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Examining executive functioning in children with autism spectrum
disorder, attention deficit hyperactivity disorder
and typical development
Blythe A. Corbetta,b,⁎, Laura J. Constantineb, Robert Hendrena,b,
David Rockec, Sally Ozonoffa,b
aDepartments of Psychiatry and Behavioral Sciences, University of California at Davis, Davis, CA, USA
bM.I.N.D. Institute, University of California at Davis, Davis, CA, USA
cDivision of Biostatistics, University of California at Davis, Davis, CA, USA
Received 27 July 2007; received in revised form 30 January 2008; accepted 8 February 2008
Executive functioning (EF) is an overarching term that refers to neuropsychological processes that enable physical, cognitive,
and emotional self-control. Deficits in EF are often present in neurodevelopmental disorders, but examinations of the specificity of
EF deficits and direct comparisons across disorders are rare. The current study investigated EF in 7- to 12-year-old children with
autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD) and typical development using a comprehensive
battery of measures assessing EF, including response inhibition, working memory, cognitive flexibility, planning, fluency and
vigilance. The ADHD group exhibited deficits in vigilance, inhibition and working memory relative to the typical group; however,
they did not consistently demonstrate problems on the remaining EF measures. Children with ASD showed significant deficits in
vigilance compared with the typical group, and significant differences in response inhibition, cognitive flexibility/switching, and
working memory compared with both groups. These results lend support for previous findings that show children with autism
demonstrate generalized and profound impairment in EF. In addition, the observed deficits in vigilance and inhibitory control
suggest that a significant number of children with ASD present with cognitive profiles consistent with ADHD.
© 2008 Elsevier Ireland Ltd. All rights reserved.
Keywords: Attention; Inhibition; Comorbidity; Working memory; CANTAB; Neuropsychology; Vigilance
Executive function (EF) is an overarching term that
refers to mental control processes that enable physical,
cognitive, and emotional self-control (Denckla, 1996;
Lezak, 1995; Pennington and Ozonoff, 1996) and are
necessary to maintain effective goal-directed behavior
(Welsh and Pennington, 1988). Executive functions
Available online at www.sciencedirect.com
Psychiatry Research 166 (2009) 210–222
⁎Corresponding author. Department of Psychiatry and Behavioral
Sciences, M.I.N.D. Institute, University of California, Davis, 2825
50th Street, Sacramento, CA 95817, USA. Tel.: +1 916 703 0232;
fax: +1 916 703 0244.
E-mail address: email@example.com
0165-1781/$ - see front matter © 2008 Elsevier Ireland Ltd. All rights reserved.
Author's personal copy
generally include response inhibition, working memory,
cognitive flexibility (set-shifting), planning and fluency
(Ozonoff and Strayer, 1997; Pennington and Ozonoff,
1996). Deficits in EF are frequently observed in
neurodevelopmental disorders, including autism and
attention deficit hyperactivity disorder (ADHD).
Autism is a severe neurodevelopmental disorder
characterized by impairment in communication, recipro-
cal social interaction, and a markedly restricted reper-
toire of activities and interests (American Psychiatric
Association, 2000). The symptoms of autism fall on a
continuum of severity referred to as autism spectrum
disorder (ASD), which include autistic disorder, Asper-
ger syndrome, and pervasive developmental disorder—
not otherwise specified (PDD–NOS). Autism is often
further divided into those with mental retardation and
those functioning in the average or above average range
of intelligence (often called high functioning autism or
et al., 2005; Hughes et al., 1994; Ozonoff et al., 1991;
Pennington and Ozonoff, 1996)). The primacy of EF
deficits in 57 autism (Russell, 1997), especially in terms
of planning, cognitive flexibility and working memory,
remains an ongoing debate (for a review, see Hill, 2004).
ADHD is also a neurologically mediated disorder
that exists on a continuum. ADHD is characterized by
varying degrees of inattention, impulsivity and hyper-
active behavior (American Psychiatric Association,
2000). ADHD is further divided into those individuals
meeting symptom criteria in all the aforementioned
areas (Combined type), those primarily evidencing
attention problems (Predominantly inattentive type) and
those with mostly hyperactive and impulsive symptoms
(Predominantly hyperactive-impulsive type). Significant
EF deficits in individuals with ADHD have been
reported; however, there is still some inconsistency
regarding particular impairments in domains of function-
ing. In a comprehensive meta-analysis, Willcutt et al.,
2005 found that studies most consistently report response
inhibition and vigilance deficits in ADHD. Other
impairments have been found in working memory (e.g.,
(Kempton et al., 1999; Rhodes et al., 2005)), planning
(e.g., (Kempton et al., 1999; Rhodes et al., 2005)), and
flexibility (Vance et al., 2003).
and ASD groups and studies investigating these groups
separately report inconsistent findings. Some have
proposed that EF deficits are core to ASD (Russell,
1997) and ADHD (Barkley, 1997). Pennington and
Ozonoff (1996) suggested that deficits in domains of EF
could be disassociated across disorders resulting in
distinct EF profiles, a notion that has received some
foundational support. Specifically, impaired motor and
response inhibition in ADHD is well supported (e.g.,
Ozonoff, 1996). Deficits in planning and set-shifting
have been shown to be more pronounced in individuals
with HFA than ADHD and typical development (Geurts
et al., 2004; Ozonoff et al., 2004; Ozonoff and Strayer,
1997; Sergeant et al., 2002). Similarly, more impairment
in HFA as compared with ADHD has also been de-
monstrated with verbal working memory (Pennington
and Ozonoff, 1996) and spatial working memory
(Goldberg et al., 2005; Landa and Goldberg, 2005).
