaccade performance on

acoustic stimuli in children.

The aim of Study I was to test the practicability of the designed experiment and to

investigate the comparability of pro- and antisaccades elicited by visual and acoustic

stimuli in a control group. Based on primary interest in the behavioural performance an

eye tracker system was used. Because of the transformation from craniotopic into

retinocentric coordinates it was assumed to extend prior results of slower acoustically

compared to visually triggered prosaccades (Goldring, et al., 1996; Zambarbieri, et al.,

1982; Zambarbieri, et al., 1981) to antisaccades and to children. Additionally, eccentricity

effects were investigated during antisaccades and in children in order to compare the

results to the described effects in prosaccades (SRTs decreasing of acoustically

triggered saccades and SRTs increasing of visually triggered saccades with larger


- 25 -

stimulus eccentricities; Frens & Van Opstal, 1995; Yao & Peck, 1997; Zambarbieri, 2002;

Zambarbieri, et al., 1995). Finally, the gap effect regarding SRTs, which appears less

pronounced for acoustically than for visually elicited prosaccades (Fendrich, et al., 1991;

Shafiq, et al., 1998; Taylor, et al., 1999; Zambarbieri, 2002), was investigated also for the

first time in acoustically triggered antisaccades.

Constructively based on the results on Study I the same experiment was carried

out with children with and without ADHD. One constriction of behavioural tests is that the

measurement of the performance is the end product of information processing. EEG

permits the measurement of brain activity – induced by task stimuli – before the

performance takes place, e.g. an answer will be observable. Therefore, Study II

measured along with the behavioural performance the brain activity. Functional brain

imaging studies have shown that a distributed fronto-parietal network is more active

when subjects perform antisaccades compared with prosaccades (McDowell, et al.,

2008). The DLPFC activity seems to provide an inhibitory signal that precedes a correct

antisaccade performance (Clementz, McDowell, & Stewart, 2001; Fitzgerald, et al., 2008;

McDowell, et al., 2005). Additionally, the ACC is relevant for the visual antisaccade

performance (Brown, Goltz, Vilis, Ford, & Everling, 2006; Ford, et al., 2005; Gaymard,

Ploner, et al., 1998; Polli, et al., 2005). Given that children with ADHD showed structural

and functional changes in a fronto-subcortical network (Bush, Valera, & Seidman, 2005;

Dickstein, et al., 2006; Seidman, et al., 2005) it was anticipated that children with ADHD

will generate more errors and altered brain activities in the antisaccade task compared to

control subjects.

Finally, children have difficulties to generate a prosaccade with a preceding

antisaccade (Hanisch, et al., 2006) or more generally, they have difficulties when mixed-

saccade tasks were applied (O'Driscoll, et al., 2005). In adults a context effect was also

detected on the behavioural (Barton, Greenzang, Hefter, Edelman, & Manoach, 2006;

Barton, Raoof, Jameel, & Manoach, 2006; Ethridge, Brahmbhatt, Gao, McDowell, &

Clementz, 2009) as well as on cortical level (Dyckman, et al., 2007). Blocked conditions

are easier and do not add a third step to the inhibition of prepotent response and the

execution of the appropriate eye movement: interpretation of the cue (Irving, Tajik-

Parvinchi, Lillakas, Gonzalez, & Steinbach, 2009). In order to simplify the experiment for

Study III a block design of the experiment was developed. It was assumed that through

this design it will be possible to differentiate children with and without ADHD on the

behavioural as well as on cortical level and, in doing so, to gain a differentiated view on

damages of children with ADHD in the different modalities.

Three antisaccade studies – Study I

- 26 -

II. THREE ANTISACCADE STUDIES

2.1 Pro- and antisaccades in children elicited by v isual and acoustic cues – does

modality matter?

2.1.1 Abstract

Background

Children are able to inhibit a prepotent reaction to suddenly arising visual stimuli,

although this skill is not yet as pronounced as it is in adulthood. However, up to now no

study investigated the inhibition mechanism to acoustic stimuli.

Methods

Reflexive (prosaccade) and inhibitory (antisaccade) responses to visual and

acoustic cues were examined with an eye tracker system in 31 children between seven

and twelve years of age using a gap-overlap task and two cue eccentricities.

Results

Acoustically cued saccades had longer reaction times than visually cued

saccades. A gap effect (i.e. shorter reaction time in the gap than the overlap condition)

was only found for visually elicited saccades, whereas an eccentricity effect (i.e. faster

saccades to more laterally presented cues) was only present in the acoustic condition.

Longer reaction times of antisaccades compared to prosaccades were found only in the

visual task. Across both tasks the typical pattern of elevated error rates in the

antisaccade condition was found. Antisaccade errors were declining with age, indicating

an ongoing development of inhibitory functions.

Conclusions

The present results lay the ground for further studies of acoustically triggered

saccades in typically as well as atypically developing children.

2.1.2 Introduction

It is a reflex-like feature of human behaviour to look towards sudden changes in

our visual field. This enables us to respond adequately to changes in our environment.

Scientifically, this reflexive behaviour is studied with prosaccade tasks. Here, participants

are required to generate a saccade to a suddenly appearing peripheral visual cue – also

called “visual grasp reflex”. Parameters such as accuracy and saccadic reaction time

(SRT) can be measured (Leigh & Zee, 1999). In order to not look towards a suddenly

appearing peripheral cue, volitional inhibition of the visual grasp reflex is required.

Scientifically, this can be investigated with antisaccade tasks (Hallett, 1978). Here,

participants are asked to suppress a prosaccade towards a visual cue and to look at its


- 27 -

mirror position in the opposite visual field instead. As antisaccades require active

inhibition of an already initiated motor response, more direction errors are made and

SRTs are longer compared to prosaccade tasks (Forbes & Klein, 1996; Ford, et al.,

2005; Fukushima, et al., 2000; Klein & Foerster, 2001; Munoz, et al., 1998; Munoz &

Everling, 2004). The timing of peripheral cue onset affects direction error rate and SRT of

both pro- and antisaccades. When the central fixation cross disappears before the onset

of the peripheral cue (gap condition), more errors are produced than when the peripheral

cue appears while central fixation is still on (overlap condition). At the same time, SRT in

gap conditions is reduced compared to overlap conditions. This “gap effect” is probably

due to the reduction in firing rate of fixation neurons in the superior colliculus and frontal

eye fields with gap onset (Munoz, et al., 1998; Munoz & Everling, 2004; Saslow, 1967).

This causes faster responses as saccade neurons in these structures start firing earlier.

The SRT gap effect is bigger for prosaccades than antisaccades (Forbes & Klein, 1996)

and more pronounced in children than in young adults (Klein, 2001; Klein & Foerster,

2001; Munoz, et al., 1998). Another factor affecting direction errors and SRT is the

peripheral position (eccentricity) of the target. Both, the number of direction errors in

response to visual targets and SRT increase with larger stimulus eccentricity (Yao &

Peck, 1997; Zambarbieri, et al., 1995). Studies of ocular motor performance in children

have shown that SRT decreases with age (Salman, et al., 2006) as does the proportion

of direction errors, although at a different pace (Fukushima, et al., 2000; Klein, 2001;

Klein & Foerster, 2001; Klein, et al., 2003; Munoz, et al., 1998).

Humans not only look towards visual stimuli, they also direct their gaze to locate

the origin of a suddenly appearing sound. This reaction is already present in babies (Muir

& Field, 1979). Although saccades towards acoustic cues are scientifically less well

investigated than saccades towards visual cues, a recent study delineated an “acoustic-

evoked ocular grasp reflex” in adults (Reuter-Lorenz, et al., in press).

Both children and adults also need to be able to inhibit reflexive visual responses

to acoustic stimuli. A child, for example, will automatically look at the person who calls

his name. But standing in the middle of a busy street it might be better to not look at the

person but to focus on the traffic coming from the opposite direction to avoid an accident.

An important difference between the visual and auditory modality is the reference

system. While input to the visual field is thought to be processed in relation to a

retinotopic reference system, acoustic cues are related to a craniotopic, i.e. head-related,

reference system (Zambarbieri, et al., 1995). The craniotopic reference system is by

definition wider than the retinotopic reference system, this being caused by the position

of the ears on the sides of our head, while the eyes face forward. The craniotopic


- 28 -

reference system is more accurate and sensitive to lateral stimuli, while the retinotopic

system is most accurate for stimuli directly in front of us. When sounds trigger a saccadic

response, it is assumed that sound representation needs to be remapped from the

craniotopic into the retinotopic reference system, in order to produce spatially correct

saccades (Yao and Peck, 1997).

Animal studies with nonhuman primates as well as experimental studies with

adults have revealed lower accuracy and longer SRTs of prosaccades towards acoustic

cues than towards visual cues (Jay & Sparks, 1990; Zambarbieri, et al., 1982;

Zambarbieri, et al., 1981). This is probably caused by the additional demand of

remapping from the craniotopic to the retinotopic reference system. Considering target

eccentricity, a reverse relationship between SRTs and target position has been found in

the auditory compared to the visual modality: SRTs of acoustically triggered saccades

decrease for larger stimulus eccentricities, but SRTs of visually triggered saccades

increase with larger stimulus eccentricities (Frens & Van Opstal, 1995; Yao & Peck,

1997; Zambarbieri, 2002; Zambarbieri, et al., 1995). Ostensibly at least in adults, a

processing advantage for centrally presented visual stimuli and a disadvantage for

centrally presented acoustic stimuli exists (Zambarbieri, et al., 1982). In adults, the gap

effect also interacts with target modality. The gap effect regarding SRTs of prosaccades

appears less pronounced for acoustic than for visual cues (Fendrich, et al., 1991; Shafiq,

et al., 1998; Taylor, et al., 1999; Zambarbieri, 2002).

The vast majority of studies on saccades triggered by acoustic cues only

investigated prosaccades. Until now there are only two studies, which investigated

acoustically triggered antisaccades in adults (Reuter-Lorenz, et al., in press; Schooler, et

al., 2008). One of these studies (Reuter-Lorenz, et al., in press) studied acoustic

antisaccades in three patients with hemispherectomy and a control group. They revealed

that patients generated more errors and showed longer SRTs than control participants.

Schooler and colleagues (2008) investigated adults with and without schizophrenia and

compared performance in antisaccade tasks using visual and acoustic cues. They found

a higher error rate for acoustically than visually triggered antisaccades in healthy young

adults while patients generated the reverse pattern of more errors during visually than

during acoustically elicited antisaccades.

The present study is – to the best of our knowledge – the first to compare SRTs

and error rates of pro- and antisaccades elicited by visual and acoustic cues in typically

developing children. We investigated children between seven and twelve years regarding

the impact of central fixation engagement (gap, overlap) as well as target eccentricity on

pro- and antisaccades elicited by visual and acoustic peripheral cues.


- 29 -

This is of relevance as studies on pro- and antisaccades triggered by acoustic and

visual cues in children will further our understanding of modality differences in ocular

motor responses and the development of basic, ecologically as well as clinically relevant

sensory-motor processing.

2.1.3 Methods

Participants

31 children between seven and twelve years participated in this study. They were

recruited at primary schools in the Konstanz area. Six children had to be excluded

because they were too small for the eye-tracker, too restless, had a partial hearing loss,

low scores on a questionnaire on auditory processing and perception, or because they

decided to not to continue after the half-time break in the experiment. The 25 remaining

children (18 girls and seven boys) had a mean age of 9.31 ±0.24 years. 24 children were

right-handed, one child was left-handed. None of the children fulfilled criteria for attention

deficit hyperactivity (ADHD) or auditory processing disorder (APD). Neither did parents

report any other neurological, psychiatric, or physiologic problems.

Procedure

The families were shown the laboratory equipment and the task was explained to

them. Children and parents then signed informed consent forms (according to the

Helsinki declaration (WMA, 2004)). Parents were asked to fill in a general information

questionnaire about their child, an ADHD symptom checklist (Lauth & Schlottke, 2002),

and an auditory processing disorder checklist (DGPP, 2002) while children completed

the Edinburgh-Handedness-Inventory (Oldfield, 1971). To ensure within-normal hearing

levels, children’s hearing thresholds were determined for frequencies 500, 1000, 2000

and 4000Hz in an acoustically shielded room. Children were then shown a computerised,

animated explanation of the task, which included examples and four training trials. For

additional motivation, children were told that they would be able to collect four “cartoon

dogs” on the computer screen if they performed well (although the dogs always

appeared after fixed intervals) which would then allow the children to pick a small gift

from a “treasure chest” after the experiment. Thus, it was ensured that all children were

motivated and perceived themselves as successful. Children were additionally

compensated with 10 Euros at the end of the experimental session.

For the eye-tracker experiment, children were comfortably seated on a height-

adjustable chair, their heads resting on a chin rest 518mm away from the computer

monitor. Brightness and contrast of the eye tracker-camera were adapted, headphones


- 30 -

were put on and the 30min - experiment was started after calibration of the eye tracker

(11 standard positions distributed over the screen).

Task

Participants were instructed to generate saccades in response to visual or

acoustic cues. The nature of the required saccade depended on the instruction.

Saccades could either be directed towards the cue (prosaccade) or away from the cue

(antisaccade). Visual cues consisting of yellow dots that that filled one of four empty

circles could appear “near” (6°) or “far” (12°) lef t or right of the fixation cross for 1000ms.

Acoustic cues were 1000Hz sine tones presented for 1000ms that were perceived either

“far” left/right (90°) or “near” left/right (45°, s ee the description below). Children were

instructed that in response to “near” acoustic cues they should generate saccades

towards the 6° circle, and upon “far” to make sacca des towards the 12° circle. Cues

could either appear 200ms after extinction of the fixation cross (gap) or with a 200ms

overlap with the fixation cross. Random combinations of the following within-group

factors were presented throughout the experiment: cue modality (visual vs. acoustic),

direction (right vs. left), type (anti- vs. prosaccade), distance (near (6° visual, 45°

acoustic) vs. far (12° visual, 90° acoustic)), dela y (gap vs. overlap). Nine runs of each

combination resulted in a total of 288 trials.

After trials 96, 129, 259 and 288 children were shown a motivation picture with 1,

2, 3 and 4 dogs, respectively. A pause-signal appeared after 144 trials indicating that

children could take a short break. The length of the break was determined by the

children.

Each trial began with a 1000ms instruction slide depicting the nature of the

required saccade by a prominent symbol the meaning of which had been explained to

the children beforehand (see procedure above). Each trial lasted 6500ms (see figure 3

for a schematic overview).


- 31 -

Figure 3: Example trial (visual prosaccade); top: overlap-condition and bottom: gap-condition

Equipment and Oculomotor Recordings

Cues were presented with the software Presentation (Neurobehavioral Systems,

Inc.). Visual cues were generated within Presentation. Sine tones were generated with

Adobe Audition 2.0®. The effect of sound lateralisation was created by intensity and

phase differences between the left and right channel. The impression of a 90°

lateralisation to either direction was created by attenuating the contra-lateral channel by

3.62dB and shifting its onset by 6.5µs. The impression of a 45° lateralisation was created

by attenuating the contralateral channel by 2.8dB and delaying its onset by 1µs.

Stimuli were presented with a PC (Intel (R) Pentium (R) 4, CPU 3.00GHz

processor, 522.928 RAM) running a Windows 2000® operating system on a monitor with

640 x 480pixels resolution (22”/51cm viewable; Iivama MA203DT; Vision Master Pro

513) and via stereo headphones (Sony Digital Reference Dynamic MDR-CD470). The

recording computer had the same specifications as the stimulus computer.

Eye movements were measured with a high-speed camera system (iView Hi-

Speed-Eye Tracker, SensoMotoric Instruments, Teltow, Germany). The eye-tracker had

a temporal resolution of 240Hz and a spatial resolution <0.01°. Data were stored for

offline analysis. During testing, eye movements were visualised on the recording

computer to enable on-line monitoring and re-calibration, if necessary.

Data analysis

SRTs and direction of saccades were analysed. Saccade onset was defined semi-

automatically with the software BeGaze® Version 1.02.0076 (SensoMotoric Instruments,

http://www.smivision.com). Individual saccades were cross-checked manually and

onsets were corrected if necessary.

Direction error rate and SRT were analysed statistically using Statistica version

6.1 (StatSoft, Inc., 2003, http://www.statsoft.de). Univariate repeated measures analyses


- 32 -

of variance (ANOVAs), using within-subject factors modality (visual/acoustic), type (anti-

/prosaccade), distance (near/far), and delay (gap/overlap) were computed. Significant

interactions were investigated further with a post hoc test (Tukey’s Honest Significant

Difference-Test). Correlations between age (in months) and dependant variables were

tested using the Bravais Pearson correlation test and Spearman Rank test. Correlations

of dependant variables in the visual and acoustic condition were tested with the Bravais

Pearson correlation test, Spearman Rank test and the partial correlation test.

