molecular imaging in adult attention deficit/hyperactivity ... · curriculum vitae (cv) ......

Molecular Imaging in Adult

Attention Deficit/Hyperactivity Disorder

Doctoral thesis at the Medical University of Vienna

in the program Clinical Neurosciences (CLINS)

at the Medical University of Vienna

for obtaining the academic degree

Doctor of Medical Science/PhD

submitted by

Thomas Vanicek, MD

Supervision by

Rupert Lanzenberger, Assoc. Prof. PD MD

NEUROIMAGING LABs (NIL) - PET & MRI & EEG & Chemical Lab Department of Psychiatry and Psychotherapy

Medical University of Vienna

Waehringer Guertel 18-20, 1090 Vienna, Austria http://www.meduniwien.ac.at/neuroimaging/

Vienna, December 2016

http://www.meduniwien.ac.at/neuroimaging/

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Declaration

This thesis project was conducted at the NEUROIMAGING LABS (NIL) - PET, MRI, EEG & Chemical

Lab (head: Assoc. Prof. PD Dr. med. Rupert Lanzenberger) of the Department of Psychiatry and

Psychotherapy (head: o.Univ.-Prof. Dr. h.c.mult. Dr. med. Siegfried Kasper), Clinical Division of

Biological Psychiatry, Medical University of Vienna, Austria

(www.meduniwien.ac.at/neuroimaging).

Positron emission tomography measurements were performed at the Department of Biomedical

Imaging and Image-guided Therapy, Division of Nuclear Medicine, former Department of Nuclear

Medicine, Medical University of Vienna, Austria. Synthesis of radioligands was done by the

working group of Radiopharmaceutical Sciences (head: Prof. Dr. Wolfgang Wadsak and Prof. Dr.

Markus Mitterhauser), Department/Division of Nuclear Medicine, Medical University of Vienna,

Austria.

Genotyping of DNA samples was conducted at the Genetics Research Center (head: Prof. Dr.

med. Dan Rujescu), Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-

University, Munich, Germany as well as at the Department of Psychiatry, University of Halle,

Halle, Germany.

Funding/Support: This research was supported by a grant from the Austrian Science Fund

(P22981) awarded to Prof. PD Dr. med. Rupert Lanzenberger and by a grant from the Austrian

National Bank (OeNB; Jubiläumsfonds; Project Nr. 13675) awarded to Prof. Dr. Markus

Mitterhauser.

Further support (personnel, infrastructure, funding by the “Forschungskostenstelle”) was also

given by the Department for Psychiatry and Psychotherapy and by the Department of Biomedical

Imaging & Image-Guided Therapy, Division of Nuclear Medicine of the Medical University of

Vienna. Further details are given in the acknowledgements section of each paper.

Role of the Sponsor: The funding agencies (FWF, OENB) had no role in the design and conduct

of the study; collection, management, analysis, and interpretation of the data; or preparation,

review, or approval of the manuscript.

http://www.meduniwien.ac.at/neuroimaging

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TABLE OF CONTENTS

Declaration ................................................................................................................................. ii

TABLE OF CONTENTS ................................................................................................................. iii

List of figures ............................................................................................................................. iv

ABSTRACT Molecular Imaging in Adult Attention Deficit/Hyperactivity Disorder (ADHD) ....... v

ABSTRACT (Deutsch) Molekulare Bildgebung in der Aufmerksamkeitsdefizit- und

Hyperaktivitässtörung (ADHS) .................................................................................................. vii

Publications arising from this thesis .......................................................................................... ix

Acknowledgments and project funding ..................................................................................... x

Abbreviations ............................................................................................................................ xi

1. INTRODUCTION ...................................................................................................................... 1

1.1. General introduction - ADHD .......................................................................................... 1

1.2. Neurobiology of ADHD .................................................................................................... 2

1.3. Monoaminergic neurotransmitter systems in ADHD ...................................................... 3

1.4. Principles of Molecular Imaging ...................................................................................... 6

1.5. Molecular imaging with PET and SPECT of the dopaminergic, noradrenergic and serotonergic neurotransmitter system .......................................................................... 8

1.5.1. PET and SPECT imaging of the dopaminergic neurotransmitter system ................. 9

1.5.2. PET and SPECT imaging of the noradrenergic neurotransmitter system .............. 10

1.5.3. PET and SPECT imaging of the serotonergic neurotransmitter system ................. 12

1.6. The genetic role in ADHD .............................................................................................. 14

1.7. Aims of this thesis.......................................................................................................... 16

2. RESULTS ................................................................................................................................ 17

2.1. The Norepinephrine Transporter in Attention Deficit/Hyperactivity Disorder Investigated with (S,S)-[18F]FMeNER-D2 ....................................................................... 17

2.2. Effects of norepinephrine transporter gene variants on NET binding in ADHD and healthy controls investigated by PET ........................................................................... 27

2.3. Altered interregional molecular associations of the serotonin transporter in attention deficit/hyperactivity disorder assessed with PET. ........................................................ 38

3. DISCUSSION .......................................................................................................................... 50

3.1. General Discussion ........................................................................................................ 50

3.2. Conclusion & future prospects ...................................................................................... 52

4. MATERIALS AND METHODS ................................................................................................. 54

REFERENCES ............................................................................................................................. 56

Curriculum Vitae (CV) ............................................................................................................... 69

Publication List (PL) .................................................................................................................. 71

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List of figures

Fig. 1 PET images showing (S,S)-[18F]FMeNER-D2 distribution in the human brain in vivo. High NET

binding is found in thalamus and midbrain, low NET binding in the basal ganglia. High NET uptake

is found in the skull bones, a phenomenon that is related to tracer deflourination. The image

depicts triplanar structural images (axial, sagittal and coronal view) and superimposed NET

availability. Mean NET distribution maps have been generated by using imaging data from the

studie “The Norepinephrine Transporter in Attention Deficit Hyperactivity Disorder (ADHD)

investigated with PET”, related to this thesis.

Fig. 2 Region specific SERT binding in the human brain in vivo. High SERT binding is found in the

midbrain. The image depicts triplanar structural images (axial, sagittal and coronal view) and

superimposed SERT availability using [11C]DASB. Mean SERT distribution maps have been

generated by using imaging data from the studie “The Serotonin Transporter in Attention Deficit

Hyperactivity Disorder investigated with Positron Emission Tomography”, related to this thesis.

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ABSTRACT

Molecular Imaging in Adult Attention Deficit/Hyperactivity Disorder (ADHD)

Attention deficit/hyperactivity disorder (ADHD) is a highly prevalent and heritable

neurodevelopment disorder, with 40 to 60% of affected children suffering from ADHD also in

adulthood. The ADHD pathophysiology is linked to dysfunctional connectivity within and

between brain regions, which are modulated by neurotransmitters systems. Frequently

prescribed treatments for ADHD, including stimulants and non-stimulants, alter

norepinephrinergic and dopaminergic signaling in the central nervous system and thereby

alleviate ADHD symptoms. In addition, serotonergic signaling is associated with hyperactivity,

impulsivity and cognitive-emotional processes, which all represent symptoms that are present in

ADHD. The causal complex neuronal mechanisms of ADHD and the way psychopharmacological

therapy unfolds its efficacy are until now not entirely disclosed, thus it is of critical and public

interest to gather more information of neurotransmitter signaling in ADHD.

This thesis project was designed to investigate proteins as the norepinephrine and serotonin

transporter (NET, SERT) in vivo in patients with ADHD. To evaluate NET and SERT expression, we

used positron emission tomography (PET) and the radioligands (S,S)-[18F]FMeNER-D2 for NET

quantification and [11C]DASB for SERT quantification. In our first study we investigated NET

binding in ADHD for the first time worldwide. There was no significant difference in NET binding

in patients with ADHD compared to healthy control subjects. In the second publication, we

showed an effect of genotype on NET binding, implicating a genetic influence on the expression

of the NET in ADHD. Furthermore in the third study, I quantified SERT levels in patients with

ADHD and found no difference in SERT binding between patients and healthy controls in specific

brain regions. However, I applied interregional molecular correlational analysis of the SERT

binding, and I revealed significant differences in interregional correlations between the

hippocampus and precuneus in patients with ADHD compared to healthy control subjects.

The results of these imaging investigations provide needed information on the NET and SERT

distribution in ADHD. The revealed lack of difference in NET and SERT binding between groups

suggest that neither NET nor SERT availability is of critical relevance for the pathophysiology of

ADHD. In addition, we found a genetic impact on NET binding in ADHD patients compared to

healthy subjects, thus supporting previous genetic findings and underling a biological component

in ADHD. To assess associations of SERT BPND between brain regions, I expanded conventional

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PET imaging analysis through performing interregional molecular correlational analysis of SERT

binding. I aimed to capture the complexity of brain interactions rather than higher or lower SERT

levels in a specific region and found differences of interregional associations in patients with

ADHD. The results help to develop a much broader understanding of the basic neurochemical

constitution of ADHD.

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ABSTRACT (Deutsch)

Molekulare Bildgebung in der Aufmerksamkeitsdefizit- und

Hyperaktivitässtörung (ADHS)

Die Aufmerksamkeitsdefizit- und Hyperaktivitässtörung (ADHS) weist hohe Prävalenzraten und

eine hohe Korrelation mit genetischen Faktoren auf, wobei bis zu zwei Drittel der Patienten, die

im Kindesalter an ADHS erkrankt sind noch im Erwachsenenalter darunter leiden. Die

Pathophysiologie von ADHS wird eng mit einer dysfunktionalen Konnektivität innerhalb und

zwischen verschiedenen Hirnregionen in Verbindung gebracht, welche durch verschiedene

Neurotransmittersysteme gesteuert wird. Stimulanzien und Nicht-Stimulanzien werden häufig

bei ADHS verordnet, modulieren vorrangig das noradrenerge und dopaminerge

Transmittersystem im zentralen Nervensystem und führen klinisch nachweislich zu einer

Reduktion der Aufmerksamkeitsstörung, Impulsivität und Hyperaktivität. Zusätzlich kommt der

serotonergen Transmission bei ADHS eine zentrale Bedeutung zu, da sie bei hyperaktivem und

impulsivem Verhalten sowie bei kognitiven Prozessen, welche durch Emotionen beeinflusst

werden, eine große Rolle spielt. Die komplexen zugrundeliegenden neuronalen Mechanismen

von ADHS sowie der Mechanismus wie Psychopharmaka ihre Wirkung erzielen sind bis dato noch

teilweise ungeklärt, weswegen mehr Informationen über diese Neurotransmittersysteme von

zentraler Bedeutung sind.

Im Rahmen dieser Doktorarbeit wurde der Noradrenalintransporter (synonym:

Norepinephrintransporter; NET) sowie der Serotonintransporter (SERT) in vivo bei Patienten mit

ADHS untersucht. Zur Quantifizierung von NET und SERT im Gehirn wurde

Positronenemissionstomographie (PET) und (S,S)-[18F]FMeNER-D2 für die Messung des NET oder

[11C]DASB für die von SERT verwendet. Die Quantifizierung des NET bei Patienten mit ADHS

wurde weltweit erstmalig im Zuge unserer ersten Studie durchgeführt. Es wurde kein

signifikanter Unterschied in der Häufigkeit von NET bei Patienten mit ADHS im Vergleich zu

gesunden Kontrollprobanden gefunden. In unserer zweiten Publikation haben wir im Zuge

genetischer Analysen einen genetischen Effekt auf die NET Expression bei Patienten mit ADHS

gefunden. Weiters habe ich den SERT bei Patienten untersucht. Die herkömmliche Datenanalyse

hat keinen Unterschied von SERT bei ADHS im Vergleich zu gesunden Kontrollprobanden gezeigt.

Zusätzlich wurde erstmalig eine interregionale Korrelationsanaylse anhand des SERT

Bindungspotentials durchgeführt, die signifikante Unterschiede in der Korrelation zwischen

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Hippokampus und Prekuneus zwischen Patienten mit ADHS und gesunden Kontrollprobanden

zeigen konnte.

Die Resultate dieser Bildgebungsuntersuchungen geben einerseits Aufschluss über die Quantität

von NET und SERT bei ADHS, wobei keine Unterschiede zwischen Patienten mit ADHS und

gesunden Kontrollprobanden nachgewiesen wurde. Dies deutet darauf hin, dass weder die

Häufigkeit von NET noch die von SERT eine zentral Bedeutung für die pathophysiologischen

neuronalen Mechanismen von ADHS haben dürfte. Andererseits konnten wir einen genetischen

Einfluss auf die Expression von NET bei ADHS darstellen und durch die erstmalige Anwendung

einer interregionalen Korrelationsanalyse gegenwärtige Analyseverfahren im Bereich der PET

Bildgebungsstudien erweitern. Insgesamt tragen die Resultate dieser PET Untersuchung zu

einem besseren Verständnis der ADHS-spezifischen neurobiologischen Mechanismen bei.

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Publications arising from this thesis

Vanicek T, Spies M, Rami-Mark C, Savli M, Höflich A, Kranz G, Hahn A, Mitterhauser M, Wadsak W,

Hacker M, Kasper S, Lanzenberger R The Norepinephrine Transporter in Attention Deficit/Hyperactivity Disorder Investigated with (S,S)-[18F]FMeNER-D2 JAMA Psychiatry. 2014 Dec 1;71(12):1340-9. [2015, IF: 14.417]

Sigurdardottir HL, Kranz GS, Rami-Mark C, James GM, Vanicek T, Gryglewski G, Kautzky A, Hienert M,

Traub-Weidinger T, Mitterhauser M, Wadsak W, Hacker M, Rujescu D, Kasper S, Lanzenberger R Effects of norepinephrine transporter gene variants on NET binding in ADHD and healthy controls investigated by PET Human Brain Mapping 2016 Mar, 37(3):884-95 [2015, IF: 4.962]

Vanicek T, Kutzelnigg A, Philippe C, Sigurdardottir HL, James GM, Hahn A, Kranz GS, Höflich A,

Kautzky A, Traub-Weidinger T, Hacker M, Wadsak W, Mitterhauser M, Kasper S, Lanzenberger R. Altered interregional molecular associations of the serotonin transporter in attention deficit/hyperactivity disorder assessed with PET. Human Brain Mapping 2016 Oct 22. Epub ahead of print [2015, IF: 4.962]

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Acknowledgments and project funding

I would like to express my thankfulness to all those who supported me in planning, executing

and completing my thesis. First of all, I like to thank my supervisor and mentor Assoc.-Prof. PD

Dr.med. Rupert Lanzenberger for his scientific and personal support from the day I started my

path in his neuroimaging lab. Special thanks to O.Prof. Dr.h.c.mult. Dr.med. Siegfried Kasper for

his mentorship, and for the opening to carry out the research at the Department of Psychiatry

and Psychotherapy.

Furthermore, great thanks go to the research team of the NEUROIMAGING LABS (NIL) - PET,

MRI, EEG & Chemical Lab, especially to Dr. Marie Spies for working with me on the first

publicating as well as for linguistic support and to Doz. Mag. Dr. Georg Kranz, DDr. Pia Baldinger

and MSc Helen Laufey Sigurdardottir for the helpful comments on the thesis, linguistic support

and throughout the years.

My special thanks are extended to the staff of my thesis committee, O.Prof. Dr.h.c.mult. Dr.med.

Siegfried Kasper and Prof. Dr. Markus Mitterhauser for supporting me, as well as Univ. Prof. Dr.

Johannes Hainfellner, the coordinator of the PhD program Clinical Neurosciences, carrying out

this task with great commitment. Furthermore, I want to acknowledge our collaboration

partners, the team of the Department of Biomedical Imaging and Image-guided Therapy,

Division of Nuclear Medicine, Medical University of Vienna, and the working group of

Radiopharmaceutical Sciences, especially Prof. Dr. Markus Mitterhauser, Prof. Dr. Wolfgang

Wadsak, Mag. Dr. Lukas Nics and Dr. Christina Rami-Mark PhD, as well as the team of the

Genetics Research Center, Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-

University, Munich, Germany, particularly Dan Rujecsu.

Finally, I am grateful to all patients and healthy controls subjects for participating in our studies.

This work was funded by Grants from the the Austrian Science Fund (“The Norepinephrine

Transporter in Attention Deficit Hyperactivity Disorder (ADHD) investigated with PET”; PI: Assoc.

Prof. PD Rupert Lanzenberger, MD; FWF Projektnr.: P 22981) and the Austrian National Bank

(“The Serotonin Transporter in Attention Deficit Hyperactivity Disorder investigated with

Positron Emission Tomography.”; PI: Prof. Dr. Markus Mitterhauser; OeNB Projekt Nr. 13675).

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Abbreviations

5-HT 5-hydroxytryptamine, serotonin

AAL automated anatomical labeling

ADHD attention deficit/hyperactivity disorder

BPND binding potential non-displaceable

HC healthy control subects

NET norepinephrine transporter

MRI magnetic resonance imaging

PET positron emission tomography

ROI region of interest

SERT serotonin transporter

SNP single nucleotide polymorphism

SPECT single photon emission tomography

SPM statistical parametric mapping

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1. INTRODUCTION

1.1. General introduction - ADHD

Attention deficit/hyperactivity disorder (ADHD) is one of the most frequent neurodevelopmental

disorders in children and adolescents. Prevalence rates of ADHD in school-aged children range

between 8 and 12% (Biederman & Faraone, 2005b) and long-term follow-up studies show that

40 to 60% of children with ADHD suffer from typical ADHD symptoms in adulthood (Volkow &

Swanson, 2013). While an age-dependent decline of ADHD symptoms as hyperactivity and

impulsivity has been described previously, ADHD is a chronic and often life-long disorder where

predominantly inattentive symptoms seem to persist into adulthood (Barbaresi et al, 2013). The

core symptoms of ADHD include inattention, motoric hyperactivity and impulsivity (Association,

2013). In addition, specifically emotional dysregulation is also frequently observed in patients

with ADHD (Shaw et al, 2014). Symptoms have to start during childhood, before puberty, and

have to exist in at least two domains of functioning (as education or work, relationship and/or

family and/or social contacts and free time/hobbies) (Association, 2013).

Adult patients with full-scale or partial ADHD symptoms are often confronted by a high personal

and social burden (Kessler et al, 2006), since they get divorced more often, get terminated by

their employee and switch labor significantly more often, are more frequently in sick leave and

generate a lower income (Biederman et al, 2006; de Graaf et al, 2008). Epidemiological studies

show that 50 % of all patients with ADHD develop psychiatric comorbidities during lifetime,

including mood disorders, anxiety disorders, substance abuse disorders (SUD), oppositional

defiant disorders and conduct disorders (Baird et al, 2012; Biederman, 2005; Kadesjo & Gillberg,

2001). Furthermore, the rate of ADHD in inmates varies from 10 to 70 %, whereas ADHD has

been implicated as a risk factor for incarceration (Ghanizadeh et al, 2011).

Diagnostic criteria is based on the Diagnostic and Statistical Manual of Mental Disorders (DSM 5;

(Association, 2013) and International Statistical Classification of Diseases and Related Health

Problems (ICD-10) where ADHD is classified by the predominance of symptoms of inattention

(the inattentive type; DSM 5: 314.00, ICD-10: F90.0), hyperactivity (the hyperactivity-impulsivity

type; DSM 5: 314.01, ICD-10: F90.1) or a combination of inattention and hyperactivity (the

combined type; DSM 5: 314.01, ICD-10: F90.0).

The psychopharmacological treatment for ADHD consists of methylphenidate (MPH) and

amphetamine (AMPH), which are stimulant medication, and of atomoxetin (ATX), a

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psychopharmaca that belongs to the group of the selective norepinephrine reuptake inhibitors.

Both stimulants and non-stimulant medication enhance dopaminergic and noradrenergic

signaling by blocking the neurotransmitter transporter in specific brain areas (Berridge et al,

2006; Bymaster et al, 2002). Randomized controlled studies have displayed that stimulant and

non- stimulant medication significantly alleviate ADHD symptoms in children and adult patients

with ADHD in comparison to placebo (Adler et al, 2009; Faraone et al, 2004; Volkow & Swanson,

2013). The clinical improvement to psychopharmacological therapy suggests that the mechanism

through which it achieves an effect is of importance to the neuro-pathophysiology and the

resulting symptomology.

1.2. Neurobiology of ADHD

Until this day, the underlying myriad and complex neuronal mechanisms of ADHD are not

entirely revealed. One reason for this is, as it is the case for other psychiatric disorders, that the

different state of the art methods in use to investigate morphological and functional brain

alterations and accompanying behavior in ADHD are most probable not sensitive enough to

detect specific neuronal correlates. Another and not less important reason is that ADHD

represents a neurodevelopmental disorder, which comprises a wide range of symptoms with a

divergent clinical phenotype, complicated by the circumstance that certain behaviors do not

exist exclusively for ADHD. For instance cognitive deficits and more specific, executive functions

such as working memory, attentional and inhibitory control are present in ADHD as well as in

affective disorders and schizophrenia (Gallo & Posner, 2016). Scientific achievements of the past

decades suggest that the etiology of ADHD is more likely explained through a multifactorial

model, including biological, social and psychological factors.

Genetic data from twin studies from different countries on different continents project

heritability to be 0.76 (Faraone et al, 2005). The genetic influence plays a main role in ADHD,

whereas unrevealed common variants with small effects and gene-environment as well as gene-

gene interactions are suggested to be involved in the genetics of ADHD (Archer et al, 2011b).

Though few genes have been associated with ADHD. Non-genetic factors comprise

environmental and psychological factors, whereas among others pregnancy and delivery

complications, prematurity, low birthrate and exposure to alcohol and nicotine during pregnancy

are counted to the former and low social class, low maternal education and single parenthood

are examples to the latter (Biederman & Faraone, 2005b).

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Neuroimaging studies investigating brain structure and the function, predominantly using

magnetic resonance imaging, in children and adult patients with ADHD revealed dysfunctional

fronto-parietal, dorsal fronto-striatal, and meso-cortico-limbic circuits, as well as abnormal

default mode and cognitive control networks (Brennan & Arnsten, 2008b; Gallo & Posner, 2016;

Volkow et al, 2007b). The neurobiological basis of ADHD symptoms has been associated to

connectivity between cortical and subcortical brain region, which are modulated by numerous

neurotransmitters and especially norepinephrine (NE) and dopamine (DA) (Del Campo et al,

2011).

Meta-analyses of structural magnetic resonance imaging (MRI) studies consistently found

reduced gray matter volume in the basal ganglia. The basal ganglia receive input from the neo

cortex and from midbrain regions and are involved in motor control, motivation and reward

processing, and goal-directed behavior (Gallo & Posner, 2016). In addition to subcortical

alterations, structural abnormalities in the frontal, parietal and temporal lobe have also been

demonstrated (Bush, 2010). Longitudinal studies and meta-analysis, including studies

investigating children as well as adult patients with ADHD, demonstrated abnormal

developmental processes (Rubia, 2007; Shaw et al, 2013). Meta-analysis of task-related

functional imaging studies, using the activation likelihood estimation method to compare and

objectify huge data sets, found hypoactivity in patients with ADHD in frontoparietal and ventral

attentional networks and hyperactivation within the default mode, ventral attention, and

somatomotor networks (Cortese et al, 2012). Further, modern analysing modalities allow

investigating huge amounts of imaging data in terms of large-scale brain networks. Thus, the

emphasis has been put on detecting differences in brain networks between ADHD and healthy

subjects in recent years. Abnormalities have been found in several brain networks in ADHD,

including fronto-parietal, dorsal attentional, motor, visual and default mode networks

(Castellanos & Proal, 2012).

