Behavioral and Cognitive Profiling in
Autism Spectrum Disorder and
Attention-Deficit/Hyperactivity Disorder
Jolanda Maria Johanna van der Meer
Behavioral and Cognitive Profiling in
Autism Spectrum Disorder and
Attention-Deficit/Hyperactivity Disorder
Een wetenschappelijke proeve op het gebied van de Medische Wetenschappen
Academisch Proefschrift
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This PhD project was supported by Karakter Child and Adolescent Psychiatry, and
the Netherlands Organisation for Scientific Research (NWO) by grants assigned
door
to Buitelaar (05613015) and Rommelse (91610024).
Jolanda Maria Johanna van der Meer
Copyright
© J.M.J. van der Meer, 2014. All rights reserved.
No part of this thesis may be reproduced, stored in a retrieval system or transmitted
in any form or by any means without prior written permission of the author.
Geboren op 6 september 1983 te Enschede.
Promotor
Prof. dr. J.K. Buitelaar
Copromotoren
Dr. N.N.J. Lambregts-Rommelse
Dr. C.A. Hartman (Rijksuniversiteit Groningen)
Manuscriptcommissie
Prof. dr. M. Willemsen (voorzitter)
Prof. dr. H. Bekkering
Prof. dr. H. Roeyers (Universiteit Gent, België)
Now nature never deals in black or white.
It is always some shade of grey.
She never draws a line without smudging it.
Winston S. Churchill
Winston And Clementine: The Personal Letters Of The Churchills
Table of Contents
Chapter 1
General introduction, aims and outline of the thesis
11
Chapter 2
Are high and low extremes of ASD and ADHD trait continua
pathological? A population-based study using the AQ and SWAN
rating scales
35
Chapter 3
Are autism spectrum disorder and attention-deficit/hyperactivity
disorder different manifestations of one overarching disorder?
Cognitive and symptom evidence from a clinic and population-based sample 61
Chapter 4
How ‘core’ are motor timing difficulties in ADHD? A latent class
comparison of pure and comorbid ADHD classes
91
Chapter 5
Homogeneous combinations of ASD-ADHD traits and their cognitive
and behavioral correlates in a population-based sample
111
Chapter 6
Using cognitive profiles to examine the relationship between
ASD and ADHD
133
7
Chapter 7
A randomized, double-blind comparison of atomoxetine and placebo
on response inhibition and interference control in children and
adolescents with autism spectrum disorder and comorbid attentiondeficit/hyperactivity disorder symptoms
161
Chapter 8
General discussion, summary, discussion, key findings, limitations,
future directions and clinical implications
183
Chapter 9
Samenvatting in het Nederlands (Summary in Dutch)
211
Chapter 10
References223
Chapter 11
Dankwoord (Acknowledgements in Dutch)
267
Chapter 12
About the author
8
275
9
General introduction
10
This thesis focuses on the shared and unique behavioral and cognitive profiles
of Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder
(ADHD). Studying ASD and ADHD together may provide the most optimal
strategy in examining both shared and unique substrates, ultimately translating
into differential prognoses and susceptibility towards treatment. The current
research approach steps away from the Diagnostic and Statistical Manual
of Mental Disorders (DSM) defined heterogeneous group comparisons, and
acknowledges the continuously distributed nature of the ASD and ADHD trait
within the population, as well as the etiological and symptomatic heterogeneity
within both disorders. In this general introduction, the co-occurrence, etiology
and treatment of ASD, ADHD and related cognitive profiles are discussed. Then,
the research approach used to reduce heterogeneity on both the behavioral and
cognitive level is described. Finally, the outline of the chapters is provided.
ASD and ADHD
With prevalence rates of about 1% for ASD and 5% for ADHD, these disorders are
among the most commonly diagnosed psychiatric developmental disorders in
children and adolescents (Baird et al., 2006; Polanczyk, de Lima, Horta, Biederman
& Rohde, 2007). ASD is characterized by impaired social interaction skills and
verbal and nonverbal communication, as well as restricted and repetitive behavior
and interests, while ADHD is characterized by severe inattention, hyperactivity
and impulsivity (American Psychiatric Association, 2013). Symptom presentations
of both disorders are rather heterogeneous, as described in the DSM (American
Psychiatric Association, 2013), see Box 1.1. The DSM-IV and previous psychiatric
classification schemes prevented a diagnosis of ADHD in the context of ASD. This
prohibition was based on the assumption that ASD is an overarching disorder
that mimics or even causes symptoms of ADHD. As a consequence, patients
were diagnosed with either ASD or ADHD, disregarding possible co-occurring
symptoms. The heterogeneous symptom presentation on the one hand and the
prohibited comorbid diagnosis of ASD and ADHD on the other hand may explain
13
Chapter 1
the not-to-be-ignored proportion of children that have been alternatively given
a diagnosis of one or the other disorder throughout development (Fein, Dixon,
Paul & Levin, 2005). In the current DSM-5, a comorbid diagnosis of ASD and
ADHD can be made (American Psychiatric Association, 2013). This step forward
will boost research on the shared and specific underlying mechanisms related to
ASD and ADHD, and can inform us on the association between both disorders.
Even though the diagnostic criteria appear to show little overlap,
symptoms of ASD and ADHD may be entangled. For example, inattention can
easily be mistaken for social inattention, and stereotyped behaviors (such as
body rocking and hand flapping) may be mistaken for hyperactivity. Although
such entangled symptoms may result in inflated ASD-ADHD comorbidity rates,
factor analyses found no overlapping diagnostic criteria, which supports the
Box 1.1 DSM-5 diagnostic criteria for ASD and ADHD
Autism Spectrum Disorder (ASD)
Diagnostic Criteria
A. Persistent deficits in social communication and social interaction across multiple
contexts, as manifested by the following, currently or by history (examples are
illustrative, not exhaustive):
1. Deficits in social-emotional reciprocity, ranging, for example, from abnormal social
approach and failure of normal back-and-forth conversation; to reduced sharing of
interests, emotions, or affect; to failure to initiate or respond to social interactions.
2. Deficits in nonverbal communicative behaviors used for social interaction, ranging,
for example, from poorly integrated verbal and nonverbal communication; to
abnormalities in eye contact and body language or deficits in understanding and use
of gestures; to a total lack of facial expressions and nonverbal communication.
3.Deficits in developing, maintaining, and understanding relationships, ranging,
for example, from difficulties adjusting behavior to suit various social contexts; to
difficulties in sharing imaginative play or in making friends; to absence of interest in
peers.
independence of ADHD and ASD diagnostic criteria (Ghanizadeh, 2010; Martin,
Specify current severity level 1 / 2 / 3, see below.
Hamshere, O’Donovan, Rutter & Thapar, 2014; see for review Rommelse, Franke,
B. Restricted, repetitive patterns of behavior, interests, or activities, as manifested
by at least two of the following, currently or by history (examples are illustrative,
not exhaustive):
Geurts, Hartman & Buitelaar, 2010). Hence, the co-occurrence of ASD and ADHD
is unlikely to be largely due to mistaken symptom interpretations. In clinic based
1. Stereotyped or repetitive motor movements, use of objects, or speech (e.g., simple
motor stereotypies, lining up toys or flipping objects, echolalia, idiosyncratic phrases).
samples, the majority of comorbidity estimates reported for ADHD in ASD fall
2. Insistence on sameness, inflexible adherence to routines, or ritualized patterns of
verbal or nonverbal behavior (e.g., extreme distress at small changes, difficulties with
transitions, rigid thinking patterns, greeting rituals, need to take same route or eat
same food every day).
within the range of 30% to 80%, whereas the presence of ASD is estimated in 20%
to 50% of the patients with ADHD (e.g. Ames & White, 2011; Leyfer et al., 2006; for
review see Rommelse et al., 2010; Ronald, Simonoff, Kuntsi, Asherson & Plomin,
2008).
The importance of comorbidity in taxonomic questions forms the basis
of critical hypotheses in both research and clinical practice, as was already
3. Highly restricted, fixated interests that are abnormal in intensity or focus (e.g, strong
attachment to or preoccupation with unusual objects, excessively circumscribed or
perseverative interest).
4. Hyper- or hyporeactivity to sensory input or unusual interests in sensory aspects of
the environment (e.g., apparent indifference to pain/temperature, adverse response
to specific sounds or textures, excessive smelling or touching of objects, visual
fascination with light or movement).
described decades ago (Caron & Rutter, 1991; Neale & Kendler, 1995). Provided
Specify current severity level 1 / 2 / 3, see below.
that comorbidity is not due to artifacts such as chance, sampling bias, population
C.Symptoms must be present in the early developmental period (but may not
become fully manifest until social demands exceed limited capacities, or may
be masked by learned strategies in later life).
stratification or symptom overlap, perhaps the most fundamental issues are at
the nosological level: Are the two disorders distinct, or do they reflect an arbitrary
division of a single syndrome (Neale & Kendler, 1995). True comorbidity may be
due to either shared or related risk factors, to a comorbid pattern constituting
a meaningful syndrome, or to one disorder creating an increased risk for the
14
D. Symptoms cause clinically significant impairment in social, occupational, or
other important areas of current functioning.
E. These disturbances are not better explained by intellectual disability (intellectual
developmental disorder) or global developmental delay. Intellectual disability
and autism spectrum disorder frequently co-occur; to make comorbid diagnoses
of autism spectrum disorder and intellectual disability, social communication
should be below that expected for general developmental level.
15
Chapter 1
Box 1.1 Continued. DSM-5 diagnostic criteria for ASD and ADHD
Box 1.1 Continued. DSM-5 diagnostic criteria for ASD and ADHD
Severity is based on social communication impairments (A) and restricted, repetitive
patterns of behavior (B):
Attention-Deficit/Hyperactivity Disorder (ADHD)
Severity level
Social communication
Restricted, repetitive
behaviors
Level 3
“Requiring very
substantial
support”
Severe deficits in verbal and nonverbal
social communication skills cause severe
impairments in functioning, very limited
initiation of social interactions, and
minimal response to social overtures from
others. For example, a person with few
words of intelligible speech who rarely
initiates interaction and, when he or she
does, makes unusual approaches to meet
needs only and responds to only very
direct social approaches
Inflexibility of behavior,
extreme difficulty coping
with change, or other
restricted/repetitive
behaviors markedly
interfere with functioning
in all contexts. Great
distress/difficulty
changing focus or
action.
Level 2
“Requiring
substantial
support”
Marked deficits in verbal and nonverbal
social communication skills; social
impairments apparent even with supports
in place; limited initiation of social
interactions; and reduced orabnormal
responses to social overtures from others.
For example, a person who speaks simple
sentences, whose interaction is limitedto
narrow special interests, and who has
markedly odd nonverbal communication.
Inflexibility of behavior,
difficulty coping with
change, or other
restricted/repetitive
behaviors appear
frequently enough to be
obvious to the casual
observer and interfere
with functioning ina
variety of contexts.
Distress and/or difficulty
changing focus or
action.
Without supports in place, deficits in
social communication cause noticeable
impairments. Difficulty initiating social
interactions, and clear examples of
atypical or unsuccessful response to
social overtures of others. May appear
to have decreased interest in social
interactions. For example, a person who
is able to speak in full sentences and
engages in communication but whose
conversation with others fails, and whose
attempts to make friends are odd and
typically unsuccessful.
Inflexibility of behavior
causes significant
interference with
functioning in one or
more contexts. Difficulty
switching between
activities. Problems
of organization and
planning hamper
independence.
Level 1
“Requiring
support”
16
A. Either (1) or (2):
(1)Six or more symptoms of inattention for children up to age 16; symptoms of
inattention have been present for at least 6 months, and they are inappropriate
for the developmental level:
Inattention
a. Often fails to give close attention to details or makes careless mistakes in schoolwork,
at work, or with other activities.
b. Often has trouble holding attention on 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 (e.g., loses focus, side-tracked).
e. Often has trouble organizing tasks and activities.
f. Often avoids, dislikes, or is reluctant to do tasks that require mental effort over a long
period of time (such as schoolwork or homework).
g. Often loses things necessary for tasks and activities (e.g. school materials, pencils,
books, tools, wallets, keys, paperwork, eyeglasses, mobile telephones).
h. Is often easily distracted.
i. Is often forgetful in daily activities.
(2)Six or more symptoms of hyperactivity-impulsivity for children up to age 16;
symptoms of hyperactivity-impulsivity have been present for at least 6 months
to an extent that is disruptive and inappropriate for the developmental level:
Hyperactivity
a. Often fidgets with or taps hands or feet, or squirms in seat.
b. Often leaves seat in situations when remaining seated is expected.
c. Often runs about or climbs in situations where it is not appropriate (adolescents or
adults may be limited to feeling restless).
d. Often unable to play or take part in leisure activities quietly.
e. Is often “on the go” acting as if “driven by a motor”.
f. Often talks excessively.
Impulsivity
g. Often blurts out an answer before a question has been completed.
h. Often has trouble waiting his/her turn.
i. Often interrupts or intrudes on others (e.g., butts into conversations or games).
In addition, the following conditions must be met:
B. Several inattentive or hyperactive-impulsive symptoms were present before age 12
years.
C. Several symptoms are present in two or more settings (e.g., at home, school or work;
with friends or relatives; in other activities).
D. There is clear evidence that the symptoms interfere with, or reduce the quality of,
social, school, or work functioning.
17
as alternate expressions of a single underlying dimension of liability, or as
symptomatic phenocopies.
18
Note. These models present ASD and ADHD as a) true comorbidity, b) comorbidity reflecting three independent disorders, c/d) alternate
expressions of a single underlying dimension of liability, e) symptomatic phenocopies. Not presented is a possible model comorbidity
due to reciprocal causation, in which ASDincreases the risk for ADHD or vice versa (‘domino effect’).
as reflecting three independent disorders (ASD, ADHD, or ASD plus ADHD),
Behavioral
symptoms
Figure 1.1. Roughly, these models present ASD and ADHD as truly comorbid,
Cognitive
impairment
A slightly adapted version of these models for ASD and ADHD is presented in
Abnormal brain
conditions
& Roessner (2007) also described several models of non-artifactual comorbidity.
Genetic and
environmental
factors
other (Caron & Rutter, 1991). More recently, Banaschewski, Neale, Rothenberger
Comorbid condition as a) true comorbidity b) independent disorders
Based on the types of symptoms, three kinds (presentations) of ADHD can occur:
- Combined Presentation: if enough symptoms of both criteria inattention and
hyperactivity-impulsivity were present for the past 6 months.
- Predominantly Inattentive Presentation: if enough symptoms of inattention, but not
hyperactivity-impulsivity, were present for the past six months
- Predominantly Hyperactive-Impulsive Presentation: if enough symptoms of
hyperactivity-impulsivity but not inattention were present for the past six months.
Figure 1.1 Models of comorbidity in ASD and ADHD, inspired by Banaschewski et al. (2007)
E. The symptoms do not happen only during the course of schizophrenia or another
psychotic disorder. The symptoms are not better explained by another mental
disorder (e.g. Mood Disorder, Anxiety Disorder, Dissociative Disorder, or a Personality
Disorder).
c) common underlying d) common underlying e) symptomatic phenocopies
dimension of liability
dimension of liability
Chapter 1
19
Chapter 1
An increasing number of studies documented on various patterns of
Devincent & Drabick, 2008; Mulligan et al., 2009), anxiety, depression and tic
association between ASD and ADHD. It has for instance been hypothesized that
disorders (e.g. Simonoff et al., 2008), sleep disorders (for review see Ming &
ASD and ADHD may both be manifestations of the same overarching disorder
Walters, 2009), and motor coordination deficits (e.g. Reiersen, Constantino &
(Rommelse, Geurts, Franke, Buitelaar & Hartman 2011; Taurines et al., 2012).
Todd, 2008). Children with more comorbid difficulties reportedly experience more
According to this hypothesis, ADHD can be seen as a mild, less impaired subtype
difficulty in daily life as compared to those with one disorder (e.g. Bauermeister
within the more severe ASD spectrum, see also Figure 1.2 that was published by
et al., 2007; Goldstein & Schwebach, 2004; Kotte et al., 2013). It is therefore
Rommelse et al. (2011). This model suggests that the genetic substrates of ‘pure’
important to assess ASD and ADHD symptoms in parallel, while co-occurring
ADHD versus ASD with comorbid ADHD are either largely similar (the difference
psychiatric symptoms should be accounted for, as has been done in this thesis.
in severity between the two primarily stems from environmental factors), or that
the genes involved in ADHD are a subset of those involved in ASD (with difference
Genetics of ASD and ADHD
in severity explained by an additional biological risk for ASD, and their interactions
ASD and ADHD are both genetically determined disorders, with moderate to high
with environmental factors). If true, this model may have implications for future
heritability estimates (e.g. Franke et al., 2012; Hallmayer et al., 2011). For ADHD,
diagnostic procedures and treatment, as treatment successful in improving
an estimated 76% of the phenotype is explained by its genetics, in ASD these
ADHD symptomatology may also prove beneficial in ASD (Davis & Kollins, 2012;
estimates vary between 60% and 90% (e.g. Faraone, Perlis et al., 2005; Freitag,
Sikora, Vora, Coury & Rosenberg, 2012). A variant of this hypothesis is that ASD
2007). Several studies indicated that the frequent comorbidity of both disorders is
and ADHD do not constitute different expressions of one overarching disorder,
likely to be related to a modest to substantial overlap in genetic and environmental
but rather reflect the presence of two distinct disorders with a common etiology
risk factors (Lichtenstein, Carlstrom, Rastam, Gillberg & Anckarsater, 2010;
(distinct disorders hypothesis) (Grzadzinski et al., 2011)
Mulligan et al., 2009; Nijmeijer et al., 2010; Rommelse et al., 2010; Ronald et al.,
2008; 2010; St. Pourcain et al., 2011; Stam, Schothorst, Vorstman & Staal, 2009;
Figure 1.2 Overarching disorder hypothesis,
inspired by Rommelse et al. (2011)
ADHD
ASD
ASD and ADHD may be different manifestations of
the same overarching disorder. The manifestations
may range from ADHD with few if any social
problems, through ADHD with greater levels of social
and communicative problems, to ASD as the most
severe subtype characterized by additional more
severe social and communicative problems mostly
combined with various levels of ADHD symptoms.
Taylor et al., 2013). However, a recent study on the largest data set currently
available from psychiatric genome-wide association studies (GWAS) did not use
quantitative genetic methods but assessed SNP-heritability and was unable to
find shared genes for ASD and ADHD (Cross-Disorder Group of the Psychiatric
Genomics, 2013). Authors suggested that the found absence of genetic overlap
may be due to the presence of ASD-affected children in previous ADHD-cohort
studies and vice versa, which may have inflated the expected shared genetic risk
factors for ASD and ADHD. This seems too early a conclusion, since shared gene-
In addition to the comorbidity of ASD and ADHD, both disorders are
environment interactions were not included in this study, rare genetic variants
also frequently associated with other neurodevelopmental disorders such as
rather than common SNPs may contribute to the overlap in genetic factor (for
oppositional defiant disorder (ODD) / conduct disorder (CD) (e.g. Gadow,
review see McCarthy et al., 2008), and still little is known on the specific genes
20
21
Chapter 1
involved in ASD and ADHD, let alone those involved in their overlap. At any rate,
Multiple ASD and ADHD riskgenes
research on the overlap in genetic and environmental risk factors for ASD and
ADHD may benefit from alternative classifications and group comparisons. Such
a classification should ideally move beyond descriptive syndromes, an approach
applied in the chapters 3 to 6 of this thesis.
Intermediate
Intermediate
Phenotype A
Phenotype B
Cognition in ASD and ADHD
ASD and/or ADHD
Cognitive measures are used frequently in the assessment of ASD and ADHD.
behavioral
symptom
These measures are also referred to as intermediate phenotypes, useful indicators
Figure 1.3 Etiological model
of ASD and ADHD, inspired
by Franke and colleagues
(2009)
Schematic representation of the
intermediate phenotype concept
in psychiatric genetics. Many
genes are involved in causing an
ASD/ADHD behavioral symptom,
while a reduced number of genes
is involved in related intermediate
phenotypes such as cognitive
functioning.
in detecting etiologically more homogeneous subgroups of patients (Gottesman
& Gould, 2003). Intermediate phenotypes form a causal link between genes and
behavioral symptoms, more closely linked to the genes in action in ASD and
of how neurocognitive impairments form a link between susceptibility genes
ADHD, and more objectively measured than behavior (Kendler & Neale, 2010).
and ASD and ADHD symptoms such as hyperactivity, impulsivity, rigidity and
Characteristic of intermediate phenotypes is that they are heritable, associated
impaired social interaction skills. It should be noted that this model is a simplified
with the disorder, state independent and present in non-affected family members
representation of reality. Most importantly, the model does not account for the
of patients (Walters & Owen, 2007). A schematic representation of the intermediate
influence of potential environmental and genetic risk or protective factors (Jaffee
phenotype concept was published by Franke, Neale & Faraone (2009), an
& Price, 2007), nor for the influence of the aforementioned frequently associated
adapted version is presented in Figure 1.3.
difficulties. Although simplified, this concept provides a comprehensive framework
The intermediate phenotype concept provides an interpretable model
for exploring the relationship between the behavioral and cognitive profiles seen
in ASD and ADHD, as was done in the chapters 3 to 6 of this thesis.
Cognitive impairments that characterize ASD are reported most frequently
in the cognitive domains of emotion recognition (also referred to as social
cognition), central coherence, cognitive flexibility and other aspects of executive
functioning (e.g. Booth, Charlton, Hughes, Happé, 2003; Booth & Happé, 2010;
Corbett, Constantine, Hendren, Rocke & Ozonoff, 2009; Pellicano, Maybery,
Durkin & Maley, 2006). In contrast, ADHD is most frequently related to cognitive
difficulties in variability, inhibitory control, motor speed and (visual) attention
and working memory (for review see Doyle, 2006; Halperin, Trampush, Miller,
Marks & Newcorn, 2008; Hervey et al., 2006; for review see Kasper, Alderson, &
Hudec, 2012; for review see Kofler et al., 2013). Furthermore, attention problems,
22
23
Chapter 1
language delay and pragmatic language problems, sensory overresponsivity,
Tavare & Gringras, 2006; Stigler, Desmond, Posey, Wiegand & McDougle, 2004).
motor problems and deficits in executive functioning are frequently disclosed in
Overall findings suggest that pharmacological treatment for ADHD symptoms is
both ASD and ADHD (e.g. Geurts, Verté, Oosterlaan, Roeyers & Sergeant, 2004;
indeed effective in reducing ADHD symptoms in patients with ASD and ADHD.
Leonard, Milich & Lorch, 2011; Mulligan et al., 2009; Nydén et al., 2010; for review
However, described benefits are smaller and adverse effects tend to be more
see Rommelse et al., 2011). This growing body of literature indicates that ASD
severe in comorbid patients when compared to patients with only ADHD. An
and ADHD traits may arise via similar cognitive processes.
increased understanding of the effects of pharmacological treatment on cognitive
functioning may provide more insight into the exact working mechanisms of the
Treatment of ASD and ADHD
pharmacological therapy in ADHD-only and comorbid patients. Thus far, little is
According to the Dutch multidisciplinary guidelines for the treatment of ASD and
known about the cognitive working mechanisms of the selective norepinephrine
ADHD, usual care for children over the age of six consists of the prescription
re-uptake inhibitor atomoxetine. The clinical trial described in chapter 7 offered
of medication and/or evidence-based psychosocial interventions (e.g. parent
the opportunity to examine whether a pharmacologic intervention in the
training, social skills training, behavioral therapies), preferably both medication
noradrenergic system hypothesized to improve symptoms of ADHD, would also
and psychosocial interventions (Trimbosinstituut, 2005; Nederlands Vereniging
improve inhibitory control and affect ADHD symptoms in comorbid patients.
voor Psychiatrie, 2009). Pharmacological treatments of ADHD have larger effects
on behavioral problems compared to psychosocial interventions, but with a
Dimensional versus Categorical Models
risk of side effects such as agitation, insomnia, loss of appetite, gastrointestinal
Given the continuous distribution of the ASD and ADHD traits, the DSM-defined
problems, and headaches (Research Units On Pediatric Psychopharmacology
cut-offs between affected and unaffected may be arbitrary, and ignorant of potential
(RUPP) Autism Network, 2009; 2012; for review on side effects see Hazell,
difficulties in children who score just below clinical cut-offs (e.g. Constantino,
2007). Current pharmacological treatments for ASD primarily target comorbid
2011; Larsson, Anckarsater, Rastam, Chang& Lichtenstein, 2012; Levy, Hay,
symptoms (e.g., irritability, aggression, hyperactivity, anxiety) rather than core
McStephen, Wood & Waldman, 1997; Lundstrom et al., 2012; Plomin, Haworth
social and communication impairments, while pharmacological treatment for
& Davis, 2009; Robinson, Munir et al., 2011). That is, samples from the general
ADHD is effective for reducing impairment associated with core ADHD symptoms
populations are usually described with a lack of precision, and lumped together
(i.e., inattention, hyperactivity, impulsivity) (for review, see Davis & Kollins, 2012).
as an unaffected group. This overlooks the evidence that not only the ASD and
This has resulted in multiple clinical trials on the effectiveness of pharmacological
ADHD affected populations, but also the general population is characterized by
treatment for ADHD symptoms in ASD-patients (Arnold et al., 2006; Benvenuto,
behavioral, cognitive and genetic variance (e.g. Barnett, Heron, Goldman, Jones
Battan, Porfirio, & Curatolo, 2012; Cortese, Castelnau, Morcillo, Roux, & Bonnet-
& Xu, 2009; Constantino, 2011; Fair, Bathula, Nikolas & Nigg, 2012; Plomin et
Brilhault, 2012; Doyle & McDougle, 2012; for review see Ghanizadeh, 2012;
al., 2009; Robinson, Koenen et al., 2011). Barnett and colleagues (2009) for
Handen, Taylor, & Tumuluru, 2011; Hanwella, Senanayake & de Silva, 2011;
instance, described that a variant in the catechol-O-methyltransferase (COMT)
Harfterkamp et al., 2012; Murray, 2010; Posey et al., 2007; Research Units On
gene that contributes to the risk of psychiatric disorders also affects normal
Pediatric Psychopharmacology (RUPP), 2005; Santosh, Baird, Pityaratstian,
cognitive variation. In addition, Fair and colleagues (2012) showed that a large
24
25
Chapter 1
part of the cognitive heterogeneity within ADHD seems actually not exclusively
contrast to this positive hypothesis, from an evolutionary perspective averageness
related to ADHD per se, but represented cognitive heterogeneity also present
may be an adaptive trade-off against the mixture of costs and benefits of more
in the ‘unaffected’ (or non-symptomatic) side of the ADHD continuum. These
extreme ends of the continuum (Nettle, 2006). That is, being at the lowest risk for
findings point out that behavioral effects of cognitive and genetic variants are best
ASD or ADHD may also come with specific disadvantages. For example, being
regarded as process specific rather than disease specific.
highly reflective instead of impulsive may lead to inertia, and very low levels of
Empirical support is strong for an alternative model which replaces
restrictive and repetitive behaviors may lead to difficulties keeping a daily routine.
DSM-defined categories with multiple dimensions based on the overall number
If true, the ASD and ADHD traits may be bipolar, and the most favorable trait may
of symptoms present (for review see Willcutt et al., 2012). The study by Marcus
be a trade-off between advantages and disadvantages of more extreme traits. To
& Barry (2011) for example, compared a categorical typology with dimensional
examine whether the positive or evolutionary perspective describes the ASD and
measures of ADHD, and found correlations among the dimensional scores and
ADHD roots best, chapter 2 examined whether the ‘lowest risk side’ of the ASD
associated features consistently higher than correlations among the categorical
and ADHD trait distributions presents with fewer comorbid problems and superior
scores and associated features. These findings indicate that ADHD symptoms all
cognitive functioning.
had a dimensional structure. Likewise, quantitative symptom measures are well
able to pick up on biological risk factors involved in ADHD (e.g. Bralten et al., 2013;
Identifying more Homogeneous Subgroups
Nigg, Goldsmith, & Sachek, 2004; Nikolas & Burt, 2010; Sonuga-Barke, 2005).
Traditionally, studies used DSM-defined and therefore heterogeneous groups of
Therefore, dimensional measures were analyzed in the chapters 3 to 6 of this
patients. Attempts to detect shared and specific underpinnings for ASD and ADHD
thesis. Furthermore, chapters 2 and 5 focused solely on the general population,
may have been hindered by this heterogeneity in symptom presentation and
using dimensional measures that were sensitive assessments for variance across
underlying mechanisms (Bernfeld, 2012; Cross-Disorder Group of the Psychiatric
the continuous ASD and ADHD traits, including the non-symptomatic ends.
Genomics, 2013; for review see Hyman, 2007; Hyman, 2010; Miller, 2010; for
Apart from acknowledging the continuously distributed nature of both
review see Uher & Rutter, 2012). That is, specific cognitive impairments, brain
ASD and ADHD within the population, thinking quantitatively may also lead to
variations or genetic deficits may underlie the disorder only in a subgroup of the
thinking positively, as suggested by Plomin and colleagues (2009). Instead of
patients studied, and clinical diagnoses of ASD and ADHD may actually include
focusing solely on the vulnerabilities of people at risk for ASD or ADHD, a new
multiple, etiologically distinct subtypes with overlapping symptom presentation
direction for research is to consider the potential resilience of individuals with low
(e.g. Brieber et al., 2007; Buhler, Bachmann, Goyert, Heinzel-Gutenbrunner &
risk rates. Individuals at the low-risk side of the ASD and ADHD continua may,
Kamp-Becker, 2011; Fair et al., 2012; Gadow et al., 2009; Maher, 2008; Reiersen
for example, present with excellent social skills and below average hyperactivity
& Todorov, 2013; Verté, Geurts, Roeyers, Oosterlaan & Sergeant, 2006). An
and impulsivity, opposite to individuals at the high-risk side of the continuum.
important strategy to overcome this heterogeneity is to empirically segment this
Increased understanding of potential invulnerabilities and motivational factors
group of individuals with ASD symptoms, ADHD symptoms or a combination of
may provide insight into mechanisms that promote favorable outcomes, which
ASD and ADHD symptoms into subgroups with possibly a more homogeneous
may be different from mechanisms that help to avoid unfavorable outcomes. In
set of underlying mechanisms. A dimensional approach that relaxes not only the
26
27
Chapter 1
DSM-defined assumption that ASD and ADHD are two separate conditions, but
Figure 1.4 Dissecting into
homogeneous groups
also that ASD and ADHD each consist of distinct subtypes, is warranted.
Each circle represents one child, each
color represents one homogeneous
subgroup. Circles of the same colour
form a subgroup (thus children
forming classes or profiles). Based
on, for example, symptom data
or cognitive data, mathematical
detection algorithms may detect
several homogeneous subgroups
with very similar profiles of behavioral
and/or cognitive data.
Using dimensions as the starting point, there are several methods of
empirically dissecting heterogeneity and defining more homogeneous disease
profiles, such as latent class analyses (LCA) or Community Detection (CD)
analyses (McCutcheon, 1987; Newman, 2006). LCA areempirical bottom-up
approaches that through mathematical detection algorithms can identify groups
of participants (also referred to as classes or profiles) who have very similar
scores on for example behavioral symptom measures (e.g. questionnaires,
interviews), see also Figure 1.4. LCA result in measures of overall fit, such as
the Bayesian Information Criterion (BIC) values and entropy. These measures
are used as indicators of whether the data support the latent class model in
question, and assess the relative efficiency with which different models fit the
data (Nylund, Asparouhov & Muthén, 2007). Multiple studies disclosed clinically
relevant, more homogeneous latent classes in ASD and ADHD with the use of
quantitative symptom measures (e.g. Acosta et al., 2008; Constantino, 2011;
Mulligan et al., 2009; Reiersen, Constantino, Volk & Todd, 2007; St. Pourcain
et al., 2011; Volk, Todorov, Hay & Todd, 2009). Overall, these studies indicated
that empirically defined ASD-ADHD classes derived from quantitative symptom
measures show partially distinct patterns of comorbid pathology and distinct
developmental trajectories. Such behaviorally homogeneous disease subtypes
may be differentially linked to etiological factors, and may help reveal shared and
unique mechanisms for ASD and ADHD. Therefore, LCA were used to examine
etiologically different subtypes in the chapters 3 to 6 of this thesis.
One step further is to base bottom-up classification on more objectively
measured cognitive performances rather than on symptoms scores. As such,
cognitive homogeneous subgroups related to dimensions of ASD and ADHD may
bring us closer to etiological homogeneity. Thus far, research on homogeneous
cognitive profiling is limited. A study of Fair and colleagues (2012) on cognitive
heterogeneity in typically developing children and children with ADHD indicated
that ADHD-behavior may be rooted in multiple cognitively distinct profiles. These
cognitive profiles did not differ in ADHD symptom presentation, and were highly
similar in the population-based and ADHD-affected samples. This may indicate
that the cognitive profiles revealed through bottom-up approaches are generic,
not only relevant for normal development and ADHD, but possibly also for other
neurodevelopmental disorders such as ASD. Cognitive profiles may provide both
research and clinical practice with cognitive substrates in children assessed for
ASD and ADHD. Thus, bottom-up cognitive profiling deserves more investigation
and may ultimately inform the development of more tailored diagnostic and
treatment procedures. Therefore, this approach was applied in chapter 6 of this
thesis.
28
29
Chapter 1
Aims and Outline of this Thesis
The overall aim of this thesis is to examine shared and unique behavioral
key findings from all chapters, points out limitations, suggests recommendations
for future research and closes with some clinical implications.
and cognitive profiles in ASD and ADHD. The approach has the following key
characteristics:
I)
Assessing ASD and ADHD symptoms in parallel.
II) Examining the relationship between behavior and cognition.
III) Applying a dimensional approach, focusing on both the lower and higher
end of the ASD and ADHD trait continua by integrating data from populationbased and clinic-based samples.
IV) Identifying subgroups that are homogeneous at the behavioral or cognitive
level, using latent class analyses.
Information with regard to behavioral and cognitive functioning is collected from
both general and clinic-based populations. For details on the study samples, see
Box 1.2. The cognitive measures under study cover affirmed cognitive strengths
and difficulties for ASD and ADHD, such as inhibition, visual and verbal attention,
visual and verbal working memory, the recognition of emotions, and motor timing
(for review see Rommelse et al., 2011).
Chapter 2 focuses on the continuum of the ASD and ADHD traits in
the general population. This study examines whether the lower end of both trait
distributions represents superior functioning, or that the lowest risk for ASD and
ADHD may also come with specific disadvantages. In chapters 3 to 5, reduced
heterogeneity on the behavioral level aims to unravel unique and shared cognitive
profiles. Next, in chapter 6 a reversed approach is used, in which a reduced
heterogeneity on the cognitive level aims to detect unique and shared ASD-ADHD
profiles. In chapter 7, the relationship between behavior and cognition is further
explored in a double blind, placebo controlled study. This study describes whether
ADHD symptom improvement is mediated by improvements in inhibitory control.
Finally, chapter 8 provides a general overview, which summarizes and discusses
30
31
Box 1.2 Study Samples
Schoolkids Project Interrelating DNA and Endophenotype Research (SPIDER)
Eligible children were 378 children from a random population cohort, largely recruited
from primary schools across the Netherlands (Breda, Enschede, Groesbeek, Hoofddorp,
Malden and Nijmegen). All children were between 6 and 13 years of age (M (SD) =
8.9 (1.7)), with boys and girls equally represented (% male = 49.5). All children were
of Caucasian descent and had an estimated total IQ of at least 70 on the Wechsler
Intelligence Scale (WISC-III) (Wechsler, 2002). ASD, ADHD and comorbid symptoms
were examined with multiple questionnaires filled in by parents. Exclusion criteria were
epilepsy, known genetic or chromosomal disorders, brain damage, and problems with
vision or hearing. ASD and ADHD diagnoses were not an exclusion criterion; children
were asked not to use medication prior to the neuropsychological assessment.
Biological Origins of Autism (BOA) study
Eligible childrenwere 274 ASD (and ADHD) affected children and their siblings, recruited
via Karakter Child and Adolescent Psychiatry Centers, and the Dutch organization for
autism (Nederlandse Vereniging voor Autisme; NVA) .All children were between 5 and
17 years of age (M (SD) = 11.2(3.1)), with boys overrepresented(% male = 65.3). ASD,
ADHD and comorbid symptoms were examined with multiple questionnaires filled in by
parents; these ratings were with regard to child’s functioning off medication. All children
were screened with the Social Communication Questionnaire (SCQ) (Rutter et al., 2003)
completed by parents and teachers. To avoid false negatives, families were included if
at least one child presented a score above 10 on the parent version or above 15 on the
teacher version of the SCQ. For all children scoring above the cut-off, a formal diagnosis
of ASD was made by a certified clinician using the Autism Diagnostic Interview-revised
(ADI-R) (Le Couteur, Lord, & Rutter, 2003). The Conners’ Rating Scales-Revised (CRS-R)
(Conners, Sitarenios, Parker & Epstein, 1998a; 1998b) completed by parents and teachers
was used to screen for ADHD. A T-score above 63 on one of the ADHD-subscales of the
CRS-R was considered clinical. For all children scoring above this cut-off, or previously
having a diagnosis of ADHD, the parental account for childhood symptoms (PACS) was
administered by a certified clinician to obtain a diagnosis of ADHD (Taylor, Sandberg,
Thorley, & Giles, 1991). Further inclusion- and exclusion criteria were identical to those
listed for the SPIDER. Total IQ was estimated via the Wechsler Preschool and Primary
Scale of Intelligence (WPPSI), Wechsler Intelligence Scale (WISC-III) or Wechsler Adult
Intelligence Scale (WAIS-III) (Wechsler 1989; 2000; 2002). Children were asked not to
use medication prior to the neuropsychological assessment.
Research on Atomoxetine in Dutch ASD/ADHD Children (RADAR)
Eligible childrenwere 97 ASD and comorbid ADHD affected children, referred to one
of nine participating child and adolescent psychiatry centers across the Netherlands
(Amsterdam, Groningen, Hoorn, Leiden, Maastricht, Nijmegen, Oosterhout, The Hague
and Utrecht). All children were between 6 and 17 years of age (M (SD) = 10.4 (2.9)), with
boys overrepresented (% male = 85.6), and had an estimated total IQ of at least 60 on the
Wechsler Intelligence Scale (WISC-III) (Wechsler, 2002). Exclusion criteria were a weight
of less than 20 kg, presence of psychosis, bipolar disorder, substance abuse, a serious
medical illness, history of seizures, ongoing use of psychoactive medications other than
the study drug, and intended start of a structured psychotherapy or in-patient treatment.
Females who were post-menarche and sexually active had to take a pregnancy test to
exclude pregnancy. Clinical Trial Registry B4Z-UT-S017, NCT00380692.
32
33
Are high and low extremes of
ASD and ADHD trait continua
pathological? A population-based
study using the AQ and SWAN
rating scales
Jolanda M. J. van der Meer*, Corina U. Greven*,
Catharina A. Hartman, Martijn G. A. Lappenschaar,
Jan K. Buitelaar, Nanda N. J. Rommelse
*joint first author
Under review
34
Abstract
Chapter 2
Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder
(ADHD) are thought to exist on a continuum, where diagnosis simply reflects
the symptomatic end of a normal distribution of quantitative traits in the general
population. One implicit assumption is that the non-symptomatic ends of these
Are high and low extremes of ASD
distributions represents superior functioning; however, this remains to be
tested, as it is possible that symptomatic and non-symptomatic ends of clinical
and ADHD trait continua pathological?
continua are both pathological. Data came from 378 children (6-13 years) from
A population-based study using the AQ
a population-based sample. Parents rated their child along the ASD and ADHD
and SWAN rating scales
trait continua using the Autism Spectrum Quotient (AQ) and the Strength and
Weaknesses of ADHD Symptoms and Normal behavior (SWAN) questionnaires,
which show a normal distribution of scores in population-based samples. Scores
on the AQ and SWAN were related to measures of internalizing and externalizing
behavioral problems and cognitive functioning using polynomial regression
analyses. Associations between the ASD and ADHD traits on the one hand,
and behavioral problems and cognitive functioning on the other hand, were
largely linear. The non-symptomatic ends of the ASD and ADHD trait continua
were not pathological; instead, they were on average related to fewer behavioral
problems and better cognitive functioning than the symptomatic ends. Finding
linear relations suggests that the non-symptomatic ends of the ASD and ADHD
trait continua differ largely quantitatively rather than qualitatively. Studying the
correlates of the non-symptomatic ends of these continua may deepen our
understanding of the mechanisms underlying superior behavioral and cognitive
functioning.
