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Early visual foraging in relationship to familial risk for autism and hyperactivity/inattention: A preliminary study


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Information foraging is atypical in both Autism Spectrum Disorders (ASD) and attention deficit hyperactivity disorder (ADHD), however while ASD is associated with restricted exploration and preference for sameness, ADHD is characterized by hyperactivity and increased novelty seeking. Here we ask whether similar biases are present in visual foraging in younger siblings of children with a diagnosis of ASD with or without additional high levels of hyperactivity and inattention. Fifty-four lowrisk controls (LR) and 50 high-risk siblings (HR) took part in an eye-tracking study at 8 and 14 months and at 3 years of age. At 8 months, siblings of children with ASD and low levels of hyperactivity/inattention (HR/ASD-HI) were more likely to return to previously visited areas in the visual scene than were LR and siblings of children with ASD and high levels of hyperactivity/inattention (HR/ASD+HI). Thus, we reveal a paradoxical effect, in which additional family risk for ADHD core-symptoms mitigates the effect of ASD risk on visual information foraging.
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Journal of Attention Disorders
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DOI: 10.1177/1087054715616490
Many human activities rely on information foraging, that is,
the seeking out and processing of information. Theoretical
accounts of information foraging draw heavily on general
models for resource foraging, which describe a trade-off
between effort or energy expenditure and the amount of
resources the organism manages to secure. Optimal foraging
requires shifting between exploitation, that is, taking advan-
tage of the resource at hand and exploration of the environ-
ment to discover new resources (Cohen, McClure, & Yu,
2007). There is no absolute best combination of exploration
and exploitation, but rather organisms gain from flexibly
adapting foraging to environmental conditions (i.e., it is better
to exploit the resources at hand if the further environment is
uncertain, and better to explore when faced with competition;
Kacelnik, Houston, & Krebs, 1981). Any strong inherent bias,
either toward exploratory behavior or for resource exploita-
tion, might therefore interfere with optimal foraging. This
article deals with the development of such potential biases in
information foraging, in infants at familial risk for autism
spectrum disorders (ASDs) or for attentional problems.
Atypical Foraging in ASD and ADHD
Individual differences in foraging behavior have been related
to the function of particular neural systems. For example,
biases for exploratory behavior were associated with differ-
ences in dopaminergic and noradrenergic function (Frank,
Doll, Oas-Terpstra, & Moreno, 2009; Jepma & Nieuwenhuis,
2011). Stimulating dopaminergic function in monkeys leads
to an increase in preference for novel stimuli over old stimuli
with larger reward value (Costa, Tran, Turchi, & Averbeck,
2014). Explorative choices, that is, choosing a new over an
old item, are preceded by increases in pupil diameter, which
index locus coeruleus/noradrenergic function (Jepma &
Nieuwenhuis, 2011). Both ASD and ADHD have been associ-
ated with atypicality in dopamine (e.g., Kriete & Noelle,
2015; Solanto, 2002) and noradrenaline functions (Biederman
& Spencer, 1999; Blaser, Eglington, Carter, & Kaldy, 2014) as
well as, separately, with atypical information foraging. In
ASD, decreased exploratory behavior was documented in a
task in which children had to discover objects hidden in
616490JADXXX10.1177/1087054715616490Journal of Attention DisordersGliga et al.
1Centre for Brain and Cognitive Development, Birkbeck, University of
London, UK
2Institute of Psychiatry, Psychology & Neuroscience, King’s College
London, UK
Corresponding Author:
Teodora Gliga, Birkbeck, University of London, Malet Street, WC1E
7HX, London, UK.
Early Visual Foraging in Relationship
to Familial Risk for Autism and
Teodora Gliga1, Tim J. Smith1, Noreen Likely1, Tony Charman2,
and Mark H. Johnson1
Objective. Information foraging is atypical in both autism spectrum disorders (ASDs) and ADHD; however, while ASD is
associated with restricted exploration and preference for sameness, ADHD is characterized by hyperactivity and increased
novelty seeking. Here, we ask whether similar biases are present in visual foraging in younger siblings of children with
a diagnosis of ASD with or without additional high levels of hyperactivity and inattention. Method. Fifty-four low-risk
controls (LR) and 50 high-risk siblings (HR) took part in an eye-tracking study at 8 and 14 months and at 3 years of age.
Results. At 8 months, siblings of children with ASD and low levels of hyperactivity/inattention (HR/ASD-HI) were more
likely to return to previously visited areas in the visual scene than were LR and siblings of children with ASD and high levels
of hyperactivity/inattention (HR/ASD+HI). Conclusion. We show that visual foraging is atypical in infants at-risk for ASD.
We also reveal a paradoxical effect, in that additional family risk for ADHD core symptoms mitigates the effect of ASD risk
on visual information foraging. (J. of Att. Dis. XXXX; XX(X) XX-XX)
infants, ASD, visual attention, eye-tracking
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2 Journal of Attention Disorders
different containers. Children with ASD spent less time in
active exploration and opened fewer containers and these
behaviors related to anatomical differences in the cerebellum,
such as differences in the size of the vermal lobules (Pierce &
Courchesne, 2001). In another similar foraging study,
Pellicano et al. (2011) reported less systematic searches and a
higher proportion of revisitations in children with ASD.
Differences were also found in free-viewing of visual scenes
(Elison, Sasson, Turner-Brown, Dichter, & Bodfish, 2012).
Typically developing (TD) and ASD participants aged 2 to 18
years were presented with large arrays of images. The number
of images visited per unit of time increased with age in both
groups, but with a steeper slope in TD participants (Elison
et al., 2012). Thus, across measures, ASD seems to be associ-
ated with less exploratory behavior. When findings could not
be explained by specific preferences related to the disorder, as
is the case in both Pellicano et al. (2011) and Pierce and
Courchesne (2001), returning to already explored locations/
objects could be driven by incomplete information processing
or poor memory. However, IQ was not a predictor of perfor-
mance in either Elison et al. (2012) or in Pierce and Courchesne
(2001), and groups were matched in IQ in Pellicano et al.
(2011). Thus, these biases seem to manifest independently of
concurrent information processing difficulties.