1.3. Monoaminergic neurotransmitter systems in ADHD

Dysfunctional dopaminergic and noradrenergic neurotransmission is widely suggested to the

pathophysiology of ADHD (Del Campo et al, 2011). This thesis was primarily generated by

pharmacological findings showing that the mechanism by which psychotropic drugs that are

prescribed for ADHD elevate DA and NE in the synaptic cleft. Subsequently performance levels

will be raised, clinical symptoms and social interaction problems commonly seen in ADHD are

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reduce. Investigations with animal models on the other hand revealed that dysregulations in

neurotransmitter pathways affect normal behavior, leading to deficits in attention and to

hyperactive and impulsive behaviors (Schneider et al, 1994; Shaywitz et al, 1978).

The noradrenergic transmitter system is critically involved in higher cognitive and affective

functions, modulating arousal states and state dependent processes. Reciprocal tonic and phasic

discharge of the locus coeruleus (LC) to cortical and subcortical regions are related to

attentiveness and the sensory to salient stimuli, modulating neuronal and behavioral activity

states that are necessary to capture and process sensory information (Aston-Jones et al, 1999;

Berridge & Waterhouse, 2003). The majority of noradrenergic cells in the brain are located in the

midbrain nucleus, the LC (Barnes, 1991). Axons of the neurons in the LC are distributed virtually

throughout the entire brain, except for main parts of the basal ganglia, which is nearly devoid of

noradrenergic transmission (Berridge & Waterhouse, 2003; Gerfen & Clavier, 1979; Morrison et

al, 1982; Morrison et al, 1979). Three noradrenergic receptor subtypes have been described,

including 1-, 2- and -receptors. The specific cellular functions across diverse terminal brain

regions that transmit into behavior are mediated by - and -receptors have been only

elucidated to a certain extent. The second messenger systems of - and -receptors are

different, where the former is coupled to the Gs/cAMP intracellular messenger system and the

latter to the phosphoinositol and Gi/cAMP systems (Dohlman et al, 1991). Furthermore, 1- and

-receptors are located mainly postsynaptically, 2-receptors are found both pre- and

postsynaptically (Berridge & Waterhouse, 2003). Since the NE system modulates arousal states

and attentional processes and dysfunctional NE signaling has been shown to cause deficits in

attention and impaired executive functions, NE has been associated with ADHD (Aston-Jones &

Gold, 2009; Chamberlain et al, 2007; Frank et al, 2007). Preclinical studies in animals and

humans have demonstrated that a moderate increase in NE transmission improves prefrontal

brain function through the 2-receptors, while high levels of NE, distributed during stress,

worsens performance via the 1-receptors in the prefrontal cortex (PFC) (Brennan & Arnsten,

2008b). Guanfacine, an -2A agonist in use to treat ADHD, has been shown to improve ADHD

symptomology and enhance performance of PFC functioning in children and adult patients with

ADHD (Arnsten et al, 2007). Studies investigating the DA system demonstrate a dopaminergic

involvement in motivational and movement coordination processes as well as in the

pathophysiology of ADHD (Frank et al, 2007; Glimcher, 2011). Phasic discharge of midbrain

dopaminergic cells is suggested to lead reward prediction error that guides learning throughout

the frontal cortex and the basal ganglia (Zhang et al, 2009). Large midbrain dopaminergic

5

neurons are centralized in the ventral tegmental area (VTA) and in the substantia nigra pars

compacta (SNc) from where long axons project to the basal ganglia and to the frontal cortex,

while little to no projections is sent to parietal, temporal and occipital regions (Glimcher, 2011).

Dopamine enfolds action via the D1 receptor family, that includes the D1 and the D5 receptor

subtype as well as via the D2 receptor family, including the D2, D3 and the D4 receptor subtype

(Brennan & Arnsten, 2008b). In particular, a moderate stimulation of the D1 receptors, which are

vastly represented in the PFC, leads to an inverted “U”-shaped response, where a processing of

irrelevant information is inhibited and extensive D1 receptor stimulation produces nonspecific

suppression of cell activation. On a behavior level, D1 receptors produce an inverted “U”-shaped

dose-dependent functioning level in regard to attention regulation and working memory

processes of the PFC (Arnsten, 2011).

The prefrontal cortex, a highly involved brain region in cognitive processes as attention,

inhibiting processing of irrelevant stimuli, sustaining attention over a long delay of time and

dividing and coordinating attention, is typically affected in ADHD (Willcutt et al, 2005) and

displays distinct noradrenergic and dopaminergic innervation from midbrain regions (Arnsten &

Li, 2005). Investigations with animal models revealed that neurotransmitter depletion in the PFC

causes detrimental effects on working memory and is as impactful as the ablation of nerve tissue

(Brozoski et al, 1979). Clinical symptoms as impairments of motivation and working memory

have been attributed to the dopaminergic signaling, while noradrenergic alterations may play a

fundamental role in vigilance, inattention and response inhibition (Bymaster et al, 2002; Frank et

al, 2007).

However, next to the well-established association between noradrenergic and dopaminergic

neurotransmission and ADHD, lines of evidence from genetic, pharmacological and animal

studies support the involvement of serotonin in the pathophysiology of ADHD (Dalley & Roiser,

2012). Serotonin is highly involved in human behavior, in multiple psychiatric disorders and in

the arrangement of cognitive-emotional processes (Archer et al, 2011a; Hoyer et al, 2002; Lowry

et al, 2008; Murakami et al, 2009).

Modification of the serotonergic neurotransmission has been implicated in the pathophysiology

of ADHD, which is mainly based on the association between impulsivity and hyperactivity, two

key traits of ADHD, and serotonergic signaling (Castellanos et al, 1994; Dalley & Roiser, 2012).

Impulsive behavior represents performance where action is taken without previous conscious

reflection, and has long been proposed to be a central clinical symptom for different

6

neuropsychiatric disorders and in particular ADHD. ADHD patients regularly respond and behaive

inappropriately, prematurely and maladaptive in daily action (Connor et al, 2010; Doerfler et al,

2011). The inability for adequate impulsive behavior is related to emotional instability and

dysfunctional motor control and is expressed in aggressive impulsivity (Robinson et al, 2008;

Sobanski et al, 2010).

Tricyclic and dual antidepressants, such as modanifil and bupropion show clinical efficacy in

ADHD patients and have been used within this indication for decades, though existing evidence

show that selective serotonin reuptake inhibitors are not effective (Faraone & Glatt, 2010). MPH

does not inhibit the serotonin transporter (Volkow et al, 2000) and therefore takes no direct

action in serotonin signaling. However, AMPH enhances serotonergic release (Kuczenski & Segal,

1997) and ATX inhibits serotonin (5-HT) reuptake (Bymaster et al, 2002), implicating that

serotonin might also be to some extent of importance in the neuropathology and treatment of

ADHD (Gainetdinov et al, 1999).

1.4. Principles of Molecular Imaging

Positron emission tomography (PET) is a non-invasive imaging method from nuclear medicine

that utilizes ionizing radiation to visualize flow metabolism, metabolic turnover or specific

proteins in vitro and in vivo (Cherry. S.; Sorenson, 2012; Innis et al, 2007). PET is a useful method

to gain more knowledge of the underlying pathophysiology of diseases, is used to determine

clinical diagnosis and prognosis and being additionally used to predict treatment effects. To

conduct a PET measurement, a positron-emitting radionuclide has to be produced in a cyclotron,

where a tracer is labeled with an isotope (Wadsak & Mitterhauser, 2010). After synthesis, the

radiotracer has to be administered to the blood stream where it distributes according to its

inherent characteristics in the tissue, binding to molecular structures such as transporters or

receptors, or imitating substances to image metabolic rate or flow processes. The constantly

emitting positrons expand for a short distance of few millimeters into the adjacent tissue and

are thereby decelerated until they interact with an electron building high-energy photons. This

interaction leads to annihilation, an event where two gamma photons move in opposite

directions (Turkheimer et al, 2014). A scintillation detection ring with a photomultiplier detects

two photons simultaneously getting captured at opposite ends, while those photons, which do

not coincidently hit the detection ring, are neglected. Many annihilation processes are recorded

7

throughout a PET measurement resulting, after reconstruction through back projections, in

tomographic images that allow the interpretation of the quantity and distribution of the tracer.

There are three basic principles for PET imaging of the human brain in vivo (Turkheimer et al,

2014). First, the radioactive compound should be administrated in small dosages in order to not

alter or disturb the investigated system. Second, the radioactive compound has to be reliable,

valid and objectifiable and lastly, the concentration of the radioligand ought to be measured

quantitatively.

The process of generating positron-emitting radionuclides starts with charged particles

accelerated to a high velocity by a cyclotron, leading to the production of isotopes. The main

radioisotopes are 11carbon with a half-life of 20 min and 18fluorine with a half-life of 110 min,

whereas 18fluorine is easily being obtained and transported due to its rather long half-life

(Wadsak & Mitterhauser, 2010). To detect the target density and distribution, the radioactive

compound has to reach equilibrium between the binding to and the dissociation from the target

molecule. An increased affinity to the target leads to slower dissociation, thus requiring a longer

scanning time to reach equilibrium. Further, the positron-emitting isotopes will be attached to

the selected substrate during a series of chemical reactions. To visualize a specific molecule in a

certain tissue in vivo, the radio tracer needs to fulfill chemical requirements as high specificity

and affinity for the particular molecule that intended to visualize, in order to keep non-specific

binding as low as possible (Heiss & Herholz, 2006; Kung, 1991). Hence, selectivity ratio for an

optimal tracer has to be larger or equal to 100 for targets over non-targets so that the non-

specific binding is low and the signal-to-noise ratio is high (Innis et al, 2007). Furthermore, the

tracer has to have little toxic power and steady labeling as well as rapid uptake and distribution

in the tissue and few, rapidly cleared, and preferably unlabeled metabolites (Heiss & Herholz,

2006; Kung, 1991).

The binding potential (BP) of a radiotracer was introduced by Mintun more than three decades

ago (Innis et al, 2007; Mintun et al, 1984). The BP is the main parameter to observe the

expression of a certain molecule, such as the norepinephrine transporter (NET) or the serotonin

transporter (SERT) in vivo and is defined as the ratio of the target density Bmax to dissociation

constant KD at equilibrium.

8

BP =Bmax

KD

= Bmax ∗ affinity

The KD is the concentration of the free ligand (F) occupying 50% of the target at equilibrium,

representing the inverse tracer affinity to the target structure.

KD =1

affinity=

kOFF

kON

=Bavail ∗ F

B=

(Bmax − B) ∗ F

B

The equilibrium is defined as the state in which the fraction of the free ligand (Bavail) equals the

fraction of the target bound (B) tracer.

F ∗ Bavail ∗ kON = B ∗ kOFF

To warrant accurate modeling and elude physiological effects on the investigated system,

administered tracer has to be, as mentioned above, of small volume and thus of high sensitivity

(Innis et al, 2007). Once arrived at the target tissue, the tracer will be present in three different

conditions: bound to the target, non-specifically bound and free, non-bound fraction. Reciprocal

changes of the target condition are defined by rates of constants. A radiotracer attains

equilibrium when no net transfer happens between two adjacent conditions.

1.5. Molecular imaging with PET and SPECT of the dopaminergic, noradrenergic and serotonergic neurotransmitter system

Since 1999 a number of PET and single photon emission tomography (SPECT) studies have

conducted to observe the involvement of neurotransmitter systems in patients with ADHD in

vivo (Fusar-Poli et al, 2012; Zimmer, 2009). Encouraged by the continuous increasing rates of

prescribed psychopharmacological treatments for ADHD that are suggested to cause symptoms

9

improvement based on modulating with the dopaminergic and noradrenergic signaling,

pharmacological and genetic studies where followed by imaging studies focusing on molecular

structures within these neurotransmitter systems (Del Campo et al, 2011).

1.5.1. PET and SPECT imaging of the dopaminergic neurotransmitter system

Especially, molecular imaging studies conducted in adults patients with ADHD focused on

exploring the contribution of dopaminergic systems to the neurobiology of ADHD, given its well

known role in the regulation of motivational processes and motoric activation as well as

attention, inhibitory and timing and functions that are mediated by fronto-striatal circuits and

found dysfunctional in ADHD (Fusar-Poli et al, 2012). Structural and functional imaging findings,

derived from MRI, EEG, PET and SPECT studies, have pointed towards DA associated striatal

deficits (Konrad & Eickhoff, 2010; Rubia, 2011; Valera et al, 2007). The dopamine transporter

(DAT) as well as dopamine receptors represent primary targets for PET and SPECT investigations.

The DAT is a cell membrane bound protein, is located presynaptically at dopaminergic axons and

clears dopamine and noradrenalin actively from the synaptic cleft back into the presynaptic

cytosol (Piccini, 2003). The active transport via the DAT represents the primary mechanism of

clearance of dopamine from the synapse. Highest levels of DAT are localized in the striatum,

lower levels of DAT are found in the brainstem nuclei VTA and SNc and very low levels are

detected in cortical regions and cerebellum (Piccini, 2003).

Several radiotracers are available to quantify the DAT binding potential, for PET and SPECT

imaging, including 11C-altropane and 11C-PE2I for PET (Elsinga et al., 2006) and123I-IPT and 99mTc-

TRODAT for SPECT (Booij & Knol, 2007). Findings of DAT binding potential in patients with ADHD

compared to healthy controls have been inconsistent. Early imaging studies in the late 90’s

found DAT binding lower in the striatum in patients than in healthy controls and two studies

found no differences between groups (Zimmer, 2009). In the largest investigated study sample

Volkow and colleagues found lower levels of DAT in 53 drug naïve patients with ADHD in the

nucleus accumbens and midbrain (Volkow et al, 2009b). Hesse et al. also found attenuated DAT

binding in 17 drug naïve patients (Hesse et al, 2009).

A meta analysis found significant higher DAT density in the striatum in patients (Fusar-Poli et al,

2012). DAT binding was increased in patients with previous medication use and decreased

medication-naive patients. No significant correlation was found for age, comorbidity, gender, or

10

imaging technique. Nevertheless, heterogeneity across PET and SPECT studies was large and

significant. Reasons for these inconsistencies are uncertain, though they are associated with

clinical factors as the inherent heterogeneity of the disorder, previous exposure to stimulants or

drugs, smoking status as well as age and gender and methodological factors as using different

reference regions or different radio ligands (Zimmer, 2009). Investigations on the D2/D3

receptor by Volkow et al. showed significantly attenuated D2/D3 receptor binding in the nucleus

accumbens and midbrain regions in patients (Volkow et al, 2009b). Though, another PET study

quantifying D2/D3 receptor levels found no differences between groups (del Campo et al, 2013).

1.5.2. PET and SPECT imaging of the noradrenergic neurotransmitter system

Neuroimaging studies exploring the noradrenergic system in vivo in the healthy human brain or

in neuropsychiatric disorders are rare. This is due to the difficulties to produce a specific

radiotracer that fulfills requirements for a reliable NET investigation in vivo (Ding, 2014). The NET

exemplifies a treatment target with several detriments as a target molecule (Ding, 2014). In

comparison to other neurotransmitter transporters as the DAT or the SERT, NET levels are lower

in general and there is a lesser contrast between NET-rich (e.g. LC, thalamus) and NET-poor brain

regions (e.g. caudatus, neocortex). Further, the NET displays a widespread distribution

throughout the brain.

In 2003 Wilson et al. showed that (S,S)-[11C]MeNER was one of the first and most promising

radioligands for NET quantification (Wilson et al, 2003), facilitating radiolabeling with high

selectivity and affinity for the NET (Ding et al, 2003). Nevertheless, specific binding equilibrium

was not reached within the timeframe of the PET-measurement (Schou et al, 2003). Due to five-

fold higher half-life, fluorine-18 labelled reboxetine analogues exhibit extended measurement

time, reaching binding equilibrium within PET scan (Schou et al, 2004). For the production of

[18F]FMeNER (Schou et al, 2004) or [18F]FERB (Ding et al, 2003), the aryl methoxy group of

(S,S)MeNER is replaced with a fluoromethoxy or fluoroethoxy group, respectively. To further

optimize the radioligand, in-vivo de-fluorination can be decreased greatly by using the

deuterated homologues [18F]FMeNER-D2 and [18F]FERB-D4.

Among others, (S,S)-[18F]FMeNER-D2 has been successfully applied in vivo in healthy human to

explore NET (Ding et al, 2013; Rami-Mark et al, 2013). PET studies using (S,S)-[18F]FMeNER-D2 in

11

healthy humans in vivo found high NET availability in the thalamus and in the midbrain region,

representing the noradrenergic neurons in the LC (Takano et al, 2008b).

Fig. 1 PET images showing (S,S)-[18

F]FMeNER-D2 distribution in the human brain in vivo. High NET binding is found in thalamus and midbrain, low NET binding in the basal ganglia. High NET uptake is found in the skull bones, a phenomenon that is related to tracer deflourination. The image depicts triplanar structural images (axial, sagittal and coronal view) and superimposed NET availability. Mean NET distribution maps have been generated by using imaging data from the studie “The Norepinephrine Transporter in Attention Deficit Hyperactivity Disorder (ADHD) investigated with PET”, related to this thesis.

Occupancy studies in the healthy human brain, found significant binding to the NET for

nortriptyline, clomipramine as well as for atomoxetin (Logan et al, 2007a; Sekine et al, 2010).

Nortriptyline and clomipramine are both tricyclic antidepressant, while atomoxetin is a selective

reuptake inhibitor. A PET study by Ding et al. found dose dependent occupancy of the NET as

well to a less extent to the SERT of atomoxetin in the rat brain (Ding et al, 2013).

Methylphenidate has also shown to occupy the NET in a dose dependent order in the healthy

brain in vivo (Hannestad et al, 2010b).

There is sparse data on NET levels and distribution in patients with psychiatric disorders. In

obese patients, Li et al. found reduced NET availability in the thalamus, though not in other NET-

rich regions (Li et al, 2014). Observations by the same group demonstrated reduced NET binding

in the LC in patients with posttraumatic stress disorder and NET availability in the LC was

associated with anxious arousal symptoms (Pietrzak et al, 2013). In a post-mortem

autoradiographic study, NET binding has shown to be attenuated in the thalamus as well as in

12

the LC in patients with Alzheimer’s disease (Gulyas et al, 2010). In opposite, thalamic and

midbrain NET levels have shown to be increased in patients with cocaine dependency (Ding et al,

2010b). In patients with depression, an occupancy study by (Nogami et al, 2013) et al. found a

dose dependent reduction of (S,S)-[18F]FMeNER-D2 levels in the thalamus subsequent to

milnacipran administration.

Since (S,S)-[18F]FMeNER-D2 exhibits both high affinity and specificity to the NET and enable

specific binding equilibrium within PET measurement and provides acceptable de-fluorination, it

is currently one of the best suited PET tracer for the in vivo quantification of the NET (Schou et

al, 2004; Takano et al, 2008b). Although there exists an extensive literature that associates the

noradrenergic signaling, and in specific the NET, in the biologic mechanisms of ADHD, there have

been no investigations carried out regarding the quantity and distribution of the NET in this

group.

1.5.3. PET and SPECT imaging of the serotonergic neurotransmitter system

Numerous PET and SPECT studies have been performed on the SERT in the last decades to gain

more insights of the main treatment target for affective disorders (Houle et al, 2000; Wong et al,

1995). Selective serotonin reuptake inhibitors (SSRIs) are potent antidepressants and are

frequently prescribed. They modulate serotonergic transmission in cortical and subcortical brain

regions by blocking the presynaptically bound SERT, thus increasing serotonin in the synaptic

cleft and modulating SERT expression. The serotonin neurotransmission is suggested to be highly

involved in the regulation of mood, emotion, appetite (Haleem, 1993), sleep (Franco-Perez et al,

2012), as well as in the processing of anxiety (Akimova et al, 2009). Further, serotonin is linked to

reward and motivation (Kranz et al, 2010), to impulsivity and attentional processes (Seo et al,

2008).

So far, of all the possible molecules capable of binding to SERT only the following were selected

for human in vivo investigations with PET (Paterson et al, 2013; Saulin et al, 2012). To this day

[11C]McN5652, [11C]DASB and [11C]MADAM are the three possible radioligands that fulfill PET

imaging criteria for in vivo in human investigations with PET, whereas [11C]DASB and

[11C]MADAM bear higher selectivity, lower non-specific binding and faster brain uptake than

[11C]McN5652 (Frankle et al, 2004; Houle et al, 2000; Lundberg et al, 2005; Suehiro et al, 1993).

In addition, studies observing occupancy of the SERT by SSRIs demonstrated displaceable

13

[11C]DASB and [11C]MADAM by SSRIs, which is why these two tracers are predominantly used for

reliable SERT quantification (Lundberg et al, 2012; Meyer et al, 2004).

High SERT concentration has been revealed in the brainstem and midbrain regions, while a

moderate binding potential has been shown in basal ganglia and diencephalic structures. Limbic

brain regions as the subgenual anterior cingulate cortex and insular cortex as well as parts of the

temporal cortex display high SERT binding, shown by human in vivo and post mortem studies

(Saulin et al, 2012; Savli et al, 2012a). These findings are replicated by post mortem data (Varnas

et al, 2004). Inconsistencies, which have been assigned to clinical and methodological reasons,

have been observed in SPECT and PET studies, showing either increased, attenuated or a lack of

differences of SERT levels between patients with depression and healthy control subjects (Herold

et al, 2006; Ichimiya et al, 2002; Parsey et al, 2006a).

Fig. 2 Region specific SERT binding in the human brain in vivo. High SERT binding is found in the midbrain. The image depicts triplanar structural images (axial, sagittal and coronal view) and superimposed SERT availability using [

11C]DASB. Mean SERT distribution maps have

been generated by using imaging data from the studie “The Serotonin Transporter in Attention Deficit Hyperactivity Disorder investigated with Positron Emission Tomography”, related to this thesis.

The SERT has also been investigated in numerous other neuropsychiatric disorders as anxiety

disorders, obsessive-compulsive disorder and eating disorders to name a few, though results

have been likewise inconsistent (Spies et al, 2015). Two molecular imaging studies have

investigated SERT concentration in adult, medication naïve patients with ADHD. A SPECT study

14

conducted by Hesse and colleges observed SERT availability with [123I]FP-CIT and SPECT in 17

patients with ADHD (Hesse et al, 2009). [123I]FP-CIT is a radiotracer that has moderate affinity to

the SERT in the midbrain regions and low affinity to the SERT in subcortical and cortical brain

regions. They found no significant alteration between patients and HC. Karlsson et al. assessed

SERT binding with [11C]MADAM and PET and found no changes in between patients with ADHD

and HC. Though, findings are preliminary and should be interpreted with caution, since Karlsson

et al. investigated SERT binding potential (BPND) in only eight patients. A sample size that is

insufficient in power to detect putative differences (Karlsson et al, 2013).

