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The studies described in this thesis were performed at the Rudolf Magnus Institute of Neuroscience, Department of Child and Adolescent Psychiatry, University Medical Center Utrecht, The Netherlands. The research in this thesis was supported by VIDI grant 91.776.384 from the Netherlands Organization for Scientific Research (NWO) to prof. dr. S. Durston. Publication of this thesis was supported by the Rudolf Magnus Institute of Neuroscience. ISBN: 978-90-393-5618-0 Cover & book design: studio ilse van klei / www.ilsevanklei.nl Printed by: Ipskamp Drukkers BV, Enschede, The Netherlands Copyright © 2011 by Patrick de Zeeuw No part of this thesis may be reproduced in any form without written permission from the author.

NEUROBIOLOGICAL HETEROGENEITY IN ADHD NEUROBIOLOGISCHE HETEROGENITEIT BIJ ADHD (met een samenvatting in het Nederlands)

Proefschrift ter verkrijging van de graad van doctor aan de Universiteit Utrecht op gezag van de rector magnificus, prof.dr. G.J. van der Zwaan, ingevolge het besluit van het college voor promoties in het openbaar te verdedigen op donderdag 13 oktober 2011 des ochtends te 10.30 uur door Patrick de Zeeuw geboren op 30 maart 1979 te ‘s-Gravendeel

Promotoren: Prof.dr. S. Durston Prof.dr. H. van Engeland

voor de kinderen en jongeren die beter weten wat ADHD is dan ik

CONTENTS Chapter 1 Introduction Chapter 2 Imaging genetics in ADHD: A focus on cognitive control Chapter 3 Decreased frontostriatal microstructural organization in ADHD Chapter 4 Deficits in cognitive control, timing and reward sensitivity are dissociable in ADHD Chapter 5 Differential brain development with low and high IQ in ADHD Chapter 6 Prenatal exposure to cigarette smoke or alcohol affects the volume of cerebellum in ADHD Chapter 7 Imaging gene and environmental effects on cerebellum in ADHD Chapter 8 General discussion

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Nederlandse samenvatting Color figures Acknowledgements/Dankwoord Curriculum Vitae Publications

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CHAPTER

INTRODUCTION Saying that no two people are exactly alike is a flagrant cliché. The counterpoint, that it will be hard to find two people with absolutely nothing in common, is no better. Both statements are more likely to have been drawn from undiscerning self-help literature rather than the start of a dissertation. However, the majority of the work described in this thesis hinges on these thoughts as they apply to the neuropsychological and neurobiological profile of Attention-Deficit/Hyperactivity Disorder (ADHD).

WHAT IS THIS THESIS ABOUT? The studies in this thesis are aimed at investigating heterogeneity in the neuropsychological and neurobiological profile of ADHD. The past decades have seen great advances in the neurobiological characterization of ADHD. Much has been learned about how brain changes and cognitive dysfunction contribute to ADHD. However, as will become evident from this introduction, much of this research has also revealed that differences in this neurobiological profile may exist between children with ADHD. It is increasingly thought that these differences may be more than “noise” and suggest that multiple neurobiological systems may be involved in multiple pathways towards ADHD. The studies in this thesis attempt to contribute to the characterization of sources of neurobiological heterogeneity in ADHD. Some of this work will show that children with ADHD may be quite dissimilar at the cognitive and neuroanatomical level. Other parts will show diversity within different aspects of the same brain network. Etiological heterogeneity is addressed when focus shifts towards environmental and genetic influences on brain alterations in ADHD. The perspective of this work is neurobiological, but placed in a broader context: if one thing should be clear from the past decades of research, it is that no unitary perspective can cover all aspects of any disorder. Clinically, ADHD presents itself in numerous behavioral forms, with and without comorbidity, and with or without complex family and psychosocial environments. The basic premise of this thesis is that the neurobiology of ADHD may be no different: not unitary, but heterogeneous. This chapter serves as an introduction to ADHD as well as an introduction to the subfields of ADHD research that are relevant to this work: research on genetic and environmental risk, neurodevelopmental differences and neuropsychological differences. The first section will discuss the definition, history, and clinical phenomenology of ADHD. Next, an

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introduction to neurobiological research of ADHD will be given. Finally, the separate studies enclosed in this thesis will be briefly introduced.

WHAT IS ADHD? ADHD is a neuropsychiatric syndrome that emerges during childhood and is characterized by impairing and developmentally inappropriate symptoms of inattentiveness and/or hyperactivity and impulsivity (American Psychiatric Association, 2000). The clinical presentation is markedly heterogeneous on a scale from subtle but pervasive attention problems or dreaminess up to excessive hyperactive, impulsive and unpredictable behavior. ADHD is associated with significant functional and psychosocial impairment (American Psychiatric Association, 2000; Biederman & Faraone, 2005).

The history of ADHD The history of what we now know as ADHD can be traced back as far as the 18th century. The Scottish physician Sir Alexander Crichton (Figure 1, left panel) may have been the first to write on “Attention and Its Diseases” (Crichton, 1789, 2008). His writing starts as a rather philosophical discussion on volitional control of attention and on harnessing it as a mental faculty. Crichton describes a disorder he refers to as “mental restlessness”, consisting of two main attention problems: the incapacity to sustain attention for longer periods of time (“constancy”) and distractibility, which he describes as attention “incessantly (being) withdrawn from one impression to another” (Crichton, 2008, p. 203). Both observations are similar to some of the current symptom criteria for ADHD (Lange et al., 2010). It is the English pediatrician Sir George Frederic Still (Figure 2, right panel) who is most often credited with the first description of ADHD, in his 1902 address to the Royal College of Physicians (Still, 1902a, 1902b). Still describes a number of patients with problems in self regulation or, as he then termed it, “moral control” (Barkley, 2006; Still, 1902a). As with most of the early accounts of ADHD, his case descriptions suggest a wide variety of problems rather than ADHD alone, including neurological states and the broader spectrum of externalizing behavior disorders (Lange et al., 2010; Still, 1902a). Still coined the term “moral imbecility” to describe the syndrome, where the “moral” may indeed be viewed to reflect the conduct problems evident in many of his patients. However, that same term also reflects what can be thought of as his main legacy: by tentatively grouping them with “nonmoral” imbecility (the term for moderate mental retardation at the time), he stressed that these behavioral problems may not be caused by an intellectual deficit but were in fact impairing and in need of medical and scientific attention (Lange et al., 2010; Rafalovich, 2001). Both Crichton and Still suggested a neurobiological origin of the behavioral phenomena, believing them to stem from a “morbid sensibility of the nerves” (Crichton, 2008, p. 203), or “the manifestation of some morbid physical condition” (Still, 1902b, p. 1165). Such ideas were corroborated by the 1917-1924 epidemic of encephalitis letargia (Mayes & Rafalovich, 2007; Rafalovich, 2001). Behavioral problems of the sort described by Still were frequently found in children who survived this illness (Lange et al., 2010; Mayes & Rafalovich, 2007). Tredgold, an

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Chapter 1 – Introduction

important scholar at the time, also suggested that nonencephalic cases were likely to constitute an inherited disorder of the brain (Mayes & Rafalovich, 2007). Subsequently, others further refined the phenotype by identifying motor hyperactivity as a core symptom (Kramer & Pollnow, 1932; Neumarker, 2005), and unifying the symptoms of attention problems, impulsivity and hyperactivity under the moniker hyperkinetic impulse disorder (Lange et al., 2010; Mayes & Rafalovich, 2007). The syndrome became more widely known in the 1950’s, when a term was proposed that reflected the inferred brain disorder of mild severity: minimal brain damage (Lange et al., 2010; Mayes & Rafalovich, 2007). In the absence of neurological evidence for actual “damage” in the pathological sense, this term gradually morphed into minimal brain dysfunction (Lange et al., 2010; Mayes & Rafalovich, 2007). Despite being rather stigmatizing and suggesting a known etiology rather than describing the syndrome, the term remained common in the literature until well after the syndrome first appeared in the Diagnostic and Statistical Manual of Mental Disorders (DSM) (Lange et al., 2010). In 1968, the syndrome was officially defined as a mental disorder for the first time in the second edition of the DSM as hyperkinetic reaction of childhood (American Psychiatric Association, 1968). In the decades following DSM-II, focus shifted back and forth between attention problems and hyperactivity as the defining symptoms of the syndrome. This is reflected in the definitions in subsequent DSM editions. DSM-III emphasized attention deficits in the diagnostic category of Attention Deficit Disorder (ADD), which could be diagnosed with or without hyperactivity. This conceptual change was short-lived: in the revision of the DSM-III in 1987, attention problems and hyperactivity were unified as Attention-Deficit/Hyperactivity Disorder. The discussion on the clinical validity of subtypes with only attention problems or only hyperactive/impulsive behavior has remained lively until this day (Lange et al., 2010). The current clinical conception ADHD reflects that diversity. Figure 1. Historical figures in ADHD.

Note. Left, Sir Alexander Crichton (1763-1856). Right, Sir George Frederic Still (1868-1941).

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ADHD today The current, fourth edition of the DSM, describes three main subtypes of the disorder (American Psychiatric Association, 2000). In the predominantly inattentive subtype, attention problems dominate the clinical picture. This subtype is largely equivalent to the older classification of Attention Deficit Disorder (ADD). Hyperactivity and impulsivity dominate the clinical picture in the hyperactive/impulsive subtype, with both symptom clusters being present in the combined subtype. Additional criteria include (a) pervasiveness, operationalized as symptoms being present in more than one setting, (b) evidence for significant social, academic, or occupational impairment and (c) age of onset before 7 years. Table 1 lists the diagnostic criteria as they appear in DSM-IV. The International Classification of Diseases (ICD-10), published by the World Health Organization (WHO) recognizes the phenotype under the Hyperkinetic Disorders (HKD) classification (World Health Organization, 2004). HKD is near identical to ADHD, but is more restrictive in terms of the requirement of pervasiveness and impairment of symptoms (Remschmidt, 2005; Swanson et al., 1998; Taylor et al., 2004). However, since the DSM has evolved into the gold standard classification in both clinical practice and research, this thesis concerns ADHD, as defined in DSM-IV. The fifth edition of DSM is due in 2013. A number of changes to the definition of ADHD are proposed (American Psychiatric Association, 2010). These include a rewording the symptoms for adults, inclusion of a more restrictive inattentive subtype (allowing a maximum of two hyperactive/impulsive symptoms), and changing the age of onset criterion to 12 years, consistent with epidemiological data (Kessler et al., 2005).