Jolanda M. J. van der Meer*, Corina U. Greven*, Catharina A. Hartman, Martijn
G.A. Lappenschaar, Jan K. Buitelaar & Nanda N.J. Rommelse
*joint first author
Under review
37
Chapter 2
Psychiatric disorders such as Autism Spectrum Disorder (ASD) and Attention-
These measures, however, attenuate variability at the low, non-symptomatic
Deficit/Hyperactivity Disorder (ADHD) are considered to represent the extreme
side of the distribution where individuals are grouped together into a single no-
problematic manifestations of a continuous distribution of quantitative traits in
symptoms group as reflected in the highly skewed distribution of scores of such
the general population. Evidence for this comes from a large body of research
measures (Delucchi & Bostrom, 2004). Two more recently developed measures
using a complementary array of research design, methodological and statistical
that cater for this methodological limitation are the Autism Spectrum Quotient
approaches (Constantino, 2011; Widiger & Samuel, 2005; Willcutt, 2005). An
(AQ) and the Strength and Weaknesses of ADHD Symptoms and Normal behavior
underlying assumption to viewing ASD and ADHD as symptomatic extremes of
(SWAN) questionnaires (Auyeung, Baron-Cohen, Wheelwright & Allison, 2008;
quantitative trait continua is that the non-symptomatic ends of these continua
Hay, Bennett, Levy, Sergeant & Swanson, 2007). The AQ and SWAN both result
represent superior functioning, or possibly even resilience rather than only
in a continuous distribution of scores in the general population (Hoekstra, Bartels,
indicating low risk (Plomin et al. 2009; 2012). However, this assumption may not
Cath, & Boomsma, 2008; Polderman et al., 2007) and can therefore be regarded
necessarily be correct.
to measure individual differences in ASD and ADHD traits on a continuum from low
Being at the lowest risk for ASD or ADHD may also come with specific
to high. It has been shown that children who show no variation on conventional
disadvantages (Plomin et al., 2009). For example, being highly focused in terms
skewed scales of ADHD behaviors, show variation across the non-symptomatic
of attention may lead to less flexible shifting of attention and cognitive rigidity,
side of the SWAN scale (Arnett et al., 2011; Polderman et al., 2007).
being highly reflective instead of impulsive and able to control activity levels
may lead to lack of spontaneity and inertia. Likewise, the opposite end of the
years) found evidence suggesting that the non-symptomatic end of the ADHD
social communication and interaction deficits seen in ASD may reflect other
distribution is indeed linked to superior functioning rather than representing
types of abnormal social behavior (e.g., very high empathy levels could also be
psychopathology (Crosbie et al., 2013). The study showed that individuals
impairing), and very low levels of restrictive and repetitive behaviors could come
with the highest possible ADHD traits performed worse on the stop signal task
with difficulties keeping a daily routine and making plans. Thus, the ASD and
(assessing response inhibition, response latency and response variability) than
ADHD traits may be bipolar, where both extremes represent maladaptation and
those with the lowest possible ADHD traits (Crosbie et al., 2013). Moreover, parent
psychopathology. In that case, the most favorable trait may be a trade-off between
reports of ADHD, anxiety, depression, learning disability and other disorders were
advantages and disadvantages of more extreme traits, and superior functioning
lowest for participants with the lowest ADHD trait scores, althoughthe study did
will be associated with people at the trade-off level (Nettle, 2006; Plomin et al.,
not present relevant statistics in support of this conclusion. It remains to be tested
2009).
whether results replicate for cognitive domains associated with ADHD other than
Viewing ASD and ADHD as symptomatic extremes of quantitative traits
A recent study using the SWAN in a community sample (ages 6-18
those assessed by the stop task, and for behavioral problems other than ADHD.
calls for a perspective and methodology different from the traditional categorical
focus described in current psychiatric classification schemes. Typically, interview
whether the non-symptomatic ends of the ASD and ADHD trait continua are
and questionnaire measures that assess complex psychiatric disorders allow fine-
associated with fewer internalizing and externalizing behavioral problems
scaled assessment of variability at the high, symptomatic end of the distribution.
and better cognitive functioning. This question is addressed using polynomial
38
The present study uses the AQ and SWAN questionnaires to examine
39
Chapter 2
regression analyses in a population-sample of 378 children. The study focuses
The SWAN consists of 18 DSM-IV-based items scored on a 7-point Likert scale
on the ASD and ADHD continua as they refer to two commonly associated
from (1) ‘far below’, (2) ‘below’, (3) ‘slightly below’, (4) ‘average’, (5) ‘slightly
complex disorders and traits that share comorbidity patterns with other behavioral
above’, (6) ‘above’ to (7) ‘far above’ (possible range of scores: 18-126; actual
problems and which are assumed to share cognitive underpinnings (see also van
range in the present sample: 25-118). Each item on the SWAN is phrased to
der Meer et al. (in press) in chapter 5; for review see Rommelse et al., 2011).
represent behavior across the ADHD continuum rather than to represent a
symptom, thereby defining a middle range of behavior. Ratings are then made in
METHODS
relation to deviation from this middle range. Scores on the SWAN were mirrored
Participants
to match the directionality of the AQ: higher scores were indicative of more ASD
Participants were part of a population based study of children sampled from
primary schools across the Netherlands (the Schoolkids Project Interrelating DNA
and Endophenotype Research – SPIDER) Data collection took place between
January 2009 and July 2011. The study sample consisted of 378 children of
Caucasian descent between the ages of 6 and 13 years (M(SD) = 8.9(1.7), %
males = 49.5). Exclusion criteria were epilepsy, known genetic or chromosomal
disorders, brain damage, problems with vision or hearing, and an estimated total
IQ of less than 70 on the Wechsler Intelligence Scale (WISC-III) (Wechsler, 2002).
and ADHD traits. Both measures have adequate reliability and validity (Arnett et
al., 2011; Hoekstra et al., 2008), and demonstrated sensitivity and specificity to
identify clinically elevated scoring children (Arnett et al., 2011; Auyeung et al.,
2008). In the current study the two measures correlated .26 (p < .001), and
showed a gamma distribution with a large shape parameter (see Supplement
2.1), which closely resembles a normal distribution.
Internalizing and externalizing behavioral problems
Informed written consent was obtained from parents. SPIDER is approved by the
Parents also rated their child’s behavior on pencil-and-paper copies of the
Committee on Research involving Human Subjects (CMO).
Conners’ Parent Rating Scale-Revised: Long version (Conners et al., 1998a). The
Conners’ scale is a validated questionnaire for assessing behavioral problems
Measures
including oppositional behavior, emotional lability, anxiety, perfectionism and
ASD and ADHD traits
psychosomatic complaints.
Parents were invited to rate their child’s ASD and ADHD traits using pencil-andpaper copies of the AQ (Auyeung et al., 2008; Hoekstra et al., 2008) and SWAN
(Hay et al., 2007) questionnaires. The AQ consists of 50 items scored on a 4-point
Likert scale from (0) ‘definitely agree’, (1) ‘slightly agree’, (2) ‘slightly disagree’
to (3) ‘definitely disagree’ (possible range of scores: 0-150; actual range in the
present sample: 8-120). Around half of the items on the AQ are phrased as to elicit
a positive endorsement from individuals high in ASD traits (e.g., ‘I am fascinated
by dates’), the other half a negative endorsement (e.g., ‘I find social situations
Cognitive functioning
The cognitive tasks included in this study are briefly described in Table 2.1.
The cognitive domains assessed by these tasks have previously shown to be
impaired in both ASD and ADHD (Oerlemans et al., 2013; Rommelse et al., 2011;
van der Meer et al., 2012, as described in chapter 3). No ceiling effects occurred.
Scores on some of the cognitive tasks were mirrored, so that higher scores on all
cognitive variables implied better performance.
easy’), which results in a continuous distribution of scores in population samples.
40
41
Chapter 2
Table 2.1 Description of the cognitive tasks
Data analyses
Task
Measurement potential
Dependent variable(s)
Polynomial regression analyses were conducted which included the linear and
Baseline
Speed a,b
Speed and variability of
motor output as response to
external cue
MRT (ms) and variability (SD of
reaction time in ms)
quadratic terms of the ASD or ADHD traits as predictors, and the internalizing
Facial
Emotion
Recognitiona,b
Capacity to identify the facial
emotional expression of
happiness, sadness, anger
and anxiety
MRT (ms) and accuracy on four
emotions
Linear relations between predictors and outcome measures would indicate
Visuo-Spatial
Sequencinga,b
Visuo-spatial attention
Number of correctly reproduced
sequences in identical (forward)
order
relations, evidenced by significant quadratic terms, could suggest that the non-
Visuo-spatial working
memory
Number of correctly reproduced
sequences in reversed (backward)
order
Centering of predictors was used (Bradley & Srivastava, 1979). All outcome
Verbal attention
Number of correctly reproduced
digits in identical (forward) order
1975) and standardized into z-scores (SPSS version 18). As an exploratory step,
Verbal working memory
Number of correctly reproduced
digits in reversed (backward) order
effects of sex and age were also examined.
Block Patterns
(WISC-III)a,c
Visual pattern recognition
Number of correctly and timely
completed geometric designs
Response
Organization
Objectsa,b
Motor inhibition
Difference in percentage of errors or
MRT (ms) between compatible and
incompatible trials
ADHD-term, to examine prediction beyond age and sex effects (step 1 linear). The
Difference in percentage of errors or
MRT (ms) between compatible trials
and mixed compatible-incompatible
trials.
(ASD*age, ASD*sex; or ADHD*age, ADHD*sex; step 2 linear). The alternative
Digit Span
(WISC-III)a,c
Cognitive flexibility
and externalizing behavioral problems and cognitive tasks as outcome measures.
that the non-symptomatic ends of the ASD and ADHD trait continua indeed
represent better functioning than the symptomatic ends. In contrast, curvilinear
Note. MRT=mean reaction time, a van der Meer et al.(2012); b de Sonneville (1999) ;c WISC-III
= Wechsler Intelligence Scale (Wechsler, 2002).
symptomatic ends may be pathological, depending on the shape of the curve.
measures were normalized using a Van der Waerden transformation (Lehman,
The basic regression model included only age and sex as predictors
(step 0). The first extended model covered this basic model plus the linear ASD or
second extended model also added interactions with age and sex as predictors
second extended model also added the quadratic term (ASD2 or ADHD2; step 2a
quadratic) and interactions with age and sex as predictors (ASD2*age, ASD2*sex;
or ADHD2*age, ADHD2*sex; step 2b quadratic). The False Discovery Rate (FDR)
controlling procedure was applied to correct for possible multiple testing effects
(p-value set at 0.05)(Benjamini, 1995).For illustrative purposes, scatterplots were
created for any analyses that revealed curvilinear effects, to examine the shape of
the quadratic curve. As a post-hoc step which further confirmed impressions from
regression analyses and scatterplots, children with the highest and lowest scores
on the ADHD and ASD distributions were compared on mean internalizing and
externalizing behavioral problems and cognitive functioning (for a description of
these analyses see Supplements 2.2 and 2.3).
42
43
Chapter 2
RESULTS
of the interactions between the linear or quadratic ADHD terms and age were
The ASD trait
significant.
The linear ASD term was significantly associated with increased scores on all
internalizing and externalizing behavioral problems (step 1 linear, Table 2.2), but
with none of the cognitive variables (step 1 linear, Table 2.3). The quadratic ASD
term was not associated with any of the behavioral problems (step 2a-b quadratic,
Table 2.2). However, the interaction between quadratic ASD term and sex was a
significant predictor of visuo-spatial working memory (step 2b quadratic, 2.3).
Post-hoc testing revealed that the quadratic ASD term was a significant predictor
of visuo-spatial working memory in girls (β=-0.33, p<.001), but not boys (β=0.09,
non-significant). No other associations between the quadratic ASD term and the
cognitive variables were significant. In addition, none of the interactions between
the linear or quadratic ASD terms and age were significant.
The ADHD trait
The linear ADHD term was significantly associated with increased scores on
oppositional behavior, emotional lability, anxiety and psychosomatic complaints,
but not with perfectionism (step 1 linear, Table 2.2).In addition, theinteraction
between the linear ADHD term and sex was a significant predictor of oppositional
behavior and perfectionism (step 2 linear, Table 2.2). Post-hoc testing indicated
that oppositional behavior and psychosomatic complaints were more strongly
related to the linear ADHD term in boys (β=0.56, p<0.001; and β=0.44, p<0.05,
respectively) than girls (β=0.20, p<0.01; and β=0.16, p<0.05, respectively). The
linear ADHD term was also significantly associated with worse performance on
visuo-spatial and verbal working memory and visual pattern recognition (step 1
linear, Table 2.3), but not with any other cognitive measures.
The quadratic ADHD term showed significant associations with
oppositional behavior, emotional lability and perfectionism (step 2a quadratic,
Table 2.2). No significant associations were found between the quadratic ADHD
term and any of the cognitive variables (step 2a-b quadratic, Table 2.3). None
44
45
46
Predictor
-
SWAN2*sex
∆R2=0.01
∆R2=0.03
∆R2=0.03
-
-
0.19 (0.00)
[0.00,0.00]
-
-
0.32 (0.02)
[0.01,0.03]
2
∆R2=0.01
∆R2=0.03
∆R2=0.01
∆R2=0.10
∆R2=0.01
∆R2=0.00
∆R2=0.00
∆R2=0.15
R2=0.00
R /∆R
2
-
-
-
-
-
0.21 (0.01)
[0.01,0.02]
-
-
-
-
-
0.48 (0.02)
[0.02,0.03]
-
-
β (B)
[95% C for B]
2
R /∆R
2
∆R2=0.00
∆R2=0.01
∆R2=0.01
∆R2=0.04
∆R2=0.01
∆R2=0.01
∆R2=0.01
∆R2=0.21
R2=0.02
Anxiety
-
-
0.18 (0.00)
[0.00,0.00]
-
-
-
-
-
-
-
-
0.50 (0.03)
[0.02,0.03]
-0.20 (-0.37)
[-0.56,-0.19]
-
2
∆R2=0.00
∆R2=0.03
∆R2=0.01
∆R2=0.00
∆R2=0.02
∆R2=0.01
∆R2=0.00
∆R2=0.23
R2=0.04
R /∆R
2
Perfectionism
β (B)
[95% C for B]
Internalizing and externalizing problems
-
-
-
-0.20 (-0.02)
[-0.04,-0.01]
-
0.32 (0.02)
[0.01,0.03]
-
-
-
-
-
0.23 (0.01)
[0.01,0.02]
-
-
β (B)
[95% C for B]
∆R2=0.01
∆R2=0.01
∆R2=0.02
∆R2=0.10
∆R2=0.00
∆R2=0.00
∆R2=0.00
∆R2=0.05
R2=0.00
R2/∆R2
Psychosomatic Complaints
Note. AQ=Autism Spectrum Quotient scale .SWAN=Strengths and Weaknesses of ADHD symptoms and Normal behavior scale.
β=standardized regression coefficient, B=unstandardized regression coefficient (with 95% confidence interval). Sex defined as boys=0
and girls=1. R 2=proportion of variance explained by variables in step 0. ∆R 2=proportion of variance explained by the additionally entered
predictors. Step 2 (linear) and steps 2a-b (quadratic) are alternative steps, not consecutive steps. Regression coefficients shown only for new
predictors entering at each step. A higher score on the AQ, SWAN, and internalizing and externalizing problems indicated more behavioral
problems. Hence, a positive β(B) value indicates that an increase in ASD/ADHD traits relates to an increase in internalizing and externalizing
problems. Values printed in bold are significant at p<0.05. β(B) values are only reported if significant after FDR correction.
-
0.19 (0.00)
[0.00,0.00]
SWAN2*age
Step 2b (quadratic) for ADHD
SWAN
2
Step 2a (quadratic) for ADHD
-0.23 (-0.02)
[-0.04,-0.01]
SWAN*age
0.39 (0.03)
[0.02,0.03]
∆R2=0.15
-
-
-
-
0.40 (0.02)
[0.01,0.02]
-
-
β (B)
[95% C for B]
-
∆R2=0.03
∆R2=0.00
∆R2=0.01
∆R2=0.15
R2=0.01
2
R /∆R
2
Emotional lability
-
SWAN*sex
Step 2 (linear) for ADHD
SWAN
Step 1 (linear) for ADHD
AQ2*sex
AQ2*age
Step 2b (quadratic) for ASD
AQ2
-
-
AQ*sex
Step 2a (quadratic) for ASD
-
AQ*age
Step 2 (linear) for ASD
AQ
0.40 (0.02)
[0.02,0.03]
-
sex
Step 1 (linear) for ASD
-
age
Step 0 (same for ASD and ADHD)
β (B)
[95% C for B]
Oppositional behavior
Table 2.2 Polynomial regressions of the ASD (AQ) and ADHD (SWAN) trait measures on internalizing and externalizing
behavioral problems
Chapter 2
47
Chapter 2
Table 2.3 Polynomial regressions of the ASD (AQ) and ADHD (SWAN) trait
measures on cognitive tasks
Cognitive tasks
Working memory
Visuo-spatial
β (B)
[95% C for B]
Block patterns
Verbal
β (B)
[95% C for B]
Visual patterns
R2/∆R2
β (B)
[95% C for B]
R2/∆R2
Predictor
Step 0 (same for ADHD and ASD)
R =0.38
R =0.19
2
R =0.03
2
2
age
0.61 (0.34)
[0.30,0.39]
0.43 (0.24)
[0.19,0.29]
-0.16 (-0.09)
[-0.14,-0.03]
sex
-
-
-
∆R2=0.00
Step 1 (linear) for ASD
AQ
-
Step 2 (linear) for ASD
∆R2=0.00
-
-
-
-
-
-
-
∆R2=0.00
-
Step 2b(quadratic) for ASD
∆R2=0.00
-
∆R =0.00
-
-
-
AQ2*sex
-0.27 (-0.00)
[-0.00,-0.00]
-
-
SWAN
∆R2=0.02
-0.15 (-0.01)
[-0.02,-0.00]
∆R2=0.02
-0.13 (-0.01)
[-0.01,-0.00]
∆R2=0.00
Step 2 (linear) for ADHD
∆R2=0.05
-
-
-
SWAN*sex
-
-
-
Step 2a(quadratic) for ADHD
SWAN2
∆R =0.00
-
∆R =0.00
-
∆R2=0.00
Step 2b(quadratic) for ADHD
-
-
-
-
SWAN *sex
-
-
-
2
Note. For a description of the table contents, see also the note to Table 2.2. A higher score
on the cognitive tasks indicated better performance. Hence, a negative β(B) value indicates
that an increase in ADHD/ASD traits relates to a decrease in cognitive functioning. Results
for the other cognitive tasks included in this study (the Baseline Speed, Facial Emotion
Recognition and Response Organization Objects tasks, as well as visuo-spatial and verbal
attention on the Visuo-Spatial Sequencing and Digit Span tasks) are not tabulated, as
neither the linear nor quadratic ASD or ADHD terms were significant predictors of these
tasks (i.e., steps 1, 2, 2a and 2b yielded non-significant results).
48
previous evidence showing that impairments in spatial working memory are linked
to ADHD, but not ASD symptoms (Sinzig, Morsch, Bruning, Schmidt, & Lehmkuhl,
∆R2=0.00
SWAN2*age
complaints). In addition, the ADHD but not the ASD trait was significantly
and verbal and visuo-spatial working memory. The latter finding is consistent with
2
∆R2=0.00
significantly associated with internalizing and externalizing behavioral problems
associated with reduced cognitive performance on visual pattern recognition
∆R =0.00
2
symptomatic ends (Plomin et al., 2009; 2012), or also represent maladaptive
(oppositional behavior, emotional lability, anxiety, perfectionism, psychosomatic
∆R2=0.01
SWAN*age
This study examined if the non-symptomatic ends of the ASD and ADHD trait
outcomes (Nettle, 2006; Plomin et al., 2009). The ASD and ADHD traits were
-0.23 (-0.02)
[-0.02,-0.01]
∆R2=0.00
2
behavioral problems and better cognitive functioning than the symptomatic ends
continua are associated with better behavioral and cognitive outcomes than the
2
AQ2*age
Step 1 (linear) for ADHD
associated with the symptomatic end. Hence, for any outcome measures showing
DISCUSSION
∆R2=0.00
∆R =0.01
∆R =0.02
ADHD scores from which children improved at a slower rate on negative outcomes
(see Supplements 2.4 and 2.5).
2
2
symptomatic end, a point was reached at more intermediate levels of ASD or
∆R2=0.01
AQ*sex
AQ2
than a u-shaped distribution: moving down from the symptomatic to the non-
non-symptomatic end of the ASD and ADHD distributions were linked to fewer
-
AQ*age
Step 2a(quadratic) for ASD
The nature of curvilinear relations was best described in terms of a j- rather
significant associations with the linear or quadratic ASD or ADHD terms, the
∆R2=0.00
∆R2=0.01
∆R2=0.01
Illustration of curvilinear relationships
2008). Any significant associations between the ASD or ADHD trait on the one
hand, and behavioral problems or cognitive functioning on the other hand were
linear, with the exception of oppositional behavior and emotional lability, which
showed curvilinear (quadratic) relations with the ADHD trait in addition to linear
associations, and perfectionism which only showed a curvilinear association with
the ADHD trait. There was some evidence for sex effects. Oppositional behavior
and psychosomatic complaints were more strongly related to the ADHD trait in
49
Chapter 2
boys, and a curvilinear relation between the ASD trait and visuo-spatial working
are likely to be larger (Preacher, Rucker, MacCallum & Nicewander, 2005).
memory was only significant in girls. Curvilinear relations could suggest that the
Normally distributed measures such as the AQ and SWAN could for example be
non-symptomatic ends represent maladaptation, depending on the shape of
helpful in selecting control individuals with particularly low ASD or ADHD scores
the quadratic curve. Given the j-shaped curve, for any behavioral and cognitive
in case-control studies, to increase power if effect sizes are expected to be small.
outcomes showing significant linear or curvilinear associations with the ASD
However, these effects can only be relevant across the distribution of traits if
or ADHD trait, the non-symptomatic ends of the trait continua were associated
high and low extremes differ quantitatively rather than qualitatively, as tentatively
with fewer behavioral problems and better cognitive performance than the
shown here for the first time for the ASD and ADHD traits.
symptomatic ends.
In contrast, neither linear nor curvilinear associations were found between
the included internalizing and externalizing behavior scales were traditional
the ADHD trait and other included cognitive measures (i.e. baseline speed,
skewed measures, which show little variation at the non-symptomatic side of
facial emotion recognition, attention, motor inhibition and cognitive flexibility).
the distribution. Although the non-symptomatic ends of the ASD and ADHD-
Moreover, the curvilinear association between the ASD trait and visuo-spatial
trait continua were associated with lower levels of behavioral problems, it is
working memory in girls represented the only significant prediction of cognitive
unclear whether this extends to the presence of more positive behaviors or even
functioning from the ASD trait, and hence interpretive caution is warranted.
resilience at the non-symptomatic ends. The current dataset contained no suitable
Together these findings suggest that most of the cognitive functioning is stable
measures to examine whether the non-symptomatic ends are associated with
across the ASD and ADHD trait continua in the general child population. Since
positive constructs such as wellbeing or quality of life and this therefore remains
associations with the cognitive measures were almost exclusively limited to the
an interesting question for future research. Nonetheless, the included internalizing
ADHD trait, the present regression analyses provide little evidence in support of
and externalizing questionnaires represent gold-standard measures, and to the
the hypothesis that ASD and ADHD traits share cognitive underpinnings, as was
best of our knowledge alternative continuously distributed behavior problem
outlined elsewhere (van der Meer et al., 2012, see also chapter 3; Rommelse et
measures do not exist. Importantly, the present study revealed a consistent pattern
al., 2011).
of association at the extremes. What explains this pattern merits examination in
Findings from this study provide further support for the assumption
future research. Second, the present study is based on a moderate-size population
that ASD and ADHD are extreme manifestations of quantitative traits that cover
sample of typically developing children. The study questions raised in this paper
quantitative rather than qualitative differences. In line with research conducted by
should be replicated in a larger normative epidemiological sample. Third, only
Fair and colleagues (2012) and the study described in chapter 5 (van der Meer
some of the associations between the ASD and ADHD traits and the cognitive
et al., in press), heterogeneity in ASD and ADHD may be rooted in heterogeneity
tasks were significant. Although using a sample of typically developing children is
in the non-symptomatic end of the trait distributions. These findings may have
also a strength, it may have weakened associations between the ASD and ADHD
implications for selecting individuals with high and low extreme scores on these
traits and the outcome measures through attenuation of range of scores at the
continua in extreme group comparison designs. Comparisons between extreme
clinical extreme. Nonetheless, the present study was able to show that the ADHD
groups can lead to increased power and larger effect sizes as group differences
trait is linked to worse visual pattern recognition and verbal and visuo-spatial
50
This study comes with some limitations and considerations. First,
51
Chapter 2
working memory, and the ASD trait to better visuo-spatial working memory in girls.
Supplemental Material
Fourth, it is unclear whether the AQ and SWAN measures successfully capture
the extreme of the far end of the ASD and ADHD traits, and further validation work
in this area is necessary to which the current study makes a first contribution.
Supplement 2.1 Gamma distribution of the ASD (AQ) and ADHD (SWAN) trait
measures
Fifth, the study exclusively relied on parent report of child behavior, and findings
should be replicated for other informants.
In conclusion, the present study suggests that the non-symptomatic
ends of the ASD and ADHD trait continua are not pathological, but represent
opposite ends of the ASD and ADHD trait continua appear to represent largely
quantitative rather than qualitative differences.
Expected gamma value
better cognitive and behavioral functioning than the symptomatic ends. The
Expected gamma value
Total score AQ
Total score SWAN
Note.The AQ and SWAN scales showed a gamma distribution with a large shape parameter.
This closely resembles a normal distribution, however, the Q-Q plot significantly deviated
from normality.
52
53
54
1.82
1.00
378
0.00
0.94
18
1.06
1.30
19
0.60
1.02
38
0.31
0.88
38
0.42
0.99
38
0.18
0.82
75
-0.20
0.92
38
-0.25
0.86
38
-0.37
0.93
38
-0.15
0.89
19
-0.33
0.68
19
-0.76
1.77
1.00
378
0.00
0.81
18
1.07
0.84
19
0.69
0.96
38
0.55
0.97
38
0.41
0.96
38
0.15
0.91
75
-0.12
0.86
38
-0.25
0.86
38
-0.28
0.84
38
-0.51
0.93
19
-0.65
0.74
19
-0.70
1.79
1.00
378
0.00
0.85
18
1.05
0.85
19
0.96
0.94
38
0.53
0.86
38
0.56
0.96
38
0.12
0.87
75
-0.22
1.00
38
-0.14
0.69
38
-0.39
0.89
38
-0.47
0.68
19
-0.74
0.62
19
-0.74
0.96
1.00
378
0.00
1.14
18
0.62
1.22
19
0.41
1.07
38
0.10
1.01
38
0.22
0.96
38
0.22
0.88
75
-0.19
1.03
38
-0.14
0.93
38
0.00
0.84
38
-0.25
1.04
19
-0.23
0.87
19
-0.34
-
1.00
368
0.00
1.10
18
0.11
1.00
18
-0.20
1.02
36
-0.01
0.95
38
0.04
0.88
37
-0.12
0.97
74
0.16
0.99
37
-0.16
1.25
37
-0.07
1.01
36
0.08
1.00
19
0.05
0.80
18
-0.14
MRT
speed
-
1.00
363
0.00
0.98
18
0.29
0.85
18
-0.10
1.07
35
0.00
1.22
38
-0.02
1.00
37
-0.01
0.96
74
0.09
0.85
36
-0.26
0.87
35
-0.17
1.02
35
0.07
1.18
19
0.33
0.89
18
-0.17
Variability
Baseline Speed
MRT
-
0.99
185
0.00
1.08
8
0.51
0.79
10
-0.17
0.86
19
-0.27
1.13
23
0.03
0.76
16
-
0.99
185
0.00
0.51
8
-0.33
1.15
10
-0.79
1.18
19
0.58
0.89
23
-0.15
0.72
16
0.18
0.94
0.92
0.14
40
-0.17
0.91
15
-0.31
0.90
14
0.64
0.98
19
0.29
0.92
10
-0.05
1.06
11
-0.23
40
0.02
1.34
15
0.08
1.13
14
0.03
0.92
19
-0.29
1.13
10
0.42
0.96
11
-0.12
Accuracy
Facial Emotion
Recognition
-
1.00
373
0.00
0.97
18
0.13
0.88
19
-0.12
0.69
38
0.12
1.15
38
0.00
0.95
38
0.14
1.00
74
-0.08
0.86
35
0.05
1.09
37
0.04
1.27
38
-0.08
0.69
19
-0.21
1.23
19
-0.03
-
1.00
374
0.00
1.17
18
0.10
0.80
19
-0.10
1.15
37
-0.16
0.92
38
0.00
1.07
38
0.10
0.92
73
0.06
1.09
37
0.20
0.90
38
0.12
0.94
38
-0.02
1.19
19
-0.23
0.87
19
-0.47
Visuospatial Verbal
Attention
2.35a
1.00
176
0.00
0.69
2
-1.98
0.49
5
-0.29
0.94
12
-0.01
0.77
12
0.14
0.94
17
0.08
1.01
38
0.09
1.29
25
-0.04
1.05
23
-0.21
0.88
20
0.11
0.69
10
-0.15
0.94
12
0.37
1.00
-
-
377
0.00
1.00
18
0.28
1.07
19
-0.19
0.85
38
-0.10
1.02
38
-0.04
1.04
38
0.02
1.01
74
0.20
1.12
38
-0.02
0.83
38
-0.20
1.14
38
0.08
0.77
19
-0.29
0.98
19
-
1.00
366
0.00
0.74
18
0.04
1.12
17
-0.20
0.78
35
0.09
0.96
37
0.24
1.08
38
-0.08
1.03
74
-0.03
0.94
36
-0.14
1.07
37
0.01
1.04
37
0.14
1.04
19
0.01
1.15
18
-0.28
-
1.00
367
0.00
0.93
18
-0.16
1.05
17
-0.21
1.28
35
-0.04
0.94
38
0.02
0.98
38
-0.02
0.94
74
0.09
1.05
36
0.25
0.99
37
0.10
0.81
37
-0.29
0.94
19
0.02
1.16
18
-0.06
Response Accutime
racy
Visual
patterns
-0.06
Motor inhibition
Block
patterns
1.00
374
0.00
1.00
18
0.23
1.01
19
-0.18
1.07
37
-0.04
0.85
38
-0.13
0.94
38
-0.11
1.00
73
0.17
0.86
37
-0.03
1.10
38
-0.02
1.22
38
-0.04
0.86
19
0.19
0.98
19
-0.16
Visuospatial Verbal
Working
memory
Cognitive tasks
-
1.00
368
0.00
1.02
18
0.04
1.17
18
-0.58
0.95
36
0.24
0.85
38
0.17
1.08
37
0.07
1.04
74
-0.02
0.92
36
0.06
1.02
37
-0.14
1.02
37
0.18
0.81
19
-0.40
0.90
18
-0.10
-
1.00
368
0.00
0.97
18
-0.15
0.99
18
0.01
1.19
36
0.09
0.98
38
0.37
1.04
37
0.00
1.03
74
0.03
0.79
36
0.16
0.88
37
-0.16
0.91
37
-0.24
1.01
19
-0.21
1.11
18
-0.20
Response Accutime
racy
Cognitive
flexibility
Note. AQ=Autism Spectrum Quotient scale. M=mean. SD=standard deviation. N=number of individuals. MRT=mean reaction time. ASD
quantiles refer to the 5%, 10% or 20% quantiles on the AQ questionnaire. The 95-100% quantile includes the children with the highest most
symptomatic scores on the AQ, the 0-5% quantile children with the lowest least symptomatic scores. All measures were corrected for age and
sex. Internalizing and externalizing problems and cognitive tasks were normalized using a van der Waerden transformation and transformed
into z-scores to achieve a mean of 0 and a standard deviation of 1 (see row labeled: Total [0-100%]).
Through comparisons of quantiles at the top and bottom extremes it was possible to examine whether the non-symptomatic ends of the ASD
distribution were on average linked to better behavioral and cognitive outcomes than the symptomatic ends. The 95-100% quantile includes
the children with the highest most symptomatic scores on the AQ, the 0-5% quantile children with the lowest least symptomatic scores. SMD
(standardized mean difference) refers to a comparison between children at the extremes comparing the 0-5% versus 95-100% quantile,
expressed as a difference in means based on a standard deviation of 1.
a
There was a significant association between the quadratic AQ term and visuo-spatial working memory that was only significant in girls, but
not boys (see main manuscript). Hence, mean performance on visuo-spatial working memory per quantile is tabulated for girls only.
1.89
1.00
SD
SMD
378
0.00
N
1.06
SD
M
18
(0-100%)
1.01
1.04
SD
N
19
M
0.42
0.88
SD
N
38
M
0.46
0.85
SD
N
38
M
0.46
0.92
SD
N
38
M
-0.08
0.94
SD
N
75
M
-0.12
0.90
SD
N
38
M
-0.13
0.79
SD
N
38
M
-0.17
0.96
SD
N
38
M
-0.35
0.97
SD
N
19
M
-0.41
0.97
SD
N
19
N
M
-0.88
M
Total
95-100%
90-95%
80-90%
70-80%
60-70%
40-60%
30-40%
20-30%
10-20%
5-10%
0-5%
ASD
quantiles
EmoOppo- tional
Perfect- Psychositional lability Anxiety ionism somatic
Internalizing and externalizing
problems
Supplement 2.2 Mean internalizing and externalizing behavior and mean cognitive task scores per ASD (AQ) trait
quantile
Chapter 2
55
56
1.76
1.00
378
0.00
1.01
18
1.25
1.11
19
0.90
0.96
38
0.18
0.84
38
-0.03
0.99
38
0.03
0.97
75
-0.23
0.95
38
-0.11
0.80
38
-0.03
0.88
38
-0.19
0.70
19
-0.39
0.98
19
-0.51
1.57
1.00
378
0.00
0.96
18
0.99
1.04
19
0.36
0.97
38
0.22
0.99
38
-0.07
1.10
38
-0.38
0.98
75
-0.07
0.89
38
0.05
0.96
38
0.04
0.78
38
-0.03
1.12
19
-0.13
0.70
19
-0.58
0.79
1.00
378
0.00
1.26
18
0.68
1.13
19
0.28
0.88
38
0.00
1.04
38
-0.07
0.96
38
-0.47
0.96
75
0.00
0.84
38
-0.02
0.99
38
-0.04
0.84
38
0.04
1.02
19
0.31
1.21
19
-0.11
1.60
1.00
378
0.00
1.20
18
0.97
1.23
19
0.51
0.91
38
0.22
1.03
38
0.21
0.75
38
0.04
0.97
75
0.03
0.87
38
-0.17
0.94
38
-0.25
0.83
38
-0.41
0.94
19
-0.18
0.94
19
-0.63
-
1.00
368
0.00
0.97
18
-0.01
1.11
17
0.05
0.98
37
-0.26
0.99
37
-0.07
0.84
35
-
1.00
363
0.00
1.04
18
-0.18
1.02
17
-0.05
0.91
37
-0.17
1.03
37
-0.05
1.01
35
0.26
1.00
1.04
0.20
74
0.02
0.92
36
0.07
1.20
37
-0.12
0.94
36
-0.07
0.72
17
0.19
1.13
19
0.15
Variability
75
0.11
1.08
37
0.05
1.16
38
-0.12
0.81
37
-0.10
0.76
18
0.04
1.14
19
0.11
MRT
speed
Baseline Speed
MRT
-
0.99
185
0.00
0.87
9
-0.29
0.71
12
-0.11
0.90
16
0.00
0.98
18
0.12
1.02
17
0.06
1.01
38
0.20
1.06
20
-0.18
0.91
20
-0.08
1.15
18
-0.15
0.99
7
0.35
1.41
10
-0.14
-
0.99
185
0.00
0.95
9
0.05
1.42
12
-0.17
0.75
16
-0.32
0.73
18
0.11
0.87
17
0.02
1.07
38
0.16
1.21
20
-0.05
1.14
20
-0.11
0.76
18
0.27
0.73
7
-0.44
1.00
10
-0.01
Accuracy
Facial Emotion
Recognition
-
1.00
373
0.00
1.16
18
-0.40
0.95
18
-0.03
1.14
38
-0.12
0.92
38
-0.05
0.86
38
0.02
0.97
74
-0.06
1.10
37
0.19
0.85
38
0.03
1.05
36
0.06
1.21
19
0.24
0.90
19
0.13
-
1.00
374
0.00
0.98
18
-0.63
0.95
18
-0.29
0.92
38
-0.28
1.13
37
-0.11
1.05
38
0.00
1.04
74
0.14
0.85
37
0.30
0.82
38
0.31
1.03
38
0.02
1.10
19
0.03
0.72
19
-0.17
Visuospatial Verbal
Attention
0.80
1.00
349
0.00
1.02
16
-0.56
0.96
17
-0.26
0.93
37
-0.13
0.95
34
-0.19
1.12
37
-0.07
0.99
71
0.05
1.01
34
0.11
1.08
36
0.03
0.81
35
0.14
0.80
16
0.65
1.05
16
0.24
0.28
1.00
374
0.00
0.63
18
-0.19
1.08
18
-0.28
1.08
38
-0.28
0.88
37
-0.10
0.97
38
-0.17
1.01
74
0.01
0.89
37
0.47
1.08
38
0.11
0.94
38
0.04
1.12
19
0.16
1.08
19
0.09
Visuospatial Verbal
Working
memory
Cognitive tasks
1.05
1.00
377
0.00
1.00
18
-0.59
0.97
18
-0.30
1.10
38
-0.35
0.78
38
-0.28
1.02
38
0.13
0.99
75
0.08
1.12
38
0.12
1.01
38
0.04
0.85
38
0.26
0.92
19
0.26
0.79
19
-
1.00
366
0.00
0.81
18
0.00
1.13
17
0.10
1.01
37
-0.11
1.18
37
0.06
0.92
34
0.04
1.01
73
-0.06
1.05
38
-0.10
1.03
38
-0.14
0.93
36
0.06
0.73
19
0.28
1.01
19
0.24
-
1.00
367
0.00
1.13
18
-0.20
1.12
17
0.24
1.16
37
-0.24
0.92
37
0.17
0.99
35
-0.13
0.96
73
-0.01
0.97
38
0.17
0.90
38
-0.07
0.97
36
-0.03
0.94
19
0.06
1.10
19
0.18
Response Accutime
racy
Visual
patterns
0.46
Motor inhibition
Block
patterns
-
1.00
368
0.00
0.83
18
-0.10
0.90
17
-0.19
1.04
38
-0.18
1.05
37
-0.05
0.95
35
0.00
1.04
74
0.00
1.02
37
0.23
1.08
38
0.01
1.07
36
0.05
0.74
19
0.20
0.95
19
-0.04
-
1.00
368
0.00
1.14
18
-0.27
1.24
17
-0.03
1.13
38
0.02
1.01
37
0.16
0.90
35
-0.08
1.04
74
-0.07
0.94
37
0.19
0.91
38
-0.10
0.94
36
0.05
0.89
19
0.06
0.92
19
0.03
Response Accutime
racy
Cognitive
flexibility
Note. SWAN=Strengths and Weaknesses of ADHD symptoms and Normal behavior scale. M=mean. SD=standard deviation. N=number of
individuals. MRT=mean reaction time. Internalizing and externalizing problems and cognitive tasks were normalized using a van der Waerden
transformation and transformed into z-scores to achieve a mean of 0 and a standard deviation of 1 (see row labeled: Total [0-100%]). All
measures were corrected for age and sex. The SWAN measure was categorized into quantiles. Quantiles divide a frequency distribution
into ordered groups each containing roughly the same number of individuals. 20%, 10% and 5% quantiles were created, which divided the
frequency distribution into 5, 10 and 20 ordered groups, respectively. Greatest resolution is provided at the top and bottom extremes of the
SWAN distribution (for which 5% quantiles are shown), and least resolution at average levels of the SWAN where data points were the most
dense (for which the 20% quantile is shown). Mean behavior problem and cognitive scores were then tabulated for each ADHD quantile.
Through comparisons of quantiles at the top and bottom extremes it was possible to examine whether the non-symptomatic ends of the
ADHD distribution were on average linked to better behavioral and cognitive outcomes than the symptomatic ends. The 95-100% quantile
included the children with the most symptomatic scores on the SWAN, the 0-5% quantile children with the least symptomatic scores. SMD
(standardized mean difference) refers to a comparison between children at the extremes comparing the 0-5% versus 95-100% quantile,
expressed as a difference in means based on a standard deviation of 1.
1.95
1.00
SD
SMD
378
0.00
N
0.95
SD
M
18
(0-100%)
1.29
0.99
SD
N
19
M
0.80
0.89
SD
N
38
M
0.45
0.84
SD
N
38
M
0.02
0.82
SD
N
38
M
-0.13
0.91
SD
N
75
M
-0.11
0.91
SD
N
38
M
-0.07
0.85
SD
N
38
M
-0.17
1.04
SD
N
38
M
-0.39
0.92
SD
N
19
M
-0.35
1.01
SD
N
19
N
M
-0.66
M
Total
95-100%
90-95%
80-90%
70-80%
60-70%
40-60%
30-40%
20-30%
10-20%
5-10%
0-5%
ADHD
quantiles
EmoOppo- tional
Perfect- Psychositional lability Anxiety ionism somatic
Internalizing and externalizing
problems
Supplement 2.3 Mean internalizing and externalizing behavior and mean cognitive task scores per ADHD (SWAN) trait
quantile
Chapter 2
57
Chapter 2
Supplement 2.4 Scatterplots of linear and quadratic relations between the
ASD (AQ) trait and visuo-spatial working memory
Supplement 2.5 Scatterplots of linear and quadratic relations between
the ADHD (SWAN) trait and oppositional behavior, emotional lability and
perfectionism
a.
a.
b.
b.
Note. Visuo-spatial working memory scores are normalized (van der Waerden transformed),
age and sex corrected z-scores. Results for visuo-spatial working memory are shown
separately for boys and girls, as the quadratic relation between the AQ and visuo-spatial
working memory was only significant in girls, but not boys.
c.
Note. Oppositional behavior, emotional lability and perfectionism scores are normalized
(van der Waerden transformed), age and sex are corrected z-scores.
58
59
Are autism spectrum disorder and
attention-deficit/hyperactivity
disorder different manifestations
of one overarching disorder?