1.6. The genetic role in ADHD

As a highly heritable disorder estimated to be 76% (Faraone et al, 2005), several

neurotransmitter-related genes have been associated to ADHD, which is a neurodevelopmental

spectrum disorder underlying in the interplay between nature, representing genetic

composition, and nurture, environmental influence on development. Although genetic studies

have failed to verify a distinct gene or genetic variations accountable for a distinct neurobiology

of ADHD, complex polygenetic pathways as gene-gene interactions as well as gene-environment

interactions are implicated in its pathophysiology (Banaschewski et al, 2010).

The majority of candidate gene studies in ADHD observed monoaminergic system, including

genes of the dopaminergic, serotonergic and noradrenergic transporters and receptor systems.

Single nucleotide polymorphisms (SNPs) within as well as transcription variations of the NET

gene have been linked to ADHD specific behavior (Faraone et al, 2005; Kim et al, 2008). Clinical

response to MPH may be sensitive to noradrenergic (Kim et al, 2008; Yang et al, 2004) or

dopaminergic gene polymorphisms (Froehlich et al, 2011; Tharoor et al, 2008), while possible

dependence on Cathechol-O Methyltransferase (Kereszturi et al, 2008) or SERT genotype

implicates the serotonergic system (McGough et al, 2009; Tharoor et al, 2008). The SERT gene

(SERT; SLC6A4) and the genes encoding for serotonergic receptors comprise SNPs were found to

be implicated in influencing the susceptibility to ADHD (Faraone & Khan, 2006; van der Meer et

al, 2014).

To conclude while considering the finding described above, profound evidence exists that the

noradrenergic as well as the serotonergic neurotransmission, and in particular the NET and SERT,

contribute to the complex pathogenesis of adult ADHD. Molecular imaging and imaging genetics

15

based on PET data represents a promising research field to observe alterations in molecules as

well as association of genes and psychiatric pheno- and endophenotypes in the healthy human

brain as well as in neuropsychiatric disorders in vivo. At present, there is a lack of imaging

studies targeting molecular structures as the NET or the SERT in patients with ADHD, hampering

the interpretation of the involvement of these structures in ADHD.

16

1.7. Aims of this thesis

The main aims of this thesis were to objectify the availability of NET and SERT binding in a priori

selected brain regions in patients with ADHD and healthy control subjects. The first and the third

publication listed in the results section deals with this scientific question. Furthermore, we

performed an interregional molecular association of SERT BPND to display possible SERT patterns,

characteristic for patients compared to controls. Finally, the second publication investigates an

effect of SNPs on the NET BPND in patients with ADHD and healthy control subjects.

This thesis will focus on quantifying NET and SERT BPND, an index for density in the investigated

tissue, in patients with ADHD and healthy control subjects based on PET. The specific aims can

be summarized as:

To test whether NET BPND is significantly different in patients with ADHD compared to

healthy control subjects using PET and the radioligand (S,S)-[18F]FMeNER-D2.

To test whether SERT BPND is significantly different in patients with ADHD compared to

healthy control subjects using PET and the radioligand [11C]DASB.

To test whether interregional molecular association of SERT BPND, is significantly

different in patients with ADHD compared to healthy control subjects.

To investigate the relationship between the effects of SNPs on the NET BPND in patients

with ADHD and healthy control subjects.

Copyright 2014 American Medical Association. All rights reserved.

The Norepinephrine Transporterin Attention-Deficit/Hyperactivity DisorderInvestigated With Positron Emission TomographyThomas Vanicek, MD; Marie Spies, MD; Christina Rami-Mark, MSc; Markus Savli, PhD; Anna Höflich, MD;Georg S. Kranz, MSc, PhD; Andreas Hahn, MSc, PhD; Alexandra Kutzelnigg, MD; Tatjana Traub-Weidinger, MD;Markus Mitterhauser, MSc, PhD; Wolfgang Wadsak, MSc, PhD; Marcus Hacker, MD; Nora D. Volkow, MD;Siegfried Kasper, MD; Rupert Lanzenberger, MD

IMPORTANCE Attention-deficit/hyperactivity disorder (ADHD) research has long focused onthe dopaminergic system’s contribution to pathogenesis, although the results have beeninconclusive. However, a case has been made for the involvement of the noradrenergicsystem, which modulates cognitive processes, such as arousal, working memory, andresponse inhibition, all of which are typically affected in ADHD. Furthermore, thenorepinephrine transporter (NET) is an important target for frequently prescribed medicationin ADHD. Therefore, the NET is suggested to play a critical role in ADHD.

OBJECTIVE To explore the differences in NET nondisplaceable binding potential(NET BPND) using positron emission tomography and the highly selective radioligand(S,S)-[18F]FMeNER-D2 [(S,S)-2-(α-(2-[18F]fluoro[2H2]methoxyphenoxy)benzyl)morpholine]between adults with ADHD and healthy volunteers serving as controls.

DESIGN, SETTING, AND PARTICIPANTS Twenty-two medication-free patients with ADHD(mean [SD] age, 30.7 [10.4] years; 15 [68%] men) without psychiatric comorbidities and 22age- and sex-matched healthy controls (30.9 [10.6] years; 15 [68%] men) underwentpositron emission tomography once. A linear mixed model was used to compare NET BPND

between groups.

MAIN OUTCOMES AND MEASURES The NET BPND in selected regions of interest relevant forADHD, including the hippocampus, putamen, pallidum, thalamus, midbrain with pons(comprising a region of interest that includes the locus coeruleus), and cerebellum. Inaddition, the NET BPND was evaluated in thalamic subnuclei (13 atlas-based regions ofinterest).

RESULTS We found no significant differences in NET availability or regional distributionbetween patients with ADHD and healthy controls in all investigated brain regions(F1,41 < 0.01; P = .96). Furthermore, we identified no significant association between ADHDsymptom severity and regional NET availability. Neither sex nor smoking status influencedNET availability. We determined a significant negative correlation between age and NETavailability in the thalamus (R2 = 0.29; P < .01 corrected) and midbrain with pons, includingthe locus coeruleus (R2 = 0.18; P < .01 corrected), which corroborates prior findings of adecrease in NET availability with aging in the human brain.

CONCLUSIONS AND RELEVANCE Our results do not indicate involvement of changes in brainNET availability or distribution in the pathogenesis of ADHD. However, the noradrenergictransmitter system may be affected on a different level, such as in cortical regions, whichcannot be reliably quantified with this positron emission tomography ligand. Alternatively,different key proteins of noradrenergic neurotransmission might be affected.

JAMA Psychiatry. 2014;71(12):1340-1349. doi:10.1001/jamapsychiatry.2014.1226Published online October 22, 2014.

Author Affiliations: Department ofPsychiatry and Psychotherapy,Medical University of Vienna, Vienna,Austria (Vanicek, Spies, Savli, Höflich,Kranz, Hahn, Kutzelnigg, Kasper,Lanzenberger); Division of NuclearMedicine, Department of BiomedicalImaging and Image-Guided Therapy,Medical University of Vienna, Vienna,Austria (Rami-Mark, Traub-Weidinger,Mitterhauser, Wadsak, Hacker);National Institute on Alcohol Abuseand Alcoholism, National Institutes ofHealth, Bethesda, Maryland (Volkow).

Corresponding Author: RupertLanzenberger, MD, Functional,Molecular, and TranslationalNeuroimaging Lab, Department ofPsychiatry and Psychotherapy,Medical University of Vienna,Waehringer Guertel 18-20, 1090Vienna, Austria ([email protected]).

Research

Original Investigation

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A ttention-deficit/hyperactivity disorder (ADHD), whichis characterized by inattention, impulsivity, andhyperactivity,1 affects between 8% and 12% of children,2

persists into adulthood in approximately 30% of cases,3 and ex-hibits rising prevalence rates.4 Attention-deficit/hyperactivitydisorder is often associated with detrimental comorbidities5-7

as well as with a large personal and social burden.7 As a result,many individuals with ADHD routinely receive psychopharma-cologic treatment.

Patients with ADHD often receive methylphenidate hydro-chloride and amphetamine sulfate, which are stimulant medi-cations that enhance dopaminergic and noradrenergic signal-ing. Alternatively, atomoxetine hydrochloride, which is anonstimulant drug that blocks the norepinephrine trans-porter (NET), is used. By blocking the NET, atomoxetineaffects noradrenergic signaling and, particularly in brainregions lacking the dopamine transporter, increases dopa-minergic transmission.8,9 Treatment using methylphenidate,amphetamine, and atomoxetine is associated with improve-ment of clinical symptoms and performance in controlledtasks eliciting executive functions, such as inhibitory control,and of cognitive functions, such as working memory andattention.10-13

Although amphetamine and methylphenidate have beensuggested14-16 to exert therapeutic efficacy via an increase in ex-tracellular dopamine, they also have been shown16,17 to modu-late noradrenergic neurotransmission, which may be therapeu-tically relevant. Methylphenidate may dose-dependently blockthe NET, thereby regulating noradrenergic and dopaminereuptake.18,19 In a similar manner, atomoxetine has beenshown20 to facilitate therapeutic response by binding the NET.In addition, quetiapine fumarate, which is not used as an ADHDmedication but has been shown21 to improve cognitive func-tion in patients with psychosis, was shown22 to bind to the NET.Ultimately, facilitation of therapeutic response by catechol-amines in general and the NET in particular suggests that thesesystems may be relevant to ADHD.

Furthermore, ADHD symptoms have long been attrib-uted to abnormalities within the frontostriatal and frontopa-rietal networks implicated in executive functions23 modu-lated by catecholaminergic systems.24,25 The noradrenergicsystem, which originates in the locus coeruleus and exerts vir-tually ubiquitous influence within the brain, modulates, amongother cortical regions, the prefrontal cortex through dynamicadaption of tonic and phasic firing.26 Studies27,28 displaying im-provement of such symptoms by application of α2 agonists fur-ther link noradrenergic influence on prefrontal cortex–mediated cognitive functions to ADHD.

More assertive investigation of underlying neurobiologi-cal correlates is made possible through positron emission to-mography (PET) imaging studies, which have focused onADHD-related changes in the dopaminergic system. Al-though changes in dopamine transporter29-31 and dopamineD2 and D3 receptor levels and distribution29,32,33 as well as dopa-mine release34,35 have been investigated, the results remain in-conclusive. However, the proposition that methylphenidate,amphetamine, and atomoxetine may induce therapeuticresponse via NET modulation suggests that noradrenergic

factors, and more specifically changes in the NET, may play arole in ADHD pathogenesis.

Therefore, we proposed a thorough investigation of ADHD-related NET distribution, as has been performed for the sero-tonin transporter and dopamine transporter. To address thisissue, we used the recently developed NET-specific radio-tracer (S,S)-[18F]FMeNER-D2 [(S,S)-2-(α-(2-[18F]fluoro[2H2]methoxyphenoxy)benzyl)morpholine], which has been suc-cessfully applied in healthy control groups.36 To investigate therole of noradrenergic changes within ADHD, NET imaging wascarried out in a region of interest (ROI) approach focusing onbrain areas integral to the noradrenergic system.

MethodsParticipantsWritten informed consent was obtained from all participantsafter detailed explanation of the study protocol, and the par-ticipants received financial reimbursement. The study was ap-proved by the ethics committee of the Medical University ofVienna and the General Hospital of Vienna (EK 552/2010).

Twenty-two adults with ADHD (mean [SD] age, 30.7 [10.4]years; 15 [68%] men) and 22 age- and sex-matched healthy in-dividuals serving as controls (30.9 [10.6] years; 15 [68%] men)(Table 1) were recruited through an ADHD outpatient clinic at

Table 1. Epidemiologic and Clinical Characteristics of Participants

Characteristic

No. (%)ADHD Group

(n = 22)Control Group

(n = 22)Age, mean (SD), y 30.7 (10.4) 30.9 (10.6)

Sex

Male 15 (68) 15 (68)

Female 7 (32) 7 (32)

Current smoker 7 (32) 11 (50)

Handedness

Right 20 (91) 17 (77)

Left 2 (9) 5 (23)

CAARS score, mean (SD)a

Inattentiveness 18.8 (5.2) 0.1 (0.4)

Hyperactivity/impulsivity 19.6 (5.6) 0.2 (0.6)

Past psychopharmacologic treatmentb NA

Stimulants 4 (18)

SNRIs 2 (9)

Stimulants and antidepressants 1 (4)

Past comorbidities NA

Depression, currently in remission 7 (32)

Drug abuse 2 (9)

Abbreviations: ADHD, attention-deficit/hyperactivity disorder; CAARS, ConnersAdult ADHD Rating Scale; NA, not applicable; SNRIs, selective norepinephrinereuptake inhibitors.a Differences between the patients with ADHD and the control participants

were significant (P < .001).b The patients had received no psychopharmacologic drugs for at least 6

months before the investigation.

Norepinephrine Transporter in ADHD Original Investigation Research

jamapsychiatry.com JAMA Psychiatry December 2014 Volume 71, Number 12 1341




the Department of Psychiatry and Psychotherapy, Medical Uni-versity of Vienna, Vienna, Austria, and from the local commu-nity via advertisement. Patients had not received psychophar-macologic treatment for at least 6 months before the screeningvisit; all control participants were naive to all psychopharma-cologic treatment. Of the original 51 study participants, 2 (4%)were excluded because of substance abuse, 2 (4%) becauseof somatic disorders, and 3 (6%) because of radiosynthesisdifficulties.

Medical Examination and Clinical ExplorationParticipants underwent standard medical examination includ-ing general physical and neurologic status evaluation, electro-cardiography, and routine laboratory tests at the screening andfinal visits to ensure their physical health. Women under-went a urine pregnancy test at the screening visit and beforePET measurement. A multidrug urine test was performed atthe screening visit to exclude current substance abuse.Participants were interviewed by experienced psychiatristsusing the Conners Adult ADHD Diagnostic Interview forDSM-IV37 to evaluate current and childhood attentional andhyperactivity/impulsivity symptoms and confirm the ADHDdiagnosis. The Conners Adult ADHD Rating Scale (CAARS)–Investigator Screening Version (Table 1) was applied to assessthe presence and severity of inattentive and hyperactivity/impulsivity symptoms, and third-party–reported and self-reported symptoms were determined with the CAARS-Observer Screening Version and the CAARS-Self-ReportScreening Version. The Structured Clinical Interview forDSM-IV Axis I and Axis II disorders was performed to excludecomorbid psychiatric disorders. Handedness was evaluatedwith the Edinburgh Inventory,38,39 and IQ was determined withthe Viennese Matrices Test–2.40 Patients with ADHD did notdiffer significantly from the controls in IQ (ADHD, 92.86 [15.22];controls, 98.77 [12.89]; P = .16; paired, 2-tailed t test). Partici-pants were subdivided into groups best describing their smok-ing status according to the quantity of consumption, which wasassessed in an open-question format (nonsmokers, 5 cigarettes/wk, 5 cigarettes/d, 5-10 cigarettes/d, 10 cigarettes/d, 10-15 ciga-rettes/d, 15 cigarettes/d, and 20 cigarettes/d; ranks were 1-8,respectively). The ADHD group did not significantly differ insmoking status compared with the control group (median rank:ADHD, 0; control, 0.5; z = −0.48, P = .63, Mann-Whitney test).Individuals with PET- or magnetic resonance imaging (MRI)–incompatible implants or in pregnancy or breastfeeding werenot included in this study.

Data AcquisitionAll PET was carried out at the Department of BiomedicalI m a g i ng a n d I m a g e - G u i d e d T h e r a p y, D i v i s i o n o fNuclear Medicine, Medical University of Vienna, using afull-ring scanner (GE Advance; General Electric MedicalSystems) in a 3-dimensional acquisition mode. We applied(S,S)-[18F]FMeNER-D2, which is among the most suitablePET tracers for in vivo NET quantification41,42 as describedpreviously43 for the following reasons: (1) fluorine F18–labeled reboxetine analogues enable specific bindingequilibrium to be reached within a reasonable time frame

for PET measurement owing to their 5-fold higherhalf-life44; (2) in vivo defluorination can be reduced consid-erably, and the interpretability of regions in proximity tobone thereby increased, through the use of deuteratedh o m o l o g u e s 4 5 ; a n d ( 3 ) ( S ,S ) - [ 1 8 F ] F Me N E R- D 2 h a sshown45 both high affinity and selectivity to the NET. A5-minute transmission scan using retractable germaniumGe 68 rod sources for tissue attenuation correction was per-formed before the emission scan. Data acquisition started120 minutes after a bolus intravenous injection of 4.7MB q /kg of body weight (ADHD, 395.1 [98.7 ] MB q;controls, 379.0 [62.2] MBq; P = .53, 2-tailed, paired t test) of(S,S)-[18F]FMeNER-D2. Mean (SD) specific radioactivity of(S,S)-[18F]FMeNER-D2 was 589.4 (399.7) GBq/μmoL (ADHD)and 440.4 (233.7) GBq/μmoL (controls) (P = .15, 2-tailed,paired t test). Brain radioactivity was measured in a series of6 consecutive time frames lasting 10 minutes each in theinterval of 120 to 180 minutes after administration of thebolus. Acquired data were reconstructed in volumes con-sisting of 35 transaxial sections (128 × 128 matrix) using aniterative filtered back-projection algorithm46 with a spatialresolution of 4.36 mm full-width at half of the maximum 1cm next to the center of the field of view. For coregistration,MRIs were acquired from all participants on a 3-T scanner(Achieva; Philips) using a 3-dimensional T1 fast field echo–weighted sequence, yielding 0.88-mm section thicknessand in-plane resolution of 0.8 × 0.8 mm.

Data QuantificationEach time frame of the dynamic PET scan was realigned to themean of frames with no head motion, identified by visual in-spection. Subsequently, each summed image (PET integralimage from realigned data) was coregistered (rigid body trans-formation) to each participant’s MRI using a mutual informa-tion algorithm implemented in SPM8 (Wellcome Trust Cen-tre for Neuroimaging; http://www.fil.ion.ucl.ac.uk/spm/).Parametric images of nondisplaceable binding potential (BPND)values were calculated using the caudate as the referenceregion because it was devoid of NET.44 According tonomenclature,47 the BPND values were defined as follows:

BPND =

180

120Ctarget

180

120Creference

− 1,

where Ctarget indicates radioactivity concentration of thetarget region and Creference, radioactivity concentration ofthe reference region.36 Caudate ROIs were delineated onMRIs in individual-participant space using image analysissoftware (PMOD, version 3.1; PMOD Technologies Ltd;http://w w w.pmod.com), which were subsequentlytransferred to coregistered summed PET images. IndividualMRIs were spatially normalized to the T1-weighted MRItemplate provided in SPM8. Resulting transformation matri-ces were applied to the coregistered parametric imageswarping them into Montreal Neurological Institute (MNI)standard space.

Research Original Investigation Norepinephrine Transporter in ADHD

1342 JAMA Psychiatry December 2014 Volume 71, Number 12 jamapsychiatry.com




Regions of InterestThe ROIs selected included NET-rich regions, based on post-mortem and in vivo human brain studies,36,44 and show agood signal to noise ratio and an acceptable bone spilloverdue to (S,S)-[18F]FMeNER-D2 defluorination.48 Bindingpotential values were extracted from parametric maps fromeither atlas-generated ROIs or manually delineated ROIs.Atlas-generated ROIs were identified from the HammersMaximum Probability Atlas49 including 6 regions: thehippocampus, putamen, pallidum, thalamus, midbrainwith pons (including the locus coeruleus), and cerebellum.Since the NET concentration in the thalamus is nothomogeneous,41 13 thalamic subnuclei were generated withthe Wake Forest University Pickatlas Tool (Table 2).50 Toverify atlas-generated ROIs, 4 atlas ROIs were delineated onthe MNI T1 single-participant brain: the midbrain (dorsallylocated including raphe nuclei, excluding pons), locus coer-uleus located according to Keren et al,51 claustrum, andhypothalamus. In addition to the above-mentioned atlasROIs, further ROIs, specifically the locus coeruleus andthalamus, which are brain regions highest in NETconcentration,41 were delineated manually for each partici-pant for confirmatory purposes. Atlas ROIs match the MNIstandard space.

Statistical AnalysisData were analyzed using linear mixed models for the out-come measure NET BPND with the ROI as a repeated factor;participant groups, sex, and ROI as fixed factors; and par-ticipants and matched participant pairs as random factors. Aseparate model was calculated for the 6 ROIs based on theHammers Maximum Probability Atlas and for the 13 tha-lamic subnuclei. Likewise, manually delineated ROIs wereassessed in 2 additional models: one using the 4 atlas-basedROIs and the other using the 2 individual-based ROIs. Fixedeffects were included in the model in a multifactorialapproach, whereas interaction effects were dropped ininstances of nonsignificance. In cases of significant interac-tions or main effects, post hoc pairwise comparisons werecomputed and Bonferroni correction was performed formultiple comparisons. In a second exploratory approach toexamine the effects of handedness, smoking status, andage, a mixed model was calculated using a stepwise proce-dure with backward elimination, starting with all candidatevariables (including participant groups and ROIs) and fol-lowed by a stepwise deletion of interactions and variableswith the largest P values. Finally, mixed-models analyseswere also applied to investigate the effects of the clinicalvariables inattentiveness and hyperactivity/impulsivity,which were assessed with the CAARS-Investigator Screen-ing Version. According to the Akaike information criterion,52

repeated measurements were modeled using a compoundsymmetric covariance structure. As an exploratory analysis,we also compared NET BPND between patients and controlsat the voxel level using SPM8 (paired t test); SPSS, version19.0 for Windows (SPSS Inc), was used for statistical compu-tations. The 2-tailed significance level was set at P = .05.Region of interest and voxel-wise analysis results were cor-

rected for multiple comparisons using Bonferroni and falsediscovery rate analysis, respectively.