2.1.4 Results

Saccadic Reaction Times

Results will be restricted to correct trials, since incorrect trials were rare in some

conditions (i.e. visual prosaccades), potentially skewing latency results. There were no

significant correlations between age and SRTs in either condition.

Therefore, it was not necessary to use age as a covariate of no interest in the

ANOVAs. Mean SRT for correct reactions was 620.76 ±163.08ms. Saccades triggered

by visual cues had shorter SRTs than saccades after acoustic cues (main effect modality

F(1,23)=97.370, p<.001, acoustic: 791.08 ±202.16ms, visual: 449.36 ±160.11ms). SRTs

of prosaccades were shorter than SRTs of antisaccades (main effect type

F(1,23)=20.488 p<.001, pro: 567.09 ±144.30ms, anti: 674.88 ±195.80ms). An interaction

between modality and type was found (F(1,23)=18.606, p<.001, see figure 4a).

Acoustically triggered pro- and antisaccades did not differ in SRTs (acoustic-pro: 769.21

±190.59ms, acoustic-anti: 812.95 ±236.44ms, p=.198). In contrast, SRTs of visual

prosaccades were significantly shorter than SRTs of visual antisaccades (visual-pro:

364.97 ±136.24ms, visual-anti: 535.01 ±196.47ms, p<.001).

SRTs in gap conditions were shorter than in overlap conditions (main effect delay:

F(1,23)=29.986, p<.001, gap: 587.68 ±165.70ms, overlap: 653.52 ±165.33ms). The

interaction modality*delay (F(1,23)=16.402, p<.001, see figure 4b) showed no SRT

difference between gap and overlap for saccades triggered by acoustic stimuli (acoustic-

gap: 776.48 ±208.02ms, acoustic-overlap: 805.68 ±209.06ms, p=.610), whereas SRTs in

gap-conditions were shorter than in overlap conditions for visually evoked saccades

(visual-gap: 397.36 ±162.49ms, visual-overlap: 501.37 ±163.45ms, p<.001, figure 4b).

Investigating SRT as a function of stimulus eccentricity showed that SRTs after

cues near the fixation cross were longer than SRTs after cues that were further away

(main effect distance F(1,23)=7.747, p<.05, near: 645.42 ±161.74ms, far: 595.74

±174.02ms). A significant interaction modality*distance was also found (F(1,23)=19.224,

p<.001, see figure 4c). Near and far stimulus cues led to equally long SRTs within


- 33 -

visually triggered conditions (visual-near: 437.59 ±167.03ms, visual-far: 460.81

±171.11ms, p=.676), whereas for acoustically evoked saccades SRTs were shorter after

cues that were further away from the fixation cross compared to near cues (acoustic-

near: 853.24 ±197.55ms, acoustic-far: 728.92 ±222.56ms, p<.001).

There was a positive correlation between SRTs in the visual and the acoustic

condition (r=.515, p<.01) across saccade type and for pro- and antisaccades,

respectively (antisaccades: r=.497, p<.05; prosaccades: r=.484, p<.05, see figure 4d).

Figure 4: a: Interaction modality*type for the dependant variable latency; b: interaction modality*delay for the dependant variable latency; c: interaction modality*distance for the dependant variable latency; d: correlation between latencies in visually and acoustically cued saccades; filed circle: antisaccades, empty circles: prosaccades

Error Rates

As there was a correlation between age and overall-error rate (age/error r=-.459,

p<.05), as well as age and antisaccade errors (see table 1), age was used as continuous

predictor in the ANOVAs.

Table 1 : Correlation of errors with age [in month] Age [in month] correlation with r(X.Y) p All -0.459 0.021 Antisaccades All -0.490 0.013 Visual -0.467 0.019 Acoustic -0.355 0.081 Prosaccades All -0.150 0.473 Visual -0.009 0.965 Acoustic -0.235 0.259


- 34 -

Across all conditions a mean of 27.38 ±9.22% direction errors were generated,

whereof 69.05 ±12.31% were corrected. The main effect type revealed that children

made more direction errors during anti- than prosaccades (antisaccades: 20.27 ±7.42%,

prosaccades: 7.11 ±3.61, F(1,23)=12.428, p<.01). The interaction type*modality was

marginally significant (F(1,23)=3.378, p=.079; visually cued antisaccades: 25.58 ±9.29%,

acoustically cued antisaccades 14.88 ±8.34%, visually cued prosaccades: 3.93 ±2.35%,

acoustically cued prosaccades 10.35 ±6.25% (see figure 5a). This result showed in the

post hoc test significant higher error rates in the anti- than in the prosaccades within in

the visual condition (p<.001) but none in the acoustic condition (p=.121) and additionally

significant higher error rates during saccades towards acoustic than visual cues within

the prosaccade condition (p<.05) and a significant opposite result within the antisaccade

condition (p<.001).

The interaction modality*distance (F(1,23)=4.616, p<.05, see figure 5b) revealed

that groups only differed in the 8° condition ( p<.05). Error rates were higher during

visually than during acoustically elicited saccades (visual condition: 15.67 ±5.58%,

acoustic condition: 12.74 ±5.38%). No further post-hoc tests were significant.

There was a positive correlation between errors in the visual and the acoustic

condition (r=.663, p<.001, see figure 5c) across saccade type, whichever is still

significant in a partial correlation corrected for age (r=.594, p<.01). However, further

inspection revealed that error rates only correlated between modalities for antisaccades

without controlling for age, not for prosaccades (antisaccades: r=.400, p<.05, partial

correlation: r=.314, p=.126; prosaccades: r=.282, p=.172).


- 35 -

Figure 5: a: Interaction modality*type for the dependant variable error rate; b: interaction modality*distance for the dependant variable error rate; c: correlation between errors in visually and acoustically cued saccades

2.1.5 Discussion

The present study is the first to investigate pro- and antisaccades following visual

and acoustic cues in children.

Using visual cues, the present study replicates a number of previous findings:

SRTs were longer for antisaccades than for prosaccades (Everling & Fischer, 1998;

Forbes & Klein, 1996; Ford, et al., 2005; Klein, et al., 2003; Munoz, et al., 1998) and

shorter for gap than for overlap trials (Everling & Fischer, 1998; Klein, et al., 2003;

Munoz, et al., 1998). More direction errors were made on antisaccade than on

prosaccade trials (Ford, et al., 2005; Fukushima, et al., 2000; Munoz, et al., 1998; Munoz

& Everling, 2004). Target eccentricity affected neither RT nor error rate. This

corresponds with findings in a study with children that used similar eccentricities as the

present one, namely 8°, 12° and 24° (Fukushima, et al., 2000), but differs from other

studies with adults (Yao & Peck, 1997; Zambarbieri, et al., 1995), suggesting that

developmental effects may account for the null finding in children. Alternatively, effects of

target eccentricity may appear only with larger visual angles.

Using acoustic cues revealed that SRTs did not differ between pro- and

antisaccades. An explanation might be that as the remapping process from the

craniotopic to the retinotopic reference system takes more time (as reflected in over-all

longer SRTs for acoustically triggered saccades), the salience of the acoustic cue


- 36 -

becomes obscured and thus the immediate “grasp-reflex” less strong. This is somewhat

supported by the findings regarding error rates. Although the main effect condition

suggests that across modalities more antisaccade errors were made than prosaccade

errors, the interaction condition*modality was marginally significant. Thus, although there

is a tendency for more errors in the anti- than in the prosaccade condition, the anti/pro-

difference is smaller than in the visual condition. This is due to more prosaccade and

fewer antisaccade errors in the acoustic than the visual condition. Correct acoustic

prosaccade generation appears to be more difficult than visual prosaccade generation in

children. In line with this finding a lower error rate on acoustically than on visually cued

antisaccades was observed by Schooler and colleagues (2008) in adult schizophrenia

patients but not in healthy adults. The authors offered the explanation that the remapping

process reduces immediate inhibitory demands on the system, making the acoustic

stimulus ‘less preemptive’. Therefore, people with reduced executive system capacities

may experience a relative benefit from different modality cues on tasks requiring

response inhibition. Children’s executive system is also less developed, as frontal lobe

maturity is only reached in adulthood (Gogtay, et al., 2004). Therefore their lower error

rate in the acoustic antisaccade task might result from their presumably less developed

executive system, which may benefit from the extra time gained in the re-mapping

process.

No auditory gap effect was found for SRTs nor error rates. In contrast to this,

previous findings in adults have described a smaller, but still significant gap effect for

acoustically cued saccades (Fendrich, et al., 1991; Reuter-Lorenz, et al., in press;

Shafiq, et al., 1998; Taylor, et al., 1999). It might be the case for children that although

mean SRTs indicate a gap-effect, it is obscured by a relatively high SRT variablity

(208.02ms gap, 209.06ms overlap condition). Alternatively, Fendrich and colleagues

(1991) suggest that a reduction (or absence) of the gap effect for acoustic cues might be

due to the fact that gap durations are usually chosen to be optimal for visual but not

necessarily for auditory saccades. In line with this notion and the present findings

Reuter-Lorenz and colleagues (in press) neither found a gap effect for error rates when

antisaccades were elicited by acoustic cues.

Target eccentricity had an effect on SRTs of acoustically cued saccades,

saccades to more peripheral targets being generated more quickly than saccades to the

nearer targets. This extends previous results in adults (Yao & Peck, 1997; Zambarbieri,

2002; Zambarbieri, et al., 1995; Zambarbieri, et al., 1982; Zambarbieri, et al., 1981) to

children between the ages of seven and twelve.


- 37 -

Comparing results across modalities showed that SRTs to acoustic targets were

generally longer than to visual targets. This finding replicates previous results for

prosaccades in adults (Fendrich, et al., 1991; Jay & Sparks, 1990; Shafiq, et al., 1998;

Zambarbieri, 2002; Zambarbieri, et al., 1995; Zambarbieri, et al., 1982; Zambarbieri, et

al., 1981) and extends them to children and to antisaccades. It shows that extra

processing time is needed to switch between references systems. Target eccentricity

affected SRTs to acoustic, but not visual cues, saccades to more distant acoustic cues

being generated more quickly than to closer cues. Presumably, within a craniotopic

reference system more lateral cues are easier to locate than nearer cues (Yao & Peck,

1997). Modality differences were found in the near condition: more errors for visually

than acoustically triggered saccades were made. One possible explanation might be the

different stimulus presentation degree between modalities. While visual cues were

presented 8° or 12° lateral of the fixation cross, acoustic stimuli represented a 45° and

90° angle. Thus, it might have been easier to disti nguish between the sides in the

acoustic condition.

Correlations between age (in months) and SRTs revealed no developmental

effects of age on saccadic RT within the age range studied. Yet, across all conditions the

slope of the regression line was negative, still indicating a small reduction of SRTs with

age until young adulthood, which, in line with other previous reports, may reach

significance for wider age ranges (Klein, 2001; Klein & Foerster, 2001; Munoz, et al.,

1998). For direction errors, significant negative correlations between age in months and

error percentage were observed for both visual and acoustic antisaccades, but not for

prosaccades, indicating a significant improvement in antisaccade performance between

the ages of seven and twelve years in both modalities. These results extend the findings

of developmental visual saccade performance (Klein, 2001; Klein & Foerster, 2001) to

the auditory modality. Thus, it can be assumed that developmental effects are

comparable for the visual and the acoustic condition.

The correlation between latencies in visual and acoustic conditions indicates that

both tasks are to some degree comparable. Children with slower latencies in visual

condition also had slower latencies in the acoustic condition. The same was true for error

rate.

2.1.6 Conclusion

The present study was the first study to investigate pro- and antisaccades elicited

by visual and acoustic stimuli in children. While many similarities between cue

presentation modalities arose, there were important differences: the “grasp-reflex” was


- 38 -

weaker for auditory saccades and they seemed less prone to be influenced by impulsivity

as their latency was longer resulting in fewer antisaccade errors. Studying different input

modalities in the context of response inhibition might be of interest for investigating

populations with disorders, such as auditory processing disorder (APD) and attention-

deficit/hyperactivity disorder (ADHD), as these diagnoses tend to overlap, although APD

should by definition be restricted to difficulties in the auditory domain. The present results

would predict that children with ADHD and children with APD should show dissociated

error and latency patterns.

Three antisaccade studies – Study II

- 39 -

2.2 Medio-frontal and anterior temporal abnormaliti es in children with attention

deficit hyperactivity disorder (ADHD) during an aco ustic antisaccade task as

revealed by electro-cortical source reconstruction

2.2.1 Abstract

Background

Attention deficit hyperactivity disorder (ADHD) is one of the most prevalent

disorders in children and adolescence. Impulsivity is one of three core symptoms and

likely associated with inhibition difficulties. To date the neural correlate of the

antisaccade task, a test of response inhibition, has not been studied in children with (or

without) ADHD.

Methods

Antisaccade responses to visual and acoustic cues were examined in nine

unmedicated boys with ADHD (mean age 122.44 ±20.81 months) and 14 healthy control

children (mean age 115.64 ±22.87 months, three girls) while an Electroencephalogram

(EEG) was recorded. Brain activity before saccade onset was reconstructed using a 23-

source-montage.

Results

When cues were acoustic, children with ADHD had a higher source activity than

control children in medio-frontal cortex (MFC) between -230 and -120ms and in the left-

hemispheric temporal anterior cortex (TAC) between -112 and 0ms before saccade

onset, despite both groups performing similarly behaviourally (antisaccades errors and

saccade latency). When visual cues were used EEG-activity preceding antisaccades did

not differ between groups.

Conclusion

Children with ADHD exhibit altered functioning of the TAC and MFC during an

antisaccade task elicited by acoustic cues. Children with ADHD need more source

activation to reach the same behavioural level as control children.

2.2.2 Introduction

Children with ADHD have difficulties with cognitive control, working memory and

response inhibition (Willcutt, et al., 2005). Response inhibition consists of two processes:

(i) the capacity to suppress a prepotent response before or after its initiation, and (ii) the

goal-directed behaviour from the interference of competing processes (Barkley, 1991b).

Antisaccades are one way to examine inhibition, as antisaccade tasks require the

suppression of the automatic response to look towards a peripheral cue and to generate


- 40 -

a saccade in the opposition direction instead (Everling & Fischer, 1998). Error rates

during antisaccade tasks reflect the ability to inhibit a response, while saccadic reaction

times (SRT) during correct trials reflect the duration of the underlying cognitive and motor

processes. There is a growing body of literature on eye movement experiments

comparing children with ADHD with control subjects (Rommelse, et al., 2008). Despite

some inconsistencies, the general finding is that subjects with ADHD have an elevated

number of direction errors during antisaccade tasks (Goto, et al., 2010; Karatekin, 2006;

Klein, et al., 2003; Loe, et al., 2009; Mahone, et al., 2009; Mostofsky, Lasker, Cutting, et

al., 2001; Mostofsky, Lasker, Singer, et al., 2001; Munoz, et al., 2003; O'Driscoll, et al.,

2005). However, until now, no study has examined brain function during antisaccade

tasks in ADHD, although this might lead to important new insight into the cortical

mechanisms of behavioural inhibition and its dysfunction in ADHD.

Inhibition difficulties are not only relevant in the visual domain, where they have

mostly been studied. Humans also redirect their gaze to locate the origin of a suddenly

appearing noise, a tendency, which is already present in babies (Muir & Field, 1979).

Still, until now, there is no study, which investigates pro- or antisaccades elicited by

acoustic cues in children. Accordingly, it is unclear, which neuronal network underlies

antisaccades following acoustic cues. There is a particular interest in analysing inhibition

deficits following acoustic cues in children with ADHD as a high number of children with

ADHD have difficulties with acoustic tasks (Breier, Fletcher, Foorman, Klaas, & Gray,

2003; Sutcliffe, Bishop, Houghton, & Taylor, 2006; Tillery, et al., 2000).

Electrophysiological and functional brain imaging studies have given insight into

which cerebral areas are active during visual saccadic tasks. The frontal eye fields

(FEF), the supplementary eye fields (SEF) and the parietal eye fields (PEF) in the

posterior parietal cortex (PPC) are active when saccades are initiated. The dorsolateral

prefrontal cortex (DLPFC) and the anterior cingulate cortex (ACC) with the Cingulate Eye

Field are associated with “higher level”, volitional and cognitive aspects of saccade

control, specifically during antisaccades (Connolly, et al., 2002; Ford, et al., 2005;

Kulubekova & McDowell, 2008; Munoz & Everling, 2004; Pierrot-Deseilligny, et al., 2004;

Pierrot-Deseilligny, et al., 2005; Pierrot-Deseilligny, et al., 2003; Pierrot-Deseilligny, et

al., 1991; Ploner, et al., 2005). DLPFC shows activity during antisaccades that is not

present during prosaccades (Clementz, et al., 2010). Its activity seems to provide an

inhibitory signal that precedes correct antisaccade performance (Clementz, et al., 2001;

Fitzgerald, et al., 2008; McDowell, et al., 2005). Directional errors are therefore generally

linked to frontal dysfunctions. The ACC is involved in the executive control of attention

and plays an important role in visual antisaccade performance (Brown, et al., 2006; Ford,


- 41 -

et al., 2005; Gaymard, Ploner, et al., 1998; Polli, et al., 2005). Given that children with

ADHD have difficulties with response inhibition and make more antisaccade errors than

children without ADHD, one might assume that activity of frontal structures involved in

the generation of antisaccades is altered. Disturbed functioning of prefrontal cortex,

ACC, and striatum are also thought to underlie other executive function deficits in ADHD

(Bush, et al., 2005). This is in line with the aetiological theory that ADHD results from

structural and functional changes in a fronto-subcortical network (Bush, et al., 2005;

Dickstein, et al., 2006; Seidman, et al., 2005).