Cognitive and symptom evidence
from a clinic and
population-based sample
Jolanda M. J. van der Meer, Anoek M. Oerlemans,
Daphne J. van Steijn, Martijn G. A. Lappenschaar,
Leo M. J. de Sonneville, Jan K. Buitelaar, Nanda N. J. Rommelse
Journal of the American Academy of Child and Adolescent
Psychiatry, 2012; 51(11), 1160-1172. 60
Abstract
Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder
(ADHD) frequently co-occur. Given the heterogeneity of both disorders, several
more homogeneous ASD-ADHD comorbidity subgroups may exist. The
current study examined if such subgroups exist and whether their overlap or
distinctiveness in associated comorbid symptoms and cognitive profiles gave
support for a gradient overarching disorder hypothesis or a separate disorders
hypothesis. Therefore, latent class analyses (LCA) were performed on Social
Communication Questionnaire (SCQ) and Conners’ Parent Rating Scale (CPRSR:L) data of 644 children (5 -17 years). Classes were compared for comorbid
symptoms and cognitive profiles of motor speed and variability, executive
functioning, attention, emotion recognition and visual spatial pattern recognition.
LCA revealed five classes: two without behavioral problems, one with only ADHDbehavior, and two with both clinical symptom levels of ASD and ADHD, but with
one domain more prominent than the other (ADHD[+ASD] and ASD[+ADHD]). In
accordance with the gradient overarching disorder hypothesis were the presence
of an ADHD class without ASD symptoms, and the absence of an ASD class
without ADHD symptoms, as well as cognitive functioning of the simple ADHDclass being less impaired than that of both comorbid classes. In conflict with
this hypothesis was that there was some specificity of cognitive deficits across
classes. The overlapping cognitive deficits may be used to further unravel the
shared etiological underpinnings of ASD and ADHD, while the non-overlapping
deficits may indicate why some children develop ADHD despite their enhanced
risk for ASD. The two subtypes of children with both ASD and ADHD behavior will
most likely benefit from different clinical approaches.
63
Chapter 3
Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder
This is a significant step forward and will likely boost research on the shared
(ADHD) are both severely impairing, highly heritable neurodevelopmental disorders.
and specific pathways related to comorbid ASD and ADHD. However, given that
ASD is characterized by impaired social interaction skills and communication,
both separate disorders are rather heterogeneous in symptom presentation,
as well as restricted and repetitive behavior and interests, whereas ADHD is
associated cognitive deficits and underlying etiological factors (Verté et al., 2006),
characterized by severe inattention, hyperactivity and impulsivity (American
it is likely that children with a comorbid diagnosis of ASD and ADHD form a very
Psychiatric Association, 2000). Even though the DSM-IV diagnostic criteria of both
heterogeneous group as well. This might hinder both clinical interventions as well
disorders appear to show little overlap, both disorders frequently co-occur. The
as scientific studies on the etiology of comorbid ASD and ADHD (Rommelse et
majority of comorbidity estimates reported for ADHD in ASD fall within the 30 to
al., 2010).
80% range, whereas the presence of ASD is estimated in 20 to 50% of the patients
with ADHD (Ames & White, 2011; Leyfer et al., 2006; Ronald et al., 2008). Features
ADHD may be achieved using an empirical bottom-up approach, such as latent
such as poor social skills, language delay, sensory over-responsivity, attention
class analyses (LCA). This statistical method allows classification into classes on
problems, oppositional defiant behavior and emotion regulation problems are
the basis of type and severity of ASD and ADHD symptoms, composing mutually
frequently disclosed in both ASD and ADHD (Gadow et al., 2009; Mulligan et
exclusive classes (Mc Cutcheon, 1987). By using quantitative symptom measures
al., 2009; Rommelse et al., 2011). Furthermore, there is an increasing number
reflecting the nature and severity of ASD and ADHD symptoms, justice is done to
of studies documenting on various patterns of association between ASD and
the continuously distributed nature of symptoms of both ASD and ADHD within
ADHD (St. Pourcain et al., 2011) and an overlap between ASD and ADHD with
the population (Kim et al., 2011; Levy, Hay, McStephen, Wood & Waldman, 1997).
respect to genetic factors (Ronald et al., 2008) and cognitive functions linked
This approach has already been successfully applied in the separate research
to a familial vulnerability for ASD and ADHD (intermediate phenotypes), such
fields of ASD and ADHD, and has with greater precision disclosed clinically
as executive functioning, motor speed and variability, emotion recognition and
relevant, more homogeneous subtypes of both disorders (Acosta et al., 2008;
visual spatial functioning (Booth & Happé, 2010; Corbett, Constantine, Hendren,
Constantino, 2011; Volk, Todorov, Hay & Todd, 2009). Of great interest is the
Rocke, Ozonoff, 2009; Fine, Semrud-Clikeman, Butcher & Walkowiak, 2008).
study of Reiersen, Constantino, Volk, & Todd (2007) that examined ASD-traits in
These findings suggest that there are shared etiological pathways for ASD and
several population-derived ADHD latent classes. Almost all classes (except one
ADHD and patients with one of both disorders should be routinely checked for the
class that merely showed hyperactive symptoms) showed more ASD symptoms
presence of the other disorder. Furthermore, as the clinical presentation of both
than the class without ADHD symptoms. However, amongst the various ADHD
disorders is strongly influenced by age, this monitoring should occur on a regular
classes, severity of ASD traits clearly differed, with the class having the most
basis (St. Pourcain et al., 2011).
severe ADHD symptoms also displaying the most severe ASD symptoms.
Although the current psychiatric classification scheme prevents a
Using DSM-IV derived ADHD subtypes, less distinctions between the subtypes
diagnosis of ADHD in the context of ASD (American Psychiatric Association, 2000),
were present, with no significant difference between the ADHD predominantly
based on the assumption that ASD mimics or even causes symptoms of ADHD,
inattentive subtype and the ADHD predominantly hyperactive-impulsive subtype.
a comorbid diagnosis of ASD and ADHD can be made in the upcoming DSM-5.
This study shows that empirically defined symptom classes may be more useful
64
Identification of more homogeneous subgroups of patients with ASD and
65
Chapter 3
in examining comorbidity patterns across the continuum of symptom severity.
studies used DSM-IV defined (hence heterogeneous) groups of patients and
However, since no previous study has used both ASD and ADHD symptom
ADHD was not always assessed in children with ASD (Rommelse et al., 2011),
measures in LCA, it is as yet unknown to what extent mutually exclusive ASD-
group comparisons may have been significantly obscured.
ADHD classes can be identified.
By examining the overlap and distinctiveness of associated traits (such
homogeneous classes exist and whether their overlap or distinctiveness in
as comorbidity patterns and cognitive problems) between the various ASD-ADHD
associated traits (comorbid symptoms, such as oppositional behavior, emotional
classes, several hypotheses can be tested. First of all, it has recently been postulated
lability, anxiety, perfectionism and psychosomatic complaints, and cognitive
that ASD and ADHD are different manifestations of one overarching disorder (H1:
profiles) gave support for the (gradient) overarching disorder hypotheses or for
overarching disorder hypothesis) (Rommelse et al., 2011). If true, symptomatic
the (partly) separate disorders hypothesis. Continuous ASD and ADHD symptom
expression can be regarded as ‘noise’ and classes (if at all identified) will not
questionnaire data were available for 644 children aged between 5 and 17 years
show distinctiveness in associated traits. Second, a variant of this hypothesis
from a clinic and population-based sample. Given the distinct developmental
states that ADHD may best be seen as a milder, less severe subtype within the
characteristics of both disorders, the influence of age was taken into account. A
ASD spectrum (H2: gradient overarching disorder hypothesis) (Rommelse et al.,
large variety of cognitive domains was assessed, most robustly associated with
2011). LCA will then identify at least one ADHD class without ASD symptoms, but
ASD (e.g. identification of facial emotions, cognitive flexibility and detail-focused
no ASD class without ADHD symptoms, and all classes will show rather similar
visual spatial processing) or ADHD (e.g. motor speed and variability, inhibition,
associated traits with lowest severity in the ADHD class without ASD symptoms
verbal and visual attention, and verbal and visual-spatial working memory), as
and highest severity in the ASD with ADHD class. Alternatively, ASD and ADHD
documented in previous studies (Booth & Happé, 2010; Corbett et al., 2009;
do not constitute different expressions of one overarching disorder. In this case,
Fine et al., 2008; Rommelse et al., 2011). LCA were used to identify phenotypical
the LCA will identify at least some classes with pure ADHD or ASD symptoms.
homogeneous classes and it was examined to what extent overlap and specificity
Further, the classes will be more different than similar in terms of associated traits
in comorbid symptoms and cognitive profiles existed between the classes.
Therefore, the current study set out to examine if different ASD-ADHD
(H0: distinct disorders hypothesis). Some previous studies using DSM-IV defined
subgroups of patients support the gradient overarching disorder hypothesis,
Methods
with ASD children having more severe, but similar type of cognitive problems
Participants
compared to children with ADHD (Gadow, DeVincent & Pomeroy, 2006; Gadow et
al., 2009; Geurts, Verté, Oosterlaan, Roeyers & Sergeant, 2004; Holtmann, Bolte,
& Poustka, 2007; Nydén et al., 2010). In contrast, there is also evidence for the
(partly) distinct disorder hypothesis, with ASD children having qualitative different
cognitive problems than children with ADHD (Booth, Charlton, Hughes, Happé,
2003; Buhler, Bachmann, Goyert, Heinzel-Gutenbrunner, & Kamp-Becker, 2011;
Sinzig, Morsch, Bruning, Schmidt, & Lehmkuhl, 2008). However, because these
66
The study has been approved by the Committee on Research involving Human
Subjects (CMO) and participants were enrolled between January 2009 and July
2011. Eligible participants were 360 children from a random population cohort
study (Schoolkids Project Interrelating DNA and Endophenotype Research;
SPIDER) and 254 children from a clinic-based ASD-ADHD genetic study
(Biological Origins of Autism; BOA). The BOA cohort consisted of siblings,
including DSM-IV based ASD, ADHD and ASD+ADHD cases and non-affected
67
Chapter 3
siblings (for a full description, see Box 1.2 regarding the study samples, or see
van Steijn et al., 2012). All children were between 5 and 17 years of age, of
Caucasian descent and had an estimated total IQ of at least 70 on the Wechsler
Preschool and Primary Scale of Intelligence (WPPSI), Wechsler Intelligence Scale
(WISC-III) or Wechsler Adult Intelligence Scale (WAIS-III) (Wechsler 1989; 2000;
2002). Exclusion criteria were epilepsy, known genetic or chromosomal disorders
(such as Down syndrome), brain damage, and problems with vision or hearing.
After complete description of the study to the parents, written informed consent
was obtained.
Measures
Data analyses
In order to identify homogeneous symptom classes, LCA were used on the
subscale outcomes of the SCQ (social interaction, communication and stereotypic
behavior) and the subscale scores of the following ten scales of the CPRS-R:L:
inattention, restlessness, cognitive problems, hyperactivity, oppositional behavior,
emotional lability, anxiety, perfectionism and psychosomatic complaints.
Subscales which represented the total of other subscales (Global Index Total)
and subscales which restructured the items by DSM-criteria (DSM-IV inattention,
DSM-IV hyperactivity-impulsivity and DSM-IV total) were excluded to prevent
overrepresentation of these items. The LCA were carried out using Mplus version
6.11 (Muthén & Muthén, 2006). Both the probability for a child to belong to each
ASD and ADHD symptom measures
of the classes and the conditional probabilities for children in a particular class to
ASD and ADHD symptom measures (parent reports) were taken from the Social
show specific behavior were estimated. Children could only be admitted to one
Communication Questionnaire (SCQ, Lifetime version) and the Conners’ Parent
of the classes. Class differences with respect to sex, age and IQ were analyzed
Rating Scale-Revised: Long version (CPRS-R:L). The SCQ and the CPRS-R:L are
to check for possible confounders. If differences were detected, only age was
both validated instruments for screening ASD and ADHD (Conners et al., 1998a;
implemented as covariate in further analyses, since both IQ and sex are inherently
Rutter, Bailey, Berument, Lecouteur, Lord, Pickles, 2003).
confounded with ASD and ADHD, and therefore could not be separated from
the effects of the disorders (Dennis et al., 2007). Class by age interaction effects
Procedure
were examined and, if significant, post-hoc analyzed and reported. If non-
The tasks described and presented in Supplement 3.1 were part of the broader
significant, these interactions were dropped from the model. Next, mean factor
neuropsychological assessment batteries used in the BOA and SPIDER projects.
sum scores of all behavioral domains were computed, and presented in a line
Children completed the battery in approximately two hours and the order of task
chart, so that quantitative differences between classes could be examined. Size
administration was counterbalanced. Due to time constraints, not all tasks were
and significance of differences were determined with a MANOVA, after which the
administered to all children. Full-Scale IQ was prorated by four subtests of the
Bonferroni correction for multiple testing was used for all post-hoc comparisons.
WPPSI, WISC-III or WAIS-III (Wechsler 1989; 2000; 2002); Block Design, Picture
Completion, Similarities and either Vocabulary (BOA) or Arithmetic (SPIDER).
All variables were successfully normalized and standardized into z-scores by
These subtests are known to correlate between .90 and .95 with Full-Scale IQ
applying a Van der Waerden transformation (SPSS version 18). Effect sizes
(Groth-Marnat 1997, Kaufman, Balgopal, Kaufman, McLean, 1994). Parents were
were defined in terms of percentage of variance explained (ηp2). Small, medium
invited to fill in several questionnaires concerning their youngster’s behavior.
and large effects were defined in variances of 0.01, 0.06 and 0.14 respectively
Secondly, the underlying cognitive profiles of the classes were examined.
(Cohen, 1988). Some of the outcome measures were mirrored, so that the
68
69
Chapter 3
scores of all variables would imply the same: a higher z-score was indicative of
0.003). The behavioral profiles of the classes are presented in Figure 3.1. For
a better performance. The classes were compared for each domain separately
the sake of clarity, the classes were labeled. Classes 1 and 2 could be viewed as
using ANCOVA’s with class-membership as a fixed factor, age as a covariate and
norm groups, showing hardly any problems on the separate behavioral domains.
speed, accuracy or variability separately for each domain as dependent variable.
Therefore, these classes were labeled ‘Normal’ (n = 268 and n = 150). Next,
Correction for multiple comparisons was applied according to the False Discovery
class 3 was best referred to as ‘ADHD’, with only ADHD-behavioral problems
Rate (FDR) controlling procedure to the analyses with a q-value setting of 0.05
(n = 109). Here, both DSM-IV-oriented CPRS-subscales for ADHD (Inattentive
(Benjamini & Hochberg, 1995). Only the effects that remained significant after the
and Hyperactive-Impulsive behavior) were above clinical cut-off, whereas the
FDR-correction were reported.
SCQ total score was substantially below cut-off (see Table 3.1). Classes 4 and
5 consisted of children who scored high on both ADHD (CPRS) as well as ASD-
Results
symptoms (SCQ). In class 4, the ADHD-symptoms were more prominent than
Identifying Homogeneous Symptom Classes
the ASD symptoms, with CPRS-subscales for ADHD-symptoms substantially
The LCA were based on fit and accuracy measures and revealed a solution with
five classes (Nylund et al., 2007). Five classes had the best fitting BIC and SSA BIC
values, entropy (.914), and p-values on the Vuong-Lo-Mendell-Rubin Likelihood
Ratio Test and Lo-Mendell-Rubin Adjusted Likelihood Ratio Test (both p’s =
Figure 3.1 Class scores on SCQ (left) and CPRS (middle and right) subscales
Class 5
ASD(+ADHD)
9.0 %
1
0,9
Mean sum scores
0,8
Class 4:
ADHD(+ASD)
9.2 %
0,7
0,6
0,5
Class 3: ADHD
16.9 %
0,4
0,3
0,2
Class 2: Normal
23.2 %
0,1
om
m
C
So
ci
al
in
te
ra
ct
io
n
un
ic
at
io
St
n
er
eo
So
t
y
ci
pi
al
c
pr
ob
le
m
s
In
at
te
nt
R
io
es
n
tle
ss
ne
ss
C
pr ogn
ob it
i
H lem ve
yp
er s
ac
tiv
O
ity
pp
os
Em
iti
o
ot
na
io
l
na
ll
ab
ilit
y
An
x
ie
Pe
ty
rfe
Ps ctio
yc nis
co ho
m
m so
pl m
ai a
nt tic
s
0
Class 1: Normal
41.6 %
0,3
70
ores
0,2
0,1
(see Table 3.1). Therefore, class 4 was described as ‘ADHD(+ASD)’ (n = 58). In
contrast, in class 5 ASD-symptoms were more prominent than ADHD symptoms,
with the SCQ total score being substantially above the clinical cut-off, the CPRSHyperactive-Impulsive subscale just above the cut-off and the CPRS-Inattention
subscale slightly below cut-off (see Table 3.1). Therefore, class 5 was described
as ‘ASD(+ADHD)’ (n = 59). No class with only ASD-behavior was identified.
A MANOVA using class as fixed factor and all behavioral domains as dependent
variables revealed that, as expected, the five classes differed significantly
on all behavioral subscales (all p < .001). Next, all classes were reciprocally
compared on the separate behavioral domains using Bonferroni corrected posthoc comparisons. Only 14 out of 130 comparisons did not reach significance.
Roughly, the non-significant differences were between the ADHD-only class and
the ASD(+ADHD) class on the ADHD-behavioral domains (in the middle of Figure
3.1), or on the comorbid behavioral domains, such as anxiety and perfectionism
(on the right side of Figure 3.1). When corrected for the influence of age, no
behavioral domain
Note. A higher mean factor sum score indicated that children in that class lacked
more competences or showed more problems on the specific domain. SCQ = Social
Communication Questionnaire, CPRS = Conners’ Parent Rating Scale, ASD = subscales
0,5
reflecting
ASD-symptoms, ADHD = subscales reflecting ADHD-symptoms,
Class 1: LL (10.1%)
0,4
above the clinical cut-off, and a SCQ-total score slightly above the clinical cut-off
changes in differences between the classes were found. Comparisons between
the two Normal classes (classes 1 and 2) were considered theoretically irrelevant.
n = 38
Class 3: MM
(54.2%) n = 203
71
Chapter 3
Hence, these classes were considered one class in further analyses. To check
whether this would affect further analyses in any possible way, both classes were
Table 3.1 Demographic characteristics of the children in the distinct classes
Normal
ADHDa
ADHD
(+ASD)a
compared on demographic characteristics as well as on all cognitive outcome
measures. None of those comparisons reached significance (all p’s
>.05);
classes 1 and 2 clearly only differed on a behavioral level, most likely ranging from
n=418
Age in years
M
9.5
n=109
SD
M
2.4
9.9
n=59
SD
2.8
M
11.2
ASD
(+ADHD)a
n=58
SD
3.3
M
SD
11.5
2.7 Normal = ADHD
< ADHD(+ASD) =
ASD(+ADHD)
Normal = ADHD
< ADHD(+ASD) =
ASD(+ADHD)
normal up to super normal behavior, but without meaningful cognitive differences.
The distribution of all children across the distinct classes, as well as the age, sex,
Contrasts based on
p-values of .05
% Male
45.7
66.1
81.4
86.2
% Population
basedb
71.4
56.4
17.2
5.8
Estimated full
scale IQc
106.2
13.8 104.2
13.9 101.5
15.5 104.2
population and IQ distributions are provided in Table 3.1.
13.9 Normal > ADHD(+ASD)
4.1
4.4
6.9
4.7
16.3
7.2
23
6 Normal < ADHD
< ADHD(+ASD) <
ASD(+ADHD)
T-score CPRSe
Inattention
47.3
6
64.7
8.3
73.3
8.8
62.6
8.2 Normal < ADHD
= ASD(+ADHD) <
ADHD(+ASD)
T-score CPRSe
HyperactiveImpulsive
48.2
6.7
64.8
9.8
79.8
8.2
66.7
11.2 Normal < ADHD
= ASD(+ADHD) <
ADHD(+ASD)
Total score
SCQd
Note. a ADHD = class with behavioral problems in ADHD only. ADHD(+ASD) = class with
severe ADHD-symptoms, who also show ASD-symptoms. ASD(+ADHD) = class with severe
ASD-symptoms, who also show ADHD-symptoms. b Percentage of the class derived from
the general population. c Full-scale IQ was estimated by four subtests of the WPPSI, WISCIII or WAIS-III (Wechsler 1989; 2000; 2002): Block Design, Picture Completion, Similarities
and either Vocabulary or Arithmetic. These subtests are known to correlate .90 to .95 with
Full-scale IQ (Groth-Marnat 1997). d The total score on the SCQ (Social Communication
Questionnaire) reflected the total amount of ASD-symptoms. The official cut-off score for
probable ASD is 15, and for definite ASD the cut-off is 21. e Subscale scores on the CPRS
(Conners’ Parent Rating Scale) subscales reflected the degree of ADHD-related symptoms.
The official cut-off for clinically relevant ADHD-symptoms is a subscale score above 63.
Cognitive Profiles of the Distinct Classes
To test in which cognitive domains the classes overlapped or differed, separate
ANCOVA’s were used for each cognitive domain, with age as a covariate. Results
are also presented in Figures 3.2a-g. In these figures, Normal refers to the classes
with no behavioral problems. ADHD refers to the class with behavioral problems
in ADHD only. ADHD(+ASD) refers to the children who most prominently show
72
73
Chapter 3
severe ADHD-symptoms, but who also show ASD-symptoms. ASD(+ADHD)
refers to the class with profound ASD-symptoms, but who also show ADHDsymptoms. The means are adjusted for the covariate age and error bars represent
1 standard error.
Identification of Facial Emotions
Significant medium and small effects of class were found for speed (F (3,379)
= 7.52, p < .001, ηp2 = .06) and accuracy (F (3,379) = 5.39, p = .001, ηp2
= .04), respectively. Pairwise comparisons revealed that responses in the
ASD(+ADHD) class were significantly slower and less accurate than the Normal
Baseline Speed and Variability
class (both p’s < .001) and significantly slower than the ADHD-class (p = .001).
A significant but small effect of class was found for speed (F (3,611) = 4.91, p
The ADHD(+ASD) class was also slower than the Normal class (p = .007), and
= .002, ηp2 = .02) and variability (F (3,611) = 3.41, p= .017, ηp2 = .02). Pairwise
formed an intermediate class that did not differ from the other classes regarding
comparisons indicated that the ASD(+ADHD) class responded significantly slower
accuracy.
and more variable than the Normal class (p = .001 and p = .008, respectively).
The ADHD-only and ADHD(+ASD) groups formed intermediate classes that did
not differ from the other classes.
b.
Figure 3.2b Differences between the classes on measures of identification
of facial emotions
Speed
a.
Figure 3.2a Differences between the classes on measures of baseline speed
p = .001
0,3
Z-score
faster and less variable
faster and more accurate
0,7
0,5
0,1
-0,1
p = .007
0,3
p = .001
0,1
-0,1
-0,3
-0,5
p < .001
-0,3
-0,7
-0,5
-0,7
p < .001
0,5
Variability
Z-score
Speed
Accuracy
0,7
p = .008
Normal (n = 405)
ADHD (n = 101)
Normal (n = 234)
ADHD (n = 64)
ADHD(+ASD)(n=45)
ASD(+ADHD)(n=41)
Note. Group differences presented were based on a mean age of 10.2 years.
ADHD(+ASD)(n=58)
ASD(+ADHD)(n=52)
Note. Group differences presented were based on a mean age of 9.9 years.
Inhibition and Cognitive Flexibility
No significant effect of class was found for speed or accuracy in motor inhibition
(F (3,605) = 2.14, p = .09, ηp2 = .01 and F (3,605) = 0.92, p = .432, ηp2 = .005,
respectively) or in cognitive flexibility (F (3,605) = 1.19, p = .31, ηp2 = .006 and F
(3,605) = 0.60, p = .614, ηp2 = .003, respectively).
74
75
Chapter 3
c.
Figures 3.2c and 3.2d Differences between the classes on measures of motor
inhibition and cognitive flexibility
Speed
Accuracy
Z-score
0,5
class (p = .003). Furthermore, all classes performed worse than the Normal
0,3
class in the verbal attention task (p = .007 for the ADHD-class, p < .001 for the
comorbid classes). Other group differences did not reach significance. A small
0,1
but significant class by age interaction effect was found in the forward trials for
-0,1
visuo-spatial attention (F (3,607) = 4.37, p =.005, ηp2 = .02). Post-hoc analyses
-0,3
indicated that the age-effect was stronger in the Normal and ADHD-only classes
compared to both comorbid classes, resulting in larger class-differences in older
-0,7
than younger children (F (1,449) = 8.50, p =.004, ηp2 = .02 for the Normal class
Normal (n = 403)
ADHD (n = 100)
Speed
ADHD(+ASD)(n=56)
ASD(+ADHD)(n=51)
Normal class compared to ADHD(+ASD) class, (F (1,149) = 7.64, p =.006, ηp2
Accuracy
= .05 for the ADHD class compared to the ASD(+ADHD) class, and (F (1,155) =
0,7
4.09, p =.045, ηp2 = .03 for the ADHD-only class compared to the ADHD(+ASD)
0,5
class).
e.
0,3
Figure 3.2e Differences between the classes on measures of attention
Visuo-Spatial Attention
0,1
Verbal Attention
0,7
-0,1
p = .003
0,5
-0,3
-0,5
-0,7
Normal (n=403)
ADHD (n=100)
ADHD(+ASD) (n=56)
ASD(+ADHD) (n=51)
Note. Group differences presented were based on a mean age of 9.9 years.
Visuo-Spatial and Verbal Attention
p < .001
0,3
Z-score
Z-score
faster and more accurate
compared to ASD(+ADHD) class, (F (1,455) = 5.34, p =.021, ηp2 = .01 for the
more accurate responses
faster and more accurate
and the ASD(+ADHD) class performed worse in comparison to the Normal
-0,5
p = .013
0,1
-0,1
p = .007
-0,3
-0,5
Small significant effects of class were found for accuracy in the forward trials for
visuo-spatial attention (F (3,607) = 7.95, p < .001, ηp = .04) and verbal attention
2
76
visuo-spatial task indicated that the ADHD(+ASD) class performed worse than
the Normal class and ADHD-only class (p < .001 and p = .013 respectively)
0,7
d.
(F (3,623) = 10.04, p < .001, ηp2 = .05), respectively. Pairwise comparisons for the
-0,7
p < .001
p < .001
Normal (n=411)
ADHD (n=105)
ADHD(+ASD) (n=56)
ASD(+ADHD) (n=56)
Note. Group differences presented were based on a mean age of 9.9 years.
77
Chapter 3
compared to the ADHD-class and F (1,434) = 5.19, p =.023, ηp2 = .01 for the
Visuo-Spatial and Verbal Working Memory
Small but significant effects of class were found for accuracy in the backward
trials for both visuo-spatial working memory (F (3,581) = 5.10, p =.002, ηp2 =
.03) and verbal working memory (F (3,623) = 5.68, p = .001, ηp2 = .03). Pairwise
comparisons indicated that in the visuo-spatial task, the ADHD(+ASD) class
performed worse than the Normal class (p < .001). In the verbal working memory
task, the ADHD-class performed worse than the Normal class (p < .001). The
ASD(+ADHD) class formed an intermediate group, not differing from the other
classes regarding both visuo-spatial and verbal working memory. A small but
significant class by age interaction effect was found in the backward trials for
visuo-spatial working memory (F (3,581) = 5.37, p =.001, ηp2 = .03). Post-hoc
analyses indicated that the effect of age was stronger in the Normal class than
in the three clinical classes, with larger class differences in older than younger
children (F (1,428) = 12.16, p =.001, ηp2 = .03 for the Normal class compared to
the ASD(+ADHD) class, F (1,474) = 4.35, p =.038, ηp2 = .01 for the Normal class
f.
Figure 3.2f Differences between the classes on measures of working memory
Visuospatial Working Memory
Verbal Working Memory
0,7
A small but significant effect of class was found for accuracy (F (3,634) = 6.93,
p < .001, ηp2 = .03) (also when processing speed or IQ was implemented as a
covariate; p < .001 and p = .003, respectively). Pairwise comparisons revealed
that the ADHD(+ASD)-class performed significantly worse compared to the
Normal class and the ASD(+ADHD) class (both p’s < .001). The ADHD-only class
formed an intermediate group, not differing from any of the other classes. The
ASD(+ADHD) class showed the highest score (although not significantly different
from the normal class), indicating a detail-focused processing style. A small but
significant class by age interaction effect was found for accuracy (F (3,631) =
4.83, p =.002, ηp2 = .02). Post-hoc analyses indicated that the effect of age was
stronger in the Normal and ADHD-only classes compared to the ADHD(+ASD)
class, resulting in larger class-differences in older than younger children (F (1,471)
g.
Figure 3.2g Differences between the classes on a measure of visual spatial
pattern recognition
Detail-focused cognitive style
0,7
0,5
0,1
0,3
-0,1
-0,3
-0,5
-0,7
p < .001
Normal (n=411)
ADHD (n=105)
ADHD(+ASD) (n=56)
ASD(+ADHD) (n=56)
Note. Group differences presented were based on a mean age of 9.9 years.
Z-score
0,3
more accurate responses
Z-score
Visual spatial pattern recognition
p < .001
0,5
more accurate responses
Normal class compared to the ADHD(+ASD) class).
p < .001
p < .001
0,1
-0,1
-0,3
-0,5
-0,7
Normal (n=416)
ADHD (n=108)
ADHD(+ASD) (n=59)
ASD(+ADHD) (n=56)
Note. Group differences presented were based on a mean age of 9.9 years.
78
79
Chapter 3
= 14.84, p <.001, ηp2 = .03 for the Normal class compared to the ADHD(+ASD)
emotional lability symptoms, were significantly lower in the ASD(+ADHD) class
class, and F (1,163 = 5.27, p =.023, ηp2 = .03 for the ADHD-only class compared
compared to the ADHD(+ASD) class. However, ADHD symptoms may actually
to the ADHD(+ASD) class).
lead to somewhat better ratings on social interaction in children with ASD, partly
because of the increased talkativeness seen with ADHD. Furthermore, some forms
Discussion
of oppositional behavior may be more closely related to ADHD, whereas others
This study aimed at examining if different ASD-ADHD symptom classes exist
may be more related to resistance to change (typical of ASD), so the degree of
and whether their overlap or distinctiveness in associated traits (comorbid
ADHD and ASD symptoms may affect which aspects of oppositional behavior
symptoms and cognitive functions) gave support for the (gradient) overarching
are most likely to be endorsed. Due to poor communication skills, symptoms
disorder hypothesis or for the (partly) distinct disorders hypothesis. If the gradient
of emotional lability may be more difficult to detect in children with higher ASD
overarching disorder hypothesis is accurate, LCA would identify at least one
symptoms. Hence, lower ratings on emotional lability in the ASD(+ADHD) group
ADHD class with no or only minor ASD symptoms and no ASD class without
may be partially explained because emotional lability goes undetected by the
ADHD symptoms. This is exactly what we found. Three patient classes could be
measures used. Based on the currently defined classes, the ADHD-only group
distinguished from two normal classes: One class with ADHD symptoms only,
was indeed the mildest affected class within the spectrum, but no such severity
one class with clinically high levels of ADHD but also clinically elevated levels of
distinction could be made between both comorbid ASD-ADHD classes; they
ASD symptoms (ADHD[+ASD]), and one class with clinically high levels of ASD
appeared qualitatively different. As yet, no previous studies have examined the
symptoms but also clinically elevated levels of ADHD symptoms (ASD[+ADHD]).
ADHD symptoms in ASD defined classes to compare our findings with. The
As hypothesized, no class with exclusively ASD-symptoms was revealed; all
current novel findings suggest that at least two ASD-ADHD comorbidity classes
children who expressed ASD-behavior also presented the less severe ‘precursor’
exist that are not merely dissociable on a quantitative basis.
of ADHD-behavior. This finding is in accordance with the higher prevalence of
children with ASD who meet criteria for ADHD (up to 80% in literature), compared
overarching disorder hypothesis, but on the other hand possible support for the
to the ASD rate in children with ADHD (up to 50% in both the literature as well
distinct disorder hypothesis emerged as well. That is, the cognitive functioning
as our sample) (Ames & White, 2011; Leyfer et al., 2006). These data are also in
in the simple ADHD-class could overall be considered at an intermediate level,
line with previous studies addressing ASD-symptoms in children with ADHD, in
performing somewhat below the normal class and better than the two comorbid-
which LCA revealed that the most severe ADHD classes were also the classes
classes. This was best visible in the domains of motor slowness and variability,
with the most severe ASD-symptoms. However, note that these studies examined
visuo-spatial and verbal attention and emotion recognition. However, qualitative
ASD symptoms in ADHD defined classes, thereby excluding the possibility of
differences were also clearly observed, which should perhaps not be too surprising
finding ASD classes without ADHD symptoms (Mulligan et al., 2009; Reiersen et
since ADHD and ASD are partly defined by specific cognitive problems (i.e.
al., 2007).
inattentiveness versus detail-focused processing, respectively). Working memory
It may seem in contrast to the (gradient) overarching disorder hypothesis
deficits were significantly more pronounced in both primarily ADHD classes
that the level of ADHD symptoms, as well as the level of oppositional and
compared to the primarily ASD class and a detail-focused cognitive style in visual
80
The cognitive profiles on the one hand further supported for the gradient
81
Chapter 3
pattern recognition (reflected in normal to superior block design performance)
severity of the deficit in social cognition was most pronounced in the ASD(+ADHD)
appeared to be present in the ASD(+ADHD) class only. This apparent double
class, followed by the ADHD(+ASD) class, but was not present in the ADHD-only
dissociation between both comorbid classes suggests that dysfunctions in the
class. Previous studies documenting on social cognition deficits in ADHD may
information processing style found in the ASD(+ADHD) class cannot merely be
therefore possibly be explained by elevated levels of ASD symptoms in these
seen as a the sum of dysfunctions in the ADHD-only and the ADHD(+ASD) classes.
ADHD-patients (Nijmeijer et al., 2008; Cadesky, Mota & Schachar, 2000; Kats-
Apparently, different ASD-ADHD comorbid subtypes exist, with overlap but also
Gold, Besser & Priel, 2007). Similarly, a detail-focused (i.e. local) processing style,
qualitative difference in cognitive deficits. Similarly, recent studies comparing
reflected by a normal to superior block pattern performance, was only apparent in
people with ASD, ADHD or ASD+ADHD to controls reported evidence for both
the ASD(+ADHD) class and not in both primarily ADHD classes, which performed
specific as well as overarching deficits (Nydén et al., 2010; Sinzig et al., 2008). In
intermediate or more poorly than the Normal and ASD(+ADHD) classes. This
any case, our cognitive findings are in contrast to the hypothesis that ASD and
normal to superior performance clearly stands out against the background of
ADHD are interchangable manifestations of the same overarching disorder and
poorer performance of this class on almost all other domains measured, and
symptomatic expression can be regarded as ‘noise’, in which case no cognitive
is in line with previous studies (Nydén et al., 2010; Happé 1999). Importantly,
differences between the classes were predicted. The overlapping cognitive
this feature was absent in the ADHD(+ASD) class, suggesting the origins of the
deficits may be used to further unravel the shared etiological underpinnings of
ASD symptoms in this class are at least partially different from the origins of the
ASD and ADHD, whereas the non-overlapping deficits may indicate why some
ASD symptoms in the ASD(+ADHD) class. Conversely, working memory deficits
children develop ADHD despite their enhanced risk for ASD and vice versa.
appeared mainly to be related to ADHD symptoms, being impaired in both ADHD
When specifically viewed at the individual cognitive domains, it is
classes but not to that degree in the ASD(+ADHD) class. However, given that
remarkable that response slowness and variability were most pronounced in
there was no class with ASD symptoms only, it remains uncertain whether ASD
the ASD(+ADHD) class and not observed in the ADHD-only class. Increased
symptoms are truly unrelated to working memory deficits. In any case, the strong
response variability is considered one of the most robust and best replicated
relation between ADHD symptoms and working memory deficits concurs with
cognitive features of ADHD as defined by the DSM nomenclature (Johnson et
previous studies (Nydén et al., 2010; Sinzig et al., 2008) and suggests working
al., 2007; Frazier-Wood et al., 2012; Klein, Wendling, Huettner, Ruder & Peper,
memory performance may shed more light on the causal pathways for ADHD.
2006). Current findings based on latent classes suggest that response variability
None of the classes showed problems in inhibition or cognitive flexibility, which
in ADHD actually reenacts on the presence of comorbid symptoms such as ASD-
was probably due to the predictable nature of the task, in which children always
symptoms, as has been described previously (Geurts et al., 2008). Noteworthy,
had to respond. Previously, this same task also did not differentiate between
verbal and visual attention were affected in all clinical classes. This finding
ADHD and controls (Rommelse et al., 2007), suggesting a more unpredictable
may imply that a dysfunction in attention is indicative for neurodevelopmental
nature of inhibitory control and cognitive flexibility may be more applicable in
disorders in general, unable to differentiate between ASD and ADHD, as has been
distinguishing patients from normally developing children (Bekker et al., 2005).
reported before (Ames & White, 2011). In contrast, impaired social cognition is a
Finally, class differences between the Normal class and the (severely) affected
substantially affirmed prime deficit specific for ASD (Loveland et al., 1997). The
classes on visuo-spatial attention, visuo-spatial working memory and visual
82
83
Chapter 3
pattern recognition were larger among older compared to younger children. This
comorbid ASD-ADHD classes may have more persistent ADHD symptoms (St.
may suggest that, within the limitations of this cross-sectional design, children in
Pourcain et al., 2011), thus setting new targets for a longitudinal research design.
the Normal class improve certain skills more during maturation than children in
the (severely) affected classes. This however needs to be further analyzed with
the help of a longitudinal research design.
Some limitations of this study warrant consideration. First, boys were
overrepresented in the three affected classes, whereas they were underrepresented
in the Normal class. This was likely due to the fact that ASD and ADHD are more
frequently diagnosed in boys than in girls (Kramer, Krueger, Hicks & 2008).
However, since this overrepresentation was present in all patient classes, this
did not affect comparisons between those classes. Second, questionnaires were
used to collect information on behavioral problems. In comparison with clinical
interviews, questionnaires tend to overestimate the degree of comorbidity, as
questionnaires do not allow for further explanation of questions. Interviews may
improve the correct interpretation of questions, and more precisely distinguish
normal-range behavior from clinical-range behavior (Tourangeau, Rips & Rasinki,
2000). However, the possible comorbidity-overestimation cannot account for the
cognitive differences in the distinct classes, nor can it explain the presence of
an ADHD-only class and the absence of an ASD-only class. Third, the nature of
our samples might have prevented us from detecting a ‘pure ASD’ class. ASD
without ADHD symptoms might be underrepresented in clinical samples and also
be relatively rare in population samples. Therefore, very large population-based
samples might be best to examine this issue further. Future studies in a purely
population-based sample may wish to include both cognitive and symptom
measures in tandem in latent class analyses or related statistical approaches,
to further increase the etiological homogeneity of the distinct classes. Fourth,
children in the currently defined ADHD-only class were younger and also mildest
affected within the spectrum. It is of great interest whether (a majority of) these
children have a childhood-limited form of ADHD, while the children in both
84
85
Chapter 3
Supplemental Material
Identification of Facial Emotions
Supplement 3.1
This task was used to measure the capacity to quickly and accurately identify
Measures
facial emotional expressions (de Sonneville, 1999) Four blocks represented
different target emotions: happiness, sadness, anger or anxiety. Children had to
Four of the tasks described were selected from the Amsterdam Neuropsychological
judge whether or not a face expressed the specified target emotion by pressing
Tasks (ANT) program (de Sonneville, 1999). Each computer task contained an
instruction trial wherein the examiner provided a typical item of the task, and a
separate practice session. Test–retest reliability and validity of the computerized
ANT-tasks are satisfactory and have been described and illustrated elsewhere (de
Sonneville, 2005).
b.
a yes/no-button as quickly and accurately as possible. The order of the targeted
emotions was randomly assigned. Dependent measures were mean reaction
time (in ms) and accuracy for all targeted emotions together.
Facial Emotion Recognition
Baseline Speed and Variability
This task was used to measure the speed and variability of motor output,
comparable to a simple reaction time task (de Sonneville, 1999). When a fixation
cross in the centre of a computer screen changed into a white square, children
pressed a key as quickly as possible. In order to prevent anticipation strategies,
the time interval between a response and the emergence of the next square varied
randomly between 500 and 2500 ms. Dependent measures were response speed
(mean reaction time in ms) and variability (SD of reaction time in ms).
Inhibition and Cognitive Flexibility
This task consisted of three blocks in which the first block measured baseline
speed and accuracy (de Sonneville, 1999). Children had to press a key as soon
Baseline Speed and Variability
as they noticed a green circle on the left or the right of a fixation cross. They were
instructed to press a key on the same side as the stimulus was presented. In the
second block, the circles were colored red, and children had to press a key on
the opposite side. Motor inhibition was calculated as the difference in percentage
of errors or in mean reaction time between blocks one and two. Finally in the third
block, both green and red circles appeared in a random order, and both same-
Fixation
Signal
side (compatible) and opposite side (incompatible) responses were required.
This was hypothesized to demand for higher levels of cognitive flexibility (Los,
1996). Cognitive flexibility was calculated as the difference in percentage of errors
or mean reaction time between block one and the compatible trials of block three.
86
87
Chapter 3
Inhibition and Cognitive Flexibility: compatible and incompatible trials
Left compatible
Right compatible
Left incompatible
Visuo-Spatial Attention and Working Memory
Right incompatible
Visuo-Spatial and Verbal Attention
The forward parts of both the Visuo-Spatial Attention task (de Sonneville, 1999)
and the Digit Span task of the WPPSI, WISC-III or WAIS-III (Wechsler, 1989; 2000;
2002) were used to obtain an indication of visuo-spatial and verbal attention. In
the Visuo-Spatial Attention task, stimuli consisted of nine squares, presented
in a three by three square. During each trial, a sequence of these squares was
pointed at, and the children were instructed to exactly reproduce the sequence.