ResultsLinear mixed-models analysis revealed an expected maineffect of ROI (F5,215 = 117.71; P < .001) but no main effects ofparticipant group (F1,41 <0.01; P = .96) (Table 2 and Figure 1)or sex (F1,41<0.01; P = .98) and no interaction effects (allP > .10). Post hoc pairwise comparisons revealed significantNET BPND differences between the 6 tested brain regions(atlas-generated ROIs; P < .05, corrected) except for the

Table 2. Norepinephrine Transporter Binding Potential by ROIa

Characteristic

Mean (SD)ADHDGroup

(n = 22)

ControlGroup

(n = 22)Hammers Maximum Probability Atlas ROIs

Thalamus 0.36 (0.08) 0.37 (0.10)

Hippocampus 0.12 (0.06) 0.11 (0.06)

Midbrain with pons 0.25 (0.11) 0.26 (0.11)

Putamen 0.18 (0.06) 0.18 (0.05)

Pallidum 0.23 (0.06) 0.22 (0.06)

Cerebellum 0.15 (0.10) 0.16 (0.08)

MNI T1 single-participant brain-delineated ROIs

Midbrain without pons 0.50 (0.12) 0.46 (0.14)

Locus coeruleus 0.41 (0.12) 0.39 (0.13)

Claustrum 0.18 (0.06) 0.18 (0.05)

Hypothalamus 0.29 (0.11) 0.28 (0.10)

Manually delineated individual ROIs

Thalamus 0.31 (0.13) 0.50 (0.12)

Locus coeruleus 0.35 (0.14) 0.47 (0.10)

Thalamic subnuclei ROIs delineatedwith WFU Pickatlas Tool

Lateral

Dorsal nucleus 0.16 (0.20) 0.23 (0.17)

Geniculum body 0.34 (0.13) 0.31 (0.12)

Posterior nucleus 0.37 (0.11) 0.40 (0.12)

Mammillary body 0.59 (0.14) 0.55 (0.16)

Medial

Dorsal nucleus 0.51 (0.41) 0.53 (0.15)

Geniculum body 0.52 (0.18) 0.47 (0.16)

Midline nucleus 0.06 (0.21) 0.13 (0.17)

Pulvinar 0.32 (0.13) 0.33 (0.13)

Subthalamic nucleus 0.40 (0.14) 0.36 (0.12)

Ventral

Anterior nucleus 0.12 (0.12) 0.16 (0.12)

Lateral nucleus 0.37 (0.10) 0.39 (0.09)

Posterior lateral nucleus 0.60 (0.15) 0.59 (0.13)

Posterior medial nucleus 0.75 (0.14) 0.73 (0.16)

Abbreviations: ADHD, attention-deficit/hyperactivity disorder; MNI, MontrealNeurological Institute; ROI, region of interest; WFU, Wake Forest University.a No significant differences could be detected in the norepinephrine transporter

nondisplaceable binding potential between the groups.






comparisons of midbrain with pallidum and putamen withcerebellum, which had similar binding values (Table 2 andFigure 2). Analogous results were obtained from the 2 mixedmodels for the manually delineated ROIs, which showedmain effects of ROI but no main effects of group and sex andno interaction effects. Similarly, the linear mixed model forNET binding within the thalamic subnuclei revealed a maineffect of ROI (F12,516 = 105.53; P < .001) but no main effect ofgroup (F1,41 = 0.08; P = .78) or sex (F1,41 = 0.39; P = .54) andno interaction effects (all P > .10). In addition, there was nosignificant difference in NET binding between patients withADHD and the controls in any brain region at the voxel level(all P > .05, corrected).

When investigating the potential effects of handedness,smoking status, and age, mixed-models analysis for ROI

NET BPND based on the Hammers Maximum ProbabilityAtlas revealed an interaction effect between ROI and age(F5,190 = 9.94; P < .001) in addition to a main effect of ROIbut no main effect of age. Post hoc correlation analysesbetween regional NET BPND and age revealed strong nega-tive correlations in the thalamus (R2 = 0.29; P < .01 cor-rected) and midbrain (R2 = 0.18 P < .01 corrected) (Figure 3),but these correlations did not differ significantly betweenthe control and ADHD groups. Handedness and smokingstatus had no effect on NET BPND, nor did they lead to anysignificant interactions. Comparable results were observedfor manually delineated ROIs, which showed strong nega-tive correlations between NET BPND and age in the midbrain(R2 = 0.28; P < .01 corrected), locus coeruleus (R2 = 0.26;P < .01 corrected), and hypothalamus (R2 = 0.26; P < .01 cor-

Figure 1. Mean (S,S)-[18F]FMeNER-D2 Distribution Normalized to the Montreal Neurological Institute T1 Template in 22 Healthy Control Participants

Norepinephrine transporter binding potential

0 >0.7

High norepinephrine transporter nondisplaceable binding potential (NET BPND)was found in the thalamus and midbrain regions of interest, and the lowest wasobserved in the basal ganglia. The highest NET uptake occurred in bones, aphenomenon associated with tracer-specific defluorination. The color bar

represents the BP at each voxel, with blue indicating the lowest andred the highest NET BPND (a unitless measure). The crosshair is seton the thalamus. (S,S)-[18F]FMeNER-D2 indicates(S,S)-2-(α-(2-[18F]fluoro[2H2]methoxyphenoxy)benzyl)morpholine.

Figure 2. Norepinephrine Transporter Nondisplaceable Binding Potential (NET BPND)in Selected Regions of Interest

0.6

0.5

0.4

0.3

0.2

0.1

0

PallidumHippocampus MidbrainCerebellum Putamen Thalamus

NET

BP N

D

ADHDControl

There were no significant differencesbetween the ADHD and controlgroups in NET BPND (a unitlessmeasure) in patients withattention-deficit/hyperactivitydisorder (ADHD) and healthy controlparticipants. The heavy rule withinthe scatterplots indicates the mean;thin rules, SD.






rected). In addition, no main or interaction effects wereobserved for clinical variables (CAARS-Inattentiveness andCAARS-Hyperactivity/Impulsivity) and ROI BPND. Finally,exclusion of 3 patients with previous methylphenidateintake in childhood (intake duration was 4, 5, and 7 years)and 2 patients with previous atomoxetine consumption inadulthood (intake duration was 5 and 6 months) did notchange NET binding results. We further excluded 2 patientsexhibiting predominantly inattentive symptoms and 1exhibiting predominantly hyperactivity/impulsivity symp-toms and, in a separate analysis, 2 patients with past drugabuse. Exclusion of these participants did not change theresults.

DiscussionTo our knowledge, this is the first PET study to investigatethe differences in brain NET distribution and availability inadults with ADHD. We found no significant differences inthe BPND of (S,S)-[18F]FMeNER-D2 between the patientswith ADHD and the controls. Furthermore, exclusion ofpatients exhibiting either predominantly inattentive or pre-dominantly hyperactivity/impulsivity subtypes and patientswith previous ADHD pharmacotherapy or past drug abusedid not change the results. Our findings validate previousstudies53 showing an age-related decrease in brain NET

availability in the healthy human brain and show an age-related decrease in brain NET availability in adults withADHD.

Randomized placebo-controlled studies54-56 have repeat-edly shown that methylphenidate, amphetamine, and atom-oxetine significantly decrease symptoms in adult ADHD pa-tient cohorts. The clinical efficacy of a pharmaceutical agentimplies that the mechanism of action through which it at-tains a response is relevant to the neurobiology and resultingsymptoms of a particular disease. Therefore, modulation of thenoradrenergic system by these 3 drugs suggests noradrener-gic abnormalities in ADHD.

Executive functions, such as response inhibition, vigi-lance, working memory, and planning, are typically impairedin ADHD.57,58 The association of these functions with the pre-frontal cortex, which exhibits pronounced noradrenergic in-nervation, once again implicates, more generally, the norad-renergic system in ADHD.59

However, investigations into the involvement of otherneurotransmitter systems in ADHD are similarly inconclu-sive. First, current data available on the dopaminergic con-tribution to ADHD are wrought with inconsistency. As is thecase with the NET, therapeutic doses of methylphenidatehave been shown60,61 using PET to reduce radiotracer stria-tal dopamine transporter binding in a dose-dependent man-ner in healthy individuals. Methylphenidate-induced dopa-mine transporter blockade has been causally linked to an

Figure 3. Negative Correlation of Norepinephrine Transporter Nondisplaceable Binding Potential (NET BPND) and Age in the Thalamusand Midbrain/Pons

0.6

0.5

0.4

0.3

0.2

0.1

0.6

0.5

0.4

0.3

0.2

0

0.1

4020 3010 50 60

NET

BP N

D: T

hala

mus

ADHDControl

Age at PET, y4020 3010 50 60

NET

BP N

D: M

idbr

ain

With

Pon

sAge at PET, y

A BThalamus Midbrain/pons

A significant negative correlation existed between the NET BPND (a unitlessmeasure) and age in the thalamus (R2 = 0.29; P < .01 corrected) (A) andmidbrain/pons (R2 = 0.18; P < .01 corrected) (B). Regions of interest wereextracted from Hammers Maximum Probability Atlas. The significance level was

set at P < .05 and the results were Bonferroni corrected for multiplecomparisons. ADHD indicates attention-deficit/hyperactivity disorder;PET, positron emission tomography. Please note the differentNET BPND ranges on the y-axis.





http://www.fil.ion.ucl.ac.uk/spm/


increase in striatal extracellular dopamine in the humanbrain,14 and this effect has been associated with therapeuticresponses to methylphenidate in ADHD.62 Moreover, striataldopamine transporter availability in patients with ADHDwas correlated with improvement of clinical symptoms aftermethylphenidate treatment.63 Brain imaging studies,31,63-65

however, have reported an array of partially contradictoryresults ranging from dopamine transporter increases to alack of change66 to decreases29,67 in the brain of adults withADHD. Although methodologic factors (eg, tracer choice)and patient characteristics (including the presence of priormedication, comorbidities, and differing sample sizes) havebeen suggested29,30 to account for this variability in results,investigations of other components of the dopaminergicsystem, such as the D2 and D3 receptors, are similarlyinconsistent.29,32 In addition, serotonergic alterations havebeen discussed in the context of ADHD68 and are primarilybased on the relationship between serotonergic innervationand impulsivity and hyperactivity, which are 2 core ADHDsymptoms.69 However, serotonergic involvement in ADHDis contradicted by data showing the limited clinical efficacyof selective serotonin reuptake inhibitors in the improve-ment of ADHD symptoms. Furthermore, serotonin trans-porter imaging studies67,70 showed no difference in seroto-nin transporter distribution between patients with ADHDand healthy controls. Therefore, although existing evidenceneither affirms nor disproves the neurotransmitter systemsdiscussed above to be involved in ADHD, background phar-macologic evidence supporting, in particular, dopaminergicand noradrenergic contribution, is strong. It was recentlysuggested by del Campo et al32 that ADHD-related dopamin-ergic changes may reflect associated symptoms rather thana disease-specific endophenotype. Therefore, approachesthat step away from the concept of endophenotypical nor-adrenergic changes in ADHD and focus on changes associ-ated with ADHD symptoms may prove to be valuable. How-ever, exclusion of patients exhibiting the predominantlyinattentive subtype and predominantly hyperactivity/impulsivity subtype of ADHD did not change our main find-ings, strongly suggesting that our results reflect a lack ofchanges in the brain NET level in ADHD in general ratherthan a subtype-specific phenomenon. In this context, futurestudies may profit from incorporating cognitive tests andgenetic data into analysis for further symptom-oriented andphenotypical classification of participants.

Despite the well-established link between modulation ofthe NET and improvement of ADHD symptoms, supported byrecent genetic studies71 implicating the NET gene in ADHD, ourstudy did not reveal differences in NET distribution betweenpatients with ADHD and the controls. Atomoxetine, methyl-phenidate, and amphetamine modulation of the NET has yetto be investigated in individuals with ADHD. Therefore, onecannot exclude the possibility that pharmacologic mecha-nisms of stimulants and nonstimulants in patients with ADHDdiffer from those in healthy individuals, as has been pro-posed to be the case by some investigators,72 although not byothers.73 However, the results of the present study may alsobe interpreted to suggest that, despite the proposed involve-

ment in the efficacy of ADHD pharmaceuticals, the NET maynot be integral to ADHD. Nevertheless, the missing differencein the NET between groups would not necessarily exclude theinvolvement of other components of the noradrenergic sys-tem in ADHD. In fact, guanfacine hydrochloride, an α2 adre-noceptor agonist and novel ADHD treatment option, appearsto be a good treatment alternative to stimulant and nonstimu-lant medications.74 Although this finding does not necessar-ily imply that α2 adrenoceptors are integral to ADHD, it againunderlines the link between noradrenergic innervation andADHD symptoms while proposing that ADHD symptoms mayalso be modulated by other noradrenergic elements.

However, several characteristics attributed to the trans-porter limit PET investigations into the role of the NET in ADHDand therefore must be considered. First, although cortical andsubcortical regions express NET, the levels of expression aregenerally considered to be low,36,75,76 particularly in frontalcortical regions. Therefore, comparability between partici-pant groups is limited in these areas. Second, evaluation ofNET levels in lateral cortical regions, including frontal re-gions, is made challenging by skull-bound radioactivity, whichspills into adjacent regions and has been associated with(S,S)-[18F]FMeNER-D2.45,48 Therefore, owing to generally lowfrontal cortex NET levels, together with image contamina-tion as a result of spillover from bone uptake, NET levels in lat-eral frontal cortical regions cannot be evaluated with(S,S)-[18F]FMeNER-D2. Thus, we cannot exclude the possibil-ity of NET differences between patients with ADHD and healthycontrols in these cortical regions.

Neuroanatomic traits intrinsic to the noradrenergic sys-tem further limit interpretability of the present study’sresults. Partial volume effects resulting from the small size ofthe locus coeruleus together with current standards of PETspatial resolution may result in an underestimation of NETlevels within this region.36 Accordingly, autoradiographystudies44 have shown locus coeruleus NET values to be 10times higher than those of other cortical and subcorticalregions, including the thalamus. However, our findings con-firm those of PET studies36,41 applying (S,S)-[18F]FMeNER-D2,showing only slight differences between the locus coeruleusand thalamus. These method-dependent differences speakfor distortion of locus coeruleus values through partial vol-ume effects. In addition, we cannot exclude the possibilitythat similar effects may influence NET values measured inthe small thalamic subnuclei evaluated.

ConclusionsThe lack of differences observed in NET distribution betweenpatients with ADHD and control participants does not ex-clude noradrenergic abnormalities in ADHD, since only onemolecular aspect and not all regional aspects of the noradren-ergic system were investigated. To further clarify NET involve-ment in ADHD, cortical brain regions must be investigated andoccupancy studies must be carried out to solidify the relation-ship between pharmacologically induced clinical improve-ment and noradrenergic changes.






ARTICLE INFORMATION

Submitted for Publication: February 11, 2014; finalrevision received May 13, 2014; accepted May 22,2014.

Published Online: October 22, 2014.doi:10.1001/jamapsychiatry.2014.1226.

Author Contributions: Drs Vanicek and Spiescontributed equally. Dr Lanzenberger had fullaccess to all the data in the study and takesresponsibility for the integrity of the data and theaccuracy of the data analysis.Study concept and design: Vanicek, Kranz,Kutzelnigg, Mitterhauser, Volkow, Kasper,Lanzenberger.Acquisition, analysis, or interpretation of data:Vanicek, Spies, Rami-Mark, Savli, Höflich, Kranz,Hahn, Kutzelnigg, Traub-Weidinger, Wadsak,Hacker, Lanzenberger.Drafting of the manuscript: Vanicek, Spies, Savli,Traub-Weidinger, Volkow, Lanzenberger.Critical revision of the manuscript for importantintellectual content: Vanicek, Spies, Rami-Mark,Savli, Höflich, Kranz, Hahn, Kutzelnigg,Mitterhauser, Wadsak, Hacker, Kasper,Lanzenberger.Statistical analysis: Savli, Kranz.Obtained funding: Kranz, Kutzelnigg, Volkow,Lanzenberger.Administrative, technical, or material support:Vanicek, Spies, Rami-Mark, Höflich, Hahn,Kutzelnigg, Traub-Weidinger, Mitterhauser, Wadsak,Hacker, Kasper, Lanzenberger.Study supervision: Kutzelnigg, Mitterhauser,Wadsak, Volkow, Kasper, Lanzenberger.

Conflict of Interest Disclosures: Without anyrelevance to this work, Dr Vanicek has received atravel grant from Eli Lilly and Company and Sanovaand compensation for workshop participation by EliLilly and Company. Dr Spies has received travelgrants from AOP Orphan Pharmaceuticals and EliLilly and Company and compensation for workshopparticipation from Eli Lilly and Company. Dr Kranzhas received travel grants from AOP OrphanPharmaceuticals and Roche. Dr Kutzelnigg hasreceived travel grants from Affiris AG, AstraZeneca,Eli Lilly and Company, and NovartisPharmaceuticals; payment for lectures, includingservice on the speakers’ bureaus of Affiris AG,AstraZeneca, Eli Lilly and Company, and NovartisPharmaceuticals Corp; and has served as aconsultant and as a member of the advisory boardsfor the Austrian Federal Ministry of Health, Biogen-Idec, Eli Lilly and Company, and MediceArzneimittel Pütter GmbH. Dr Wadsak has receivedresearch support from ABX, Advion, Iason GmbH,Raytest Austria GmbH, and Rotem GmbH and hasserved as a consultant/trainer for Bayer and THPPharma. Dr Hacker has received conferencespeaker honoraria from Covidian, Endocyte, GEHealthcare, and IBA and consults for the advisoryboard of Endocyte. Dr Kasper has received grant/research support from the Austrian National Bank,Bristol-Myers Squibb, Dr Willmar Schwabe GmbH &Co KG, Eli Lilly and Company, Fonds fürwissenschaftliche Förderung, GlaxoSmithKline,Lundbeck A/S, Organon, Servier, and SunovionPharmaceuticals; has served as a consultant for oron the advisory boards of AOP OrphanPharmaceuticals, AstraZeneca, Austrian NationalBank, Austrian Sick Foundation, Bristol-Myers

Squibb, Eli Lilly and Company, German ResearchFoundation (Deutsche Forschungsgemeinschaft),Generali Insurance Company, GlaxoSmithKline,Janssen, Lundbeck A/S, Novartis, Organon, Pfizer,and Sepracor; and has served on speakers’ bureausfor AOP Orphan Pharmaceuticals, AstraZeneca, EliLilly and Company, Janssen, Lundbeck A/S,Neuraxpharm, Servier, and SunovionPharmaceuticals. Dr Lanzenberger has receivedtravel grants and conference speaker honorariafrom AstraZeneca, Lundbeck A/S, and RocheAustria GmbH. No other disclosures were reported.

Funding/Support: This research was supported bygrant P22981 from the Austrian Science Fund(FWF) (Dr Lanzenberger).

Role of the Funder/Sponsor: The Austrian ScienceFund (FWF) had no role in the design and conductof the study; collection, management, analysis, andinterpretation of the data; preparation, review, orapproval of the manuscript; and decision to submitthe manuscript for publication.

Additional Contributions: Medical support wasprovided by Mara Stamenkovic, MD (Department ofPsychiatry and Psychotherapy, Medical Universityof Vienna), Claudia Klier, MD (Department of Childand Adolescence Medicine, Medical University ofVienna), Brigitte Hackenberg, MD (Department ofChild and Adolescence Medicine, MedicalUniversity of Vienna), Anastasios Konstantinidis,MD (Department of Psychiatry and Psychotherapy,Medical University of Vienna), Pia Baldinger, MD(Department of Psychiatry and Psychotherapy,Medical University of Vienna), Diana Meshkat, MD(Department of Psychiatry and Psychotherapy,Medical University of Vienna), Jan Losak, MD(Department of Psychiatry and Psychotherapy,Medical University of Vienna), and Ralf Gößler, MD(Department of Child and Adolescence Psychiatry,Neurological Centre Rosenhügel, Vienna, Austria).Technical support was provided by the PET team,especially Georgios Karanikas, MD, Lucas Nics, MSc,PhD, Daniela Häusler, MSc, PhD, and CecilePhilippe, MSc, PhD (Department of BiomedicalImaging and Image-guided Therapy, Division ofNuclear Medicine, Medical University of Vienna).Administrative support was provided by GregorGryglewski, Marian Cotten, Jakob Unterholzner,and Mathis Godber Godbersen (Department ofPsychiatry and Psychotherapy, Medical Universityof Vienna). These individuals received no financialcompensation.

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Effects of Norepinephrine Transporter GeneVariants on NET Binding in ADHD and Healthy

Controls Investigated by PET

Helen L. Sigurdardottir,1 Georg S. Kranz,1 Christina Rami-Mark,2

Gregory M. James,1 Thomas Vanicek,1 Gregor Gryglewski,1

Alexander Kautzky,1 Marius Hienert,1 Tatjana Traub-Weidinger,2

Markus Mitterhauser,2 Wolfgang Wadsak,2 Marcus Hacker,2 Dan Rujescu,3

Siegfried Kasper,1 and Rupert Lanzenberger1*

1Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna,Austria

2Department of Biomedical Imaging and Image-Guided Therapy, Division of NuclearMedicine, Medical University of Vienna, Vienna, Austria

3Department of Psychiatry, University of Halle, Halle, Germany

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Abstract: Attention deficit hyperactivity disorder (ADHD) is a heterogeneous disorder with a stronggenetic component. The norepinephrine transporter (NET) is a key target for ADHD treatment and theNET gene has been of high interest as a possible modulator of ADHD pathophysiology. Therefore, weconducted an imaging genetics study to examine possible effects of single nucleotide polymorphisms(SNPs) within the NET gene on NET nondisplaceable binding potential (BPND) in patients with ADHDand healthy controls (HCs). Twenty adult patients with ADHD and 20 HCs underwent (S,S)-[18F]FMeNER-D2 positron emission tomography (PET) and were genotyped on a MassARRAYMALDI-TOF platform using the Sequenom iPLEX assay. Linear mixed models analyses revealed agenotype-dependent difference in NET BPND between groups in the thalamus and cerebellum. In thethalamus, a functional promoter SNP (23081 A/T) and a 50-untranslated region (50UTR) SNP (2182T/C), showed higher binding in ADHD patients compared to HCs depending on the major allele. Fur-thermore, we detected an effect of genotype in HCs, with major allele carriers having lower binding.In contrast, for two 30UTR SNPs (*269 T/C, *417 A/T), ADHD subjects had lower binding in the cere-bellum compared to HCs depending on the major allele. Additionally, symptoms of hyperactivity andimpulsivity correlated with NET BPND in the cerebellum depending on genotype. Symptoms correlatedpositively with cerebellar NET BPND for the major allele, while symptoms correlated negatively toNET BPND in minor allele carriers. Our findings support the role of genetic influence of the NE systemon NET binding to be pertubated in ADHD. Hum Brain Mapp 37:884–895, 2016. VC 2015 The Authors

Human Brain Mapping Published by Wiley Periodicals, Inc.

Contract grant sponsor: the Austrian Science Fund (FWF; R.L.;Project No: 22981)

Conflict of interest: The authors declare no conflict of interestwith regards to this paper.*Correspondence to: Rupert Lanzenberger, Neuroimaging Labs(NIL) – PET & MRI & EEG & Chemical Lab, Department of Psy-chiatry and Psychotherapy, Medical University of Vienna, Waeh-

ringer Guertel 18-20, 1090 Vienna, Austria. E-mail: [email protected]

Received for publication 30 June 2015; Revised 18 November2015; Accepted 18 November 2015.

DOI: 10.1002/hbm.23071Published online 17 December 2015 in Wiley Online Library(wileyonlinelibrary.com).

r Human Brain Mapping 37:884–895 (2016) r

VC 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution andreproduction in any medium, provided the original work is properly cited.