Studies are beginning to emerge directly comparing
autism and ADHD groups with their typically develop-
ing counterparts (Corbett and Constantine, 2006; Gold-
berg et al., 2005; Ozonoff and Jensen, 1999; Verte et al.,
2006). These studies have generally found EF deficits
across both diagnostic groups, with ostensibly more
severe and global deficits in ASD. Deficits within the
ADHD groups tend to be more consistently restricted
to behavioral disinhibition and vigilance (Corbett and
Constantine, 2006; Goldberg et al., 2005; Ozonoff
and Jensen, 1999; Verte et al., 2006). However, there is
evidence that individuals with ADHD may also have
deficits in planning, set-shifting, and spatial working
memory (e.g., Kempton et al., 1999).
Recently, Goldberget al. (2005) examined inhibition,
planning, set-shifting and working memory in a sample
of children 8 to 12 years of age with HFA, ADHD and
typical development. Participants were carefully
assessed to screen out comorbid impulsivity or hyper-
activity in autism. Using a computerized battery
(CANTAB®), the study reported that response inhibi-
tion, planning, and set-shifting were similar across the
three groups of ASD, ADHD and typical development
and HFA groups were reported (Goldberg et al., 2005).
However, age and level of functioning on this measure
may explain the limited sensitivity (Goldberg et al.,
2005; Landa and Goldberg, 2005).
Since deficits in EF are present in several neurode-
velopmental disorders, the issue of discriminant validity
must be considered as to how disorders with different
behavioral phenotype can share similar neuropathologi-
cal substrates (Ozonoff and Jensen, 1999). Geurts et al.
(2004) expanded on this notion using a comprehensive
neuropsychological battery in children between 6 and
12 years and reported that children with ADHD de-
monstrated EF deficits in inhibition and verbal fluency
while children with HFA showed deficits across most of
the EF measures.
211 B.A. Corbett et al. / Psychiatry Research 166 (2009) 210–222
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Verte et al. (2006) recently reported significant
and response variability compared to children with
Tourette Syndrome or typical development. Further,
poorer inhibition and more response variability were
associatedwith symptoms ofADHD,whilepoorworking
memory was associated with more symptoms of autism.
Taken together, the majority of the studies conclude that
than children with ADHD.
Converging evidence from a variety of methods
including chart review (Goldstein and Schwebach,
2004), parent and teacher questionnaires (Gadow
et al., 2004) and neuropsychological measures (Corbett
and Constantine, 2006) conclude that a high percentage
of children with ASD evidence symptoms of ADHD,
some warranting a comorbid diagnosis. The etiology of
both neurodevelopmental disorders has a strong genetic
basis with heritability estimates for autism to be 0.9
(Baron-Cohen and Belmonte, 2005; Zafeiriou et al.,
2007) and estimates for ADHD to be 0.7 (Faraone et al.,
2005). Furthermore, there are some preliminary findings
of a genetic linkage between these disorders at
chromosomal locations 2q24 and 16p13 (Fisher et al.,
2002; Ogdie et al., 2003; Smalley et al., 2005). Even
without consideration of comorbid features, various
neuroscientific models have highlighted the common
behavioral features, biological pathways and neuroana-
tomical correlates between ASD and ADHD implicating
the frontostriatal system including the frontal lobes and
basal ganglia (Damasio and Maurer, 1978; Ozonoff and
Jensen, 1999; Pennington and Ozonoff, 1996; Stuss and
Benson, 1984). Structural and functional neuroimaging
studies show frontal lobe dysfunction in autism (e.g.,
(Carper and Courchesne, 2000, 2005; Courchesne et al.,
2001; McAlonan et al., 2005; Muller et al., 2001), and
ADHD (e.g., (Faraone and Biederman, 1998; Kates
et al., 2002; Lou et al., 1984; Mostofsky et al., 2002;
Smith et al., 2006; Sowell et al., 2003; Zang et al.,
2005)). These brain regions are important in EF, and, as
discussed, both disorders have been associated with
deficits in EF (e.g., (Barkley, 1997; Goldberg et al.,
2005; Pennington and Ozonoff, 1996; Russell, 1997)).
Furthermore, many children with ASD display
ADHD symptomatology, suggesting that the disorders
may share similar traits or are often comorbid (Corbett
and Constantine, 2006; Gadow et al., 2004; Geurts et al.,
2004; Ghaziuddin et al., 1992; Goldberg et al., 2005;
Goldstein and Schwebach, 2004; Verte et al., 2006). Yet,
the limited studies that have compared these two
disorders generally exclude comorbid features (Geurts
et al., 2004; Goldberg et al., 2005). Conversely, based
on population-based investigation, it has been shown
that children diagnosed with ADHD may show autistic
traits (Reiersen et al., 2007), which punctuates the
importance of investigating comorbid features.