The first aim of the present study was to investigate how children with and without

ADHD differ in brain activation during an antisaccade task. The second aim was to

investigate, whether children with ADHD have comparable inhibition difficulties when

cues are visual and acoustic.

2.2.3 Methods

Participants

Sixteen children with ADHD and sixteen children without ADHD were investigated.

Children with ADHD were recruited at two child psychiatric outpatient clinics, diagnoses

being made by the head psychiatrist and his/her team of psychologists based on

questionnaires, anamnestic biographical interviews and psychometric tests. Control

children were recruited at a local school. However, data of seven children with ADHD

and data of two control children had to be discarded due to insufficient data quality (too

many movement artefacts). Data of nine children with ADHD (mean age 122.44 ±20.81

months, boys only) and 14 healthy control children (mean age 115.64 ±22.87 months,

three girls) were further analysed. All but one child with ADHD were diagnosed with

ADHD combined type; the remaining child was diagnosed with ADHD primarily

inattentive type. All children were investigated off medication. Three children with ADHD

who were prescribed with methylphenidate refrained from taking it at least 24 hours

before the experiment in concordance with their respective psychiatrist and their parents.

All children with ADHD had at least one comorbid disorder (mostly specific

developmental disorder of motor function) and 44% had at least two comorbid disorders

(mostly specific developmental disorders of scholastic skills). Control children did not

have any clinically relevant diagnoses or took any medication as reported by the parents.

Procedure

Children and parents were shown the laboratory equipment and the task was

explained to them. They then signed informed consent forms (according to the Helsinki


- 42 -

declaration (WMA, 2004)). Parents were asked to fill in an ADHD symptom checklist

(Lauth & Schlottke, 2002), an auditory processing disorder (APD) checklist (DGPP,

2002) and a routine questionnaire while children completed the Edinburgh-Handedness-

Inventory (Oldfield, 1971). To ensure within-normal hearing levels, children’s hearing

thresholds were determined for frequencies 500, 1000, 2000 and 4000Hz in an

acoustically shielded room. Children were then shown a computerised, animated

explanation of the task, which included examples and four training trials. To ensure that

all children were motivated and perceived themselves as successful, children were told

that they would be able to collect four “cartoon dogs” on the computer screen if they

performed well (the dogs always appeared after fixed intervals) which would then allow

the children to pick a small gift from a “treasure chest” after the experiment. Children

were additionally compensated with 20 Euros at the end of the experimental session.

For the EEG experiment, children were comfortably seated in a chair, their heads

resting on a chin rest 500mm away from the computer monitor. Headphones were put on

and the 30min - experiment was started after impedance measurement. After the EEG

experiment intelligence was assessed by the Coloured Progressive Matrices (CPM;

Raven, Raven, & Court, 2002).

Task

Participants were instructed to generate saccades in response to visual or

acoustic cues. The nature of the required saccade depended on the instruction.

Saccades could either be directed towards the cue (prosaccade) or away from the cue

(antisaccade). Visual cues, consisting of yellow dots that filled one of four empty circles,

could appear “near” (6°) or “far” (12°) and left or right of the fixation cross for 1000ms.

Acoustic cues were 1000Hz sine tones presented for 1000ms that were perceived either

“far” left/right (90°) or “near” left/right (45°, s ee the description below). Children were

explained that in response to “near” acoustic cues they should generate saccades

towards the 6° circle, and upon “far” to make sacca des towards the 12° circle. Cues

could either appear 200ms after extinction of the fixation cross (gap) or with a 200ms

overlap with the fixation cross. Random combinations of the following within-group

factors were presented throughout the experiment: cue modality (visual vs. acoustic),

direction (right vs. left), type (anti- vs. prosaccade), distance (near (6° visual, 45°

acoustic) vs. far (12° visual, 90° acoustic)) and d elay (gap vs. overlap). Nine runs of each

combination resulted in a total of 288 trials. This random design was chosen to avoid

ceiling effects and enable better group differentiation.


- 43 -

After trial 96, 129, 259 and 288 children were shown a motivation picture with 1, 2,

3 and 4 dogs, respectively. A pause-signal appeared after 144 trials indicating that

children could take a short break. The length of the break was determined by the

children.

Each trial began with a 1000ms instruction slide depicting the nature of the

required saccade by a prominent symbol the meaning of which had been explained to

the children beforehand (see procedure above). Each trial lasted 6500ms (see figure 6

for a schematic overview).

Figure 6: Temporal structure of an exemplary trial (visual prosaccade); top: overlap-condition and

bottom: gap-condition

Equipment and Recordings


Inc.). Visual cues were generated within Presentation. Sine tones were generated with

Adobe Audition 2.0®. The effect of sound lateralisation was created by intensity and

phase differences between the left and right channel. The impression of a 90°

lateralisation to either direction was created by attenuating the contra-lateral channel by

3.62dB and shifting its onset by 6.5µs. The impression of a 45° lateralisation was created

by attenuating the contralateral channel by 2.8dB and delaying its onset by 1µs.

Stimuli were presented with a PC Dell precision 390 with Intel ® Core ™ 2CPU

2.13Hz-processor with 2GB Ram operating system on a monitor with 365 x 270mm

resolution (Samtron 96 BDF) and via stereo headphones (Sennheiser HD 280 pro

(64Ω)).

Electrical brain activity was measured using EEG. Recording was done with a 257

channel system from EGI Electrical Geodesics Inc. using NetStaionTM12 on a Mac OSX

with 1,25GHz PowerPC G4 processor and 1GB DDR SD RQM. Sample rate was 250Hz

and an online filter of 100Hz lowpass and 0.1Hz highpass were applied.


- 44 -

Data analysis

Data were analysed with BESA software (Brain Electrical Analysis, version

5.2.4.52, MEGIS Software GmbH, Graefelfing, Germany). Vertical and horizontal eye

movements artefacts (blinks and saccades) were systematically removed using an

algorithm implemented in BESA (Berg & Scherg, 1994; Lins, Picton, Berg, & Scherg,

1993). For each condition, data were segmented into epochs from 500ms pre to 2000ms

post stimulus (notch filter at 50Hz). For the identification of saccades, data were filtered

digitally from 0.01-8Hz (6dB/octave forward and 12dB/octave zerophase). The

percentage of correct saccades was determined and saccade latency was measured to

the nearest sampling point. Saccades with latencies <80ms were excluded, as they can

be classified as anticipations rather than responses (Klein, 2001). Next, unfiltered

response-locked averages of antisaccades (merged across direction, distance and delay

to gain higher statistical power and more averages for source reconstruction) were

generated i.e. epochs (500ms pre and 500ms post response) were exported, which were

centred at saccade onset. Source analysis was carried out with a 23-source-model

(generated on the basis of talairach coordinates of structures known to be involved in

saccade generation), data being filtered digitally from 0.1-30Hz (6dB/octave forward and

24dB/octave zerophase). The source montage was generated to cover activity of

structures relevant for the processing and production of saccades (SEF, FEF, DLPFC,

PPC – left and right, frontal midline and medio-frontal cortex (MFC)). Further, sources

were placed that covered activity of structures relevant for the processing of acoustic and

visual stimuli (supplemental temporal cortex (STC), temporal parietal cortex (TPC),

temporal anterior cortex (TAC) – left and right). Additional sources of no interest

(cerebellum – left and right) were placed to increase the sensitivity of the sources of

interest. The sensitivity of a source describes its ability to pick up the activity generated

by the brain volume of interest. Source sensitivity is dependant on the position of the

source in the brain model, the number of sources in the montage, as well as the distance

between the sources. The sensitivity of relevant sources was carefully tested with

sensitivity maps in BESA (see figure 7 for the sensitivity map). The output of a source

montage is each individual source’s activity over time. Source positions in space are

fixed.


- 45 -

Figure 7: Sensitivity map of the MFC (top) and the TAC left (bottom)

Statistical analysis

Only antisaccades were analysed, as the leading question of the present article

concerned response inhibition. SRTs and the percentage of correctly generated

antisaccades (merged across direction, distance and delay) were compared between

groups using Statistica (StatSoft, Inc., 2003). T-tests or Mann-Whitney-U tests were

computed after testing for normal distribution of the dependant variables using Shapiro-

Wilks-W-test. Scores of questionnaire data were analysed accordingly. In order to

objectively identify time-windows, throughout which the experimental groups differed in

activity of one or more sources, non-parametric cluster-based analysis of EEG source

data was performed using FieldTrip, an open-source signal processing toolbox for Matlab

(Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen,

The Netherlands. http://www.ru.nl/neuroimaging/fieldtrip). Groups were compared for

each sampling point and each source via independent t-tests. In order to prevent

chance-findings, data were re-shuffled 1000 times using a cluster-based Monte-Carlo

randomization.

This method effectively controls for multiple comparisons (Maris & Oostenveld,

2007). Clusters (here: clusters of sampling points) were defined as significant when the

probability of observing larger effects in the shuffled data was below 5%. As response

inhibition takes place before the onset of the saccade and in accord with already existing

findings (Clementz, et al., 2001; McDowell, et al., 2005), data analysis was carried out for

the time-windows -230ms until -120ms before response and -120ms until 20ms after

response.


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2.2.4 Results

Sample characteristics

Groups did not differ in age (t(21)=0.689, p=.499) or gender distribution

(χ2(1)=2.22, p=.135). Children with and without ADHD had comparable intelligence

scores as measured by the CPM (ADHD: 71.00 ±29.97 percentile rank, Control: 66.15

±29.84 percentile rank; t(19)=0.361, p=.722). Children with and without ADHD had

hearing sensitivities of 20dB or better in each ear for all measured frequencies (American

Speech-Language-Hearing Association, 1997). Groups did not differ from each other

(see table 2).

Table 2: Results hearing levels ADHD (n=9) Control (n=14)

Side tested test Frequency (Hz)

Mean SD Mean SD t/Z- Value

df p

t-test 500 4.67 5.05 3.50 4.15 0.605 21 0.552 t-test 1000 1.56 4.98 0.21 3.93 0.721 21 0.479 t-test 2000 -0.89 4.83 -0.79 4.92 -0.049 21 0.961

Right

t-test 4000 0.33 5.36 -0.93 6.81 0.469 21 0.644 t-test 500 3.00 7.45 3.36 5.42 -0.133 21 0.895 MWU 1000 -1.33 8.02 -0.86 6.77 -0.031 21 0.975 MWU 2000 -2.67 5.55 0.07 8.40 -0.661 21 0.508

Left

MWU 4000 -2.00 6.08 -0.43 9.49 -0.504 21 0.614

Children with ADHD had higher values than control children for both subscales of

the ADHD questionnaire (see table 3). Groups also differed on the subscales Speech

Perception and Auditory Memory of the APD questionnaire (see table 3).


- 47 -

Table 3: Results parental ratings of ADHD/APD symptoms

ADHD Control

Symptoms Subscales Test n Mean SD n Mean SD t/Z-value

df p

Inattention MWU 9 34.00 7.38 14 14.71 2.40 3.874 21 0.000 ADHD

Hyperactivity/ Impulsivity

MWU 9 3.09 0.67 14 1.34 0.22 3.969 21 0.000

Speech Perception

t-test 9 1.89 0.73 13 1.29 0.25 2.767 20 0.012

Auditory Discrimination

MWU 9 1.38 0.72 14 1.14 0.23 0.787 21 0.380

Sound Localisation

MWU 9 1.27 0.53 14 1.01 0.05 1.134 21 0.086

Hearing in background noise

MWU 9 1.63 0.78 14 1.48 0.41 0.157 21 0.874

Auditory Memory

MWU 9 1.81 0.65 14 1.30 0.42 2.331 21 0.019

APD

Auditory Hypersensitivity

t-test 9 2.77 0.64 13 2.48 0.62 1.058 20 0.303

Saccadic reaction and latencies

Groups did not differ regarding correct antisaccade reactions in the visual

condition (ADHD 50.52 ±16.54% correct, Control 48.84 ±20.53% correct, t(21)=0.205,

p=.839) and in the acoustic condition (ADHD: 57.20 ±12.88% correct, Control: 65.38

±12.32% correct, t(21)=-1.527, p=.142).

There were neither group differences in antisaccade latency in the visual condition

(ADHD: 493.36 ±196.43ms, Control: 441.00 ±146.65ms, Z(21)=0.504, p=.614), nor in the

acoustic condition (Antisaccades: ADHD: 696.25 ±258.34ms, Control: 639.94

±226.71ms, t(21)=0.551, p=.588).

Pre-saccadic brain activity

A significant group difference was identified for the acoustic antisaccade condition

between 228 and 140ms before antisaccade onset (t(21)=74.707, p<.05) in the MFC

source and at 112-0ms before antisaccade onset (t(21)=76.294, p<.05) in the TAC left

source. Children with ADHD showed higher source activity than control children (MFC:

ADHD: 67.09 ±40.16nAm, Control. 34.59 ±13.49nAm, see figure 8; TAC left: ADHD:

61.83 ±31.80nAm, Control 31.34 ±20.18nAm, see figure 9).

In contrast, no significant group differences were revealed in the visual

antisaccade condition in either of these sources or any other source.


- 48 -

Figure 8: Group effect for the dependant variable source power of correct antisaccades in the MFC

Figure 9: Group effect for the dependant variable source power of correct antisaccades in the TAC left

2.2.5 Discussion

Aim of this study was to investigate differences in response inhibition and

corresponding brain activity between children with and without ADHD. Response

inhibition was measured in an antisaccade task where saccades were either elicited by

acoustic or visual cues.

The main finding of the study was that children with and without ADHD differed in

brain activity when saccades were elicited by acoustic cues. Children with ADHD had a

higher source activity than control children in the MFC source between -228 and -140ms

and in the left-hemispheric TAC source between -112 and 0ms before saccade onset.

These time windows overlap with the critical period for response inhibition in visual

antisaccade tasks (Clementz, Brahmbhatt, McDowell, Brown, & Sweeney, 2007;

Clementz, et al., 2001; McDowell, et al., 2005).


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Behavioural data

No group differences regarding the correctness of saccade execution were found

in the present study. Other studies on antisaccades using only visual cues revealed an

elevated number of direction errors in children with ADHD (Rommelse, et al., 2008),

indicating that these children are less able than control children to inhibit inappropriate

responses. However, there are also studies in line with the present findings (Aman, et al.,

1998; Hanisch, et al., 2006; Rothlind, et al., 1991) without group differences. The random

design of experimental presentation in the present study was chosen to increase task

difficulty in order to differentiate between the groups. However, it might have been the

case that the task was equally more difficult for both, control children and children with

ADHD, as supplementary task switching between pro- and antisaccades is required

(Irving, et al., 2009; O'Driscoll, et al., 2005), thus concealing group effects.

Another explanation for the negative finding of behavioural group differences

might be related to the age range of the children in the present study. Rothlind and

colleagues (1991) investigated a group of children with a similar age range. The mean

age of their ADHD group was 10.5 ±2.4 years (range: 6.9 – 13.9 years), mean age of the

control group was 9.9 ±2.8 years (range: 6.8 – 14.4 years). As in the present study,

Rothlind and colleagues did not find any group differences in saccadic errors. Other

studies have used groups of children with a smaller age-range and were able to find

more errors in children with ADHD (Goto, et al., 2010; Karatekin, 2006; Klein, et al.,

2003; Mahone, et al., 2009; Munoz, et al., 2003; O'Driscoll, et al., 2005). A reason might

be that boys younger than 11 years have difficulty with oculomotor inhibition in general

(Fischer, et al., 1997; Klein & Foerster, 2001). However, a study with younger children

has also found differences between children with and without ADHD (Goto, et al., 2010)

and thus questions the assumption of a general oculomotor inhibition deficit in younger

children. Finally the subtype of ADHD might be an influencing factor on performance in

saccade tasks. Children with ADHD combined type made more antisaccade errors than

control children, while no group differences were found between children with ADHD

inattentive type and control children (O'Driscoll, et al., 2005). In the present study eight of

nine children with ADHD had the diagnose ADHD combined type. Thus, ADHD subtype

is not likely to have influenced the response pattern in the present study.