Key words: norepinephrine transporter; positron emission tomography; single nucleotide polymor-phisms; neuroimaging genetics; attention deficit hyperactivity disorder

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INTRODUCTION

Attention deficit hyperactivity disorder (ADHD) is themost frequent neurodevelopmental disorder diagnosed inchildren. It is characterized by inattention, hyperactivity,and impulsiveness which frequently leads to severe social,academic, and vocational dysfunction [De La Fuente et al.,2013]. In around 30% of ADHD cases, the symptoms per-sist through adolescence into adulthood [Barbaresi et al.,2013]. Symptoms differ in adults compared to children,such as hyperactivity decreases while problems with inat-tention persist [Volkow and Swanson, 2013]. ADHD has astrong genetic component with a heritability estimated tobe around 0.77 [Curatolo et al., 2009]. Though the heritabil-ity is rather high in ADHD, studies have failed to indicate asingle gene responsible for the course of ADHD, suggestingcomplex polygenetic mechanisms and gene environmentinteractions to be of importance [Banaschewski et al., 2010].

Norepinephrine (NE) neurotransmission has beenhypothesized to be altered in various disorders, such asdepression, PTSD, Alzheimer’s disease, and ADHD [Bie-derman and Spencer, 1999; Gulyas et al., 2010; Klimeket al., 1997; Pietrzak et al., 2013]. NE has long been dis-cussed to be dysregulated in ADHD since frequently pre-scribed psychopharmaca such as methylphenidate (MPH)and atomoxetine (ATX) target the dopaminergic and NEsystems by increasing the extracellular neurotransmitterlevels through inhibition of the respective reuptake trans-porters [Hannestad et al., 2010; Logan et al., 2007]. MPHand ATX, a selective NE reuptake inhibitor, have beenproven clinically effective in improving core symptoms inADHD [Asherson et al., 2014], although up to 40% ofpatients being ascribed to stimulant and nonstimulant medi-cation do not respond [Newcorn et al., 2008, 2009]. Recently,guanfacine, an alpha-2 adrenergic receptor agonist, has alsobeen used as an effective treatment option for patients withADHD [Newcorn et al., 2013]. It is, therefore, likely that alter-ations in the NE system may predispose to ADHD and thus,the norepinephrine transporter (NET) gene is suspected toplay a major role in ADHD pathogenesis. The gene encodingfor the NET (SLC6A2) contains certain single nucleotidepolymorphisms (SNPs) that have been investigated in patho-logical conditions [Hahn and Blakely, 2007]. In associationand linkage studies, various SNPs have been found to beinvolved throughout the ADHD population [Kim et al.,2006a; Sengupta et al., 2012]. Results, however, have varied,and there is some contradictory results confounding thistheory [Barr et al., 2002; Xu et al., 2005].

As for in vivo brain quantification of the NET, specifi-cally in ADHD, literature is quite scarce until now. In a

recently published study, our group demonstrated no dif-ferences in NET nondisplaceable binding potential (BPND)in patients with ADHD compared to healthy controls(HCs) [Vanicek et al., 2014]. It is of high interest to exam-ine whether genetic variants in the NE system have aneffect on NET BPND which could shed light on individualdifferences in susceptibility to ADHD. Additionally, to thebest of our knowledge, no positron emission tomography(PET) study is available so far investigating polymor-phisms in the NE system on the NET binding, neither inHCs nor in patients with ADHD.

Thus, the aim was to examine the relationship betweenthe effects of SNPs in the NE system and the NET BPND ina cohort comprising of ADHD subjects and HCs matchedfor age and sex. We hypothesized that ADHD subjects car-rying either major or minor alleles will have higher bindingcompared to their healthy matched controls. High bindingsubcortical regions believed to be principal areas in behav-ioral and attentional control were selected [Arnsten andRubia, 2012], whereas cortical regions were dismissed dueto the defluorination and bone spill over of the radioligand(S,S)-[18F]FMeNER-D2. Moreover, we hypothesized that thesymptoms of hyperactivity and impulsivity would correlateto with NET BPND in areas related to motoric activity (puta-men, cerebellum, midbrain) whilst symptoms of inattentionwould correlate with NET BPND in the thalamus.

MATERIALS AND METHODS

Subjects

Twenty adult ADHD patients (age 6 SD: 30.8 6 10.9, 14males) and 20 HCs (age 6 SD: 30.4 6 10.9, 14 males) wererecruited through ADHD outpatient clinic at the Depart-ment of Psychiatry and Psychotherapy, Medical Universityof Vienna, and from the local community via advertise-ment as previously published elsewhere [Vanicek et al.,2014]. All patients had been free from psychopharmacolog-ical treatment for at least 6 months prior to screening visit.During the prescreening, medical examinations includingwithdrawal of blood samples were performed to ensurephysical well being of participants. All participants under-went a multidrug urine test to assess current substanceabuse. For inclusion, patients had to have a currentADHD diagnosis as well as a history of childhood ADHD.Five of the 20 patients had their first diagnosis in child-hood. Subjects were interviewed using the Conners’ AdultADHD Diagnostic Interview for DSM-IV (CAADID, Con-ners, 1999), Conners’ Adult ADHD Rating ScaleInvestigater-Screen Version (CAARS-Inv:SV), Conners’

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Adult ADHD Rating Scale: Observer-Screen Version(CAARS-O:SV) and the Conners‘Adult ADHD RatingScale: The self-report screening Version (CAARS-S:SV). Toexclude any current comorbidities, subjects were inter-viewed using the Structural Clinical Interview for DSM-IVAxes I and II disorders. HCs were na€ıve to psychopharma-cological treatment. Participants signed written consentforms for the study and were reimbursed financially fortheir participation. The study was approved by the Ethicscommittee of the Medical University of Vienna.

Selection of Single Nucleotide Polymorphisms

Eleven SNPs (Fig. 1) were considered for inclusion,which were selected upon previous association studies[Bobb et al., 2005; Sengupta, et al., 2012; Thakur et al.,2012] and functional effect studies [e.g., functional pro-moter 23081 A/T (rs28386840), where the minor allele (T)has been shown to decrease promoter activity] [Kim et al.,2006a]. Three SNPs were included in genotyping to extendthe 30 flanking region (rs15534, rs40615, rs7188230). Fur-thermore, three SNPs were chosen to extend the 50 regionof NET (rs2397771, rs168924, rs2242246). Haploview ver-sion 4.2 (http://www.broad.mit.edu/mpg/haploview/)was used to test whether frequencies were according toHardy–Weinberg equilibrium. Furthermore, the tag func-tion in Haploview was used to identify SNPs in high-

linkage disequilibrium. It refers to the nonrandom associa-tion of alleles at different loci, allowing an identification ofgenetic variations by the information derived from one ormore SNPs [Hu et al., 2004; Stram, 2004]. Thus, these taggedSNPs were used for further analysis. For final analysis, thefollowing four SNPs were used as identified using the tagfunction in Haploview: 23081 A/T (rs28386840) and 2182T/C (rs2242446) (r2 5 869), and *269 T/C (rs15534) and *417A/T (rs40615) (r2 5 0.866). For simplicity’s sake, they willbe referred to by their rs number in this article.

Genotyping

Procedures were preformed as previously described [Bal-dinger et al., 2014]. In short, 9 ml. EthyleneDiamineTetraace-tic Acid (EDTA) blood samples were drawn from eachsubject and DNA was isolated from whole blood using theQiaAmp DNA blood maxi kit (Qiagen, Hilden, Germany).Genotyping was performed using the iPLEX assay on theMassARRAY MALDI-TOF mass spectrometer as described[Oeth et al., 2009]. Allele specific extension products wereidentified and genotypes allocated by Typer 3.4 Software(Sequenom, San Diego, CA). All applied quality criteria weremet [individual call rate >80%, SNP call rate >99%, identityof genotyped of CEU trios (Coriell Institute for Medicalresearch, Camden, NJ) with HapMap database >99%].

Positron Emission Tomography

Scans were conducted at the Department of Biomedicaland Image-guided Therapy, Division of Nuclear Medicineat the Medical University of Vienna. Each subject under-went a PET (General Electric Medial Systems, Milwaukee,WI) scan using the tracer (S,S)-[18F]FMeNER-D2, synthe-sized as previously described [Rami-Mark et al., 2013].(S,S)-[18F]FMeNER-D2 is currently the most suitable radio-ligand for in vivo NET quantification previously described[Vanicek et al., 2014]. Briefly, fluorine-18-labelled reboxe-tine analogue allows, due to its long half-life (t1/2 5 109.77min) and excellent affinity and selectivity, to reach the spe-cific binding equilibrium within the time-frame of the PETmeasurement. A 5-min transmission scan using a retracta-ble 68Ge rod sources for tissue attenuation correction wasperformed prior to the dynamic emission scan acquired in3-D mode. Data acquisition started 120 min after a bolusi.v. injection of 4.7 MBq/kg body weight (ADHD patients:393 6 95 MBq, HC: 384 6 61 MBq; P> 0.05, t-test) of (S,S)-[18F]FMeNER-D2. Mean specific radioactivity of (S,S)-[18F]FMeNER-D2 was 537 6 383 GBq/lmol (ADHDpatients) and 473 6 218 GBq/lmol (HC) (P >0.05, t-test).Brain radioactivity was measured in a series of six consec-utive time frames lasting 10 min each in the interval of120–180 min after tracer bolus application. Acquired datawere reconstructed in volumes consisting of 35 transaxialsections (128 3 128 matrix) using an iterative filtered backprojection algorithm (FORE-ITER) with a spatial resolution

Figure 1.

Linkage disequilibrium plot of single nucleotide polymorphisms

(SNPs) considered for inclusion. Depicted are 11 genotyped

SNPs and the pairwise R2 between them. At the top are the rel-

ative positions of the SNPs to one another on the NET gene.

Below are the rs numbers for each corresponding SNP and the

color scheme shows the strength of the of their R2 value.

White5 R25 0, shades of gray 5 0< R2< 1 and black 5 R2 5 1.

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of 4.36-mm full-width at half maximum 1 cm next to thecenter of the field of view. For coregistration, magnetic res-onance (MR) images were acquired from all participantson a 3-Tesla Philips scanner (Achieva) using a 3-D T1 FFE-weighted sequence, yielding 0.88-mm slice thickness andin plane resolution of 0.8 3 0.8 mm [Vanicek et al., 2014].

Data Preprocessing and Quantification of NET

As described previously [Vanicek et al., 2014], each timeframe of the dynamic PET scan was realigned to the mean offrames with no head motion, which was identified by visualinspection. These summed realigned images were then core-gistered to each individual’s MRI scan using a mutual infor-mation algorithm in SPM8 (Wellcome Trust Centre forNeuroimaging, London, UK: http://www.fil.ion.ucl.ac.uk/spm/). Parametric images of NET BPND were computedusing the caudate as the reference region. The quantificationwas done as previously described [Arakawa et al., 2008].Briefly, the ratio method was used to express the BPND asarea under the time-activity curve of the target region/areaunder the time-activity curve for the reference region. Theratio method was highly correlated to the golden standardused in their study, values were r 5 0.88 (y 5 0.71x 1 0.29) inthe thalamus and r 5 0.88 (y 5 0.86x 1 0.12) for other brainregions. The integration interval of 120–180 min was used.Manual delineation of the caudate ROI was performed onindividual MR images using PMOD image analysis software,version 3.1 (PMOD Technologies, Zurich, Switzerland,www.pmod.com). MRI scans were spatially normalizedusing SPM8 (Wellcome Trust Centre for Neuroimaging, Lon-don, UK; http://www.fil.ion.ucl.ac.uk/spm/) and theresulting transformation matrices applied to the coregisteredparametric images warping them into MNI standard space.

Regions of Interest

Four regions of interest (ROIs) were selected includingNET rich regions [Schou et al., 2005] as well as regionsthought to be “core” regions in behavioral control (inatten-tion, impulsivity, hyperactivity) [Arnsten and Rubia, 2012].These were the thalamus, midbrain with pons (including thelocus coeruleus), putamen, and cerebellum. Cortical regions,such as the prefrontal cortex (PFC) were not taken intoaccount due to the bone spill over of (S,S)-[18F]FMeNER-D2

inherent to the radioligand. NET BPND for each region wasextracted from parametric maps from the Hammers Maxi-mum Probability Atlas [Hammers et al., 2003].

Statistical Analysis

Descriptive statistics were computed and regional NETBPND values were evaluated for normality using the Sha-piro–Wilk test. For each analysis, subject were groupedaccording to their genotype, that is, minor allele carriersversus major allele homozygotes. Genotype frequencies

were determined and found to be distributed according tothe Hardy–Weinberg equilibrium (P> 0.1).

To examine the effect of genotypes on NET BPND, linearmixed models for each SNP were computed, using thegenotype (homozygous major vs. minor allele carriers) andgroup (ADHD patients vs. HC) as fixed factors and ROI asa repeated factor and the NET BPND as the dependentvariable. Possible effects of cofactors (age and sex) werealso tested for and were excluded if insignificant.

A separate model for each SNP was computed as fol-lows; linear mixed model with the factors group, ROI, andgenotype as the independent variables and the BPND asthe dependent variable. For each model, main effects weretested for, and interactions among ROI, group, and geno-type. If rendered significant, further analysis included test-ing for interaction between group and genotype, separatedby ROI. Further analysis included post hoc t-tests.

The model prevailing the best fit was the autoregressive1 (AR(1)). Individual slopes and intercepts were fitted forsubjects and for random effects the variance componentsstructure was used.

To test whether there was any effect of behavioural sub-scales on NET BPND depending on genotypes, Pearson’scorrelation coefficient was used. All analyses were com-puted using SPSS version 22.0 (IBM Corp. Released 2013.IBM SPSS Statistics for Windows, Armonk, NY: IBMCorp). Each model was corrected for multiple comparisonsusing the false discovery rate (FDR) at a significance levelof a 5 0.05 [Benjamini et al., 2001].

RESULTS

Demographics and allele counts of study subjects can beseen in Table I. Control and ADHD groups did not differsignificantly in terms of age and sex.

TABLE I. Demographics, psychological tests, past

comorbidities, and allele frequencies between patients

with ADHD and HCs

Controls(n 5 20)

ADHD(n 5 20)

Age 30.4 6 10.9 30.8 6 10.9Sex M/F 14/6 14/6

SNP rs28386840 A/T 9/10 14/6SNP rs2242446 T/C 9/9 12/8SNP rs15534 C/T 13/7 11/9SNP rs40615 T/A 12/8 10/10CAARS Total score 0.32 6 0.82* 37.45 6 8.23*CAARS Hyperactive/

Impulsive0.21 6 0.63* 19.45 6 5.89*

CAARS Inattention 0.11 6 0.32* 18 6 4.78*Past comorbiditiesDepression n 57Drug abuse n 5 2

Significant differences between groups are indicated with * atP< 0.001. Genotype frequencies are shown for major/minor allele


r 887 r

For the functional promoter SNP (rs28386840) a signifi-cant three-way interaction was detected between ROI, sta-tus and genotype (F3.08 5 104.6, P 5 0.002, P< 0.05,corrected). On a ROI-based level, a further analysis detectedan interaction between status and genotype in the thalamus(F11.16 5 34.87, P 5 0.002, P< 0.05, corrected). Post hoc t-testsrevealed that ADHD subjects had higher NET BPND thancontrols for the major allele (A) (t 5 23.5, P 5 0.006,P< 0.05, corrected) and no difference was detected for theminor allele (T) between groups (t 5 0.73, P> 0.05). This islikely due to the difference in HCs between major andminor allele groups, with major allele having lower bindingthan the minor allele group (t 5 23.06, P 5 0.007, P< 0.05,corrected) (Table II and Fig. 2a).

For rs2242446, three-way interaction was detectedamong ROI, group, and genotype (F2.90 5 103.57, P 5 0.003,P< 0.05, corrected). Based on different ROIs, the analysisdemonstrated an interaction between group and genotypein the thalamus (F10.05 5 33.90, P 5 0.003, P< 0.05, cor-rected). Post hoc t-test revealed that ADHD subjects hadhigher binding for the major allele (T) (t 5 23.0, P 5 0.008,P< 0.05, corrected) than controls and no difference wasdetected for minor allele (C) between groups (Table IIIand Fig. 2b). Which is likely due to difference in HCsbetween major and minor allele groups, which did notsurvive corrections (t 5 22.54, P 5 0.022, P> 0.05,corrected).

A three-way significant interaction was detected amongrs15534 genotype, group, and ROIs (F2.75 5 117.52,P 5 0.004, P< 0.05, corrected). After separating the analysisby each ROI to determine where the difference was, aninteraction was detected between rs15534 genotypes andgroup in the cerebellum (F7.73 5 35.63, P 5 0.009, P< 0.05,corrected). Post hoc t-test revealed that controls carryingthe major allele (C) in rs15534 had higher binding com-pared to major allele carrying patients (t 5 3.19, P 5 0.004,P< 0.05, corrected) (Table IV and Fig. 3a). No differencewas detected between minor allele (T) groups, and a trendbetween patients was detected between major and minorallele groups (t 5 2.09, P 5 0.051) and between minor andmajor allele in HCs (t 5 1.80, P 5 0.088).

For the SNP rs40615, a three-way interaction was alsoobserved between genotypes, ROI and group(F2.65 5 108.78, P 5 0.006, P< 0.05, corrected). Further anal-ysis demonstrated an interaction in the cerebellumbetween genotypes and status (F8.94 5 35.41, P 5 0.005,

TABLE II. Linear mixed model effects summary for the

SNP rs28386840

Model rs28386840

Fixed effects df F value P value

Intercept 12.78 864.80 <.000Group 26.34 0.70 0.41ROI 83.66 195.59 <.000rs28386840 39.97 1.77 0.19group*ROI*rs28386840 104.60 3.08 0.002Separated by ROICerebellum group*rs28386840 33.27 0.74 0.20Midbrain group*rs28386840 34.31 4.10 0.049Putamen group*rs28386840 32.97 1.16 0.29Thalamus group*rs28386840 34.87 11.16 0.002

Values given are degrees of freedom (df), F values, and P values

Figure 2.

Differences in NET BPND in the thalamus between alleles

rs28386840 (23081 A/T) (a) and rs2242446 (2182 T/C) (b).

The white bars depict the major alleles while the gray ones

depict the minor alleles. Major allele (A) carriers for

rs28386840 were (n 5 9) for HCs and (n 5 14) for ADHD sub-

jects. Minor allele (T) carriers for rs28386840 were (n 5 10) for

controls and (n 5 6) for ADHD subjects. Major allele (T) car-

riers for rs2242446 were (n 5 9) for HCs and (n 5 9) for

ADHD subjects. Minor allele (T) carriers for rs2242446 were

(n 5 12) for controls and (n 5 8) for ADHD subjects. Error bars

indicate 95% confidence interval. Difference between groups is

marked with an * in which the difference is at P< 0.05 cor-

rected level significance.


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P< 0.05, corrected). Post hoc t-test revealed that controlscarrying the major allele (T) in rs40615 had higher bindingcompared to major allele carrying patients (t 5 3.53,P 5 0.002, P< 0.05, corrected) (Table V and Fig. 3b). Nodifference was detected between minor allele (A) groups,nor between genotypes in patients and HCs (P> 0.05).

Mean NET BPND of controls and ADHD group depend-ing on genotype grouping is listed in Table VI. In this rel-atively small sample, no significant associations betweenSNPs and ADHD were detected (P> 0.05). Highest signifi-cance was reached with the rs28386840 SNP for the Aallele with a P value of 0.09.

Behavioral Correlation

To test whether these effects were associated with spe-cific ADHD symptoms, scores from CAARS-Inattentiveness and CAARS hyperactivity/impulsivenesswere tested between genotype groups and NET BPND. Nocorrelation of symptoms scores with NET binding wasdetected in any region in patient groups separated bySNPs rs28386840 and rs2242446 with any region. Con-versely, a significant correlation was detected between thebehavioral subscales CAARS hyperactivity/impulsiveness(P< 0.05) and CAARS total score (P< 0.05) with NETBPND in the cerebellum depending on genotype forrs15534 and rs40615. For the major allele in rs15534,CAARS hyperactivity/impulsivity was positively associ-ated with NET BPND (r 5 0.664, P 5 0.026). For the minorallele group, the scale was negatively associated with NETBPND (r 5 2729, P 5 0.026) (Fig. 4a). For the CAARS totalscore, in the major allele group the positive correlationwas r 5 0.772, P 5 0.005 (Fig. 5a). No association wasdetected for the minor allele. For rs40615, differential asso-ciation between CAARS hyperactivity/impulsivity wasalso detected depending on genotype. Depending on themajor allele, the postive association detected was r 5 0.689(P 5 0.028). On the contrary, the negative association for

the minor allele was r 5 20.669 (P 5 0.034) (Fig. 4b). Forthe CAARS total score, the positive association with NETBPND was r 5 0.827 (P 5 0.003) in the major allele group(Fig. 5b). The association for the minor allele did not reachsignificance.

In addition, for the minor allele group, a negative corre-lation was detected between NET BPND in the midbrainwith CAARS hyperactivity/impulsivity (r 5 20,831,P 5 0.006, rs15534) and (r 5 20877, P 5 0.001, rs40615).

DISCUSSION

Here, we report the influence of genetic variants withinthe NE system and its effect on in vivo NET binding usingPET and the radioligand (S,S)-[18F]FMeNER-D2. Ourresults showed significant differences in cerebellar andthalamic NET binding dependent on genotypes betweenpatients with ADHD and HCs. These were largely due tothe impact of NET gene polymorphisms on NET BPND inHCs which is not as pronounced in patients with ADHD.Strikingly, in patients with ADHD, a high correlationbetween specific behavioural symptoms, that is, hyperac-tivity/impulsivity, and NET BPND in the cerebellum wasdetected, an effect which was strongly moderated bygenotype.

Our results for the functional promoter SNP(rs28386840) deviate from in vitro experiments whichfound that the minor (T) allele resulted in decreased pro-moter activity and the major allele (A) in higher expres-sion [Kim et al., 2006a]. We detected high NET binding forthe minor (T) allele carriers which, indicating high expres-sion of this allele. However, a possible reason for thisopposite effect are changes in gene expression based epi-genetic mechanisms. The T allele was found to bind totranscriptional repressors, slug, and scratch, which resultin decreased expression [Kim et al., 2006b]. Slug recruits acorepressor, which in turn recruits histone deacelytase(HDAC) [Shirley et al., 2010] resulting in tighter packing

TABLE III. Linear mixed model effects summary for the

SNP rs2242446

Model rs2242446


Intercept 9.10 930.46 <.000group 20.97 0.42 0.52ROI 80.45 200.78 <.000rs2242446 32.83 1.61 0.21group*ROI*rs2242446 103.57 2.90 0.003Separated by ROICerebellum group*rs2242446 31.50 0.57 0.46Midbrain group*rs2242446 34.66 6.47 0.026Putamen group*rs2242446 32.12 1.41 0.24Thalamus group*rs2242446 33.90 10.05 0.003


TABLE IV. Linear mixed model effects summary for the

SNP rs15534

Model rs15534


Intercept 16.92 733.19 <.000group 33.25 0.29 0.59ROI 88.09 194.08 <.000rs15534 51.62 0.61 0.44group*ROI*rs15534 117.52 2.75 0.004Separated by ROI

Cerebellum group*rs15534 35.63 7.73 0.009Midbrain group*rs15534 35.97 0.48 0.56Putamen group*rs15534 35.62 2.78 0.89Thalamus group*rs15534 33.49 0.95 0.34

Values given are degrees of freedom (df), F values, and P values.