In the Digit Span task, children had to repeat a sequence of verbally presented
numbers. In both tasks, the difficulty level increased after each succeeded trial.
Dependent variables were the total number of correct sequences in identical
order, for both tasks separately.
Detail-focused processing style
The Block Design task of the WPPSI, WISC-III or WAIS-III (Wechsler, 1989; 2000;
2002) was used to measure detail-focused (local) processing. Children had to
copy geometric white-and-red designs using four to nine plastic cubes. All cubes
had two completely white, two completely red and two diagonally white-and-red
sides. Dependent measure was the score based on the amount of correct and
timely completed geometric design.
Visuo-Spatial and Verbal Working Memory
The backward parts of the Visuo-Spatial Attention task (de Sonneville, 1999) and
the Digit Span task (Wechsler, 1989; 2000; 2002) were used to obtain an indication
of working memory. Here, children were asked to reproduce the verbal and
visuo-spatial sequences (such as described above) in backwards order. Again,
the sequence increased if a child reproduced the previous trial successfully.
Dependent measures were the total number of correct sequences in backwards
order, for both tasks separately.
88
89
How ‘core’ are motor timing
difficulties in ADHD? A latent
class comparison of pure and
comorbid ADHD classes
Jolanda M. J. van der Meer, Catharina A. Hartman,
Andrieke J. A. M. Thissen, Anoek M. Oerlemans, Marjolein Luman,
Jan K. Buitelaar, Nanda N. J. Rommelse
Under review
90
Abstract
Children with Attention-Deficit/Hyperactivity Disorder (ADHD) often have motor
timing difficulties. This study examined whether affected motor timing accuracy
and variability are specific for ADHD, or that comorbidity with Autism Spectrum
Disorder (ASD) contributes to these motor timing difficulties. Therefore, an
80-trial motor timing task measuring accuracy (μ), variability (σ) and infrequent
long response times (τ) in estimating a 1-second interval was administered to
283 children and adolescents (8-17 years) from both a clinic and populationbased sample. They were divided into four latent classes based on the Social
Communication Questionnaire (SCQ) and Conners’ Parent Rating Scale (CPRSR:L) data. These classes were: without behavioral problems ‘Normal-class’ (n=
154), with only ADHD symptoms ‘ADHD-class’ (n=49), and two classes with
both ASD and ADHD symptoms, but with one domain more prominent than
the other; ADHD(+ASD)-class (n= 39) and ASD(+ADHD)-class (n= 41). The
pure ADHD-class did not deviate from the Normal class on any of the motor
timing measures (mean RTs 916 ms and 925 ms, respectively). The comorbid
ADHD(+ASD) and ASD(+ADHD) classes were significantly less accurate (more
time underestimations) compared to the Normal class (mean RTs 847 ms and 870
ms, respectively). Variability in motor timing was reduced in the younger children
in the ADHD(+ASD) class, which may reflect a tendency to rush a tedious task.
Findings suggest that comorbid ASD symptoms contribute to motor timing
difficulties in ADHD, as ADHD symptom severity in the pure ADHD-class and the
ASD(+ADHD) class was highly similar, with the former class showing no motor
timing deficits.
93
Chapter 4
Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder
difficulties in motor time processing can be found in ‘pure’ ADHD, or rather are
that is typified by developmentally inappropriate degrees of inattention, impulsivity
associated with comorbid ASD (Cormier, 2008; Rommelse et al., 2011). Adamo
and hyperactivity (American Psychiatric Association, 2000; 2013). Broad patterns
and colleagues (2013) compared response time variability in normally developing
of neuropsychological impairments have been associated with ADHD, among
children, children with ADHD, and children with ASD with and without substantial
which deficits in time processing (Castellanos & Tannock, 2002; Falter & Noreika
comorbid ADHD symptoms. Findings suggested that both children with ADHD
2011; Noreika, Falter & Rubia, 2013). Falter & Noreika (2011) suggested that
and children with ASD and comorbid ADHD had elevated levels of response time
deficits in time processing may play an important role in neurodevelopmental
variability. In contrast, children with ASD without substantial comorbid ADHD
disorders like ADHD by interacting with and modulating primary symptoms. For
symptoms did not differ from normally developing children regarding response
example, previous studies suggested that difficulties in complex functions such as
time variability, suggesting that response time variability is more strongly related
attention, language and inhibition are associated with reduced time processing,
to ADHD. However, in addition to subtyping along traditional lines of DSM-
as these functions are characterized by specific temporal patterns (Rubia et
based categories, a comparison of motor timing across homogeneous groups
al., 2009; Szelag et al., 2004). Time processing, measured with a motor timing
within comorbid ASD-ADHD children may be a powerful method to further
paradigm in which the accuracy and variability of motor timing, and infrequent long
our understanding of both disorders. Such homogeneous groups based on
response times are disentangled by using mu, sigma and tau, may differentially
quantitative symptom measures reflect the continuously distributed nature and
affect cognitive functions that rely on accurate motor timing (Hervey et al., 2006;
severity of ASD and ADHD symptoms across the general population, as shown
Leth-Steensen et al., 2000; Thissen et al., (under revision)). Reduced motor
by several studies (Constantino, 2011; Fair et al., 2012; Spiker et al., 2002; St
time processing has frequently been associated with ADHD, despite systematic
Pourcain et al., 2010; 2011).
differences across studies, and has shown to be highly heritable, suggestive of an
etiological role in ADHD (Andreou et al., 2007; For a review, see Kofler et al., 2013;
subgroups of comorbid ASD-ADHD children when studying shared etiological
Marx et al., 2010; Noreika et al., 2013; Toplak, Dockstader, & Tannock, 2006).Of
pathways in a clinic and population-based sample(van der Meer et al., 2012, see
note, abnormalities in motor timing are predominantly related to deficient motor
also chapter 3). In that study, classes were derived using a latent class analysis
timing processes rather than to general deficient motor functioning in children and
(LCA), an empirical method which allows classifications based on the type and
adolescents who suffer from ADHD (Rommelse et al., 2008).
severity of ASD and ADHD symptoms. We showed that ADHD-symptoms were
Despite this compelling evidence for motor timing difficulties in ADHD,
present both in the absence and presence of ASD-symptoms. This resulted in
reduced time processing has not exclusively been found in ADHD. It has also been
a pure ADHD-class that showed no comorbid symptoms of ASD, and an ADHD-
observed in other disorders including Autism Spectrum Disorders (ASD)(Allman,
class with comorbid ASD (ADHD(+ASD)). Furthermore, ASD-symptoms were
DeLeon, & Wearden, 2011; Falter, Noreika, Wearden, & Bailey, 2012; Geurts et
reported in the presence of less severe ADHD-symptoms (ASD(+ADHD)), but
al., 2008; Martin, Poirier, & Bowler, 2010). Since ADHD is frequently comorbid
no class with pure ASD-behavior was identified. The empirical validity of these
with ASD (in clinical samples, 20% to 50% of ADHD-patients meet criteria for
distinct classes was affirmed by the overlap and distinctiveness of associated
ASD; for a review see Rommelse et al. (2011)), it remains to be seen whether
comorbidity patterns and cognitive profiles. Classes with children suffering from
94
We previously reported on the advantages of more homogeneous
95
Chapter 4
both types of symptoms were overall cognitively more impaired than children
METHODS
with only ADHD-symptoms, indicative for an overlapping cognitive background
Participants
in ASD and ADHD. Importantly, cognitive specificity was found in that the
ADHD(+ASD) class showed the more typical ADHD neurocognitive problems
(working memory deficits) while the ASD(+ADHD) class showed more typical
ASD neurocognitive problems (emotion recognition problems and superior block
pattern performance). This cognitive double dissociation between comorbid
classes with either more profound ASD or more profound ADHD symptoms can
increase our understanding of the distinct etiological pathways for ASD and
ADHD (van der Meer et al., 2012).
The cognitive domain of time processing is an additional candidate for
furthering our understanding of these more homogeneous classes of children
affected with pure ADHD or affected with both ASD and ADHD symptomatology.
The current study was set out to examine the overlap and distinctiveness in
motor timing abilities betweenhomogeneous subgroups with the use of a wellvalidated motor timing paradigm (Rommelse et al., 2008; van Meel et al., 2005).
This paradigm measures the accuracy, variability, and infrequent long response
times of 1 second interval motor time productions with the use of the parameters
mu, sigma and tau, respectively. In sum, the aims were to examine whetherthe 1)
accuracy, 2) variability and 3) infrequent long response times differed across the
four homogeneous ADHD-ASD symptom classes. Given the previous findings
in more homogeneous subgroups (van der Meer et al., 2012), we hypothesized
that motor timing is affected (i.e. reduced accuracy, increased variability of motor
timing, and increased infrequent long response times) in classes with both ADHD
and ASD symptoms, and to a lesser extent, although still different from the Normal
class, in the pure ADHD class.
The task was randomly assigned to 283 children between 8 and 17 years of age
from a population and clinic-based sample. This sample originally consisted of
644 children (van der Meer et al., 2012, see also chapter 3); because of task
demands the current task was not administered to the 5, 6 and 7-year olds. 81
children originated from a random population cohort study (Schoolkids Project
Interrelating DNA and Endophenotype Research; SPIDER) and 202 children and
adolescents from a clinical ASD-ADHD genetic study (Biological Origins of Autism;
BOA). The BOA cohort consisted of children with DSM-IV based ASD, ADHD and
ASD+ADHD diagnoses and non-affected siblings (for a full description, see Box
1.2 regarding the study samples, or see van Steijn et al., 2012).
In the previous study (van der Meer et al., 2012), children were divided
into homogeneous symptom classes with the use of a LCA on the raw subscale
outcomes of the SCQ (social interaction, communication and stereotypic behavior)
and the T-scores of the following ten scales of the CPRS-R:L: social problems,
inattention, restlessness, cognitive problems, hyperactivity, oppositional behavior,
emotional lability, fear, perfectionism and psychosomatic complaints (for a full
description see van der Meer et al., 2012). The raw subscale outcomes of the
SCQ and the T-scores of the CPRS used were either unrelated to age (SCQ)
or corrected for the influence of age (CPRS), limiting the impact of age on the
definition of the latent classes. Five classes had the best fitting BIC and SSA
BIC values and entropy (.914), combined with informative class profiles (Nylund
et al., 2007). Between class contrasts indicated that the current subsample was
comparable to the complete sample regarding ASD symptom severity (all p’s >
.06), ADHD symptom severity (all p’s > .08), sex (all p’s > .21), and IQ (all p’s
> .21). Consequently, the current sample was older (M (SD) 11.57 (2.5)) than
the complete sample (M (SD) 9.5 (2.4)). The distribution of children across the
distinct homogeneous symptom classes, as well as the ASD and ADHD symptom
severity, age, sex, population and IQ distributions are provided in Table 4.1. These
96
97
Chapter 4
distributions are well in line with the distributions in the complete sample (see also
chapter 3).
Table 4.1 Demographic characteristics of the children in the distinct classes
Normal
ADHDa
n=49
M
SD
11.6 2.5
ADHD
(+ASD)a
n=39
M
SD
12.3 2.7
ASD
(+ADHD)a
n=41
M
SD
12.1 2.5
Age in years
n=154
M
SD
11.2 2.3
% Male
42.2
69.4
79.5
85.4
%Population
basedb
Estimated full
scale IQc
Total score
SCQd
39.0
30.6
15.4
0.0
106.7 12.1
104.7 11.8
100.5 12.5
102.2 10.6
Normal > ADHD(+ASD)
4.3
5.4
8.2
5.2
16.4
6.6
22.4
6.0
T-score CPRSe
Inattention
48.2
6.5
65.0
7.1
73.3
8.8
62.5
8.6
T-score CPRSe
HyperactiveImpulsive
T-score CPRS
Oppositional
behaviore
T-score CPRS
Cognitive
problemse
T-score CPRS
Anxietye
48.4
7.2
66.3
10.6
79.9
8.8
67.1
12.4
48.3
6.8
57.22 8.3
74.5
9.0
59.4
10.9
49.0
6.5
65.0
8.0
71.8
7.6
59.7
8.3
50.7
11.1
55.4
11.4
71.5
13.6
69.6
13.1
46.6
6.4
49.7
6.4
60.5
11.3
64.8
9.9
T-score CPRS 50.7
Psychosomatic
complaintse
T-score CPRS 46.1
Emotional
labilitye
9.7
54.7
11.8
72.1
15.6
63.0
14.6
7.1
54.5
10.7
71.3
13.9
58.3
10.5
Normal < ADHD
< ADHD(+ASD) <
ASD(+ADHD)
Normal < ADHD
= ASD(+ADHD) <
ADHD(+ASD)
Normal < ADHD
= ASD(+ADHD) <
ADHD(+ASD)
Normal < ADHD
= ASD(+ADHD) <
ADHD(+ASD)
Normal < ASD (+ADHD)
< ADHD < ADHD
(+ASD)
Normal = ADHD
< ASD(+ADHD) =
ADHD(+ASD)
Normal = ADHD
< ADHD(+ASD) =
ASD(+ADHD)
Normal = ADHD
< ASD(+ADHD) <
ADHD(+ASD)
Normal < ADHD
= ASD(+ADHD) <
ADHD(+ASD)
For the sake of clarity, the classes were labeled. Children in class
‘Normal’ showed hardly any problems on ASD-and ADHD behavioral domains
(n = 154). Next, class ‘ADHD’ contained children with only ADHD-symptoms
(n = 49) without comorbidities. Here, both DSM-oriented CPRS-subscales for
ADHD (Inattentive and Hyperactive-Impulsive behavior) were above clinical cutoff, whereas the SCQ total score was substantially below cut-off (see Table 4.1).
Children in the class ‘ADHD(+ASD)’ scored above clinical cut-off on both ADHD
and ASD-symptoms, with the ADHD-symptoms more prominent than the ASD
symptoms (n = 39). Finally, children in the class ‘ASD(+ADHD)’ scored above / at
clinical cut-off on both ASD and ADHD-symptoms, with the ASD-symptoms more
prominent than the ADHD symptoms (n = 41). No class with only ASD-behavior
was identified. All children were of Caucasian descent and had an estimated
total IQ of at least 70 on the Wechsler Intelligence Scale (WISC-III) or Wechsler
Adult Intelligence Scale (WAIS-III) (Wechsler, 2002; 2000). Exclusion criteria were
epilepsy, known genetic or chromosomal disorders (such as Down syndrome),
brain damage, and problems with vision or hearing.
T-score CPRS
Perfectionisme
Contrasts based on
p-values of .05
Normal < ADHD
< ADHD(+ASD) =
ASD(+ADHD)
Normal < ADHD
= ADHD(+ASD) =
ASD(+ADHD)
Note.a ADHD = class with behavioral problems in ADHD only. ADHD(+ASD) = class with
severe ADHD-symptoms, who also show ASD-symptoms. ASD(+ADHD) = class with
severe ASD-symptoms, who also show ADHD-symptoms. b Percentage of the class derived
from the general population. c Full-scale IQ was estimated by four subtests of the WPPSI,
WISC-III or WAIS-III:Block Design, Picture Completion, Similarities and either Vocabulary or
Arithmetic (Wechsler, 1989; 2000; 2002). These subtests are known to correlate .90 to .95
with Full-scale IQ(Groth-Marnat, 1997). dThe total score on the SCQ (Social Communication
Questionnaire) reflected the total amount of ASD-symptoms. The official cut-off score for
probable ASD is 15, and for definite ASD the cut-off is 21.eSubscale scoreson the CPRS
(Conners’ Parent Rating Scale) subscales reflected the degree of ADHD-related and
comorbid symptoms. The official cut-off for clinically relevantsymptoms on the CPRS is a
T-score above 63
98
99
Chapter 4
Measures
description of the study to the parents and adolescents, written informed consent
Motor Timing Task
was obtained. Parents were invited to fill in several questionnaires concerning
This 1-second time production task measured the accuracy, variability and
infrequent long response times of motor timing (Rommelse et al., 2008; van
Meel et al., 2005). Children had to press a button with their preferred index finger
when they thought a 1-second time interval had elapsed. The start of the interval
was announced by a tone. After the button press, visual feedback concerning
the accuracy of the response was presented on screen, indicating whether the
their youngster’s behavior.
Data analyses
Raw responses higher or lower than 4 SD from a subject’s mean, with a minimum
response time of 200 ms, were considered outliers and excluded (Leth-Steensen
et al., 2000), which was < 0.1% of the data. Slow responses less than 4 SD
response was correct, too fast or too slow. A response was regarded as correct
above a subject’s mean were not excluded, but represented by τ, the mean of
if it fell between the lower and upper boundary set by a dynamic (self-paced)
the exponential part of the distribution. Since τ-data were positively skewed,
tracking algorithm. Boundaries were set at 500 to 1,500 ms at the beginning
normalized z-scores for τ were used in all analyses. These z-scores were obtained
of the task (van Meel et al., 2005). If the response fell within these boundaries,
the boundaries of the subsequent trial were narrowed by 100 ms. Likewise, the
boundaries of the subsequent trial were widened with 100 ms if the response on
the previous trial fell outside the boundaries. The practice session consisted of 20
trials, the experimental session consisted of 80 trials.
Accuracy of motor timing was represented by μ, the mean of time
productions in ms, corrected for the tail of the distribution (infrequent long
response times). Variability in motor timing was represented by σ, the variability
of time productions in ms corrected for the tail of the distribution (infrequent
long response times). Infrequent long response times were represented by τ,
the mean of the exponential part of the distribution (Leth-Steensen et al., 2000).
Dependent measures μ (mu), σ (sigma) and τ (tau) were calculated with the use
of ex-Gaussian analyses performed in MATLAB.
by Van der Waerden transformations (SPSS version 20). Effect sizes were defined
in terms of percentage of variance explained (ηp2). Small, medium and large
effects were defined as explained variances of .01, .06 and .14 respectively
(Cohen, 1988).
The classes were compared using Repeated Measures ANCOVAs
with class-membership as a fixed factor, age, age2 and sex as covariates and,
respectively, μ, σ and τ as dependent variables. Age2 was included to adjust for
possible nonlinear improvement in task performance across age. Interaction
effects were examined and, if nonsignificant, dropped from the model. Correction
for multiple comparisons was applied according to the False Discovery Rate
(FDR) controlling procedure to the post-hoc analyses with a p-value setting of .05
(Benjamini, 1995). Only the effects that remained significant after FDR-correction
were reported. Finally, in light of possible cognitive impairments in unaffected
siblings, analyses were repeated excluding unaffected siblings of ASD, ADHD
Procedure
The task described was part of the broader neuropsychological assessment
and ASD+ADHD affected children in the Normal class, to examine a potential
influence on the findings.
batteries used in the SPIDER and BOA projects. These studies have been
approved by the Committee on Research involving Human Subjects (CMO) and
children were enrolled between January 2009 and July 2011. After complete
100
101
Chapter 4
RESULTS
Variability (σ)
Accuracy (μ)
No significant class effect was found for the σ (F(3,282) = 0.86, p = .46, ηp2 =
A significant class effect, however with small effect size, was found for μ (F (3,
.01). A significant class*age interaction effect with a medium effect size, was
282) = 4.20, p = .006, ηp2 = .04). All children seemed to underestimate the 1
found for σ (F (3, 282) = 5.58, p = .001,ηp2 = .06), see also Figure 4.2. Post hoc
second interval (see Figure 4.1). Pairwise comparisons indicated that the deviation
analyses including two age groups per class indicated that older children in all
from the aimed response time (1000 ms) of the ADHD(+ASD) (M = 847 ms) and
classes except for the ADHD(+ASD)class showed less variability compared to
ASD(+ADHD) (M = 870 ms) classes deviated significantly from that of the Normal
their younger counterparts.
the only ADHD-class did not differ from that of the other classes (M = 916 ms).
No significant class*age interaction effect was found for μ (F(3,282) = 1.71, p =
.16, ηp2 = .02). A significant positive linear age effect, however with small effect
size, was found for μ (F(1,282) = 10.37, p = .001, ηp2 = .04), with more accurate
Figure 4.2 The variability
of time productions (ms)
corrected for infrequent long
response times across age
in the distinct classes
responses in older than younger children.
µ; accuracy of time productions (ms) (95% CI)
Figure 4.1 The accuracy of time productions (ms) corrected for infrequent
long response times in the distinct classes
Normal class
ADHD -only class
ADHD(+ASD) class
ASD(+ADHD) class
Age (years)
Infrequent long response times (τ)
No significant class effect nor significant class*age interaction effect was found
for the τ (F(3,282) = 1.53, p = .21, ηp2 = .02 and F(3,282) = 0.20, p = .90, ηp2 =
.00, respectively). A significant positive linear effect of age with a medium effect
Normal-class
ADHD-only class ADHD(+ASD)-class ASD(+ADHD)-class
Classes
102
; variability of time productions (ms)
class (M = 925 ms) (p = .002 and p = .025, respectively), while the accuracy of
size and a significant effect of age2 with a small effect size were found for these
infrequent long response times (F(1,282) = 36.42, p < .001, ηp2= .12 and F(1,282)
103
Chapter 4
= 5.50, p = .02, ηp2= .02, respectively), see also Figure 4.3. Findings indicated
problems, purely ADHD-behavior without any comorbidity, or both ASD and
reduced infrequent long response times in older compared to younger children.
ADHD-symptomatology, with one more prominent than the other. In contrast
to our hypotheses, the pure ADHD-class did not deviate from the Normal class
on any of the motor timing abilities (mu, sigma and tau). In fact, motor timing
difficulties were found only in classes where both ADHD and ASD-symptoms
Normalized; infrequent long response times (ms)
Figure 4.3 Infrequent long
response times (ms) across
age in the distinct classes
were present. The ADHD(+ASD) and ASD(+ADHD) classes showed a reduced
motor timing accuracy (i.e. increased underestimation) compared to the Normal
class. In addition, younger children in the ADHD(+ASD) class had a reduced
variability in motor timing when compared to younger children in the Normal and
ASD(+ADHD) classes, a pattern which was diminished across older children.
The finding that the pure ADHD-class did not deviate from the Normal
class on the motor timing abilities may seem to contrast previous studies that
used the same motor timing paradigm and found a tendency to underestimate
time and an elevated motor timing variability in ADHD (Rommelse et al., 2008;
Normal class
ADHD -only class
ADHD(+ASD) class
ASD(+ADHD) class
Thissen et al. (under revision); van Meel et al., 2005). This contrast is likely due
Age (years)
to differences in groups across the studies; children who were DSM-defined
as ‘ADHD’ in previous studies may actually have suffered from comorbid ASD-
Finally, as a check on the interpretation of our findings, these analyses were
symptoms as well. Additionally, our pure ADHD-class may have milder problems
repeated without unaffected siblings of ASD, ADHD and ASD+ADHD affected
than those typically included in case-control studies, suggesting that children
children in the Normal class. This resulted in minor changes in outcomes, which
with only the more severe behavioral symptoms show motor timing deficiencies.
could not explain the absence of a difference between the ADHD-only class and
In our data, both the ADHD(+ASD) class (with highest ADHD symptoms), and the
the Normal class. Thus, the presence of unaffected siblings of affected children in
ASD(+ADHD) class (with highest ASD symptoms), differed from the pure ADHD
the Normal class does not change the conclusions.
and Normal classes. This shows that current motor timing results cannot merely
be explained by high ADHD severity with ASD playing no role. This, because
DISCUSSION
The present study examined whether reduced motor timing accuracy, increased
motor timing variability, and infrequent long response times are specific for ADHD,
or -in part- due to comorbidity with ASD. We compared motor timing difficulties
across four homogeneous classes derived from a clinic and populationbased sample. These homogeneous classes presented either no behavioral
104
ADHD symptom severity in the pure ADHD-class and the ASD(+ADHD) class was
highly similar, while the former class showed no timing deficits. Furthermore, the
current findings parallel our previous study which indicated that homogeneous
classes with children suffering from both types of symptoms were cognitively more
impaired than children with pure ADHD symptoms, suggesting an overlapping
cognitive background in ASD and ADHD (van der Meer et al., 2012). The current
105
Chapter 4
findings are well in line with studies that found deficits in time processing in
profile of problems fits well with the symptom presentation of children with ASD
children with ASD regardless of ADHD-comorbidity (Geurts et al., 2008; Maister &
and comorbid ADHD, it follows that no claim can currently be made regarding the
Plaisted-Grant, 2011). It has been suggested that deficits in temporal processing
necessity of ADHD-symptoms for motor timing deficiencies to emerge when ASD-
interact with primary symptoms such as the poor development of social cognition
symptoms are present.
in children with ASD (Falter & Noreika, 2011). Current underestimation of
time across children and adolescents with both ASD and ADHD symptoms is
commonalities in pure ADHD and ASD with comorbid ADHD can be further
potentially also related to real-life difficulties in planning and organizing tasks and
elucidated by analyzing brain-behavior relationships. The extent to which
task completion. For example, children and adolescents with ASD and ADHD may
substrates of motor timing related to pure ADHD are also related to ASD with
perceive the time set for a given (school)task as very long, and may underestimate
comorbid ADHD, and vice versa, can increase our understanding of the role of
the time needed to complete tasks.
time processing in the development of behavioral symptoms in ASD and ADHD.
A recent meta-analysis on reaction time variability compared ADHD-
It has been suggested that motor time processing deficits in ASD are due to
affected children, adolescents (aged 13 to 18 years) and adults with clinical control
an abnormal cortical connectivity and synchrony as well as more diffuse and
groups (Kofler et al., 2013). Findings suggested that children but not adolescents
widespread neural abnormalities, with reduced volumes reported in the parietal
with ADHD had a slightly elevated variability compared to the clinical control
lobe, limbic and cortical regions and white matter tracts (Belmonte et al., 2004;
groups. In contrast, our findings suggest a reduced motor timing variability in the
Gepner & Feron, 2009; Ivry, 2003).Functional magnetic resonance imaging (fMRI)
class of youngest children with ADHD(+ASD) symptoms. This reduced variability
data specifically focusing on the neural substrates of motor timing in children
may reflect impulsivity or the tendency to rush a tedious task, one of the primary
with ADHD indicated more confined deficits in the anterior cingulate gyrus,
symptoms of ADHD. As also discussed by Falter et al. (in press), the interpretation
supplementary motor area and their connections to fronto-striatal pathways
of motor timing abnormalities in ASD and ADHD is obscured by the variety of tasks,
(Rubia et al., 2009). Future fMRI studies across empirically defined homogeneous
modalities, exposure durations and classifications used across studies. Our study
ASD and ADHD classes may be better apt to inform us on not only the neural
adds important knowledge to this topic by reducing the clinical heterogeneity
mechanisms of motor timing, but also the possible shared and distinct neural
present in DSM-defined ASD and ADHD group comparisons. Our comparisons of
substrates of motor timing in pure ADHD and comorbid ASD and ADHD.
motor timing abilities in empirically defined homogeneous ASD and ADHD classes
suggest that ASD-symptoms contribute to motor timing abnormalities. However,
in the three affected classes, whereas they were underrepresented in the Normal
the role of ADHD in these combined classes is unclear, since a) no homogeneous
class. This is because symptoms of ASD and ADHD are more frequently seen
class with only ASD-symptoms emerged from the latent class analyses, and b)
in boys than in girls (Kramer et al., 2008). Note that this overrepresentation was
the class with most severe ADHD-symptoms presented with ASD-symptoms as
present in all affected classes, and therefore did not affect comparisons between
well (van der Meer et al., 2012). In addition, the classes that presented with ASD-
those classes. Second, the latent classes were based on questionnaires. In
symptoms also suffered from more symptoms on other behavioral domains such
comparison with clinical interviews, questionnaires tend to overestimate the
as oppositional behavior, fear, perfectionism and emotional lability. Although this
degree of comorbidity, as questionnaires do not allow for further probing
106
Evaluation of the significance of motor timing differences and
There are some limitations worthy of note. First, boys were overrepresented
107
Chapter 4
or explanation of questions (Tourangeau et al., 2000). However, a possible
overestimation of comorbidity cannot account for the differences in motor timing
abilities in the distinct latent classes (van der Meer et al., 2012). Third, the nature of
our samples might have prevented us from detecting a homogeneous class with
pure ASD-behavior. ASD without ADHD symptoms might be underrepresented
in clinic-based samples and rare in population-based samples. Therefore, very
large samples are required to examine this issue further.
108
109
Homogeneous combinations of
ASD-ADHD traits and their
cognitive and behavioral
correlates in a population-based
sample
Jolanda M. J. van der Meer, Martijn G. A. Lappenschaar,
Catharina A. Hartman, Corina U. Greven, Jan K. Buitelaar,
Nanda N. J. Rommelse
Journal of Attention Disorders (in press)
110
Abstract
ASD and ADHD are assumed to be the extreme manifestations of continuous
heterogeneous traits that frequently co-occur. This study aims to identify
subgroups of children with distinct ASD-ADHD trait profiles in the general
population, using measures sensitive across both trait continua, and aims to
show how these subgroups differ in cognitive functioning. We examined 378
children (6-13 years) from a population-based sample. Latent class analyses
(LCA) detected three concordant classes with low (10.1%), medium (54.2%) or
high (13.2%) scores on both traits, and two discordant classes with more ADHD
than ASD characteristics (ADHD>ASD, 18.3%) or vice versa (ASD>ADHD, 4.2%).
Findings suggest that the ASD and ADHD traits usually are strongly related in the
general population, and that a minority of children displays atypical discordant trait
profiles characterized by differential visual-spatial functioning. This dissociation
suggests that heterogeneity in ASD and ADHD is rooted in heterogeneity in the
lower non-symptomatic end of the distribution.
113
Chapter 5
Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder
have been made in the identification of such discordant subtypes using LCA or
(ADHD) are highly heritable developmental disorders that frequently co-occur
LCGA (Latent Class Growth Analyses), adopting a longitudinal perspective (St.
(Ames & White, 2011; Ronald et al., 2008). Twin studies reveal a moderate degree
Pourcain et al., 2011; van der Meer et al., 2012; as described in chapter 3). St.
of phenotypic overlap between ASD and ADHD both throughout the whole range
Pourcain and colleagues (2011) suggested that children with ADHD symptoms
of scores and at the upper extreme end (Reiersen et al., 2008; Ronald et al.,
without ASD symptoms may more often have a childhood-limited form of ADHD,
2008), and there is evidence for shared etiological factors for ASD and ADHD
while children having both ASD and ADHD symptoms may have more persistent
(Rommelse et al., 2010; 2011; St. Pourcain et al., 2011). Further, clinical studies
ADHD symptoms, and possibly are more resistant towards treatment. Furthermore,
have documented poor social skills, language delay, sensory overresponsivity,
we previously used both ASD and ADHD clinical symptom measures as well
attention problems, oppositional defiant behavior and emotion regulation problems
as measures for comorbid internalizing and externalizing problems in LCA and
in both ASD and ADHD (Gadow et al. 2009; Mulligan et al., 2009; Rommelse et
identified amongst others two mutually exclusive classes of children with clinical
al., 2011). An increasing number of studies showed an overlap between ASD and
symptoms of both ASD and ADHD. One of these classes had proportionally more
ADHD with respect to cognitive functions (Booth et al., 2010, Corbett et al., 2009,
ASD than ADHD symptoms and the other class just the other way round (van
Fine et al., 2008), and therefore, studying ASD and ADHD together may provide
der Meer et al., 2012). Importantly, these classes showed opposite visual-spatial
the most optimal strategy in examining both shared and unique underpinnings.
processing capacities, suggesting the identification of behavioral subtypes may
For an in-depth discussion on differing models of co-existence of ASD and ADHD,
increase our understanding of the cognitive heterogeneity in both disorders as
see also Banaschewski et al. (2007) and Rommelse et al. (2011).
well as the etiology of their co-occurrence.
ASD and ADHD are both highly heterogeneous disorders; however the
A frequently overlooked issue that may have hindered progress in
optimal approach to describe this heterogeneity remains unclear. Latent class
identifying more homogeneous, etiologically distinct disorder subgroups is
analyses (LCA) have been used with the aim to identify more homogeneous
that not only ASD and ADHD populations, but also general populations are
subgroups of both traits. This approach in the separate fields of ASD and ADHD
characterized by heterogeneity. General populations are usually described with
research previously resulted in the identification of subgroups that mainly differ
a lack of precision and lumped together into a single group without symptoms.
by disorder severity rather than in truly distinct categories in a general population
This hinders the study of heterogeneity in this group, and ignores strong evidence
sample (Acosta et al., 2008; Constantino, 2011; Volk et al., 2009). Recent studies
that ASD and ADHD as well as other internalizing and externalizing behavioral
using both ASD and ADHD symptom measures also disclosed concordant classes
disorders exist on a continuum (Constantino, 2011; Levy et al., 1997; Lundstrom
differing mainly in severity (Reiersen et al., 2007; Mulligan et al., 2009). Such
et al., 2012; Plomin et al., 2009; Robinson, Munir, et al., 2011). Hence, cognitive
concordant ASD-ADHD trait profiles highlight the shared etiology of both traits,
and symptom heterogeneity at the upper end of the symptom distribution (i.e.
with both disorders sharing a common genetic and biological basis. In contrast,
in the clinical range) may well be reflective of similar cognitive and symptom
discordant ASD-ADHD trait profiles, that are highly symptomatic on one trait but not
heterogeneity at the lower end of the distribution. Such cognitive heterogeneity
the other, may have atypical underpinnings. These underpinnings may translate
was recently examined in a sample of ADHD-affected children and typically
into differential prognoses and susceptibility towards treatment. Some successes
developing children (Fair et al., 2012). Individual-based analyses on a range of
114
115
Chapter 5
cognitive tasks revealed that some of the cognitive heterogeneity in children with
ADHD seemed to be nested within the variation in typically developing children:
studying it using measures that are sensitive assessments across the continuous
largely similar cognitive subtypes (i.e. neuropsychological subgroups) were
ASD and ADHD traits. LCA were used to identify distinct ASD-ADHD profiles in
revealed in both populations. The authors also showed that diagnostic accuracy
the general population, and these profiles were examined for their internalizing
increased somewhat when the ADHD versus control contrast was made within
and externalizing problem and cognitive correlates. Given the correlations
each cognitive subtype instead of whole group analyses. Furthermore, the study
usually reported between both quantitative traits and the comorbidity between
showed that a large part of the previously unexplained cognitive heterogeneity
ASD and ADHD as extreme ends, it was expected that mostly concordant ASD-
within ADHD seems actually not to be related to ADHD as a disorder (i.e. the
ADHD classes would be detected that differed quantitatively but not qualitatively.
upper end of the symptom distribution), but more so to cognitive heterogeneity
However, by providing greater resolution of scores across the trait continua, we
also present in the non-clinical part of the ADHD spectrum.
also expected to have greater power to identify discordant classes with distinct
behavioral and cognitive profiles.
Ordinary assessments of psychopathology would not disclose such
In the present study our focus is on the population-based sample,
heterogeneity across the continuous ASD and ADHD traits, as they give resolution
only to the affected end of disorders, reflected in skewed distributions of symptom
METHODS
measures. An exception are questionnaires that provide greater resolution across
Participants
the entire distribution, taking into account difficulties as well as possible strengths
such as well-developed social-communication and attention traits. This approach
seemed quite promising in distinguishing ADHD subtypes in population data on
the Strengths and Weaknesses of ADHD symptoms and Normal behavior (SWAN)
rating scale (Arcos-Burgos et al., 2010), which revealed latent classes with less
than average hyperactivity and impulsivity. While the former study focused on
symptom data, further work on identifying distinct non-affected subtypes may
also provide us with a better understanding of the previously unexplained
cognitive heterogeneity in ASD and ADHD. Therefore, the present study aimed
to identify distinct ASD-ADHD trait profiles that are typified by distinct cognitive
and behavioral profiles across the ASD and ADHD trait continua, including the
non-symptomatic ends. These children were also part of our previous study in
a combined clinical and population-based sample, where more than 80 % of
them were lumped into ‘normal classes’ on the basis of clinical ADHD and ASD
measures (van der Meer et al., 2012).
The study has been approved by the Committee on Research involving Human
Subjects (CMO) and children were enrolled between January 2009 and July
2011. Eligible children were 378 children from a random population cohort study
(Schoolkids Project Interrelating DNA and Endophenotype Research; SPIDER).
All children were between 6 and 13 years of age (M (SD) 8.9 (1.7), % male =
49.5). All were of Caucasian descent and had an estimated total IQ of at least 70
on the Wechsler Intelligence Scale (WISC-III) (Wechsler, 2002). Exclusion criteria
were epilepsy, known genetic or chromosomal disorders, brain damage, and
problems with vision or hearing. After complete description of the study to the
parents, written informed consent from all parents was obtained.
Measures
ASD and ADHD symptom measures
ASD and ADHD trait measures according to parents were obtained using the
Autism Quotient (AQ) (Baron-Cohen et al., 2001; Hoekstra et al., 2008) which
provides a quantitative measure of ASD-like traits in the general population, and
116
117
Chapter 5
the Strengths and Weaknesses of ADHD symptoms and Normal behavior (SWAN)
rating scale (Hay et al., 2007), respectively. Both measures have shown adequate
reliability and validity (Arnett et al., 2011; Hoekstra et al., 2008). Both are based on
a Likert-type rating scale and show scores that followed a continuous distribution
in the general population (Baron-Cohen et al., 2001; Polderman et al., 2007). The
distribution of these measures resembled a poisson distribution rather than a
normal distribution. Therefore, the subscales were modeled as count variables in
the latent class model (Muthén & Muthén, 2006).
Cognitive measures
Table 5.1 Description of the cognitive measures
Task
Measurement potential
Dependent variable(s)
Baseline
Speeda,b
Speed and variability of
motor output as response
to external cue
Mean reaction time (ms) and variability
(SD of reaction time in ms)
Facial
Emotion
Recognitiona,b
Capacity to identify
the facial emotional
expression of happiness,
sadness, anger and
anxiety.
Mean reaction time (ms) and accuracy
on four emotions
Response
Organization
Objectsa,b
Motor Inhibition
Difference in percentage of errors or
mean reaction time (ms) between
compatible and incompatible trials
Cognitive Flexibility
Difference in percentage of errors
or mean reaction (ms) between
compatible trials and mixed compatibleincompatible trials.
Visuo-Spatial Attention
Number of correct reproduced
sequences in identical (forward) order
Visuo-Spatial Working
Memory
Number of correct reproduced
sequences in reversed (backward)
order
Verbal Attention
Number of correct reproduced digits in
identical (forward) order
Verbal Working Memory
Number of correct reproduced digits in
reversed (backward) order
Visual pattern recognition
Number of correct and timely
completed geometric designs
The six neurocognitive tasks analyzed in this study have been described
elsewhere (van der Meer et al., 2012; see chapter 3), a brief description of all
cognitive dependent variables is provided in Table 5.1. Ceiling effects did not
occur on any of the tasks as indicated by boxplot analyses on raw data (not
Visuo-Spatial
Sequencinga,b
presented).
Digit Spana,c
Block
Patternsa,c
Note. a van der Meer et al.(2012) see also chapter 3; bde Sonneville (1999); c Wechsler (2002).
Other internalizing and externalizing problems
In addition to the normally distributed ASD and ADHD trait measures, two
questionnaires measuring clinical symptom levels of ASD, ADHD, oppositional
behavior, emotional lability, anxiety, perfectionism and psychosomatic complaints
were obtained. These were the Social Communication Questionnaire (SCQ,
Lifetime version; parent ratings) and the Conners’ Parent Rating Scale-Revised:
Long version (CPRS-R:L), both validated instruments for screening developmental
problems (Conners et al., 1998a, Rutter et al., 2003).
118
119
Chapter 5
Procedure
The tasks described were part of the neuropsychological assessment battery
used in the SPIDER project. Children completed the battery in approximately two
hours and the order of task administration was counterbalanced. Due to time
constraints, not all tasks were administered to all children. Full-Scale IQ was
prorated by four subtests of the WISC-III; Block Design, Picture Completion,
Similarities and Arithmetic. These subtests are known to correlate between .90 and
.95 with Full-Scale IQ (Groth-Marnat, 1997; Kaufman, 1994). Parents were invited
to fill in the aforementioned questionnaires concerning their child’s behavior.
inherently confounded with symptoms of ASD and ADHD, and could therefore
not be separated from the effect of class (Dennis et al., 2009). Class*age,
class*sex, age*sex and class*age*sex interaction effects were examined and
reported if significant. If non-significant, interactions were dropped from the
model. Dependent variables were speed and/or accuracy measures for each task
separately, or subscale scores on the internalizing and externalizing problems. All
dependent variables were successfully normalized and standardized into z-scores
by applying a Van der Waerden transformation (SPSS version 20). Some of the
outcome measures were mirrored, so that the scores of all variables would imply
the same: a higher z-score was indicative of a better performance. Correction for
Data analyses
To identify homogeneous ASD-ADHD trait classes, LCA were used on the subscale
outcomes of the AQ, ranging from 0 to 30 (social skills, attention switching, local
details, communication, imagination) and the subscale outcomes of the SWAN
(inattention and hyperactive-impulsive). Subscale scores on the SWAN, ranging
from 9 to 63, were mirrored so that the scores on all subscales would imply the
same: a higher score was indicative of more symptoms. The LCA were carried
out using Mplus version 6.11 (Muthén & Muthén, 2006). Both the probability for a
multiple comparisons was applied according to the False Discovery Rate (FDR)
controlling procedure with a p-value setting of .05 (Benjamini, 1995). Effect sizes
were defined in terms of percentage of variance explained (ηp2). Small, medium
and large effects were defined in variances of .01, .06 and .14 respectively (Cohen,
1988).