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Table 1. DSM-IV diagnostic criteria for Attention-Deficit/Hyperactivity Disorder A. Either (1) or (2):

(1) six (or more) of the following symptoms of inattention have persisted for at least 6 months to a degree that is maladaptive and inconsistent with developmental level: Inattention a. often fails to give close attention to details or makes careless mistakes in schoolwork, work, or other activities b. often has difficulty sustaining attention in tasks or play activities c. often does not seem to listen when spoken to directly d. often does not follow through on instructions and fails to finish schoolwork, chores, or duties in the workplace (not due to oppositional behavior or failure to understand instructions) e. often has difficulty organizing tasks and activities f. often avoids, dislikes, or is reluctant to engage in tasks that require sustained mental effort (such as schoolwork or homework) g. often loses things necessary for tasks or activities (e.g., toys, school assignments, pencils, books, or tools) h. is often easily distracted by extraneous stimuli i. is often forgetful in daily activities (2) six (or more) of the following symptoms of hyperactivity/impulsivity have persisted for at least 6 months to a degree that is maladaptive and inconsistent with developmental level: Hyperactivity a. often fidgets with hands or feet or squirms in seat b. often leaves seat in classroom or in other situations in which remaining seated is expected c. often runs about or climbs excessively in situations in which it is inappropriate (in adolescents or adults, may be limited to subjective feelings of restlessness) d. often has difficulty playing or engaging in leisure activities quietly e. is often “on the go” or often acts as if “driven by a motor” f. often talks excessively Impulsivity g. often blurts out answers before questions have been completed h. often has difficulty awaiting turn i. often interrupts or intrudes on others (e.g., butts into conversations or games) B. Some hyperactive-impulsive or inattentive symptoms that caused impairment were present before age 7 years. C. Some impairment from the symptoms is present in two or more settings (e.g., at school [or work] and at home). D. There must be clear evidence of clinically significant impairment in social, academic, or occupational functioning. E. The symptoms do not occur exclusively during the course of a Pervasive Developmental Disorder, Schizophrenia, or other Psychotic Disorder and are not better accounted for by another mental disorder (e.g., Mood Disorder, Anxiety Disorder, Dissociative Disorder, or a Personality Disorder). Code based on type: – 314.01 Attention-Deficit/Hyperactivity Disorder, Combined Type: if both Criteria A1 and A2 are met for the past 6 months – 314.00 Attention-Deficit/Hyperactivity Disorder, Predominantly Inattentive Type: if Criterion A1 is met but Criterion A2 is not met for the past 6 months – 314.01 Attention-Deficit/Hyperactivity Disorder, Predominantly Hyperactive-Impulsive Type: if Criterion A2 is met but Criterion A1 is not met for the past 6 months

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The epidemiology of ADHD Prevalence estimates of ADHD vary strongly across studies, mostly due to methodological factors. A meta-analysis of all available epidemiological data from samples across the globe, accounting for methodological limitations, arrived at a pooled worldwide prevalence of 5.3% (Polanczyk et al., 2007), decreasing from childhood (6.5%) to adolescence (2.7%). The worldwide prevalence in adults is estimated at 2.5% (Simon et al., 2009). Lifetime prevalence of ADHD in The Netherlands has recently been reported at 2.9% (de Graaf et al., 2011). ADHD is more prevalent in boys than in girls. Across studies in non-referred, epidemiological samples, a 2.45 times higher prevalence rate has been found for boys in comparison to girls (Polanczyk et al., 2007; Polanczyk & Rohde, 2007). However, in clinical samples, the male:female ratio may be as high as 9:1 (American Psychiatric Association, 2000). There is evidence that the clinical presentation differs between boys and girls, with girls more commonly affected with the inattentive subtype (Bauermeister et al., 2007; Biederman & Faraone, 2004; Rucklidge, 2008). Comorbidity is frequent in ADHD, Oppositional Defiant Disorder (ODD) and Conduct Disorder (CD) in particular. Comorbidity with ODD/CD is found in about 30% of cases in community samples (Nock et al., 2007; Tuithof et al., 2010) and up to 50% or even more in clinical samples (Jensen et al., 1997). Anxiety disorders and learning disorders are also frequently comorbid with ADHD (Jensen et al., 1997), the former of which appears more common in girls with ADHD (Bauermeister et al., 2007).

Diagnosis and treatment Diagnosis of ADHD is arrived at by assessing behavioral symptoms in a standardized way. Most clinical guidelines recommend multiple informants (typically parents and teachers) and a thorough psychiatric and/or (neuro)psychological examination allowing for direct observation of the child’s behavior by professionals (Atkinson & Hollis, 2010; Taylor et al., 2004). There are currently no non-behavioral tests, either psychological or (neuro)biological that provide enough sensitivity and specificity to be used as a dichotomous diagnostic criterion. As we will see below, neuropsychological and neurobiological heterogeneity within the ADHD phenotype may be one explanation for this phenomenon. Typically, a diagnostic workup will include an assessment of the psychosocial, family, and school environment. Psychosocial interventions are recommended in moderately affected cases, including parent management training, support groups for children and parents, and training of general social skills and (social) problem solving of the child. In the more affected cases and in moderately affected cases where psychosocial intervention leads to no avail, pharmacological treatment, typically psychostimulants, is indicated (Atkinson & Hollis, 2010; Taylor et al., 2004). The efficacy of psychostimulants in treating ADHD has had profound influence on the study of its neurobiology and was discovered by Charles Bradley in the 1930’s (Bradley, 1937). He initially prescribed the stimulant benzedrine to relieve headaches in hyperactive children who had undergone a pneumoencephalogram. It provided little relief of the headaches but markedly improved symptoms, school functioning and general emotional well being (Bradley, 1937; Mayes & Rafalovich, 2007). However, it took until 1967 before a newer class of psychostimulant, methylphenidate (now better known under one of its commercial

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names: Ritalin), was first approved in the United States. Some form of controversy has always surrounded its use (Mayes & Rafalovich, 2007). However, its efficacy and safety is well researched and documented (Biederman et al., 2004; MTA Cooperative Group, 2004).

ADHD grown up The degree of chronicity of ADHD remains a widely discussed topic. Originally thought to be primarily a childhood disorder, current data suggest that ADHD may manifest in adults just the same (Antshel & Barkley, 2009). When defined as full remission (not meeting formal diagnostic criteria), persistence is estimated low, at approximately 15% (Faraone et al., 2006). However, partial remission with continued functional impairment is estimated to be present in as many as 50-70% of cases (Antshel & Barkley, 2009; Faraone et al., 2006). Hyperactive/ impulsive symptoms decline more strongly from childhood to adulthood, with inattentive symptoms showing stronger persistence (Biederman et al., 2000). Children diagnosed with ADHD in childhood are at increased risk for functional impairments in adulthood including occupational and academic problems, difficulty in maintaining personal relationships, and financial problems (Antshel & Barkley, 2009; Barkley et al., 2006). Antisocial activity and psychiatric morbidity, particularly of mood, anxiety and substance abuse disorders, are also more common in this group than in the general population (Antshel & Barkley, 2009; Barkley et al., 2004; Elkins et al., 2007).

THE NEUROSCIENCE OF ADHD A neurobiological etiology for ADHD has been suggested as early as the reports of Crichton and Still. Recent history has brought us both better agreement on the phenotypic characteristics of ADHD and rapid technological advances allowing detailed in-vivo study of the neurobiological factors. These two together have propelled the field into developing an increasingly refined picture of the neurobiology of ADHD. It is important to note however, that important starting points were the observations of Bradly, who showed stimulant effects in ADHD (Bradley, 1937), and the observations of resemblance between frontal lesion patients and ADHD patients (Lange et al., 2010). Both were found to implicate prefrontal brain regions, the striatum and impairments in cognitive control on behavior. This thesis includes studies on a wide variety of issues, which require a brief introduction: genetic and environmental risk factors and neurobiological and neuropsychological correlates of ADHD. Discussing such factors requires stipulating a framework to connect elements from disparate but related levels of analysis. One frequently applied model is the endophenotype model, where the syndrome is placed at the end of a neurobiological cascade (Durston et al., 2009; Gottesman & Gould, 2003; Kendler & Neale, 2010; Rommelse et al., 2008a). A highly simplified version of such a model is presented in Figure 2. At the top of the hierarchy are genetic and environmental effects which drive developmental changes in neurobiology (brain anatomy for example). These changes may be associated with certain neuropsychological changes that eventually place an individual on the continuum from no symptoms to the full syndrome. The intermediate levels of alterations in neurobiology and

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cognition are termed endophenotypes. An important assumption of these endophenotypic models is that the relationship between genetic risk factors and brain alterations will be more direct and neurobiologically informative than the relationship between, for example, genetic risk factors and behavioral measures of ADHD. One important requirement for a measure to be a viable endophenotype that follows from this assumption is that the disorder-related state of the endophenotype should also be present in a more attenuated degree in unaffected relatives of the affected child (state independency). Theory of endophenotypes will be discussed in some more detail in Chapter 2 and will return in the Discussion. In the next sections, levels of genetic risk factors, environmental risk factors, brain anatomy, and neuropsychology will be separately discussed. Figure 2. A simplified version of the endophenotype model.

endophenotypes

genetic risk factors

{

no symptoms

environmental risk factors

neurodevelopmental changes

neuropsychological changes

full syndrome

Note. This model represents a causal hierarchy, where genetic and environmental risk factors drive neurodevelopmental alterations, which may produce cognitive alterations that ultimately place a person on the continuum from no symptoms to the full syndrome. The model is simplified in that genetic and environmental factors may interact and may also influence other pathways in the cascade. For example, environmental factors may exacerbate or dampen the effects of cognitive alterations on symptom profile.

The genetics of ADHD The first studies reporting that ADHD runs in families appeared as early as the 1970’s (Morrison & Stewart, 1971). Behavioral genetic studies employing twin and adoption designs have converged at a heritability estimate of 77% (Faraone et al., 2005). As such, 77% of the variance in ADHD symptoms can be attributed to additive genetic effects. These results have encouraged molecular genetic studies of ADHD, which are discussed in more detail in Chapter 2. With the beneficial effects of stimulants in mind, early candidate gene studies mainly focussed on genes known to affect dopaminergic and noradrenergic neurotransmission in particular (DAT1, DRD1-4, NET1, ADRA2A). Another notable early candidate gene was the synaptosomal associated protein 25kb (SNAP-25) gene, which was discovered when the Coloboma mouse model of hyperactivity was found to have a deletion in a chromosome-2

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region encompassing this gene (Mill, 2007). However, not unlike other complex behavioral phenotypes, results from molecular genetic studies of ADHD have been heterogeneous. Consistent replication of the genetic association of most candidate genes has been rare (Gizer et al., 2009). In addition, associations that have been repeatedly (though not universally) demonstrated, such as the dopamine transporter gene (DAT1), the dopamine D4 receptor gene (DRD4) and SNAP-25, are of small magnitude (Gizer et al., 2009). Odds ratios rarely exceed 1.3, which is roughly equivalent to a 1.5 percent-point increase in risk for ADHD, assuming a baseline prevalence of 5%. In contrast to hypothesis driven candidate gene studies, genome-wide association studies (GWAS) take a data driven approach, searching for genetic association across the genome in large samples of cases and controls. The studies performed in ADHD samples thus far have not identified any genome-wide significant results (Franke et al., 2009; Neale et al., 2010). Moreover, the lower-threshold top hits from these studies not only show little overlap, but have typically not confirmed the classic candidate genes of theoretical interest (Franke et al., 2009). Thus, molecular genetic studies have not been able to identify genetic variance that can sufficiently account for the high heritability of ADHD. This situation, that is common across the whole of psychiatry and research of complex phenotypes in general, is often referred to as the missing heritability problem (Manolio et al., 2009). Typically, involvement of either many genes of small effect size (and therefore hard to detect) or (structural) variants of large effect (too uncommon to detect in GWAS) are suggested to underly the missing heritability. One example of the latter are copy number variations that have been identified in some cases of ADHD (Elia et al., 2010). Another informative approach has been to examine how top GWAS hits may fit into a genetic network that serves a specific function. In ADHD, one such network may be involved in directed neurite outgrowth (Poelmans et al., 2011). The advantage of such an approach is that biological plausibility, rather than statistics alone is reemphasized. This approach may point us towards pivotal neural processes or cascades that are involved in the development of ADHD but are missed when GWAS findings are taken at face value. A final and potentially more important source of missing heritability relevant to this thesis lies within the phenotype itself, in its inherent complexity and heterogeneity (Bilder et al., 2009; Sabb et al., 2009; Van der Sluis et al., 2010). Finding genes specific to ADHD as a discrete diagnostic category may be difficult when the phenotype may in reality be best described as a multidimensional space of a number of symptom classes and cognitive deficiencies that, each in turn, may or may not be present in an affected individual. Indeed, some authors claim that the success of future genetic studies in psychiatric phenotypes will depend on parsing these phenotypes along more neurobiologically informative dimensions (Bilder et al., 2009; Durston et al., 2011).