As for saccadic correctness, no group differences were found for SRTs in the

present study. The latency of correct antisaccades was not investigated in all saccade

studies and results are inconsistent. Some studies found slower antisaccade latencies in

children with ADHD compared with control children (Goto, et al., 2010; Karatekin, 2006;

Klein, et al., 2003; Mostofsky, Lasker, Cutting, et al., 2001; Mostofsky, Lasker, Singer, et


- 50 -

al., 2001; Munoz, et al., 2003). Other studies found no group differences in antisaccades

latencies (O'Driscoll, et al., 2005; Rothlind, et al., 1991), which is in line with the present

result.

Thus, it is still unclear why no group differences were found in the rate of correct

saccades and its latencies. The small sample size – which resulted from the fact that

only ADHD children off medication were included – and the relatively big age range

seem to be the most likely explanation. However, an absence of behavioural differences

reduces ambiguities in the interpretation of any effects in brain measures.

Pre-saccadic brain activity

Indeed, source activation differed between groups in the acoustic condition.

Children with ADHD had higher activation of the MFC and the left-hemispheric TAC

compared to control children during time-windows likely to reflect response inhibition.

MFC includes parts of the dorsal ACC, which is connected with the prefrontal cortex and

parietal cortex as well as the motor system and the frontal eye fields (Brown, Vilis, &

Everling, 2007; Ding, Powell, & Jiang, 2009; Wang, Matsuzaka, Shima, & Tanji, 2004). It

is crucially involved in the executive control of attention. The ACC plays an important role

in visual antisaccade performance (Brown, et al., 2006; Ford, et al., 2005; Gaymard,

Ploner, et al., 1998; Polli, et al., 2005) and ACC activity seems to be altered in patients

with ADHD (Bush, et al., 1999; Colla, et al., 2008; Fallgatter, et al., 2004; Paul-Jordanov,

et al., 2010). In the present study, children with ADHD had higher activity in the MFC

source than control children preceding an auditory antisaccade. Still, behavioural

performance, i.e. the percentage of correctly executed saccades did not differ between

the groups. It thus appears that children with ADHD needed more activation of the MFC

to reach the same level of response inhibition as control children. The present results

were found only when saccades were elicited by acoustic cues. Still, a comparable

pattern of brain activation results was found in studies investigating response inhibition in

a visual go/nogo task design (Dickstein, et al., 2006; Durston, et al., 2003; Vaidya, et al.,

1998). The present results are also in line with a meta - analysis (Dickstein, et al., 2006),

which concluded that there are two brain areas, in which ADHD patients have

significantly more activation than controls: the medial frontal gyrus and the right

secondary somatosensory area.

Activation of the left TAC source was higher in children with ADHD than in control

children preceding antisaccades. Results from other experiments regarding temporal

lobe activity during cognitive tasks are inconsistent. There seems to be some evidence of

dysfunction and also of compensatory use of the temporal lobes in ADHD (Cherkasova &


- 51 -

Hechtman, 2009). However, the current finding is in line with a go/nogo study in which

children with ADHD showed more activation than the control children in the

middle/inferior/superior temporal gyrus (Tamm, et al., 2004). This might be also related

to structural abnormalities in children with ADHD (Seidman, et al., 2005). Castellanos

and colleagues (2001; 2002) showed that children with ADHD have a reduced volume of

frontal and temporal gray matter, caudate, and cerebellum. These volume reductions

were related with measures of symptom severity in an ADHD sample (Casey, et al.,

1997; Castellanos, et al., 2002). Another study detected reduced brain volumes in the

lateral anterior and midtemporal cortices bilaterally (Sowell, et al., 2003). Lateral

temporal and parietal regions are part of the cross-modal association cortex, which also

includes the DLPFC. This system integrates information from lower order sensory

systems into higher order rules and functions. It is assumed that these regions together -

beside their anatomical interconnection - form a broadly distributed action-attention

system that supports the maintenance of attentional focus and successful inhibition

(Mesulam, 1998; Peterson, et al., 1999; Sowell, et al., 2003). It might be speculated that

because of the smaller volume of the temporal cortex, children with ADHD showed more

reflexive reaction to acoustic cues. Because of that, more frontal activation might have

been needed as well in order to control behavioural output.

Finally, group differences in brain activation during acoustically elicited

antisaccades are in line with auditory deficits (in Speech Perception and Auditory

Memory) as detected in the APD questionnaire in the present study. The results are also

in line with a suggested symptom overlap of children with ADHD and children with APD

(Cacace & McFarland, 2006; Dawes & Bishop, 2009; Keller & Tillery, 2002; Witton,

2010). APD is characterised by disturbed hearing despite a normally functioning

periphery. Typical symptoms are poor recognition, discrimination, separation, grouping,

localisation, ordering of non-speech sounds and difficulties with acoustic tasks when

competing acoustic signals are present (American Speech-Language-Hearing

Association, 2005; British Society of Audiology Steering Group, 2007). Both, children

with APD and children with ADHD, have difficulty paying attention and remembering

information presented orally, are easily distracted, have difficulty following complex

acoustic directions or commands, and show low academic performance. The present

results also demonstrate that acoustic processing should be a focus of interest in ADHD

research. Knowing more about alterations of the auditory systems and according

consequences might enable better differentiation of the ADHD/APD diagnosis.


- 52 -

In summary, both structures - MFC and the left-hemispheric TAC - are part of

functional brain areas involved in attention and response inhibition, and seem to be

functionally or structurally altered in children with ADHD.

Against expectations, no differences in brain activity were found in the visual

antisaccade condition. There might be many contributing factors such as sample size,

task design, and age range, as mentioned above. It is not possible to directly compare

the present results to previous findings, as no other studies have investigated brain

activation during antisaccades in children with ADHD. However, it should be noted that

there are inconsistent findings in imaging studies of other visual inhibition tasks. Some

studies reported that ADHD children exhibit a smaller P300 amplitude than control

children (Kemner, et al., 1996; Liotti, et al., 2005; Paul-Jordanov, et al., 2010; Paul, et al.,

2007), and showed lower activation of inferior prefrontal cortex and other brain regions

(Dickstein, et al., 2006; Rubia, et al., 1999; Rubia, Smith, Brammer, Toone, & Taylor,

2005). Other authors found increased activation in prefrontal brain regions (Durston, et

al., 2003; Vaidya, et al., 1998) and in the medial frontal gyrus respectively (Dickstein, et

al., 2006). Again, it is difficult to compare studies using different inhibition tasks. More

research with bigger sample sizes and a smaller age range are needed to answer to the

question if there are differences in brain activity between children with and without ADHD

during visually cued antisaccades.

2.2.6 Conclusion

In sum, the present study for the first time provides insight in the cortical network

underlying the production of antisaccades elicited by acoustic stimuli in children with and

without ADHD. While no group differences were found when visual cues were used,

results showed that functioning of the anterior temporal lobe and medio-frontal cortex is

altered in children with ADHD when acoustic cues are used to trigger antisaccades. The

present results support the hypothesis that cortical structures underlying response

inhibition are more active in children with ADHD to achieve the same behavioural output

as children without ADHD, possibly as a compensatory mechanism.

Three antisaccade studies – Study III

- 53 -

2.3 Brain activation differences during the generat ion of visually and acoustically

guided antisaccades between children with and witho ut attention deficit

hyperactivity disorder (ADHD)

2.3.1 Abstract

Background

Disinhibition is a symptom of ADHD. It can be investigated with the antisaccade

task, where the reflex to look towards an appearing stimulus needs to be suppressed.

Children with ADHD have difficulties with this task. Until now, only response inhibition

after visual cues has been explored in ADHD. However, research into the cross-modality

of disinhibition is necessary in order to better understand the executive function deficit in

ADHD and to distinguish ADHD from auditory processing disorder. The present study

investigated cross-modal response inhibition.

Methods

16 children with ADHD and 16 healthy control children participated in two

antisaccade experiments with visual and acoustic cues while an Electroencephalogram

(EEG) was recorded. Behavioural data (errors and latencies) as well as response-locked

EEG-activity were analysed. Source localisation (CLARA) was performed in time-

windows of significant effects between and within groups.

Results

Children with ADHD had an inhibition deficit only when cues were visual. Children

with ADHD made more saccadic errors than control children when cues were acoustic,

yet they were not specifically impaired with response inhibition. Groups differed in brain

activity in a time-window corresponding to response inhibition. Source reconstruction of

this effect implied frontal hypoactivation and a parietal-cerebellar compensatory network

in ADHD.

Conclusion

Although children with ADHD do seem to have greater difficulties generating

saccades when acoustic cues are used, they do not have an inhibition deficit. Children

with ADHD only had difficulties with response inhibition after visual cues. This supports

the idea that deficient response inhibition is not a cross-modal phenomenon.

2.3.2 Introduction

ADHD is characterised by inattention, hyperactivity and impulsivity (American

Psychiatric Association, 2000). One feature of impulsivity is disturbed response

inhibition, i.e. the ability to suppress prepotent responses. This has been demonstrated


- 54 -

in many experiments using different tasks like the Continuous Performance Test (CPT;

Losier, McGrath, & Klein, 1996) or a modified version of the Stop-Signal Task (Paul-

Jordanov, et al., 2010; Paul, et al., 2007; Willcutt, et al., 2005). One further possibility to

investigate response inhibition is the antisaccade task (Everling & Fischer, 1998). While

in the prosaccade condition subjects are required to direct their gaze at an appearing

stimulus as quickly as they can, they are asked to look at the mirror position of the

stimulus in the antisaccade condition. The generation of correct antisaccades involves at

least two processes: (i) the suppression of the reflexive saccade towards the stimulus

and (ii) the generation of the intentional antisaccade. The repression of the reflexive

saccade can be considered as impulse control or response inhibition. Thus, errors during

the antisaccade condition are an indicator for impulsivity.

Several studies investigating antisaccades elicited by visual stimuli in participants

with ADHD showed an elevated number of direction errors compared to control

participants (Goto, et al., 2010; Karatekin, 2006; Klein, et al., 2003; Loe, et al., 2009;

Mahone, et al., 2009; Mostofsky, Lasker, Cutting, et al., 2001; Mostofsky, Lasker, Singer,

et al., 2001; Munoz, et al., 2003; O'Driscoll, et al., 2005).

To the best of our knowledge, until now only one study from our group has

investigated antisaccades triggered by acoustic cues (Goepel, Kissler, Rockstroh, &

Paul-Jordanov, 2011). It was demonstrated that more cortical activation in the medial

frontal and anterior temporal areas is needed in children with ADHD to perform on the

same behavioural level as control children when antisaccades are elicited by acoustic

cues. Yet, impulse control in response to acoustic stimuli is behaviourally highly relevant.

An example might be a pupil in class listening to the teacher, while other sounds are

present (the neighbour whispering, noises from outside, etc.). In order to pay attention to

the teacher it is important to not respond to competing acoustic sounds.

The need for more research into cross-modality of symptoms in ADHD becomes

apparent when considering the diagnosis central auditory processing disorder ((C)APD).

APD is considered as a central disorder, i.e. children have difficulties with hearing and

psychoacoustic tasks despite the peripheral hearing system being fully functional

(American Speech-Language-Hearing Association, 2005; British Society of Audiology

Steering Group, 2007). However, there is much dispute about the nature of APD in the

literature. It has been suggested that ADHD and APD are the same disorder, the label

simply defined by the person who diagnoses the child (psychiatrist vs. audiologist), or

whether they are different disorders overlapping in phenotype (Cacace & McFarland,

2005b). The attempts to differentiate APD children with and without ADHD using electro-

physiological measures have not been successful (Ptok, et al., 2004). Both groups had


- 55 -

comparable late acoustic potentials. One difficulty is that multi-modal testing is necessary

to differentiate between ADHD and APD. It is assumed that children with APD show poor

performance only on acoustic tasks, while children with ADHD show poor performance

on auditory and visual attention tasks (Dawes & Bishop, 2009; Jerger & Musiek, 2000).

However, only few studies on APD have focused on more than one modality. For

example, Starzacher (2006) demonstrated in 20 children with APD and 31 control

children, that patients performed poorer than control children on all subtests of the

continuous performance attention test, i.e. on subtests involving the auditory and visual

modality. Another experiment using a visual and auditory CPT showed in 68 children with

suspected APD that a greater proportion of children had problems in both modalities than

with auditory or visual attention alone. Additionally, the authors suggested that 30% of

the children had normal auditory attention and a diagnosis of APD, whereas 8% had poor

auditory attention but no APD diagnosis (Sharma, et al., 2009). These findings

demonstrate that the strict definition that deficits of children with APD are restricted to the

auditory modality does not hold true.

Regarding ADHD, Jonkam and colleagues (1997b) showed that children with

ADHD indeed had higher error rates and amplitude-reduced event-related potentials

during a CPT compared to control children in both modality conditions (visual and

auditory). Further, Breier and colleagues (2003) used a task assessing the perception of

auditory temporal and nontemporal cues. Participants were children with (a) reading

disability (RD), (b) ADHD, (c) RD with ADHD and (d) control children. The presence of

ADHD was associated with a general reduction of performance across all

psychoacoustic tasks. Another study showed that children with ADHD have difficulties

with frequency discrimination (FD), but not with the detection of frequency modulation

(FM) (Sutcliffe, et al., 2006). The latter result is particularly interesting, as it shows that

poor performance on psychoacoustic tasks is not necessarily the result of inattention,

otherwise it should have affected both, performance on the FD and the FM task. These

results demonstrate that children with ADHD have difficulties with psychoacoustic tasks

that might be independent of an attention deficit.

In order to differentiate between ADHD and APD, a multi-modal approach is

nevertheless the only option. It is important to understand, whether typical ADHD

symptoms are restricted to one modality in order to be able to compare children with

ADHD and APD. We followed this idea and aimed at investigating response inhibition in

ADHD using an antisaccade task, where saccades were either triggered by visual or

acoustic cues. Previous studies (Rommelse, et al., 2008) suggest that children with

ADHD make more antisaccade errors when visual cues are used. Barely results exist


- 56 -

regarding acoustic cues. Thus the present study lays the ground for further comparison

of children with ADHD and children with APD.

2.3.3 Methods

Participants

16 children with ADHD and 16 healthy control children participated in the

experiment (see results section for demographic information). One child per group was

excluded from analysis of the visual experiment due to a high artefact level in the EEG

data. For the acoustic experiment two children per group were excluded. Control children

were recruited at a local school and did not have any clinically relevant diagnoses or took

any medication as reported by the parents. Children with ADHD were recruited at two

child psychiatric outpatient clinics, diagnoses being made by the head psychiatrist and

his/her team of psychologists based on questionnaires, anamnestic interviews and

psychometric tests. None of the children with ADHD took any kind of medication.

Procedure

The families were shown the laboratory equipment and the task was explained to

them. Children and parents then signed informed consent forms (according to the

Helsinki declaration (WMA, 2004)). Parents were asked to fill in a standardised ADHD

symptom checklist, a standardised conduct disorder symptom checklist (Diagnose-

Checkliste Aufmerksamkeitsdefizit-/ Hyperaktivitätsstörungen (DCL-ADHS), Diagnose-

Checkliste Störung des Sozialverhaltens (DCL-SSV); Döpfner, et al., 2008) and an

auditory processing disorder checklist (DGPP, 2002). Children completed the Edinburgh-

Handedness-Inventory (Oldfield, 1971) and their non-verbal intelligence was measured

by the Coloured Progressive Matrices (CPM; Raven, et al., 2002). To ensure within-

normal hearing levels children’s hearing thresholds were determined for frequencies 500,

1000, 2000 and 4000Hz in an acoustically shielded room. Children were then shown a

computerised, animated explanation of the task, which included examples and 16

training trials. For additional motivation, children were told that they would be able to

collect eight “cartoon mice” on the computer screen if they performed well (although the

mice always appeared after fixed intervals) which would then allow the children to pick a

small gift from a “treasure chest” after the experiment. Thus, it was ensured that all

children were motivated and perceived themselves as successful. Children were

additionally compensated with 20 Euros at the end of the experimental session.

For the EEG experiment, children were comfortably seated on a chair, their heads

resting on a chin rest 500mm away from the computer monitor. The 30min-experiment


- 57 -

(with a flexible break in the half of the session) was started after ensuring that

impedances were below 30kOhm.

After the experiment children rated the difficulty and their perceived success.

Groups found the experiment equally difficult (visual experiment: Z(27)=0.655, p=.513,

acoustic experiment: t(25)=.629, p=.535) and perceived themselves as equally

successful (visual experiment: Z(27)=0.655, p=.513, acoustic experiment: Z(25)=.946,

p=.344). Children with and without ADHD described the task as easy (visual experiment

mean: 32.07 ±22.46, acoustic experiment mean: 30.37 ±21.07, “0” representing very

easy, “100” representing very difficult) and assumed that they had made mistakes in

27.59 ±18.01% and 28.81 ±19.44% of trials, respectively.