RESULTS
Identifying Homogeneous Symptom Classes
child to belong to each of the classes and the conditional probabilities for children
The LCA on the AQ and SWAN subscales were based on fit and accuracy
in a particular class to show specific behavior were estimated. Next, children were
measures (Nylund et al., 2007), and revealed a solution with five classes. Five
admitted to the class with the highest probability. Mean subscale sum scores on
classes had the best fitting BIC and SSA BIC values, and entropy (.887), and a
the seven aforementioned subscales were computed, and presented in a line
bootstrapped lo-mendell-rubin likelihood ratio test p-value < .001 (see also Table
chart, so that quantitative differences between classes could be examined. Size
5.2). This, combined with the most informative class profiles and all correlation
and significance of class differences on these subscales were determined with a
matrix probabilities > .900, indicated accurate classification. The AQ and SWAN
MANOVA.
profiles of the classes are presented in Figure 5.1. For the purpose of simplicity,
Next, class differences with respect to age and sex were analyzed to
the classes were labeled. Three concordant classes emerged which had either
check for possible confounders. The identified classes were examined regarding
low, medium or high levels of both ASD and ADHD traits (see also Table 5.3).
their cognitive profiles and their internalizing and externalizing problems
We refer to those as ‘LL’ (Low ASD, Low ADHD; 10.1%), ‘MM’ (Medium ASD,
separately, using ANCOVA’s with class-membership as a fixed factor, and
Medium ADHD; 54.2%) and ‘HH’ (High ASD, High ADHD; 13.2%), and two
age and sex as covariates. IQ was not implemented as a covariate since IQ is
discordant classes with either higher scores on the ADHD traits than on the ASD
120
121
An
Figure 5.1 Class scores on AQ (left) and SWAN (right) subscales
trait ‘ASD>ADHD’ (4.2%). The LL-class scored low on both the AQ and the SWAN,
0,5
the MM-class scored intermediate on both measures and the HH-class scored
0,4
-0,1
SWAN. Roughly 30% of the children in the ASD>ADHD class passed the clinical
-0,3
cut-off for the ASD-measure. Again all scores were below clinical cut-off on the
-0,4
Class
General tests of model fit
Entropy
.878
BIC
16558.88
SSA BIC
16511.29
Technical output
VLMR
LRT
p-value
LMR adj. LRT
p-value
.00
.00
-Im
pu
At
te
n
ls
iv
e
tio
n
tio
n
in
a
ag
H
yp
er
ac
tiv
e
Im
C
om
Lo
m
ca
un
ic
a
lD
et
ai
ls
Sk
ills
At
te
n
Table 5.2 Results of latent class analyses on AQ and SWAN measures
tio
n
-0,5
ia
l
have been undisclosed in the ordinary assessments of ASD and ADHD.
Class 4: HL (4.2%)
n = 15
So
c
ASD and ADHD clinical symptom scales, indicating that these distinctions would
Class 2: LM (18.3%)
n = 68
-0,2
in
g
while the ASD>ADHD-class scored relatively high on the AQ and low on the
Class 5: HH (13.2%)
n = 46
0
Sw
itc
h
The ADHD>ASD-class scored intermediate on the SWAN and low on the AQ,
0,1
tio
n
scored below the clinical cut-off on the ASD and ADHD clinical symptom scales.
Class 3: MM
(54.2%) n = 203
0,2
Mean sum scores
the HH-class passed the clinical cut-off for both measures. Still, all three classes
Class 1: LL (10.1%)
n = 38
0,3
relatively high on the AQ as well as the SWAN. Roughly 30% of the children in
2
Pe
r
behavioral domain
trait ‘ADHD>ASD’(18.3%), or higher scores on the ASD trait than on the ADHD
No.
fe
Ps ctio
yc n
co ho
m so
pl
ai
nt
s
nt
er
ac
om
m
un
ic
a
St
er
e
ot
So
y
ci
al
pr
ob
l
In
at
te
R
es
tle
ss
n
C
pr ogn
ob
H l em
yp
er
ac
t
O
pp
os
Em
iti
ot
io
na
ll
a
li
C
So
ci
a
Chapter 5
Class 1: Normal
41.6 %
cognive domain
Note. Social skills, attention switching, local details, communication and imagination are
subscales of the AQ (Autism Spectrum Quotient), attention and hyperactive-impulsive are
subscales of the SWAN (The Strengths and Weaknesses of ADHD symptoms and Normal
behavior scale). A higher mean factor sum score indicated that children in that class lacked
population-based sample
more competences or showed more problems on the specific domain.
1,5
1,0
.857
16179.36
16106.39
.24
.00
4
.862
16017.76
15919.40
.03
.00
speed(21.9
%)
as dependent
variables revealed that, as expected, the five classes
differed
0
5
.887
15912.57
15788.83
.34
.00
medium accuracy-high
overall-0,5significantly (p< .001). Next, all classes were pairwise compared
on the
6
.862
15839.38
15690.26
.09
.00
separate
-1,0 ASD and ADHD subscales. Only 11 out of 70 comparisons did not reach
a
mean sum score
3
A MANOVA
using class as a fixed factor and the ASD and ADHD subscales
0,5
high accuracy-medium
speed (24.2 %)
low accuracy-medium
speed (35.3 %)
significance.
Roughly, the non-significant differences were on ASD-measures
-1,5
on
on
gn
iti
co
re
re
re
co
co
gn
iti
gn
iti
on
y
or
m
ng
ng
em
or
la
tte
em
m
te
at
nt
io
n
y
io
n
nt
pu
t
ro
ut
ve
rb
al
ot
ot
o
or
o
ut
pu
t
low accuracy-low speed
(on the left side of Figure 5.1) between either the LL and ADHD>ASD
class or
(18.6 %)
n
n
ot
ot
io
io
rn
em
em
su
sp
at
ia
lw
pa
tte
or
ki
ia
os
pa
t
ki
or
w
al
ve
rb
of
y
lit
va
ria
bi
ee
d
of
m
m
between the ASD>ADHD and HH class, or on ADHD-measures (on
the right
mean
of
d
sp
ee
ra
cy
of
su
al
vi
cu
vi
su
o-
vi
side of Figure 5.1) between the LL and ASD>ADHD class. The distribution of all
sp
Note. Entropy refers to classification accuracy, BIC refers to Bayesian Information Criterion,
SSA BIC refers to Sample Size Adjusted BIC, VLMR LRT refers to the vuong-lo-mendell-rubin
likelihood ratio test, LMR adj. LRT refers to the bootstrapped lo-mendell-rubin likelihood
ratio test. a From a 6 classes solution onwards, the p-value may not be trustworthy due to
local maxima.
ac
children across the distinct classes, as well as the sex, age, and IQ distributions
cognive domain
are provided in Table 5.3. Boys were overrepresented in the classes with higher
levels of ASD and/or ADHD traits (classes HH and ASD>ADHD), whereas girls
clinic-based
sample
were overrepresented in
the class
with the lowest levels of both traits (class LL).
1,5
122
ean sum score
1,0
0,5
0
123
high accuracy-medium
speed (16.5 %)
medium accuracy-high
Chapter 5
When corrected for the influence of age and sex, no changes in differences
between the classes were found.
Figure 5.2 Differences between the classes on measures of block patterns
and visual-spatial working memory
Block patterns
1,0
0,8
To test in which cognitive domains the classes overlapped or differed, separate
0,6
ANCOVAs were used for each cognitive domain, with age and sex as covariates.
0,4
in their block pattern performance (F (4,376) = 5.61, p< .001, ηp2 = .06). The
ASD>ADHD class showed superior block pattern performance compared to the
ADHD>ASD class, while the ADHD>ASD class also performed worse compared
-0,2
-0,4
to the MM-class. The HH-class performed comparable to the ADHD>ASD class,
and worse than the ASD>ADHD class. Next, the overall class effect reached
-0,8
trend-level significance for visual-spatial working memory (F (4,348) = 2.04, p
-1,0
= .09, ηp2 = .02), significant post-hoc results did not survive the correction for
Other Internalizing and Externalizing Problems of the Distinct Classes
concordant classes
LL (n=38)
MM (n=204)
discordant classes
HH (n=50)
ADHD>ASD (n=69) ASD>ADHD (n=16)
0,8
0,6
0,4
0,2
Z-score
more accurate responses
classes did not differ on the other cognitive measures studied.
p = .001
p = .017
Visual-spatial working memory
and the ADHD>ASD class indicated a significant difference in visual-spatial
performance and visual-spatial working memory are presented in Figure 5.2. The
p = .026
1,0
multiple comparisons. Still, pairwise comparison between the ASD>ADHD class
discordant classes were relatively small. Overall class effects on block pattern
p = .005
0,0
-0,6
working memory (F (1,79) = 5.94, p = .02, ηp2 = .07), despite the fact that both
p = .003
p = .001
0,2
Z-score
The discordant classes differed from the concordant classes and from each other
more accurate responses
Cognitive Profiles of the Distinct Classes
0,0
-0,2
-0,4
-0,6
Scores on the clinical questionnaire (CPRS) indicated that all classes represent
the non-symptomatic side of the continuum: none of the classes scored in the
clinical range on any of the subscales (i.e. oppositional behavior, emotional
lability, anxiety, perfectionism and psychosomatic complaints). The concordant
classes with intermediate or relatively high scores on the ASD and ADHD traits
also presented elevated scores on the other internalizing and externalizing
traits. For the discordant classes, the highest levels of the other internalizing and
externalizing problems were present in the ASD>ADHD class. In contrast, the
124
-0,8
-1,0
concordant classes
LL (n=32)
MM (n=188)
HH (n=49)
discordant classes
ADHD>ASD (n=66) ASD>ADHD (n=14)
Note. The means are adjusted for the covariate age. Group differences presented were
based on a mean age of 8.9 years. Error bars represent 1 standard error. LL refers to the
concordant class with low levels of ASD and ADHD symptoms, MM refers to the concordant
class with intermediate scores on both traits, HH refers to the concordant class with high
levels of both symptoms. ADHD>ASD refers to the discordant class with intermediate levels
of the ADHD and low levels of ASD symptoms, ASD>ADHD refers to the discordant class
with high levels of ASD symptoms and low levels of ADHD symptoms. The overall class
effect reached trend-level significance for visual-spatial working memory, (p = .09, ηp2 =
.02), and significant post-hoc differences in visual-spatial working memory did not survive
FDR-correction.
125
Chapter 5
Table 5.3 Demographic characteristics of the children in the distinct classes
Concordant classes
Discordant classes
LLa
MMa
HHa
ADHD>
ASDa
ASD>
ADHDa
n=38
(10.1%)
n=205
(54.2%)
n=50
(13.2%)
n=69
(18.3%)
n=16
(4.2%)
Contrasts based on
p-values of .05
Switching, Local Details, Communication, Imagination), the clinical cut-off of the total score
on the AQ in children is 76 (Auyeung et al., 2008). d The total score on the SCQ (Social
Communication Questionnaire) reflected the total amount of ASD-symptoms. e The mirrored
total scores on the SWAN (The Strengths and Weaknesses of ADHD symptoms and Normal
behavior scale) reflected the degree of ADHD-related symptoms. f Subscale scores on the
CPRS (Conners’ Parent Rating Scale) subscales reflected the degree of domain-specific
symptoms. The official cut-off for clinically relevant symptoms is a subscale score above 63.
M (SD)
M (SD)
M (SD)
M (SD)
M (SD)
Age in years
9.3 (1.6)
8.8 (1.8)
9.1 (1.7)
8.5 (1.7)
9.7 (1.5)
ns
ADHD>ASD class did not present increased scores on the other internalizing
% Male
28.9
47.8
76.0
40.6
75.0
LL = ADHD>ASD = MM
< ASD>ADHD = HH
and externalizing traits. When corrected for the influence of age, sex, class*age,
101.9 (9.5)
104.1 (8.5) 110.4
(12.0)
HH <ASD>ADHD
class*sex and class*age*sex interaction effects, results did not change. Results
Estimated full 105.7 (9.3) 106.1
(10.2)
scale IQb
are also presented in Table 5.3.
ASD measures
Total score
AQc
29.7 (8.1)
43.0 (7.9)
74.4 (15.7)
21.9 (5.8)
70.1 (11.3) LL <ADHD>ASD< MM
< ASD>ADHD = HH
Total score
SCQd
2.8 (2.8)
4.3 (3.2)
9.1 (5.7)
2.2 (2.2)
7.9 (4.3)
LL = ADHD>ASD < MM
< ASD>ADHD = HH
41.8 (7.6)
68.1 (9.7)
82.8 (12.2)
68.1 (8.4)
44.3 (8.2)
LL = ASD>ADHD < MM
= ADHD>ASD < HH
population-based sample. As hypothesized, the individual-based analyses
T-score CPRSe 44.4 (3.9)
ADHD
50.4 (8.9)
62.1 (10.9)
47.6 (8.2)
45.6 (5.5)
all< HH
revealed mostly quantitatively differing, concordant ASD-ADHD classes (77.5%)
ADHD measures
Total score
SWANe
with either low, medium or high scores on both traits, and two discordant
Oppositionalf
46.1 (6.0)
49.6 (7.7)
59.8 (11.1)
46.8 (7.1)
48.1 (5.3)
all< HH
Emotional
Labilityf
43.6 (4.4)
47.3 (7.7)
55.9 (10.9)
44.2 (6.1)
47.3 (8.4)
LL = ADHD>ASD<MM
= ASD>ADHD< HH
Anxietye
46.0 (6.0)
50.0 (9.2)
59.3 (11.6)
45.2 (5.8)
53.7 (12.0) LL = ADHD>ASD < MM
= ASD>ADHD< HH
Perfectionism 45.5 (4.9)
47.5 (7.2)
54.8 (11.6)
43.2 (4.4)
56.1 (10.2) LL =ADHD>ASD< MM
<ASD>ADHD= HH
47.2 (5.5)
49.8 (9.7)
58.1 (14.1)
47.8 (8.0)
48.8 (7.3)
Psycho­
somatic
Complaintse
all< HH
Note. a LL is the class with low levels of ASD and ADHD symptoms, MM refers to the
class with intermediate levels of ASD and ADHD, and HH is the class with relatively high
levels of ASD and ADHD symptoms. ADHD>ASD is the class with intermediate levels of
ADHD symptoms and low levels of ASD symptoms, ASD>ADHD refers to the class with
relatively high levels of ASD symptoms and low levels of ADHD symptoms. b Full-scale
IQ was estimated by four subtests of the WISC-III (Wechsler, 2002): Block Design, Picture
Completion, Similarities and Arithmetic. These subtests are known to correlate .90 to .95
with Full-scale IQ (Groth-Marnat, 1997). c The total score on the AQ (Autism Spectrum
Quotient) reflected the total amount of ASD-symptoms (subscales Social Skills, Attention
126
The present study examined whether differentiated ASD-ADHD latent classes
typified by distinct cognitive and behavioral profiles can be identified in a
Other internalizing and externalizing problems
e
DISCUSSION
classes with either more ADHD symptoms than ASD symptoms (18.3%), or more
ASD symptoms than ADHD symptoms (4.2%). When comparing the latter two
discordant classes, the specific combination of ASD>ADHD was characterized
by a superior visual-spatial processing, whereas the ADHD>ASD combination
was characterized by inferior visual-spatial processing. Furthermore, the class
with elevated scores on both traits (HH) presented a cognitive profile which
closely resembled the profile of the ADHD>ASD class. The elevated levels of
internalizing and externalizing problems in the classes with either high scores on
both traits or more ASD symptoms than ADHD symptoms did not translate into
more performance deficits than in the other classes.
Intriguingly, many of the findings in these non-clinical ASD-ADHD classes
were an extension of our previous study on clinical ASD-ADHD classes as well
as other studies across clinical ASD and ADHD samples (Mulligan et al., 2009;
127
Chapter 5
Reiersen et al., 2007; St. Pourcain et al., 2011; Todd et al., 2002; van der Meer et
as recently discussed by Fair and colleagues (2012), our findings suggest that
al., 2012). A first parallel was that one discordant ADHD>ASD profile was much
heterogeneity in clinical developmental disorders are rooted in comparable
more common (18.3%) than the other discordant profile ASD>ADHD (4.3%); the
heterogeneity present in the general population. Given that previously more than
latter profile even remained undisclosed in our previous study. This may suggest
80% of our population sample was lumped together into ‘normal’ classes (van
that across the general population, just as across the clinical population, most of
der Meer et al., 2012), the current findings can be seen as an extension of those
the children who express ASD-behavior also present the less severe ‘precursor’ of
findings across the non-symptomatic end of the ASD and ADHD traits.
ADHD-behavior, as has been hypothesized previously (Rommelse et al., 2011). A
second resembling finding was that the ASD>ADHD class is again characterized
dissociation in visual-spatial functioning (block pattern performance and working
by superior visual spatial functioning, whereas the ADHD>ASD class is
memory) between classes displaying either more ASD traits than ADHD traits or
characterized by inferior visual spatial functioning. Thus, superior visual-spatial
vice versa, which was also found previously for clinical cases. This dissociation
functioning in children with higher scores on the ASD trait than the ADHD trait
might pinpoint towards differential organization and/or functioning of neural
holds across the general population and the clinical population alike. This finding
substrates underlying visual-spatial information processes that are oppositely
is in keeping with recent studies across the general population reporting that a
involved in ASD and ADHD pathology. While normative visual-spatial attention
higher level of autistic traits measured with the AQ across the general population
is biased towards global processing (i.e. global interference), children with
was also associated with an enhanced visual working memory (Grinter et al.,
ASD are said to have a visual perceptual processing style that facilitates local
2009; Richmond et al., 2012). A third similarity was that the class with high scores
rather than global processing (Booth et al., 2003; Happé & Frith, 2006). Such
on both traits (class HH) showed a cognitive profile that most closely resembled
local processing is favorable in the completion of cognitive tasks like the block
the ADHD>ASD class. This may suggest that children with elevated scores on
pattern task and embedded figures test (EFT). Individuals with ASD or high levels
both traits primarily suffer from cognitive problems with an ADHD-alike etiology.
of mild ASD-like traits often show a superior performance in such tasks since
Alternatively, the behavior in the HH-class may actually reflect a true co-occurring
no global perceptual bias needs to be surpassed (Grinter et al., 2009; Shah &
of cognitive features of both ASD and ADHD, but the prime cognitive deficits
Frith, 1983). A reduced global-to-local perceptual process in ASD has also been
specific for ASD may be obscured by the cognitive deficits most robustly found in
approved in recent fMRI studies (Just, Keller, Malave, Kana & Varma, 2012; Liu,
ADHD. A fourth parallel was that boys were overrepresented in the classes with
Cherkassky, Minshew & Just, 2011; McGrath et al., 2012), which may indicate
higher levels of ASD and/or ADHD, which corresponds with the upper extreme
less top-down control and increased local connectivity in ASD. Such increased
ASD and ADHD traits being more easily recognized in boys than in girls (Kramer
local connections were not found in studies in ADHD; neural activity patterns
et al., 2008). This may indicate that sex-differences at the upper extreme end of
rather indicated a frontal, striatal and parietal hypofunction in ADHD (Bush, Valera
phenotypic traits are also embedded in more typically developing children, as
& Seidman, 2005; Silk et al., 2005; Vance et a., 2007). The widespread reduced
has been discussed previously (Neuman et al., 2005). This finding may suggest
top-down control described in those studies is not specific for visual processes
that sex differences in clinical referral and diagnoses of ASD and ADHD are not
in ADHD, and may pinpoint towards an overall reduced attentional network. We
based on a clinical bias, but rather reflects a true predisposition in males. Hence,
aim to follow-up on these findings by comparing children with elevated scores on
128
Of particular relevance is the extension into the general population of the
129
Chapter 5
either ASD or ADHD, children with elevated scores on both, and control children
regarding brain activation patterns during visual-spatial task performances.
This study was not without limitations. First, information on all phenotypic
domains relied on parent reports. Compared to clinical interviews, surveys tend
to overestimate the degree of co-occurrence of ASD and ADHD, since the degree
of response variability that can be measured is limited (Tourangeau et al., 2000).
This limitation may have affected the distribution of children across the classes
in favor of the concordant classes and may have hampered the disclosure of
discordant classes. Therefore, the optimal approach for studying these traits is
the use of structured psychiatric interviews. Second, questionnaires measuring
the symptom levels of the other internalizing and externalizing traits are not
designed to examine the lower extreme end of the phenotypic spectrum. This
however, did not impede differentiation across the classes, as can be seen in
differences among the classes in other internalizing and externalizing problems
(see also Table 5.3). Third, apart from the visual-spatial functioning, the classes
did not differ on the other cognitive domains (see also Table 5.1). This may be
due to the weakened associations between the cognitive measures on the one
hand and the reduced range of scores on both ASD and ADHD traits on the other.
Fourth, as aforementioned, sex differences were profound across all classes, with
boys overrepresented in the classes with higher levels of the ASD (and ADHD)
traits and girls overrepresented in the class with the lowest levels of both traits.
However, we do not believe this has affected the results, since the effect of sex
was analyzed and, when necessary, accounted for in the study.
In sum, the present study showed that, in the general population, 77.5%
of the children presents with concordant ASD-ADHD trait profiles, while 22.5% of
the children displays atypical discordant trait profiles characterized by differential
visual-spatial functioning. This dissociation was previously also reported in
classes with clinical symptoms of ASD and ADHD, suggesting that heterogeneity
in ASD and ADHD is rooted in heterogeneity present in the non-symptomatic end
of the distribution.
130
131
Using cognitive profiles to
examine the relationship
between ASD and ADHD
Jolanda M. J. van der Meer, Catharina A. Hartman,
Jan K. Buitelaar*, Nanda N. J. Rommelse*
*shared last author
Under review
132
Abstract
The objective was to examine if segmenting in cognitively homogeneous classes
is a useful approach in detecting shared and unique mechanisms underlying
Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder
(ADHD). Therefore, latent class analyses (LCA) were performed on motor speed
and variability, verbal and visual-spatial attention, verbal and visual-spatial
working memory, visual pattern recognition and emotion recognition in 360
children from a population-based sample and 254 children from a clinic-based
sample (5 - 17 years). Classes were compared on several behavioral symptom
scales. LCA in the population and clinic-based samples revealed a similar four
class solution typified by qualitatively different speed-accuracy trade-offs: high
accuracy-medium speed (21.9% of the population sample and 16.5% of the clinic
sample), medium accuracy-high speed (24.2% and 24.4%), low accuracy-medium
speed (35.3% and 39.0%) and low accuracy-low speed (18.6% and 20.0%).
These classes were respectively associated with lowest and highest levels of
ASD and ADHD symptoms in the clinical sample, with an overall strong predictive
effect. Associations with clinical symptoms were much weaker in the population
sample. Classes were not characterized by domain specific cognitive strengths
or weaknesses. Cognitive subtyping appears a powerful strategy to uncover the
mechanisms underlying ASD and ADHD. The relevance of cross-domain generic
cognitive factors fits current models of abnormal neural connectivity in ASD and
ADHD. The weak associations between cognition and behavior in the population
sample suggest that cognitive functioning may only predict behavior when other
risk or protective factors are present.
135
Chapter 6
Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder
anxiety and motor problems. Preliminary evidence further suggested exposure to
(ADHD) are neuropsychiatric developmental disorders that frequently co-occur
perinatal factors may differ between these ASD-ADHD classes. Finally, St. Pourcain
(for review, see Rommelse et al., 2010). The frequent comorbidity of both
and colleagues (2011) used Latent Growth Curve Analyses (LGCA) to examine
disorders is likely due to a substantial overlap in genetic factors and functional
to what degree changes in ASD and ADHD symptoms during development are
and structural brain characteristics between ASD and ADHD (for review, see
associated. They showed that children with ADHD symptoms persisting into late
Rommelse et al., 2011). Disclosing these shared underpinnings is complicated
adolescence were more ASD symptomatic compared to children with other ADHD
because both disorders encompass multiple distinct subtypes with overlapping
symptom trajectories. Overall, these studies indicate that empirically defined
symptom presentations. A basic assumption is that specific genetic deficits, brain
ASD-ADHD classes show partially distinct patterns of comorbid pathology and
abnormalities or cognitive impairments may underlie these disorders only in a
distinct developmental trajectories, and suggest they may be differentially linked
subgroup of the patients (Brieber et al., 2007; Maher, 2008; Veatch, Veenstra-
to etiological factors.
Vanderweele, Potter, Pericak-Vance & Haines, 2014; Verté et al., 2006). Hence,
an important strategy is to empirically segment this heterogeneous group of
on ASD, ADHD and comorbid symptom data (van der Meer et al., 2012, as
individuals with ASD, ADHD or a combination of ASD and ADHD into subgroups
described in chapter 3). Our results pointed to at least two ASD-ADHD comorbid
with possibly a more homogeneous set of underlying mechanisms.
subtypes that could be differentiated by the pattern of ASD and ADHD symptoms.
The merits of empirically defining more homogeneous disease subtypes
Subsequently, we were able to document that each class was also characterized
have already been demonstrated in separate studies of ASD and ADHD. For
by a quite distinct cognitive profile. That is, one ASD-ADHD class showed poor
instance, subtypes with a homogeneous symptom profile defined by Latent
recognition of facial emotions in combination with superior visual spatial (working
Class Analyses (LCA) show less heterogeneity with respect to age (Elia et al.,
memory) skills, whereas the other ASD-ADHD class showed normal abilities
2009), comorbid symptoms (Acosta et al., 2008; Beuker et al., 2013), associated
in recognizing facial emotions, yet poor visual spatial (working memory) skills.
cognitive deficits (Fair et al., 2012; Munson et al., 2008), and prognosis (St.
These findings suggest cognitive functions may be used in our search for more
Pourcain et al., 2011). These subtypes are also possibly more stable across
homogeneous classes of ASD-ADHD patients.
informants (Althoff et al., 2006) than DSM based subtypes. Three previous
studies used LCA or an equivalent technique to study empirically defined ASD-
symptoms is potentially even more promising in identifying distinct ASD-ADHD
ADHD classes. Reiersen and colleagues (2007) described several LCA defined
classes. Cognitive performances can be measured more objectively than
ADHD classes with distinctive degrees of increased levels of ASD symptoms.
clinical symptoms, and is potentially more closely linked to the neurobiological
Particularly the most severely impaired ADHD class showed the highest levels
underpinnings (Gottesman & Gould, 2003). A proof of concept study on
of ASD symptoms. Mulligan and colleagues (2009) extended these results by
neuropsychological heterogeneity in typically developing children and children
showing that children with ADHD combined type can be subdivided into several
with ADHD reported that children with ADHD could be segmented in four
classes with different levels of ASD symptoms. These ASD-ADHD classes also
subgroups characterized by specific cognitive difficulties for response variability,
showed distinct patterns of comorbid pathology, such as oppositional behavior,
executive functioning, temporal information processing and signal detection
136
We recently took this line of research a step further by performing LCA
Segmenting based on cognitive performance rather than on clinical
137
Chapter 6
(Fair et al., 2012). As these subgroups did not differ in ADHD symptoms, the
III) or Wechsler Adult Intelligence Scale (WAIS-III) (Wechsler, 1989; 2000; 2002).
clinical phenotype of ADHD may be rooted in multiple distinct cognitive subtypes.
Exclusion criteria were epilepsy, known genetic or chromosomal disorders, brain
Moreover, the cognitive subgroups disclosed across typically developing children
damage, and problems with vision or hearing. The studies have been approved
largely resembled the cognitive subgroups disclosed in ADHD. This suggests
by the local Committee on Research involving Human Subjects (CMO). After the
that part of the heterogeneity in ADHD is nested in normal variation. The broader
study procedures had been fully explained, informed consent was signed by all
implication is that cognitive profiles disclosed through a bottom-up approach are
participants, whereby parents signed informed consent for children younger than
generic, i.e. not only relevant for normal development and ADHD but possibly
12 years of age.
also for other neurodevelopmental disorders such as ASD.
Therefore, the present study aimed to extend these lines of research
Measures
by examining if homogeneous cognitive segmenting is a useful approach in
A large variety of cognitive domains was assessed, robustly associated with ASD
detecting shared and unique cognitive substrates for ASD and ADHD. Based on
(i.e. emotion recognition and visual pattern recognition) or ADHD (i.e. motor
our previous results (van der Meer et al., 2012), we hypothesized that a cognitive
speed and variability, verbal and visual attention, and verbal and visual–spatial
subtype might be identified with superior visual spatial skills and inferior emotion
working memory), as documented in previous studies (Booth & Happé, 2010;
recognition abilities that was most strongly linked to ASD; and a cognitive subtype
Corbett et al., 2009; Fine et al., 2008; Rommelse et al., 2011; van der Meer et
with inferior visual-spatial skills and normal emotion recognition abilities that was
al., 2012). These cognitive domains are also summarized in Table 6.1. Children
most strongly linked to ADHD.
completed the assessment battery in approximately two hours and the order of
METHODS
task administration was counterbalanced. Due to time constraints, not all tasks
were administered to all children. Full-Scale IQ was prorated by four subtests of
Participants
the WPPSI, WISC-III or WAIS-III; Block Design, Picture Completion, Similarities
Between January 2009 and July 2011, 360 eligible children were recruited from
and either Vocabulary (BOA) or Arithmetic (SPIDER) (Wechsler, 1989; 2000;
a random population cohort study (Schoolkids Project Interrelating DNA and
2002). These subtests are known to correlate between .90 and .95 with Full-Scale
Endophenotype Research; SPIDER) and 254 children from a clinical ASD-ADHD
IQ (Groth-Marnat, 1997). Parents were invited to fill in several questionnaires
genetic study (Biological Origins of Autism; BOA). The SPIDER cohort consisted
concerning their youngster’s behavior.
of the full range of children who attend primary schools, the BOA cohort consisted
of ASD, ADHD and ASD+ADHD affected children and their non-affected siblings
(for a full description, see Box 1.2 regarding the study samples, or see van Steijn
et al., 2012). Both samples aimed to cover wide behavioral and cognitive trait
distributions. All children were between 5 and 17 years of age, of Caucasian
descent and had an estimated total IQ of at least 70 on the Wechsler Preschool
and Primary Scale of Intelligence (WPPSI), Wechsler Intelligence Scale (WISC-
138
139
Chapter 6
Table 6.1 Description of the cognitive measures
population-based sample, behavioral symptom measures (parent reports) were
also taken from the Strengths and Weaknesses of ADHD symptoms and Normal
Task
Measurement potential
Dependent variable(s)
Baseline
Speeda,b
Speed and variability of
motor output as response
to external cue
Mean reaction time (ms) and
variability (SD of reaction time in ms)
Digit Spana,c
Verbal Attention
Number of correct reproduced digits
in identical (forward) order
Data analyses
Verbal Working Memory
Number of correct reproduced digits
in reversed (backward) order
To identify homogeneous cognitive classes, latent class analyses (Mplus version
Visuo-Spatial Attention
Number of correct reproduced
sequences in identical (forward) order
Visuo-Spatial Working
Memory
Number of correct reproduced
sequences in reversed (backward)
order
Block
Patternsa,c
Visual pattern recognition
Number of correct and timely
completed geometric designs
Facial Emotion
Recognitiona,b
Capacity to identify the
facial emotional expression
of happiness, sadness,
anger and anxiety.
Mean reaction time (ms) and accuracy
on four emotions
Visuo-Spatial
Sequencinga,b
Note. a van der Meer et al. (2012) see also chapter 3; b de Sonneville (1999); c Wechsler
(1989; 2000; 2002).
Procedure
Behavioral symptom measures (parent reports) were based on the Social
Communication Questionnaire (SCQ, Lifetime version), the Conners’ Parent
Rating Scale-Revised: Long version (CPRS-R:L) and the Autism Quotient (AQ)
in both samples. The SCQ and the CPRS-R:L are both validated instruments for
screening ASD and ADHD (Conners et al., 1998a, Rutter et al., 2003) while the
AQ provides quantitative measures of ASD-like traits in the general population
(Baron-Cohen et al., 2001; Hoekstra et al., 2008). For children in the clinic-based
sample scoring above clinical cut-offs on the SCQ and CPRS (DSM-subscales),
the Parental Account for Childhood Symptoms (PACS) and Autism Diagnostic
Interview Revised (ADI-R) were administered by a certified clinician to obtain a
diagnosis of ADHD and/or ASD (Le Couteur et al., 2003; Taylor et al., 1991). In the
140
behavior (SWAN) rating scale that provided a quantitative measure of possible
strengths in the general population (Hay et al., 2007).
6.11, Muthén & Muthén, 2006) were conducted in both samples separately on
the following 9 cognitive measures: speed and variability of motor output, verbal
attention, verbal working memory, visuo-spatial attention, visuo-spatial working
memory, visual pattern recognition and speed and accuracy of identification
of facial emotions. The cognitive measures were corrected for the influence of
age by calculating age regressed residuals, and next successfully normalized
and standardized into z scores by applying a Van der Waerden transformation.
Cognitive measures were uncorrected for sex and IQ as both are inherently
confounded with and therefore cannot be separated from ASD and ADHD (Dennis
et al., 2009). The measures for speed (response time) and variability were mirrored,
so that for all variables a higher score was indicative of a better performance. The
mean factor sum scores of all cognitive domains were computed and presented
in line charts, so that quantitative differences between classes could be examined.
Size and significance of differences were determined with multivariate analyses
of variance (MANOVAs), after which the correction for multiple comparisons was
applied according to the false discovery rate (FDR) controlling procedure with a
p-value setting of .05 (Benjamini, 1995).
Secondly, we examined whether the cognitive subtypes differed for
ASD and/or ADHD symptoms, comorbid oppositional behavior, cognitive
problems, anxiety, perfectionism, psychosomatic complaints and emotional
lability symptoms. To this end, we ran MANOVAs with the cognitive classes as
independent variable, and all behavioral domains as dependent variables in
both samples separately. Polynomial contrasts tested for linear and quadratic
141
Chapter 6
class differences regarding symptom data. Effect sizes were defined in terms of
off (medium accuracy-high speed). Class 3 (35.3% and 39.0%, respectively) was
Cohen’s d, mall, medium and large effects were defined as explained variances of
best described as low accuracy-medium speed. Class 4 could be viewed as low
.01, .06 and .14 respectively (Cohen, 1988).Correction for multiple comparisons
accuracy-low speed (18.6% and 20.0%, respectively). The characteristics of the
was again applied according to the FDR-controlling procedure with a p-value
classes are provided in Table 6.2. Supplemental separate analyses of affected and
setting of .05 (Benjamini, 1995). Only the effects that remained significant after
unaffected individuals from the clinic-based sample confirmed an LCA solution of
the FDR correction were reported.
four classes typified by the same qualitatively different speed-accuracy trade-offs
(see Supplement 6.1).
In a supplementary analysis we examined the representativeness and
stability of the cognitive profiles. Therefore children from the clinic-based (BOA)
sample were divided into two categories: affected if the ADI-R and/or PACS
scores were above clinical cut-offs, and unaffected if, despite familial risk, these
scores were below clinical cut-offs. Latent class analyses were conducted on
the same cognitive measures (separately normalized and standardized) in both
subsamples, as in our main analyses.
RESULTS
Identifying Homogeneous Cognitive Classes in Both Samples
Latent class analyses were based on fit and accuracy measures and visual
inspection of the figures (Nylund et al., 2007). This revealed a solution with four
classes in both samples. Four classes had the best fitting BIC values and satisfying
entropy (.719 for the population-based sample and .775 for the clinic-based
sample), combined with the most informative class profiles. The cognitive profiles
of the classes are presented in Figures 6.1a and 6.1b. For ease of interpretation,
the classes were labeled according to their cognitive profiles. Given the emerged
similarities in the cognitive profiles across the clinic and population-based
samples, these labels were applicable to classes from both samples.
These classes were typified by qualitatively different speed-accuracy
trade-offs rather than strengths or weaknesses on specific cognitive domains.
Class 1 (21.9% of the population-based sample and 16.5% of the clinic-based
sample) was best referred to as high accuracy-medium speed, whereas class
2 (24.2% and 24.4%, respectively) showed an opposite speed-accuracy trade-
142
143
of
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mean sum mean
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of
mean sum mean
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1,5
0,5
a.
1,0
b.
144
1,0
0
high accuracy-medium
speed(21.9 %)
0,5
-0,5
0
-1,0
medium accuracy-high
high
accuracy-medium
speed
(24.2 %)
speed(21.9 %)
-0,5
-1,5
low accuracy-medium
medium
accuracy-high
speed (35.3
%)
speed (24.2 %)
-1,0
-1,5
1,5
0,5
1,0
0
0,5
-0,5
0
-1,0
-0,5
-1,5
low accuracy-low speed
low
accuracy-medium
(18.6
%)
speed (35.3 %)
mean
low accuracy-low speed
(18.6 %)
cognive domain
mean
cognive domain
1,5
clinic-based sample
clinic-based sample
high accuracy-medium
speed (16.5 %)
medium accuracy-high
high
accuracy-medium
speed
(24.4 %)
speed (16.5 %)
low accuracy-medium
medium
accuracy-high
speed (39.0
%)
speed (24.4 %)
low accuracy-low speed
low
accuracy-medium
(20.1
%)
speed (39.0 %)
mean
low accuracy-low speed
(20.1 %)
cognive domain
mean
Note. A higher mean sum score indicated that children in that class had more competencies
or showed less problems on the
specific cognitive domain. The cognitive measures were
cognive domain
corrected for the influence of age.
a
49.2
T-score CPRS
Social problems
47.8
49.5
30.7
31.5
T-score CPRS
Inattention
T-score CPRS
Hyperactivity
SWAN
Attention
SWAN
Hyperactivity/
Impulsivity
ADHD measures
4.5
43.6
Total score AQ
8.8
8.0
8.1
8.5
7.9
18.8
4.0
34.0
32.7
51.7
49.0
48.9
43.2
4.5
8.5 108.6
111.2
8.0
50.6
1.6
49.4
9.5
M
SD
M
Total score SCQ
ASD measures
Estimated IQ
% Male
Age in years
Class 2
n= 87
Class 1
n = 79
53.5
9.2
M
n=127
Class 3
3.8
7.1
7.0
10.2
8.8
8.0
20.3
4.2
33.4
34.8
53.6
52.3
50.3
43.7
38.8
8.6
M
7.8
8.1
10.7
10.4
8.8
18.3
3.7
32.8
33.4
52.4
51.2
50.9
39.4
4.5
8.5 103.1
1.7
SD
n=67
Class 4
9.3
M
7.4
7.4
9.8
9.7
9.2
15.8
3.8
n.s.
1<3
1<3
1<3
n.s.
n.s.
n.s.
54.2
53.1
57.7
58.1
8.7
11.5
11.5
12.4
29.6
8.9
58.8
56.6
58.7
60.1
9.7
10.9 102.6
2.0
SD
Class 2
n = 62
63.6
12.4
M
13.3
13.3
14.3
32.0
9.0
63.3
60.4
65.0
69.1
11.4
10.0 103.1
1.9
SD
n = 99
Class 3
66.7
11.4
M
14.3
12.4
17.0
32.7
9.4
68.6
64.8
69.4
78.1
15.7
12.0 100.4
3.0
SD
n = 51
Class 4
All < 1
n.s.
2 < all
12.9
12.1
16.4
n/a
n/a
1=2<
4&
1<3
1 = 2 < 4&
1<3
1=2<4
31.1 1 = 2 < 4
10.8 1 = 2 < 4
12.4
4.3
SD
Class
Class
low accuracy- low accuracymedium
low speed
speed
Clinic-based sample
Class
medium
accuracyhigh speed
58.1
11.6
M
n = 42
Class 1
Class
high
accuracymedium
speed
71.4
n.s.
2=4
<1
Contrasts
based on
p-values
of .05
3=4
9.9
116.1
< 2 = 1
2.1
SD
Class
Class
low accuracy- low accuracymedium
low speed
speed
8.1 101.7
1.2
SD
Class
medium
accuracyhigh speed
Population-based sample
Contrasts
based on
p-values
of .05
population-based sample
Class
high
accuracymedium
speed
Figures 6.1a and population-based
6.1b Latentsample
cognitive classes across population and clinic1,5
based samples
1,0
hi
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cognive domain
Table 6.2 Demographic characteristics and behavioral symptoms in the distinct cognitive classes
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Chapter 6
cognive domain
145
146
Note. a Full Scale IQ was estimated by four subtests of the Wechsler Preschool and Primary Scale of Intelligence (WPPSI), Wechsler Intelligence
Scale (WISC-III), or Wechsler Adult Intelligence Scale (WAIS-III) (Wechsler; 1989; 2000; 2002). To prevent a restriction of variance in symptom
measures in the population-based sample, normally distributed ASD (AQ) and ADHD (SWAN) trait measures were included. SWAN-scores
were mirrored so that the scores on all questionnaires would imply the same: a higher score was indicative of more symptoms / less favorable
outcome.
1<4
13.7
59.6
56.1
55.2
9.9
8.9
8.6
47.3
T-score CPRS
Emotional
lability
8.0
48.1
8.2
47.2
47.2
n.s.
50.1
12.7
14.1
1<4
62.6
57.6
57.3
12.3
9.6
11.7
48.8
T-score CPRS
Psychosomatic
complaints
9.0
49.8
8.8
51.4
50.0
n.s.
53.2
14.7
15.0
16.0
n.s.
11.9
55.3
53.9
52.9
9.6
7.6
7.6
48.1
T-score CPRS
Perfectionism
7.8
48.0
9.9
47.8
47.1
n.s.
51.3
11.5
12.8
n.s.
16.4
63.4
60.5
57.2
16.5
8.4
11.5
49.8
T-score CPRS
Anxiety
8.8
48.6
7.4
51.4
49.2
n.s.
56.1
14.3
14.8
1=2<
4&
1<3
11.6
62.7
59.2
55.2
9.8
9.5
10.4
48.7
T-score CPRS
Cognitive
problems
8.1
49.1
8.6
53.2
51.7
1=2
<3
52.8
11.8
10.8
1< 3
=4
62.0
59.2
56.3
9.3
9.0
49.7
T-score CPRS
Oppositional
behavior
Comorbid measures
8.5
50.2
8.8
50.2
8.6
49.0
n.s.