Although we recognize the value of elucidating EF in
clearly defined prototypical cases of autism and ADHD,
this may not be representative or generalizable to many
children on the spectrums of autism or ADHD. Thus, we
conducted a comprehensive neuropsychological study
to compare and contrast six domains of EF (response
inhibition, working memory, flexibility/shifting, plan-
ning, fluency and vigilance), in children with ASD,
ADHD and typical development deliberately allowing
comorbid ADHD features in the children with ASD. We
hypothesized that children with ASD would demon-
strate greater impairment across a broad range of EF
tasks. Simultaneously, we investigated the performance
of EF and related it to the level of ADHD symptoms
across these groups. We predicted that specific measures
of vigilance and behavioral inhibition would be
associated with ADHD symptoms across the groups.
The participants in this study included 18 children
with high functioning (IQN70) ASD (autism=12,
Asperger=3, PDD-NOS=3); 18 children with ADHD
(combined=16, primarily inattentive=1, primarily
hyperactive/impulsive=1); and 18 typically developing
children (TYP). The demographic information for the
groups is presented in Table 1. Regarding medication,
seven ADHD participants were receiving stimulant
medication, one of whom was also being treated with
clonidine, and two of whom were receiving hormone
medications. Of the ASD participants, six were being
treated with stimulants, neuroleptics, selective serotonin
reuptake inhibitors, or a combination of these. While
stimulant medication was withheld for 24 h prior to
testing (Greenhill, 1998), other longer term medications
were not stopped for ethical reasons. Inclusion criteriafor
all participantsconsistedofhavinganIQN70, anabsence
of Fragile X or other serious neurological (e.g., seizures),
psychiatric (e.g., bipolar disorder) or medical conditions.
The University of California, Davis Institutional
Review Board (IRB) approved the study. The child's
parent completed written informed consent and the
child assented to participate in the study. Approximately
one third of the children participated in a previous
investigation (Corbett and Constantine, 2006). The
diagnostic groups were recruited from the University
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of California, Davis M.I.N.D. (Medical Investigation of
the participants already had a confirmed ASD diagnosis
with the Autism Diagnostic Observation Schedule (Lord
et al., 1999) and the Autism Diagnostic Interview (Lord
et al., 1994). For children who were not already
evaluated, the following diagnostic procedures were
conducted. The diagnosis of autism spectrum disorder
(i.e., Autistic Disorder, PDD-NOS, Asperger) was based
on DSM-IV criteria (American Psychiatric Association,
2000) and established by 1) a previous diagnosis by
either a psychologist, psychiatrist or behavioral pedia-
trician, 2) clinical judgment by a licensed clinical
psychologist (bac), and 3) confirmation by a score on
the social-communication scale of the Autism Diagnos-
tic Observation Schedule within or above the autism
spectrum threshold (Lord et al., 1999).
(American Psychiatric Association, 1994) established by
by a licensed clinical psychologist (bac), and 3) a semi-
structured parent interview extracted from the Diagnostic
Interview Schedule for Children (DISC) (Shaffer et al.,
for children who had a primary diagnosis of ADHD but
none of the ADHD children had to be excluded based on
this criteria. The typically developing children were
selected based on age and gender and recruited from
area schools and recreation centers, then screened via
parent interview for the absence of neurodevelopmental
disorders, including ASD and ADHD, using the DISC.
Diagnostic and neuropsychological measures were
completed in one visit using standardized procedures.
A few of the participants were unable to complete every
subjects who had data for the measures. Participants
received minimal financial compensation and toys, and
their parents/guardians were sent a letter summarizing
the assessment results from the published, standardized
Autism Diagnostic Observation Schedule (ADOS)
(Lord et al., 1999). The ADOS comprises semi-struc-
tured interactive activities designed to assess current
behaviors indicative of autism involving social beha-
vior, communicative functioning, and restricted activ-
ities (Lord et al., 1999).
Wechsler Abbreviated Intelligence Scale (WASI;
(Wechsler, 1999)). The WASI is a measure of general
intelligence used to obtain an estimated IQ for inclusion/
exclusion into the study.
The following dependent neuropsychological mea-
sures are conceptualized based on a previous theoretical
model (Pennington and Ozonoff, 1996) and grouped
into six EF domains (inhibition, working memory,
flexibility, planning, and fluency, vigilance). In addition,
a measure of ADHD symptoms was included.
Response Inhibition was measured using the Inte-
grated Visual and Auditory response control quotients
and the D-KEFS Color Word Interference Test.
The Integrated Visual and Auditory (IVA) Continuous
Performance Test (CPT) (Sandford and Turner, 2000)
in a counter-balanced design across both visual and
auditory modalities. The Visual Response Control Quo-
tient (VRCQ) and Auditory Response Control Quotient
(ARCQ) are the primary dependent variables.