In contrast to ASD, ADHD is associated with extreme
novelty seeking, especially in the case of the hyperactive
and combined (hyperactive and inattentive) subtypes
(Salgado et al., 2008). Intriguing evidence that genes asso-
ciated with ADHD (e.g., DRD4 gene variants) are more fre-
quent in populations that have a history of migration (Chen,
Burton, Greenberger, & Dmitrieva, 1999; Matthews &
Butler, 2011) led to the suggestion that hyperactivity might
be an adaptation to the food-scarce and volatile environ-
ment our ancestors lived in (Jensen et al., 1997). Some
rather indirect evidence for a bias toward exploratory
behavior in ADHD comes from a study in which the num-
ber of regions visited when free-viewing a visual scene cor-
related with differences in curiosity (Risko, Anderson,
Lanthier, & Kingstone, 2012), which some have associated
with ADHD (Williams & Taylor, 2006).
The existing literature suggests ASD and ADHD might
be associated with opposing biases in foraging. However, as
ASD and ADHD often co-occur (approximately 20% of
U.K. 7-year-olds with ASD meet criteria for ADHD, and
vice versa; Russell, Rodgers, Ukoumunne, & Ford, 2014),
this raises the question of how ASD and ADHD-specific
foraging biases interact during development. Additive phe-
notypic effects were previously described in children with
comorbid ASD and ADHD, for example, neural processing
of human gaze in these children was similar to both profiles
of children with ASD only or with ADHD only (Tye et al.,
2013). An increase in symptom severity, compared with the
single diagnosis cases, was also documented (e.g., Goldin,
Matson, Tureck, Cervantes, & Jang, 2013). These findings
raise the intriguing possibility that, when disease-specific
phenotypes are at opposite ends of a spectrum (e.g.,
increased exploration in ADHD and decreased exploration
in ASD), risk for one disorder may mitigate the effects of
risk for the other disorder.
We were therefore interested in investigating the impact
ASD and ADHD risk has on information foraging during
development. Both occulo-motor behaviors and object
manipulation have been used to measure information forag-
ing in infants (Bornstein, Hahn, & Suwalsky, 2013). Of the
two measures, visual scanning is less confounded by gen-
eral motor development.
Visual Foraging and Its Development
It has been suggested that the type of cognitive processes that
derived from spatial foraging for resources, such as food,
may also be associated with the control of visual attention
(Hills, Todd, & Goldstone, 2008). For example, when freely
exploring new visual scenes, participants make shorter fixa-
tions and longer saccades during the first few seconds, cover-
ing the whole visual scene. However, as participants continue
to look, their fixations gradually become longer and their sac-
cades shorter (Fischer, Graupner, Velichkovsky, & Pannasch,
2013; Pannasch, Helmert, Roth, Herbold, & Walter, 2008).
This suggests that the optimum visual information strategy
relies on an initial exploratory phase, where the new scene is
mapped to discover potential interesting sights, followed by
information exploitation, where chosen locations (sources of
information) are repeatedly investigated (Krebs, Kacelnik, &
Taylor, 1978). There is still limited understanding of devel-
opmental changes in visual foraging, but the limited existing
evidence suggests an increase in exploration with age. During
the first month after birth, infants restrict their scanning to a
small portion of an image, but by 3 months they produce lon-
ger saccades and gaze patterns that are more systematically
distributed over visual scenes (Bronson, 1991). However,
even beyond 6 months of age, scanning patterns remain
restricted to particular locations of the visual scene
(Schlesinger & Amso, 2013). In the current study we will ask
whether measures of visual scanning capture information
foraging atypicalities early in development.
The Current Study
The current work is a reanalysis of a previously published
data set (Elsabbagh et al., 2013). The “face-pop out” para-
digm was designed to measure face orienting as an early
marker for ASD. Infants freely explored displays contain-
ing a face and four other objects while their eye movements
were measured with an eye tracker (Figure 1). Because
infants had 15 s to explore each new display, by analyzing
the sequences of visits to faces and other objects, this para-
digm can inform about visual foraging strategies.
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Gliga et al. 3
Study participants were infants with an older sibling with
ASD. Approximately 20% of children at-risk will receive a
diagnosis of ASD themselves (Ozonoff et al., 2011), and
another 30% will manifest elevated levels of ASD symptoms
(Messinger et al., 2013). Thus, by comparing visual foraging
in this population with a low-risk cohort, we aim to capture
differences due to genetic susceptibility for ASD. As men-
tioned earlier, ASD and ADHD are often co-occurring and,
like ASD, ADHD has moderate heritability (Larsson, Chang,
D’Onofrio, & Lichtenstein, 2014). To investigate the effect
ADHD risk has on visual foraging, we characterized the
older siblings’ hyperactivity and inattention profile using the
Strengths and Difficulties Questionnaire (SDQ). The SDQ
has been extensively used as a screener for ADHD and has
good sensitivity, especially for children with the combined
subtype (hyperactivity/inattention; Carballo, Rodríguez-
Blanco, García-Nieto, & Baca-García, 2014; Ullebø,
Posserud, Heiervang, Gillberg, & Obel, 2011). To reflect the
fact that the SDQ is a screening, not a diagnostic, instrument
we refer to the risk conferred by a high score on the SDQ as
hyperactivity/inattention risk (HI risk). We can thus compare
the effect of ASD risk, and that of additional HI risk, on
visual foraging during early development. Infants contrib-
uted data at three age points (8 months, 14 months, and 3
years). We analyzed the temporal dynamics of attention as
participants scanned the different areas of interest (AOIs),
focusing in particular on the likelihood of images being revis-
ited. We expected ASD risk to be associated with decreased
exploration (i.e., higher likelihood of revisiting AOI) and that
additional HI risk may moderate this effect.
Participants were 54 infants at high familial risk (HR) and
50 infants at low familial risk (LR) for ASD. Infants
attended lab-based testing 3 times, first between 6 and 10
months, a second time around 14 months, and a third time
around 3 years of age. Only a subset of participants contrib-
uted data at the 3-year visit (Table 1). At the time of enroll-
ment, none of the infants had been diagnosed with any
medical or developmental condition. HR infants all had an
older sibling (proband) with a community clinical diagnosis
of ASD. Proband diagnosis was confirmed by two expert
clinicians (GP, TC) based on information from the
Development and Well-Being Assessment (DAWBA) and
the parent-report Social Communication Questionnaire
(SCQ, Rutter, Bailey, & Lord, 2003). Infants in the low-risk
group were recruited from a volunteer database at the xxx.