Environmental riskfactors in ADHD Throughout the history of psychiatry and psychology, deviant behavior has frequently been explained in terms of either a genetic or environmental etiology: the nature/nurture debate. Today, it is increasingly recognized that this is a false dichotomy; despite a high heritability, environmental risk factors affect the development and course of ADHD. Moreover, both domains may interact in increasing the risk of ADHD: gene by environment interaction (Nigg et

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al., 2010). This will be dicussed briefly in a later section. Environmental risk factors in ADHD by themselves may be crudely divided into two main categories: those assumed to directly affect neurobiology, and those of a psychological or psychosocial nature. Research in the first category has typically focussed on teratogenic substances which alter the development of the unborn child through exposure of the mother. Prime examples that have been repeatedly related to ADHD are maternal prenatal smoking and alcohol use (Banerjee et al., 2007; Ernst et al., 2001; Linnet et al., 2003). Such exposures may directly affect brain development through a multitude of mechanisms (Dwyer et al., 2009; Roussotte et al., 2010; Thompson et al., 2009). However, since risk for substance abuse and ADHD may share a common genetic basis, it has been suggested that the link between these prenatal exposures and ADHD is genetically mediated rather than by the exposures per se (Knopik, 2009; Thapar et al., 2009). More generally, low birth weight, for which prenatal substance exposure may be a causative factor, is one of the more robustly associated non-genetic factors in ADHD (Nigg & Breslau, 2007). Effects of birth weight and prenatal exposure to cigarette smoke and alcohol are discussed in more detail in Chapters 6 and 7. Postnatally, low-level lead or polchlorinated biphenyl (PCB) exposure have been related to ADHD (Nigg et al., 2007; Williams & Ross, 2007). Finally, popular media have given much attention to the potential role of food additives, excessive TV watching or computer games, for all of which the evidence is equivocal at best (Banerjee et al., 2007). However, despite the fact that the association of food additives and colorants with ADHD is now considered disproven (Banerjee et al., 2007), recent work where highly restrictive diets were successfully used to treat ADHD suggests that in a circumscribed set of patients, food allergies may be related to the symptoms (Pelsser et al., 2011). Psychosocial adversity may play a role in the development of many mental disturbances. Seminal work by Rutter and colleagues implicated socio-economic status, parental marital conflict, large family size, foster placement and parental mental disorder or criminality (Rutter et al., 1975). Importantly, it is most likely the aggregate of adversities rather than any one of these factors in isolation that increases risk for ADHD (Biederman et al., 1995; Rutter et al., 1975). Some of the increased risk conveyed by parental mental disorder and criminality may be related to an underlying genetic liability shared with the affected child, similar to prenatal exposure. A related environmental risk factor is child maltreatment. Across the spectrum of severity of maltreatment, from profound institutional deprivation (Stevens et al., 2008), to physical or emotional maltreatment in family situations, ADHD is the most frequently occurring type of psychiatric morbidity, often requiring specialized non-standard treatments (Garland et al., 2001). Parenting style may affect development and course of the disorder as well (Deault, 2010). Despite not having a directly appreciable effect on neurobiology, psychosocial factors may affect neurobiological development. High levels of maternal stress during pregnancy have been associated with ADHD, despite some inconsistency in findings (Linnet et al., 2003; Rodriguez & Bohlin, 2005). Preclinical studies in animal models suggest that such stressors may directly influence brain development through endocrine mechanisms (Charil et al., 2010). Emotional maltreatment during childhood may equally affect brain development. For example, in a study of adult patients with depression and anxiety disorders, emo-

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tional maltreatment during childhood was related to reductions in prefrontal cortex volume (Van Harmelen et al., 2010). It is not unlikely that similar effects may be present in ADHD samples.

Neurodevelopmental changes: how is the brain affected in ADHD? This section first focuses on the brain areas and systems that have been implicated in ADHD. Second, a brief discussion of structural neuroimaging studies in ADHD is given. The neuroimaging literature, including functional neuroimaging, will be discussed in more detail in Chapter 2. As stated previously, the prefrontal cortex (PFC) and frontostriatal networks were the primary targets for research in ADHD. Indeed, after some decades of research it now appears that prefrontal dysfunction is the cornerstone in the pathophysiology of ADHD and is related to its main neurocognitive endophenotypes: deficits in cognitive control, timing and reward processing (Arnsten, 2011; Durston et al., 2011; Nigg & Casey, 2005; Sonuga-Barke, 2003). A detailed discussion of PFC structure and function is beyond the scope of this introduction. The overview offered here emphasizes prefrontal functions in relation to the main cognitive endophenotypes of ADHD.

Prefrontal cortex, frontostriatal and frontocerebellar systems The prefrontal cortex has direct connections to nearly every other part of the brain, including subcortical structures (Fuster, 2008). It receives afferents from the multimodal association cortices (such as the posterior parietal and inferior temporal cortices) and as such, processes already highly abstract information. The PFC can be thought of as the top of a processing hierarchy, where it has a modulatory role on other multimodal association cortices (Arnsten, 2009; Fuster, 2008; Posner & Rothbart, 2007). This modulatory role on processing across the brain forms the basis of cognitive control, the ability to adapt behavior to environmental circumstances (Aron & Poldrack, 2005; Fuster, 2008; Miller & Cohen, 2001, see also Chapter 2). Guided by its anatomical connectivity, the PFC may be divided into an orbitomedial section and a lateral section (Fuster, 2008). The orbitomedial section is the anatomical substrate for affect, motivation and reward-related modulation of behavior and is strongly connected with the limbic structures through the medial thalamus: amygdala, medial temporal cortex, hippocampus and anterior cingulate cortex (Fuster, 2008). This part of the PFC also projects to the ventral striatum, which is thought to be an important network to motivation and reward processing (Alexander et al., 1986; Haber, 2003; Middleton & Strick, 2000; Sagvolden et al., 2005; Tripp & Wickens, 2007). Through the lateral thalamus, the lateral PFC is strongly connected with large parts of the neocortex, through the midbrain with the cerebellum, and sends direct efferents to the (dorsal) caudate and putamen. This part of the PFC can be thought of as the substrate for higher cognitive functions and cognitive and motor control (Fuster, 2008). However, the orbitomedial and lateral sections are highly interconnected; as demands of the task at hand vary, they will both be important (Dalley et al., 2008; Fuster, 2008; Miller & Cohen, 2001). Moreover, frontostriatal networks may play a pivotal role here since spiraling loops from ventral striatum appear to influence the dorsal frontostriatal network, providing a neurobiological substrate for influence of reward and motivation on cogni-

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tive and attentional control (Durston et al., 2011; Haber, 2003). The prefrontal cortex is highly sensitive to its neurochemical environment, particularly neuronal modulation by dopamine (DA) and noradrenalin (NA) (Arnsten, 2011; Arnsten & Li, 2005). In ADHD research, the focus has classically been on DA, as stimulant medication was thought to primarily block the DA transporter in the striatum and PFC, effectively increasing DA availability, and thereby the signal-to-noise ratio of neural processing (Volkow et al., 2001; Volkow et al., 2005). However, the common classes of stimulants appear to stimulate α-2a NA receptors as well (Arnsten & Li, 2005). Indeed, recent additions to the pharmacotherapeutic options for ADHD, such as atomoxetine and guanfacine, selectively target NA transporter and receptor respectively (Arnsten, 2009; Biederman et al., 2004). Arnsten has suggested that an optimum level of DA and NA is necessary for the PFC to function optimally, where levels that are too low (such as in ADHD) or too high (such as in chronic stress) will impair PFC function (Arnsten, 2011; Arnsten & Li, 2005). The relevance of the PFC-cerebellum loops has only become apparent in recent years, partly because neuroimaging studies consistently implicated the cerebellum in ADHD (Durston et al., 2009; Valera et al., 2007). This loop mainly links dorsolateral PFC with the cerebellar hemispheres (Fuster, 2008; Ramnani, 2006). Cerebellar involvement in cognitive functions is now increasingly acknowledged after patients with cerebellar lesions had been reported to display cognitive changes that were not consistent with the prior conception of the cerebellum as solely a motor structure (Ito, 2008; Ramnani, 2006; Schmahmann, 2001; Schmahmann & Sherman, 1998). The cerebellum had long been implicated in organizing timing and force of motor sequences and motor learning through internal models of action (Ito, 2008; Ramnani, 2006). It now appears that it may act similarly on cognitive functioning, affecting processing of temporal information and sequence (Ivry et al., 2002). As such, it may be essential to the correct buildup of expectancies in internal models of thought and action (Ito, 2008).