The Dellis-Kaplan Executive Function System
(D-KEFS) (Dellis et al., 2001) consists of nine
tests that measure a variety of EFs. The D-KEFS Color
Word Interference Test consists of four conditions
including: color naming (word finding), word reading
(reading speed), and inhibition (verbal inhibition) that
expose the child todifferent reading conditions (fourth
condition see Cognitive Flexibility/Switching below).
Means and standard deviations for the demographic variables.
M (S.D.)M (S.D.)M (S.D.)FP Eta-squared
Note. TYP=18 (12 males, 6 females) typically developing children; ADHD=18 (12 males, 6 females) children with attention deficit/hyperactivity
disorder; and ASD=18 (17 males, 1 female) children with autism spectrum disorders.
213 B.A. Corbett et al. / Psychiatry Research 166 (2009) 210–222
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The task is analogous to a Stroop test and performance
is based on speed of completion. The inhibition por-
tion requires the ability to verbally inhibit the more
salient response of reading words in order to name the
color of the discordant ink. These tasks are designed
for children 8 years and older. As such, 34 of the 54
participants in the study were administered or able to
complete these measures.
Working Memory was measured using the CANTAB
Spatial Span and Spatial Working Memory subtests.
The Cambridge Neuropsychological Test Automated
Battery (CANTABexpedio) assesses cognitive domains
including attention, executive function, memory, proces-
sing speed, and visuospatial ability.
Spatial Span (SSP) measures both forward and
reverse spatial memory span. At the onset, white
squares are displayed, some of which momentarily
change color in an unpredictable pattern. The individual
is required to touch the boxes in the same sequence
order as they changed color. Throughout the task, the
number of boxes in the sequence is increased, and the
order and color change with each sequence to minimize
interference. The total raw score was used as the depen-
Spatial Working Memory (SWM) measures the
ability to maintain spatial information and to subse-
quently manipulate the presented items in working
memory. The objective is to find a blue “token” in each
of the boxes, then select and place them in an empty
column. The individual must resist returning to a box
where a token was previously found, which constitutes
an error. The number of boxes is progressively
increased. For each trial, the color and position of the
boxes are changed. The total spatial working memory
between search errors (SWM Btwn Error) and strategy
scores (SWM Strat) were used as dependent variables.
Cognitive Flexibility/Switching was measured using
the D-KEFS Total Switching Accuracy, CANTAB ID/
ED Set Shifting, and Children's Color Trails Test 2.
The D-KEFS Category Switching (DK T-Switch);
(Dellis et al., 2001) condition is a measure of cognitive
flexibility that requires the individual to shift between
color naming, word reading and inhibition (see Color
Word Interference Test above), and was used as a de-
pendent variable for flexibility.
is a test of rule acquisition and reversal that measures
shifting and flexibility of attention, visual discrimination,
attention set formation, and maintenance. The ID/ED task
consists of colored shapes and white lines that increase in
complexity throughout the test. Following 6 consecutive
correct responses, the correct shape becomes the incorrect
stimuli, and the formerly incorrect shape becomes the
correctstimuli (intra-dimensionalshift). Through Stage 7,
it is the shape that determines which picture is correct.
However, beginning with Stage 8, the line identifies the
correct picture, (extra-dimensional shift) and cognitive
flexibility is required. As this shift is the key measure of
cognitive flexibility and many subjects were unable to
complete Stage 8, only the number of errors committed in
Stage 8wasusedasthedependentvariable.As directedin
the CANTAB manual, if a subject did not complete Stage
8, 25 errors were assigned.
Children's Color Trails Test 1 and 2 (CCTT-1 & 2);
(Llorente et al., 1998) are used to measure alternating
and sustained visual attention, sequencing ability, psy-
chomotor speed, cognitive flexibility and inhibition. It
is analogous to Trail Making tests for adults but modi-
fied for children. The CCTT-1 requires the individual to
rapidly connect different colored circles in the correct
numerical order (1-2-3…). The CCTT-2 requires con-
necting circles numerically while switching between
colored numbers. An Interference Index is obtained that
rates the effect of interference in processing time
needed to complete one task (CCTT2) that is more
complex than another (CCTT1). The CCTT2 inter-
ference score was the primary dependent variable used
for this measure.
Planning was measured using the CANTAB SOC.
Stockings of Cambridge (SOC). The SOC is based on
the “Tower of London” test and is a spatial planning task.
Two views are shown, each with three colored balls
which appear to be in “stockings” or stacks. The
individual must repeat the pattern shown in the example
view by touching the ball and then touching where the
ball is to be moved. The individual's planning ability is
based on how quickly and accurately the pattern is
imitated. The SOC number of problems solved in
minimum number of moves (SOC Min Moves), initial
thinking (SOC Initial Thinking) and subsequent thinking
(SOC Sub Thinking) were the dependent variables used.
Fluency was measured using the D-KEFS Letter
Fluency and Category Fluency (see D-KEFS above).
The D-KEFS Letter Fluency and Category Fluency
Tests (Dellis et al., 2001) provide information regarding
with the same letter, and ability to retrieve lexical items
from a designated category, respectively. The DK Letter
and DK Category were the dependent variables used.