51.7
13.3
13.8
12.8
Chapter 6
MANOVAs using class as fixed factor and all cognitive domains as dependent
variables revealed that the four classes differed significantly on all cognitive
measures in the clinic-based sample as well as the population-based sample
(both p’s < .001). In FDR-corrected post hoc comparisons for the clinic-based
sample, 11 of 54 comparisons did not reach significance, for the populationbased sample, 16 of 54 comparisons did not reach significance. Roughly, the
nonsignificant differences were found on comparisons with the low accuracymedium speed class in both the clinic and population-based sample, and in the
population-based sample on comparisons between all classes regarding the
accuracy of identification of facial emotions.
Behavioral Profiles of the Homogeneous Cognitive Classes
Next, we examined whether the cognitive classes differed in ASD, ADHD and
comorbid symptoms, by running MANOVAs for all behavioral domains in both
samples separately. The results are also presented in Table 6.2. We found linear
class contrasts for severity of ASD, ADHD, oppositional behavior, cognitive
problems (all p-values < .001), psychosomatic complaints (p = .01) and emotional
lability (p = .001) in the clinic-based sample. The high accuracy-medium speed
class had the lowest amount of symptoms, the low accuracy-low speed class
the highest amount of symptoms (d’s between .58 and .69 for ASD-measures,
between .91 and .96 for ADHD-measures and between .62 and .86 for comorbid
problems). In contrast, effect sizes were much smaller in the population-based
sample, with linear class contrast only present for ADHD symptoms and cognitive
problems (both p-values < .005), with only a small difference in symptom severity
between the high-accuracy-medium speed class and low accuracy-low speed
class (average d = .28). Results are presented in Figures 6.2a and 6.2b.
147
Chapter 6
Figure 6.2 Differences between the cognitive classes on a) measures of
ASD and ADHD, and b) oppositional behavior, cognitive problems, anxiety,
perfectionism, psychosomatic complaints and emotional lability symptoms
Clinic-based sample
,50
,50
,30
SCQ
AQ
CPRS Hyperactivity
CPRS Inattention
,10
-,10
-,30
Z-score (effect size)
,70
more behavioral problems
Z-score (effect size)
more behavioral problems
Clinic-based sample
,70
-,50
-,70
,70
,30
,10
-,10
-,30
-,50
high accuracy medium accuracy
medium speed
high speed
low accuracy
medium speed
-,70
low accuracy
low speed
low accuracy
low speed
,10
SCQ
AQ
CPRS Hyperactivity
CPRS Inattention
SWAN
-,10
-,30
-,50
Z-score (effect size)
,50
more behavioral problems
Z-score (effect size)
more behavioral problems
medium accuracy low accuracy
high speed
medium speed
,70
,30
-,70
high accuracy
medium speed
Population-based sample
Population-based sample
,50
a.
Oppositional
Cognitive problems
Anxiety
Perfectionism
Psychosomatic
Emotional lability
,30
,10
Oppositional
Cognitive problems
Anxiety
-,10
Perfectionism
Psychosomatic
Emotional lability
-,30
-,50
high accuracy medium accuracy low accuracy
medium speed
high speed
medium speed
low accuracy
low speed
b.
-,70
high accuracy medium accuracy low accuracy
medium speed
high speed
medium speed
low accuracy
low speed
Note. Error bars represent 1 standard error. SCQ = Total score on the Social Communication
Questionnaire, AQ = Total score on the Autism Quotient. ADHD and comorbid scores were
based on the Conners’ Parent Rating Scale-Revised (CPRS-R:L), SWAN = Total score on
the Strengths and Weaknesses of ADHD symptoms and Normal behavior rating scale,
population sample only.
148
149
Chapter 6
DISCUSSION
This study examined if segmenting ASD and ADHD into homogeneous cognitive
classes is a useful approach in detecting shared and unique substrates for ASD
and ADHD. Our main finding is that LCA in a population and a clinic-based
sample revealed similar four class solutions typified by qualitatively different
speed-accuracy trade-offs: high accuracy-medium speed, medium accuracy-high
speed, low accuracy-medium speed and low accuracy-low speed. These classes
were respectively associated with lowest and highest levels of ASD and ADHD
(and several comorbid) symptoms in the clinical sample, with an overall strong
predictive effect. Effects were much weaker or absent in the population sample.
Of note, classes were not characterized by domain specific cognitive strengths
or weaknesses.
In the clinic-based sample the speed-accuracy trade-off pattern was
strongly linked to between-class differences in ASD and ADHD symptom severity.
Children with inaccurate and slow performance across a range of tasks (i.e. blue
line in Figure 6.1) had the highest levels of ASD and ADHD as well as comorbid
symptoms, while children performing accurate at a normal pace showed the
lowest levels of ASD, ADHD as well as comorbid symptoms. The comparison of
these two most extreme groups resulted in moderate to large class differences.
This is in sharp contrast to previously reported effect sizes (small to moderate at
best) when comparing measures of cognition in DSM-defined groups (for reviews
see Gargaro, Rinehart, Bradshaw, Tonge & Sheppard 2011; Taurines et al., 2012).
This seems to indicate that cognitive subtyping results in more homogeneous
groups than DSM-based categories, and may therefore be a powerful strategy in
examining causal factors underlying ASD and ADHD.
The profile associated with the lowest amount of ASD and ADHD (and
comorbid) symptoms (i.e. the ‘protective’ profile) was also associated with
higher intelligence and somewhat older age. This may reflect a trade-off favoring
accuracy over speed that is associated with brain maturational processes.
In contrast, the profile associated with the highest amount of ASD, ADHD and
150
comorbid symptoms (i.e. the ‘risk’ profile) was characterized by slow and
inaccurate performances across a range of tasks. Clearly, children in this latter
group had difficulty with any cognitive task that was presented to them (regardless
of the specific aim of measurement), even tasks with very low cognitive demands
(i.e. simple baseline speed). These ‘generally at risk’ and ‘generally favorable’
cognitive profiles may suggest that generic cognitive skills strongly influence
cognitive performances across domains in a consistent manner. It is tempting to
speculate about the neural basis of these generic neurocognitive factors in ASD
and ADHD by linking them to abnormalities in brain connectivity (Di Martino et
al., 2013). Compromised small-world properties, i.e. reductions in local efficiency
and modularity within several functional networks, have previously been reported
for both ASD and ADHD (Rudie et al., 2013; Wang et al., 2009; Wass, 2011),
although findings are still somewhat inconclusive, and may be cognitive statedependent (for reviews, see de la Fuente, Xia, Branch & Li, 2013; Vissers, Cohen
& Geurts, 2012). Future studies might examine how the current generic cognitive
profiles map into the efficiency and modularity of functional neural networks. In
any case, our findings indicate that a task-transcending cognitive impairment,
resulting in an overall slower and more inaccurate performance, is an aspecific
but strong predictor of psychiatric dysfunctioning. This calls into question the use
of extensive cognitive batteries that are now often used for research and clinical
purposes, when performance across these tasks is mainly driven by a generic
cognitive impairment (Kuntsi et al., 2010; Millan et al., 2012) that may also be
measurable using much shorter paradigms.
The absence of cognitive profiles with domain specific strengths/
weaknesses contrasts with the idea of multiple cognitively different developmental
pathways to ADHD as suggested in several theory papers (for review, see Nigg
et al., 2005). At first sight, findings may also seem in contrast to those of our
previous study that described two classes of children with ASD and ADHD with
some distinctiveness in their cognitive profiles regarding emotion recognition and
visual spatial functioning (van der Meer et al., 2012). However, this may be due
151
Chapter 6
to the reversal of our dependent and independent variables in both approaches.
In the previous study we started to derive classes with homogeneous symptom
samples, one highly enriched in ASD/ADHD affected individuals and one
profiles and then detected virtually no cognitive differences between both groups,
population derived sample. One the one hand, the derived cognitive subtypes
with the exception of the two aforementioned domains. In contrast, in the current
were very similar in both samples, indicating that the LCA solution was fairly
study we first constructed cognitively homogeneous classes, and then identified
robust and not driven by sampling bias. On the other hand, the predictive value of
large between-class differences in clinical symptoms. The large degree of shared
cognitive subtype regarding symptom measures was much stronger in the clinic
variance was apparently more powerful and relevant for the formation of cognitive
sample compared to the population sample. It remains puzzling how this can
subtypes than the cognitive parameters (i.e. in visual spatial functioning and
be explained. A restriction of variance in symptom measures in the population
emotion recognition abilities) that were differentially related to ASD and ADHD
sample is unlikely to account for this, since we used amongst others also normally
(van der Meer et al., 2012).
distributed ASD and ADHD traits. However, when comparing both samples for
Our findings do contrast sharply though with those reported by Fair et
each cognitive subtype separately (see Supplement 6.2), it can be noticed that
al. (2012). Their findings suggested that subgroups of ADHD-affected children
the ‘risk’ profile (low accuracy and low speed) in the clinic sample performs
have specific strengths or weaknesses in distinct cognitive domains involved in
overall somewhat worse than the ‘risk’ profile in the population sample, whereas
the disorder. It remains to be seen why these outcomes diverge. Since substantial
the ‘protective’ profile in the clinic sample (high accuracy and medium speed)
overlap was present in the task batteries used in both studies (motor speed and
performs overall somewhat better than the ‘protective’ profile in the population
variability, verbal and visual spatial attention, and verbal and visual spatial working
sample. In other words, there may be less cognitive variance in the population
memory), it seems unlikely that the content of the current cognitive battery explains
sample compared to the clinical sample, which may explain the (much) lower
the absence of cognitive subtypes with domain specific strengths/ weaknesses.
predictive value regarding behavioral measures, as is also found in another recent
A prominent difference between the two studies that is more likely to explain
study (Rajendran, Rindskopf, et al., 2013). This may suggest cognitive functioning
the difference in results is the use of either community detection (CD) analyses
only predicts behavior when other risk or protective factors are present and that
(Newman, 2006) or latent class analyses (LCA), both are techniques that aim to
in every day life, favoring speed over accuracy or the other way around is overall
derive homogeneous subgroups. Current data were also examined with the use
equally adaptive.
of CD. This however resulted in very unstable outcomes in split-half analyses. This
instability in outcomes led us to prefer the LCA approach, providing replicable
was younger than the clinic-based sample, and age has proven to be an important
results in both samples as well as within the clinic sample when split into an
factor in the pattern of association between ASD and ADHD (St. Pourcain et al.,
affected and non-affected subsample. In any case, our findings challenge the
2011). Therefore, the cognitive measures were corrected for the influence of
currently wide held view of multiple different (sometimes viewed as independent;
age by calculating age regressed residuals. Furthermore, the cognitive profiles
de Zeeuw, Weusten, van Dijk, van Belle & Durston, 2012; Sonuga-Barke, 2005)
were replicated in the clinic and population-based sample despite these age-
developmental pathways to ADHD, by indicating that these pathways are not
differences, which underscores that the current patterns of association between
independent, but likely to be related to underlying generic cognitive impairment.
ASD and ADHD could not be explained by age-differences. Second, we had to
152
We took the opportunity to perform our analyses in two independent
This study was not without limitations. First, the population-based sample
153
Chapter 6
be selective in choosing the cognitive measures used for constructing cognitive
Supplemental Material
homogeneous classes, and different cognitive tests and paradigms might have
Identifying Homogeneous Cognitive Classes Within the Clinic-Based
been selected. In particular, extension of our findings by including other ASD and/
Sample
or ADHD related measures such as Theory of Mind abilities, and processing of
social and non-social reward (Ames & White, 2011) may be worthwhile.
In conclusion, cognitive subtypes -defined by different speed-accuracy
trade-offs instead of domain specific strengths and weaknesses- are similar in
clinic and population-based samples and strongly related to ASD, ADHD and
several other symptom domains in the clinic sample. Cognitive subtypes did not
(or much weaker) relate to behavior in the population-based sample, suggesting
cognitive functioning may only predict behavior when other risk or protective
factors are present.
In two additional latent class analyses it was examined if the four cognitive profiles
found in the clinic-based sample could also be identified in cases and unaffected
siblings, respectively. These subsamples consisted of either affected children (n
= 144) or their siblings who had ASD and ADHD symptoms below clinical ADI-R
and PACS cut-offs (n = 110). The cognitive profiles of the four classes solutions
in these subsamples are presented in Supplement 6.1a and 6.1b. These profiles
closely resembled the cognitive profiles in the complete clinic-based sample,
indicating that the cognitive profiles used are stable and representative for the
complete ASD and ADHD continuum from no problems to mild and up to severe
problems.
154
155
Chapter 6
Supplement 6.1a and 6.1b Latent cognitive classes across clinic-based
subsamples
Supplement 6.2 Class comparisons between population and clinic-based
samples
mean sum score
Clinic-based affected subsample
High accuracy – medium speed
1,5
1,0
0,5
high accuracymedium speed(15.3%)
0
-0,5
medium accuracyhigh speed(22.2 %)
-1,0
sp
ee
d
va
of
ria
m
bi
ot
lit
or
y
of
ou
m
tp
ot
ut
or
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al
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w
tte
or
n
v
k
vi
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su
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n
om
s
sp
em
pa
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tia
or
ia
y
la
lw
vi
ac
t
te
or
su
cu
nt
k
a
in
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ra
lp
g
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e
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n
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sp of e
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o
of
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on
em n re
co
ot
gn
io
n
iti
re
on
co
gn
iti
on
-1,5
low accuracy-medium
speed (40 %)
low accuracy-low
speed (22.2 %)
mean
cognitive domain
a.
1,5
1,0
0,5
0
-0,5
-1,0
-1,5
*
*
Medium accuracy – high speed
1,5
1,0
0,5
0
-0,5
-1,0
-1,5
Low accuracy-medium speed
Clinic-based unaffected subsample
mean sum score
1,5
1,0
0,5
high accuracy-medium
speed (25.5 %)
0
-0,5
medium accuracy-high
speed (38.2 %)
-1,0
sp
e
va ed
of
ria
m
bi
ot
lit
or
y
of
ou
m
tp
ot
ut
or
ve
ve
ou
rb
rb
tp
al
al
ut
w
at
or
te
k
n
in
vi
tio
vi
g
su
su
n
m
ooe
sp
sp
m
at
or
at
ia
ia
la y
lw
ac
vi
tte
s
o
cu
ua
r
nt
ra
io
l p king
cy
n
at
m
of
te
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r
m
of
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sp
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io
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of
n
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io
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re
n
co
ot
gn
io
n
i
tio
re
n
co
gn
iti
on
-1,5
b.
1,5
1,0
0,5
0
-0,5
-1,0
-1,5
*
*
low accuracy-medium
speed (22.7 %)
low accuracy-low
speed (13.6 %)
mean
Low accuracy – low speed
1,5
1,0
0,5
0
-0,5
-1,0
-1,5
*
cognitive domain
Note. Affected subsample (a): ADI-R and PACS scores above clinical cut-offs. Unaffected
subsample (b): ADI-R and PACS scores were below clinical cut-offs, despite familial risk of
ASD (and ADHD). A higher mean sum score indicated that children in that class had more
competencies or showed less problems on the specific cognitive domain. The cognitive
measures were corrected for the influence of age.
156
157
Chapter 6
population
sample
clinic sample
1,0
0,5
att
en
tio
n
ve
rba
me l wor
mo kin
ry g
al
vis
uo
att -spa
en
tio tial
n
vis
wo uo
rki -sp
ng
a
me tial
mo
ry
vis
ua
rec l pa
og tte
nit rn
ion
ac
cu
e rac
rec moti y of
og on
nit
ion
sp
ee
do
rec
f
og emo
nit
ion tion
-1,5
ve
rb
sp
-1,0
va
ria
b
-0,5
ility
ou of m
tpu
t otor
0
ee
d
ou of m
tpu oto
t
r
Z-score
more cognitive competencies
1,5
cognitive domain
Note. A higher standardized score indicated that children in that class had more
competencies or showed less problems on the specific cognitive domain. The scores
were standardized for both samples together, cognitive measures were corrected for the
influence of age. Significant differences were marked with an asterisk.
158
159
A randomized, double-blind
comparison of atomoxetine and
placebo on response inhibition
and interference control in
children and adolescents with
autism spectrum disorder and
comorbid attention-deficit /
hyperactivity disorder symptoms
Jolanda M. J. van der Meer, Myriam Harfterkamp,
Gigi van de Loo-Neus, Monika Althaus, Saskia W. de Ruiter,
A. Rogier T. Donders, Leo M. J. de Sonneville, Jan K. Buitelaar,
Pieter J. Hoekstra, Nanda N. J. Rommelse
Journal of Clinical Psychopharmacology, 2013; 33 (6), 824-827.
160
Abstract
The aim of this study was to investigate whether atomoxetine led to improvement
of response inhibition and interference control in children with Autism Spectrum
Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD), and whether
ADHD symptom improvement was mediated by improvement in response
inhibition and interference control. Therefore, 97 children (6-17 years) with ASD
and ADHD and a Total IQ of ≥ 60 were randomly assigned to double blind treatment
with either atomoxetine or placebo for 8 weeks, followed by a 20 weeks openlabel phase. Neuropsychological assessments and behavioral questionnaires
were completed at baseline, after the double blind phase and after the open-label
phase. Atomoxetine treatment was associated with improvement of response
inhibition but not of interference control. Next, atomoxetine decreased ADHD
symptoms as assessed by parents and teachers, yet no significant correlation was
found between improvement in measure of inhibitory control or ADHD symptoms.
Atomoxetine improved response inhibition but not interference control in children
with both ASD and ADHD, independent from progress in ADHD-symptomatology.
These findings suggest that cognitive improvements and improvements on
the behavioral level do not necessarily co-occur, and that distinct pathogenic
pathways may play a role in the occurrence of clinical symptoms and cognitive
dysfunctions.
163
Chapter 7
With prevalence rates of 1% for Autism Spectrum Disorder (ASD) and 5% for
except for a pilot study (Arnold et al., 2006), well-controlled clinical trials of
Attention-Deficit/ Hyperactivity Disorder (ADHD), these disorders are among the
atomoxetine in ASD patients are lacking. Open-label studies suggest decreased
most commonly diagnosed psychiatric syndromes in children (Baird et al., 2006;
hyperactivity and improved attention and learning after atomoxetine treatment
Polanczyk et al., 2007). Impairments in ASD are characterized by decreased
(Charnsil, 2011; Fernández-Jaén et al., 2012; Hazell, 2007; Jou et al., 2005; Posey
communication and social interaction skills, and repetitive and restricted behavior
et al., 2006; Troost et al., 2006; Zeiner, Gjevik & Weidle, 2011). These effects of
and interests. Impairments in children with ADHD reflect severe inattention,
open-label studies need confirmation by double-blind, placebo-controlled studies
hyperactivity and impulsivity impulsivity (American Psychiatric Association, 2013).
to establish treatment benefits of atomoxetine on the ADHD-symptoms in ASD.
Although the clinical descriptions of both disorders are quite distinctive, ASD and
ADHD co-occur frequently: about 20 to 50% of the patients with ADHD meet criteria
severity in children with both ASD and ADHD using a double-blind placebo-
for ASD and about 30 to 80% of the patients with ASD meet criteria for ADHD
controlled design in 97 children (Harfterkamp et al., 2012). Results indicated that
(Reiersen & Todd, 2008; Rommelse et al., 2010). Despite the fact that the DSM-
atomoxetine improved ADHD symptoms in children with ASD according to both
IV prohibits a diagnosis of ADHD in the context of ASD, it has now widely been
parent and clinician-based ratings and was generally well tolerated. Furthermore,
recognized that both disorders can co-occur and share a substantial proportion
effects were stronger for symptoms of hyperactivity-impulsivity compared to
of etiological risk factors (Rommelse et al., 2010; 2011; Ronald et al., 2008; St.
symptoms of inattentiveness, which is in accordance with the only placebo-
Pourcain et al., 2011). Therefore, the DSM-5 draft includes the possibility of a
controlled crossover pilot trial available (Arnold et al., 2006). All in all, previous
comorbid diagnosis. This has given impulse to several clinical trials investigating
open-label studies and our double-blind placebo controlled study suggest
the effectiveness of pharmacological treatments such as methylphenidate for
atomoxetine is indeed effective in reducing ADHD symptoms in patients with ASD
ADHD symptoms in ASD-patients (Murray, 2010; Posey et al., 2007; Research
and ADHD.
Units On Pediatric Psychopharmacology, 2005; Santosh et al., 2006; Stigler, et
al., 2004). Overall, it was concluded that the benefits seen from methylphenidate
associated with ADHD provides more insight into the working mechanisms of
are smaller amongst patients suffering from both ASD and ADHD-symptoms
the pharmacological agent. For the selective norepinephrine re-uptake inhibitor
as opposed to pure ADHD patients. Further, adverse effects such as agitation,
atomoxetine, little is known about the cognitive working mechanisms. Several
reduced appetite, gastrointestinal symptoms and fatigue were more frequently
(animal-)studies suggest that inhibitory control, one of the core deficits associated
reported amongst comorbid patients. These side effects are reportedly severe
with ADHD symptoms (Barkley, 1997; Pennington & Ozonoff, 1996), improves
enough to lead to discontinuation and suspended treatments. Therefore, recent
with atomoxetine (Bari, Eagle, Mar, Robinson & Robbins 2009; Chamberlain et
reviews on the efficacy and tolerability of medical treatment options for ADHD-like
al., 2007; 2012; Faraone, Biederman et al., 2005; Gau & Shang, 2010; Robinson
symptoms in individuals with ASD suggest that in addition to methylphenidate,
et al., 2008), but others have found no effects (de Jong et al., 2009; Nandam et
atomoxetine may be a reasonable choice to target ADHD-symptoms in ASD
al., 2011) or even deleterious effects (Graf et al., 2011). In contrast, for the mixed
(Benvenuto et al., 2012; Cortese, et al., 2012; Doyle & McDougle, 2012; for review
dopamine and norepinephrine re-uptake inhibitor methylphenidate, research has
see Ghanizadeh, 2012; Handen et al., 2011; Hanwella et al., 2011). However,
consistently shown its improvement of the neural mechanisms of inhibitory control
164
We previously reported the effect of atomoxetine on ADHD symptom
Knowledge of the effects of medical treatment on cognitive deficits
165
Chapter 7
in ADHD-patients (Ashare et al., 2010; Chamberlain et al., 2011; Groom et al., 2010;
2003). Moreover, all children were screened for the presence of ADHD-combined
Lee, Han, Lee & Choi, 2010; Nandam et al., 2011; Rubia et al., 2011; Scheres et
subtype (ADHD-C) and had to meet DSM-IV-TR criteria A through D for ADHD
al., 2003). The contrasting findings for atomoxetine may partially be explained by
(American Psychiatric Association, 2000). Both parents and children over the age
the broad definition of inhibitory control. Inhibitory control encompasses amongst
of 12 years had to give written informed consent after procedures and possible
others inhibition of an ongoing response and interference control (Scheres et
side effects were explained to them. Exclusion criteria included an IQ below 60 on
al., 2004). Inhibition of an ongoing response (or response inhibition) is best
a Wechsler Intelligence Scale (WISC-III)(Wechsler, 2002) a weight of less than 20
described as the ability to suppress pre-potent behavior that is inappropriate
kg, presence of psychosis, bipolar disorder, substance abuse, a serious medical
or no longer required, an output-process reflecting stimulus selection, whereas
illness, history of seizures, ongoing use of psychoactive medications other than
interference control refers to the cognitive control needed to prevent interference
the study drug, and intended start of a structured psychotherapy or in-patient
due to competition of relevant and irrelevant stimuli, an input-dependent process,
treatment. Females who were post-menarche and sexually active had to take a
reflecting response organization. These aspects do not necessarily correlate with
pregnancy test to exclude pregnancy. The study was approved by the national
each other, hence a selective norepinephrine re-uptake inhibitor (atomoxetine)
and local institutional review board committees, and children were enrolled
and a mixed dopamine and norepinephrine re-uptake inhibitor (methylphenidate)
between October, 2006 and March, 2008.
may influence distinct mechanisms of inhibitory control. This is in accordance
with earlier findings that suggested both overlapping and distinct cognitive effects
which 5 were excluded based on their intelligence quotient and/or cut-off scores
of methylphenidate and atomoxetine (Chamberlain et al., 2011; Robinson et al.,
on the ADI-R. In total, 97 children (83 boys, 14 girls) were randomly assigned to
2008). It should be noted that these studies have focused on ADHD-only, healthy
either placebo (n=49) or atomoxetine (n=48) treatment. Five children randomized
controls or rodents. Change in the neural mechanisms of inhibitory control as a
to atomoxetine and three children randomized to placebo discontinued during
result of atomoxetine treatment has thus far not been studied in patients suffering
the double blind treatment, see also Figure 7.1. Of the 89 children who completed
from both ASD and ADHD. Our aims were to examine 1) whether atomoxetine
the double-blind placebo controlled phase one child decided to stop and not to
improved two forms of inhibitory control (response inhibition and interference
continue in the open label treatment period. 88 started the open-label phase;
control), and 2) whether ADHD symptom improvement (Harfterkamp et al., 2012)
42 previously on atomoxetine and 46 previously on placebo. Fifteen children
was mediated by improvements in inhibitory control.
withdrew from this phase of the study because of adverse events or lack of
At the start of the study, 102 children were assessed for eligibility, after
efficacy, resulting in 73 children completing the open-label phase. No differences
METHODS
between the placebo and atomoxetine group were found regarding age, sex or
Participants
IQ, see also Table 7.1.
Eligible children were referred to one of nine participating Dutch medical centers
across the country, between 6 and 17 years of age at their initial visit, and suffering
from ASD and ADHD. ASD diagnoses were established by clinical assessment
and at least two ADI-R subscale scores had to be above the clinical cut-off (Rutter,
166
167
Chapter 7
Table 7.1 Baseline demographic characteristics of subjects randomized to
the placebo and atomoxetine group
ATXa (n = 48)
M (SD)
Placebo group (n = 49)
M (SD)
F, p
Age in years
10.5 (2.7)
10.4 (2.9)
0.01 NS
Male (%)
87.5
83.7
0.16 NS
WISC-III IQb
91.0 (16.4)
94.6 (17.7)
0.46 NS
Note. ATX= Atomoxetine. In 2 children, estimated total intelligence quotient (IQ) was
not available. 1 child (randomized to atomoxetine) had a nonverbal IQ above 60, but was
nottestable with regard to his verbal IQ, and 1 child (randomized to placebo) had widely
differing nonverbal and verbal IQs (of 55 and 85,respectively); therefore a total IQ could not
be validly determined.
a
b
Measures
Both tasks contained an instruction trial wherein the examiner provided a typical
item of the task, and a separate practice session (de Sonneville, 1999) Test–retest
reliability and validity of the computerized ANT-tasks are satisfactory and have
been described and illustrated elsewhere (de Sonneville, 2005).
Response Inhibition
The Go-No Go task was administered to measure the response inhibition of prepotent responses. In this task, 24 Go signals, in response to which the subjects
had to press a key, were randomly mixed with 24 No Go signals, to which
responses had to be suppressed. The Go signals were open squares, whereas
Study Design
The study consisted of three periods, starting with a screening/washout period
with a duration of 3 to 28 days, followed by an 8 week double blind treatment with
either atomoxetine or placebo in a 1:1 ratio and then a 20 week open label phase.
During the first phase (T0), all children were screened for eligibility, including
a diagnostic and medical evaluation. The first neuropsychological assessment
was administered in order to correct for potential differences in previous test
experience. At the start of the double blind phase (T1), children were randomly
assigned to a double blind treatment with either atomoxetine or placebo capsules,
the No Go signals were closed squares. In each trial the signal was preceded
by a warning tone lasting 500 ms, signals were presented every 3000 ms. The
valid response window was 200 ms to 2300 ms post stimulus onset. The primary
outcome measure was the proportion of false alarms after No Go signals. The
secondary outcome measures not specifically related to response inhibition were
the proportion of missed Go signals, and the response time and response time
variability of correct responses.
Interference Control
titrated to a fixed once-daily dose (first week: 0.5 mg/kg/day; second week: 0.8
The Focused Attention task was administered to measure interference control,
mg/kg/day; then 1.2 mg/kg/day for six weeks). Atomoxetine and placebo were
i.e., the degree of distractibility by irrelevant information. In this task, 20 relevant
identical in appearance. The neuropsychological measures were administered
target trials, in response to which the subject had to press a ‘yes-button’, were
at the start (T1) and at the end (T2) of the second phase. The open label phase
randomly mixed with both 10 non-target trials and 10 trials with foils (interference
had a duration of 20 weeks; at the end of this period, the final neuropsychological
trials) to which the subject had to respond by pressing a ‘no-button’. In the
measures were administered (T3). During this open-label phase both groups
target trials, the target fruit (a cherry) was shown at a vertical position, whereas
were treated with atomoxetine, titrated identically as in the second period. All
in the non-target trials the stimulus was absent. In the foil-trials, the stimulus was
study personnel, parents and children were blinded to treatment assignment for
presented in a horizontal position, which children had to ignore. The stimulus
the complete duration of the study.
remained on the screen until the child pressed a key. The next stimulus was
presented 250 ms after the response. There was no feedback on the response.
168
169
Period 3:
Open label,
20 weeks
Period 2:
Double blind,
8 weeks
Period 1:
Screening and
medication
washout
Chapter 7
The valid response window was 200 ms to 6000 ms post stimulus onset; trials
with responses faster than 200 ms or slower than 6000 ms were replaced by trials
of a similar type. Primary outcome measure was the proportion of false alarms on
170
Discontinued
(n = 11) due to
-adverse event (7)
-lack of efficacy (4)
Discontinued
(n = 3) due to
-protocol violation (2)
-physician decision
(1)
interference control were the proportion of false alarms on the non-target trials,
the proportion of relevant targets missed, and response time and response time
variability of correct responses.
ADHD symptomatology
T3 (End of open label phase):
Previously Placebo (n=35)
Assessment + Questionnaires
T2(End of double blind phase):
Placebo (n=46)
Assessment + Questionnaires
T1 (Baseline):
Placebo (n=49)
Assessment + Questionnaires
(ADHD-RS)(DuPaul, 1998) and the Conners’ Teacher Rating Scale-Revised:
Short Form (CTRS-R:S) (Conners et al., 1998b). The ADHD-RS is a DSM-IV based
rating scale administered by a clinician and contains 18 items on inattentive and
hyperactive-impulsive symptoms to be scored on a four-point scale in order to
assess symptom severity over the past week. The subscales of inattention and
hyperactivity-impulsivity only summed the scores of the respective items. If a
single item of a subscale was missing, the mean score for all other items in the
T3 (End of open label phase):
Atomoxetine (n=38)
Assessment + Questionnaires
T2(End of double blind phase):
Atomoxetine (n=43)
Assessment + Questionnaires
subscale was imputed as the score for the missing item. If more than one item of
T1 (Baseline):
Atomoxetine (n=48)
Assessment + Questionnaires
a subscale was missing, the score for the subscale as well as the total score were
considered missing. The CTRS-R:S is a 28-item questionnaire to be completed
by the child’s teacher in order to assess ADHD-related problem behavior in the
school setting. Outcomes on the ADHD-RS and the CTRS-R:S were also reported
previously (Harfterkamp et al., 2012).
Data analyses
Discontinued
(n = 4) due to
-adverse event (4)
Last observation carried forward (LOCF) analyses were conducted (Hamer &
Discontinued
(n = 5) due to
-protocol violation (2)
-adverse event (1)
-lack of efficacy (1)
-parent/caregiver
decision (1)
Randomized children
(N = 97)
Excluded
(n = 5)
based on
stated
inclusion
criteria
ADHD symptom improvement was measured using the ADHD Rating Scale
Eligible children
(N = 102)
Baseline Assessment
Figure 7.1 Flow diagram for all eligible children suffering from ASD and ADHD symptoms
the interference trials. Secondary outcome measures not specifically related to
Simpson, 2009), with the requirement of having at least a neuropsychological
assessment at T1. In total, 94 out of 97 had a T1 assessment of the response
inhibition task and 95 out of 97 children of the interference control task. First, to
examine the effect of atomoxetine on both forms of inhibitory control, two analyses
171
Chapter 7
were conducted: a) groups were compared using only the blinded measurements
prior (T1) and post treatment (T2); and b) change after first treatment was analysed
combining change during the blind phase (T2-T1) for the atomoxetine group and
change during the open label phase (T3-T2) for the placebo group. Reaction time
and reaction time variability were continuously distributed and were analysed
using a) ANCOVA’s, with group as factor, the pre-treatment (T1) as covariate
and post-treatment (T2) as dependent measure (Gueorguieva & Krystal, 2004)
and b) One-sample T-tests against a test-value of 0 (no change after treatment).
Proportions of false alarms and misses showed substantial underdispersion and
were therefore analysed using a) negative binominal models with group as factor,
the pre-treatment (T1) as covariate and post-treatment (T2) as dependent measure
and b) one sample nonparametric tests comparing the median against a testvalue of 0 (no change after treatment). Correlations between congruent measures
of both tasks (e.g. reaction time variables of both tasks) were calculated at T1, T2
and T3 to examine the (in)dependence of both inhibitory control domains.
Second, inhibitory control measures that significantly changed in
response to atomoxetine treatment were used to examine the possible mediating
effects of inhibitory control on the change in ADHD symptoms. Parametric (reaction
time and reaction time variability) and non-parametric (proportion of false alarms
and misses) correlations were calculated between Δ inhibitory control (T2-T1 for
the blind group and T3-T2 for the open label group) and Δ ADHD symptoms
(T2-T1 for the blind group and T3-T2 for the open label group), in which negative
scores reflected improvement (less ADHD symptoms and less erroneous and
RESULTS
Group characteristics for measures of inhibitory control and ADHD symptom
severity are presented in Table 7.2.
Effect of atomoxetine on inhibitory control
A significant treatment effect was found on the primary outcome measure of
response inhibition (proportion false alarms on a Go-No Go paradigm) according
to both the blind phase analyses as well as change after first treatment analyses
(see Table 7.2). The secondary outcome measure of response inhibition
(proportion of misses on the Go-No Go paradigm) only improved during the
blind phase. In contrast, the primary outcome measure of interference control
(proportion false alarms on interference trials) did not improve after atomoxetine
treatment in the blind or open label phase. A secondary measure of interference
control (proportion misses) deteriorated after atomoxetine treatment during the
blind phase, but not according to change after first treatment analyses. Another
secondary measure of interference control (response time) improved according
to change after first treatment analyses, but not according to blind phase
analyses. Both primary outcome measures (proportion false alarms on a Go-No
Go paradigm and proportion false alarms on interference trials) did not correlate
significantly (r = .15, p = .17). Except for the proportion of misses in both tasks
(r = .38, p < .001), none of the other cognitive or behavioral outcome measures
correlated significantly either.
faster performances as a results of treatment)
172
173
174
3.39, .07
14.9 (7.3)
-2.27 [3.70 - -0.87]
13.83, <.001 -9.39 [-11.52 - -7.26]
25.3 (12.2)
n=42/49
-45.57 [-94.91 – 3.77]
Note. ATX = Atomoxetine, LOCF = Last observation carried forward. ADHD-RS= ADHD
Rating Scale, CTRS-R:S = Conners’ Teacher Rating Scale-Revised: Short Form.
94/97 children had a T1 assessment of the Response inhibition task and 95/97 children of
the Interference control task a For 2 children the response inhibition measurement and for
3 children the interference control measurement of T0 was used for T1 because of missing
data at T1. b Difference between atomoxetine and placebo post treatment, corrected for
pre-treatment. ANCOVAs were used for reaction time (variability) with group as factor, the
pre-treatment (T1) as covariate and post-treatment (T2) as dependent measure. Negative
binominal models were used for error measures, since these data showed substantial
underdispersion, with group as factor, the pre-treatment (T1) as covariate and posttreatment (T2) as dependent measure. c Change after first treatment using one-sample t-test
(response time (variability)) or non-parametric tests (error measures) against a test-value of
0 (no change after treatment): double blind phase for the atomoxetine group (T2-T1), open
label phase for the placebo group (T3-T2).
13.4 (8.7)
27.5 (12.0)
n=39/48
Mediating effect of inhibitory control on ADHD symptomatology
A significant treatment effect was found on ADHD symptoms as assessed by the
ADHD-RS during the blind and open label phases. For the CTRS-R:S, significant
17.6 (9.0)
treatment analyses. Correlations between ADHD-symptoms, error measures and
response time (variability) in response inhibition were found to be non-significant
(correlations ranging from .00 - .13 for parents and from .02 - .16 for teachers,
15.5 (9.8)
37.6 (9.8)
32.6 (11.0)
n=42/48
n=47/49
ADHD symptom improvements were only reported according to change after first
all p’s > .10). For interference control, correlations between error measures
and response time (variability) and ADHD-symptoms were non-significant either
18.1 (7.5)
38.6 (8.4)
n=47/49
(ranging from .04 - .16 for parents and from -.15 - .02 for teachers, all p’s > .10).
See also Figures 7.2a and 7.2b for the change of both types of inhibitory control
(measured with the proportion of false alarms and proportion misses) in relation
40.7 (7.5)
18.5 (9.3)
- ADHD-RS10
11
- CTRS-R:S
n=43/48
to the change of ADHD symptoms after both phases.
ADHD SYMPTOM
SEVERITY
-47.22 [-129.60 -18.85]
1081.4 (419.7) 1045.4 (480.7) 985.7 (465.8) 1012.9 (462.3) 931.9 (389.5) 948.1 (458.9) 0.88, .35
- Response time
- Response time variability 454.1 (299.6) 459.9 (334.6) 356.0 (263.9) 387.6 (290.3) 372.6 (326.2) 383.2 (276.1) 0.30, .58
0.03 [0.01 – 0.05]
4.88, .03
.06 (.09)
.10 (.11)
.05 (.06)
.06 (.06)
.07 (.11)
.05 (.06)
- Proportion misses
-0.02 [-0.05 – 0.01]
0.95, .33
.10 (.12)
.10 (.13)
.13 (.14)
.11 (.14)
.12 (.13)
.13 (.16)
- Proportion false alarms
on non-target trials
0.00 [-0.02 – 0.02]
0.10, .75
.05 (.08)
.06 (.10)
.03 (.07)
.04 (.07)
.03 (.05)
- Proportion false alarms .06 (.10)
on interference trials
n=46/49
n=42/48
n=47/49
n=43/48
n=48/49
n=47/48
INTERFERENCE
CONTROL
-2.30 [-17.17 – 12.57]
-1.51 [-15.53 – 12.51]
0.00, .99
118.3 (69.4)
133.2 (72.2)
137.7 (79.6)
144.2 (86.5)
120.0 (77.2)
532.7 (144.9) 485.0 (120.7) 525.7 (132.8) 490.5 (104.6) 501.7 (134.8) 491.4 (117.4) 0.07, .80
- Response time
- Response time variability 125.1 (64.0)
-0.02 [-0.04 - -0.001]
0.00 [-0.01 – 0.01]
3.64, .05
3.83, .05
.06 (.08)
.02 (.04)
.06 (.07)
.01 (.04)
.02 (.05)
.08 (.11)
.04 (.05)
.03 (.06)
.02 (.05)
.06 (.08)
.02 (.06)
- Proportion false alarms .07 (.10)
n=46/49
n=42/48
n=47/49
n=47/48
n=47/49
n=47/48
RESPONSE INHIBITION
- Proportion misses
LOCF
M [95% CI]
LOCF
F/Wald-χ², p
Formerly
placebo
M (SD)
ATX
M (SD)
Placebo
M (SD)
ATX
M (SD)
Placebo
M (SD)
ATX
M (SD)
T3: End of open label phase Group
Change after first
comparison treatmentc
blind phaseb
T2: End of double blind
phase
T1: Baselinea
Table 7.2 Means and standard deviations of ADHD measures and inhibitory control measures before atomoxetine
treatment (T1), after double blind placebo controlled treatment (T2) and after an open label extension (T3)
Chapter 7
175
AD
open label, proportion misses
Interference Control: r = .09, p = .38 for false alarms, r = .16, p = .14 for misses.
Interference Control: r = .09, p = .38 for false alarms, r = .16, p = .14 for misses.
Response Inhibition: r = .00, p = .99 for false alarms, r = .00, p = .98 for misses.
open label, proportion false alarms
open label, proportion misses
double blind, proportion false alarms
double blind, proportion misses
Response Inhibition: r = .00, p = .99 for false alarms, r = .00, p = .98 for misses.
improved RI,
deteriorated ADHD
improved RI and ADHD
ADHD symptom severity
deteriorated RI
and ADHD
deteriorated RI,
improved ADHD
Response inhibition (false alarms, misses)
Change in response inhibition in relation to change
in ADHD symptoms
Response inhibition (false alarms, misses)
176
Change after first treatment: Δ Response
inhibition, Δ Interference control and Δ
double blind, proportion false alarms
ADHD symptoms: T2-T1 for the blind
double blind, proportion misses
phase and T3-T2 for the open label
open label, proportion false alarms
phase
ADHD symptom severity
improved IC,
deteriorated ADHD
improved IC and ADHD
ADHD symptom severity
deteriorated IC
and ADHD
deteriorated IC,
improved ADHD
Change in interference control in relation to change
in ADHD symptoms
Interference control (false alarms, misses)
in ADHD symptoms
Figures 7.2a and 7.2b Change in ADHD symptoms and inhibitory control during exposure to atomoxetine in the
double blind and open label phases
Interference control (false alarms, misses)
in ADHD symptoms
Chapter 7
DISCUSSION
This double-blind placebo controlled study with an open-label extension phase
aimed at examining the effects of atomoxetine on two forms of inhibitory control
(response inhibition and interference control) in children with both ASD and ADHD,
and whether ADHD symptom improvement was mediated by improvement in
inhibitory control. The primary outcome measure of response inhibition (number
of false alarms on no-go trials) improved as well as the number of misses after
atomoxetine treatment; the former both in the blind phase and change after first
treatment analyses, the latter only in the change after first treatment analyses
(combining blind and open label phase for the atomoxetine and placebo group,
respectively). In contrast, the primary outcome measure of interference control
(proportion of errors on interference trials) did not improve after atomoxetine
treatment and a secondary measure (proportion of misses) actually worsened
after atomoxetine treatment during the blind, but not the open label phase.