Task

Four blocks with 60 trials each were presented in randomised order for each

experiment (visual, acoustic). Two blocks involved prosaccades, the other two blocks

involved antisaccades. Thus, a trial could either cue an anti- or a prosaccade, directed to

the left or the right of the screen. It was counter-balanced, whether a child started with

the visual or acoustic experiment. In the visual experiment, the cue was a cartoon owl; in

the acoustic experiment, the cue was the sound of an owl-call. For prosaccade trials

participants were asked to look towards the cue as quickly as possible, while they were

asked to look at the mirror-location of the cue for antisaccade trials. Each block started

with an instruction slide (anti- or prosaccade) of variable length as children were asked to

repeat the instruction to make sure they had understood the task. Each trial began with a

1300ms fixation time with a black fixation cross in the middle of a blue screen and two

black owl silhouettes to the left and right of the fixation cross (±30°). Then the cross

disappeared and after a 200ms gap period the cue (the visual cue filled one of the owl

silhouettes; the acoustic cue was played on one of the loudspeakers ±39° in front of the

participant) was presented for 1000ms. Subsequently, the fixation cross appeared again

for 500ms. After each block children were shown a picture of a clock for 5sec, which

graphically depicted how many blocks they had already finished (the clock was filled

subsequently). This was followed by a motivation picture (5sec) with 1 to 8 already

collected cartoon mice. A pause-signal appeared after 120 trials indicating that children

could take a short break. The length of the break was determined by the children.

Before the actual experiment, a calibration block was run, in which the children

were requested to look towards a visual cue left, right, above and below the fixation

cross. These calibration saccades were later used to build template eye-movements for

EEG data analysis.


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Equipment and Oculomotor Recordings


Inc.). The visual cue (picture of an owl) had been painted by hand and was (25 x 25mm).

The fixation cross was generated within Presentation and was (10 x 12mm).

Presentation was run on a PC (Dell precision 390 with Intel® Core™ 2CPU 2.13Hz-

Prozessor with 2GB Ram operating system) and the stimuli were presented on a monitor

with 27.5” (1920x1200 pixels) resolution (HANNS. G HG 281 DJ). The acoustic cue (owl

call) was downloaded from an open internet database (http://www.findsounds.com) and

modified with Adobe Audition 2.0® in order to last 1000ms. The sound was presented at

67dB through external loudspeakers (Creative Inspire 280 2.0) placed at an angle of 39°

before the participant.

EEG-recordings were made with a high-density 257-channel system from EGI

Electrical Geodesics Inc. using NetStaionTM12 (run on a Mac OSX with 1.25GHz

PowerPC G4 processor and 1GB DDR SD RQM). The sampling rate was 250Hz using

an online filter of 100Hz lowpass and 0.1Hz highpass.

Data and statistical analysis

Questionnaire values (Handedness in %, positive values indicating right-

handedness; CPM raw values with a maximum of 36; DCL-ADHS and DCL-SSV

questionnaire in stanine values; APD questionnaire in raw values ranging from 1 to 4,

where 1 represents “few problems” and 4 represents “many problems”) were compared

between groups using Statistica (StatSoft, Inc., 2003). T-tests, Mann-Whitney-U tests or

Chi2-Test were chosen after testing for normal distribution with the Kolmogorov Smirnov

Test (Lillifors adaption).

EEG data were analysed with the software package BESA (Brain Electrical

Analysis, version 5.3.4, MEGIS Software GmbH, Gräfelfing, Germany). Bad channels

were identified and interpolated for each individual EEG data set. For the identification of

eye movements, data were notch filtered at 50Hz, low-pass filtered at 8Hz (12dB/octave

zerophase) and high-pass filtered at 0.5Hz (6dB/octave forward). Data were manually

scored for saccade onset (saccadic reaction time, SRT) and correctness. Epochs

containing saccades with latencies <80ms or eye movements in the 100ms time window

before stimulus onset were discarded. Eye movement artefacts (blinks and saccades)

were then systematically removed using an algorithm implemented in BESA (Berg &

Scherg, 1994; Lins, et al., 1993).

Saccadic correctness and SRTs were statistically probed using analyses of

variance (ANOVAs), T-tests, Mann-Whitney-U tests or sign tests after testing for normal


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distribution with the Kolmogorov Smirnov Test (Lillifors adaption) using Statistica. Post-

hoc testing of significant interactions was done using the Tukey test. Age was introduced

as a covariate in the ANOVA when the correlation between age and the dependant

variable (Bravais Pearson correlation test or Spearman Rank test) became significant.

In order to investigate inhibition skills in children with and without ADHD

response-locked averages of correct saccades (-500 until 900ms after response;

baseline -500 to -400ms) were generated for each condition with all filters turned off

except the notch filter. Averaged epochs were then filtered between 1Hz (6dB/octave

forward) and 30Hz (24dB/octave zerophase). In order to reduce the number of statistical

tests, data were resampled to 83.3Hz using Matlab (Version 7.5.0.342 (R2007b)). In

order to objectively identify time-windows of interest, non-parametric cluster-based

analyses of EEG sensor data were performed using FieldTrip, an open-source signal

processing toolbox for Matlab (Donders Institute for Brain, Cognition and Behaviour,

Radboud University Nijmegen, The Netherlands. http://www.ru.nl/neuroimaging/fieldtrip).

As response inhibition takes place before the onset of a saccade and in accord with

already existing findings (Clementz, et al., 2001; McDowell, et al., 2005), data analysis

was carried out for the time-windows -156ms until -24ms before response. In order to

objectively determine time-windows, during which brain activity of ADHD and control

children would differ, the difference waveforms anti- minus prosaccades waveforms were

compared between groups using independent t-tests. Cluster α was set at 0.1.

Additionally anti- and prosaccades were compared via dependant t-tests (cluster α=0.05)

within groups. In order to prevent chance-findings, data were re-shuffled 1000 times

using a cluster-based Monte-Carlo randomization. This method effectively controls for

multiple comparisons (Maris & Oostenveld, 2007). Clusters were defined as significant

when the probability of observing larger effects in the shuffled data was below 5%.

Significant interactions (i.e. in this case significant group difference of anti- minus

prosaccades equals an interaction group (ADHD, Control) x condition (anti-,

prosaccades)) were further probed with a post-hoc test, comparing mean amplitudes of

pro- and antisaccades within groups and individual conditions (anti-, prosaccades)

between groups for statistically significant electrode clusters. Additionally grand averages

of anti- minus prosaccade difference waveforms, as well as grand averages of individual

conditions (anti-, prosaccades) were computed per group. In time-windows of statistical

significance, the underlying brain activity of the grand average difference waveforms

(ADHD (anti- minus prosaccade) minus Control (anti- minus prosaccade)) was modelled

using CLARA (realistic head-model approximation for ages 10-12 years). The result of

the CLARA image only reveals brain regions that differ in activation between two groups.


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They do not allow the interpretation of the direction of the effect, i.e. which condition

(anti- or prosaccades) led to more activity. Thus, source montages (regional dipoles)

were generated based on the CLARA results. These source montages were then applied

to the individual conditions (e.g. anti- minus prosaccades, antisaccades or prosaccades

within control children and children with ADHD) and the mean magnitude in the time-

windows of interest was extracted.

2.3.4 Results

Sample characteristics

Groups did not differ in age, gender distribution, handedness or intelligence

scores (table 4). Children with and without ADHD had hearing levels of 20dB or better in

each ear for all measured frequencies.

Table 4: Demographic characteristics ADHD (n=16) Control (n=16) test t/Z/χ2 FG p

Age [in month] 129.38 ±22.47 122.63 ±20.71 t-test 0.884 30 0.384 Gender (male : female) 14:2 13:3 Chi 0.240 1 0.626 Handedness [in %] 93.75 ±8.06 65 ±60.22 MWU 1.300 30 0.194 CPM [raw scores] 30.88 ±3.16 32.19 ±3.37 MWU -1.489 30 0.137

Children with ADHD had higher values than control children for all three subscales

of the ADHD questionnaire. Further, groups differed on the subscale oppositional

aggressive behaviour of the conduct disorder questionnaire (table 5).


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Table 5: Parental ratings of ADHD/APD symptoms ADHD Control

Symptoms Subscale n n test Z FG p Inattention 15 7.33 ±0.98 16 4.56 ±1.67 MWU 4.091 29 0.000 Hyperactivity 15 7.20 ±1.21 16 4.06 ±2.93 MWU 3.320 29 0.001 ADHD Impulsivity 15 7.27 ±2.02 16 4.44 ±2.94 MWU 2.965 29 0.003 Oppositional aggressive behaviour 15 7.27 ±1.75 16 5.13 ±2.45 MWU 2.510 29 0.012

Conduct Disorder Dissocial

aggressive behaviour 15 6.53 ±2.45 16 5.06 ±2.62 MWU 1.957 29 0.050

Speech Perception 16 1.51 ±0.67 16 1.21 ±0.26 MWU 1.432 30 0.152 Auditory Discrimination 16 1.30 ±0.64 16 1.16 ±0.2 MWU 0.094 30 0.925 Sound Localisation 16 1.06 ±0.38 16 1.04 ±0.08 MWU 0.528 30 0.598 Hearing in background noise

16 1.71 ±0.77 16 1.22 ±0.2 MWU 2.506 30 0.012

Auditory Memory 16 1.70 ±0.74 16 1.29 ±0.3 MWU 2.205 30 0.027

APD

Auditory Hypersensitivity 16 2.40 ±0.87 15 2.34 ±0.82 MWU 0.217 29 0.828

Visual experiment

Correctness of saccades

As age and saccade correctness were correlated (table 6), age [in months] was

used as continuous predictor in the ANOVA. Besides the main effects group (more

correct saccades in control children than in children with ADHD: F(1,27)=19.159, p<.001)

and condition (more correct saccades in the pro- than in the antisaccade condition:

F(27)=29.761, p<.001) the interaction condition x group was significant (F(1,27)=6.542,

p<.05). Children with ADHD made fewer correct antisaccades than control children

(p<.01), while groups did not differ regarding prosaccades. Within groups more correct

prosaccades were generated than antisaccades (ADHD: p<.001; Controls: p<.001, table

7).

Table 6: Correlation of correct reactions [in %] with age [in months] in the visual experiment Age [in month] correlation with r(X.Y) p All Antisaccades 0.681 0.000 Prosaccades 0.586 0.001 ADHD Antisaccades 0.700 0.004 Prosaccades 0.518 0.048 Control Antisaccades 0.891 0.000 Prosaccades 0.570 0.026

Table 7: Mean ± standard deviation [in %] of correct saccades in the visual experiment All children (n=30) ADHD (n=15) Control (n=15)

All 82.61 ±9.20 79.36 ±8.02 85.87 ±9.39 Antisaccades 72.55 ±14.61 67.42 ±12.70 77.68 ±14.99 Prosaccades 93.67 ±5.71 92.56 ±5.27 94.78 ±6.08


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Latency

Latency of antisaccades also correlated with age (table 8). Therefore, age was

used as continuous predictor in the ANOVA.

Table 8: Correlation of latency [in ms] with age [in months] in the visual experiment Age [in month] correlation with r(X.Y) p All Antisaccades -0.482 0.007 Prosaccades -0.118 0.534 ADHD Antisaccades -0.531 0.042 Prosaccades 0.152 0.590 Control Antisaccades -0.687 0.005 Prosaccades -0.067 0.813

The main effect group (F(1,27)=6.182 p<.05) revealed that saccade onset was

later in children with ADHD than in control children. The main effect condition was found

(F(1,27)=44.877, p<.001). Antisaccade onset was later than prosaccade onset. The

interaction condition x group did not reach significance (table 9).

Table 9: Mean ± standard deviation [in ms] of correct saccades in the visual experiment

All children (n=30) ADHD (n=15) Control (n=15)

All Antisaccades

268.64 ±59.67 335.22 ±90.32

289.8 ±71.17 362.63 ±110.88

247.49 ±36.73 307.8 ±54.70

Prosaccades 202.07 ±44.12 216.97 ±52.37 187.17 ±28.55

Brain activity

Group-comparison of the difference waveform anti- minus prosaccades revealed

a negative cluster of 76 electrodes (t(14)=-763.029, p<.05) and a positive cluster of 88

electrodes (t(14)=881.185, p<.05, table 10 and figure 10). Here, a negative cluster

represents a more negative difference anti- minus prosaccades in children with ADHD,

while a positive cluster represents a more positive difference anti- minus prosaccades in

the ADHD compared to the control group.

Table 10 : Mean amplitudes and standard deviations [in µV] in the time window -156 to -24ms before response onset in the visual experiment

Antisaccade Prosaccade Mean SD Mean SD

ADHD -2.00 0.60 -0.46 0.72 Negative Cluster Control -1.54 0.65 -1.51 0.88 ADHD 2.22 0.66 0.51 0.84 Positive Cluster Control 1.19 0.74 1.46 1.01

Post hoc mean amplitudes were compared between and within groups. This

comparison revealed that control children had higher negative amplitudes in the


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prosaccade condition than children with ADHD (t(28)=3.091, p<.01). Groups did not differ

for the positive cluster after bonferroni correction. Additionally, higher activity in the

antisaccade than in the prosaccade condition in both clusters (negative cluster:

Z(15)=3.098, p<.01, positive cluster: Z(15)=3.615, p<.001) within the ADHD group was

found (table 11). Source reconstruction of the difference ADHD minus Control revealed

activity of the sources described in table 12 and displayed in figure 10.

Table 11: Post-hoc results of the interaction group x condition in the visual experiment Negative Cluster

Group Condition test t/Z FG p ADHD vs. Control Antisaccades MWU -0.809 28 0.419 ADHD vs. Control Prosaccades t-test 3.091 28 0.004 ADHD Anti- vs. Prosaccades Sign test 3.098 15 0.002 Control Anti- vs. Prosaccades t-test -0.078 14 0.939

Positive Cluster ADHD vs. Control Antisaccades MWU 0.809 28 0.419 ADHD vs. Control Prosaccades t-test -2.132 28 0.042 ADHD Anti- vs. Prosaccades Sign test 3.615 15 0.000 Control Anti- vs. Prosaccades t-test -0.395 14 0.699

Note: Bold p-values are significant after Bonferroni correction.

Table 12: Sources underlying the difference (ADHD (anti- minus prosaccades) minus control group (anti- minus prosaccades)) in the visual experiment

Talairach

coordinates Mean Amplitude

[in nAm]

x y z Val

[in nAm/cm3] % ADHD Control ADHD vs. Control

Right anterior cingulate cortex 3.5 46.1 2.7 2.3400 100 13.25 16.56 Right insula 31.5 11.1 -4.3 2.0000 85.6 11.39 24.22 Left inferior frontal lobe -24.5 18.1 2.7 0.8628 36.9 4.72 17.31 Right cerebellum anterior lobe 3.5 -58.9 -25.3 0.8620 36.8 21.12 10.72 Right parietal lobe 17.5 -44.9 37.7 0.2465 10.5 15.02 11.69


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Figure 10: Children with ADHD (anti- minus prosaccades) vs. control children (anti- minus prosaccades) during the visual experiment: from left to right: (1) topoplot: top: negative cluster, bottom: positive cluster, (2) mean amplitude of the significant electrode cluster for anti- (dashed line) and prosaccades (continuous line) and for children with ADHD (red) and control children (black), (3) sources of the anti- minus prosaccade group-difference: (a) right anterior cingulate cortex, (b) right insula, (c) left inferior frontal lobe, (d) right cerebellum anterior lobe, (e) right parietal lobe

In the time window -156 to -24ms before response onset a negative cluster

(higher negative amplitudes during anti- than prosaccades) of 104 electrodes (t(14)=-

3035.000, p<.001) and a positive cluster (higher positive amplitudes during anti- than

prosaccades) of 92 electrodes (t(14)=2689.800, p<.001, table 13 and figure 11) were

found in the group of children with ADHD. For control children, a negative cluster of 60

electrodes was also found (t(14)=-637.806, p<.01, table 13 and figure 12), while no

positive cluster reached significance. Source reconstruction of the difference anti- minus

prosaccades within groups revealed activity of the sources described in table 14 and

displayed in figure 11 and 12.