Attention Quotient (AAQ) (Sandford and Turner, 2000)
are based on equal weights of Vigilance (inattention),
Focus (speed of mental processing), and Speed (reaction
214 B.A. Corbett et al. / Psychiatry Research 166 (2009) 210–222
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time). The VAQ and AAQ were the primary dependent
variables used for this measure (see IVA above).
ADHD Symptomology was measured using the Con-
ners' Parent Rating Scale-Revised (Short) (CPRS-R:S)
(Conners, 2001). The CPRS-R:S provides information
about behaviors associated with attention and/or hyper-
activity as well as oppositional behavior. The CPRS
was used as an index of ADHD symptoms rather than
for diagnostic purposes. The four domain Standard
Scores were used as dependent measures and the
ADHD Index (C-ADHD) was used as an index of
2.4. Statistical analysis
Statistical analyses were performed using SPSS®
(Norusis, 1993). As a first step, we did a multivariate
analysis of variance, while covarying for IQ (MAN-
COVA) on the six EF domains. Secondly, of those
results that were significant, independent Analyses of
Variance (ANOVAs) were conducted on the dependent
measures. The partial eta square was reported as an
index of effect size. Subsequently, we conducted post
hoc multiple pairwise comparisons using the Tukey
HSD to control for overall Type 1 error and to determine
what group comparisons were significant across the
Simultaneously, we investigated the extent to which
ADHD symptoms predict attention and EF performance
across these groups using linear and stepwise multiple
regression modeling. The predictor variables entered
the criterion variables were the EF variables previously
shown to be statistically significant using ANOVA. We
predicted that measures of vigilance and behavioral
inhibition would be more predictive of ADHD across
the groups. Exploratory analyses were conducted with
MANCOVA excluding children with combined ASD
Descriptivestatisticsfor the54 participants across the
demonstrated that the three groups did not differ relative
to gender, χ2(2, N=54)=14.52, PN0.05. Univariate
ANOVA demonstrated that the three groups did not
differ relative to age, F(2,51)=0.04 PN0.10. There was
a significant difference in Full Scale IQ, F(2,51)=6.38,
Pb0.005. Post hoc planned comparisons revealed
that this difference was due to the ASD group being
significantly lower than both the ADHD, t=(1,34)=
2.13, Pb0.05, and TYP, t=(1,34)=3.31, Pb0.01,
groups. Subsequently, IQ was used as a covariate in
The means and standard deviations and post hoc
multiple pairwise comparisons (Tukey HSD) for the
Conners Parent Rating Scale are reported in Table 2. The
average range on this measure is defined by T scores
from 40 to 60. ADHD was defined by a score greater
than 1.5 standard deviations above the mean (Conners,
2001). Based on this criterion, all of the ADHD children
met criteria, eight of the ASD children, and none of the
typical children qualified for a diagnosis of ADHD.
The results of the MANCOVAs for each domain are
presentedbelow.The means andstandarddeviations and
by domains are presented in Table 3. Post hoc analyses
for each domain are presented below. Due to concerns
about the possible effects of medication, analyses were
conducted comparing the clinical groups between those
with and without medication and there were no
significant differences (all F'sb1.0 and PN0.05).
3.1. Response inhibition
Based on MANCOVA, there was a significant differ-
ence between the groups regarding Inhibition: F(6,56)=
3.99, Pb0.005; Wilks' lambda=0.548. Post hoc
Means and standard deviations for the Conners' diagnostic measure.
M (S.D.)M (S.D.)M (S.D.)FP Eta-squared
Note. TYP=18 (12 males, 6 females), ADHD=18 (12 males, 6 females), ASD=18 (17 males, 1 female), C=Conners Parent Rating Scale-Revise,
C-OPP=Oppositional, C-INA=Inattention, C-HYP=Hyperactivity, ADHD=Conners' ADHD Index.
215 B.A. Corbett et al. / Psychiatry Research 166 (2009) 210–222
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multiple pairwise comparisons (Tukey HSD) for the
VRCQ showed significant differences between the ASD
and TYP group and ASD and ADHD: F(2,51)=5.33,
Pb0.01. The ARCQ revealed significant differences be-
tween the ADHD and TYP groups, F(2,51)=4.59,
Pb0.01, and between the ASD and TYP groups,
F (2,51)=5.36, Pb0.01. The ASD group demonstrated
the lowest performance, followed by the ADHD group
and then the TYP group. There were no differences
between the ADHD and TYP groups on the DK INH, but
the ASD group performed significantly lower than the
TYP group, F(2,34)=6.20, Pb0.01.
3.2. Working memory
Working Memory, F(6,92)=2.67, Pb0.05, Wilks'
and for SWM Btwn Errors, F(2,48)=3.95, Pb0.05, and
SWM Strategy, F(2,48)=3.97, Pb0.05. There were
significant differences between the ADHD and ASD
children for SWM Btwn Errors, F(2,48)=3.95, Pb0.01,
and SWM Strategy,F(2,48)=3.97,Pb0.05, with the ASD
group performing more poorly. Regarding the SSP, there
was a significant difference between the ADHD and TYP
group, F(2,51)=4.72, Pb0.05.