All low-risk infants had at least one older sibling with typi-
cal development and no first-degree relatives with ASD.
None of the older siblings scored above instrument cutoff
for ASD on the SCQ (15, 1 score missing).
Assessing Hyperactivity/Inattention-Risk
Proband ADHD risk was attributed based on scores on the
SDQ, filled in by the parent of HR participants. The SDQ
is composed of 25 items that ask about behavioral attri-
butes of the child and are combined to form five sub-
scales. The hyperactivity/inattention subscale covers
restlessness, fidgeting, concentration, distractibility, and
impulsivity. Each item can be answered with not true,
somewhat true, or certainly true and they are scored 0, 1,
or 2, respectively, giving a total score out of a possible 10
for each subscale. Screening studies suggest a cutoff of
8/10 as providing the best ADHD diagnostic accuracy
(Carballo et al., 2014). Using a cutoff of 8, we separated
the HR-ASD group into a HR/ASD-HI group (n = 26),
including children whose probands scored below 8 (aver-
age = 5.19, SD = 1.7) and a HR/ASD+HI group (n = 20),
when probands scored 8 or above (average = 9.25, SD =
0.78). Hyperactivity and Inattention subscale scores did
not relate to proband social communication abilities (cor-
relation with SCQ, r = .246, p > .1).
Visual Scanning Task and Procedure
The same stimuli and procedure as previously described in
Elsabbagh et al. (2013) was used. At 8 and 14 months,
infants saw 14 different slides (example in Figure 1), each
for 15 s. At 3 years, only 10 slides were shown. Each slide
contained 5 images, one from each of the following catego-
ries: faces, mobile phones, birds, cars, and scrambled faces.
Figure 1. Example stimulus, AOIs, and visit coding.
Note. Successive fixations within an AOI were coded as one visit (e.g.
fixations 1 and 2 and fixations 3 and 4, in this example). The third visit, in
green, is a revisit to the face. AOI = area of interest.
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4 Journal of Attention Disorders
A central attention getter was presented before each slide to
reorient infant’s attention. Infants were seated on their par-
ent’s lap in front of the eye-tracking monitor (at 8 and 14
months) or on their own in front of the eye-tracking monitor
(3 years of age). Parents were asked to refrain from pointing
to the screen or naming any of the images.
Revisitation Analysis
Gaze data were recorded with a Tobii 1,750 eye tracker at
50 Hz. Data were parsed into fixations defined as “gaze
remaining within a 30 pixel radius (~.80° of visual angle)
for a minimum of 60 ms.” A list of fixations, in the order in
which they occurred and their corresponding AOI, was
extracted for each participant and used to compute visit
durations and order during each trial. A visit was defined as
the sum of all consecutive fixations made within an AOI.
Each visit was coded as either a first visit (the first visit to
that AOI, coded as 0) or a revisit (the AOI had been visited
before within the trial, coded as 1). The probability of
choosing an old item was always zero at the first visit as
none of the other items had been fixated at this point within
the trial. Likewise, the probability of choosing an old item
at the second visit was always zero because all of the pos-
sible target AOIs were new items. We coded up to 10 visits
per trial. For each participant, we calculated the revisitation
likelihood for each visit, by averaging across data obtained
from all slides.
Younger Sibling Outcome Characterization
A standard measure of mental development level, the
Mullen Scales for Early Learning (MSEL; Mullen, 1995),
was collected. The MSEL is a standardized direct develop-
mental assessment that yields a standardized score (M =
100, SD = 15) of overall intellectual ability (Early Learning
Composite standard score). The HR group was also
assessed with the parent-report Autism Diagnostic
Interview–Revised (ADI-R; Lord et al., 1994) and the
Autism Diagnosis Observation Schedule (ADOS; Lord
et al., 2000). A “best estimate clinical consensus” approach
to diagnosis was taken following a review by experienced
clinical researchers (TC) taking account of all information
about the child (i.e., MSEL, informal observation), in addi-
tion to information from the ADI-R and ADOS-G. Children
were included in the ASD group if they met International
Classification of Diseases–Revision 10 (ICD-10; World
Health Organization, 1993) criteria for any pervasive
developmental disorder (PDD). Given the young age of the
children, and in line with the changes to Diagnostic and
Statistical Manual of Mental Disorders (5th ed.; DSM-5;
American Psychiatric Association, 2013), no attempt was
made to assign specific subcategories of PDD/childhood
autism diagnosis. Children from the HR group were con-
sidered TD (high-risk-typical) if they (a) did not meet ICD-
10 criteria for an ASD, (b) did not score above the ASD
cutoff on the ADOS or ADI, and (c) scored within 1.5 SD
Table 1. Participant Characteristics.
F:M 29:21 16:10 10:10
Time 1 (8 months)
Age months (SD) 7.87 (1.1) 8.01 (1.1) 7.56 (1.4)
Trial no. (min-max) 13.83 (12-14) 13.85 (13-14) 13.17 (4-14)
N148 26 18
Mullen ELC2 (SD) 104.42 (11.31)a,b 91.30 (11.21)a95.21 (14.24)b
Time 2 (14 months)
Age months (SD) 13.91 (3.1) 14.48 (1.2) 13.80 (1.6)
Trial no. (min-max) 13.67 (4-14) 13.71 (11-14) 13.94 (13-14)
N145 24 18
Mullen ELC (SD) 106.10 (15.72) 99.07 (17.31) 98.20 (18.94)
Time 3 (3 years)
Age months (SD) 38.22 (3.05) 37.42 (1.4) 37.20 (1.7)
Trial no. (min-max) 9.60 (7-10) 9.95 (5-14) 10 (10-10)
N126 20 13
Mullen ELC2 (SD) 115.79 (16.27)a100.92 (22.61)a111.94 (22.61)
ADOS SC 9.42 (5.2) 7.40 (5.3)
Note. Superscript letters indicate significant paired comparisons (Bonferroni correction, p < .05). LR = low familial risk; HR = high familial risk;
ASD = autism spectrum disorder; HI = hyperactivity/inattention; Mullen ELC = Early Learning Composite standard scores; ADOS SC = Autism Diag-
nostic Observation Scale, Social Interaction and Communication Composite.