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of the DNA of thus lower transcription of the gene. Acounteraction of a repressor, such as degradation, inactiva-tion by interaction with other elements, such as smallinterfering RNAs or HDAC inhibitors, could lead to over-expression and thus result in a reversed effect of the poly-morphism in vivo as observed in our findings [Prelich,2012; Tuschl, 2001]. Further research is needed to deter-mine the functional effect of the major (A) allele. No sig-nificant difference was detected between major and minorallele in ADHD subjects indicating that this effect is notpronounced in ADHD. Moreover, Kim et al. [2006b] foundthe T allele to be overtransmitted in ADHD, and thus con-cluded it to be a risk allele for ADHD. Even though noSNP reached significance for association to ADHD in oursample, the strongest effect was seen for the SNPrs28386840 with the A as the associative allele. This iscompatible with a recent study by Hohmann et al. [2015]which reports homozygotic A allele carriers to have ahigher rate of lifetime ADHD diagnosis. Additionally,another study reported higher response times for ADHDsubjects carrying the A allele [Kim et al., 2013]. Nonethe-less, one has to bear in mind that studies have been incon-sistent, possibly due to confounding factors, such asmedication history, individual differences, comorbidities,and differences in sample sizes [de Zubicaray et al., 2008;Leo and Cohen, 2003].

The SNP rs2242446, first determined by Zill et al. [2002]also showed this similar binding in the thalamus as for thefunctional promoter SNP. This SNP is located on the 50

flanking region of the NET and the functional effects ofthe 50 flanking region is crucial in transcription regulation[Kim et al., 1999; Meyer et al., 1998]. It has been implicated

in antidepressant response to milnacipran in depressedsubjects. Yoshida et al. [2004] found that major allele (T)carriers responded better to the treatment than the minor(C) allele. The comorbidity of ADHD and depressionranges from 5% to 40%. Symptoms of depression oftenoverlap with those in ADHD, such as distractibility, poorconcentration, and impulsivity [Goodman and Thase, 2009;McIntosh et al., 2009]. In addition to the antidepressanteffect of milnacipran, it has also been demonstrated to alle-viate symptoms of inattention and impulsivity [Hiraideet al., 2013; Kako et al., 2007]. Here, the major allele carrierADHD group had higher binding than the major allele HCcarriers. Thus, the major allele may lend support to the

Figure 3.

Differences in NET BPND in the cerebellum between alleles

rs15534 (*269 T/C) (a) and rs40615 (*417 A/T) (b). The white

bars depict the major alleles while the gray ones depict the

minor alleles. Major allele (C) carriers for rs15534 were

(n 5 13) for HCs and (n 5 11) for ADHD subjects. Minor allele

(T) carriers for rs15534 were (n 5 7) for controls and (n 5 9)

for ADHD subjects. Major allele (T) carriers for rs40615 were

(n 5 12) for HCs and (n 5 10) for ADHD subjects. Minor allele

(A) carriers for rs40615 were (n 5 8) for controls and (n 5 10)

for ADHD subjects. Error bars indicate 95% confidence interval.

Difference between groups is marked with an * in which the dif-

ference is at P< 0.05 corrected level significance.

TABLE V. Linear mixed model effects summary for the

SNP rs40615

Model rs40615


Intercept 14.95 749.56 <.000group 29.72 0.30 0.59ROI 86.53 200.42 <.000rs40615 51.60 0.26 0.62group*ROI*rs40615 108.78 2.65 0.006Separated by ROI

Cerebellum group*rs40615 35.41 8.94 0.005Midbrain group*rs40615 35.94 0.08 0.78Putamen group*rs40615 35.79 2.05 0.16Thalamus group*rs40615 34.97 0.46 0.51



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use of milnacipran to treat ADHD patients with comorbiddepression.

Noticeable, our major novel findings was the inverseeffect of genotype on NET BPND between controls andpatients for rs15534 and rs40615. Even more intriguingly,we detected correlation of scores on CAARS Hyperactiv-ity/Impulsivity and CAARS total score scales with theNET BPND in the cerebellum for ADHD subject which wasstrongly modulated by genotype. For both SNPs, inpatients carrying the major allele higher NET availabilitywas associated with higher symptom scores. On the con-trary, as NET availability decreased, scores increased forthe minor allele. From a pharmacological point of view,these findings can only explain the higher NET binding

found for the minor allele as it reflects lower NE levels.However, further influencing factors remain unclear. Theseresults resemble the classic inverted U-shaped effect asAston–Jones and associates (1999) established for interac-tion of LC and NE on task performance. The hypothesisstates that ADHD symptoms are due to increased tonicactivity in the LC which in turn inhibits the basal activityin the cerebellum and other areas and thus increasesmotor hyperactivity and impulsivity [Berridge and Water-house, 2003; Howells et al., 2012]. Aston–Jones and associ-ates research on monkeys showed that with increasedtonic discharge of the LC, phasic activity of LC neurons isdecreased. This results in poorer performance on focusingon task stimuli. Moreover, they state that for optimal

TABLE VI. Rounded mean 6 SD for NET BPND values in selected ROIs, shown depending on genotype (major/

minor allele) in patients with ADHD and controls

Controls

rs28386840 rs2242446 rs15534 rs40615ROI A/T T/C C/T T/A

Putamen 0.16 6 0.04/0.18 6 0.04 0.16 6 0.04/0.19 6 0.05 0.18 6 0.04/0.21 6 0.03 0.19 6 0.04/0.16 6 0.02Midbrain/pons 0.22 6 0.08/0.30 6 0.12 0.22 6 0.08/0.29 6 0.12 0.26 6 0.10/0.26 6 0.11 0.27 6 0.10/0.25 6 0.11Thalamus 0.39 6 0.07/0.52 6 0.12 0.39 6 0.07/0.52 6 0.13 0.45 6 0.13/0.48 6 0.11 0.45 6 0.13/0.48 6 0.10Cerebellum 0.20 6 0.06/0.26 6 0.06 0.20 6 0.06/0.26 6 0.07 0.24 6 0.07/0.21 6 0.06 0.25 6 0.06/0.20 6 0.06

ADHD

rs28386840 rs2242446 rs15534 rs40615ROI A/T T/C C/T T/A

Putamen 0.18 6 0.05/0.17 6 0.05 0.18 6 0.05/0.17 6 0.05 0.17 6 0.03/0.19 6 0.06 0.17 6 0.03/0.18 6 0.06Midbrain/pons 0.24 6 0.09/0.22 6 0.12 0.25 6 0.10/0.22 6 0.10 0.25 6 0.10/0.22 6 0.11 0.26 6 0.10/0.22 6 0.11Thalamus 0.48 6 0.08/0.48 6 0.03 0.48 6 0.08/0.47 6 0.04 0.49 6 0.05/0.47 6 0.08 0.49 6 0.05/0.47 6 0.08Cerebellum 0.20 6 0.09/0.21 6 0.09 0.20 6 0.09/0.21 6 0.09 0.17 6 0.06/0.24 6 0.10 0.17 6 0.06/0.23 6 0.10

Figure 4.

Association between NET BPND in the cerebellum and the

CAARS hyperactive/impulsive scale depending on genotype in

rs15534 (*269 T/C) (a) and rs40615 (*417 A/T) (b). The scatter

plot shows the correlation split by major (white circles) and

minor (gray circles) alleles in ADHD subjects only. The signifi-

cance for the SNP rs15534, depending on major allele C was

P 5 0.026, and for minor allele T, P 5 0.026. For the SNP

rs40615, depending on major allele T; P 5 0.028, for minor allele

A, P 5 0.034.


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performance, balanced levels of tonic, and phasic activityare needed. If the levels of tonic discharge are too low ortoo high, attentional performance suffers vastly [Aston-Jones et al., 1999]. Extracellular levels of NE have beendemonstrated to follow a positive linear relationship withtonic discharge from the LC [Berridge and Abercrombie,1999; Florin-Lechner et al., 1996]. With that in mind, forthe major allele carriers, the tonic release may be too highas NET binding is lower indicating high levels of extracel-lular NE. This inverted-U relationship between tonic activ-ity and task performance may explain why we detectedthis inverse genotype effect. The level of this inverse effectis, therefore, likely determined by the genotype. Alongthese lines, studies indicate a region specific LC stimula-tion and LC–NE effect. The LC effect may differ in termsof interaction with receptor subtypes and sensitivity aswell as for NE concentration in that region [Berridge andWaterhouse, 2003; Devilbiss et al., 2006]. A study done onhealthy rats revealed that tonic stimulation of the LC haddifferential effects on cortical cells versus cells in the thala-mus [Devilbiss and Waterhouse, 2004]. Another studyrevealed projections to the PFC and the motor cortex todiffer [Chandler et al., 2014]. They revealed that neuronsprojecting from LC to the PFC show more spontaneousactivity and are more excitable than those projecting to themotor cortex. The LC might have a differential effect onthe thalamus and the cerebellum and this may explainwhy we detected opposite binding for the major alleles onSNPs located in the 50UTR versus those in the 30UTRregion.

Another explanation involves the location of these SNPs.They are located in the 50UTR and 30UTR regions whichhave been demonstrated to play an important part intranslation, stability and localization of the mRNA. TheSNPs r28386840 and rs2242446 are located within the pro-moter regions while rs40615 and rs15534 are located

downstream at the termination codon. The NET may bederegulated by changes in gene expression, mRNA trans-lation or stability, post-translational modifications such asphosphorylation, protein trafficking, cytoskeleton interac-tion, and oligomerization [Chatterjee and Pal, 2009]. Sub-strates involved in the aforementioned processes have alsoshown to have a regulatory effect on the NET. An injectionof the enzyme inhibitor a–methyl-p-tyrosine (AMPT),resulted in around 50% reduced levels of NE as well aslower mRNA levels in the brainstem indicating a compen-satory mechanism for reduced extracellular NE levels[Xiao et al., 1995]. Furthermore, activation of proteinkinase C (PKC) has also been shown to regulate the NET.Activation of PKC is believed to result in redistribution ofsurface NET as radioligand binding demonstrated a reduc-tion in BMAX to NET without any change to KD [Appa-rsundaram et al., 1998]. Different location of the SNPs andfunction may explain why we only detected differencesfor one allele and why we detected opposite binding onmajor alleles between SNPs in the 50UTR and the SNPs inthe 30UTR.

Limitations and Future Directions

A limitation of this study is that with (S,S)-[18F]FMeNER-D2 cortical areas cannot be properly assesseddue to spill over from suspected bone uptake. Therefore,genetic influence in the neocortex could not be examined.Studying the cortex, specifically the frontal cortex due toits vast role in cognitive and behavioral control would bevery intriguing to test whether and what effects polymor-phisms in the NET gene would have on the binding.Another limitation is that we did not include the CAARSinconsistency index and, therefore, we could not assesswhether there was any inconsistencies or irregularities in

Figure 5.

Association between NET BPND in the cerebellum and the CAARS total score depending on

genotype in rs15534 (*269 T/C) (a) and rs40615 (*417 A/T) (b). The scatter plot shows the cor-

relation split by major (white circles) and minor (gray circles) alleles in ADHD subjects only. Sig-

nificance was only found depending on the major alleles, for rs15534 (C, P 5 0.005) and for

rs40615 (T, P 5 0.003).


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responses within subjects, nor could we analyze that inrespect to our data.

The evidence presented in this article gives rise to a rolefor genetic influence of the NE system and alterations inNE signalling as a part of the pathophysiology contribut-ing to ADHD and thus strengthening the hypothesis ofimbalances in NE system in the neurobiological mecha-nism of ADHD. However, we did not detect any differen-ces in the midbrain and the putamen. We can onlyhypothesize about the possible reasons for these distincteffects. Our results may suggest that the NET genotypeeffects modulate the NET availability in a region specificmanner. Conversely, we cannot exclude other possibilities,such as other influential genetic or nongenetic factors thatinteract with these regions. In addition, we did not detectany association of any SNP to ADHD, which is probablydue to insufficient power of this sample to assess thesesubtle effects. Due to the heterogeneous nature of ADHDfurther research requires larger samples for the validationand for the establishment of potential endophenotypes inADHD. Replication in vivo and in vitro studies is neededto establish if NET genotypic influence could serve as anendophenotype for NE neurotransmission. Moreover,SNPs within the NET may also be very important in rela-tion to the dopaminergic system. The NET is also respon-sible for reuptake of dopamine in cortical regions [Mor�onet al., 2002], and therefore, SNPs could possibly affect theavailability of dopamine within the cortex. Future studiescould explore the possibility whether there are any effectsof the NET on the dopaminergic system.

To conclude, this is the first imaging genetic studyshowing significant differences in NET BPND in patientswith ADHD compared to HCs, depending on their geno-type. We find genotypic difference in the thalamusbetween major and minor alleles for a functional promoterSNP in HCs only. The inverse effect of genotype whichwas detected in the cerebellum indicates genetic influenceof NET on the binding in the cerebellum to differ betweengroups of ADHD subjects and HCs. The results are com-patible with the theory that NE follows an inverted-U-shaped curve. Its effect on differential association ofbehavioral scales with binding further demonstrates afunctional and neuropsychological activity to be imbal-anced in ADHD.

ACKNOWLEDGMENTS

We are grateful to Nora D. Volkow, MD (National Insti-tute on Alcohol Abuse and Alcoholism, National Institutesof Health, Bethesda, Maryland, USA) for scientific supportin planning the study. We thank Anna H€oflich, MD, PiaBaldinger, MD, PhD, Marie Spies, MD, Mara Stamenkovic,MD, Anastasios Konstantinidis, MD, Alexandra Kutzel-nigg, MD, Diana Meshkat, MD, Jan Losak, MD (Depart-ment of Psychiatry and Psychotherapy, Medical Universityof Vienna), and Claudia Klier, MD and Brigitte Hacken-berg, MD (Department of Child and Adolescence Medi-

cine, Medical University of Vienna), and Ralf G€oßler, MD(Department of Child and Adolescence Psychiatry, Neuro-logical Centre Rosenh€ugel, Vienna, Austria) for medicalsupport, and Georgios Karanikas, MD, Lucas Nics, MSc,PhD, Daniela H€ausler, MSc, PhD, and Cecile Philippe,MSc, PhD (Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, MedicalUniversity of Vienna), Markus Savli, PhD and AndreasHahn, MSc, PhD (Department of Psychiatry and Psycho-therapy, Medical University of Vienna) for technical sup-port. Administrative support was provided by MarianCotten (Department of Psychiatry and Psychotherapy,Medical University of Vienna).

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Altered Interregional Molecular Associations ofthe Serotonin Transporter in Attention Deficit/

Hyperactivity Disorder Assessed with PET

Thomas Vanicek,1 Alexandra Kutzelnigg,1 Cecile Philippe,2

Helen L. Sigurdardottir,1 Gregory M. James,1 Andreas Hahn,1

Georg S. Kranz,1 Anna H€oflich,1 Alexander Kautzky,1

Tatjana Traub-Weidinger,2 Marcus Hacker,2 Wolfgang Wadsak,2

Markus Mitterhauser,2 Siegfried Kasper,1 and Rupert Lanzenberger1*

1Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria2Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided

Therapy, Medical University of Vienna, Austria

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Abstract: Altered serotonergic neurotransmission has been found to cause impulsive and aggressivebehavior, as well as increased motor activity, all exemplifying key symptoms of ADHD. The main objec-tives of this positron emission tomography (PET) study were to investigate the serotonin transporterbinding potential (SERT BPND) in patients with ADHD and to assess associations of SERT BPND betweenthe brain regions. 25 medication-free patients with ADHD (age 6 SD; 32.39 6 10.15; 10 females) withoutany psychiatric comorbidity and 25 age and sex matched healthy control subjects (33.74 6 10.20) weremeasured once with PET and the highly selective and specific radioligand [11C]DASB. SERT BPND mapsin nine a priori defined ROIs exhibiting high SERT binding were compared between groups by means ofa linear mixed model. Finally, adopted from structural and functional connectivity analyses, we per-formed correlational analyses using regional SERT binding potentials to examine molecular interregionalassociations between all selected ROIs. We observed significant differences in the interregional correla-tions between the precuneus and the hippocampus in patients with ADHD compared to healthy con-trols, using SERT BPND of the investigated ROIs (P< 0.05; Bonferroni corrected). When correlating SERTBPND and age in the ADHD and the healthy control group, we confirmed an age-related decline in brainSERT binding in the thalamus and insula (R2 5 0.284, R2 5 0.167, Ps< 0.05; Bonferroni corrected). Theresults show significantly different interregional molecular associations of the SERT expression for theprecuneus with hippocampus in patients with ADHD, indicating presumably altered functional cou-pling. Altered interregional coupling between brain regions might be a sensitive approach to demon-strate functional and molecular alterations in psychiatric conditions. Hum Brain Mapp 00:000–000, 2016.

VC 2016 Wiley Periodicals, Inc.

Key words: neuroimaging; ADHD; positron emission tomography; PET; serotonin; SERT; interregionalmolecular associations

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*Correspondence to: Rupert Lanzenberger, Department of Psychiatryand Psychotherapy, NEUROIMAGING LABs (NIL) - PET & MRI &EEG & Chemical Lab, Medical University of Vienna, WaehringerGuertel 18-20, Vienna 1090, Austria.E-mail: [email protected]

Received for publication 30 March 2016; Revised 17 August 2016;Accepted 20 September 2016.

DOI: 10.1002/hbm.23418Published online 00 Month 2016 in Wiley Online Library(wileyonlinelibrary.com).

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VC 2016 Wiley Periodicals, Inc.

INTRODUCTION

Attention deficit hyperactivity disorder (ADHD) is char-acterized by inappropriate inattention, hyperactivity, impul-sive behaviour and emotional dysregulation [AmericanPsychiatric Association, 2013; Rosler et al., 2010], as well asby a certain constellation of deficits in executive functions.ADHD is considered to be the most prevalent neurodeve-lopmental disorder, prevalance rates are estimated to rangebetween 8% and 12% in childhood [Biederman and Far-aone, 2005]. In about 30% of children diagnosed withADHD [Barbaresi et al., 2013], especially inattentive symp-toms persist into adulthood.

Frequently prescribed stimulant and non-stimulant psy-chopharmacological treatment for patients with ADHD aresuggested to unfold efficacy through modulation of dopa-minergic (DA) and norepinephrinergic neurotransmissionin cortical and subcortical brain circuits and improvementof neurocognitive deficits [Castells et al., 2011; Chamber-lain et al., 2009; Retz et al., 2011]. Although the serotonin-ergic system is not a direct target for ADHD medication,evidence from pharmacological, genetic and animal stud-ies suggest an involvement of the serotonergic neurotrans-mission in the neurobiological mechanisms of ADHD [forreview see (Banerjee and Nandagopal, 2015)].

Although methylphenidate does not inhibit the seroto-nin transporter, amphetamines enhance serotonergicrelease [Bymaster et al., 2002; Kuczenski and Segal, 1997].A recently published positron emission tomography (PET)animal study found that atomoxetine applied at clinicaldosage blocks the norepinephrine transporter as well asthe serotonin transporter (SERT) [Ding et al., 2014] andatomoxetine has been shown to significantly alleviatesymptoms in adult ADHD patients [Adler et al., 2009].This has led some researchers to suggest that serotonergictransmission might also be of relevant to ADHD treatmentand neuropathology [Gainetdinov et al., 1999].

Several lines of evidence suggest that serotonin isinvolved in impulsive behaviour and extensive motoractivity [Dalley and Roiser, 2012; Winstanley et al., 2006].Serotonergic neurons in the medial and dorsal raphe pro-ject into the striatum, ventral tegmental area and nucleusaccumbens as well as into the amygdala, hippocampusand the frontal cortex [Muller and Jacobs, 2009]. Serotoninregulates dopaminergic neurotransmission via projectionsto the dopaminergic neurons in the midbrain and neuronalinteractions between these neurotransmitters are found toprofoundly modulate impulsive behaviour [Oades, 2008;

Wood and Wren, 2008]. Furthermore, a deficit to withholdattention for an adequate time, related to a specific con-text, can lead to emotional dysregulation, a symptom ofADHD that affects patients markedly throughout lifetime.Brain regions implicated in emotional dysregulation com-prise the striatum, amygdala and the medial prefrontalcortex, regions that are strongly modulated by serotonergicneurotransmission [Shaw et al., 2014].

Neuroimaging studies have been demonstrating thatpatients with ADHD display altered neural activation forinhibition and attention in frontal, parietal and thalamicbrain regions as well as in the basal ganglia [Aron and Pol-drack, 2005; Hart et al., 2013]. In comparison to healthy con-trol subjects (HC), the administration of fluoxetine, aselective serotonin reuptake inhibitor, prior to functionalmagnetic resonance imaging (fMRI) measurements, hasbeen shown to normalize neuronal activation during a stopsignal task measuring motor inhibition in the orbitofrontalcortex and in the basal ganglia in 18 patients with ADHD[Chantiluke et al., 2015]. Fluoxetine, as well as its metabolitenorfluoxetine, also binds to the norepinephrine transporter,although to a far lesser extent [Wong et al., 1993]. In addi-tion, Fluoxetine has been found to be effective to improveattention and alleviate hyperactivity in children withADHD and non-bipolar comorbid mood-disorders [Barrick-man et al., 1991; Quintana et al., 2007].

With a remarkable heritability estimated to be 77%[Faraone et al., 2005], ADHD exemplifies a spectrum disor-der with behavioural and personality traits, which under-lie a combination and an interaction of genetic andenvironmental factors [Fliers et al., 2012]. The gene encod-ing the serotonin transporter (SERT; SLC6A4) as well asthe genes encoding certain serotonergic receptors comprisevarious single nucleotide polymorphisms that have beenexamined in ADHD and other neuropsychiatric disordersand were found to be influencing the susceptibility toADHD [Faraone and Khan, 2006; van der Meer et al.,2014]. Thus, the SERT gene is alleged to play a main rolein ADHD pathogenesis.