Anatomical neuroimaging in ADHD With the arrival of magnetic resonance imaging (MRI) in the late 1980s, the development of brain structure and function could now be investigated in vivo in a non-invasive manner. As this thesis focuses on anatomical MRI (more commonly referred to as structural MRI), a brief discussion of methods and results of anatomical imaging is given below. A more detailed overview of MRI studies in ADHD, including functional MRI, is given in Chapter 2. Structural MRI studies have applied a variety of technical approaches, depicted in Figure 3. In volumetric studies (Figure 3A), volumes of circumscribed brain areas are measured by manually or automatically delineating these structures on the MRI scans. Chapters 4, 6 and 7 of this thesis include volumetric data. In ADHD, such studies have typically found reductions in volumes of total brain, cerebrum, cerebellum, caudate and cerebral and cerebellar gray matter. Interestingly, a large longitudinal study found that caudate volume reductions appeared to converge to the volumes of controls around mid-adolescence (Castellanos et al., 2002). A second study from the same group showed comparable results for the cerebellum in children with good developmental outcome (Mackie et al., 2007). Studies on lobular volumes have typically shown reductions across the brain, but have most consistently reported pre-

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frontal reductions (Valera et al., 2007). Interestingly, not all volumetric studies have reported on white matter volumes, resulting in rather mixed results for white matter volumes, with a recent meta-analysis reporting evidence for publication bias for this tissue type (Valera et al., 2007). Voxelbased Morphometry (VBM, Figure 3B) is a method that has become frequently used in structural neuroimaging as it is much less labour intensive than volumetric approaches (Ashburner & Friston, 2000). It involves normalizing all brains to a standard template for patients and controls separately, and can therefore be seen as comparing a “mean” patient brain with a “mean” control brain. Any significant results represent group differences in tissue density rather than volume per se, frequently complicating interpretation. The validity of the method as a replacement for volumetric analyses has been criticized (Bookstein, 2001; Giuliani et al., 2005). In ADHD, despite some very recent exceptions, VBM has typically been applied in small samples, yielding large clusters of results across the brain that have been somewhat hard to interpret univocally. A method more suited to detailed analysis of cortical changes is cortical thickness estimation (Figure 3C). In this procedure, for each individual subject, a mesh model surface is fitted to the boundary of gray and white matter and is then inflated to meet the boundary between gray matter and cerebrospinal fluid. The distance between two homologous points on each of these surfaces is taken as a measure of local cortical thickness. A seminal series of longitudinal studies by Shaw and colleagues on the largest ADHD sample worldwide has shown widespread cortical thinning in ADHD, most pronounced in dorsolateral prefrontal cortex and parietal association cortex (Shaw et al., 2006). However, not all subsequent studies have replicated this result (Batty et al., 2010; De Zeeuw et al., submitted; Wolosin et al., 2009). Shaw and colleagues also reported that cortical development follows a pattern in ADHD that is similar to controls but is delayed by three to five years for large parts of the cortex (Shaw et al., 2007a). Finally, a steadily increasing number of studies use diffusion tensor imaging (DTI) to investigate the microstructural organization of white matter tracts that connect gray matter regions (Konrad & Eickhoff, 2010). To date, most of these studies have used analytical methods that do not allow specific interpretation of a circumscribed anatomical connection. In addition, findings have shown considerable inconsistency, possibly as a result of analytic strategy and small sample size. These technique, and studies applying them in ADHD are discussed in Chapter 3. In sum, ADHD is associated with widespread reductions in brain volume but with notable and much replicated reductions in key brain regions such as the caudate, prefrontal cortex and cerebellum. A number of studies further suggest a delayed developmental pattern of cortex and caudate nucleus. Cerebellar development has been shown to converge to the level of controls in patients with good clinical outcome.

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Figure 3. Methods in structural MRI.

A

B

C

Note. A. Volumetric analysis. An example for the cerebellum is given here, the basic procedure is similar for other parts of the brain. The area of the cerebellum is delineated on every slice where the cerebellum is visible for every individual MRI scan in the study. This procedure results in a so called segmentation of the cerebellum, of which the bottom picture is a 3-dimensional rendering. B. Example output of voxel based morphometry. In VBM, a groupwise regression of gray or white matter density is performed, which requires normalizing all brains to a model brain. Results are usually presented as depicted here, as a colored map of voxels where the density is significantly different between patients and controls, superimposed on the model brain. C. Cortical thickness. The top picture is a schematic representation of the procedure, where gray matter is colored purple and white matter is colored red. For each scan in a study, a mesh model is fitted to the gray/white boundary (green line) and is then inflated to meet the gray/cerebrospinal fluid boundary (blue line). The distance between homologous points on both surfaces (white lines, i.e. before and after inflating the model) is taken as a measure of local cortical thickness. Tens of thousands of point are typically measured in these procedures. Results are depicted as colored cortical maps that can show actual thickness or local t-values, presented on a model brain. A color version of this figure can be found in the color figure supplement (page 213).

The neuropsychology of ADHD In keeping with the hierarchy of the enodophenotype model presented in Figure 2, the neuropsychology of ADHD is our last stop. The neuropsychological literature of ADHD has very much propelled theory and research into the disorder. This literature is vast: a quick search in PubMed yields more than 3500 non-review papers related to the neuropsychology of ADHD. However, a good way to parse that literature is to take the major neuropsychological theories that are derived from it as a starting point. These concern deficiencies in cognitive control, timing, reward processing, and attention. The largest part of neuropsychological work in ADHD emphasizes problems in cognitive control amongst other issues (Barkley, 1997; Nigg & Casey, 2005; Sergeant, 2005; Sonuga-Barke, 2002, 2003). As mentioned above, cognitive control has been related to the dorsal frontostriatal system (Arnsten & Li, 2005; Aron & Poldrack, 2005; Durston et al., 2009; Durston et al., 2011). Common paradigms to measure cognitive control ability are variations of the go/nogo task, the stop signal task and response interference tasks such as

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flanker, Stroop or antisaccade tasks (Aron & Poldrack, 2005). This by now classic feature in the neurocognitive profile of ADHD was suggested by some to be its defining cognitive feature (Barkley, 1997). However, meta-analyses report that only about half of all children with ADHD are deficient on this type of neuropsychological task (Nigg, 2005; Willcutt et al., 2005). The second neuropsychological factor is temporal processing, which may be related to frontocerebellar dysfunction (Durston et al., 2011; Makris et al., 2009; Nigg & Casey, 2005). Children with ADHD are known to have problems in duration discrimination, duration reproduction, anticipatory timing (judging when in time an event will happen), and finger tapping (Sonuga-Barke et al., 2010; Toplak et al., 2006). Such timing functions are thought to be related to the intrinsic timing functions of the cerebellum (Ivry et al., 2002). Importantly, these dysfunctions may culminate into the aberrant buildup of expectancies about the timing of events in the environment (Nigg & Casey, 2005). Reward processing deficiencies have been addressed in ADHD from two main directions. One is the concept of delay aversion (Sonuga-Barke, 2002; Sonuga-Barke et al., 1992), which captures the observation that children with ADHD have difficulty with delayed reward. The second angle comes from work with animal models, which yielded hypotheses of decreased dopamine signaling towards reward cues in the ventral striatum (Luman et al., 2005; Sagvolden et al., 2005; Tripp & Wickens, 2007). Such hypotheses have been confirmed in human subjects using functional neuroimaging (Plichta et al., 2009; Rubia et al., 2009; Scheres et al., 2007; Stark et al., 2011). Reward processing theories may explain diminished control of intermittent reinforcement schedules on behavior in ADHD (Sagvolden et al., 1998), that may be a factor in the relative inefficacy of behavioral interventions, as most of these interventions employ intermittent reinforcement schedules. In addition, since these models predict that larger rewards, delivered with higher frequency are more likely to work in children with ADHD (Sagvolden et al., 2005), they may explain part of the delay aversion phenomenon. Deficits in sustained attention, that is, maintaining a basic state of vigilance across a long period of time, have long been proposed in ADHD (Barkley, 1997). Other theories underscored the importance of deficiencies in more basic state regulation (arousal/vigilance) as an explanation for problems in sustained attention (Sergeant, 2005). Indeed, it has been suggested that problems in sustaining attention originate from periodical lapses of attentional control that may occur more often in children with ADHD than in typically developing children (Sonuga-Barke & Castellanos, 2007). This has been linked behaviorally to increases in reaction time variability that have been shown to periodically occur in children with ADHD and amount to higher variability in reaction times across entire cognitive tasks (De Zeeuw et al., 2008; Johnson et al., 2007; Klein et al., 2006). However, increased response variability may be confounded with timing difficulties (Ivry et al., 2002), since children with ADHD have also been shown to be deficient in basic finger tapping (Toplak et al., 2006). Finally, most children with ADHD show average intellectual performance on formal tests of intelligence. Despite this, ADHD is associated with reduced intelligence at group level (Frazier et al., 2004). Neurobiological repercussions of this will be further discussed in Chapter 4.

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ADHD AS A HETEROGENEOUS PHENOTYPE As will be evident from the discussion above, the endophenotype model as represented in Figure 2 is an oversimplification. ADHD is highly multifactorial: both genetic and environmental influences affect a number of neurobiological systems and subsequent neuropsychological changes in the disorder through an unknown number of possible mechanisms. Finding etiological pathways towards the disorder is highly complicated by such heterogeneity for many reasons. To name a few: First, genetic risk and environmental risk may interact (gene by environment interaction, GxE) (Nigg et al., 2010). For example, psychosocial adversity has been shown to interact with DAT1 and 5-HTT genotype in ADHD (Nigg et al., 2010). GxE results for prenatal factors, such as cigarette smoke exposure, have been more diverse, with the majority of studies reporting main effects of exposure but mixed results for interactions with DAT1 or DRD4 genotype (Nigg et al., 2010; Plomp et al., 2009). A related issue is gene by environment correlation (rGE). For example, since ADHD is heritable, children at risk for ADHD are more likely to grow up in a pedagogically suboptimal environment because of parental symptoms, which may further increase their risk of developing ADHD. Importantly, certain types of GxE and rGE may be contained in the estimate of additive genetic effects, biasing the estimated heritability (Nigg et al., 2010; Rutter et al., 2006; Visscher et al., 2008). Environmental factors may theoretically interact with a genetic cascade at any point. Figure 4 shows an extension to the model proposed in Figure 1, showing that genes may interact directly with environmental factors to produce brain changes at the top of the hierarchy, but other factors, parental support for example, might play a role in how strongly neuropsychological changes spiral towards expression of ADHD symptoms. Second, any of the specific cognitive deficits, neurobiological alterations, and genetic or environmental factors need not be common to all children with ADHD. For example, the neuropsychological literature in particular shows that the typical cognitive endophenotypes such as cognitive control, timing and reward sensitivity need not be impaired in every child with ADHD, nor do children that have an impairment in one necessarily be impaired in the others (Durston et al., 2011; Nigg, 2005; Sonuga-Barke et al., 2010; Willcutt et al., 2005). This suggests that neurobiological cascades towards ADHD may differ between affected children. Third, a multitude of studies have tried to explain heterogeneity in ADHD reasoning from clinical phenomenology, e.g. comparing the clinical subtypes or groups with specific comorbidities. Since this approach has returned a just as heterogeneous neurobiological picture, it may not be the best way forward for fundamental research on ADHD. In fact, there is no a-priori reason to assume that clinical heterogeneity will directly map onto neurobiological heterogeneity. In sum, finding a unitary, unifying etiology of ADHD is highly unlikely. Many theorists of ADHD have suggested that the way forward for research is to somehow homogenize the groups that we do research on. In other words, to parse the phenotype not so much along phenomenological lines, but along characteristics that are more likely to inform us on neurobiological cascades (Durston et al., 2011; Makris et al., 2009; Nigg & Casey, 2005; SonugaBarke, 2005).