However, performance on the interference control task was somewhat faster
after treatment with atomoxetine when change after first treatment analyses were
conducted. None of the measures mediated the effect of atomoxetine on ADHD
symptom improvement; improvement in ADHD symptom scores and inhibitory
control occurred independently from each other.
The finding that atomoxetine improves response inhibition is in line with
the majority of previous studies documenting response inhibition in children with
ADHD (Alderson, Rapport & Kofler, 2007; de Jong et al., 2009; Gau & Shang,
2010; Lijffijt, Kenemans, Verbaten & van Engeland, 2005; Nigg, 1999; Nigg,
Blaskey, Huang-Pollock & Rappley, 2002; Oosterlaan, Logan & Sergeant, 1998;
Rucklidge & Tannock, 2002; Schachar, Mota, Logan, Tannock & Klim, 2000),
response inhibition in adults with ADHD (Chamberlain et al., 2007) and response
inhibition in rodents (Bari et al., 2009; Blondeau & Dellu-Hagedorn, 2007;
Paterson, Ricciardi, Wetzler & Hanania, 2011; Pattij, Schetters, Schoffelmeer
& van Gaalen, 2012; Robinson et al., 2008) although in contrast to a minority
(Kuntsi, Oosterlaan, & Stevenson, 2001). Our study adds to these findings by
177
Chapter 7
illustrating that response inhibition can also be improved in patients with ASD
& Taylor, 2005), whereas for interference control, a reduced activitity of the frontal-
and comorbid ADHD, tentatively suggesting that the origins of response inhibition
striatal cortex (including the cingulate cortex) as well as parietal cortex has been
problems in these comorbid patients may be of similar nature as those observed
reported in ADHD (Bunge et al., 2002; Lee, 2010; Vaidya et al., 2005). Atomoxetine
in ADHD-only patients. This hypothesis is further supported by the improvements
appears to mainly exert its effects on the prefrontal cortex underlying response
of ADHD symptoms as a result of treatment with atomoxetine in this patient
inhibition, and not on the parietal brain regions also required for interference
group(Harfterkamp et al., 2012) as well as in previous (mostly open-label) studies
control in patients with ASD and ADHD. In other words, the enhanced frontal-
(Arnold et al., 2006; Charnsil, 2011; Fernández-Jaén et al., 2012; Hazell, 2007;
striatal functioning via the selective norepinephrine re-uptake inhibitor may be
Jou et al., 2005; Posey et al., 2006; Troost et al., 2006; Zeiner et al., 2011). All in
sufficient to improve response inhibition, but insufficient to improve interference
all, atomoxetine appears to improve both response inhibition problems as well
suppression because parietal-temporal contributions are necessary (Bunge et
as ADHD symptoms in patients with ASD and ADHD, suggesting this to be an
al., 2002; Casey et al., 2000). Further functional imaging studies are required to
effective method of treatment in these patients.
confirm or refute this hypothesis.
In contrast, the effect of atomoxetine on interference control was absent,
An intriguing finding reported here was the lack of association between
which runs counter to previous studies examining the effect of atomoxetine on
ADHD symptom improvement and improvements in inhibitory control. The
interference control in ADHD patients (Grodzinsky & Diamond 1992; Spencer
absence/presence of such a relationship is a relatively neglected topic in studies
et al., 1998; Yang et al., 2011), but is in line with others (Faraone, Biederman,
reporting on pharmaceutical effects on cognitive and symptom data (Boonstra,
et al., 2005; Schwartz & Verhaeghen, 2008; Spencer et al., 2006; van Mourik,
Kooij, Oosterlaan, Sergeant & Buitelaar, 2005; Fernández-Jaén et al., 2012;
Oosterlaan & Sergeant, 2005).The differential effect of atomoxetine on response
Turner, Clark, Dowson, Robbins & Sahakian, 2004), yet is of great relevance in
inhibition on the one hand and interference control on the other hand, is not
understanding the mechanisms of action of a pharmacological agent. Some
surprising given that these functions do not necessarily correlate with each other,
previous studies on the effects of atomoxetine on ADHD symptoms and inhibitory
which was the case in our study as well as previous studies (Barkley, Grodzinsky
control did report on this relationship, and found no connection between
& DuPaul, 1992; Scheres et al., 2003; van der Oord, Geurts, Prins, Emmelkamp &
improvement of one and the other (Gualtieri & Johnson, 2008; Spencer et al.,
Oosterlaan, 2012). The neural substrates of response inhibitionand interference
2006; Vaughn et al., 2011). Similarly, independent effects of methylphenidate on
control in ADHD are not yet fully understood. It has been suggested that alteration
ADHD symptoms and associated cognitive deficits have been found (Epstein et
in the neural basis of response inhibition and interference control in childhood
al., 2006; Scheres et al., 2003), suggesting this is not a rare phenomenon or a
ADHD are characterized by distinct patterns of functional abnormality (Bunge,
finding only reported in atomoxetine treatment studies. It has been suggested that
Hazeltine, Scanlon, Rosen & Gabrieli, 2002; Vaidya et al., 2005). The functional
cognitive deficits in psychiatric disorders may act as epiphenomena: related to
activity associated with response inhibition in ADHD is a decreased activation of
the same etiological underpinnings as the disease symptoms, but not mediating
the prefrontal and striatal brain regions (including the inferior frontal gyrus and
between both (Kendler & Neale, 2010), it is still unclear for which functions and
cingulate cortex) (Aron & Poldrack, 2005; Durston, 2003; Durston, Mulder, Casey,
for which disorders this is the case. However, it may suggest that predicting or
Ziermans & van Engeland, 2006; Rubia et al., 1999; Rubia, Smith, Brammer, Toone
monitoring treatment response using cognitive tests may not be of clinical utility,
178
179
Chapter 7
at least in the case of atomoxetine treatment in children with ASD and ADHD
using inhibitory control tasks.
Several limitations warrant consideration. First, we did not include an
unaffected control group and can therefore not be certain of a baseline deficit in
inhibitory control. However, since all children were clinically diagnosed with ADHD,
at least a proportion was likely to have inhibitory control difficulties (Barkley, 1997;
Pennington, 1996), and our results showed that for the overall group room for
improvement was present given the significant effect of atomoxetine treatment
on response inhibition. Second, we used only two tests to measure inhibitory
control, perhaps the use of a more comprehensive task battery could have led
to more firm conclusions on the efficacy of atomoxetine on inhibitory control in
children affected with both disorders. Third, our study sample had relatively few
adolescents, female subjects, and children with IQ’s in the lower range, making
findings possibly less accountable for these groups.
In sum, this is the first double blind, placebo controlled study to
demonstrate selective beneficial effects of atomoxetine on motor response
inhibition but not interference control in children with both ASD and ADHD
symptoms. This cognitive benefit occurs independently from improvements in
ADHD-symptomatology, which may suggest that distinct pathogenic pathways
play a role in clinical symptoms and cognitive dysfunctions. Increased
understanding of the effects of medical treatment on both levels of functioning
can help the development of personalized medicine; medication with a higher
probability of desired outcomes and reduced side effects.
180
181
General discussion
182
ASD and ADHD are frequently co-occurring neurodevelopmental disorders that
are rather heterogeneous in symptom presentation and underlying etiological
factors. ASD is characterized by impaired social interaction, impaired verbal
and nonverbal communication, as well as restricted and repetitive behavior and
interests, while ADHD is characterized by severe inattention, hyperactivity and
impulsivity (American Psychiatric Association, 2013). Most estimates for the
presence of ADHD among patients with ASD fall within the range of 30% to 80%,
whereas the presence of ASD is estimated in 20% to 50% of the patients with
ADHD (e.g. Ames & White, 2011; Leyfer et al., 2006; for review see Rommelse
et al., 2010; Ronald et al., 2008). ASD and ADHD are both typified by cognitive
impairments (for review see Rommelse et al., 2011). The overall objective of
this thesis was to examine shared and unique behavioral and cognitive profiles
in ASD and ADHD, and whether these profiles provided more insight into the
shared and unique etiology of ASD and ADHD. The disclosure of shared and
unique underlying mechanisms of ASD and ADHD is complicated by the
heterogeneity across diagnostic categories in the DSM classification system, for
group comparisons based on DSM classifications reflect symptoms rather than
causes (e.g. Lord & Jones, 2012; Miller, 2010). In addition, approaches based on
DSM-cut-offs overlook the evidence that the general population (i.e. under the
clinical cut-off) is also characterized by behavioral, cognitive and genetic variance
(e.g. Barnett et al., 2009; Constantino, 2011; Fair et al., 2012; Plomin et al., 2009;
Robinson, Koenen et al., 2011).
In line with these insights, this dissertation used dimensional measures
of ASD and ADHD symptoms, which more accurately reflect the continuously
distributed and multifaceted nature of behavioral symptoms and cognitive profiles
of both disorders within the population. To empirically dissect heterogeneity and
define more homogeneous disease profiles, an empirical bottom-up approach
(i.e. latent class analyses (LCA) (McCutcheon, 1987)) was applied. In sum, the
approach of this thesis had the following key characteristics:
185
Chapter 8
I)
Assess ASD and ADHD symptoms in parallel.
were largely linear. That is, for any behavioral and cognitive outcomes that showed
II)
Examine the relationship between behavior and cognition.
significant associations with the ASD or ADHD traits, the non-symptomatic ends
III)Apply dimensional measurements by focusing on both the lower and
of the trait continua were associated with fewer behavioral problems and better
higher end of the ASD and ADHD trait continua and integrating data from
cognitive performances than the symptomatic ends. This provided support
population-based and clinic-based samples.
to the assumption that ASD and ADHD are both extreme ends of continuous
IV)Identify subgroups that are homogeneous at the behavioral or cognitive
level by using latent class analyses.
traits that cover quantitative rather than qualitative differences. Future studies
on the correlates of the non-symptomatic ends of both continua may deepen
This general discussion summarizes and discusses key findings from all chapters,
our understanding of protective mechanisms underlying superior behavioral and
points out limitations, suggests recommendations for future research, and closes
cognitive functioning.
with some clinical implications.
The relationship between behavior and cognition: reduced heterogeneity
Summary
on the behavioral level
The continuum of ASD and ADHD traits
In chapter 3, LCA on ASD and ADHD symptom data were conducted in order
Chapter 2 focused on normally distributed ASD and ADHD traits in a populationbased sample of 378 children between 6 and13 years of age. ASD and ADHD are
thought to exist on a continuum, where diagnosis simply reflects the symptomatic
end of a normal distribution of quantitative traits in the general population.
This study was conducted in order to find support for or evidence against the
hypothesis that the lower extreme ends of the trait continua are associated with
favorable cognitive and behavioral outcomes. Alternatively, extreme deviations in
either direction on the continua may be pathological. That is, being at the lowest
risk for ASD or ADHD may also come with specific disadvantages. For example,
very low levels of restrictive and repetitive behaviors may lead to difficulties
keeping a daily routine, and being highly reflective instead of impulsive may lead
to inertia. We examined if the association of ASD and ADHD traits with cognitive
and other behavioral measures was linear or curvilinear. Findings indicated
that the non-symptomatic ends of the ASD and ADHD trait distributions indeed
represented some superior cognitive (for ADHD) and behavioral (for ASD and
ADHD) functioning. The associations between the ASD and ADHD traits on the
one hand and behavioral problems and cognitive functioning on the other hand
186
to find support for or evidence against the hypothesis that ASD and ADHD are
different manifestations of one overarching disorder. We applied LCA on Social
Communication Questionnaire (SCQ) and Conners’ Parent Rating Scale (CPRSR:L) data of 644 children between 5 and 17 years of age from both a population
and clinic-based sample. Classes were compared for comorbid symptoms and
cognitive profiles of motor speed and variability, executive functioning, attention,
emotion recognition and visual spatial functioning. LCA identified three patient
classes that could be distinguished from two normal classes: one class with
ADHD symptoms only, one class with clinical levels of ADHD but also clinically
elevated levels of ASD symptoms (ADHD[+ASD]), and one class with clinically
high levels of ASD symptoms but also clinically elevated levels of ADHD symptoms
(ASD[+ADHD]). As hypothesized, no class with exclusive ASD symptoms was
revealed; all children who expressed ASD behavior also presented the less
severe ‘precursor’ of ADHD behavior. These findings gave support for the gradient
overarching disorder hypothesis, which states that ADHD may best be seen as
a milder, less severe subtype within the ASD spectrum. In addition, the cognitive
profiles partially supported the gradient overarching disorder hypothesis as
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Chapter 8
well. That is, the cognitive functioning in the ADHD-only class could overall be
In chapter 5, we used measures that provide greater resolution of the scores in
considered at an intermediate level, but qualitative differences were also observed,
the lower end of the ASD and ADHD trait distributions, subjected these to LCA,
with working memory deficits being more pronounced in both primarily ADHD
and examined how subgroups differed in terms of cognitive functioning. Analyses
classes compared to the primarily ASD class, and a detail-focused cognitive style
revealed five classes; three mostly quantitatively differing concordant ASD-ADHD
in visual pattern recognition in the ASD(+ADHD) class only, despite the fact that
classes with either low, medium or high scores on both traits (77.5%), which
both classes showed clinically elevated ASD and ADHD symptoms. Thus, we
indicated that ASD and ADHD traits are usually also strongly related in the general
concluded that different ASD–ADHD comorbid subtypes exist, with quantitative
population. Two discordant ASD-ADHD classes presented with either more ASD
overlap, but also qualitative difference in cognitive deficits. This has clinical
symptoms than ADHD symptoms or vice versa (22.5%) were characterized by
relevance, since these children may respond differently to medication treatment,
differential visual spatial functioning. These findings across the non-symptomatic
social skills therapy and behavioral therapy.
end of the ASD and ADHD traits closely resembled those across the clinical ASD-
In chapter 4, the 8 to 17 year old children from the Normal, ADHD-only,
ADHD classes described in chapter 3, which suggested that heterogeneity in ASD
ADHD(+ASD) and ASD(+ADHD) classes described in chapter 3were examined
and ADHD may be rooted in heterogeneity present in the general population.
with regard to their motor timing abilities. Motor timing abilities are frequently
reported to be affected in children with ADHD, and deficits in time processing may
The relationship between behavior and cognition: reduced heterogeneity
play an important role by modulating primary symptoms. For example, attention,
on the cognitive level
language and inhibition are associated with time processing, as these functions
Chapter 6 examined the association between behavior and cognition in the
are characterized by specific temporal patterns. Results indicated that motor
reversed direction, subjecting cognitive measures to LCA, a rather novel approach.
timing accuracy was more affected in children with ADHD with comorbid ASD
Given that reduced heterogeneity on the behavioral level resulted in informative
symptoms (ADHD[+ASD]) compared to the children in the ADHD-only class.
cognitive profiles in chapter 3, segmenting on the basis of cognitiveperformances
This finding led to the conclusion that only patients with more severe behavioral
may be a useful complementary approach in detecting shared and unique
symptoms show motor timing deficiencies. However, this could not merely be
mechanisms underlying ASD and ADHD. That is, cognitive functioning can be
explained by high ADHD severity with ASD playing no role, as ADHD symptom
measured more objectively than clinical symptoms, and is possibly more closely
severity in the pure ADHD-class and the ASD(+ADHD) class was highly similar,
linked to the neurobiological underpinnings than behavior (Gottesman & Gould,
with the former class showing no motor timing deficits. This was well in line
2003). Based on the findings described in chapter 3, it was hypothesized that
with the gradient overarching disorder hypothesis described in chapter 3, and
cognitive subtypes might be identified with a) superior visual pattern recognition
underlined the relevance of a reduction of the behavioral heterogeneity present in
skills and inferior emotion recognition abilities that was most strongly linked to ASD;
DSM-defined ASD and ADHD group comparisons.
and b) inferior visual pattern recognition skills and normal emotion recognition
In chapter 5, the focus shifted back to the population-based sample only.
abilities that was most strongly linked to ADHD. Contrary to these hypotheses, the
This sample was mostly grouped together through LCA into ‘Normal’ classes on
main finding was that LCA in the clinic and population-based samples revealed a
the basis of clinical (thus skewed) ASD and ADHD measures used in chapter 3.
similar four class solution typified by qualitatively different speed-accuracy trade-
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offs instead of domain specific strengths/weaknesses. Furthermore, in the clinic-
that cognitive deficits in psychiatric disorders may act as epiphenomena: related
based sample, these speed-accuracy trade-off patterns were strongly linked to
to the same etiological underpinnings as the disease symptoms, but not mediating
between-class differences in ASD and ADHD (and comorbid) symptom severity.
between both. This is clinically relevant, since it may suggest that predicting or
More specifically, the cognitive profile that overall favored accuracy over speed
monitoring treatment response using cognitive tests is not of clinical utility in
was associated with the lowest amount of ASD and ADHD symptoms, while the
atomoxetine treatment in children with ASD and ADHD using inhibitory control
profile characterized by slow and inaccurate performance across the cognitive
tasks.
tasks was associated with the highest amount of ASD and ADHD symptoms.
These associations between cognitive functioning and behavioral symptoms
Discussion
were much weaker in the population-based sample, which was not due to a
On the relationship between ASD and ADHD
restriction of variance in symptom measures, since normally distributed ASD and
ADHD trait measures were additionally used. The absence of cognitive profiles
with domain specific strengths/weaknesses contrasts with the idea of multiple
cognitively different developmental pathways to ASD and ADHD and the cognitive
profiles detected in chapter 3. This finding suggested that cognitive functioning
only predicts behavior when other risk or protective factors are present.
Further investigation of the relationship between behavior and cognition:
clinical trial data
The clinical trial described in chapter 7 examined whether a pharma­cologic
intervention in the noradrenergic system hypothesized to improve symptoms of
ADHD would also improve cognitive impairments in children with both ASD and
ADHD. This double blind, placebo controlled study demonstrated that children
improving in response inhibition were not necessarily the ones also improving in
ADHD symptoms, since none of the cognitive measures mediated the effect of
atomoxetine on ADHD symptom improvement. That is, although both response
inhibition and ADHD symptoms improved under the influence of atomoxetine,
improvement in response inhibition did not correlate with improvement in ADHD
symptoms. This lack of association between ADHD symptom improvement and
improvements in inhibitory controlmay suggest that distinct pathogenic pathways
play a role in clinical symptoms and cognitive dysfunctions. It has been suggested
190
With respect to the relationship between ASD and ADHD, the most fundamental
issues are at the nosological level: Are ASD and ADHD distinct disorders, or do they
reflect an arbitrary division of a single syndrome (Neale & Kendler, 1995)? Multiple
findings appeared to suggest that ASD and ADHD are alternate expressions of
a single underlying dimension of liability, as described by Banaschewski et al.
(2007) and Rommelse et al. (2010). That is, ASD and ADHD seemed to present
with shared etiological substrates, in which ADHD may best be seen as a milder,
less severe subtype within the ASD spectrum (chapters 3, 4 and 5). In short, the
most important findings in favor of this hypothesis were:
a) the presence of a latent class with children that presented with ADHD
symptoms without comorbid ASD symptoms, in contrast to the absence of
a latent class with children who present with ASD without comorbid ADHD
symptoms;
b) the latent class with only ADHD-symptoms presented with behavioral and
cognitive difficulties which were on an intermediate level when compared to
the class without behavioral problems and the class with ADHD symptoms
and comorbid ASD symptoms;
c) mostly quantitatively differing concordant ASD-ADHD classes with either
low, medium or high scores on both traits were found in both clinic-based
samples and across the population-based sample.
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Chapter 8
Considering ADHD as a less severe subtype within the ASD spectrum calls for
liability model. Nonetheless, no definite conclusions can be drawn since a study
a broader view on developmental disorders, as will be discussed in the future
design with repeated measures that monitors behavior over time is warranted
directions below. In contrast, another finding was more in favor of a distinct
to distinguish between a shared underlying liability and reciprocal causation.
disorder hypothesis for ASD and ADHD:
Such a research design was applied in the Avon Longitudinal Study of Parents
a) some specificity of cognitive deficits with regard to the visual spatial
and Children (ALSPAC) study conducted by St.Pourcain and colleagues (2011).
processingstyle (i.e. detail-focused or not), working memory and emotion
They stated that the clinical presentation of both ASD and ADHD is influenced by
recognition across latent ASD-ADHD classes (chapters 3 and 5).
age, with ASD symptoms being more stable compared to ADHD symptoms. As
The difference in visual spatial functioning between ASD and ADHD reflected a
changes in symptom severity for ASD and ADHD varied, both traits were strongly
double dissociation, reflective of a qualitatively different cognitive deficit. This was
but not reciprocally interlinked. That is, the majority of children with a persistent
in line with recent studies that reported ADHD-subgroups with unique cognitive
ADHD symptomatology also showed persistent ASD deficits but not vice versa,
and/or comorbid behavioral profiles (Fair et al., 2012; Pauli-Pott, Dalir, Mingebach,
suggesting that a complex and variable relationship exists between the ASD and
Roller & Becker, 2013). These findings led the authors to suggest that the clinical
ADHD traits (St. Pourcain et al., 2011).
phenotype of ADHD may be rooted in multiple distinct cognitive subgroups and/
or developmental pathways. If true, the identification and description of such
ASD-ADHD comorbidity, we can conclude that some substrates of ASD and
subgroups may help clinical practice to meet the specific deficits of these children
ADHD may be shared between both disorders, while others may be unique.
by more tailored diagnostic and treatment procedures within a personalized
The gradient overarching disorder hypothesis, in which ASD and ADHD are
(medicine) framework.
quantitatively different from one and another and in which ADHD may best be
Finally, a third potential model of comorbidity that cannot be rejected
seen as a milder subtype within the ASD spectrum, seems a reasonable model.
on the basis of the findings in this thesis is that ASD-ADHD comorbidity is due
That is, most findings are well in line with this hypothesis, except for visual spatial
to reciprocal causation (Banaschewski et al., 2007). That is, ADHD may increase
processing styles, which may indicate why some children develop ADHD despite
the risk for ASD (‘domino effect’), which can explain the presence of pure ADHD
their enhanced risk for ASD. Future research on causal models for ASD, ADHD
as well as comorbid ASD-ADHD. However, the reverse pattern, i.e. that ASD may
and ASD-ADHD comorbidity may benefit from longitudinal research designs, as
increase the risk of ADHD, would suggest the presence of pure ASD, which we did
patterns of association between ASD and ADHD symptoms change over time
not find. The nature of our samples might have prevented the detection of pure
(St. Pourcain et al., 2011). In addition, future research may wish to use not only
ASD, as ASD without comorbid ADHD symptoms may be underrepresented in
behavioral and cognitive measures, but also account for the influence of potential
clinical samples and may also be relatively rare in population samples. Therefore,
environmental and genetic risk or protective factors in order to resolve this
very large population-based samples might be best to examine whether children
nosological uncertainty (Jaffee & Price, 2007).
Based on the current findings, and regardless of the exact cause for
who present with ASD without comorbid ADHD exist, but their rarity by itself may
suggest that a reciprocal risk is an unlikely route. Rather, our data suggest that if
ASD is present, ADHD seems present as well, which is more in line with the shared
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On the relationship between behavior and cognition
Cognitive measures are used frequently in the assessment of ASD and ADHD.
These measures are also referred to as intermediate phenotypes, useful indicators
in detecting etiologically more homogeneous subgroups of patients (Gottesman
& Gould, 2003). Intermediate phenotypes form a causal link between genes and
behavioral symptoms, more closely linked to the genes in action in ASD and
ADHD, and more objectively measured than behavior (Kendler & Neale, 2010).
Characteristic of intermediate phenotypes is that they are heritable, associated
with the disorder, state independent and present in non-affected family members
of patients (Walters & Owen, 2007). A growing body of evidence strongly suggests
that, although several cognitive domains can impact on symptom severity, none
of them is necessary or sufficient in causing the developmental disorders (see for
review Coghill, Seth, et al., 2013). Kendler and Neale (2010) aimed to clarify the
relationship between behavior and cognition through a conceptual analysis of the
intermediate phenotype construct. Among the concerns discussed were that a)
no distinction could be made between a liability-index model and a mediational
model for intermediate phenotypes, b) the association between an intermediate
phenotype and a psychiatric disorder could either be unidirectional and/or
bidirectional, and c) intermediate phenotypes may reflect both environmental
and/or genetic risk factors for the development of a disorder. For those reasons,
a relevant and powerful strategy to study the relationship between behavior
and cognition via intermediate phenotypes would be a longitudinal research
design with multiple measurements of behavior and cognition over time. Such
designs have the capacity to clarify the causal relationships between intermediate
phenotypes and psychiatric disorders.
The complexities in the relationship between behavior and cognition
only when additional risk factors are present. The associations between cognitive
measures and symptom severity were weak or absent in the population-based
sample, providing little evidence in support of the hypothesis that ASD and ADHD
traits share cognitive underpinnings in the general population. In contrast, the
associations between cognition and symptoms were related to symptom severity
in the clinic-based sample, suggesting that the measures used did uncover
cognitive mechanisms underlying ASD and ADHD. Such a missing link between
symptom severity and neurocognitive functioning in the general population was
also reported in another recent population-based study (Rajendran, Rindskopf,
et al., 2013), while observed in a clinic-based sample (Rajendran, Trampush, et
al., 2013). In the studies described in this thesis, it is unlikely that a restriction of
variance in symptom measures can explain this difference in findings between
both samples, since the findings pertained also to normally distributed ASD
and ADHD trait measures. Rather, it seems likely that the relationship between
cognitive functioning and symptom severity may be more complex than
expected. Coghill, Hayward and colleagues (2013) examined this complex
relationship further through a prospective study of the association between
compensatory improvements in executive neuropsychological functioning and
clinical symptom improvement. Their findings suggested that cognitive deficits
and behavioral symptoms are not linearly related, such as depicted in Figure
8.1, which is a slightly adapted version of a traditional causal model for ASD and
ADHD described by Coghill, Hayward, et al. (2013). Rather, the relative lack of
association between clinical and cognitive changes over time may suggest that
clinical and cognitive aspects of ASD and ADHD make independent contributions
to overall impairment; see the slightly adapted version of a potential alternative
causal model depicted in Figure 8.2.
are illustrated by our data. The treatment study described in chapter 7 indicated
that none of the inhibitory control measures was associated with ADHD symptom
improvement. In addition, the findings described in chapter 6 suggest that the
relationship between behavior and cognition may be stronger and can be detected
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Chapter 8
Figure 8.1 Traditional causal model for ASD and ADHD, inspired by Coghill,
Hayward, et al. (2013)
addition, McAuley, Crosbie, Charach & Schachar (2013) also concluded that
cognitive performances (i.e. response inhibition) in ADHD improved with age
regardless of changes in ADHD symptoms and impairment. Thus, cognitive
Environment
deficits were state-independent, present irrespective of changes in the behavioral
symptoms. Third, non-affected siblings of ASD and/or ADHD affected children
present with resembling cognitive difficulties, as was also outlined by Oerlemans
Brain
structure and
function
Genes
ASD/ADHD
symptoms
Cognition
Impairment
and colleagues (2013). Here, the cognitive performances of unaffected siblings
were overall at an intermediate level, performing somewhat worse than healthy
control children and better than their ASD affected siblings. These findings add
Figure 8.2 Potential alternative causal model for ASD and ADHD, inspired by
Coghill, Hayward, et al. (2013)
Environment
up to the idea that distinct pathogenic factors underlie behavioral symptoms on
the one hand, and specific cognitive dysfunctions on the other hand. Although
multiple findings are in line with this alternative causal model, another possible
explanation may be that the extent to which cognitive tasks accurately measure
daily life difficulties (e.g. difficulties in time management and self motivation for
ADHD, difficulties in social reciprocity and changes in daily routines for ASD) may
Cognition
Genes
Brain
structure and
function
be limited (Barkley & Fisher, 2011; Knouse, Barkley & Murphy, 2013). Rather,
Impairment
ASD/ADHD
symptoms
research indicated that the recently developed questionnaire Barkley Deficits in
Executive Functioning Scale (BDEFS) correlated more closely to daily difficulties
in ADHD than did cognitive deficits (Barkley, 2011). In any case, the uncertainty
and complexity of the relationship between symptoms and cognition calls for
Multiple findings are in line with this latter model. First, the change in
performances on the specific cognitive measures used in chapter 7 (i.e. inhibitory
control, which is a robustly validated neurocognitive deficit in ASD and ADHD (for
meta-analyses see Geurts, van den Bergh & Ruzzano, epub ahead of print; for
review see Rommelse et al., 2011) appeared to be unrelated to change in ADHDsymptomatology. Second, the systematic review on the cognitive predictors of
persistence of ADHD conducted by van Lieshout, Luman, Buitelaar, Rommelse
& Oosterlaan (2013) did not provide evidence to support the hypothesis that
either automatically controlled or more consciously controlled cognitive functions
differentiated between persistence and remittance of ADHD symptoms. In
196
cautiousness in the clinical neurocognitive field and the use of intermediate
phenotypes.
The studies described in this thesis tentatively discussed potential
relations between the cognitive performance patterns in ASD and ADHD, and
their neural basis. The more generic cognitive profiles detected in chapter 6 are
in line with theories of more diffuse abnormal cortical connectivity and synchrony
as well as more widespread neural abnormalities in ASD and ADHD (Rudie et al.,
2013; Wang et al., 2009; Wass, 2011). Long-range cortical under-connectivity and
local (sub)cortical over-connectivity are found in the majority of studies in ASD; an
increased local connectivity was found in some but not all studies across children
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Chapter 8
with ADHD (e.g. Bush et al., 2005; for review see de la Fuente et al., 2013; Silk et
brain on the other hand, a humble attitude towards our current knowledge of the
al., 2005; Vance et al., 2007; for review see Vissers et al., 2012). This difference
relationship between behavior and the brain seems appropriate.
between ASD and ADHD seems in accordance with the double dissociation in
visual-spatial functioning described above. In short, these findings indicated that
On the use of a dimensional approach
children displaying more ASD traits than ADHD traits presented with a visual
Previous studies have shown that ASD and ADHD exist on a continuum that
perceptual processing style that facilitated local rather than global processing,
extends from the symptomatic end of ASD and ADHD affected populations to
while the opposite pattern was seen in children that displayed more ADHD traits
the non-symptomatic end without ASD and ADHD symptoms (Constantino, 2011;
than ASD traits. Although this dissociation may pinpoint towards differences in
Fair et al., 2012; Plomin et al., 2009; Robinson, Koenen, et al., 2011). In chapter 2,
connectivity (i.e. favoring local processing in ASD, and favoring global processing
the lower ends of the ASD and ADHD trait continua appeared to represent largely
in ADHD), it is still too early to draw conclusions. That is, some inconsistency in
quantitative rather than qualitative differences. That is, the non-symptomatic ends
findings may also be due to the multiple definitions that are used for ‘long range’
of the ASD and ADHD trait continua were associated with lower levels of behavioral
and ‘local’ connectivity (Vissers et al., 2012). Future research therefore warrants
problems on gold-standard measures for internalizing and externalizing behavior,
an explicit definition of ‘long-range’ and ‘local’ connectivity, in order to develop a
and some advantages in cognitive performances for the ADHD continuum. In
better understanding of brain connectivity in ASD and ADHD. In general, future
chapter 5, we did demonstrate that many of the findings in non-clinical ASD-
research should aim at relating cognitive processes to the diffuse abnormal
ADHD classes were in line with the findings across the clinical ASD-ADHD classes
cortical connectivity and synchrony and widespread neural abnormalities
described in chapter 3. This indicated that variance in ASD and ADHD is rooted
seen in ASD and ADHD. This can for example be achieved through functional
in variance in the non-symptomatic end of the trait distributions. Furthermore,
neuroimaging during cognitive task completion. The cognitive performances
in chapter 6, resembling four class solutions typified by qualitatively different
mapped on connectivity measures may provide more precise knowledge on the
speed-accuracy trade-offs emerged in both the population-based and clinic-
relationship between activity in certain brain areas and specific cognitive -and
based samples. This suggests that the cognitive profiles disclosed through a
ultimately behavioral- measures.
bottom-up approach are generic, i.e. not only relevant for neurodevelopmental
Although structural and functional alterations in widespread brain
disorders but also for normal development. These conclusions are in line with the
regions and their connections may be central in ASD and ADHD pathophysiology
perception of ASD and ADHD as quantitative traits rather than as categorically
(for reviews see de la Fuente et al., 2013; Vissers et al., 2012), still little is known
defined conditions. And clearly, some of the heterogeneity in neurodevelopmental
about their exact relevance for the expression of ASD and ADHD behavior.
disorders is rooted in heterogeneity present in the general population.
Stronger behavioral links are necessary to deepen our understanding of the
actual importance and influence of abnormalities in brain connectivity to the
heterogeneity between affected and normal populations has implications for
manifestation of ASD and ADHD. Given the complexity of the relationships
not only diagnostic classification and detection of underlying mechanism of
between behavior and cognition on the one hand, and between cognition and the
neurodevelopmental disorders, but also for public health efforts to identify and
As described by Constantino (2011), this finding of continuity of
support affected children. Public health monitoring can be more accurate if it
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Chapter 8
is not limited by clinical diagnoses and cut-offs. Ideally, everyone should be
was concluded that ASD and ADHD were typified by some uniqueness in their
characterized by quantitative risk scores on an established amount and selection
cognitive profiles (regarding emotion recognition and visual spatial functioning).
of trait continua. Research thus far indicated that continuum scores are stable over
The study described in chapter 6, where a cognition-based multivariate bottom-
time for ASD, and of moderate stability for ADHD across the general population
up classification was conducted, resulted in cross-domain generic cognitive
(Constantino et al., 2009; Lakes, Swanson & Riggs, 2012; Robinson, Munir, et al.,
factors rather than domain specific strengths/weaknesses. Thus, it appears that
2011). The use of continuous risk scores may potentially lead to a public health
results partially depend on the measures used. It is therefore recommended that
care model that focuses on prevention rather than solely on treating patients,
not only symptom counts, but also aspects such as its context-dependency and
as elegantly outlined by Plomin and colleagues (2009). In chapter 2, it was
longitudinal clinical course are explored in the development of valid symptom
concluded that the non-symptomatic ends of the ASD and ADHD trait continua
dimensions for bottom-up classification (Uher & Rutter, 2012). In addition to
represented some superior functioning, possibly reflective of not only low risk, but
these measures, future research may also wish to conduct gene-based and/or
potentially even of resilience for developmental disorders. Increased insight into
functional neuroimaging-based multivariate bottom-up classifications.
the correlates of this low risk / resilient end of the trait distribution may deepen
our insights in protective factors. As described in chapter 6, potential protective
care with insights in protective factors and resilience, opening doors towards
factors for the development of behavioral problem may be a cognitive profile that
prevention rather than cure. A new taxonomy however does require an established
favors accuracy over speed, a higher intelligence, and a somewhat older age
amount and type of measures to accurately ascertain their relative contributions
(maturation).
to neurodevelopmental problems. See also the future directions below for the
It is tempting to speculate about the usefulness of a bottom-up
promising Research Domain Criteria project (RDoC) approach developed by the
classification instead of DSM-based classification system for clinical practice. As
National Institute of Mental Health (NIMH)(http://www.nimh.nih.gov/research-
described by Uher & Rutter (2012), symptom dimensions can only be considered
priorities/rdoc/index.shtml).
To conclude, a dimensional approach may provide public health
as alternatives to diagnosis, after a systematic approach to the selection of
symptom variables tackled the hard questions of how many and which dimensions
Key findings
are really needed. That is, dimensions have the potential to enhance the validity
•
Bottom-up subtyping into behaviorally homogeneous groups reveals
of classification, but the sensitivity to input methodology and lack of consensus
associations between behavior and cognition that easily remain undisclosed
suggest that it is still too soon to replace categories with dimensions. To be of
in DSM-defined heterogeneous subgroups.
clinical utility, findings should not be affected by differences in for example the
•
Findings in line with the gradient overarching disorder hypothesis are that
samples or measures used across studies. For instance, despite the fact that
ASD and ADHD do differ quantitatively but not qualitatively from one and
both studies were conducted across the same populations, the conclusions from
another in baseline motor speed and reaction time variability, executive
the complementary behavioral and cognitive empirical bottom-up approaches
functioning, attention and emotion recognition. For these measures, ADHD
in the chapters 3 and 6 may appear contradictory at first sight. In chapter 3,
may best be seen as a milder subtype within the ASD spectrum.
where a behavior-based multivariate bottom-up classification was conducted, it
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Chapter 8
•
•
•
•
•
•
•
Findings in contrast to the gradient overarching disorder hypothesis are
corrected for the influence of age, for example by calculating residuals, by
that visual spatial processing styles do differ qualitatively between ASD and
covarying for age and by using age-corrected subscale scores of behavioral
ADHD, with a detail focused visual spatial processing style typical for ASD but
questionnaires. Notwithstanding, the optimal approach to tackle a potential age-
not ADHD. This may indicate why some children develop ADHD despite their
effect would be the use of a longitudinal research design.
enhanced risk for ASD.
It is less likely to observe pure ASD in clinical practice; generally ASD is
ASD and/or ADHD, in both the clinic-based and population-based sample. This
presented with co-occurring ADHD symptoms.
corresponds with the referral bias in clinical practice, and the upper extreme ASD
Motor timing difficulties, possibly interacting with primary ASD and ADHD
and ADHD traits being more easily recognized in boys than in girls (Kramer et al.,
symptoms, cannot merely be explained by ADHD only, as ADHD symptom
2008). As sex is inherently confounded with ASD and ADHD, the influence of sex
severity in the pure ADHD-class and the ASD(+ADHD) class was highly
could not be separated from the effects of the ASD and ADHD symptomatology.
similar, with the former class showing no motor timing deficits.
In addition, the sex-differences at the upper extreme end of phenotypic traits
An overall cognitive slower and more inaccurate performance is an aspecific
are also observed in the general population. This suggests that sex differences
but strong predictor of psychiatric dysfunctioning, which calls into question
in clinical referral patterns and diagnoses of ASD and ADHD are not based on
the use of extensive and specific cognitive batteries that are now often used
a clinical bias, but rather reflect a true predisposition in males (Neuman et al.,
for research and clinical purposes.
2005).
The cognitive profiles observed in the clinic-based sample resemble those
across the population-based sample, which may indicate that part of the
information on behavioral problems. This did not affect comparisons between
cognitive heterogeneity in ASD and ADHD is nested in normal variation.
classes and between studies, but in contrast to clinical interviews, questionnaires
Cognitive functioning may only predict behavioral problems when other risk
do not allow for further probing or explanation of questions. In addition,
factors are present. In every day life, favoring speed over accuracy or the
information from multiple informants instead of parents-only may have provided
other way around may be equally adaptive.
a more generalized behavioral profile. Information on children by means of
Although both response inhibition and ADHD symptoms improved under
questionnaires completed by teachers was collected, and can be used for further
the influence of atomoxetine, improvement in response inhibition did not
analyses.
Second, boys were overrepresented in the classes with higher levels of
Third, questionnaires completed by parents were used to collect
correlate with improvement in ADHD symptoms.
Limitations
These studies come with some limitations and considerations. First, the children
varied widely in age (5-17 years). Since age has a strong effect on both cognitive
performances as well as type of behavioral symptoms presented, the influence
of age may have affected between class differences. However, analyses were
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Future directions
unintended consequences of reifying the current diagnoses that probably do not
Development of diagnostic classification schemes
mirror nature. Such a process may help develop and adjust the classification
When comparing the current DSM-5 with its antecedents (i.e. DSM-III, DSM-III-R,
DSM-IV and DSM-IV-TR), some progress can be observed. In earlier psychiatric
classification schemes, many of the diagnoses included hierarchical exclusionary
rules such that certain diagnoses could not be assigned if the symptoms occurred
during the course of another disorder that occupied a higher level in the hierarchy
(e.g. ADHD was excluded in the presence of ASD). These exclusion rules were
later seen as problematic because these were not empirically based and made
the study of lower-ranked diagnoses (e.g. ADHD) difficult. Therefore, the options
to diagnose multiple disorders were extended, and diagnostic criteria were more
specific and sensitive in later versions of the manual (Beuter & Malik, 2002). Now,
the best next step would be to no longer rely on a categorical approach (i.e. the
disorder is either present or absent), but rather to adopt a dimensional model
where deficits can be conceptualized as falling somewhere along a continuum
that ranges from normal to pathological.
An important pioneer in this field is the National Institute of Mental Health
(NIMH), which decided to no longer adhere to the current classification system,
and to apply an experimental approach to the classification of mental disorders.
The NIMH recently launched the Research Domain Criteria project (RDoC) to
implement this strategy that incorporates not only behavioral symptoms, but
also measures from neurocognitive, neurobiological and genetic research (http://
www.nimh.nih.gov/research-priorities/rdoc/index.shtml). The inclusion of multiple
domains provides a broader view on developmental disorders in general, and
a framework that ultimately brings the approach to disorders such as ASD and
criteria long before it is time to start thinking about a DSM-6.