Studies applying PET or single photon emission tomog-raphy (SPECT) in adult patients with ADHD haveexplored glucose, blood flow metabolismand [for reviewsee (Zimmer, 2009)] and especially the dopaminergic andnoradrenergic neurotransmitter systems. Dysfunctionaldopaminergic signaling, including investigations on thedopamine transporter [Fusar-Poli et al., 2012] and dopa-mine receptors [del Campo et al., 2013; Volkow et al.,2009], has been identified in different brain regions,though results remain inconsistent. In a recently publishedPET study, we found no difference in norepinephrinetransporter in subcortical regions between patients withADHD and HC [Vanicek et al., 2014]. A PET study hasinvestigated serotonin transporter binding potential (SERTBPND) in patients with ADHD [Karlsson et al., 2013],using [11C]MADAM, which is a frequently used tracer forestimating brain SERT levels. The results depict no

Abbreviations

AAL Automated anatomical labellingADHD Attention deficit hyperactivity disorderHC Healthy control subectsPET Positron emission tomographySERT Serotonin TransporterSPECT Single photon emission tomography

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differences compared to HC. However the findings arepreliminary, since the sample size is too small to exempli-fy reasonable size for power analysis and the tracer. Asmentioned above, evidence from behavioral, neuroimagingand genetic studies suggest an involvement of the seroto-nergic system in ADHD. The SERT terminates serotoninfrom the synaptic cleft, therefore withholding a pivotalrole in the regulation of serotonergic signaling. The SERTBPND has been investigated in the past with [11C]DASBand PET in various neuropsychiatric disorders [Spieset al., 2015].

In the last decades neuroimaging investigations havebegun to change the conceptual focus from activationparadigms towards connectivity analysis, from univariate,where activation in cue-related regions is explored, to mul-tivariate analysis, where correlations of activation acrossbrain regions are evaluated [Bullmore, 2012]. To disclose apossible involvement of a specific neurotransmitter systemin neuropsychiatric disorders, PET imaging has predomi-nantly been used to observe regional availability of a par-ticular transporter or receptor in a specific brain region.Though, through performing interregional correlation anal-yses, PET imaging has also been applied to explore brainconnectivity in HC, major depressive disorder, autism andobsessive-compulsive disorder, Alzheimer’s disease andepilepsy [Baldinger et al., 2014; Horwitz et al., 1984; Leeet al., 2008; Morbelli et al., 2013; Vanicek et al., 2016].

The serotonergic system represents one of the chiefmodulatory neurotransmitter systems in the human brain,where neurons from the raphe nuclei innervate nearly allcortical regions and several subcortical structures. There-fore, serotonin is associated with almost all emotional andcognitive functions. Since the SERT expression is modifiedvia available and released serotonin [Benmansour et al.,2002], investigations on the relation of SERT expressionbetween different brain regions may exemplify a valuablemethod to understand the function on a more global levelof this neurotransmitter system. Studies from our groupshowed that molecular associations of the serotonergicneurotransmitter system (serotonin-1A receptor and SERT)differed between depressive patients and HC [Baldingeret al., 2014; Hahn et al., 2014; Lanzenberger et al., 2012],implicating that interregional molecular correlation analy-ses is a promising method to generate more insight to thecomplexity of neurotransmitter systems and their role inneuronal pathophysiology.

Therefore, we applied [11C]DASB and PET to assessSERT BPND in SERT rich regions to observe differences inSERT availability between adult patients with ADHD andHC. Furthermore, we performed a correlational analysis,to examine interregional association of SERT binding as anindex for interregional molecular balance of serotonergicneurotransmission. We hypothesized that SERT BPND andinterregional molecular associations of SERT availabilityacross brain areas will reflect a characteristic pattern thatdiffers between patients with ADHD and HC.

METHODS

Subjects

Twenty-five adult patients with ADHD (age 6 SD;32.39 6 10.15; 10 females) and 25 age and sex matched HC(aged 33.74 6 10.20) were recruited through the ADHD out-patient clinic at the Department of Psychiatry and Psycho-therapy, Medical University of Vienna and from the localcommunity via advertisement. Patients were free from psy-chopharmacologic treatment for at least six months prior tothe screening visit while HC were na€ıve to all psychophar-macologic treatment. Four patients used methylphenidatein the past, one patient atomoxetine and one antidepressantmedication. Written informed consent was obtained fromall participants after detailed explanation of the study pro-tocol and subjects received financial reimbursement fortheir participation. This study was approved by the EthicsCommittee of the Medical University of Vienna and theGeneral Hospital of Vienna (EK 552/2010).

Medical Examination and Clinical Exploration

Subjects underwent standard medical examinationincluding a general physical and neurological status, elec-trocardiography and routine laboratory tests at the screen-ing- and final visit in order to ensure physical health.Female participants underwent a urine-pregnancy test atthe screening visit and prior to PET measurement. Amultidrug-urine test was performed at the screening visit inorder to exclude current substance abuse. Participants wereinterviewed by experienced psychiatrists using Conners’Adult ADHD Diagnostic Interview for DSM IV (CAADID,Conners 1999) to evaluate current and childhood attentionaland hyperactivity/impulsivity symptoms and to attestADHD diagnosis. (ADHD: impulsive symptoms: 20.056

4.34 hyperactive symptoms: 20.05 6 4.42; HC: impulsivesymptoms: 0.556 0.92 hyperactive symptoms: 0.35 6 0.79).For five patients hyperactivity/impulsivity symptoms werenot recorded, thus we excluded these patients and theirmatched HC from this analysis. Structured Clinical Inter-view for DSM IV Axis I and Axis II disorders (SCID-I,SCID-II) was performed to exclude comorbid psychiatricdisorders. Smoking status was recorded and subjects weresubdivided into groups best describing their smoking statusaccording to quantity of consumption (non-smokers, fivecigarettes/week, five cigarettes/day, five to ten cigarettes/day, ten cigarettes/day, ten to 15 cigarettes/day, 15 ciga-rettes/day and 20 cigarettes/day; ranks 1-8, respectively).ADHD patients did not significantly differ in smoking sta-tus compared to HC (Mann-Whitney U 5 161.5, Z521.25, P5 0.30). Subjects with PET- or MRI-incompatible implantsor in pregnancy or breastfeeding were also excluded.

Data Acquisition

All PET scans were carried out at the Dept of Biomedi-cal Imaging and Image-guided Therapy, Division of

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Nuclear Medicine, Medical University of Vienna using afull-ring scanner (General Electric Medical Systems, Mil-waukee, WI, USA) in 3D acquisition mode. We applied[11C]DASB [Haeusler et al., 2009], which is currentlyamong the most suitable PET tracers for in vivo SERTquantification as reported previously in detail [Lanzen-berger et al., 2012]. A 5 min transmission scan usingretractable 68Ge rod sources for tissue attenuation correc-tion was performed prior to the emission scan. Data acqui-sition started with a bolus i.v. injection. Brain radioactivitywas measured in a series of 50 consecutive time frames(12 3 5 s, 6 3 10 s, 3 3 20 s, 6 3 30 s, 4 3 1 min, 5 3 2 min,14 3 5 min) with a total measurement time of 90 min afterbolus. Acquired data were reconstructed in volumes con-sisting of 35 transaxial sections (128 3 128 matrix) usingan iterative filtered back-projection algorithm (FORE-ITER)with a spatial resolution of 4.36 mm full-width at halfmaximum 1 cm next to the center of the field of view. Forcoregistration, magnetic resonance (MR) images wereacquired from all participants on a 3 Tesla (T) Philipsscanner (Achieva) using a 3D T1 FFE weighted sequence,yielding 0.88 mm slice thickness and inplane resolution of0.8 3 0.8 mm.

Data Quantification

Each time frame of the dynamic PET scan was realignedto the mean of frames with no head motion, identified byvisual inspection. Subsequently, each summed image (PETintegral image from realigned data) was coregistered (rigidbody transformation) to each subject’s MRI using a nor-malized mutual information algorithm implemented inSPM12 (Wellcome Trust Centre for Neuroimaging, Lon-don, UK; http://www.fil.ion.ucl.ac.uk/spm/). IndividualMRIs were spatially normalized to the T1-weighted MRItemplate provided in SPM. Resulting transformation matri-ces were applied to the coregistered PET images, warpingthem into MNI standard space. Parametric images ofBPND [Innis et al., 2007] values were calculated using themultilinear reference tissue model with two parameters(MRTM2) implemented in PMOD image analysis software,version 3.509 (PMOD Technologies Ltd., Zurich, Switzer-land; http://www.pmod.com). Thalamus was used as thereceptor-rich region and cerebellar grey matter as the ref-erence region because it contains negligible availability ofSERT and has been demonstrated to represent the optimalreference region for [11C]DASB [Parsey et al., 2006].

Regions of Interests (ROIs)

Selected ROIs included SERT rich brain regions, basedon previous PET, in vivo, human brain studies [Savli et al.,2012], including the anterior cingulate cortex, amygdala,dorsal raphe nuclei as well as the hippocampus, insula,precuneus, posterior cingulate cortex, striatum and thala-mus. Binding potential values were extracted from an

automated anatomical labelling (AAL)-based atlas [Savliet al., 2012], including manually delineated ROIs for thedorsal and medial raphe nucleus.

Statistical Analysis

Data was analysed using linear mixed models for theoutcome measure SERT BPND with group, sex, and ROI asfixed factors, with ROI as repeated factor, and subjectsand matched participant pairs as random factors. Fixedeffects were included in the model in a multifactorialapproach whereas interaction effects were dropped in caseof non-significance. In case of significant interactions ormain effects, post-hoc pairwise comparisons were comput-ed and Bonferroni corrected for multiple comparisons. In asecond exploratory approach to examine the effects of ageand smoking status, a mixed model was calculated using astepwise procedure with backward elimination, i.e., start-ing with all candidate variables (including subject groupsand ROI) followed by a stepwise deletion of interactionsand variables with largest P-values. Finally, mixed modelsusing the same procedure were applied to investigate theeffects of clinical variables CAARS-inattentiveness andCAARS-hyperactivity/impulsivity. According to Akaike’sinformation criterion [Akaike, 1974], repeated measure-ments were modelled using the diagonal structure. SPSSversion 19.0 for Windows was used for statistical compu-tations. The two-tailed significance level was set at 0.05.

Interregional molecular association matrices were calcu-lated between each ROI pair using Spearman’s rank corre-lation coefficient (Dq) for each group separately. For theassessment of statistically significant differences (P< 0.05)in balance between patients with ADHD and HC, correla-tion matrices were transformed using Fisher’s r-to-z-transformation and a 10,000 fold permutation test was per-formed. Results were Bonferroni correction for multiplecomparisons.

RESULTS

Linear mixed models analysis revealed a main effect ofROI (F800.22 5 72.08, P< 0.001) and of subject group(F29.35 5 261.37, P< 0.001; Table 1; Fig. 1), but no maineffects for sex (F1.21 5 21.26, P 5 0.1) and no interactioneffects (all P> 0.1). Post-hoc pairwise comparisons revealedsignificant attenuated SERT BPND in patients with ADHDcompared to HC in the striatum (P 5 0.029; uncorrected) aswell as trend in the anterior cingulate cortex and insula(P 5 0.066 and P 5 0.085; uncorrected). After applying Bon-ferroni correction for multiple comparisons, we were notable to detect any significant differences (Table I).

When investigating the potential effects of age, mixedmodels analysis for ROI SERT BPND based on AAL atlasrevealed an interaction effect between ROI and age(F3.53 5 69.79, P< 0.002), in addition to a main effect of ROImain effect of age (F7.15 5 21.26, P 5 0.014). Post-hoc

r Vanicek et al. r

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correlation analyses between regional SERT BPND and agerevealed negative correlations in the thalamus and insula(R2 5 0.284, R2 5 0.167, Ps< 0.05; Bonferroni corrected;Fig. 2). Furthermore, negative correlations in the anteriorcingulate cortex (R2 5 0.128), posterior cingulate cortex(R2 5 0.119) and the precuneus (R2 5 0.129) were detected,however not significant after Bonferroni correction. Thesecorrelations did not differ between HC and ADHD patients.Smoking status had no effect on SERT BPND nor did theylead to any significant interactions. Additionally, no main orinteraction effects were observed for clinical variables(CAARS-Inattentiveness, CAARS-Hyperactivity/Impulsivity)and SERT BPND.

When comparing interregional SERT BPND correlationsbetween patients with ADHD and HC, we found a signifi-cant difference in the correlation of precuneus with amyg-dala, hippocampus, insula, DRN and ACC, of thehippocampus with insula and ACC as well as of the PCCand the ACC. Only the differences in interregional molec-ular correlations of precuneus with hippocampus survivedBonferroni correction for multiple comparisons (P 5 0.0324;see Table II, Figs. 3 and 4).

DISCUSSION

In this cross-sectional PET study we aimed to investigateSERT availability in adult, medication free patients withADHD. When comparing groups, we observed lowerSERT availability for all ROIs pooled together in patientswith ADHD compared HC. For separate brain regions andafter correction for multiple comparisons, results show nosignificant differences in SERT BPND between patientswith ADHD and HC. When comparing interregional SERTBPND correlations between groups, we found a significantincrease for interregional SERT BPND correlations of theprecuneus with hippocampus. In addition, we observed anegative correlation for SERT BPND and age for patientsand HC in the thalamus and the insula.

Previously published PET and SPECT imaging studiesfound no changes in SERT availability between patientswith ADHD and HC [Hesse et al., 2009; Karlsson et al.,2013]. Though, findings are preliminary and should beinterpreted with caution, since Karlsson et al. investigatedSERT BPND in eight patients with ADHD, a sample sizeinsufficient in power to detect putative differences [Karlssonet al., 2013]. Another study observed SERT availability with[123I]FP-CIT, a SPECT radiotracer showing only moderate spe-cificity to the SERT in subcortical regions [Hesse et al., 2009]

TABLE I. SERT BPND by region of interest

Region of interest HC ADHD p-value

Anterior cingulate cortex 0.318 60.071 0.278 60.076 0.066Amygdala 1.025 60.158 0.958 60.181 0.183Dorsal raphe nucleus 3.522 60.653 3.463 60.526 0.697Hippocampus 0.605 60.127 0.563 60.118 0.194Insula 0.558 60.103 0.518 60.101 0.085Precuneus 0.240 60.052 0.198 60.070 0.723Posterior cingulate cortex 0.246 60.078 0.228 60.085 0.433Striatum 1.748 60.248 1.603 60.209 0.029*Thalamus 1.880 60.266 1.772 60.256 0.096

Mean SERT BPND and standard deviations are listed from auto-mated AAL, including manually delineated ROI dorsal raphenuclei for patients with ADHD and HC.*Marks significant differences between patients with ADHD andHC, though, after Bonferroni correction for multiple comparisons,differences are not significant different.AAL: anatomical labelling atlas; ADHD: attention deficit/hyperac-tivity disorder; HC: healthy control subjects; SERT BPND: seroto-nin transporter binding potential; ROIs: regions of interest.

Figure 1.

Average [11C]DASB distribution in 25 patients with ADHD normalized to MNI T1 template.

Highest SERT BPND is found in the dorsal raphe nuclei ROIs. The color table represents binding

potential at each voxel, blue indicates lowest and red highest SERT BPND. Crosshair is set on the

dorsal raphe nuclei in MNI space. [Color figure can be viewed at wileyonlinelibrary.com]


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and demonstrated no alteration between patients and HC.We found attenuated SERT binding in patients with ADHDat uncorrected P-value in the striatum, a region that hasbeen found to exhibit ADHD specific morphological andfunctional alterations [Plichta et al., 2009; Qiu et al., 2009].Elevated SERT availability has been shown to be correlatedwith cognitive performance in the caudate as well as in oth-er brain regions in HC [Madsen et al., 2011] whereas a neg-ative association has been found between SERT bindingand impulsive behavior in suicide attempters [Ryding et al.,2006]. Our finding may suggest a contribution of the SERT

to the pathophysiology in ADHD, which may be key forimpulsive symptoms. Nevertheless, and in line with previ-ous SERT imaging in ADHD, we found no differences inSERT BPND after correction for multiple testing in patientswith ADHD in comparison to HC.

The findings further demonstrate a decrease of SERTBPND with increasing age in the thalamus, insula, precu-neus and anterior and posterior cingulate cortex in patientsand HC. This validates previous SERT investigations [Hesseet al., 2003; Yamamoto et al., 2002] as well as PET studiesobserving the noradrenergic transmitter system [Ding et al.,

Figure 2.

Negative correlation of SERT BPND and age in the thalamus and

insula in both patients with ADHD and HC. Scatterplots show-

ing a significant negative correlation between SERT BPND and

age in the thalamus (R2 5 0.284) and insula (R2 5 0.167). ROIs

were extracted from automated AAL. Age is given in years,

significance level was set to P< 0.05 and results were Bonfer-

roni corrected for multiple comparisons. AAL: anatomical label-

ling atlas; ADHD: attention deficit/hyperactivity disorder, SERT

BPND: serotonin transporter binding potential, ROIs: regions of

interest. [Color figure can be viewed at wileyonlinelibrary.com]

TABLE II. Significant differences in interregional molecular correlations of the SERT BPND

Region of interest Dq P-valueP-value Bonferroni

corrected

Precuneus—amygdala 0.3846 0.0206a 0.7416Precuneus—hippocampus 0.74308 0.0009a 0.0324b

Precuneus—insula 0.52231 0.0072a 0.2592Precuneus—dorsal raphe nucleus 0.68 0.0134a 0.4824Precuneus—anterior cingulate cortex 0.50923 0.0077a 0.2772Hippocampus—posterior cingulate cortex 0.42 0.0218a 0.7848Hippocampus—insula 0.30385 0.0243a 0.8748Anterior cingulate cortex—posterior cingulate cortex 0.42769 0.0259a 0.9324

We observed significant stronger interregional associations of SERT BPND between the listed ROIs in patients with ADHD and healthycontrol subjects (Spearman’s delta rho; P< 0.05; corrected for multiple comparisons).aMarks significant differences between patients with ADHD and HC.bMarks significant differences between patients with ADHD and HC after Bonferroni correction for multiple comparisons.ADHD: attention deficit/hyperactivity disorder; HC: healthy control subjects; SERT BPND: serotonin transporter binding potential; ROIs:regions of interest.

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2014; Vanicek et al., 2014], which demonstrated a negativecorrelation of monoaminergic transporters with age in HCas well as in patients with ADHD.

In addition to the comparison of SERT BPND betweengroups, we performed an interregional molecular correla-tional analysis to evaluate associations of SERT BPND

between the selected ROIs. Our approach is similar to MRIstudies, where structural and functional connectivity analy-ses aim to capture the complexity of large-scale brain net-works and findings show widespread and distinctalterations in connectivity in ADHD [Matthews et al., 2014].These methods were adapted to PET imaging in order toexplore if interregional correlations of SERT mirror morpho-logical correlates in the pathophysiology of ADHD. Theassumption that regional up- or down-regulation of asingle protein, such as the SERT, might be sufficient to dif-ferentiate healthy from disordered brains may seem over-simplifying. Therefore, this approach might allow for aneven more precise understanding of inherent specificities ofthe serotonergic system rather than simply comparing trans-porter binding in various regions between subject groups.

Using this interregional molecular correlational analysiswe found significant interregional differences of SERT BPND

correlations for the precuneus and the hippocampus. Theserotonergic system projects from raphe nuclei to the precu-neus and the hippocampus, therefore modulating regionaland network specific function of these brain regions. Theprecuneus is part of the posterior components of the defaultmode network, a network that is activated during no-goal

directed processes, which has been shown to be dysfunc-tional in ADHD [Castellanos et al., 2008]. Using resting-statefMRI, altered functional connectivity between the precuneusand other brain regions, specifically the ventromedial pre-frontal cortex, a region which is highly innervated and mod-ulated by serotonin action, has been demonstrated. Inaddition, the precuneus has also been found to be involvedin timing functions, exhibiting increased activation patternsin patients with ADHD relative to HC using fMRI [Hartet al., 2012]. Timing deficits have been observed in patientswith ADHD and linked to impulsiveness [Rubia et al.,2009].

The hippocampus is associated with learning and mem-ory and is implicated in encoding novel stimuli, process-ing spatial information as well as in attention [Goldfarbet al., 2016; Jarrard, 1995; Kaplan et al., 2014; Van Petten,2004]. Being a component of the limbic region, the hippo-campus is highly modulated by serotonergic neurotrans-mitter system, receiving projections from midbrainserotonergic cells [Hensler, 2006]. During a decision-making task, measured with [15O]H2O and PET, regionalblood flow has been found to be reduced in the hippocam-pus in patients with ADHD [Ernst et al., 2003]. The alteredactivation in the hippocampus as well as in other regionsin ADHD is interpreted as a diminished involvement ofbrain regions associated with complex cognitive-emotionalfunctions. A MRI study found reduced hippocampal vol-ume and connectivity of the hippocampus with the pre-frontal cortex, whereas structural findings were associated

Figure 3.

Molecular interregional molecular correlations of patients with

ADHD and HC. Left map shows the correlation (Spearman’s q)

of SERT BPND in HC indicating interregional differences in func-

tional coupling. Right map denote the condition in patients with

ADHD. The color table represents the strength of interregional

associations, red indicates lowest and yellow highest interregion-

al associations. ADHD: attention deficit/hyperactivity disorder,

SERT BPND: serotonin transporter binding potential, ROIs:

regions of interest. HC: healthy controls. [Color figure can be

viewed at wileyonlinelibrary.com]


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with depressive symptoms [Posner et al., 2014]. Thoughother structural MRI investigations have showed inconclu-sive data in ADHD, depicting higher or no differences inhippocampal volume between patients with ADHD andHC [Castellanos et al., 1996; Plessen et al., 2006].

Imaging and behavioral studies have demonstrated thatserotonergic neurotransmission affect impulsive behavior[Dalley and Roiser, 2012], motor planning and sensoryperception [Biskup et al., 2016] and modulates the defaultmode network [Hahn et al., 2010]. Recently, it has beenfound that, compared to HC, patients with ADHD showelevated functional connectivity of the default mode net-work and attenuated functional connectivity in a state ofdiminished brain serotonin levels, evoked through acutetryptophan depletion [Biskup et al., 2016]. We found ahigher molecular correlation of the SERT between the pre-cuneus and the hippocampus in patients with ADHD andin general lower correlations in HC, which may reflecthigher impulsivity in patients and might be explained bya more diverse, region specific modulated serotonergicsystem in HC and by more rigid and less variable seroto-nergic signaling in ADHD.

This PET study has limitations that compromise the inter-pretation of its results. Regarding group differences in

regional SERT binding a main effect was observed, but onlytrends for significant differences were obtained in separatebrain regions. Although the sample size of this study iscommon for investigations with PET [Kranz et al., 2015; Vol-kow et al., 2007], it is still possible that more subjects arerequired to identify more subtle differences. On the otherhand, the significance in the main effect but not for singleROIs might be driven by a more reliable variance estimatefor the former one. Next to the thalamus and the insula, wefound an association between age and SERT binding in theanterior cingulate cortex, posterior cingulate cortex and theprecuneus, though not significant after applying Bonferronicorrection. Previous PET studies found a decline in SERTwith age in the raphe nuclei, though, we did not observe anage-related decline of SERT in the dorsal raphe nuclei. Thedorsal raphe nuclei is relatively small structures in the mid-brain where signal to noise ratio is rather low. Therefore, itis possible that there is an age-related decline in SERT inthis region, although we did not detect an association. Inaddition, no blood sampling was carried out in this study.This impedes the evaluation of potential differences in thecerebellum, which was however suggested to represent anoptimal reference region [Parsey et al., 2006].