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Endophenotype studies have already started to map the pathways from genes to behavior by investigating how genetic and/or environmental factors are related to circumscribed neuroanatomical or neuropsychological alterations in ADHD. Many different approaches can be taken here. For example, cognitive endophenotypes have been used to discover new genes related to ADHD (Rommelse et al., 2008c). In addition, twin and sibling studies have started to chart overlap between genetic pools affecting cognitive endophenotypes and ADHD and may in that way contribute to parsing the phenotype. For example, both IQ and cognitive control have both shown to be viable endophenotypic candidates in ADHD: they show separate familial segregation in ADHD families (Rommelse et al., 2008b) which is likely to be mediated by overlapping genetic pools associated with both ADHD and these endophenotypes (Kuntsi et al., 2004). Moreover, the associations are likely to be driven by disparate genetic pools for IQ and cognitive control (Kuntsi et al., 2010; Wood et al., 2010a; Wood et al., 2010b), suggesting two separate pathways from genes to behavior for both the IQ and cognitive control endophenotype. In addition, as discussed in the sections on PFC neurobiology, work in animal models has suggested that neuronal pathways underlying deficits in cognitive control, timing and reward processing may be separable as well (Del Campo et al., 2011; Durston et al., 2011). Neuroimaging studies have begun charting how brain changes between children with ADHD and controls may vary as a function of genetic background (see also Chapter 2). For example, variation in the DRD4 gene has been related to frontal cortex thickness and volume (Durston et al., 2005; Shaw et al., 2007b). More examples of this type of work that tries to connect levels of analysis will appear throughout this thesis. Finally, a similar approach may be taken for environmental effects and interactions between genes and environment. However, at this time, very little is known about how environmental factors contribute to neurobiological development in ADHD. Figure 4. The endophenotype model including gene by environment interactions.

genetic risk factors

environmental risk factors

neurodevelopmental changes

neuropsychological changes

no symptoms

full syndrome

Note. This model is an extension of the simple endophenotype model and includes possible gene by environment interplay, which may occur at any stage of the cascade.

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THIS THESIS The studies in this thesis specifically attempt to address aspects of heterogeneity in ADHD: etiological heterogeneity, neurobiological heterogeneity, and cognitive heterogeneity. Chapter 2 reviews recent findings of neuroimaging and genetic approaches that study links between candidate genes and brain alterations in ADHD. Neurobiological heterogeneity is addressed in Chapters 3 and 5: what aspects of neurobiology are affected and in which children? In Chapter 3 we measure the microstructural organization and myelination of frontostriatal white matter tracts. As discussed above, this system is essential to the recruitment of cognitive control. The study described in this chapter aimed to investigate whether both microstructural organization as well as myelination is affected in white matter connectivity within this network. In Chapter 5 we investigate whether the pattern of cortical development in ADHD is different between children with low and high IQ. The impetus for this study was reports of overlap between the genetic pools associated with IQ and ADHD, described above. Differences in intelligence have been related to neurodevelopmental differences in typically developing children, suggesting that the neuroanatomical signature of ADHD may be vary across the IQ spectrum. Chapter 4 addresses cognitive heterogeneity. Specifically, we investigated whether deficits in cognitive control, timing, and reward processing were separable at the cognitive level, such as recent theoretical models of ADHD predict. Chapter 6 and 7 address etiological heterogeneity in relation to cerebellum morphology in ADHD. In Chapter 6, we investigate the effect of prenatal maternal smoking or alcohol use on global brain volumes in ADHD. Exposure to any of these substances is a putative environmental risk factor for ADHD. Since the cerebellum appears particularly sensitive to the effects of such exposure, we hypothesized that cerebellar alterations would be stronger in children with ADHD who had been exposed in comparison to children who had not. Finally, in Chapter 7 we test main effects and interaction effects between a novel-candidate gene for ADHD expressed in cerebellum and birth weight. Finally, the Discussion summarizes what has been learned from these studies and discusses implications for research and practice.

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CHAPTER

IMAGING GENETICS IN ADHD: A FOCUS ON COGNITIVE CONTROL Sarah Durston, Patrick de Zeeuw, & Wouter G. Staal Neuroscience and Biobehavioral Reviews, 33 (2009), 674-689 Neuroimaging Lab Department of Child and Adolescent Psychiatry Rudolf Magnus Institute of Neuroscience University Medical Center Utrecht The Netherlands

ABSTRACT This paper evaluates neuroimaging of cognitive control as an endophenotype for investigating the role of dopamine genes in ADHD. First, this paper reviews both data-driven and theory-driven approaches from genetics and neuroimaging. Several viable candidate genes have been implicated in ADHD, including the dopamine DRD4 and DAT1 genes. Neuroimaging studies have resulted in a good understanding of the neurobiological basis of deficits in cognitive control in this disorder. Second, this paper discusses imaging genetics in ADHD. Papers to date have taken one of two approaches: Whereas early papers investigated the effects of one or two candidate genes on many brain areas, later papers constrained regions of interest by gene expression patterns. These papers have largely focused on cognitive control and the dopamine circuits involved in this ability. The results show that neuroimaging of cognitive control is useful as an endophenotype in investigating dopamine gene effects in ADHD. Other avenues of investigation are suggested by a combination of dataand theory-driven approaches in both genetics and neuroimaging.

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Chapter 2 – Imaging genetics in ADHD: A focus on cognitive control

INTRODUCTION Control of action and cognition is of longstanding interest to investigators interested in Attention-Deficit/Hyperactivity Disorder (ADHD), as it is often compromised in individuals with this disorder. In ADHD research, this ability is often referred to as cognitive control and is defined as the ability to suppress inappropriate behaviors in favor of appropriate ones, leading to the ability to flexibly adapt behavior in the face of changing circumstances. Its relevance to ADHD is reflected by the diagnostic criteria for the disorder, which include descriptions of ‘not being able to sit still in class’ and ‘blurting out answers to questions before they have been completed’ (suppressing inappropriate behavior), as well as ‘having difficulty organizing tasks and activities’ and ‘losing things necessary for tasks or activities (e.g., toys, school assignments, pencils, books, or tools)’ (adapting behavior as required by circumstances) (American Psychiatric Association, 2000). Problems in cognitive control have even been suggested to be central to this disorder (Barkley, 1997), although this simple explanation cannot explain all the phenotypic variance in ADHD, as only 30-50% of children with ADHD have significantly impaired performance on tests of this ability (Casey & Durston, 2006; Nigg et al., 2005). This paper aims to evaluate neuroimaging measures of cognitive control as an endophenotype for investigating the neurobiology of ADHD, and specifically for investigating the role of dopamine genes in this disorder. The brain circuitry underlying cognitive control is widely investigated and, as a result, is relatively well understood (see Chambers et al., 2009, for a comprehensive discussion). Catecholamine systems are central to cognitive control: Small variations in noradrenaline or dopamine levels in prefrontal cortex have profound effects on cognitive functioning in animal models (Arnsten & Li, 2005; Castner et al., 2005). Furthermore, these neurotransmitter systems have been implicated in ADHD in a wide variety of studies. To name but one example: DAT1 knock-out mice show hyperactivity in novel surroundings that can be improved using stimulant medication that works on catecholamine systems. Interestingly, this effect appears to be mediated by serotonin systems, illustrating the subtle nature of monoamine interactions that are involved in fine-tuning behavior (Gainetdinov et al., 1999). Endophenotypes are intermediate between a behavioral classification (such as ADHD) and the biological variables that are the cause of the disorder (whether genetic or environmental). Using such intermediate phenotypes can be advantageous in studying psychiatric disorders, such as ADHD, as they have the potential to overcome some of the limitations of approaches using diagnostic categories as end-points: The use of diagnostic categories may create heterogeneous groups, as subjects are included based on a behavioral phenotype that may reflect an array of biological causes. This is obviously problematic in investigations of the neurobiology of such disorders, including both genetic association and fMRI studies (see Castellanos & Tannock, 2002; Gottesman & Gould, 2003). A particular strength of the endophenotype approach is that it aims to identify neurobiological markers within more homogeneous subgroups and, as such, is less susceptible to the noise inherent to heterogeneity. A number of theorists have outlined criteria that endophenotypes should meet in order to be beneficial the study of causative agents in psychiatry. These are summarized in Table 1.

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Endophenotype approaches by their nature require a theoretical approach to a disorder, as a phenotype associated with it is linked to a biological pathway. This is not necessarily true of genetics or neuroimaging, where whole-genome or whole-brain analyses can be conducted in a data-driven manner. In this paper, we first review the genetic and neuroimaging literature on ADHD. We discuss data-driven and theory-driven approaches from both. Next, we review the literature using neuroimaging measures as endophenotypes in ADHD and assess the value of cognitive control as such a measure.

METHOD Genetic and MRI original research papers were retrieved through PubMed and ISI Web of Science. Search terms were ‘Attention deficit and hyperactivity disorder’; ‘ADHD’ and ‘association study’, ‘genetic study’, ‘heritability’, ‘linkage study’, ‘genome wide study’, ‘genome wide scan’ or ‘genetic risk’ for genetic studies. ‘ADHD’, ‘MRI’ and ‘fMRI’ were used for neuroimaging studies. Brain tissue expression and receptor localization for candidate genes were assessed by searches using the human UCSC Genome Browser, release March 2006 (Kent et al., 2002); http://genome.ucsc.edu) and the Allen Brain Atlas / Allen Institute for Brain Science (http://www.brain-map.org) and an additional PubMed search. Papers were excluded if they contained only data described in earlier publication (i.e., reports of new analyses or subgroups of subjects were allowed). In the overview of whole-genome scans in Table 2, regions with LOD-score > 2.0 were included, although scores > 2.2 are typically considered suggestive of linkage (Albayrak et al., 2008; Lander & Kruglyak, 1995). We chose a slightly lower minimum score to minimize the chance of type II errors, where a suggestive replication is narrowly missed. For neuroimaging studies (Tables 5-7), only studies of subjects with a clinical ADHD diagnosis were included; studies of population-based samples were excluded. The naming of brain regions in Table 5 is based on the talairach co-ordinates reported and not on the nomenclature used in the original papers, as naming is not always consistent across studies and groups. In Table 5 and 6, only results from the direct comparison of subjects with ADHD to controls are reported (i.e., findings within group, in comparison with other clinical samples and medication effects are not). Table 1. Criteria for endophenotypes for investigating gene effects in psychiatry. Criteria for intermediate phenotypes in psychiatric research (1) continuously quantifiable (2) stable (a trait as opposed to a state measure) (3) closer to the causative agent (e.g., genes and gene expression) than the disorder (4) associated with disorder (5) probabilistically predictive of the disorder (6) cluster in families where the disorder is found (7) found in unaffected relatives of affected individuals (8) grounded in neuroscience Note. Adapted from Almasy et al., 2001; Castellanos & Tannock, 2002; Gottesman & Gould, 2003.

