Examining Autistic Traits in Children with ADHD:
Does the Autism Spectrum Extend to ADHD?
Rebecca Grzadzinski•Adriana Di Martino•Emily Brady•
Maria Angeles Mairena•Matthew O’Neale•Eva Petkova•
Catherine Lord•F. Xavier Castellanos
Published online: 25 November 2010
? Springer Science+Business Media, LLC 2010
reports of autistic traits in some children with Attention
Deficit Hyperactivity Disorder (ADHD) are the result of
ADHD-related symptoms or qualitatively similar to the
core characteristics of autism spectrum disorders (ASD).
Results confirm the presence of a subgroup of children with
ADHD and elevated ratings of core ASD traits (ADHD?)
not accounted for by ADHD or behavioral symptoms.
Further, analyses revealed greater oppositional behaviors,
but not greater ADHD severity or anxiety, in the ADHD?
We examined to what extent increased parentsubgroup compared to those with ADHD only. These
results highlight the importance of specifically examining
autistic traits in children with ADHD for better character-
ization in studies of the underlying physiopathology and
Attention-deficit/hyperactivity disorder ?
Social reciprocity ? Social responsiveness scale ?
Children’s communication checklist-2
Autism ? Autism spectrum disorders ?
Clinical anecdotes, case reports, and empirical studies
demonstrate that many children display both ADHD and
ASD symptoms (Hattori et al. 2006; Holtmann et al. 2007;
Mulligan et al. 2009; Nijmeijer et al. 2008; Reiersen and
Todd 2008; Nijmeijer et al. 2009; Rommelse et al. 2009,
2010). Yet, the Diagnostic and Statistical Manual of
Mental Disorders, fourth edition (American Psychiatric
Association 1994), does not allow for the comorbid diag-
noses of Attention-Deficit/Hyperactivity Disorder (ADHD)
and Pervasive Developmental Disorders (referred to from
here on as autism spectrum disorders, ASD). The preva-
lence of ADHD symptoms in individuals with a primary
clinical diagnosis of ASD ranges between 13 and 50% in
population and community based studies (Bradley and
Isaacs 2006; Icasiano et al. 2004; Keen and Ward 2004;
Montes and Halterman 2006; Ronald et al. 2008; Simonoff
et al. 2008) and between 20 and 85% in clinical samples
(de Bruin et al. 2007; Gadow et al. 2006; Gillberg 1989;
Goldstein and Schwebach 2004; Holtmann et al. 2005,
2007; Ogino et al. 2005; Lee and Ousley 2006; Sinzig et al.
2008a; Sturm et al. 2004; Wozniak et al. 1997). The
presence and nature of ASD-like symptoms (i.e., autistic
traits) in individuals with a primary diagnosis of ADHD
R. Grzadzinski ? A. Di Martino (&) ? E. Brady ?
M. A. Mairena ? M. O’Neale ? F. X. Castellanos
Phyllis Green and Randolph Co ¯wen Institute for Pediatric
Neuroscience, Child Study Center of the NYU Langone Medical
Center, 215 Lexington Avenue, New York, NY 10016, USA
A. Di Martino
Department of Neuroscience, Division of Child and Adolescent
Neuropsychiatry, University of Cagliari, Cagliari, Italy
Division of Biostatistics, Child Study Center of the NYU
Langone Medical Center, New York, NY, USA
E. Petkova ? F. X. Castellanos
Nathan Kline Institute for Psychiatric Research, Orangeburg,
University of Michigan Autism and Communication Disorders
Center, Ann Arbor, MI, USA
M. A. Mairena
Hospital Sant Joan de De ´u, Barcelona, Spain
New York University, College of Arts & Science, New York,
J Autism Dev Disord (2011) 41:1178–1191
has been increasingly noted (Hattori et al. 2006; Nijmeijer
et al. 2008, 2009; Reiersen et al. 2007; Reiersen et al.
2008a; Luteijn et al. 2000; Rommelse et al. 2009; Mulligan
et al. 2009). However, this remains an understudied area.
Social difficulties are often reported in children with
ADHD but these difficulties are typically interpreted as
resulting from ADHD symptoms rather than reflecting the
qualitative impairments in social-communicative func-
tioning characteristic of ASD (Biederman et al. 1999;
Greene et al. 1996; Hoza et al. 2005; Matthys et al. 1999;
Bagwell et al. 2001; McQuade and Hoza 2008). For
example, several authors suggest that because of their
impulsivity, children with ADHD are more likely to be
rated as inappropriately intrusive during conversations or
play (Abikoff et al. 2002), and are more likely to be
rejected by their peers (Hoza et al. 2005; Greene et al.