There were significant differences across the groups for
For DK T-Switch, significant differences were found
between the ADHD and ASD groups, F(2,33)=7.56,
Therewere nosignificantdifferencesfor the CCTT2orthe
ID/ED tasks across the groups.
There were no significant differences between the
groups based on MANCOVA for Planning, F(6,56)=
Means and standard deviations and analysis of variance for the executive functioning and attention measures.
Measure TYP ADHD ASD
M (S.D.)M (S.D.)M (S.D.)P
SWM Btwn Errors
SOC Min Moves
SOC Initial Thinking
SOC Sub Thinking
Note:aASD vs. Typical, Pb0.05;bADHD vs. Typical, Pb0.05;cASD vs. ADHD, Pb0.05; ns, PN0.05.⁎N varies depending on test.
VRCQ=Visual Response Control Quotient, ARCQ=Auditory Response Control Quotient, DK=D-KEFS, DK INH=Inhibition, SSP=Spatial Span,
SWM=Spatial Working Memory, ID/ED-Shift=Extradimensional Shift errors, DK T-Switch=Total Switching, CCTT2=Children's Color Trails
Thinking=Stockings of Cambridge Subsequent Thinking Time; DK Letter=Letter Fluency, DK Category=Category Fluency, VAQ=Visual
Attention Quotient, AAQ=Auditory Attention Quotient.
216B.A. Corbett et al. / Psychiatry Research 166 (2009) 210–222
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1.73, PN0.05, Wilks' lambda=0.79, which included
SOC Min Moves, SOC Initial Thinking and SOC
There were no significant differences between the
ASD and TYP groups for Fluency, F(4,58)=2.38,
PN0.05; Wilks' lambda 0.738, which included the DK
Letter and DK Category measures.
Based on MANCOVA, there were significant dif-
ferences in Vigilance between the groups, F(4,98)=
4.63, Pb0.01; Wilks' lambda=0.707. Subsequently,
significant differences were found between the ADHD
and TYP groups for AAQ, F(2,50)=8.28, Pb0.001,
and VAQ F(2,50)=5.58, Pb0.01, with the ADHD
group performing more poorly. There were significant
differences between the ASD and TYP groups on the
Fig. 1. a and b. Scatterplots of the relationship between the C-ADHD (Conners ADHD Index) and the 1a. ARCQ (Auditory Response Control
Quotient) and 1b. AAQ (Auditory Attention Quotient) across the groups: TYP=Typical, ADHD=ADHD, ASD=Autism Spectrum Disorder, and
ASD/ADHD=Autism Spectrum Disorder with ADHD.
217 B.A. Corbett et al. / Psychiatry Research 166 (2009) 210–222
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IVA for AAQ, F(2,50)=8.28, Pb0.001, and VAQ,
F (2,50)=5.58, Pb0.01.
3.7. Prediction and exploratory analysis
Next, we used stepwise multiple regression to examine
the relationship between the dependent variables and
regression showed that the ADHD index (C-ADHD) was
the variance (t=10.09, Pb0.001); when IQ was also
entered into the model it predicted 40.7% of the variance
(t=3.24, Pb0.01). For VAQ, the C-ADHD explained
27.5% of the variance individually (t=9.41, Pb0.05) and
when IQ (t=4.57) and age (t=3.96) were entered into the
equation, 56.3% of the variance was explained (both
Pb0.05). For D-KEFS Inhibition, diagnosis (t=13.30)
along with the ADHD index (t=2.47) explained 35.4% of
the variance (Pb0.05). Using linear regression analysis,
the ADHD index predicted 16.1% and 11.1% of the
variance for ARCQ (t=9.27, Pb0.001) and VRCQ
(t=7.96, Pb0.05), respectively. The ADHD index was
not predictive for the remaining variables.
Using an exploratory approach, we investigated the
influence of ADHD within the ASD group by removing
these subjects. The ASD group was divided into those
without ADHD (N=10) and with ADHD (N=8) based
on the ADHD index (N65), and MANCOVAs were
conducted excluding the ASD/ADHD participants on
group; namely Inhibition (ARCQ, VRCQ) and Vigilance
(AAQ, VAQ). The MANCOVAwith all subjects included
was highly significant for Inhibition (F(4,98)=3.81,
MANCOVA without the ASD/ADHD group fell to trend
level F(4, 82)=2.24, P=0.07; Wilks' lambda=0.81 (see
ARCQ, Fig. 1a), suggesting that the ASD/ADHD
comorbid group significantly contributed to differences in
these domains. The original Vigilance MANCOVA, F
(4,98)=4.63, Pb0.0001, Wilks' lambda=0.71, conducted
without the ASD/ADHD group was reduced but remained
statistically significant, F(4,82)=4.241, P=0.004, Wilks'
important to note, however, that this approach resulted in
unequal groups and a smaller sample size, which reduced
the power to detect differences.