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HERITABILITY AND GENES IN ADHD ADHD is a common disorder with a significant heritable component. The disorder tends to cluster in families and additive genetic effects explaining up to 80% of the variance in the phenotype (e.g., Albayrak et al., 2008; Thapar et al., 1999). Concordance rates are estimated to lie between 50-80% for monozygotic twins and at around 30% dizygotic twins (Bradley & Golden, 2001; Thapar et al., 1999). Interestingly, although ADHD clusters in families, ADHD subtypes do not (Smalley et al., 2001). This suggests that there may not be a direct link between risk genes and the clinical phenotype, but rather that mediating factors may be involved. The search for genes that convey risk for ADHD is ongoing. In a genome scan, all chromosomes are screened for linkage with markers spaced throughout the genome. When a region of interest is identified, it can be further explored using high-density mapping. This approach has the advantage that it does not require an a-priori hypothesis of which genes (or regions on the genome) are involved. A disadvantage is that it cannot directly identify risk genes, as many genes lie under a single linkage peak and the linkage signal could be caused by any one of those. Candidate gene studies use a different approach, where a gene is selected on theoretical grounds. Its involvement in the disorder can then be investigated in a case-control or family-based design. Case-control designs have the disadvantage that they can be susceptible to population stratification effects, whereas family-based designs use transmission disequilibrium testing to test for preferential transmission of high-risk alleles to ADHD probands. This approach has been used in most candidate gene studies in ADHD to date.

Whole genome linkage studies Table 2 shows regions implicated in whole genome linkage studies. Seventeen regions have been implicated in ten studies published to date, including data from four independent samples from Germany, the Netherlands, the USA and an isolated region of Colombia. Of those, only two regions have been implicated in more than one independent sample: 5p13 and 17p11. The region on 5p13 includes the DAT1 gene that has also been implicated in candidate gene studies (see Table 3). One group recently confirmed that their linkage peak on 5p13 (Hebebrand et al., 2006) was due to this gene by genotyping 30 single nucleotide polymorphisms (SNPs) within it and its 5’ region (Friedel et al., 2007). They found that their linkage peak was related to genetic variation at the DAT1 locus. The serotonin transporter gene (5-HTT) lies on 17q11, relatively close to the peak on 17p11 in some analyses (Ogdie et al., 2004). It is notable that whole genome linkage studies include many more differences between studies than overlap. This suggests that ADHD is not related to a small number of genes of large effect, but is more likely caused by combinations of larger numbers of genes of small effect (Faraone et al., 2005).

41

Table 2. Regions from whole-genome scans with maximum LOD Score > 2.0 associated with ADHD diagnosis. Region

Publication

2q35 5p13 5q13 5q33 6q12 6q22-23 7p13 7q21 9q22 9q33 11q22 12p13 14q12 15q15 16q23 16q24 16p13 17p11 No region

Romanos, et al., 2008 Friedel, et al., 2007; Hebebrand, et al., 2006; Ogdie, et al., 2004 Romanos, et al., 2008 Arcos-Burgos, et al., 2004 Fisher, et al., 2002; Ogdie, et al., 2004 Romanos, et al., 2008 Bakker, et al., 2003 Romanos, et al., 2008 Asherson, et al., 2008; Romanos, et al., 2008 Bakker, et al., 2003 Arcos-Burgos, et al., 2004 Fisher, et al., 2002 Romanos, et al., 2008 Bakker, et al., 2003 Asherson, et al., 2008 Romanos, et al., 2008 Ogdie, et al., 2003;Ogdie, et al., 2004 Ogdie, et al., 2004; Arcos-Burgos, et al., 2004; Ogdie, et al., 2003 Faraone, et al., 2007; Fisher, et al., 2002

Candidate gene studies There have been many more studies investigating genes that are considered to be candidate risk genes for ADHD than genome wide explorations. Genes located in regions of suggestive or significant linkage have often been studied, as have genes in the catecholamine systems, given their theoretical relevance to the disorder (Arnsten, 2006; Casey & Durston, 2006; Durston, 2003; Durston, 2008). These systems can be viewed as particularly relevant to ADHD, given the mechanisms of action for ADHD medication: Methylphenidate (MPH) is thought to primarily exert its therapeutic effects by blocking the dopamine transporter (Volkow et al., 1998), although noradrenaline and serotonin systems may also be involved (Gainetdinov et al., 1999). A newer medication that is sometimes effective in ADHD is atomoxetine, a norepinephrine transporter blocker. Another monoamine system that has received attention is the serotonin system, given its implication in impulsive and hyperactive behavior (Oades, 2007). Table 3 lists candidate genes that have been investigated in ADHD and the number of positive and negative associations reported for each gene. A table listing all references is available upon request from the authors. A total of 170 reports of the effects of 49 candidate genes have been published. There have been 96 reports of positive associations, including for 16 catecholamine genes, although many of these have failed to replicate in new samples. These studies are likely to have been hindered by the small effect many ADHD risk genes, in line with observations of weak linkage signals. A further confound may be that most candidate gene studies have linked potential risk alleles to diagnosis, as opposed to a phenotype closer to the biology of a disorder. Diagnostic criteria reflect behavioral phenotypes and may therefore include multiple biological subtypes, with potentially different genetic underpinnings. The two genes that have been most frequently investigated are the dopamine DRD4

42


and DAT1 genes (see Table 3). Both positive and negative associations have been reported for both, with somewhat more positive reports. A fairly recent meta-analysis concluded that, to date, only seven candidate genes have risk alleles where the pooled odds ratio is significantly greater than 1.0, and that are therefore over-transmitted in ADHD. These genes include four catecholamine genes (the dopamine-4 and dopamine-5 receptors (DRD4 & DRD5), dopamine transporter (DAT1), and dopamine-beta-hydroxylase (DBH)), two in the serotonin system (the serotonin transporter (5-HTT) and serotonin 1B-receptor (HTR1B) genes) and SNAP-25 (Faraone et al., 2005). Table 3. Candidate genes in ADHD research. Gene

Published negative associations

Published positive associations

Catecholamines COMT 7 Catecholamines: dopamine

1

DAT1 (SLC6A3) 9 DRD1 DRD2 2 DRD3 1 DRD4 10 DRD5 3 DBH 3 TH 1 DARPP-32 1 NR4A2

17 2 1

Catecholamines: norepinephrine

21 2 4

1

NET1 (SLC6A2) 3 ADRA2A 1 ADRA2C 1 ADRB2 PNMT Other monoamine related genes

MAOA MAOB Other monoamines: serotonin

3 2

2 5 1 1

5 1

5-HTT (SLC6A4) 5 5HT1B 1 5HTR2A 3 5HTR2B 1 HTR4 TPH 1 TPH2 1 DDC 1

4 2 3

Other neurotransmitter systems: acetylcholine

CHRNA4 CHRNA7 1 Other genes SNAP25 1

1

1 1 3 1

4

43

BDNF 2 GDNF 2 NGF 1 NT3 1 GRIN2A FGF10 1 ISL1 1 HCN1 1 ITGA1 1 HLA-DRB1 1 SLC1A3 1 CLOCK ARRB2 SYP HES1 FADS2 IL-1 AR LIN-7

2

1

1 1 1 1 1 1 1 1 1

Gene expression Gene expression in the brain is summarized in Table 4 for 13 of the most frequently replicated genes in ADHD. From this table, it is clear that many of these genes are expressed at a moderate level throughout the human brain. Expression appears to be somewhat more differentiated in the mouse brain. Furthermore, an additional PubMed search suggested that there may be some differentiation in brain expression patterns, even in the human brain (last column). The best replicated candidate genes for ADHD, the DAT1 and DRD4 genes are expressed throughout the brain in humans, although protein localization and human gene expression studies suggest there may be relative over-expression in striatum (and midbrain) and prefrontal cortex, respectively. For the 5-HTT gene (which has been suggestively implicated by whole genome approaches and candidate gene studies), there is a suggestion of over-expression in the medial temporal lobe structures, the amygdala in particular. Not all changes in DNA structure of putative risk genes for ADHD necessarily have functional consequences. Theoretically, effects of a risk allele can only be relevant if they are associated with changes in expression of the gene in vivo. The 5-HTT gene is a good example of a gene where the risk allele has been shown to have functional consequences, as the short allele of the promoter polymorphism is associated with reduced expression of the gene and lessened availability of the transporter (Lesch et al., 1996). Furthermore, we know from fMRI studies that this polymorphism influences amygdala activity (Hariri et al., 2002). This is a major advantage as these data can now be integrated to build models of how changes in neurochemistry affect physiology. The data are not as strong for the DRD4 or DAT1, although the 9R-allele of a variable number of tandem repeats (VNTR) in the DAT1-gene has been shown to be associated with changes in expression of the gene. A more comprehensive discussion of this gene follows later.

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IMAGING OF BRAIN STRUCTURE AND FUNCTION IN ADHD Magnetic resonance studies of ADHD have often started from a theoretical perspective, where cognitive functions (in functional studies) and brain regions (in anatomical studies) of interest were selected. With the advent of whole-brain voxelbased analysis techniques and resting state fMRI approaches, this is no longer the case, and data-driven approaches can be used to suggest new avenues of theoretical interest.

Imaging brain function Problems with cognitive control are the best-established cognitive deficit associated with ADHD (Lijffijt et al., 2005; Nigg et al., 2005). Functional imaging studies have often focused on aspects of this ability using paradigms that require overriding a prepotent response (e.g., go/no-go or stopsignal tasks), ignoring salient information (e.g., flanker and stroop paradigms) or switching from one behavior to another (e.g., switch tasks). Table 5 lists cognitive domains that have been investigated using fMRI in ADHD. The criteria that were used for including studies in this table are described in the Methods section. It is not an exhaustive overview of the literature, as other, more comprehensive reviews are available (Bush et al., 2005; Casey & Durston, 2008; Durston, 2003). From the table, it is obvious that cognitive control is the most investigated domain, although it is not the only one. At a first glance, the widely diverse findings appear too disparate to be informative in terms of underlying neurobiological changes. However, a common theoretical framework can be discerned: Many of the tasks involve aspects of cognitive control. For example, a working memory task such as the N-back is not typically conceptualized as a cognitive control task. However, it does involve aspects of this ability, as it requires the subject to override the obvious response to a stimulus (i.e., press the button corresponding to the stimulus on the screen) and rather to react to a stimulus that occurred one or more trials ago. Similar arguments can be made for many of the cognitive tasks listed in Table 5. As, such, many of the studies showing reduced activity in fronto-striatal dopamine circuits involve aspects of this ability. Other changes in brain activity are reported, depending on the nature of the task: For example, tasks involving vigilance have shown (additional) changes in temporal regions (Rubia et al., 2007) and working memory tasks have shown changes in more dorsolateral prefrontal regions (Sheridan et al., 2007). These different studies all focus on different aspects of cognitive functioning, but as cognitive (and neural) processes are intrinsically linked, deficits in one system are likely to affect others in secondary ways (see also Chambers et al., this issue for a more comprehensive discussion). An interesting and relatively new direction in functional neuroimaging of ADHD is an interest in reward-related circuitry. This follows from theoretical accounts of ADHD that attribute importance to reward in this disorder and relate changes in sensitivity to reinforcement to symptoms of hyperactivity and impulsivity (Sagvolden, 1998; Sonuga-Barke, 2002). These studies have shown reduced activity in ventral striatum in response to reward (Scheres et al., 2007; Strohle et al., 2008).