CONCLUSION

In conclusion, we observed altered interregional SERTBPND correlation of the precuneus and the hippocampusin patients with ADHD, underlining the involvement ofthese brain areas in the pathophysiology of ADHD. On theother hand, SERT binding does not differ after applyingcorrection for multiple comparisons on a regional levelbetween patients with ADHD and HC. Given the fact thatthe SERT expression is modulated by regional serotonergicrelease, our results are compatible with alterations of inter-regional coupling within the serotonergic system inADHD.

ACKNOWLEDGMENTS

The authors thank M Stamenkovic, C Klier, B Hackenberg,A Konstantinidis, P Baldinger, D Meshkat, J Losak, CKraus and R G€oßler for medical support. Further theythank the PET team, especially G Karanikas, L Nics, DH€ausler, C Rami-Mark for the technical support. Foradministrative support, we thank G Gryglewski, M Cot-ton, J Unterholzer, and MG Godbersen.

DISCLOSURE OF BIOMEDICAL FINANCIAL

INTERESTS AND POTENTIAL CONFLICTS OF

INTEREST

All authors declare no competing financial interests inrelation to the work described. Without any relevance tothisƒ work, M Hacker has received conference speakerhonoraria from Covidian, GE Healthcare, IBA and

Figure 4.

Difference in the interregional SERT balance between patients

with ADHD and HC subjects. Color marked squares demon-

strate significant differences in interregional SERT coupling in

patients with ADHD compared to HC subjects (P< 0.05). After

Bonferroni correction for multiple comparisons, we found signif-

icant differences in interregional correlations of precuneus with

hippocampus (P 5 0.0324), marked with *. The color table rep-

resents the difference in interregional associations (in Spear-

man’s delta rho), red indicates lowest and yellow highest

interregional associations. ADHD: attention deficit/hyperactivity

disorder, SERT: serotonin transporter, ROIs: regions of interest.

[Color figure can be viewed at wileyonlinelibrary.com]

r Vanicek et al. r

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Endocyte and consults advisory boards of Endocyte. SKasper declares that he has received grant/research sup-port from Austrian National Bank (OENB), Eli Lilly, Lund-beck A/S, Bristol-Myers Squibb, Fonds f€urwissenschaftliche F€orderung (FWF), Servier, Sepracor,GlaxoSmithKline, Organon, Dr. Willmar Schwabe GmbH& Co. KG and has served as a consultant or on advisoryboards for AOP Pharma, AstraZeneca, Austrian SickFound, Austrian National Bank (OENB), Bristol-MyersSquibb, German Research Foundation (DFG), GeneraliInsurance Company, GlaxoSmithKline, Eli Lily, LundbeckA/S, Pfizer, Organon, Sepracor, Janssen, and Novartis,and has served on speakers’ bureaus for AOP Pharma,AstraZeneca, Eli Lilly, Lundbeck A/S, Neuraxpharm,Servier, Sepracor and Janssen. GS Kranz received travelgrants from Roche and AOP Orphan. A Kutzelnigg hasreceived travel grants from Eli Lilly and Company, AffirisAG, Novartis Pharmaceuticals Corporation, and AstraZe-neca, payment for lectures including service on speakers’bureaus from Eli Lilly and Company, Novartis Pharma-ceuticals Corporation, AstraZeneca and Affiris AG and hasserved as a consultant and on advisory boards for theAustrian Federal Ministry of Health, Eli Lilly and Compa-ny, Biogen-Idec and Medice Arzneimittel GmbH. R. Lan-zenberger received travel grants and/or conferencespeaker honoraria from AstraZeneca, Lundbeck A/S, Dr.Willmar Schwabe GmbH, AOP Orphan PharmaceuticalsAG, Janssen, and Roche Austria GmbH. T. Vanicekreceived travel grants and compensation for workshopparticipation from Pfizer and Eli Lilly. W Wadsak hasreceived research support from Rotem GmbH, ABX, Iason,Advion and Raytest Austria and has served as a consul-tant/trainer for Bayer and THP. The authors A Hahn, AH€oflich, Gregory M. James, A Kautzky, M Mitterhauser,Cecile Philippe, Helen L. Sigurdardottir, T Traub-Weidinger report no financial relationships with commer-cial interests. Funding/Support: This research was sup-ported by a grant from the Austrian National Bank(OeNB), FONDS/Jubil€aumsfonds (Project Nr. 13675)awarded to M Mitterhauser.

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3. DISCUSSION

3.1. General Discussion

Within the scope of this thesis, I aimed to address fundamental neuroscientific questions by

observing molecular structures of the noradrenergic and serotonergic neurotransmitter systems

in ADHD. PET studies are cost and labor intensive and are methodologically highly complex.

Though, PET imaging is the most sensitive and promising method to gather information on

neurotransmitter systems in vivo, which are hypothesized to cause or mediate neuropsychiatric

disorders. To shed light on the underlying neurochemical mechanisms I used PET and (S,S)-

[18F]FMeNER-D2 or [11C]DASB to quantify NET and SERT BPND in patients with ADHD and healthy

control subjects.

There is a lack of imaging studies targeting noradrenergic molecules and in particular the NET,

which is caused by several methodological issues in producing a reliable and suitable radioligand

for the NET (see section 1.5.2.). Since the NET represents a main treatment target for ADHD

specific psychopharmacological treatment, expression of the NET in the cell membrane in

patients is of high interest. In the first publication, patients with ADHD and matched healthy

control subjects were measured once with PET and (S,S)-[18F]FMeNER-D2 (Vanicek et al, 2014).

This is the first PET study to explore differences in brain NET binding in ADHD. Based on post

mortem and in vivo studies we a priori selected ROIs that show high levels of NET, including

subcortical region as the hippocampus, the putamen, the pallidum, the thalamus, the midbrain

with pons (stating a ROI which includes the LC), and the cerebellum, in adults with ADHD. We

found no significant differences in NET binding between patients and healthy control subjects.

Further, we revealed an age-associated decline in NET binding in the healthy human brain as

well as in adult patients with ADHD. As previously described (Schou et al, 2004; Takano et al,

2008b), we found highest NET levels in the thalamus and LC and lower levels of NET in the

pallidum, putamen, cerebellum and hippocampus. Lines of evidence suggest that noradrenergic

signaling in cortical regions, and in particular in the prefrontal cortex, is important for regulating

arousal, vigilance and executive functions (Berridge et al, 2006). Though, due to general low

levels of NET in cortical brain regions and skull bound radioactivity, which is associated with

(S,S)-[18F]FMeNER-D2 and superimposes NET quantification in bordering cortical regions, it is

currently not possible to objective NET levels in the neocortex (Ding, 2014; Rami-Mark et al,

2013). In relation to NET findings in different neuropsychiatric disorders, where lower levels

were found in obese patients, patients with posttraumatic stress disorder and in Alzheimer’s

51

disorder and higher levels were found in cocaine dependency (Ding et al, 2010b; Gulyas et al,

2010; Li et al, 2014; Pietrzak et al, 2013), results are diverse and do not point toward an unitary

up- or down-regulation in different disorders. Nevertheless, more studies on the NET have to be

executed in patients with ADHD to replicate and to underline the non-findings demonstrated in

our study, as well as in other neuropsychiatric disorder to gain more knowledge of the

noradrenergic transmitter system.

In our second publication, we investigated the effect of genotypes on NET BPND between groups

(Sigurdardottir et al, 2016). Imaging genetics is an approach that is derived from MRI

investigations (Meyer-Lindenberg and Weinberger, 2006). This approach also seems suitable for

PET data, since genotypes are related to measureable proteins. In our imaging genetics analysis

we deteced genotype-differences between groups in the thalamus and cerebellum.

Furthermore, we demonstrated an effect of genotypes in healthy control subjects, with major

allele carriers exhibiting lower NET binding while patients with ADHD had lower levels of NET

binding dependent on major allele expression. Moreover, depending on genotype, ADHD specific

symptoms as hyperactivity and impulsivity significantly correlated with NET BPND in the

cerebellum. A positive correlation was found between symptoms NET BPND in the cerebellum for

the major allele, whereas a negative correlation was found in minor allele carriers. Though, we

did not observe any association of SNPs to ADHD. This is possibly due to insufficient power,

caused by a small sample size, which is too small to assess subtle effects. The findings implicate a

genetic influence on noradrenergic signaling contributing to ADHD. To establish endophenotypes

for ADHD future research requires larger samples sizes as well as replication studies.

Genetic, pharmacological, as well as behavioral and imaging studies suggest an involvement of

the serotonergic system in the ADHD pathophysiology. The SERT critically modulates

serotonergic signaling, therefore representing a central serotonergic molecule for scientific

examinations. The SERT has been inspected twice in adult patients with ADHD, though in one

study methodological issues and in the other study a small sample size hamper the ability to

interpret the results. In this cross-sectional PET study, we detected no differences in SERT

availability between patients with ADHD and healthy subjects (Vanicek et al, 2016b). PET has

primarily been used to examine a targeted transporter or receptor density. Based on

interregional correlation analyses, PET imaging has also been used to investigate neuronal

connectivity (Baldinger et al, 2014; Horwitz et al, 1984; Lee et al, 2008; Morbelli et al, 2013;

Vanicek et al, 2016a). We performed an interregional correlation of SERT binding in every ROI

52

and found significant higher correlations in patients with ADHD in numerous ROIs and after

correction for multiple comparisons between the hippocampus and the precuneus. These results

describe SERT associations between brain regions, thus leading to a more realistic understanding

of an altered serotonergic system in ADHD. In addition and similar to the age related findings of

the NET, we found a negative correlation of age and SERT binding in patients with ADHD and

healthy control subjects.

3.2. Conclusion & future prospects

To summarize, this thesis intended to quantify essential transporter proteins of the

noradrenergic and serotonergic neurotransmitter system in ADHD in vivo and to detect the

extent of the involvement of genotypes on noradrenergic signaling. Three publications arose

from this thesis, whereas PET and the radioligands (S,S)-[18F]FMeNER-D2 or [11C]DASB as well as

genotyping was used to test study hypothesis. With these investigations I was able to shed light

on previously not studied neurochemical pathway, as demonstrated in the first publication,

where NET distribution has been described for the first time in patients ADHD. In addition, a

genotype effect on the expression on NET has been demonstrated, suggesting a genetic

influence on the NET in ADHD. Furthermore, I was the first to apply interregional correlational

analysis of the SERT binding, which represents an approach that aims to reveal alterations on

more global level throughout the brain to capture a complex pattern of the SERT distribution,

and demonstrate significant differences in patients with ADHD. These publications improve

knowledge about fundamental neuroscientific understanding on NET and SERT availability and

function.

On the one hand future research will have to replicate imaging findings in larger study samples,

especially results describing the NET distribution, since the NET plays a critical role in

noradrenergic and dopaminergic pathways that are highly involved in the neurobiology of ADHD

and since these are the first and only data of NET in this patient group. On the other hand,

studies have to expand our aims by applying a new and suitable radioligands to investigate NET

levels in cortical regions. Furthermore, molecular imaging studies will have to target different

receptors within the noradrenergic and dopaminergic system and genotypes of different

proteins. Lastly, occupancy studies are needed to be executed in vivo in patients with ADHD to

53

disclose the neurobiological mechanisms of the neurotransmitters system in ADHD, influenced

by psychotropic medication frequently prescribed in ADHD.

54

4. MATERIALS AND METHODS

This doctoral thesis was designed, organized, coordinated and executed by the “NEUROIMAGING

LABS (NIL) - PET, MRI, EEG & Chemical Lab” at the Department of Psychiatry and Psychotherapy,

Clinical Division of Biological Psychiatry, Medical University of Vienna and the “Doctoral

Programme Clinical Neurosciences – CLINS”. The thesis project was followed through within the

scope of the projects “The Norepinephrine Transporter in Attention Deficit Hyperactivity

Disorder (ADHD) investigated with PET” (PI: Assoc. Prof. PD Rupert Lanzenberger, MD) and “The

Serotonin Transporter in Attention Deficit Hyperactivity Disorder investigated with Positron

Emission Tomography.” (PI: Prof. Dr. Markus Mitterhauser). The study protocols where approved

by the Ethics Committee of the Medical University of Vienna and the General Hospital of Vienna

(EK 552/2010; EK 784/2010). The project “The Norepinephrine transporter in Attention Deficit

Hyperactivity Disorder investigated with PET” was funded by the Austrian Science Fund FWF

(FWF Projektnr.: P 22981) and “The Serotonin Transporter in Attention Deficit Hyperactivity

Disorder Investigated with Positron Emission Tomography” by the Jubiläumsfonds der

Oesterreichischen Nationalbank (OeNB Projektnr.: 13675).

Patients with ADHD where recruited through the focus outpatient clinic for ADHD in adults at

the Department of Psychiatry and Psychotherapy, Clinical Division of Biological Psychiatry.

Healthy control subjects were recruited from the local community via advertisement.

Adult patients with ADHD and age and sex matched healthy control subjects were measured

once with the GE Advance PET scanner (General Electroc Medical Systems, Milwaukee,

Wisconsin, USA) at the Department of Biomedical Imaging and Image-guided Therapy, Division

of Nuclear Medicine Department of Nuclear Medicine, Medical University of Vienna, in

accordance to the specific study procedures with PET and (S,S)-[18F]FMeNER-D2 or [11C]DASB.

The radiotracers have been used successfully in previous studies (Lanzenberger et al., 2007;

Rami-Mark et al. 2014) and have been shown to be suitable to quantify the norepinephrine and

serotonin transporter in vivo in humans. PET data analysing techniques have to be acquired in

order to perform quantitative tracer kinetic modeling using reference tissue compartmental

models and the kinetic modelling tools, which are implemented in the biomedical image

quantification software PMOD 2.9 (Burger and Buck 1997; http://www.pmod.com).

Further Statistical Parametric Mapping (SPM) was used to co-register PET- and MRI images. Main

outcome measures were either NET or SERT BPND in areas of interest (region-of-interest

http://www.pmod.com/

55

approach). General linear mixed model (GLM) was performed to reveal significant differences in

NET and SERT binding potential according to study groups, gender and age as well as to the

genotype groups and other variables. Interregional molecular associations analysis were

calculated using Spearman’s rank correlation coefficient (Δρ) for each group separately and

compared between ROIs and added to the analysis of significant differences in SERT BPND

between groups. Interregional correlation matrices were transformed using Fisher’s r-to-z-

transformation and a 10,000 fold permutation test was performed. All results where corrected

for multiple testing.

56

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Curriculum Vitae (CV)

University Education Since 09.2011 Doctoral studies „Clinical Neurosciences“ (N790) at the Medical University

of Vienna (Supervision: Assoc.-Prof. PD Dr. Rupert Lanzenberger). 2008 – 2011 Diploma thesis „Anschlussdegenerationen bei dorsalen Lumbalfusionen.

Eine retrospektive Datenanalyse.“ at the Orthopädische Spittal Speising, Vienna.

2004 – 2011 Human Medicine studies at the Medical University of Vienna

Professional Education Since 09.2014 Training in Systemic Psychotherapy Since 08.2013 Medical resident for Psychiatry and Psychotherapeutic Medicine at the

Department for Psychiatry und Psychotherapy of the Medical University of Vienna (Head: O. Univ. Prof. Dr. hc. mult. Dr. med. Siegfried Kasper)

09.2012 – 06.2013 Medical resident for Child and Adolescent Psychiatry at the Department for

Child and Adolescent Medicine of the Medical University of Vienna, der MUW (Head: O. Univ. Prof. Dr. Arnold Pollak)

Research Collaborator Since 09.2011 Research assistance at the NEUROIMAGING LABs (NIL) - PET & MRI & EEG

& Chemical Lab (Head: Assoc.-Prof. PD Dr. Rupert Lanzenberger) at the Department for Psychiatry und Psychotherapy of the Medical University of Vienna.

Thomas Vanicek MD E-Mail: [email protected]


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Honors MUW researcher of the month Januar 2016.

Medical University of Vienna, AKH Wien Vanicek T, Spies M, Rami-Mark C, Savli M, Höflich A, Kranz G, Hahn A, Mitterhauser M,

Wadsak W, Hacker M, Kasper S, Lanzenberger R The Norepinephrine Transporter in Attention Deficit/Hyperactivity Disorder Investigated with (S,S)-[18F]FMeNER-D2 JAMA Psychiatry. 2014 Dec 1;71(12):1340-9.

Research award in clinial psychiatry OeGPB, Nov. 2014 Vanicek T, Spies M, Rami-Mark C, Savli M, Höflich A, Kranz G, Hahn A, Mitterhauser M,

Wadsak W, Hacker M, Kasper S, Lanzenberger R The Norepinephrine Transporter in Attention Deficit/Hyperactivity Disorder Investigated with (S,S)-[18F]FMeNER-D2 JAMA Psychiatry. 2014 Dec 1;71(12):1340-9.

Herbert Reisner Preis 2016 für klinische Epileptologie der ÖGfE Österreiches Gesellschaft für Epileptologie, 11.11.2016, Innsbruck, Austria

Poster Award of the 30th CINP Congress, Seoul, KoreaJuly 3-5, 2016 Vanicek T, Kutzelnigg A, Cecile P, Sigurdardottir HL, James GM, Hahn A, Kranz GS, Höflich

A, Kautzky A, Traub-Weidinger T, Hacker M, Wadsak W, Kasper S, Mitterhauser M, Lanzenberger R. Interregional Correlations of SERT in Attention Deficit/Hyperactivity Disorder compared to Healthy Controls; Investigated with PET and [11C]DASB

30th CINP Congress, Seoul, KoreaJuly 3-5, 2016 Collegium Internationale Neuro-Psychopharmacologicum (CINP)

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Publication List (PL)

Original Investigations– First Author Publications (Top)

1. Vanicek T, Spies M, Rami-Mark C, Savli M, Höflich A, Kranz G, Hahn A, Mitterhauser M, Wadsak W, Hacker M, Kasper S, Lanzenberger R

The Norepinephrine Transporter in Attention Deficit/Hyperactivity Disorder Investigated with (S,S)-[18F]FMeNER-D2 JAMA Psychiatry. 2014 Dec 1;71(12):1340-9. [2015, IF: 14.417]

2. Vanicek T, Hahn A, Traub-Weidinger T, Hilger E, Spies M, Wadsak W, Lanzenberger R,

Pataraia E, Asenbaum-Nan S. Insights into intrinsic brain networks based on graph theory and PET in right- compared to left-sided temporal lobe epilepsy. Scientific Reports 2016. Epub 2016 Jun 28 [2015, IF: 5.228]

3. Vanicek T, Kutzelnigg A, Philippe C, Sigurdardottir HL, James GM, Hahn A, Kranz GS, Höflich A, Kautzky A, Traub-Weidinger T, Hacker M, Wadsak W, Mitterhauser M, Kasper S, Lanzenberger R. Differences in interregional molecular balance of the serotonin transporter in attention

deficit/hyperactivity disorder revealed by PET. Human Brain Mapping 2016 Oct 22. Epub ahead of print [2015, IF: 4.962]

Co-Author Publications (Top) 1. Höflich A, Hahn A, Küblböck M, Kranz GS, Vanicek T, Ganger S, Spies M, Windischberger C, Kasper

S, Winkler D, Lanzenberger R. Ketamine-dependent neuronal activation in healthy volunteers. Brain Structure and Function Epub 2016 Aug 30. [2015, IF: 5,811]

1. Hahn A, Gryglewski G, Nics L, Hienert M, Rischka L, Vraka C, Sigurdardottir H, Vanicek T, James

GM, Seiger R, Kautzky A, Silberbauer L, Wadsak W, Mitterhauser M, Hacker M, Kasper S, Lanzenberger R. Quantification of task-specific glucose metabolism with constant infusion of [18F]FDG. Journal of Nuclear Medicine Epub 2016 Jul 7 [2015, IF: 5,849]

2. Kautzky A, Baldinger-Melich P, Kranz GS, Vanicek T, Souery D, Montgomery S, Mendlewicz J, Zohar

J, Serretti A, Lanzenberger R, Kasper S. A new prediction model for evaluating treatment resistant depression. J Clin Psychiatry 78:0, 2017. [2015, IF: 5.408]

3. Hahn A, Kranz GS, Sladky R, Kaufmann U, Ganger S, Hummer A, Seiger R, Spies M, Vanicek T,

Winkler D, Kasper S, Windischberger C, Swaab DF, Lanzenberger R. Testosterone affects language areas of the adult human brain. Human Brain Mapping 2016 Feb 15, Epub ahead of print [2014, IF: 5.969]

4. Sigurdardottir HL, Kranz GS, Rami-Mark C, James GM,Vanicek T,Gryglewski G, Kautzky A, Hienert

M, Traub-Weidinger T, Mitterhauser M, Wadsak W, Hacker M, Rujescu D , Kasper S, Lanzenberger R

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Effects of norepinephrine transporter gene variants on NET binding in ADHD and healthy controls investigated by PET Human Brain Mapping 2016 Mar, Epub 2015 Dec 7 [2014, IF: 5.969]

5. Ganger S, Hahn A, Küblböck M, Kranz GS, Spies M, Vanicek T, Seiger R, Sladky R, Windischberger C,

Kasper S, Lanzenberger R. Comparison of continuously acquired resting state and extracted analogues from active tasks. Human Brain Mapping 2015 Oct;36(10):4053-63. [2014, IF: 5.969];

6. Höflich A, Hahn A, Küblböck M, Kranz GS, Vanicek T, Windischberger C, Saria A, Kasper S, Winkler

D, Lanzenberger R Ketamine-induced modulation of the thalamo-cortical network in healthy volunteers as a model for schizophrenia International Journal of Neuropsychopharmacology, 2015 Apr 19 [2011, IF: 5.264]

7. Kranz GS, Hahn A, Baldinger P, Häusler D, Philippe C, Kaufmann U, Wadsak W, Savli M, Höflich A, Kraus C, Vanicek T, Mitterhauser M, Kasper S, Lanzenberger R. Cerebral serotonine transporter asymmetry in males and male-to-female transsexuals: a PET

study with [11C]DASB. Brain Structure and Function. Epub 2014 Jan 2. [2011, IF: 5.628]

Co-Author Publications (Standard) 1. Rami-Mark C, Eberherr N, Berroterán-Infante N, Vanicek T, Nics L, Lanzenberger R, Hacker M,

Wadsak W, Mitterhauser M. [18F]FMeNER-D2: A systematic in vitro analysis of radio-metabolism. Nuclear Medicine and Biology 2016 Aug;43(8):490-5. [2015, IF: 2.429]

Vienna, 22.12.2016

molecular imaging in adult attention deficit/hyperactivity ... · curriculum vitae (cv) ......

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