The overarching goal of this investigation was to
profile EF deficits for two major childhood disorders,
ASD and ADHD, compared with children with typical
development while not controlling for ADHD symp-
toms. The initial aim was accomplished by assessing
performance using a comprehensive neuropsychological
battery of EF measures across six domains (response
inhibition, vigilance, working memory, flexibility/shift-
that children with ASD demonstrate pervasive impair-
ment across a broad range of EF tasks. Specifically,
children with ASD showed poor performance relative to
the typical group in inhibition, working memory,
flexibility/shifting and vigilance. The ASD performed
more poorly than the ADHD group in regards to
inhibition, working memory, and flexibility. There
were no significant differences observed on measures
of planning and fluency across the groups. As can be
seen in Table 3, the ASD group consistently showed
more impairment than the control or ADHD group on all
of the aforementioned EF measures. Thus, the current
investigation supports previous findings that children
with ASD demonstrate generalized and profound
impairment in EF skills (Geurts et al., 2004; Goldberg
et al., 2005). It is also consistent with recent studies in
autism reporting working memory deficits (Goldberg
et al., 2005; Landa and Goldberg, 2005; Pennington and
Ozonoff, 1996; Verte et al., 2006), as well as set-shifting
deficits (Hughes et al., 1994; Ozonoff et al., 2004;
Ozonoff and Strayer, 1997; Sergeant et al., 2002). The
finding that children with ASD performed more poorly
than children with ADHD on measures of flexibility has
also been previously reported (Geurts et al., 2004).
Conversely, it has been shown that some ASD subjects
have less severe and persistent EF deficits than ADHD
children (Happe et al., 2006). It appears, however, that
(81%) compared to the current study in which the
majority had autism and few ASD participants had
Asperger syndrome (17%). Thus, developmental and
diagnostic issues likely serve asimportantdistinctions in
EF within ASD.
The ADHD group exhibited deficits in vigilance
and response inhibition when compared to the TYP
group corroborating our previous findings (Corbett and
Constantine, 2006) and consistent with a meta-analytic
review showing these as the most consistently reported
domains of executive dysfunction in ADHD (Willcutt
et al., 2005). Other comparative investigations report
similar and specific deficits in inhibition (Geurts et al.,
2004; Happe et al., 2006; Pennington and Ozonoff,
1996; Verte et al., 2006). Our ADHD group also showed
some impairment in working memory, but they did not
show statistically significant deficits in the remaining
218B.A. Corbett et al. / Psychiatry Research 166 (2009) 210–222
Author's personal copy
areas of EF. The current findings are in contrast to
studies, which found impairments across working
memory, planning, and attentional set-shifting simulta-
neously (Kempton et al., 1999; Rhodes et al., 2005,
2006; Vance et al., 2003). Although the sensitivity of
some of the measures may be called into question
(Goldberg et al., 2005), the results in the current inves-
tigation are consistent with the notion that children with
ADHD demonstrate variable deficits on neuropsycholo-
Ozonoff, 1996; Rhodes et al., 2005, 2006; Vance et al.,
2003; Willcutt et al., 2005). Further, it was shown that
such variability, as in our own investigation, is not
attributed to medication (Doyle et al., 2000).
While EF deficits are associated with ADHD, they
complex cognitive and behavioral profile (Willcutt et al.,
2005). In consideration of the heterogeneity, more recent
neuropsychological models are emerging suggesting that
there may be additive or interactive effects arising from
multiple neural networks contributing to the complexity
of the symptom profile of ADHD (e.g., Nigg et al., 2005;
been suggested that studies of both ADHD and autism
need to take into account the overlapping symptoms of
these neurodevelopmental disorders (Verte et al., 2006);
thus, a more dimensional (symptom profile) rather than a
categorical approach (diagnostic grouping) may be
warranted (Frazier et al., 2007).
Thus, the next aim of the study was to examine the
relationship of ADHD symptoms to the neuropsycho-
logical measures. We hypothesized that ADHD symp-
toms would predict task performance on measures of
vigilance and behavioral inhibition across the groups,
and we confirmed our hypothesis. The results suggest
that symptoms of ADHD are associated with poor
performance on measures of visual and auditory
vigilance and response inhibition as previously reported
(Corbett and Constantine, 2006). Further, exploratory
analysis excluding the ASD/ADHD children from the
analysis provided support that symptoms of ADHD are
especially associated with deficits in inhibitory control.
Taken together, the results indicate that symptoms of
ADHD are associated with inattention and inhibition
deficits across the groups (Verte et al., 2006). The
finding supports the utility of the IVA (Sandford and
Turner, 2000) as a neuropsychological tool in identify-
ing symptoms of poor vigilance and inhibitory control
within and across neurodevelopmental disorders,
including ADHD and ASD. These results also support
the inclusion of a diagnostic parent report measure, such
as the Conners (Conners, 2001) in an assessment battery
as being able to assist in reliably capturing and classi-
fying children with symptoms of ADHD in ASD.