1Number of participants contributing eye-tracking data at each age.
2Indicates a main effect of group.
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Gliga et al. 5
of the population mean on the Mullen Early Learning
Composite (ELC) score (>77.5) and Receptive Language
(RL) and Expressive Language (EL) subscale T-scores
(>35). Children from the HR group were considered to
have atypical development if they did not fall into any of
the above groups. That is, they either scored above the
ADOS or ADI cutoff for ASD or scored <1.5 SD on the
Mullen ELC or RL and EL, but did not meet ICD-10 crite-
ria for an ASD. From the 47 at-risk participants taking part
in this task, 17 met criteria for an ASD diagnosis, 18 were
at-risk typical, and 12 were in the HR-atypical group (9
scoring above the ADOS ASD cutoff, 1 scoring above the
ADOS ASD cutoff and <1.5 SD Mullen ELC cutoff, 1
scoring above the ADI ASD cutoff, and 1 scoring <1.5 SD
Mullen ELC cutoff).
Preliminary Analyses
We computed the average number of AOI visits per trial for
each age group. On average, participants contributed 7 vis-
its per slide at 8 months (min average 3, max average 9.9),
8 visits at 14 months (min 2.42, max 10), and 9 visits at 3
years (min 6.8 and max 10). Thus, to avoid data loss, we
restricted the analysis to the first three visits, which meant
restricting the analysis of revisitation likelihood to Visit 3
(as the likelihood of revisitation is zero at the first and sec-
ond visits). Given that at the third visit, all previous visits
had been on new AOIs, the one other advantage of this mea-
sure is that it is not affected by visit history (and by poten-
tial group differences in visit history). Because at both 8
months and 3 years, the HR/ASD-HI group had the lowest
scores on the Mullen Scales of Early Learning of the three
groups, (Table 1), we investigated the relationships between
revisitation likelihood and concurrent Mullen scores. None
of these relationships reached significance (see Suplemental
Online Material).
Does ASD and HI Risk Influence Visual
We carried out a repeated measures ANOVA on likelihood
of revisitation at the third visit, with age (8, 14 months, and
3 years) and group (24 LR, 20 HR/ASD-HI, 12 HR/
ASD+HI). This yielded a main effect of age, F(2, 106) =
17.97, p < .001, as well as a significant effect of group, F(2,
53) = 3.81, p = .028. With age, all groups became more
exploratory, in the sense that they were increasingly more
likely to visit a new AOI rather than returning to a previ-
ously seen one. Bonferroni-corrected post hoc t tests
revealed that LR participants were less likely to revisit than
the HR/ASD-HI group (p = .051), while the HR/ASD+HI
group was not significantly different from either the HR/
ASD-HI participants (p = .091) or the LR participants. We
followed up on a significant Age × Group interaction, F(4,
106) = 3.40, p = .012, with three repeated measures
ANOVAs for each age (Figure 2). At 8 months, the effect of
group was significant, F(2, 95) = 7.39, p = .001, HR/
ASD-HI were significantly different from both LR (p =
.009) and HR/ASD+HI (p = .001), the last two being indis-
tinguishable statistically. Likelihood of revisitation was
above chance level (.25) in all groups, LR: t(47) = 7.07,
p < .001; HR/ASD-HI: t(25) = 10.54, p < .001; HR/
ASD+HI: t(17) = 3.17, p = .006. At 14 months, the effect of
group was again significant, F(2, 86) = 4.65, p = .012; only
LR and HR/ASD-HI were significantly different from each
other (p = .012). Likelihood of revisitation was above
chance level in all groups, LR: t(43) = 3.17, p = .003; HR/
ASD-HI: t(23) = 4.53, p < .001; HR/ASD+HI: t(17) = 5.41,
p < .001. Groups did not significantly differ at 3 years and
for all groups, the likelihood of revisitation was not
Figure 2. Likelihood of revisitation at all ages depending on family risk; bars represent 1 Standard Error of the Mean; dimensional
relationship between revisit likelihood at 8 months and proband hyperactivity/inattention score.
Note. HR = high familial risk; ASD = autism spectrum disorder; HI = hyperactivity/inattention.
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6 Journal of Attention Disorders
different from chance, LR: t(25) < 1; HR/ASD-HI: t(19) <
1; HR/ASD+HI: t(13) < 1.
Given that a dimensional measure of HI risk was avail-
able (the SDQ scores vary between 0 and 10), and to
strengthen our findings, we investigated the quantitative
relationship between revisitation likelihood at 8 and 14
months and this measure. This analysis yielded a significant
negative correlation between revisitation likelihood at 8
months and SDQ scores (r = −.430, p = .003; Figure 2) but
no significant relationship with visual foraging at 14
Were Group Differences Driven by Returns to
the Face?
As many revisits at the third visit are returns to the face
(about 50% of first looks are to the face, Elsabbagh et al.,
2013), it is important to know whether the group differences
described mainly reflect differences in returns to this stimu-
lus. To ask this question, we ran a repeated measures
ANOVA with type (Face, non-Face) and group (LR, HR/
ASD-HI, HR/ASD+HI), at each age point. At both 8 and 14
months, this analysis yielded significant main effects of
type, 8 months: F(1, 88) = 42.35, p < .001; 14 months: F(1,
83) = 23.14, p < .001, and group, 8 months: F(2, 88) = 3.64,
p = .030; 14 months: F(2, 83) = 3.47, p = .036, but a nonsig-
nificant interaction of Type × Group, 8 months: F(2, 88) =
2.06, p > .1; 14 months: F(2, 83) < 1, which suggests that
group differences in revisitation likelihood concern both
types of images. At 3 years, the effects of type and group
were not significant, type: F(1, 55) = 2.84, p > .05; group:
F(2, 55) < 1, but there was a marginally significant interac-
tion between type and group, F(2, 55) = 2.99, p = .058.
However, follow-up paired t tests yielded no significant
effects of type.