45

46


High expression in striatum, amygdala, olfactory areas, hippocampus Whole brain, enriched in ventral striatum, retro hippocampal region, olfactory areas, amygdala

DRD1 (5q35)

Medulla, pons

DBH (9q34)

MAOA (Xp11)

Moderate expression in pallidum

Very low expression and receptor density in the brain

ADRA2A (10q25)

Other monoamines

Very low expression and receptor density in the brain

SLC6A2 / NET1 (16q12)

Catecholamines: norepinephrine

Whole brain high expression and receptor density

DRD5 (4p16)

DRD4 (11p15)

Midbrain

Expression in mouse

SLC6A3/ DAT1 (5p15)

Catecholamines: dopamine

Candidate gene (locus)

Moderate expression whole brain. High expression in thalamus, amygdala, occipital lobe and prefrontal cortex

Moderate expression whole brain.

Whole brain moderate expression



Whole brain high expression, particularly caudate nucleus and prefrontal cortex Moderate expression in prefrontal, parietal and temporal lobes, cingulate cortex, cerebellum, basal ganglia


Expression in human

Table 4. Expression in the brain of genes associated with ADHD in at least two studies.

Hypothalamus, nucleus coeruleus, substantia nigra (Westlund et al., 1988)

Cerebellum (Schambra et al., 2005)

Locus coeruleus (Eymim et al., 1995) Amygdala (Smith et al., 2008)

Prefrontal cortex (De La Garza et al., 2000; Noain et al., 2006; Primus et al., 1997) Hippocampus (De La Garza et al., 2000; Primus et al., 1997) Hypothalamus, thalamus, entorhinal cortex, lateral septal nucleus (Mrzljak et al., 1996; Primus et al., 1997;) Globus pallidus (Mrzljak et al., 1996) Cerebellum, substantia nigra, hypothalamus, striatum, cerebral cortex, nucleus accumbens, hippocampus and olfactory tubercle. (Beischlag et al., 1995; Khan et al., 2000) Noradrenergic brain stem nuclei, sympathetic ganglion neurons (Hoyle et al., 1994) Hypothalamus, substantia nigra (Westlund et al., 1988)

Striatum (Garris et al., 1994) Midbrain (Brookes et al., 2007; Giros et al., 1992; Shimada et al., 1992) Temporal lobe, cerebellum (Mill et al., 2002) Prefrontal cortex (Garris et al.,1994) Amygdala (Garris et al.,1994) Striatum (Augood et al., 2000; Hurd et al., 2001) Frontal cortex (Hurd et al., 2001)

Additional expression or protein localization

47

Whole brain high expression

Moderate expression in whole brain. High expression in prefrontal cortex and amygdala High expression in the whole brain, particularly the prefrontal cortex Whole brain moderate expression. High expression in cingulate cortex and globus pallidus

Moderate whole brain expression

Whole brain high expression (Garbelli et al., 2008)

Raphe nuclei (Zill et al., 2007) Pons (Lim et al., 2007)

Nl raphe (Bidmon et al., 2001) Substantia nigra, globus pallidus, striatum, amygdala, hippocampus, and the cerebral cortex (Varnäs et al., 2001) Prefrontal cortex (de Almeida et al., 2007; Hall et al., 2000)

Midbrain, pons (Lim et al., 2006) Amygdala (O’Rourke et al., 2006)

Note. The frontal lobes include prefrontal and orbitofrontal cortex. The basal ganglia include putamen, caudate nucleus, nucleus accumbens, globus pallidus, subthalamic nucleus and substantia nigra. The diencephalon includes thalamus, hypothalamus , subthalamus and pretectum. The midbrain includes internal structures such as the raphe nuclei, the red nucleus and the reticular formation (including the locus coeruleus).

Other genes SNAP25 (20p11.2)

TPH2 (12q21)

Whole brain high expression and receptor density

Very low expression and receptor density in the brain Very low expression and receptor density in the brain

5HTR2A (13q14)

5HT1B (6q14)

Limited expression and density in striatum and amygdala, pons, medulla, midbrain High expression in striatum and cerebellum

SLC6A4, 5-HTT (17q11)

Other monoamines: serotonin

Studies using resting state approaches are not tied to a theoretical vantage point in the same way as studies that choose a paradigm to tax a particular function in ADHD. In these studies, fMRI scans are typically acquired while the subject lies quietly with his/her eyes closed. The resulting images can then be used to investigate synchronization of activity in neural circuits in rest. These patterns of activity have been shown to correlate with behavioral and cognitive measures and could potentially lead to task-independent biomarkers of neuropsychiatric disorders (Greicius, 2008; Kelly et al., 2007). Such an a-theoretical approach may be advantageous as such investigations can provide unexpected results, suggesting new directions for research. However, a caveat is that these approaches do not have the constraint of task performance, allowing researchers to monitor what the subject is doing while in the scanner. It is noteworthy that this very different approach has shown changes in some of the same fronto-striatal regions that have been reported using more traditional fMRI-designs.

Imaging brain structure Studies of brain structure can also take a data-driven approach to investigating ADHD: For example, whole-brain voxelbased methods search for differences between groups throughout the brain without requiring a-priori hypotheses of where they may be found. In contrast, studies using volumetric approaches of necessity take a more theoretically driven approach, as they must select the regions to compare between groups. Studies of brain structure in ADHD are listed in Table 6, by the approach taken. It is not an exhaustive overview and other, more comprehensive reviews and a meta-analysis are available in the literature (Durston 2003; Seidman et al., 2005; Valera et al., 2007) Studies using data-driven, whole-brain approaches have shown changes in a wide variety of regions, including fronto-striatal areas that would be predicted from deficits in cognitive control. Other regions that show changes in ADHD include areas in cingulate cortex (Brieber et al., 2007; Carmona et al., 2005; Overmeyer et al., 2001), all other major cortical lobes (occipital, parietal and temporal cortex) (Brieber et al., 2007; Carmona et al., 2005; McAlonan et al., 2007; Wang et al., 2007) and the medial temporal lobe (Brieber et al., 2007; Carmona et al., 2005). Studies using cortical thickness measures have similarly shown changes throughout the cortex, with the most striking differences between children with ADHD and controls perhaps in the developmental trajectories, where children with ADHD show a lag in cortical development of several years, particularly in prefrontal areas (Shaw et al., 2007). Studies defining regions of interest from a theoretical approach have consistently shown smaller than average total brain volumes in ADHD, as well as smaller volumes of frontostriatal regions and changes in cerebellum (see Table 6). Studies that have looked at the volume of other cortical lobes have sometimes shown reductions in volume here too (Durston et al., 2004; Filipek et al., 1997; Wolosin et al., 2007). Interestingly, the volume of the medial temporal lobe structures has also been reported to differ between children with ADHD and control subjects (Castellanos et al., 1996b; Plessen, et al., 2006), although in one case total hippocampus was reported to be larger in ADHD while only the posterior sections were smaller (Plessen, et al., 2006). It is interesting that these structures have been implicated using both hypothesis- and data-driven approaches. These changes could be related to symptoms of anxiety or aggression in ADHD and may suggest new directions for imaging genetics in this disorder, as will be discussed later.

48


49

Baeyens et al., under Go/NoGo review Booth et al., 2005 Durston et al., 2007 Durston et al., 2006 Durston et al., 2003 Epstein et al., 2007 (adolescents and adults) Mulder et al., 2008 Schulz et al., 2004 Schulz, Newcorn et al., 2005 Smith et al., 2006 Suskauer et al., 2008 Tamm et al., 2004 Vaidya et al., 1998 Vaidya et al., 2005

Response Suppresion

Task

Studies

Domain

Table 5. FMRI-studies in ADHD by domain.

NoGo vs Go

Contrast

DLPFC: ADHDNC (Durston et al., 2003) VLPFC: ADHDADHD remitters>NC)) Secondary motor cortex (pre/supplementary motor): ADHD12yr (n=31)

.159

.028

.124, ADHD: all |r| < .16, p > .239). 32 Subjects with ADHD (56%) had a deficit on one or more component. 30 Subjects (52.6%) had deficits on at least one of the three predicted components (cognitive control, timing, and reward sensitivity), Of those, 24 (80%) had a deficit on only one component. There were no individuals with deficits on more than two components (Table 3). Figure 1 shows a Venn diagram for the components predicted by the model. Loglinear analysis was run to test the statistical independence of the classification categories. It terminated after 11 iterations with the final model including only the main effects of cognitive control, timing, reward sensitivity and vigilance. This confirms that any overlap of these deficits within subjects can be attributed to chance. A comparison of clinical characteristics between those subjects with ADHD who did and those who did not reach criterion for a deficit showed that subjects with a deficit had a lower total IQ (t(55) = 2.756, p < .05), and were more likely to be co-morbid for ODD (χ2(1) = 4.18, p < .05). There were no differences between these groups on any of the CBCL scales (all p > .179).

103

104

Chapter 4 – Deficits in cognitive control, timing and reward sensitivity are dissociable in ADHD

0.041

0.050

-0.513

-0.296

0.208

0.174

0.880

0.931

-0.031

-0.015

-0.105

-0.088

12.6%

RTBenefit

ICVExpectedGO

ICVUnexpectedGO

AccuracyExpectedGO

AccuracyUnexpectedGO

AccuracyExpectedNOGO

AccuracyUnexpectedNOGO

B0vs5ct

B0vs15ct

ICV0ct

MRT0ct

Variance explained

16.5%

0.204

0.083

0.004

0.024

0.877

0.825

0.138

0.099

-0.376

-0.397

0.058

0.308

0.331

12.6%

0.000

-0.413

-0.027

-0.105

0.934

0.902

0.021

0.080

-0.175

-0.457

-0.103

0.104

0.205

14.2%

0.040

0.083

-0.083

0.117

0.000

-0.049

-0.029

-0.175

0.708

0.396

0.889

0.726

0.300

13.5%

0.076

-0.077

-0.041

0.069

0.087

0.022

0.149

-0.016

0.565

0.277

0.916

0.722

0.167

≤12yr

16.2%

-0.284

0.208

-0.111

0.156

-0.006

-0.020

-0.099

-0.180

0.816

0.347

0.857

0.777

0.402

>12yr

10.6%

-0.287

-0.034

0.876

0.862

0.027

-0.041

-0.011

-0.029

0.007

0.108

0.004

0.025

0.028

All

10.8%

-0.302

-0.149

0.887

0.887

0.054

-0.070

-0.041

-0.134

0.044

0.299

0.038

0.038

0.017

≤12yr

Reward Sensitivity

11.0%

-0.192

-0.108

0.844

0.830

-0.082

-0.150

0.022

0.045

-0.028

-0.019

-0.019

0.195

0.263

>12yr

31.6%

0.699

-0.415

0.057

-0.096

0.006

-0.067

-0.680

-0.753

0.322

0.459

-0.192

0.600

0.844

All

Vigilance

27.8%

0.647

-0.150

0.110

-0.105

0.002

0.029

-0.754

-0.796

0.546

0.539

-0.182

0.546

0.808

≤12yr

29.5%

0.589

-0.261

-0.063

-0.007

-0.087

-0.109

-0.733

-0.724

0.252

0.620

-0.077

0.525

0.728

>12yr

Note. Component loadings >.400 are printed in boldface. Abbreviations: B, Regression Coefficient; ICV, Intra-Individual Coefficient of Variation; MRT, Mean Reaction Time; RT, Reaction Time.