The observed deficits in vigilance and inhibition in
our ASD group provide additional evidence that a
significant number of children with ASD present
with ADHD-like cognitive impairments (Corbett and
Constantine, 2006; Goldstein and Schwebach, 2004;
Happe et al., 2006). Our results differ from some
previous investigations (Goldberg et al., 2005) that do
not report significant differences in vigilance and
inhibition in ASD. The lack of replication may be due
to distinctions in subject inclusion criteria. Goldberg
et al. (2005) excluded subjects with ASD who had
ADHD features, while we did not. This suggests that
these deficits in our ASD group were due to comorbid
ADHD symptoms, rather than fundamental deficits of
autism. This notion is supported by the relatively high
CPRS scores of our ASD subjects, two-thirds of whom
fell in the at risk range or above on this measure.
Whether symptoms of ADHD seen in children with
ASD represent an “ADHD-like” disorder unique to
autism or represent a distinct co-occurring ADHD may
have important treatment implications. It has been
proposed that children with both sets of symptoms are
more impaired functionally and may respond differently
to treatment (Arnold et al., 2006; Ghaziuddin et al.,
1992; Goldstein and Schwebach, 2004; Kadesjo and
Gillberg, 2001; Posey et al., 2006). Pharmacologic
treatment reports for ADHD symptoms in children with
ASD are mixed, with older studies suggesting poor
results (Quintana et al., 1995) and more recent studies
suggesting benefit in a subgroup of children (Stigler
et al., 2004). Thus, the identification of subtype
treatment predictors for medications and for cognitive
and behavioral interventions may be helpful in treatment
effectiveness and side effect avoidance. Future studies
should determine if there is a subgroup of children who
have ASD with ADHD symptoms that are more likely to
respond to particular interventions.
Despite these findings, there are important limita-
tions to report. It is unclear if the current sample of
subjects is truly representative of most children with
ASD or ADHD. It is possible that some families
enrolled the children with ASD in the study knowing
that an investigation of ADHD was underway. As such,
we may have enrolled a higher proportion of children
with ADHD symptoms within ASD. Even so, recent
reports showing the notable preponderance of ADHD
symptoms in ASD (Corbett and Constantine, 2006;
219B.A. Corbett et al. / Psychiatry Research 166 (2009) 210–222
Author's personal copy
Gadow et al., 2004; Ghaziuddin et al., 1992; Goldberg
et al., 2005; Goldstein and Schwebach, 2004; Happe
et al., 2006) enhance the generalizability of our findings.
In addition, our investigation included a rather small
sample size and multiple data points. In particular, the
exploratory analysis was conducted with very few sub-
jects and uneven groups; thus, it must be interpreted
with extreme caution until the findings are replicated in
larger samples. For clinical utility, we chose to use
standardized instruments rather than conducting a factor
analytic study merging domains of functioning; thus,
there is an increased chance of Type 1 error. Our com-
promise was to conduct multivariate analysis followed
up by analysis of variance on individual subtests.
Further, we were unable to collect a medication naïve
sample for this investigation. In an attempt to control for
this, stimulant medication was withheld for 24 h, which
is a sufficient washout period (Greenhill, 1998). Some
investigations have shown benefit from stimulant
medication on EF measures e.g., (Kempton et al.,
1999; Vance et al., 2003), while other recent studies
show modest or a lack of benefit on EF measures
following stimulant use (Coghill et al., 2007; Rhodes
et al., 2006). Other medications were few (ADHD=3,
ASD=4) and evenly distributed across the two disorder
groups. Even so, we conducted analyses across the
clinical groups comparing those with and without
medication and the results remained the same. Despite
these efforts, we acknowledge that there may have been
some modest effects on the results attributed to
medication use in some of the participants. Finally,
contradictory findings across studies may be, in part,
explained by differences in inclusion criteria, age, level
of functioning and task demands, which inadvertently
contribute to inconsistencies in the literature.
Due to the converging evidence reporting a high
prevalence of ADHD in ASD, a serious reconsideration
is necessary regarding the current diagnostic practice of
not providing a diagnosis of ADHD within a pervasive
developmental disorder when appropriate (APA, 2000).
The issues and challenges of diagnosing ADHD,
especially within special populations, must be carefully
considered (Barkley, 2003). The presence of symptoms
from more than one neurodevelopmental disorder
likely leads to exponential risks and greater treatment
challenges. Additional research is needed to help
delineate patterns of performance within and between
ADHD and ASD. This comorbid profile of ADHD in
ASD may represent a distinct phenotype in autism that
requires further study. Neuropsychological models
are beginning to emerge that could account for the
heterogeneity of ASD and ADHD. Across both
disorders there may be an additive or interactive effect
stemming from dysfunction from various neural net-
works that contributes to the heterogeneous profile (e.g.,
(Castellanos et al., 2006; Nigg et al., 2005; Sergeant
et al., 2003; Sonuga-Barke, 2005). It may also be the
case that a more dimensional approach to understanding
neurodevelopmental disorders is warranted (Frazier
et al., 2007). It is our hope that elucidating the overlap
and distinctions between ASD and ADHD profiles will
enable clinicians and researchers to better assess,
characterize and treat these complex disorders.
Funding was provided by a NIH Career Develop-
ment Award to Blythe Corbett (5 K08NMHO72958).
Additionally, the authors thank the Perry Family
Foundation and the Debber Family Foundation for
their generous support of our research. We express our
sincere gratitude to Josh Day and Meridith Brandt for
the participant recruitment.
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