Table 13: Mean amplitudes and standard deviations [in µV] in the time window -156 to -24ms before response onset in the visual experiment

Antisaccade Prosaccade Mean SD Mean SD

ADHD Negative Cluster -1.97 0.59 -0.48 0.73 Positive Cluster 2.29 0.67 0.57 0.85

Control Negative Cluster -1.78 0.84 -1.26 0.72


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Table 14: Sources underlying the difference antisaccades minus prosaccades in the visual experiment

Talairach


[in nAm]

X y z Val

[in nAm/cm3] % Antisaccades Prosaccades ADHD

Right medial frontal gyrus 3.5 46.1 9.7 1.1500 100 7.23 8.87 Left cerebellum posterior lobe -10.5 -58.9 -32.3 0.7491 65.1 15.55 10.33 Left paracentral lobule -3.5 -23.9 44.7 0.4668 40.6 25.84 11.20 Right medial temporal lobe 31.5 -2.9 -11.3 0.2597 22.6 6.64 5.65

Control Right insula 31.5 11.1 -4.3 1.9500 100.0 32.58 12.87 Left insula -31.5 11.1 -4.3 0.7118 36.5 30.75 16.89 Right cerebellum posterior lobe 24.5 -58.9 -32.3 0.6944 35.6 20.01 17.04 Right anterior cingulate cortex 3.5 46.1 2.7 0.5616 28.8 16.87 4.91 Right cuneus 3.5 -72.9 16.7 0.2429 12.4 17.92 17.93

Figure 11: Children with ADHD during the visual experiment; from left to right: (1) topoplot: top: negative cluster, bottom: positive cluster, (2) mean amplitude for significant electrode clusters for anti- (dashed line) and prosaccades (continuous line) (3) sources of the anti- minus prosaccade difference: (a) right medial frontal gyrus, (b) left cerebellum posterior lobe, (c) left paracentral lobule, (d) right medial temporal lobe

Figure 12: Control children during the visual experiment; from left to right: (1) topoplot: negative cluster, (2) mean amplitude for significant electrodes for anti- (dashed line) and prosaccades (continuous line), (3) sources of the anti- minus prosaccade subtraction: (a) right insula, (b) left insula, (c) right cerebellum posterior lobe, (d) right anterior cingulate cortex, (e) right cuneus


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Acoustic experiment

Correctness of saccades

The number of correct saccades correlated with age in the prosaccade condition

and marginally in the antisaccade condition (table 15). Thus, age was introduced as a

continuous predictor in the ANOVA.

Table 15: Correlation of correct reactions [in %] with age [in months] in the acoustic experiment Age [in month] correlation with r(X.Y) p

All Antisaccades Prosaccades

0.345 0.443

0.072 0.018

ADHD Antisaccades Prosaccades

0.195 0.242

0.504 0.405

Control Antisaccades Prosaccades

0.626 0.790

0.017 0.001

The main effect group (F(1,25)=4.307, p<.05) revealed that control children made

more correct saccades than children with ADHD. The main effect condition showed that

antisaccades were less accurate than prosaccades (F(1,25)=6.624, p<.05, table 16).

Table 16: Mean ± standard deviation [in %] of correct saccades in the acoustic experiment

All children (n=28) ADHD (n=14) Control (n=14) All 80.89 ±6.48 79.39 ±6.39 82.39 ±6.43 Antisaccades 74.25 ±8.24 72.24 ±7.37 76.25 ±8.83 Prosaccades 88.11 ±5.36 87.14 ±6.16 89.09 ±4.45

Latency

Age did not correlate with latency in the acoustic experiment (table 17). Therefore,

it was not used as a covariate in further statistics.

Table 17: Correlation of latency [in ms] with age [in months] in the acoustic experiment Age [in month] correlation with r(X.Y) p

All Antisaccades Prosaccades

-0.115 -0.310

0.559 0.109

ADHD Antisaccades Prosaccades

0.004 -0.277

0.988 0.337

Control Antisaccades Prosaccades

-0.218 -0.402

0.455 0.154

No group differences were found for antisaccade or prosaccade latency.

Antisaccade onset was later than prosaccade onset (Z(26)=4.725, p<.001, table 18).


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Table 18: Values represent mean ± standard deviation [in ms] of correct saccade latencies

All children (n=28) ADHD (n=14) Control (n=14) All 369.42 ±137.45 358.17 ±83.73 380.66 ±178.76 Antisaccades 427.20 ±170.10 412.66 ±108.84 441.74 ±218.61 Prosaccades 311.63 ±111.78 303.68 ±65.98 319.57 ±146.50

Brain activity

In the acoustic experiment the group comparison of the difference waveform anti-

minus prosaccades did not result in any significant clusters (figure 13).

Figure 13: Topoplot (t-Values) difference antisaccades minus prosaccades per group elicited by acoustic stimuli – no interaction was found in the time window -156 to -24ms before response onset

In the time window -156 to -24ms before response onset a marginally significant

negative cluster (higher negative amplitudes for anti- than prosaccades, t(13)=-544.286,

p=.064) of 57 electrodes and a significant positive cluster (higher positive amplitudes for

anti- than prosaccades, t(13)=953.064, p<.05, table 19) of 68 electrodes were found in

the group of children with ADHD. Source reconstruction of the difference anti- minus

prosaccades revealed the sources listed in table 20 and displayed in figure 14. In the

control group no significant clusters were found.

Table 19: Mean amplitudes and standard deviations for the positive and negative cluster within the group of children with ADHD in the acoustic experiment

Antisaccade Prosaccade Mean SD Mean SD Negative Cluster -2.44 0.89 -1.18 0.88 Positive Cluster 2.22 0.90 1.05 0.71


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Table 20 : Sources underlying the difference antisaccades minus prosaccades within the group of children with ADHD in the acoustic experiment

Talairach


[in nAm]

x y z Val

[in Am/cm3x10-3] % Antisaccades Prosaccades ADHD Right cerebellum posterior lobe 24.5 -58.9 -32.3 0.4505 100 15.76 21.19 Left precuneus -3.5 -44.9 44.7 0.4412 97.9 31.20 17.37 Left medial temporal lobe -31.5 -2.9 -4.3 0.2943 65.3 11.97 7.19 Right medial temporal lobe 31.5 -16.9 -11.3 0.2549 56.6 22.93 28.21 Right medial frontal gyrus 10.5 46.1 16.7 0.2413 53.6 5.42 6.46

Figure 14: Children with ADHD during the acoustic experiment; from left to right: (1) topoplot: top: negative cluster, bottom: positive cluster, (2) mean amplitude for significant electrode clusters for anti- (dashed line) and prosaccades (continuous line) (3) sources of the anti- minus prosaccade difference: (a) right cerebellum posterior lobe, (b) left precuneus, (c) left medial temporal lobe, (d) right medial temporal lobe and (e) right medial frontal gyrus

2.3.5 Discussion

The present study aimed at investigating, whether response inhibition in children

with ADHD is equally disturbed when it is triggered by visual and acoustic cues. Another

goal was to uncover brain activity underlying differences in performance between

children with and without ADHD. Results of the present study suggest that children with

ADHD are more impaired when response inhibition is cued by visual stimuli than after

acoustic stimuli on the behavioural level. Likewise, groups only differed in brain activity

after visual stimuli. This shall be discussed in more detail in the following.

Behavioural data

Behavioural data of the visual experiment are in line with previous findings on

antisaccade tasks comparing children with and without ADHD (Karatekin, 2007;

Rommelse, et al., 2008). More errors were made during the anti- than the prosaccade

condition, indicating that more errors were made, when reflexive behaviour was to be


- 69 -

inhibited and a volitional antipodal response needed to be generated. This was true for

the control group and for children with ADHD. The interaction group x condition revealed

that children with ADHD made fewer correct antisaccades than control children (Goto, et

al., 2010; Karatekin, 2006; Klein, et al., 2003; Loe, et al., 2009; Mahone, et al., 2009;

Munoz, et al., 2003; O'Driscoll, et al., 2005), whereas no group differences were found

during the prosaccade condition (e.g. O'Driscoll, et al., 2005). This shows that children

with ADHD have more difficulties with response inhibition than control children (Willcutt,

et al., 2005). No group differences in performance were found in a previous antisaccade

study of our group (Goepel, et al., 2011). However, the main difference was that trials

were not presented block-wise as in the present study, but randomly. I.e. with every trial,

children did not know whether to expect a prosaccade or an antisaccade and whether it

would be triggered by a visual or an acoustic cue. Task difficulty in a random

presentation design is thus much higher. This might be the reason, why group

differences were not found in the previous study. They might have been obscured by a

ceiling effect.

Antisaccade latencies were longer than prosaccade latencies. This also reflects

the greater difficulty of the antisaccade task and the fact that more processes are

involved and more actions need to be executed in the antisaccade condition compared to

the control condition (Everling & Fischer, 1998; Forbes & Klein, 1996; Ford, et al., 2005;

Klein, et al., 2003; Munoz, et al., 1998). Further, children with ADHD had longer latencies

than control children. A comparable finding was reported by Munoz and colleagues

(2003) and Klein and colleagues (2003) and has been interpreted as an indicator that

children with ADHD have difficulty in saccade initiation (Rommelse, et al., 2008).

However, no group differences in saccade latency have also been found (O'Driscoll, et

al., 2005; Rothlind, et al., 1991). One reason for this discrepancy might be that latencies

of children with ADHD are more variable and less consistent, which can lead to group

effects in one or the other direction.

During the acoustic experiment both groups made more errors on antisaccade

trials than on prosaccade trials. This demonstrates that acoustic cues – like visual cues –

have a “grasping” effect. As in the visual experiment, children made more errors when

they were asked to inhibit their automatic reaction to look towards the cue. Children with

ADHD made more errors than control children across conditions. However, no group x

condition interaction was found. It thus appears that although children with ADHD have

more difficulties with the acoustic task than control children, they do not specifically have

greater difficulties with behavioural inhibition. In line with the results on visually triggered

saccades, latencies of antisaccades were longer than after prosaccades. No group


- 70 -

effects were found. The higher error rate in children with ADHD on acoustic trials might

be a consequence of their deficit in auditory processing (Riccio & Hynd, 1996). Data of

the APD questionnaire indeed revealed lower scores in children with ADHD for auditory

memory and hearing in background noise.

In conclusion, behavioural results of the visual experiment in the present study

show that children with ADHD are less able than control children to suppress

inappropriate responses and need a longer time to initiate their saccades. When

saccades are cued by acoustic stimuli, children with ADHD make more errors than

control children, but are not specifically more impaired regarding behavioural inhibition.

EEG data

EEG data after visual cues

Groups differed in brain activation of the difference correct anti- minus

prosaccades after visual stimulation. This is equal to an interaction group x condition.

The group difference occurred between -156 and -24ms before saccade onset. This

overlaps with the time-range usually connected to response inhibition (Clementz, et al.,

2001; McDowell, et al., 2005). Post-hoc testing revealed that the interaction on the

sensor level mainly stemmed from activation differences in the prosaccade condition.

Here, control children had higher amplitudes in the prosaccade condition than children

with ADHD, effectively making the difference anti- minus prosaccades smaller in the

control group than in the ADHD group. Accordingly amplitudes of anti- and prosaccades

differed significantly in children with ADHD for both sensor clusters, while no differences

were found for control children. As only correct saccades were analysed, this means that

brain activation in children with ADHD was higher for correct anti- than correct

prosaccades, while brain activation did not differ for control children. Taking into account

that control children performed better than children with ADHD, i.e. they made less

antisaccade/inhibition errors, the EEG sensor space results imply that children with

ADHD needed a clearer distinction between anti- and prosaccades in terms of brain

activation, i.e. higher activation for anti- than prosaccades in order to perform correct

saccades. In control children, smaller activation differences, which did not lead to

statistically significant differences between conditions, seem to have been sufficient. In

other words, children with ADHD had greater activation for response inhibition than

control children for the correct execution of saccades. This could be interpreted as a

compensatory mechanism in children with ADHD. In control children, the neural network

underlying response inhibition seems to be more efficient, thus resulting in less

activation.


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Looking at the source reconstruction of the interaction ((ADHD: anti-pro) minus

(Control: anti-pro)) revealed that five brain regions were involved. The anterior cingulate

cortex (ACC) was most active followed by the insula, the inferior frontal lobe, the

cerebellum and the parietal lobe. Functional Magnetic Resonance Imaging (fMRI) studies

have shown that the ACC is involved in antisaccade tasks (Brown, et al., 2006;

Gaymard, Ploner, et al., 1998; Pierrot-Deseilligny, et al., 2005; Polli, et al., 2005). Activity

in the ACC was higher before correct than before incorrect antisaccades, whereas higher

activity for incorrect antisaccades than prosaccades was found during the response

period. Therefore, the ACC was interpreted to be involved in the signalling of an error

and error monitoring during the antisaccade task (Ford, et al., 2005). The ACC has also

been found to be the relevant structure in studies investigating response inhibition with a

Go/Nogo task (Bokura, et al., 2001), a modified stop-paradigm (Paul-Jordanov, et al.,

2010; Paul, et al., 2007), and a continuous performance task (Fallgatter, et al., 2004).

Thus, the ACC seems to be robustly activated when response inhibition is involved.

Along with the ACC Lerner and colleagues (2009) found the insula to be active in a study

on blinking suppression. The authors concluded that both structures mediate the control

and suppression of physiological urges and play a role in behavioural inhibition. In a

meta-analysis of studies on response inhibition, Dickstein and colleagues (2006) also

revealed the inferior frontal lobe to be active in various studies on response inhibition and

reduced activation in this structure was found in children with ADHD (Rubia, et al., 2005).

Further, it has been shown that patients with inferior frontal lesions have specific deficits

with response inhibition and working memory (Aron, Fletcher, Bullmore, Sahakian, &

Robbins, 2003; Clark, et al., 2007). The cerebellum is classically linked to motor fine-

tuning and timing of motor actions (Ivry, Spencer, Zelaznik, & Diedrichsen, 2002). In the

context of saccades it seems to be important for steering and stopping saccades, thus

determining their accuracy (Jenkinson & Miall, 2010). The relative volume of the vermis

was related to saccadic accuracy in humans. Larger volumes were associated with

greater accuracy during a prosaccade task (Ettinger, et al., 2002). The cerebellum is also

increasingly becoming of interest in ADHD research (Schneider, Retz, Coogan, Thome,

& Rosler, 2006) because of its connection to the frontal lobe. In mice it was shown that

the cerebellum can influence the dopamine outflow in the frontal cortex (Mittleman,

Goldowitz, Heck, & Blaha, 2008). The parietal lobe also has connections to frontal motor

regions (Kulubekova & McDowell, 2008) and is more active during voluntary than

reflexives saccades (Dyckman, et al., 2007; Ettinger, et al., 2008; Ford, et al., 2005;

Kulubekova & McDowell, 2008). Thus, the parietal lobe also seems to be involved in the

voluntary control of actions.


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Looking at activation differences between groups in these structures, it appears

that control children had more activity in the ACC, in the insula and in the inferior frontal

lobe. These structures seem to be responsible for better inhibition performance in control

children. This finding also goes in line with the hypothesis that children with ADHD have

a frontal hypoactivation (Dickstein, et al., 2006). In contrast, children with ADHD had

higher activity in the cerebellum and in the parietal lobe. This could again reflect a

compensatory mechanism. The absolute values should not be interpreted, as the source

models were computed on grand average waveforms across the children within each

group. Still, they give an idea of how the activity pattern differs between groups. It should

be noted at this point that the source structure as described above underlies the

interaction on sensor level. Thus, although on sensor level only children with ADHD had

higher activation for anti- than prosaccades, and although the group-difference was only

significant for prosaccades, the underlying activity pattern leading to this interaction on

sensor level suggests stronger activation during inhibition in frontal regions in control

children, while children with ADHD show stronger activation in the cerebellum and the

parietal cortex.

Looking for inhibition within group (without the requisite that groups need to differ)

revealed two sensor cluster with higher activity for anti- than prosaccades in the ADHD

group and one sensor cluster in the control group. Source reconstruction of this effect

within the ADHD group revealed four active sources. The medial frontal gyrus was most

active, followed by the posterior lobe of the cerebellum, the paracentral lobule and the

medial temporal lobe. In control children, also four structures were involved. Bilateral

insulae were most active, followed by the posterior lobe of the cerebellum, the ACC and

the cuneus. The medial frontal gyrus was more active during prosaccades than during

antisaccades in children with ADHD. The ACC is in close proximity to the medial frontal

gyrus and was clearly more active during antisaccades than during prosaccades in

control children. This is in line with the notion that the ACC is strongly involved in

successful response inhibition in control children. The frontal lobe in general (more

specifically the dorsolateral prefrontal cortex (DLPFC), the frontal eye fields (FEF) and

the supplementary eye fields (SEF)) is involved in the control of voluntary saccades. The

DLPFC is involved in the inhibition of prosaccades and the generation and of

antisaccades, memory-guided saccades and in decision processes (Pierrot-Deseilligny,

et al., 2004). Several lesion studies showed that error percentage in the antisaccade task

is increased after DLPFC lesion (Pierrot-Deseilligny, et al., 2003; Pierrot-Deseilligny, et

al., 1991; Ploner, et al., 2005). Further, it was shown that children with ADHD have

reduced brain activation in the right inferior prefrontal cortex reaching deep into the


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insula during successful motor response inhibition compared to control subjects (Rubia,

et al., 2005). Thus, the fact that activation in the medial frontal cortex was bigger during

pro- than antisaccades in children with ADHD is in line with the notion of a hypoactivation

in structures relevant for inhibition (Rubia, et al., 1999).