Is Foraging Related to the Child’s ASD
As ASD diagnosis was available for the younger sibling, we
also ran a repeated-measures ANOVA on the revisitation
likelihood, with age as a within participant variable and out-
come (LR, HR-TD, HR-Atypical, and HR-ASD) as between-
participants factor. This yielded a significant interaction
between age and outcome, F(6, 108) = 2.43, p = .030, and a
nonsignificant effect of outcome, F(3, 54) < 1. We followed
up this analysis with three univariate ANOVAs for each age
point. Although HR-ASD did have the highest likelihood of
revisitation at 8 months, outcome was not a significant pre-
dictor, F(3, 102) = 2.23, p = .089 (see Supplemental Figure
S1). Follow-up paired t tests against the LR group (Dunnett,
1955) yielded marginally significant effects (LR vs.
HR-ASD, p = .084). At 14 months, there was a significant
effect of outcome, F(3, 92) = 2.78, p = .045, but none of the
follow-up paired t tests were significant. The effect of out-
come was not significant at 3 years.
To explore whether proband HI affected all outcome
groups, at 8 months a second repeated measures ANOVA
was run on the high-risk group only, this time with both
outcome (HR-TD, HR-atypical and HR-ASD) and HI level
(low, high) as between-participants variables. This yielded
a significant effect of HI level, F(1, 43) = 10.51, p = .002,
which did not interact with outcome, F(2, 43) < 1 (see
Supplemental Figure S2). Thus, high proband HI was asso-
ciated with stronger exploratory biases in all high-risk chil-
dren, independent of their ASD diagnosis.
Borrowing the foraging framework developed for appeti-
tive decision-making in animals (Cohen et al., 2007;
Kacelnik et al., 1981), we investigated the development of
exploratory biases in visual information foraging. We
observed age-related changes. Six-month-olds showed a
strong tendency to revisit previously seen AOIs, with 40%
of their third visits being revisits. In contrast, by 3 years of
age, when faced with a new visual scene, children randomly
choose whether to explore a new AOI or return to a previ-
ously seen one. Thus, although we observe a decrease with
age in exploitative choices (i.e., preferring to return to old
AOIs), not even at 3 years of age does foraging become
driven by exploration (i.e., consistently preferring to sam-
ple new AOIs). The main question that motivated this
research was whether background family risk for ASD and
hyperactivity and inattention (core symptoms of ADHD)
impact on visual foraging early in life. In terms of the likeli-
hoods of revisitation, the HR/ASD-HI group was more
likely to revisit old locations than were LR participants, at
both 8 and 14 months. When the sibling’s own ASD diagno-
sis was taken into account, a trend was found for higher
revisitation likelihood in 8-month-old HR-ASDs, compared
with LR controls. This is reminiscent of previous findings
in children with a diagnosis of ASD, who also show high
return rates when exploring visual scenes or real environ-
ments (Elison et al., 2012; Pellicano et al., 2011; Pierce &
Courchesne, 2001).
We also predicted that proband hyperactivity/inattention
would be associated with a bias toward exploration. Indeed,
at 8 months, likelihood to revisit AOIs inversely relates to
proband SDQ hyperactivity and inattention scores.
However, when groups are compared, HR/ASD+HI partici-
pant performance was similar to that of the control group at
8 months and was not different from either the HR/ASD-HI
or LR at 14 months. Our findings are compatible with an
additive effect, where ASD and ADHD risk contribute
opposing biases. Additive effects of dual ASD and ADHD
diagnosis have been previously documented, but more often
then not, the literature reports increased symptom severity
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Gliga et al. 7
in children with a dual diagnosis of ASD and ADHD (e.g.,
Goldin et al., 2013; Yerys et al., 2009). This is the first evi-
dence for a potential moderating effect of one risk type on
another. However, because we are assessing the effects of
co-occurring ASD and HI risk and not those of co-occur-
rence of these symptoms in a particular individual, we are
unable to say at this point whether the “typical” perfor-
mance of the HR/ASD+HI group reflects an interaction
between the two risk backgrounds or whether it results from
some children having inherited the ASD risk factors and
other HI risk factors. Some indication that the former
hypothesis might be true comes from the fact that HR/
ASD+HI performance variance is comparable to those of
the other two groups, Levene’s F(2, 89) = 1.17, p > .1
(Supplemental Figure S1). It is important to highlight that
whether co-occurring conditions result in additive, multipli-
cative, or a completely new phenotype may vary from one
phenotypic trait to another and therefore the current find-
ings should not be generalized to imply that co-occurring
risk for both ASD and ADHD might be overall beneficial.
Why are the effects of proband hyperactivity/inattention
more prominent earlier in development? Transitory signa-
tures of risk have been reported previously. For example,
9-month-old performance in a visual search task is a predic-
tor of later ASD symptoms, but the performance of
15-month-olds does not relate to later outcome (Gliga,
Bedford, Charman, Johnson, & the BASIS Team, 2015).
Similarly, the amount of looking toward faces is a predictor
of later face recognition when measured at 8 months, but
not at 14 months (deKlerk, Gliga, Charman, & Johnson,
2014). It is possible that these developmental changes
reflect adaptive mechanisms following initial perturbations
in brain functioning, and that eventually mask the perturba-
tions, but not before development has been set on an atypi-
cal pathway (Johnson, Jones, & Gliga, 2015).
Our findings are also relevant for the debate surrounding
the origin of ASD and ADHD. Some scholars have sug-
gested a common developmental origin, which would also
explain the high rates of comorbidity (Rommelse, Geurts,
Franke, Buitelaar, & Hartman, 2011), but developmental
studies have revealed limited overlap in terms of early
markers for these disorders (Johnson, Gliga, Jones, &
Charman, 2015). The current findings may add to this list
and suggest an alternative view, according to which comor-
bidity may result from the expression of a few general vul-
nerability factors, such as poor executive functions
(Johnson, 2012). However, it remains possible that our find-
ings do not reflect heritable genetic background but envi-
ronmental effects. Growing up alongside a sibling with a
developmental disorder may in itself affect development.