Genetic research
A bottom-up classification also allows for the inclusion of quantitative information
on comorbid symptoms into genetic studies. Since the LCA also permit the
inclusion of milder but still impairing (‘below the clinical cut-off’) behavior, it
may improve correspondence between phenotypic variance and susceptibility
genes. For example, Acosta et al. (2008) performed LCA on symptom measures
for ADHD, oppositional defiant disorder (ODD), conduct disorder (CD), anxiety
and depression to improve correspondence between phenotypic variance and
susceptibility genes. Their findings indicated that a dimensional approach that
includes milder but still impairing phenotypes (i.e. below the clinical cut-off)
may even be essential for analyses in large molecular genetic studies, relevant
for future clinical classifications, and may help resolve the contradictory results
presented in current molecular genetic studies. Studies conducted by Li & Lee
(2010; 2012) additionally illustrated that since LCA minimize the within-group
heterogeneity seen in traditional diagnostic categories, they improve the grip on
complex phenotypes in genetic studies. Unfortunately, the homogeneous groups
within our current samples were too small to analyze susceptibility genes, but we
are currently in the process of merging genetic, cognitive, and behavioral data
from multiple research projects.
Biomarkers in clinical practice
ADHD closer to the development of more sophisticated treatment. Although these
Thus far, neuroimaging studies mainly focused on cortical regions and their
neurocognitive, neurobiological and genetic domains have not made it into the
connections, and demonstrated global cortical maturation delay based on
DSM-5, it is acknowledged that it may not be too early to use neurobiology as a
reduced cortical thickness and reduced grey and white matter volumes in ADHD
central tool to rethink the current approach to mental disorders (Hyman, 2007;
(see for review de la Fuente et al., 2013). Future research may wish to put more
2010; Miller, 2010). That is, ongoing research could detach science from the
effort on understanding the roles of the subcortical structures such as the basal
204
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Chapter 8
ganglia, and their structural and functional pathways in ADHD, which may also
individual’s experience of the learning environment would provide insight into
significantly contribute to the pathophysiology of ADHD. A systematic review and
how individual differences in brain development interact with formal education.
meta-analysis of diffusion-weighted MRI studies (van Ewijk, Heslenfeld, Zwiers,
Second, how the education is provided, for example with regard to the amount of
Buitelaar & Oosterlaan, 2012) indeed described positive associations between on
rehearsal offered, can be altered according to these individual differences. Ideally,
the one hand ADHD-behavior or cognitive deficits typical for ADHD (e.g. attentional
the educational system should be able to monitor and adapt to the learner’s
focus, interference control, verbal fluency), and on the other hand white matter
current repertoire of skills and knowledge. A promising approach involves the
abnormalities in amongst others the inferior and superior longitudinal fasciculus,
development of technology-enhanced learning applications that are capable
and areas within the basal ganglia.
of adapting to individual needs (for review see Butterworth & Kovas, 2013).
Based on the qualitative differences between homogeneous ASD-ADHD
Improving educational quality by meeting the needs of children with ASD and
classes in visual-spatial task performances described in the chapters 3 and 5,
ADHD is likely to positively affect their general wellbeing, since these children will
studying brain activation during such tasks may be a promising future research
experience less frustration and more success. Current research did not contain
avenue. By comparing children with elevated scores on either ASD or ADHD,
suitable measures to examine whether the non-symptomatic ends of the ASD
children with elevated scores on both, and control children regarding their brain
and ADHD traits were associated with positive constructs such as wellbeing and
activation patterns during such visual-spatial tasks, (mal)adaptive structural and
quality of life; this therefore remains an interesting question for future research.
functional pathways unique or shared in ASD and ADHD may be detected. Of
note, given that a task-transcending cognitive impairment (i.e. overall slower and
Clinical implications
more inaccurate performance) such as described in chapter 6 was an aspecific
Given the high comorbidity rates between ASD and ADHD (an estimated
but strong predictor of psychiatric dysfunctioning in general, future studies might
30% to 80% ADHD in ASD, and about 20% to 50% ASD in ADHD; for review
also wish to examine how generic cognitive profiles map into the efficiency and
see Rommelse et al., 2010), and given the homogeneous classes with mostly
modularity of functional neural networks. Ultimately, the detection of biomarkers
similar levels of both disorders detected across the general population (chapter
for ASD and/or ADHD can be of clinical utility in children with an unclassifiable
5), findings suggest that children who are referred to a clinical centre for either
behavioral profile, i.e. behavioral problems that are milder than full blown
ASD or ADHD should be examined for symptoms of the other disorder as well.
diagnoses, but still impairing. Such biomarkers can for instance provide extra
As the clinical presentation of both disorders is strongly influenced by age, this
information with regard to expected response to treatment.
examination should occur on a regular basis (St. Pourcain et al., 2011). Well
in line with this recommendation is that children with solely ADHD symptoms
Life outside clinical practice – education and learning
(i.e. the ADHD-only class described in chapter 3) were slightly younger than the
Butterworth & Kovas (2013) described two ‘grand challenges’ that have to be met
children who presented with both ASD and ADHD symptoms. These ADHD-only
to understand disorders such as ASD and ADHD, and to improve education for
children may have a childhood-limited form of ADHD, a hypothesis that calls
children with ASD and ADHD and formal education in general. First, unraveling
for regular examinations. As children with ‘pure’ ASD are rarely seen in clinical
how cognitive processes, their neural basis, and genetic etiology influence the
populations (despite the fact that we included children in our study on the basis
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207
Chapter 8
of the presence of ASD), clinicians will most of the time see patients with ASD and
comorbid ADHD. Therefore, clinicians should keep in mind that ADHD-specific
difficulties may make treatment interventions harder to grasp for ASD-affected
children. For instance, social skills training may be more difficult due to comorbid
ADHD problems such as inattention. Literature reports greater impairment in
adaptive functioning and a poorer health-related quality of life for children with
ASD and clinically significant ADHD symptoms in comparison with children with
ASD and fewer ADHD symptoms (Sikora et al., 2012). Therefore, clinicians may
wish to adjust treatment plans to ensure comprehensive and effective treatment
for both ASD and associated ADHD. In addition, the two subtypes of children
with both ASD and ADHD behavior described in chapter 3(i.e. with one or the
other more profound) differed not only with regard to comorbid internalizing and
externalizing symptoms, but also qualitatively with regard to their visual-spatial
skills and emotion recognition abilities. Therefore, these children may benefit
from different clinical approaches.
Based on the absence of an association between behavioral symptoms
and inhibitory control (chapter 7), consideration should be given to the cognitive
measures used in the assessment of medical treatment outcome in ASD and
ADHD. Perhaps predicting or monitoring treatment response using cognitive
tests may not always be of clinical utility. Furthermore, since the cognitive
profiles in theclinic and population-based sample largely overlapped, and since
these profiles were defined by similar strengths and difficulties across cognitive
tasks, standard cognitive batteries may mainly tap into the shared underlying
variance relevant to psychiatric dysfunctioning in general. Even though this is
quite informative in its own right, as this shared cognitive variance may link to
etiological factors relevant to dysfunctioning in general (Caspi et al., 2013; CrossDisorder Group of the Psychiatric Genomics, 2013), it does not bring us any
closer to understanding the heterogeneity in ASD/ADHD affected populations, let
alone in developing more tailored diagnostic and treatment approaches.
208
209
Summary in Dutch
210
Wat zijn Autismespectrumstoornis en Aandachtstekortstoornis met
Hyperactiviteit?
Autismespectrumstoornis (ASS) en Aandachtstekortstoornis met Hyperactiviteit
(ADHD) zijn psychiatrische ontwikkelingsstoornissen. ASS wordt gekenmerkt
door problemen in de sociale communicatie en repetitief en/of stereotiep gedrag
en interesses (American Psychiatric Association, 2013). In het contact met een
kind met ASS is er bijvoorbeeld weinig/geen sprake van wederkerigheid, en door
onvoldoende begrip van humor en abstract taalgebruik is het voor kinderen met
ASS moeilijker om een gesprek te voeren. Het repetitief en stereotiep gedrag
blijkt bijvoorbeeld uit geobsedeerd bezig zijn met bepaalde voorwerpen of
onderwerpen, vrijetijdsbestedingen en stereotiepe bewegingen. Voor ADHD zijn
aandachtsproblemen en/of hyperactiviteit en impulsiviteit het meest kenmerkend
(American Psychiatric Association, 2013). Aandachtsproblemen bij kinderen
met ADHD zijn bijvoorbeeld waarneembaar als verhoogde afleidbaarheid van
schoolwerk of dagdromen. Hyperactief en impulsief gedrag blijken bijvoorbeeld
uit voortdurend in de weer zijn en anderen onderbreken tijdens het praten.
ASS en ADHD zijn gedragsdiagnoses; ze worden vastgesteld op
basis van diagnostische interviews en observaties door experts in de kinderen jeugdpsychiatrie. Leidraad voor dit diagnostisch onderzoek is de Diagnostic
and Statistical Manual of Mental Disorders (DSM-5) (American Psychiatric
Association, 2013). De DSM-5 is een classificatiesysteem waarin de precieze
gedragskenmerken van onder andere ASS en ADHD staan omschreven. In
dit classificatiesysteem zijn criteria vastgelegd aangaande de hoeveelheid
symptomen, de mate van ernst van deze symptomen en het eerste voordoen van
deze symptomen die voorwaarde zijn om een diagnose te mogen stellen. Op basis
van deze criteria wordt bij ongeveer 1% van de kinderen ASS gediagnosticeerd
en bij ongeveer 5% van de kinderen ADHD (Baird et al., 2006; Polanczyk et al.,
2007). Hiermee zijn ASS en ADHD een van de meest voorkomende psychiatrische
ontwikkelingsstoornissen wereldwijd.
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Chapter 9
De waarde van de DSM is dat experts overal ter wereld over exact hetzelfde
en unieke oorzakelijke factoren van beide stoornissen. In de recent verschenen
praten en naar exact hetzelfde onderzoek doen wanneer zij het ASS of ADHD
DSM-5 mogen beide diagnoses wel samen gesteld worden, hetgeen een impuls
noemen. Vrijwel alle kennis die we nu over ASS en ADHD hebben is gebaseerd op
kan geven aan wetenschappelijk onderzoek naar de oorzakelijke factoren voor
volgens de DSM gedefinieerde groepen kinderen. Dit classificatiesysteem heeft
ASS en ADHD.
echter ook een belangrijk nadeel. De DSM is gebaseerd op consensus tussen
experts aangaande categorieën en niet op de meest actuele wetenschappelijke
nemen van een breder perspectief door gebruik te maken van dimensionele maten.
inzichten. Het is onwaarschijnlijk dat deze DSM-categorieën goed afgrensbaar
Bij een dimensionele benadering wordt geen afkappunt gehanteerd, maar wordt
zijn op basis van oorzaken en prognose van bepaalde symptomen. Het afkappunt
het gehele continuüm van gedragskenmerken meegenomen. Kinderen worden
voor wanneer er wel/niet sprake is van een diagnose ASS of ADHD is arbitrair
gekarakteriseerd op basis van de mate waarin zij verschillende symptomen
zolang hiervoor geen objectieve criteria zijn.
laten zien. Hierbij wordt geen onderscheid gemaakt tussen types symptomen,
Dit probleem speelt op meerdere fronten. Ten eerste is het afkappunt tussen
bijvoorbeeld of symptomen passen bij ASS of ADHD. Bij een dimensionele
bijvoorbeeld ‘normale’ repetitieve gedragingen en ‘normale’ aandachtsproblemen
beoordeling van gedragskenmerken worden alle kinderen, ongeacht de aan-of
versus klinisch repetitief gedrag en klinische aandachtsproblemen arbitrair. ASS
afwezigheid van DSM-classificaties, op meerdere dimensies beoordeeld.
Een goede methodiek voor het detecteren van deze grondslagen is het
en ADHD-gedragskenmerken zijn continu verdeeld in de algemene populatie.
Dit betekent dat ook kinderen die geen DSM-classificatie hebben variëren in
Neuropsychologie bij ASS en ADHD
de aan-en afwezigheid van gedragskenmerken die passen bij ASS en ADHD.
Neuropsychologie bestudeert de relatie tussen gedrag en breinfunctioneren,
Door gebruik te maken van DSM-categorieën worden eventuele problemen bij
hiermee
kinderen die onder het afkappunt voor DSM-classificaties zitten miskend. Ten
Neuropsychologen bestuderen de werking van de hersenen op gedragsniveau.
tweede is niet iedereen die een ASS-diagnose heeft hetzelfde, en iedereen die
Dat betekent dat neuropsychologen proberen te ontrafelen hoe onze hersenen
een ADHD-diagnose heeft evenmin. Groepsvergelijkingen op basis van DSM-
ons in staat stellen om ons gedrag te sturen. Veel neuropsychologisch onderzoek
indelingen zijn hierdoor gebaseerd op heterogene groepen (zie bijvoorbeeld Lord
richt zich hierbij op zogenaamde cognitieve functies zoals aandacht, geheugen
& Jones, 2012; Miller, 2010). Dit probleem is mede ontstaan doordat de DSM-
en cognitieve flexibiliteit. Deze neurocognitieve taken zijn bijvoorbeeld geschikt
classificaties ASS en ADHD tot voor kort formeel niet samen gesteld mochten
voor het maken van sterkte-zwakte profielen van kinderen. Kinderen met ASS en
worden. Veel kinderen met een diagnose ASS laten echter ook symptomen zien
ADHD-diagnoses zijn bekend met cognitieve problemen, en neuropsychologen
die kenmerkend zijn voor ADHD, en vice versa laten ook relatief veel kinderen
proberen verbanden te leggen tussen de gedragskenmerken enerzijds en de
met een ADHD-diagnose symptomen zien die passen bij ASS. In de klinische
cognitieve problemen anderzijds. Een cognitief probleem dat kenmerkend
praktijk lopen de schattingen voor deze comorbiditeit uiteen; ongeveer 30% tot
is voor ASS is bijvoorbeeld een zwakke centrale coherentie, wat betekent dat
80% van de kinderen met ASS heeft ook ADHD, en ongeveer 20% tot 50% van de
kinderen met ASS gemiddeld genomen niet gehelen maar details waarnemen
kinderen met ADHD heeft ook ASS (voor review zie Rommelse et al., 2011). Door
(zie bijvoorbeeld Booth & Happé, 2010). Een cognitief probleem dat kenmerkend
de eerdere DSM-restricties zijn tot dusverre weinig studies verricht naar gedeelde
is voor ADHD is bijvoorbeeld een zwakke inhibitie, wat betekent dat kinderen met
214
is
neuropsychologie
een
van
de
hersenwetenschappen.
215
Chapter 9
ADHD gemiddeld genomen meer moeite hebben met het remmen van gedrag (zie
zijn in meer of mindere mate identiek in de patronen die zij vertonen. Zij laten
bijvoorbeeld Crosbie et al., 2013). Naast cognitieve problemen die meer typisch
bijvoorbeeld veel ADHD-gedragskenmerken gecombineerd met weinig ASS-
zijn voor ASS of ADHD zijn er meerdere cognitieve problemen die bij zowel ASS
gedragskenmerken zien, of zij laten bijvoorbeeld zien heel vlot, maar weinig
als ADHD gerapporteerd worden (zie voor review Rommelse et al., 2011).
accuraat te kunnen werken. Op basis van deze patronen kunnen kinderen
Zowel
het
frequent
samen
voorkomen
van
ASS
en
ADHD-
ingedeeld worden in subgroepen (classes) die meer homogeen zijn. Door meer
gedragskenmerken en -diagnoses, als de overeenkomsten in cognitieve problemen
homogene classes te vergelijken kunnen de grondslagen van ASS en ADHD-
indiceert dat er mogelijk gelijke processen aan beide ontwikkelingsstoornissen
gedragskenmerken of cognitieve profielen beter gedetecteerd worden.
ten grondslag liggen. Deze unieke en gedeelde cognitieve factoren onderliggend
aan ASS en ADHD-gedragskenmerken kunnen het veld wijzen in de richting van
Wat zijn de belangrijkste bevindingen?
meer objectieve diagnostiek. Net als voor gedragskenmerken geldt ook voor
In hoofdstuk 2 bekijken we het continuüm van ASS en ADHD-symptomen in de
cognitieve problemen dat deze continu verdeeld zijn in de algemene populatie.
algemene populatie. We onderzoeken of de beide uiteinden van het continuüm
Dit betekent dat ook kinderen die geen DSM-classificatie hebben variëren in hun
(het onaangedane uiteinde zonder ASS en ADHD-gedragskenmerken en
cognitieve sterke en zwakke kanten (zie bijvoorbeeld Fair et al., 2012). Door ook
het aangedane uiteinde met ASS en ADHD-symptomen) meer kwantitatief
cognitieve profielen dimensioneel te beschouwen, dus het hele continuüm mee
dan kwalitatief van elkaar verschillen. De resultaten laten zien dat zowel de
te nemen en geen afkappunt te hanteren, wordt ook deze cognitieve variatie in de
hoeveelheid comorbide gedragsproblemen als (in mindere mate) het cognitief
algemene populatie erkend.
presteren gunstiger is met minder ASS en ADHD-symptomen; zowel qua gedrag
als qua cognitie worden geen nadelen gevonden voor het vrij zijn van ASS en
Het onderzoek in dit proefschrift
ADHD-symptomen.
Dit proefschrift heeft ten doel de cognitieve profielen die horen bij de symptomen
van ASS en ADHD te ontrafelen. Welke cognitieve sterktes en zwaktes horen
3, 4, 5 en 6 de heterogeniteit op een van beide domeinen beperkt. Hiervoor wordt
specifiek bij ASS-symptomen, welke horen specifiek bij ADHD-symptomen en
gebruik gemaakt van gedragsmatig of cognitief meer homogene latente classes.
welke passen bij allebei? De bijbehorende cognitieve profielen bieden mogelijk
In hoofdstuk 3 wordt de hypothese getoetst of ADHD een minder ernstige variant
meer inzicht in de gedeelde en unieke grondslagen van ASS en ADHD.
binnen het ASS-spectrum is. Als dat klopt, dan zou er wel zoiets kunnen bestaan
Het detecteren van deze grondslagen wordt in dit proefschrift nagestreefd
als enkelvoudige ADHD, maar zal ASS altijd gepaard gaan met comorbide ADHD-
door gebruik te maken van dimensionele data, afkomstig van kinderen (5 -17
symptomen. In de LCA wordt inderdaad wel een class met enkelvoudige ADHD
jaar) uit zowel de algemene populatie als een klinische ASS-populatie. Deze
en geen class met enkelvoudige ASS gevonden. Er wordt bovendien veel overlap
dimensionele maten kunnen worden ingevoerd in zogenaamde latente klasse
in cognitieve problemen passend bij ASS-symptomen en passend bij ADHD-
analyses (latent class analyses: LCA) (McCutcheon, 1987). LCA is een data-
symptomen gevonden, bijvoorbeeld voor motorische vaardigheid, aandacht en
analysemethode waarbij ervan uit wordt gegaan dat er patronen bestaan in de
emotieherkenning. Grofweg hangt een toename in ASS en ADHD-symptomen
data, bijvoorbeeld in gedragskenmerken of cognitieve symptomen. Kinderen
samen met een toename in cognitieve problemen. Echter, het visuo-spatieel
216
Aangaande de relatie tussen gedrag en cognitie wordt in de hoofdstukken
217
Chapter 9
functioneren blijkt verschillend voor kinderen met voornamelijk ASS-symptomen
ASS en ADHD stelt het klinisch nut van uitgebreide neurocognitieve taakbatterijen
en kinderen met voornamelijk ADHD-symptomen.
bij de diagnostiek naar ASS en ADHD ter discussie.
Een zwakke motorische timing wordt in verband gebracht met primaire
In hoofdstuk 7 komt tenslotte een dubbelblind placebo-gecontroleerde
ADHD-symptomen zoals moeite met het remmen van gedrag en het richten en
klinische studie onder kinderen met zowel een ASS-diagnose als ADHD-
vasthouden van aandacht. De studie beschreven in hoofdstuk 4 laat zien dat
symptomen aan bod. In deze studie wordt het effect van de noradrenaline-
de motorische timing ernstiger is aangedaan bij kinderen die niet alleen ADHD-
heropnameremmer atomoxetine op de prestaties op twee inhibitietaken
symptomen, maar ook ASS-symptomen presenteren. Dit resultaat sluit aan bij de
bestudeerd. De resultaten laten zien dat een afname in ADHD-symptomen niet
hypothese dat ADHD een minder ernstige variant binnen het ASS-spectrum is.
samenhangt met een verbetering in inhibitie. Deze bevinding kan betekenen
In hoofdstuk 5 worden, evenals in hoofdstuk 2, alleen kinderen uit
dat er verschillende oorzaken aan ADHD-symptomen en inhibitieproblemen bij
de algemene populatie bestudeerd. In deze studie worden gedragsmaten
kinderen met ASS en ADHD ten grondslag liggen. Er kan geconcludeerd worden
gebruikt die sensitief zijn voor de variatie in ASS-gedragskenmerken en ADHD-
dat dit type cognitieve taken niet geschikt is voor het monitoren van vooruitgang
gedragskenmerken in de algemene populatie. Uit de resultaten komt naar voren
in ADHD-gedragskenmerken bij kinderen met zowel ASS als ADHD-problematiek.
dat de gedragsprofielen en cognitieve profielen binnen de algemene populatie
grotendeels overeenkomen met de resultaten binnen de klinische populatie.
Concluderend
Hieruit kan geconcludeerd worden dat een deel van de heterogeniteit die
• Een dimensionele benadering is geschikt voor het detecteren van associaties
kenmerkend is voor ontwikkelingsstoornissen als ASS en ADHD, ook gevonden
kan worden binnen de algemene populatie.
tussen gedragskenmerken en cognitief functioneren.
•
In hoofdstuk 6 wordt een innovatieve aanpak gehanteerd, waarbij
homogene classes gebaseerd worden op cognitieve maten in plaats van
populatie is te herleiden tot variatie in de algemene populatie.
•
gedragsmaten. Uit deze studie komt naar voren dat de cognitieve profielen van
kinderen met en zonder ASS en ADHD-symptomen grotendeels vergelijkbaar zijn.
Een deel van de gedragsmatige en cognitieve variatie binnen de klinische
ASS-gedragskenmerken gaan gepaard met ADHD-gedragskenmerken,
terwijl ADHD-gedragskenmerken ook frequent enkelvoudig voorkomen.
•
Een overwegend ASS-profiel en een overwegend ADHD-profiel verschillen
Er worden geen cognitieve sterktes en zwaktes gevonden op specifieke domeinen,
qua cognitieve prestaties voornamelijk in kwantitatieve zin van elkaar, ten
maar profielen die te onderscheiden zijn op basis van specifieke combinaties van
nadele van het ASS-profiel.
snelheid en accuratesse over de verschillende domeinen. Classes die minder
•
Enkel het visueel-spatieel functioneren verschilt kwalitatief voor kinderen met
accuraat en langzamer presteren laten meer ASS en ADHD-symptomen zien, en
overwegend ASS-gedragskenmerken en kinderen met overwegend ADHD-
classes die accurater en vlotter presteren laten minder ASS en ADHD-symptomen
gedragskenmerken, met meer oog voor detail bij een overwegend ASS-
zien. Er is sprake van vergelijkbare heterogeniteit binnen de klinische populatie
profiel.
en algemene populatie, met kwantitatieve meer dan kwalitatieve verschillen over
het continuüm. Het vinden van meer aspecifieke cognitieve voorspellers voor
218
•
Cognitieve profielen wijzen op een generiek minder accuraat en minder vlot
presteren bij meer ASS en ADHD-gedragskenmerken.
219
Chapter 9
•
Het maken van cognitieve sterkte-zwakte profielen en het monitoren van
gedragsmatige vooruitging middels cognitieve taken vereist een duidelijke
samenhang tussen gedrag en cognitie.
Welke richting geven deze bevindingen voor de toekomst?
Uit dit proefschrift zijn twee hoofdbevindingen te destilleren die richting
kunnen geven aan toekomstig wetenschappelijk onderzoek en de toekomstige
behandeling van kinderen met ASS en ADHD. Ten eerste is gebleken dat door
gebruik te maken van dimensionele maten voor ASS en ADHD, associaties
tussen gedrag en cognitie naar voren komen die met een DSM-groepsindeling
mogelijk niet gedetecteerd zouden worden. Deze kennis pleit voor een breder
perspectief in toekomstig wetenschappelijk onderzoek, zoals ook geïnitieerd
door het National Institute of Mental Health (NIMH) in het Research Domain
Criteria project (RDoC). Hierbij wordt niet uitgegaan van DSM-classificaties
op basis van gedragskenmerken, maar van meer objectiveerbare domeinen
zoals het cognitief functioneren, het breinfunctioneren en de genetica. Een
toekomstig classificatiesysteem zou dan niet langer een categoriale indeling
hebben, maar gebaseerd worden op multidimensionele uitkomstmaten met
meer constructvaliditeit. Over de keuze van geschikte uitkomstmaten moet
consensus bestaan, en hierbij moet rekening gehouden worden met de context
en ontwikkeling van de stoornissen.
Ten tweede laten de resultaten zien dat de cognitieve en gedragsmatige
heterogeniteit binnen klinische populaties met ASS en ADHD-symptomen deels
geworteld zijn in vergelijkbare heterogeniteit binnen de algemene populatie.
Eventuele verschillen tussen de niet-symptomatische en symptomatische
uiteinden van de ASS en ADHD-continua blijken veelal kwantitatief van aard te zijn.
Dit inzicht kan aanleiding zijn tot het bestuderen van mogelijke beschermende
factoren voor het ontwikkelen van ASS en ADHD-gedragskenmerken binnen de
algemene populatie. Wanneer beschermende factoren gevonden worden biedt
dit mogelijke aanknopingspunten voor preventieve interventies.
220
221
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Acknowledgements in Dutch
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Het zit er op. Wauw.
Chapter 11
Het promotietraject heb ik als een avontuur ervaren. Ik werd omringd door ijzersterke
experts die me stimuleerden om veel te leren & fijne collega’s die ervoor zorgden
dat ik ondertussen niet sociaal achterop raakte. Perfect. Ik houd er van om hoge
Acknowledgements in Dutch
doelen na te streven en hard te werken. Altijd al gedaan. Een strebertje to the limit?
Misschien wel. Ik had mijzelf ten doel gesteld om met mijn promotieonderzoek
toch minimaal tot zorgverbetering voor de kinder-en jeugdpsychiatrie te komen.
Daarvoor moest gedegen wetenschappelijk onderzoek bedreven worden. Met
die missie in gedachten ben ik het promotietraject, al was het bij vlagen ietwat
neurotisch, goed doorgekomen.
Er zijn veel mensen die een cruciale bijdrage hebben geleverd aan de totstand­
koming van dit proefschrift, enkelen van hen wil ik hieronder expliciet noemen.
Mijn promotor prof. dr. Buitelaar. Jan, uiteraard heb ik grote bewondering voor je
expertise en uiteraard heeft jouw feedback mijn papers verbeterd. Dat zegt alleen
nog weinig over de verstandhouding tussen Jolanda-de-junior-onderzoeker en
de Jan-de-promotor. Ik denk met veel plezier terug aan de overleggen met onze
Israëlische vrienden, het stijldansen op een personeelsfeest (dat moet er toch
wonderlijk uit hebben gezien :)), en je lachsalvo’s om toch wel voornamelijk je
eigen grappen. Dat jij op afstand meestuurde en meedacht in dit promotietraject
heeft het tot een succes gemaakt; bedankt voor het vertrouwen dat je in me
gesteld hebt.
Mijn eerste co-promotor dr. Lambregts-Rommelse. Nanda, ik heb het getroffen
met je. Van meet af aan zaten we op één lijn. Qua ambities, qua werkstijl, qua
persoonlijkheid. Onze overleggen gingen ook altijd even over de belangrijke
zaken die zich buiten het werk om afspeelden. Ik vind het tof dat we zo’n
vriendschappelijk contact hebben ontwikkeld. Werkinhoudelijk ben ik je dankbaar
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voor al je zinnige, vlotte en opbouwende kritieken. Meer dan eens mailde je ‘Dit
van deze ouders en kinderen zijn we meer te weten gekomen over de relatie
kunnen we hoog wegzetten, moeten we even 100% perfect opschrijven’. Zonder
tussen gedrag en cognitieve prestaties. Met deze kennis begrijpen we weer iets
jou was dit proefschrift er niet geweest. Period. Ik bewonder niet alleen hoe jij
beter waardoor het ene kind ASS of ADHD ontwikkelt en het andere kind niet.
tien promovendi op de rit weet te houden, hoe jij drie kleintjes thuis weet groot
Allen hartelijk dank voor jullie inzet!
te brengen, of hoe jij dat weet te combineren met de GZ-opleiding. Ik bewonder
vooral hoe je dat allemaal met oprechte aandacht voor iedereen en een niet
uitvoeren, hiervoor zijn we veel dank verschuldigd aan enkele leerkrachten en
aflatend enthousiasme kunt. Dankjewel!
directies. Onmisbaar was de betrokkenheid van De Hazesprong, De Bloemberg
Veel kinderen mochten de cognitieve taken onder schooltijd en op school
en Klein Heyendaal in Nijmegen, de Alfonsusschool te Enschede, Sinte Maerte in
Mijn tweede co-promotor, dr. Hartman. Catharina, jouw inhoudelijke adviezen
Breda, Gerardus Majella in Groesbeek, De Regenboog in Malden en Floriande en
hebben de laatste papers naar een hoger niveau getild, terwijl je persoonlijke
de 1e Montessorischool te Hoofddorp.
begeleiding het laatste jaar van mijn promotietraject de nodige luchtigheid heeft
gegeven. Wanneer ik naar jouw smaak te ongeduldig was, wist je me daar
hulp gehad van vele studenten. Anne, Annemie, Dominique, Gertie, Gosia,
met een ‘Je bent al net zo verschrikkelijk als Nanda!’ tactvol op te attenderen.
Imke, Jennie, Jill, Kim, Lenthe, Maartje, Marike, Marjolein, Marthe, Myrthe,
Geregeld heb ik hartelijk gelachen om je gebrekkige inhibitievermogen en
Nienke, Nina, Noortje, Renate en Véronique, ik heb er veel plezier aan beleefd
bondig geformuleerde antwoorden. Je scherpe inzicht enerzijds en je spontaniteit
om jullie te coachen en enkelen van jullie te begeleiden bij het schrijven van de
anderzijds is een unieke combinatie in de academische wereld. Ik heb van beide
mastherthesis. Het was iedere week weer een uitdaging om de planning rond
geprofiteerd, veel dank daarvoor!
te krijgen. Wie gaat er op welke dag, naar welke stad, welk kind testen? Hebben
Gelukkig hoefde ik alle kinderen niet in mijn eentje te zien en heb ik
ze dan geen pauze, gymles, open dag of jarige juf? De dataverzameling kon vlot
Leden van de manuscriptcommissie prof. dr. Willemsen, prof. dr. Bekkering en
verlopen doordat jullie zo gemotiveerd en flexibel waren. Dank jullie wel. Ik hoop
prof. dr. Roeyers en leden van de promotiecommissie mw. prof. dr. Geurts, mw. dr.
dat de opgedane ervaringen jullie helpen in jullie verdere carrière en hoor graag
Polderman en dr. Staal, hartelijk dank voor het bestuderen van mijn proefschrift
hoe het jullie vergaat!
en voor het opponeren bij de verdediging op woensdag 3 september. Mrs. prof.
dr. Simonoff, thank you for the time and effort spent on reading my dissertation
Er zijn veel collega’s die op directe of indirecte wijze bijgedragen aan mijn
and for your willingness to act as opponent of my dissertation on Wednesday the
werkvreugde. Patricia, bedankt voor het aanwakkeren van mijn liefde voor
3rd of September.
academisch onderzoek. Rutger-Jan, Dorith, Sascha, Dorine en Kina bedankt
voor de geboden mogelijkheden om de feeling voor het klinisch werk niet te
Dit proefschrift is gebaseerd op data uit neuropsychologisch onderzoek
verliezen. Shireen, Brechje en alle andere KCK-leden, bedankt voor de hulp bij
bij honderden kinderen en jongeren uit heel Nederland, een flinke stapel
het organiseren van het Karakter symposium ‘De brug tussen wetenschappelijk
gedragsvragenlijsten die ouders over hun kinderen hebben ingevuld en
onderzoek en klinische praktijk’. Het is zo’n interessant en relevant onderwerp;
diagnostische interviews die bij ouders zijn afgenomen. Dankzij de medewerking
hopelijk zullen er nog vele symposia volgen!
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Chapter 11
Barbara Franke en Kimm van Hulzen, het paper dat we samen beoogden
en ontwikkelgesprekken, op andere dagen waren we beiden zo ijverig dat
te schrijven over susceptibility genes voor ASS en ADHD is helaas nog niet af. Ik
we nauwelijks een woord wisselden. Tegenpolen op menig maar blijkbaar
hoop dat deze belangrijke studie met behulp van meer samples alsnog doorgang
verwaarloosbaar vlak. Het maakt jou & mij een zowel singulier en buitenissig
zal vinden. Martijn Lappenschaar, Marcel Zwiers en Rogier Donders bedankt voor
als flagrant en apert duo. Het promotietraject heeft onze vriendschap werkelijk
jullie onmisbare deskundigheid aangaande statistiek. Heel fijn dat mails met als
verdiept en verrijkt, wat een briljante benefit.
onderwerp ‘Een heel kort vraagje’ zo vlot werden beantwoord! Corina Greven,
Gigi van de Loo, Leo de Sonneville, Marjolein Luman, Monika Althaus, Myriam
Eindhovenaren, Hagenaren en Utrechters. Door me in de weekenden samen
Harfterkamp en Pieter Hoekstra hartelijk dank voor de prettige samenwerking.
met jullie met volstrekt andere zaken bezig te houden dan promoveren kon ik
Enschedese crew, ten dele inmiddels rasechte Amsterdammers,
weer opladen voor nieuwe werkweken. De ‘Hé Jo, hoe gaat het eigenlijk met je
De onderzoeksafdeling bij Karakter wordt bestierd door een tiental ambitieuze
verslagje / scriptie / rapportje / werkstukje?’ werkte bovendien zeer relativerend.
vrouwen. Geen 9-tot-5 mentaliteit te bekennen. Heerlijk. Publicaties, presentaties
Dankjulliewel! Marlijn en Ingeborg, ik koester onze vriendschap die al zoveel
en gewonnen beurzen samen vieren, tegenvallende inclusie, eindeloze analyses
doorstaan heeft. Moniek, wat had ik je graag nog hier bij ons gehad!
en bergen feedback samen vervloeken; het is heel belangrijk voor me geweest.
Anoek, Daphne, Jennifer, Karlijn, Kirsten, Leonie, Loes, Mireille, Mirjam, Saskia
Marloes, Annemiek en Martijn; mijn dierbare zus, zusje en broertje. De vraag of ik
en Yvette, heel veel dank. Ik hoop en verwacht dat de toekomst veel goeds voor
ooit weer terugkeer naar Het Twentsche Land hangt voor altijd in de lucht. Daaruit
jullie in petto heeft!
spreekt zoveel verbondenheid! Of ik nu tweehonderd kilometer verderop woon of
Ook buiten Karakter heb ik veel leuke en slimme promovendi ontmoet.
niet, de lijntjes met jullie zijn ijzersterk. Jesper, Karlijn, Daan en Ties, ik vind jullie
Sanne, jij maakte IMFAR in San Sebastian tot een feest! Boudewijn, Daan, Daniël,
geweldig. Met geen mogelijkheid had ik meer van jullie kunnen genieten dan ik
Danique, Denise, Desirée, Janna, Marloes, Marten, Melanie en Niels bedankt voor
heb gedaan!
de maandagmiddagmeetings en social-e-vents.
Pap en mam, een dochter kan zich geen lievere ouders wensen dan jullie.
Extrèmmers Andrieke, Janneke, Marloes en Vera, stelletje geweldige allrounders!
Jullie zijn liefhebbende ouders pur sang. Ik heb grote bewondering voor jullie
Dat ook jullie psycholoog en/of promovenda zijn maakt onze dates soms tot
ongelimiteerde betrokkenheid en zorgzaamheid, en ben jullie heel dankbaar voor
halve werkoverleggen. Ik geniet echter nog veel meer van ons scala aan andere
het warme thuis dat jullie altijd geboden hebben en nog altijd bieden.
gedeelde interesses. Wanneer ronden we een gespreksonderwerp nu eindelijk
eens af, zonder dat we er ‘even tussendoor...’ nooit meer op terugkomen? Hoewel
Sander, aanhoudende jeugdliefde. Op macroniveau betekent het niets, op
ik werkelijk mijn bést moet doen om verbaal boven jullie uit te komen, houd ik erg
microniveau betekent het alles. Alles. Ik ben je dankbaar dat je me altijd
van onze groepsdynamiek. Laten we gauw weer samen op pad gaan!
aangemoedigd hebt to go explore. Je hebt mijn wereld verrijkt en verruimd. Nu
het promotietraject afgerond is komt er weer ruimte voor iets anders. Ik heb heel
Andrieke, vele, vele uren hebben we samen in ons kleine kantoor
doorgebracht. Op sommige dagen besteedden we uren aan functionerings-
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veel zin in een nieuw avontuur samen met jou; wat zullen we gaan doen?!
273
About the author
274
Jolanda van der Meer (1983) was born in Enschede. She moved to Nijmegen in
2003 to study psychology at the Radboud University. During college, Jolanda
worked as a student assistant at the Max Planck Institute for Psycholinguistics,
where she coordinated and implemented a study on language acquisition in
children 6 to 12 years of age. After obtaining her bachelor’s degree, she decided
to aim for a master’s degree in the field of neuropsychology and rehabilitation
psychology. She wrote her master thesis at Karakter University Centre for Child and
Adolescent Psychiatry in Nijmegen. The focus of her thesis was on the attentional
bias towards mood-congruent emotional stimuli amongst depressed and nondepressed adolescents. After thesis completion, Jolanda spent half a year in
Jakarta, Indonesia. She taught neuropsychology at the Atma Jaya University and
coordinated the data-collection for several cognitive tasks across the Indonesian
child and adolescent population. At the same time, she volunteered for Yayasan
Kampung Kids and Werkgroep ’72.
Consecutively, she completed her master’s degree cum laude in 2009
with an internship at the department of Medical Psychology of the Radboud
University Nijmegen Medical Centre, and continued working there. In parallel, she
commenced with her Ph.D-project, which resulted in this thesis. During her Ph.D,
she attended multiple courses and gave lectures, organised a symposium on
the relationship between clinical practice and academic research, participated in
the writing of a successful research proposal for innovative ADHD-treatment, and
presented at international conferences. Noteworthy are her conference talks at
the International Meeting for Autism Research (IMFAR) and the European Network
of Hyperkinetic Disorders (EUNETHYDIS); for the latter she won the Sagvolden
Scholarship. In her leisure time she committed herself to a local political party and
voluntarily attended demented people. Currently, she works as a senior policy
maker at the Dutch Knowledge Centre for Child and Adolescent Psychiatry on the
changes in the Dutch child welfare system (i.e. decentralization and transformation)
by 2015. In addition, she recently started her own business, Therapeutic Smile, for
the treatment of ASD and ADHD affected children and adolescents.
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Chapter 12
Jolanda van der Meer (1983) werd geboren te Enschede. In 2003 verhuisde zij naar
Hier richt zij zich op de aanstaande decentralisatie en transformatie van de
Nijmegen om daar psychologie te studeren aan de Radboud Universiteit. Naast
kinder-en jeugdpsychiatrie. Daarnaast heeft zij onlangs als zelfstandig gevestigd
haar studie was zij als student-assistent werkzaam bij het Max Planck Instituut
psycholoog Therapeutic Smile opgestart, om kinderen en adolescenten met ASS
voor psycholinguïstiek. Op de afdeling taalverwerving heeft zij een taaltraining
en ADHD te behandelen.
opgezet en uitgevoerd bij kinderen tussen de 6 en 12 jaar oud. Na het afronden
van haar bachelor koos zij voor de master neuro-en revalidatiepsychologie. Haar
afstudeerscriptie heeft zij geschreven bij Karakter, expertisecentrum voor complexe
kinder- en jeugdpsychiatrie te Nijmegen. Haar scriptie richtte zich op de invloed
van stemming op de aandacht voor emotioneel geladen stimuli bij depressieve en
niet-depressieve adolescenten. Na afronding van haar scriptie heeft Jolanda een
half jaar gewerkt aan de Atma Jaya Universiteit te Jakarta, Indonesië. Hier heeft
zij onderwijs verzorgd in de neuropsychologie en de coördinatie gevoerd over
de dataverzameling van cognitieve taken ter normering voor de Indonesische
kinder-en jeugdpopulatie. Daarnaast heeft zij aldaar vrijwilligerswerk gedaan voor
Yayasan Kampung Kids en Werkgroep ’72.
Terug in Nederland heeft Jolanda haar klinische stage doorlopen aan het
Radboud UMC te Nijmegen, op de kinderafdeling van medische psychologie. In
2009 is zij cum laude afgestudeerd, waarna zij als neuropsycholoog werkzaam
bleef op de afdeling medische psychologie. Parallel hieraan is zij gestart aan het
promotieonderzoek dat geresulteerd heeft in dit proefschrift. Tijdens dit traject
heeft zij verscheidene cursussen gevolgd, onderwijs verzorgd, een symposium
georganiseerd over de brug tussen de klinische praktijk en wetenschappelijk
onderzoek, meegeschreven aan een succesvolle ZonMw subsidieaanvraag voor
een innovatieve behandeling van ADHD, en gepresenteerd op internationale
congressen. Hoogtepunten waren haar presentaties op de International Meeting
for Autism Research (IMFAR) en European Network of Hyperkinetic Disorders
(EUNETHYDIS), voor laatstgenoemde heeft zij de Sagvolden beurs gewonnen.
In haar vrije tijd was Jolanda actief betrokken in de lokale politiek en deed zij
vrijwilligerswerk met dementerende ouderen. Momenteel is Jolanda werkzaam als
senior beleidsmedewerker bij het Kenniscentrum voor kinder-en jeugdpsychiatrie.
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