Both groups had higher activation during anti- than prosaccades in the

cerebellum. Analogue to the source reconstruction of the group interaction, children with

ADHD had a slightly larger activation in the cerebellum than control children. This might

be part of a compensatory mechanism to increase saccadic accuracy. Only children with

ADHD had greater activation in the paracentral lobule during anti- than during

prosaccades. The paracentral lobule includes the medial part of the superior frontal

gyrus. The posterior part is part of the parietal lobe, whereas the anterior section is part

of the frontal lobe and is often described as the supplementary motor area. Successful

antisaccades are linked to activity in the pre-supplementary motor area (Fitzgerald, et al.,

2008). Tamm and colleagues (2004) also revealed activation in the supplementary motor

area in a Go/Nogo task in children with ADHD. The authors concluded that this is

probably related to motor planning and response execution. Similarly, in the meta-

analysis of neuroimaging studies on response inhibition it was demonstrated that in

children with ADHD the paracentral lobule was active (Dickstein, et al., 2006). As this

area was not activated in control children and as only correct saccades were analysed,

one might assume that the present activation pattern in children with ADHD reflects a

compensatory mechanism to overcome difficulties in response inhibition.

According to the source reconstruction of the group interaction, the insula was

found to be more active during anti- than prosaccades only in control children. As

mentioned above, the insula has been shown to be active in a study on blinking

suppression in control children (Lerner, et al., 2009) and also in the meta analysis on

response inhibition (Dickstein, et al., 2006). Thus, in control children, the insula seems to

be part of a cortical network responsible for response inhibition. In contrast, children with

ADHD had higher activation in the right medial temporal lobe during anti- than

prosaccades. As the right temporal lobe has been linked to spatial memory (Nunn,

Graydon, Polkey, & Morris, 1999), it is conceivable that the antisaccade task places a

higher demand on spatial memory in children with ADHD. This might compensating for

the lack of activity in the frontal inhibition system.

The cuneus was also activated in control children. However, its activation was

nearly equal for anti- and prosaccades. Thus, it is unlikely that it plays a relevant role for

response inhibition.


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EEG data after acoustic cues

No group effects were found in brain activation after acoustic cues. This is in line

with behavioural findings, where control children and children with ADHD also did not

differ in response inhibition performance. Neither were there any significant brain

activation differences between anti- and prosaccades within the control group. It might

have been the case that the task was easier for control children. This is supported by

lower error rates compared to children with ADHD. Consequently, it might have been the

case that activation of structures involved in saccade generation and response inhibition

were not activated as strongly by antisaccades as in the visual experiment.

Consequently, the comparison of EEG data during anti- and prosaccades did not reach

statistical significance. Within the ADHD group, two electrode clusters with higher

amplitudes for anti- than prosaccades were found. Source reconstruction of this effect

revealed four underlying brain-regions: the cerebellum was most active, followed by the

precuneus, the medial temporal lobe and the medial frontal gyrus. Similar to the visual

experiment higher activity in pro- compared to antisaccades was found in the medial

frontal gyrus. Therefore the theory of frontal hypoactivity during inhibition may extend to

auditory modality, as well. Also comparable to the visual experiment was the finding of

higher activity during anti- than prosaccades in the precuneus, which borders with the

paracentral lobule. This activation pattern in the parietal cortex in children with ADHD

might compensate for the frontal under-activation. However, the direction of the effect in

the other structures was reversed compared to the visual experiment, although the

structures themselves were the same. In the acoustic experiment, activity in the

cerebellum was higher during pro- than antisaccades. In the visual experiment it was

higher during anti- than prosaccades. The same was true for the right medial temporal

lobe.

Taken together, the cerebellum and the right medial temporal lobe do not seem to

be part of a compensatory inhibition network when acoustic stimuli trigger saccades as

seen in the visual experiment. It is more likely that task demand is different when

saccades are triggered by acoustic cues compared to visual cues. Latencies of saccades

were approximately 100ms longer when saccades were triggered by acoustic cues. This

was true for both, anti- and prosaccades. Thus, it appears that the task was more

demanding when acoustic cues were used resulting in a longer reaction time to initiate

pro- and antisaccades. This is conceivable, as for the correct execution of saccades the

position of the acoustic cue, which is believed to be represented in a craniotopic, i.e.

head-related reference system needs to be re-mapped to a retinotopic reference system

before the generation of acoustically triggered saccades (Jay & Sparks, 1990;


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Zambarbieri, et al., 1982). Given that also the execution of prosaccades is more difficult

when cues are acoustic, this could explain, why in the same structures that were more

active during antisaccades in the visual experiment were more active during

prosaccades in the acoustic experiment. Interestingly, the left medial temporal lobe was

more active during anti- than prosaccades. In the study of Tamm and colleagues (2004)

children with ADHD had higher activity in Nogo than in Go stimuli in the left

middle/inferior temporal gyrus. The authors suggested that this may reflect the adoption

of verbally mediated strategies to rehearse or hold the instruction in mind to enhance

task performance. Thus, this could again reflect a compensatory mechanism in children

with ADHD to enhance inhibition performance by internal rehearsal rather than the

activation of a control-like inhibition network. Thus it appears that the medial temporal

lobes fulfil different purposes in the context of visually and acoustically triggered

saccades.

In summary, interpreting the behavioural results together with the source-

reconstruction results implies that children with ADHD have a deficit in response

inhibition when it is cued by visual cues. This is accompanied by frontal hypoactivation

and activation of a compensatory posterior-cerebellar network. Children with ADHD do

not seem to have an inhibition deficit when acoustic cues are used. However, the

underlying source structure of response inhibition in ADHD suggests that the same

structures are involved when acoustic and visual cues are used, while the functionality of

the structures is not always the same. The present study was the first to investigate

response inhibition in a cross-modal block-design. Although children with ADHD do seem

to have greater difficulties generating saccades when acoustic cues are used, they do

not have an inhibition deficit. Children with ADHD only had difficulties with response

inhibition after visual cues. This supports the idea that deficient response inhibition is not

a cross-modal phenomenon.

General discussion

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III. General discussion

Inhibition is an evolutionary-biological principle to prevent situations inadequate

reactions, in order to activate operation relevant responses, which is linked to structural

and functional brain maturation. Patients with ADHD often are conspicuous due to

inappropriate behaviour. Their impulsivity – one of the three core deficits – is manifested

as difficulty in delaying responses, blurting out answers, having difficulties to wait,

interrupting others or grabbing objects from others (American Psychiatric Association,

2000).

Inhibition control is measured with SST, CPT and other Go/Nogo paradigms and

the majority of functional image studies identified reduced activation in frontal regions,

ACC and striatum in children with ADHD compared to control participants (Cherkasova &

Hechtman, 2009). This hypo-frontality was found in fMRI studies applying prior Go/Nogo

tasks (e.g. Booth, et al., 2005; Durston, et al., 2003; Rubia, et al., 1999; Smith, Taylor,

Brammer, Toone, & Rubia, 2006; Tamm, et al., 2004). The typical Go/Nogo task makes

use of visual stimuli and disregards acoustic cues and their everyday life relevance.

Thus, aim of the thesis was to delineate possible brain areas – involved in

inhibition control of reactions to visual and acoustic suddenly arising stimuli – revealing

altered activity patterns in children with ADHD and, thus, to answer the question whether

children with ADHD are handicapped in suppression of reflexive responds to acoustic

cues in order to advance the basics for a differential diagnostic. As a measuring

instrument the antisaccade task was chosen because the task as well as the underlying

brain activity are well investigated.

Bringing together the results of these saccade studies presented here indicate

that the degree to which children with ADHD are impaired in inhibition control depends

on the complexity of the task. This was varied by the amount of stimulus x response

compatibility (one versus two modalities) and the kind of design (blocked versus

random). In doing so, the integration of two modalities alleviated whereas the random

design increased the task demand. Judging the complexity by the mean error rate of

both groups (as far as possible) – on the behavioural level, children with ADHD showed

inhibition impairment only at the second least complexity experiment, i.e. during the

blocked visual experiment (Study III). On the physiological level group differences were

apparent in the previously mentioned condition and at the second highest extensive

condition, i.e. during the randomly presented acoustic experiment (Study II). Thus, it is

assumed that in the two other conditions (visual random and acoustic blocked condition)

floor and ceiling effects caused the missing incidence of group differences: one condition

General discussion

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was too difficult and the other too easy. This and further consideration shall be discussed

in more detail below.

To make sure that the severity of the mono modality condition (visual condition) is

comparable to the cross-modality condition (acoustic condition), the eye tracker Study I

(a pilot study) tested the experiment with a control group of children comparing pro- and

antisaccade performance elicited by visual and acoustic stimuli, something that has

never been done before. The results showed that the saccade performances are

comparable: in both conditions the typical anti-/prosaccade pattern emerged (increased

error rate in anti- compared to prosaccades). Additionally, the correlations between the

two conditions within the error rate and within latency have strengthened the similarity.

Based on these findings it was assumed that both modality conditions measured

inhibition control and the comparability of both conditions could be embraced.

Furthermore, important differences between both conditions were found: primary,

acoustically cued saccades had elongated SRTs compared with visually triggered

saccades, which could have been caused by the remapping from the craniotopic, i.e.

head-related reference system to the retinotopic reference system (Yao & Peck, 1997).

In the visual condition, a gap effect (i.e. shorter reaction time in the gap than the overlap

condition) and longer reaction times in anti- compared to prosaccades were found but

not so in the acoustic condition, whereas an eccentricity effect (i.e. faster saccades to

more laterally presented cues) was only presented in the acoustic experiment. That is,

the influence of different task conditions on the behavioural performance depended on

the cue modality.

The interaction condition x modality was marginally significant and showed that

the anti-/prosaccade difference was smaller pronounced in the acoustic than in the visual

condition due to more pro- and fewer antisaccade errors during acoustically rather than

during visually elicited responses. The advantage in the acoustic antisaccade condition

compared with the visually triggered antisaccades was explained by a relative benefit

from the remapping process, reducing the immediate inhibitory demand on the system

for children, whose executive system is not completely developed yet. It seems that

acoustically elicited saccades were less prone to be influenced by impulsivity. This

interpretation was deduced from the same behavioural pattern, which Schooler and

colleagues (2008) found in adult schizophrenia patients during a blocked antisaccade

task.

As a second step, Study II was carried out – the comparison of children with and

without ADHD. The measuring instrument of Study I (eye tracker) was replaced by an

General discussion

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EEG because neural activity shows a higher sensitivity than pure behavioural data. Due

to the interest in inhibition control in particular, only the antisaccade performance was

analyzed. It was hypothesized that children with ADHD – described to have difficulties in

acoustic tasks (Riccio & Hynd, 1996) – have, compared to control subjects, a higher

error rate not only in the visual but also in the acoustic antisaccade experiment. Study II,

however, revealed a completely different pattern: although children with ADHD

generated slightly fewer correct antisaccades than control subjects in the acoustic

condition, no significant differences were found between the children groups in the

antisaccade task performance. The high difficulty of the task might be an explanation for

this – the demand of the task was too high for both groups. On the physiological level,

children with ADHD generated a higher brain activity in the MFC between -230 and -

120ms and in the left-hemispheric TAC between -112 and 0ms before saccade onset

(time windows in which inhibition processes were found; McDowell, et al., 2005) during

the acoustic condition. I.e. because of the slightly higher error rate in children with ADHD

during the acoustic antisaccade task it was assumed that this task is more difficult for

them compared to the control children. Therefore, group differences in the brain activity

were not surprising in the task that seems to have differential demands for the children

groups.

This increased activity on the physiological level has to be explained. Based on

functional studies, Johnston and Everling (2008) assumed that the preparation for an

antisaccade leads to a suppression of preparatory and stimulus-related activities in the

FEF and SC. Top-down signals required for this could be elicited in the DLPFC, the ACC

or the SEF, whereas the ACC is recruited when task demands increase (Johnston,

Levin, Koval, & Everling, 2007). Due to the random experimental design, in which

supplementary task switching between pro- and antisaccades is needed (O'Driscoll, et

al., 2005), the task is very demanding. Thus, it was assumed that both, control subjects

and children with ADHD, had the ability to suppress the activity in FEF and SC. However,

in children with ADHD this suppression required a higher activity of the ACC – mapped in

higher activity of the MFC compared to control children. The ACC – involved in

antisaccade tasks (Brown, et al., 2006; Gaymard, Ploner, et al., 1998; Pierrot-

Deseilligny, et al., 2005; Polli, et al., 2005) – seems to be responsible for error monitoring

and signalling (higher activity before correct than incorrect antisaccades; Ford, et al.,

2005). Supposed the task was more difficult for children with than without ADHD, the

higher activity in the MFC is necessary to generate correct demanding cross-modal

antisaccades. Similarly the higher activity in the left TAC was interpreted as a

compensatory mechanism in children with ADHD, suggesting the possibility of an internal

General discussion

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rehearsal of the instruction mechanism (Tamm, et al., 2004), although the underlying

mechanisms are not fully understood.

Summing it up to this point – children with ADHD presented the same behavioural

inhibition pattern in a random task design as control subjects but in the acoustic condition

it seemed only possible because of a higher fronto-temporal activity – interpretable as a

compensatory mechanism.

Based on the results of Study II, it was assumed that the random design was very

difficult for both children groups. Therefore, for Study III a new paradigm which should

differentiate adequately between the groups was created in a – compared to the random

paradigm easier – block design. This design change was motivated by former studies

with blocked designs, which revealed group differences in visual inhibition tasks (e.g.

Klein, et al., 2003; Munoz, et al., 2003). Additionally, the conditions were diminished – no

overlap condition and no different eccentricity conditions were inserted – to include more

trials per condition, given that error rates are the key measure of inhibitory skill on this

task and that RTs are confounded with the number of trials on which they are based in

comparisons between groups with widely different accuracy rates (Everling & Fischer,

1998). Finally, in order to make the experiment more attractive for children, stimuli were

changed to owl calls and cartoons. The visual experiment results of Study III next to the

condition main effects in error rate and latency (more correct saccades and longer

latencies in the pro- than in the antisaccade condition) revealed that children with ADHD

made fewer correct antisaccades than control children and showed elongated latency in

pro- as well as antisaccades – a result that confirms the inhibition deficit in children with

ADHD and replicates results of former studies (Rommelse, et al., 2008).

Looking at the source reconstruction, several brain areas were active during a

time window between -156 and -24ms before response onset, in which inhibition

processes take place. The group x condition interaction showed higher activation

differences (anti- minus prosaccades) for control children in the ACC, the insula and the

inferior frontal lobe, whereas smaller activity was found in the cerebellum anterior lobe

and the parietal lobe. Thus, it seems that children with ADHD exhibited a frontal

hypoactivation but were able to reach a correct saccade reaction with the support of a

compensatory posterior-cerebellar network. This assumption was enhanced by findings

in the within-group comparisons: here, children with ADHD showed less medial frontal

activations and a higher activation in the cerebellum, the parietal and temporal lobe in

the anti- compared to the prosaccade condition, whereas in control children higher

General discussion

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frontal-cerebellar activation in the anti- than in the prosaccade condition was found,

strengthening the assumption of frontal inhibition mechanism.

In the acoustic experiment next to the simplification through the block design the

remapping process provided additional time resulting in no differences between children

groups. Children with ADHD did not seem to benefit from this additional time, because it

was revealed that children with ADHD made more errors than control children in both

conditions – interpreted as a consequence of their deficit in auditory processing (Riccio,

et al., 1996), although both children groups were constricted by higher error rates and

elongates latencies in the anti- compared to the prosaccade condition. In other words,

both groups showed inhibition deficits but the expected enhanced handicap in children

with ADHD during antisaccades was lacking, rather a general deficit in both conditions

was apparent. Thus, it seemed that the task was easier for children in the control group.

In line with the behavioural results on source level no group x condition interaction

was found and also no condition effect within the control group. Because of the easiness

of the task it might have been the case that activation of structures involved in volitional

saccade generation and response inhibition were not activated as strongly by

antisaccades as in the visual experiment. Children with ADHD, however, showed higher

activation in the medial frontal gyrus during pro- compared to antisaccade, extending the

theory of frontal hypoactivity to the auditory modality. Higher and therefore compensatory

activity was found in the precuneus and left medial temporal lobe during anti- compared

to prosaccades. Not expected was the antisaccade related higher activity in the right

cerebellum and the right temporal lobe because of an opposite pattern in the visual

experiment.

Summing it up to this point – children with ADHD presented in the visual

experiment compared to control children an inhibition deficit which was accompanied by

frontal hypoactivation and activation of a compensatory posterior-cerebellar network. In

contrast when cues were acoustic, children with ADHD did not seem to have an inhibition

deficit whereas the underlying structures resembled the structures of the visual

experiment, while the functionality of the structures was not always the same.