Proband hyperactivity symptoms may be more perturbing
when the sibling is younger, thus explaining the stronger
relationship found at 8 months of age. Despite the uncer-
tainty regarding the mechanisms behind the effects we
document here, these findings remain novel and important
as they add to our understanding of both early learning and
psychopathology. How infants explore information has
long-term consequences on their learning abilities
(Bornstein et al., 2013, and see SOM). We also reveal para-
doxical mechanisms for resilience, where high levels of
hyperactivity/inattention in older siblings, through shared
genetic or environmental mechanisms, can have paradoxi-
cal effects, being associated with optimal information for-
aging in infants at-risk for ASD.
Author Contributions
T.G. designed the eye-tracking study, T.G., T.S., and N.G. ana-
lyzed the data; T.G. and T.S. wrote the article with contribution
from M.H.J. and T.C. M.H.J. and T.C. led the BASIS program.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.
The author(s) disclosed receipt of the following financial support for
the research, authorship, and/or publication of this article: This work
was supported by Medical Research Council (MRC) Program Grant
G0701484 and the BASIS funding consortium led by Autistica
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Author Biographies
Teodora Gliga is Programme Leader for the Infat Sibling Studies
at the Centre for Brain and Cognitive Development, Birkbeck,
University of London.
Tim J. Smith is a Senior Lecturer in Psychological Sciences at
Birkbeck, University of London.
Noreen Likely is a Trainee Clinical Psychologist at the University
of Limerick, Ireland. She contributed to the current work as part of
her Masters degree, at Birkbeck, University of London.
Tony Charman holds the Chair in Clinical Child Psychology in
Clinical Child Psychiatry at the Institute of Psychiatry, Psychology
& Neuroscience, King’s College London.
Mark H. Johnson is an MRC Programme Leader and Director of
the Centre for Brain and Cognitive Development, Birkbeck,
University of London.
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... However, revisitations in visual scanning may serve as an early marker of atypical attention development in infants at risk of autism. Gliga, Smith, Gilhooly, Charman, and Johnson (2015) calculated the probability of revisitations by coding up to 10 visits (i.e., the sum of all consecutive fixations within an AOI). However, information about local temporal gaze patterns (i.e., LAM) gets lost by collapsing consecutive fixations into one visit. ...
Full-text available
Standard looking-duration measures in eye-tracking data provide only general quantitative indices, while details of the spatiotemporal structuring of fixation sequences are lost. To overcome this, various tools have been developed to measure the dynamics of fixations. However, these analyses are only useful when stimuli have high perceptual similarity and they require the previous definition of areas of interest (AOIs). Although these methods have been widely applied in adult studies, relatively little is known about the temporal structuring of infant gaze-foraging behaviors such as variability of scanning over time or individual scanning patterns. Thus, to shed more light on the spatiotemporal characteristics of infant fixation sequences we apply for the first time a new methodology for nonlinear time-series analysis-the recurrence quantification analysis (RQA). We present how the dynamics of infant scanning varies depending on the scene content during a ''pop-out'' search task. Moreover, we show how the normalization of RQA measures with average fixation durations provides a more detailed account of the dynamics of fixation sequences. Finally, we link the RQA measures of temporal dynamics of scanning with the spatial information about the stimuli using heat maps of recurrences without the need for defining a priori AOIs and present how infants' foraging strategies are driven by the image content. We conclude from our findings that the RQA methodology has potential applications in the analysis of the temporal dynamics of infant visual foraging offering advantages over existing methods.
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Sensory regulation, the ability to select and process sensory information to plan and perform appropriate behaviours, provides a foundation for learning. From early in development, infants manifest differences in the strategies used for sensory regulation. Here, we discuss the nature and characteristics of sensory seeking, a key behavioural strategy for sensory regulation often described as atypical in children with Neurodevelopmental Disorders. We evaluate theoretical models proposed to clarify mechanisms underlying individual differences in sensory seeking and discuss evidence for/against each of these models. We conclude by arguing that the information prioritization hypothesis holds the greatest promise to illuminate the nature of individual differences in sensory seeking across participant cohorts. This proposal aligns to molecular genetic animal and human evidence, provides a coherent explanation for developmental findings, and generates testable hypotheses for future research.
Objective To evaluate which early neurocognitive and behavioural precursors are associated with the development of attention-deficit/hyperactivity disorder (ADHD) and whether these are currently targeted in early interventions. Method We conducted two systematic reviews and meta-analyses of empirical studies to examine (1) early-life (0-5 years) neurocognitive and behavioural precursors associated with familial likelihood for ADHD, an early ADHD diagnosis/elevated ADHD symptoms, and/or the presence of later-childhood ADHD, and (2) interventions delivered to children aged 0-5 years targeting the identified precursors or measuring these as outcomes. Standardised mean differences (Hedge's g) and pre-post-treatment change scores (SMD) were computed. Results One-hundred-and-forty-nine studies (165,095 participants) investigating eight neurocognitive and behavioural domains met inclusion criteria for part (1). Multi-level random-effects meta-analyses on 136 studies revealed significant associations between ADHD and poorer cognitive (g=-0.46 [95% CIs:-0.59,-0.33]), motor (g=-0.35 [CIs:-0.48,-0.21]) and language (g=-0.43 [CIs:-0.66,-0.19]) development, social (g=0.23 [CIs:0.03,0.43]) and emotional (g=0.46 [CIs:0.33,0.58]) difficulties, early regulatory (g=0.30 [CIs:0.18,0.43]) and sleep (g=0.29 [CIs:0.14,0.44]) problems, sensory atypicalities (g=0.52[CIs:0.16,0.88]), elevated activity levels (g=0.54 [CIs:0.37,0.72]) and executive function difficulties (g=0.34 [CIs:0.05,0.64] to -0.87 [CIs:-1.35,-0.40]). Thirty-two trials (28 randomised, four non-randomised, 3,848 participants) testing early interventions that targeted the identified precursors met inclusion criteria for part (2). Multi-level random-effects meta-analyses on 22 studies revealed significant intervention-related improvements in ADHD symptoms (SMD=0.43[CIs:0.22,0.64]) and working memory (SMD=0.37[CIs:0.06,0.69]). Conclusion Children aged 0-5 years with current or later-emerging ADHD are likely to experience difficulties in multiple neurocognitive/behavioural functions. Early interventions show some effectiveness in reducing ADHD symptoms but their effects on neurocognitive/behavioural difficulties require further study.