0.016

MRTUnexpectedGO

All

>12yr

All ≤12yr

Timing

Cognitive Control

MRTExpGO

Table 2. Rotated component loadings from the PCA analysis.

Table 3. Number of ADHD subjects scoring below the 10th percentile of the distribution in controls. ADHD (Age ≤ 12yr) (n=26)

ADHD (Age > 12yr) (n=31)

Whole ADHD group (n=57)

1. Cognitive Control only

3

9

12 (21.1%)

2. Timing only

4

3

7 (12.3%)

3. Reward only

1

0

1 (1.8%)

4. Vigilance only

1

1

2 (3.5%)

5. Cognitive Control + Timing

1

4

5 (8.8%)

6. Cognitive Control + Reward

1

0

1 (1.8%)

7. Cognitive Control + Vigilance

1

2

3 (5.3%)

8. Timing + Vigilance

0

1

1 (1.8%)

9. No deficit

14

11

25 (43%)

Note. For the analysis of the two age-groups, norm data were used from the age-matched controls. Abbreviations: ADHD, Attention-Deficit/Hyperactivity Disorder.

Figure 2. Venn diagram of deficits in the ADHD group for the predicted cognitive components.

Cognitive Control N=21

15/21.1%

5/8.8%

Timing N=13

8/12.3%

0/0% 1/1.8%

0/0%

1/1.8% Reward N=2

No Deficit N=27

105

DISCUSSION In this study, we tested the prediction that cognitive deficits theoretically arising from different neurobiological pathways should be separable in ADHD (Durston et al., 2011). We found three separable cognitive components that corresponded to three cognitive domains suggested by the model. Furthermore, loglinear analysis confirmed that deficits on these components segregated between individuals with ADHD, providing support for a multiple pathway account of ADHD (Durston et al., 2011; Nigg & Casey, 2005; Sonuga-Barke, 2002). The finding of a fourth component representing vigilance was not predicted by the model, but may relate to a fourth neurobiological system involved in ADHD. Our results converge with those from a recent study by Songa-Barke and colleagues (Sonuga-Barke et al., 2010a). They used a very different neuropsychological battery, rooted in a psychological rather than neurobiological framework, with nine tasks, and found deficits in cognitive control, timing and delay aversion that segregated between individuals with ADHD. In the current study, of those individuals with ADHD and a deficit, 80% had a deficit on only 1 of the predicted components (69% if all 4 components are taken into account), similar to the 70% in the Sonuga-Barke dataset (Sonuga-Barke et al., 2010a). Both studies therefore support separable pathways to ADHD at the neuropsychological level. One difference between these two studies is the proportion of subjects showing a deficit score on any of the 4 components present (56% in our data versus 70% in the report by Sonuga-Barke). This may be related to differences in the way age effects were treated: Sonuga-Barke and colleagues (2010) linearly regressed out any variance associated with age from all measures prior to conducting the PCA, whereas we used separate norms for younger and older participants. For many of the variables in our study, age effects were clearly not linear. As such, a regression would not have been appropriate. We specifically set out to test predictions of a neurobiological model in the cognitive domain (Durston et al., 2011). The neurobiological underpinnings of the deficits found by Sonuga-Barke and colleagues (2010) are not as clear. For example, both sensitivity to reward and temporal processing may be related to the construct of delay aversion. Basic neuronal temporal processing, contextual (task-related) factors and idiosyncratic factors including the perceived magnitude or emotional valence of stimuli are all known to affect the perception of how long events last (Grondin, 2010; Ivry & Schlerf, 2008; Toplak et al., 2006). As there is some evidence of a reduced response to reward in ADHD (e.g. reduced dopamine response to reward cues; Sagvolden et al., 2005; Tripp & Wickens, 2007) this may be related to reports of delay aversion in this disorder, where the interval preceding reward may be perceived as longer simply due to a reduced sensitivity to reward (Rubia et al., 2009). Therefore, we explicitly aimed to separate the effects of timing and reward sensitivity. We found only few subjects with ADHD with a deficit on the component relating to reward sensitivity. This may be related to the version of the task used, where a relatively high reward frequency schedule was applied (80%). Earlier studies have suggested that high reward frequency may reduce reward sensitivity problems in ADHD, whereas these are more obvious in designs using low reward frequencies (Sagvolden et al., 2005). In a newer version of our task, we use two block types, one with relatively high and one with relatively low

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reward frequency. Indeed, differences between subjects with ADHD and controls are most obvious in the low reward frequency blocks (see Supplementary Material). Interestingly, we found a fourth independent component in our data that was not predicted by the model and that appears to correspond to vigilance. Subjects with a deficit on this component had a pattern of slow responding and low target detection. This is a pattern of impairments that has received relatively little attention in ADHD theory, although one model, the Cognitive Energetic Model (CEM) did underscore its relevance (Sergeant, 2000). Empirical work has reported problems in vigilance or state regulation, particularly in studies using low event rates (Sonuga-Barke et al., 2010b). Our finding of an independent component corresponding to this response pattern suggests that impairments in vigilance may constitute a fourth neurobiological pathway in ADHD, possibly related to attention networks (Makris et al., 2009). Indeed, studies using Posner’s Attention Network Test, where alerting, orienting and cognitive control are separated, have also suggested that both alerting and control components may be affected in ADHD (Johnson et al., 2008; Konrad et al., 2006). Furthermore, these findings tie into recent evidence from a sibling study that suggests familial effects on cognitive impairments in ADHD separate into independent vigilance and cognitive control components (Kuntsi et al., 2010). Finally, it is important to assess how these results map onto clinical heterogeneity in ADHD. First, in line with previous work (Geurts et al., 2005; Willcutt et al., 2005), we found no differences in DSM-IV ADHD subtype between those subjects who did or did not have cognitive deficits. However, co-morbid ODD was more frequent in subjects with deficits. Most earlier work has concluded that neurocognitive impairments are independent of comorbid oppositional and aggressive symptoms, with greater ADHD symptom severity in co-morbid cases accounting for any differences (Biederman et al., 2004; Hummer et al., 2011; Lambek et al., 2010; Loo et al., 2007; Oosterlaan et al., 1998; Sergeant et al., 2002; Willcutt et al., 2005). This may apply here equally. The unexpected finding of lower IQ in subjects with a deficit is intriguing and converges with recent results reporting lower IQ in children with ADHD and neurocognitive deficits (Lambek et al., 2010). However, this finding could be an artifact of the slightly higher rate of deficits and a trend towards lower IQ in the older age group in our study. Furthermore, none of the neurocognitive component scores correlated with IQ. In sum, we find that cognitive control, timing and reward sensitivity are separable at the level of cognition and that deficits in these domains segregate between individuals with ADHD. This is in line with neurobiological models of ADHD positing that symptoms may arise from dysfunction in separate brain circuits underlying these cognitive domains. Furthermore, our data are suggestive of a fourth neurobiological pathway to ADHD involving deficits in vigilance. Such a stratification of the ADHD-phenotype into neurobiologically meaningful subtypes may facilitate future neurobiological and genetic research.

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SUPPLEMENTARY MATERIAL Supplementary Material 1 – The reward sensitivity task Background This task was designed to estimate sensitivity to reward by measuring change in reaction times (RT) as a function of reward. RTs in choice reaction time tasks may be modulated by reward in such a way that responses are faster in the presence of reward cues (Mir et al., 2011; Shadmehr et al., 2010). This supplement describes the task in more detail and provides data showing between-group comparisons of the effect of task manipulations.

Task design The task was a simple 2-choice reaction time task that was based on the Monetary Incentive Delay (MID) task and that was adapted to be suitable for young children. It addressed only the effect of reward, rather than the effect of delay and reward. We manipulated two parameters, reward magnitude (between 0-15 Eurocents per trial) and reward frequency (20% of trials rewarded versus 80%). The task consisted of 4 blocks of 4 minutes each, with 4 x 60 trials. Supplementary Figure 1, Panel A, shows the trial sequence. Each trial started with a 2000ms cue of a wallet showing the amount of money that could be won on the upcoming trial. Next, two cartoon figures were presented. Children were instructed to guess which character was hiding the wallet, and responded by a button press on the left button for the left image and the right button for the right image. There was a 1250ms window in which to respond. The target remained on screen for 750ms, followed by a 500ms blank screen. If children responded within the 750ms window, the target remained on screen until 750 ms had passed and then went straight to the feedback screen. If children responded during the 500ms blank screen, the task jumped directly to the feedback screen. The feedback screen stated whether the guess was correct, in which case a green “thumbs up” image was shown, alongside the awarded money. If the guess was incorrect, a red “thumbs-down” image was displayed. If a child did not respond within the 1250 ms window, the feedback screen displayed “TOO LATE!” in a large font. The total accumulated reward was also displayed on the feedback screen. The feedback screen remained on screen until the full trial time of 4000ms had passed, thus for a minimum of 750ms.

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Supplementary Figure 1. Task design.

The task was rigged so that it was predetermined which trials were “correct”. In other words, the choice made by the subject did not affect whether the trial was rewarded or not. This permitted experimental control of the reward frequency. There were two types of blocks, with a reward frequency of either 20% or 80% (Supplementary Figure 1, Panel B). Reward magnitude was controlled, where on 1/3 of trials no reward was available (an empty wallet), on 1/3 of trials a small reward was available (5 eurocents) and on 1/3 a larger reward was available. (15 eurocents; Supplementary Figure 1, Panel C). All trial types were presented 20 times per block. We used a Latin square design to ensure that all trial types were rewarded an equal number of times and that each trial type preceded every other an equal number of times during each block. This also ensured that no pattern of rewarded versus unrewarded trials was present. Prior to the actual task, a number of instruction screens explaining the task were shown. Experimenters who administered the task explained the procedure in a standardized manner. Next, a practice block of 15 trials was administered, where 50% of trials was rewarded. The task blocks were administered either in Low-High-Low-High or High-Low-High-Low reward frequency order. The software randomly chose this order for each subject.

Between group differences in task performance In order to compare the effect of the reward frequency manipulation between groups, we computed the mean RTs in each group across bins of 10 trials for 80% and 20% reward blocks separately. The dataset used here partly overlaps with the one reported in the main paper. Supplementary Table 1 shows the sample characteristics.

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Supplementary Table 1. Participant Characteristics. Measure

Control

ADHD

n=42

n=30

Age, M(SD)

12.9(4.0)

12.4(3.9)

Boys/Girls

27/15

22/8

TIQ, M(SD)**

114.0(19.4)

101.1(9.5)

Note. ** p