To compare the findings of Study II and III it has to be mentioned that design and

data analyses in both studies were different: both, Study II and III investigated pro- and

antisaccades but first in a random (very difficult) and second in a blocked (more easy)

design, perhaps causing different influences of attention and motivation. The cue

property in Study II was compared to Study III very simplistic (pure sinus tone and yellow

General discussion

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dot vs. owl call and owl cartoon) and perhaps responsible for different brain area

activations. Furthermore, the angle of visual and acoustic cue was more comparable in

Study III than in Study II. Study II included gap and overlap conditions, whereas in Study

III only gap conditions were presented. Study II compared both groups in the antisaccade

conditions whereas Study III considered anti- as well as prosaccades; therefore, direct

group comparisons within the different conditions were not considered anymore in order

to accomplish an interaction analysis. Source reconstruction was done in Study II with an

established 23-source-model and in Study III sources were searched with CLARA,

perhaps resulting in different sensitivity dispersions.

In the following section both studies are compared also with regard to implications

for further research – based on the previously described background the analogy of both

findings should be considered carefully.

On the behavioural level it was illustrated that more correct antisaccades could be

generated in the blocked design compared to the random design – confirming the

facilitation by lapse of task switching. A further group differentiation was possible in the

visual antisaccade performance when the task was presented in a blocked manner:

children with ADHD made more errors and were slower during correct antisaccades,

inferring that the visual blocked experiment is the most differentiating condition.

Although it was not analysed, it seems that in Study II the simplification by a less

strong stimulus x response compatibility (acoustic condition) was higher for control

children than for children with ADHD. When comparing the correct response rate per

condition, children with ADHD showed in the visual condition 50.52 ±16.54% and in the

acoustic condition 57.20 ±12.88% correct antisaccades, whereas control children

generated 48.84 ±20.53% visual and 65.38 ±12.32% acoustic correct antisaccades –

replicating the pattern of Study I.

However, considering the behavioural performance in Study III it seems that here

children with ADHD compared to control subjects benefited more from the additional time

(slightly higher correct performance in the acoustic than in the visual condition while

there was no difference in the control group) – which means that results were contrary to

those of Study II. In comparison to the study of Schooler and colleagues (2008) –

mentioning that two blocked studies were matched now – children with ADHD and adult

patients with schizophrenia were similar. This resemblance is eminent because patients

with schizophrenia reveal comparable performance patterns in visual elicited saccades

like patients with ADHD (Hutton & Ettinger, 2006). It seems that the advantage in the

control children group in the acoustic over the visual antisaccade condition – seen in the

random design – was lacking in the blocked design. Perhaps this benefit reverts in

General discussion

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adulthood in support of a better performance in the visual condition (Schooler, et al.,

2008).

The comparison between performance in visual, acoustic and also combined

tasks during different designs in further investigations is worthwhile due to accentuating

loss-making and compensatory mechanisms in patient groups: thus, at nearly no time in

life an inhibition is requires on a pure stimulus. A siren for example triggers always a

visual search for an ambulance parallel to the auditory orientation. Combined tasks are

necessary for finding an adequate treatment for children with ADHD to adjust their

performance in the different modalities to that of normally developed children.

However, it seems that in the random design comparable behavioural

performances were generated during the acoustic condition based on fronto-

hyperactivation whereas in the blocked design a fronto-hypoactivity was present in both

modality conditions. Below structures which overlapped in both studies and their

functions are discussed.

The frontal lobe: Study II revealed a frontal hyperactivation in children with ADHD

compared to control subjects differing from previous reports of reduced activation during

Go/Nogo tasks, SSTs, CPTs or Stroop tests (Cherkasova & Hechtman, 2009) and also

from the results of Study III. Although it seems that there is consistence in current ADHD

literature about frontal hypoactivity reflecting a decrease in the intensity of activation in

this region, authors indicated to be cautious in making rapid inferences. For example,

Dickstein and colleagues (2006) pointed out that the altered activity pattern could also

reflect decreases in the spatial extent of activations, more spatial dispersion of

activations, decreases in functional connectivity or more statistical noise. This noise is

perhaps conditional on factors such as more variable responses in or greater motions in

patients. Additionally, it has to be mentioned that some regions show a greater activation

for patients compared to controls as seen in Study III, suggesting that ADHD is not

purely constituted by hypofunction and accentuating compensatory mechanism

(Dickstein, et al., 2006).

Furthermore, some studies conform to results of Study II (Schulz, et al., 2004;

Schulz, et al., 2005; Vaidya, et al., 1998), therefore, indicating that the participants of

Schulz and colleagues (2004; 2005) were older compared to the other studies, which

could have had an influence on the results. In a Go/Nogo task Schulz and colleagues

(2004) investigated adolescents with a history of ADHD with fMRI design and found

compared to control subjects higher frontal activations. Additionally an inverse relation

was found between the anterior cingulate gyrus activity and the performance on the

Go/Nogo task of both groups and between the frontopolar activation and the behavioural

General discussion

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performance of the control group: the more difficult in inhibiting the prepotent response

the greater the brain activation. Vaidya and colleagues (1998) assumed that the

inhibition task including selection, online maintenance of stimuli in working memory, and

switching attentional sets (Go and Nogo trials) and therefore the widespread and greater

activation in children with ADHD may reflect greater inhibitory efforts.

While results of Study III are in line with the hypothesis of frontal hypoactivation in

children with ADHD, the findings of Study II are conflictive to the state of inhibition

research (established on Go/Nogo tasks). Therefore, the question arises whether a

comparison between inhibition performances and their underlying brain activity during

Go/Nogo and antisaccade tasks should be allowed. Both tasks involve an inhibition effort

but the antisaccade task requests an additional volitional eye movement. Recent studies

tend to investigate both conflicting responses separately (Brown, et al., 2007; Ettinger, et

al., 2008; Reuter, et al., 2009) but without any consistent findings. Thus, further research

is essential to clarify the nature of both findings of decreased and increased activities

associated with ADHD.

The temporal lobe – as a second overlapping structure – is a region that is

involved in more complex and integrative tasks and develops later (Gogtay, et al., 2004).

Independent of the design in children with ADHD higher activity was found in the left

temporal lobe during acoustically elicited antisaccades. It was assumed that this activity

reflects a kind of compensatory mechanism. This mechanism could be an instruction

holding in mind (Tamm, et al., 2004) i.e. to strengthen the working memory. This is in line

with Barkley’s (1997) theory, hypothesizing that the primary deficit in ADHD is a

behavioural disinhibition, and that deficits in working memory, self-regulation,

reconstitution and internalization of speech are secondary to the behavioural inhibition

deficit. Therefore, both studies strengthen the assumption that in inhibition tasks also the

working memory is involved and should be respected. Children with ADHD seem to

benefit from the temporal activity and further research should consider the temporal lobe

beside the frontostriatal circuitry in investigations of ADHD.

Concluding, advantages and limitations of the present studies with implications of

further research – if not mentioned until now – are presented in the following section.

Study III replicated the well investigated fact that children with ADHD are impaired

in oculomotor inhibition control compared to normal developed children. And at the first

time this differences could be demonstrated on a physiological level during the visual

experiment. To a large extent, physiological inhibition control investigations make use of

fMRI as a method containing significant motion artefacts, resulting in up to 50% of data

General discussion

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loss in some cases (Yerys, et al., 2009). Thus, the advantage of the used EEG system in

the present studies is that this method is also applicable in groups of children with

psychiatric disorders, who are often restless. This advantage should be maintained in

further research, also because of the low cost and the related possibility for diagnostic

initiation.

Due to the findings of the pilot study, it was assumed that both modality conditions

are equivalent. This seems not be confirmed. The acoustic experiment has a higher

complexity than the pure visual task. Therefore, children with ADHD showed a higher

error rate in this condition than control children, whereas no group differentiation

between the inhibition and the control task was possible. Additionally, it seems that in all

three studies acoustically elicited saccades were less prone to be influenced by

impulsivity – possibly due to the remapping process. Consequently, the question, which

arises, is whether the present task is the right method for investigating inhibition control

in the auditory modality. Or if the obvious conclusion can be drawn from the present

experiment that children with ADHD are impaired in the visual but not in the cross-modal

inhibition. Higher comparable tasks have to be designed – for example an experiment

including visual and acoustic stimuli with a key press as response. This kind of

experiment would resemble the CAPT, measuring attention. It was shown that children

with ADHD are more impaired in a response inhibition network than in the selective

attention network (Booth, et al., 2005), thus, the considered task could be described as a

helpful completive diagnostic tool to investigate impaired fields of inhibition control

children with ADHD.

The present studies provided insight in the cortical network underlying the

production of antisaccades elicited by acoustic stimuli in children with and without ADHD

for the first time. It was shown that conclusions regarding functional roles of the

prefrontal cortex regions in adults may not be directly applicable to children (Hwang,

Velanova, & Luna, 2010; Tamm, Menon, & Reiss, 2002). Additionally, up to now little is

known about the inhibition control of acoustic cues and also nothing about the

development of this skill over the life time. Therefore, further developmental studies –

comparable to studies investigating visual inhibition development (Luna & Sweeney,

2004) – as well as complementary longitudinal investigations should investigate this

question in control participants in order to develop templates or growth curves to which

children with an atypical development, cognition or behaviour can be compared.

Although disinhibition in children with ADHD may result from discrete dysfunction in

specific brain regions, the possibility of a developmental dysmaturation (Tamm, et al.,

General discussion

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2002) or late development of frontal lobe (Gogtay, et al., 2004) as a contributing factor

should not be forgotten.

Due to the small participant number in the present studies, further investigations

should give more priority to the sample size – the bigger the sample the higher the

generalization. Approaches for modifications of the design – only to mention the high

relevant alterations – should be a random fixation time so that the predictability of the

stimulus will be reduced to enhance the complexity of the task minimal in order to

heightening the differentiability in turn. Furthermore, the aspect of correction of

erroneous saccades seems to be important because children with ADHD correct less

than control subjects (Klein, et al., 2003). An analysis of the EEG data of the erroneous

saccades in comparison to the correct reaction is equally important, in order to

understand the underlying brain physiological mechanism in deficits.

Thus, it should not be forgotten that all the major, developmental disorders –

inclusive ADHD – are closely linked to one another (Witton, 2010). It is not important to

find the correct label for the symptoms of a patient but an appropriate description of the

patient’s symptoms to be able to develop the most adapted therapy. The results of the

present thesis show: a multi-professional approach to diagnosis and management is

critically indispensable.

List of tables and figures

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LIST OF TABLES AND FIGURES

Tables

Table 1: Correlation of errors with age [in month] ............................................................................- 33 -

Table 2: Results hearing levels ........................................................................................................- 46 -

Table 3: Results parental ratings of ADHD/APD symptoms ............................................................- 47 -

Table 4: Demographic characteristics ..............................................................................................- 60 -

Table 5: Parental ratings of ADHD/APD symptoms .........................................................................- 61 -

Table 6: Correlation of correct reactions [in %] with age [in months] in the visual experiment........- 61 -

Table 7: Mean ± standard deviation [in %] of correct saccades in the visual experiment................- 61 -

Table 8: Correlation of latency [in ms] with age [in months] in the visual experiment......................- 62 -

Table 9: Mean ± standard deviation [in ms] of correct saccades in the visual experiment ..............- 62 -

Table 10: Mean amplitudes and standard deviations [in µV] in the time window -156 to -24ms before response onset in the visual experiment.....................................................................- 62 -

Table 11: Post-hoc results of the interaction group x condition in the visual experiment ................- 63 -

Table 12: Sources underlying the difference (ADHD (anti- minus prosaccades) minus control group (anti- minus prosaccades)) in the visual experiment..................................................- 63 -

Table 13: Mean amplitudes and standard deviations [in µV] in the time window -156 to -24ms before response onset in the visual experiment.....................................................................- 64 -

Table 14: Sources underlying the difference antisaccades minus prosaccades in the visual experiment ........................................................................................................................................- 65 -

Table 15: Correlation of correct reactions [in %] with age [in months] in the acoustic experiment ........................................................................................................................................- 66 -

Table 16: Mean ± standard deviation [in %] of correct saccades in the acoustic experiment .........- 66 -

Table 17: Correlation of latency [in ms] with age [in months] in the acoustic experiment ...............- 66 -

Table 18: Values represent mean ± standard deviation [in ms] of correct saccade latencies .........- 67 -

Table 19: Mean amplitudes and standard deviations for the positive and negative cluster within the group of children with ADHD in the acoustic experiment.................................................- 67 -

Table 20: Sources underlying the difference antisaccades minus prosaccades within the group of children with ADHD in the acoustic experiment .................................................................- 68 -

Figures

Figure 1: The parameters of eye movement ....................................................................................- 11 -

Figure 2: Cortical areas involved in saccade generation and execution. After processing visual information in the visual cortex and after integration of visuo-spatial integration in the PPC, reflexive saccades are triggered by the PEF. In contrast, intentionally saccades are elicited by the FEF. If a saccade is inhibited the DLPFC will play a primary role. Furthermore, the SEF are responsible for the control of motor programming, whereas the CEF inherit a motivational role for areas controlling intentional saccades. ACC: anterior cingulate cortex; CEF: cingulate eye fields; DLPFC: dorsolateral prefrontal cortex; FEF: frontal eye fields; PEF: parietal eye fields; PPC: posterior parietal cortex; SC: superior colliculus; SEF: supplementary eye field Modified to Pierrot-Deseilligny, Milea, & Muri, 2004; Pierrot-Deseilligny, Muri, Nyffeler, & Milea, 2005 .......................................................................................................................................- 19 -

Figure 3: Example trial (visual prosaccade); top: overlap-condition and bottom: gap-condition ...........................................................................................................................................- 31 -

List of tables and figures

- 87 -

Figure 4: a: Interaction modality*type for the dependant variable latency; b: interaction modality*delay for the dependant variable latency; c: interaction modality*distance for the dependant variable latency; d: correlation between latencies in visually and acoustically cued saccades; filed circle: antisaccades, empty circles: prosaccades...........................................- 33 -

Figure 5: a: Interaction modality*type for the dependant variable error rate; b: interaction modality*distance for the dependant variable error rate; c: correlation between errors in visually and acoustically cued saccades ..........................................................................................- 35 -

Figure 6: Temporal structure of an exemplary trial (visual prosaccade); top: overlap-condition and bottom: gap-condition ................................................................................................- 43 -

Figure 7: Sensitivity map of the MFC (top) and the TAC left (bottom) .............................................- 45 -

Figure 8: Group effect for the dependant variable source power of correct antisaccades in the MFC............................................................................................................................................- 48 -

Figure 9: Group effect for the dependant variable source power of correct antisaccades in the TAC left.......................................................................................................................................- 48 -

Figure 10: Children with ADHD (anti- minus prosaccades) vs. control children (anti- minus prosaccades) during the visual experiment: from left to right: (1) topoplot: top: negative cluster, bottom: positive cluster, (2) mean amplitude of the significant electrode cluster for anti- (dashed line) and prosaccades (continuous line) and for children with ADHD (red) and control children (black), (3) sources of the anti- minus prosaccade group-difference: (a) right anterior cingulate cortex, (b) right insula, (c) left inferior frontal lobe, (d) right cerebellum anterior lobe, (e) right parietal lobe................................................................................- 64 -

Figure 11: Children with ADHD during the visual experiment; from left to right: (1) topoplot: top: negative cluster, bottom: positive cluster, (2) mean amplitude for significant electrode clusters for anti- (dashed line) and prosaccades (continuous line) (3) sources of the anti- minus prosaccade difference: (a) right medial frontal gyrus, (b) left cerebellum posterior lobe, (c) left paracentral lobule, (d) right medial temporal lobe ........................................................- 65 -

Figure 12: Control children during the visual experiment; from left to right: (1) topoplot: negative cluster, (2) mean amplitude for significant electrodes for anti- (dashed line) and prosaccades (continuous line), (3) sources of the anti- minus prosaccade subtraction: (a) right insula, (b) left insula, (c) right cerebellum posterior lobe, (d) right anterior cingulate cortex, (e) right cuneus.....................................................................................................................- 65 -

Figure 13: Topoplot (t-Values) difference antisaccades minus prosaccades per group elicited by acoustic stimuli – no interaction was found in the time window -156 to -24ms before response onset......................................................................................................................- 67 -

Figure 14: Children with ADHD during the acoustic experiment; from left to right: (1) topoplot: top: negative cluster, bottom: positive cluster, (2) mean amplitude for significant electrode clusters for anti- (dashed line) and prosaccades (continuous line) (3) sources of the anti- minus prosaccade difference: (a) right cerebellum posterior lobe, (b) left precuneus, (c) left medial temporal lobe, (d) right medial temporal lobe and (e) right medial frontal gyrus ......................................................................................................................................- 68 -

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