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Slower habituation to repeating stimuli characterises Autism, but it is not known whether this is driven by difficulties with information processing or an attentional bias towards sameness. We conducted eye-tracking and presented looming geometrical shapes, clocks with moving arms and smiling faces, as two separate streams of stimuli (one repeating and one changing), to 7–15 years old children and adolescents (n = 103) with Autism, ADHD or co-occurring Autism+ADHD, and neurotypical children (Study-1); and to neurotypical children (n = 64) with varying levels of autistic traits (Study-2). Across both studies, autistic features were associated with longer looks to the repeating stimulus, and shorter looks to the changing stimulus, but only for more complex stimuli, indicating greater difficulty in processing complex or unpredictable information.
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Full-text available
Background: Reduced executive functions (EF) are commonly associated with developmental conditions (e.g., autism spectrum disorder, ASD; attention deficit/hyperactivity disorder, ADHD), although EF seems to be typical in children with callous unemotional (CU) traits. Regulatory function (RF) is a proposed infant precursor that maps on onto factors driving later EF. Here, we first test whether RF is specifically and negatively associated with ASD and ADHD traits, but not CU traits. Second, we test whether RF can act as a protective factor, by moderating the association between infant markers and subsequent ASD and ADHD traits. Methods: Participants were 79 infants at high (N = 42) and low (N = 37) familial risk for ASD. Data come from the 14-month infant visit (Autism Observational Scale for Infants; AOSI; activity level and RF from the Infant Behavior Questionnaire; IBQ) and the 7-year visit (ASD traits: Social Responsiveness Scale, SRS; ADHD traits: Conners 3, CU traits: Inventory of Callous Unemotional Traits). Results: Infant RF was negatively associated with later traits of ASD (B = - 0.5, p = 0.01) and ADHD inattention (B = - 0.24, p = 0.02) but not hyperactivity (B = - 0.25, p = 0.10) or CU traits (B = 0.02, p = 0.86). RF moderated the association between infant AOSI score and ASD traits, with a significant effect in those with low RF (B = 0.10, p = 0.006), not high RF (B = 0.01, p = 0.78). Similarly, for ADHD, infant activity level was associated with later ADHD inattention in those with low (B = 0.17, p = 0.04) but not high RF (B = 0.07, p = 0.48). For ADHD hyperactivity symptoms, activity level was predictive at both high and low levels of RF. Conclusions: Strong RF may allow children to compensate for other atypicalities, thus attenuating the association between infant markers and later disorder traits. Whilst infant RF was associated with both ASD and ADHD inattention traits, there was no association with ADHD hyperactivity or CU traits. This suggests that any protective effect may not be universal and emphasises the need for a better understanding of the underlying moderating mechanisms.
Full-text available
We conducted a replication study of our prior report that increased alpha EEG connectivity at 14-months associates with later autism spectrum disorder (ASD) diagnosis, and dimensional variation in restricted interests/repetitive behaviours. 143 infants at high and low familial risk for ASD watched dynamic videos of spinning toys and women singing nursery rhymes while high-density EEG was recorded. Alpha functional connectivity (7–8 Hz) was calculated using the debiased weighted phase lag index. The final sample with clean data included low-risk infants (N = 20), and high-risk infants who at 36 months showed either typical development (N = 47), atypical development (N = 21), or met criteria for ASD (N = 13). While we did not replicate the finding that global EEG connectivity associated with ASD diagnosis, we did replicate the association between higher functional connectivity at 14 months and greater severity of restricted and repetitive behaviours at 36 months in infants who met criteria for ASD. We further showed that this association is strongest for the circumscribed interests subdomain. We propose that structural and/or functional abnormalities in frontal-striatal circuits underlie the observed association. This is the first replicated infant neural predictor of dimensional variation in later ASD symptoms.
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The way in which the behavioral manifestations of autism spectrum disorder (ASD) emerge in infancy is variable. Regression—loss of previously acquired skills—occurs in a subset of children. However, the etiology and significance of regression remains unclear. Until recently, investigation of regression relied on retrospective report by parents or examination of home videos from early in life. However, home videos and retrospective report of the nature and timing of regression, and association with factors such as illness or immunization, is potentially subject to bias. The advent of prospective studies of infant siblings at familial high‐risk of ASD has the potential to document regression as it occurs. Recent research has suggested that subtle loss of skills occurs in a larger proportion of children with ASD than previously assumed; however, there are few reports of clear‐cut regressions, such as that involving dramatic loss of language and other established skills, in the prospective literature. This could be because of the following: clear‐cut regression occurs less commonly than parent report suggests, study design limits the potential to detect regression, or there are differences between multiplex and simplex families in the rate of de novo genetic mutations and therefore regression risk. This review will bring together literature from retrospective and prospective research and attempt to reconcile diverging findings, with a specific focus on methodological issues. Changing conceptualizations of regression will be discussed, as well as etiological factors that may be associated with regression. The main challenges that need to be addressed to measure regression in prospective studies will be set out. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. Lay Summary Regression—a loss of previously established skills—occurs in a subset of children with ASD. Parental recall is not always accurate but studying younger siblings of children with ASD, 10–20% of whom will develop ASD, should make it possible to measure regression as it occurs. Clear‐cut regression, like loss of language, has not often been reported in infant sibling studies, but recent research suggests that gradual loss of social engagement might be more common. This review looks at the evidence for regression from infant sibling studies and asks how study design affects the likelihood of capturing regression.
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In addition to core symptoms, i.e., social interaction and communication difficulties and restricted and repetitive behaviors, autism is also characterized by aspects of superior perception [1]. One well-replicated finding is that of superior performance in visual search tasks, in which participants have to indicate the presence of an odd-one-out element among a number of foils [2-5]. Whether these aspects of superior perception contribute to the emergence of core autism symptoms remains debated [4, 6]. Perceptual and social interaction atypicalities could reflect co-expressed but biologically independent pathologies, as suggested by a "fractionable" phenotype model of autism [7]. A developmental test of this hypothesis is now made possible by longitudinal cohorts of infants at high risk, such as of younger siblings of children with autism spectrum disorder (ASD). Around 20% of younger siblings are diagnosed with autism themselves [8], and up to another 30% manifest elevated levels of autism symptoms [9]. We used eye tracking to measure spontaneous orienting to letter targets (O, S, V, and +) presented among distractors (the letter X; Figure 1). At 9 and 15 months, emerging autism symptoms were assessed using the Autism Observation Scale for Infants (AOSI; [10]), and at 2 years of age, they were assessed using the Autism Diagnostic Observation Schedule (ADOS; [11]). Enhanced visual search performance at 9 months predicted a higher level of autism symptoms at 15 months and at 2 years. Infant perceptual atypicalities are thus intrinsically linked to the emerging autism phenotype. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.
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Resilience and adaptation in the face of early genetic or environmental risk has become a major interest in child psychiatry over recent years. However, we still remain far from an understanding of how developing human brains as a whole adapt to the diffuse and widespread atypical synaptic function that may be characteristic of some common developmental disorders. The first part of this paper discusses four types of whole-brain adaptation in the face of early risk: redundancy, reorganization, niche construction, and adjustment of developmental rate. The second part of the paper applies these adaptation processes specifically to autism. We speculate that key features of autism may be the end result of processes of early brain adaptation, rather than the direct consequences of ongoing neural pathology.
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Background Autism spectrum disorders (ASD) and attention deficit hyperactivity disorder (ADHD) are two of the most common neurodevelopmental disorders, with a high degree of co-occurrence.Methods Prospective longitudinal studies of infants who later meet criteria for ASD or ADHD offer the opportunity to determine whether the two disorders share developmental pathways.ResultsProspective studies of younger siblings of children with autism have revealed a range of infant behavioral and neural markers associated with later diagnosis of ASD. Research on infants with later ADHD is less developed, but emerging evidence reveals a number of relations between infant measures and later symptoms of inattention and hyperactivity.Conclusions We review this literature, highlighting points of convergence and divergence in the early pathways to ASD and ADHD.
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Novelty seeking refers to the tendency of humans and animals to explore novel and unfamiliar stimuli and environments. The idea that dopamine modulates novelty seeking is supported by evidence that novel stimuli excite dopamine neurons and activate brain regions receiving dopaminergic input. In addition, dopamine is shown to drive exploratory behavior in novel environments. It is not clear whether dopamine promotes novelty seeking when it is framed as the decision to explore novel options versus the exploitation of familiar options. To test this hypothesis, we administered systemic injections of saline or GBR-12909, a selective dopamine transporter (DAT) inhibitor, to monkeys and assessed their novelty seeking behavior during a probabilistic decision making task. The task involved pseudorandom introductions of novel choice options. This allowed monkeys the opportunity to explore novel options or to exploit familiar options that they had already sampled. We found that DAT blockade increased the monkeys' preference for novel options. A reinforcement learning (RL) model fit to the monkeys' choice data showed that increased novelty seeking after DAT blockade was driven by an increase in the initial value the monkeys assigned to novel options. However, blocking DAT did not modulate the rate at which the monkeys learned which cues were most predictive of reward or their tendency to exploit that knowledge. These data demonstrate that dopamine enhances novelty-driven value and imply that excessive novelty seeking-characteristic of impulsivity and behavioral addictions-might be caused by increases in dopamine, stemming from less reuptake. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
Background: Although limited environmental exploration in autism is an obvious behavioral feature and may be a manifestation of “restricted interests” as described in DSM-IV criteria, there have been no behavioral or neurobiological studies of this important aspect of the disorder. Given consistent reports of cerebellar abnormality in autism, combined with animal research showing a relationship between exploration and the cerebellum, this study aimed to test the possible link between cerebellar abnormality and exploration in autism.
Is there any relationship between visual fixation durations and saccade amplitudes in free exploration of pictures and scenes? In four experiments with naturalistic stimuli, we compared eye movements during early and late phases of scene perception. Influences of repeated presentation of similar stimuli (Experiment 1), object density (Experiment 2), emotional stimuli (Experiment 3) and mood induction (Experiment 4) were examined. The results demonstrate a systematic increase in the durations of fixations and a decrease for saccadic amplitudes over the time course of scene perception. This relationship was very stable across the variety of studied conditions. It can be interpreted in terms of a shifting balance of the two modes of visual information processing.
Saccade latencies are longer prior to an eye movement to a recently fixated location than to control locations, a phenomenon known as oculomotor inhibition of return (O-IOR). There are theoretical reasons to expect that O-IOR would vary in magnitude across different eye movement tasks, but previous studies have produced contradictory evidence. However, this may have been because previous studies have not dissociated O-IOR and a related phenomenon, saccadic momentum, which is a bias to repeat saccade programs that also influences saccade latencies. The present study dissociated the influence of O-IOR and saccadic momentum across three complex visual tasks: scene search, scene memorization, and scene aesthetic preference. O-IOR was of similar magnitude across all three tasks, while saccadic momentum was weaker in scene search. © 2014 ARVO.
To examine the screening ability of the Strengths and Difficulties Questionnaire (SDQ) for ADHD subtypes in a clinical sample. Parents of 523 children (3 to 17 years old) referred to Child and Adolescent Mental Health Services completed the Spanish version of the SDQ and the ADHD Rating Scale-IV. Receiver operating curve (ROC) curve analyses and likelihood ratios (LRs) were conducted. The LR results indicated that the 8/10 cutoff showed the highest diagnostic accuracy. The sensitivity of the SDQ 8/10 cutoff for the three subtypes was significantly different: 84.0% (95% confidence interval [CI] = [75.58, 89.90]) of ADHD combined subtype (ADHD-Co), 25.0% (95% CI = [17.55, 34.30]) of ADHD predominantly inattentive subtype (ADHD-I), and 77.8% (95% CI = [68.71, 84.83]) of ADHD predominantly hyperactive/impulsive subtype (ADHD-H). Sex and age differences were found. These screening differences were also found when using the 7/10 cutoff or the SDQ predictive algorithm. Our study supports the use of the SDQ in the screening for ADHD. However, not all ADHD subtypes are equally screened. © 2014 SAGE Publications.