1996). Using the Social Adjustment Inventory for Children
and Adolescents (SAICA; Orvaschel and Walsh 1984),
Greene et al. (1996) identified a group of children with
ADHD as ‘‘socially disabled.’’ These children showed
greater impairments in items assessing their ability to
‘‘get along with siblings,’’ ‘‘make friends easily,’’ or ‘‘be
affectionate’’ across different contexts (home, school, and
free-time). However, social difficulties often remain even
after treating ADHD symptoms (see McQuade and Hoza
More recently, authors have examined the presence of
social and communicative profiles qualitatively similar to
those associated with ASD in individuals with ADHD
(Nijmeijer et al. 2009; Reiersen et al. 2007; Carpenter et al.
2009; Geurts et al. 2004a; Mulligan et al. 2009; Clark et al.
1999). These studies have used a variety of parent-based
instruments to measure deficits in social functioning. With
one exception (Mulligan et al. 2009), authors have selected
instruments that encompass a broad range of symptoms to
capture milder forms on the autism spectrum. For example,
Reiersen et al. (2007) identified a subgroup of children with
ADHD and autistic traits using parent ratings on the Social
Responsiveness Scale (SRS; Constantino et al. 2000, 2003,
2004; Constantino and Todd 2000, 2003), a continuously
distributed, single-factor measure of social reciprocity
skills associated with ASD (Constantino et al. 2004). Other
authors have used the Children’s Communication Checklist
(CCC) and its revision, the CCC-2 (Geurts and Embrechts
2008; Bishop and Baird 2001; Geurts et al. 2004a), to
measure pragmatic aspects of language and found that
children with ADHD are impaired in a similar manner as
many children with ASD. Both the SRS and the CCC-2 are
broad screening questionnaires that are designed to mea-
sure social-communicative impairment which is considered
a core characteristic of ASD. They have been shown to
discriminate ASD with high sensitivity but only moderate
specificity, particularly in the presence of behavioral
problems such as ADHD (Charman et al. 2007). Thus, it
remains unclear whether elevated SRS or CCC-2 scores in
some children with ADHD reflect underlying ASD symp-
toms or non-specifically increased behavioral difficulties.
The overarching aim of this study was to confirm the
presence of elevated ratings of autistic traits in children
with ADHD, and to characterize the children who exhibit
elevated scores with respect to the severity of autistic traits,
ADHD symptoms, and other measures of psychopathology.
To accomplish this goal, we examined whether signifi-
cantly elevated total SRS scores of children with ADHD
were due to the severity of items tightly related to autistic
traits or to those probing broader behavioral symptoms
related to ADHD. Then, we contrasted the ADHD sub-
group with elevated SRS ratings (ADHD?) to the
remaining children with only ADHD (ADHD-) with
respect to the CCC-2 (Bishop 1998, 2003) as a separate
measure of autistic traits, as well as to other parent-based
measures of psychopathology. Though the focus of this
study was on children with ADHD with or without elevated
SRS scores, a sample of typically developing children was
included for illustrative purposes.
Data were collected from 75 children with DSM-IV-TR
based ADHD (60 boys) between the ages of 7.1 and 17.8.
Children with ADHD were recruited through referrals from
the NYU Child Study Center Child & Family Associates,
parent support groups, newsletters, flyers, and web/news-
paper advertisements. We screened out prospective ADHD
participants with a history of an ASD diagnosis.
Children with ADHD were included in the study if they
presented elevated ratings (T score C 60) on the Conners’
Parent Rating Scale-Revised: Long-Version (CPRS-R:L;
Conners et al. 1998; Conners 1998) in at least one of the
ADHD summary scales and if they were not referred for an
explicit ASD diagnostic concern. Diagnosis was confirmed
by administration of the Schedule of Affective Disorders
and Schizophrenia for Children—Present and Lifetime
Version (K-SADS-PL; Kaufman et al. 1997) to the parent/
legal guardian and child, separately, with 65 of the cases.
To accommodate participants’ schedules, the remaining 10
interviews were conducted with a parent/legal guardian
only. Of the ADHD children, 38 (51%) were diagnosed
with ADHD-Combined type (ADHD-C), 27 (36%) with
ADHD-Predominantly Inattentive type (ADHD-I), six
(ADHD-HI), and four (5%) as ADHD Not Otherwise
J Autism Dev Disord (2011) 41:1178–11911179
present, individually or in combination, in 26 children; they
included Anxiety Disorders (two with Specific Phobia, two
with Social Phobia, two with Generalized Anxiety Disor-
der, two with Obsessive–Compulsive Disorder, one with
Panic Disorder, and three Anxiety Disorder NOS), Enuresis
(n = 5), Learning Disorders (n = 2), Specific Language
Disorder (n = 2); Oppositional Defiant Disorder (ODD;
n = 7), one with Dysthymia, one with Tourette’s Disorder,
one with Tic Disorder, and one with Adjustment Disorder.
Twenty-eight (37%) children were treated with psychoac-
tive medication (25 with psychostimulants and one each
with risperidone, atomoxetine, or paroxetine).
Sixty-nine typically developing children (TDC; 30 boys)
were also included for comparison (see Appendix). TDC
were recruited from the local community through flyers/
advertisements, and word of mouth. Inclusion as a TDC
required T-scores below 60 on all four CPRS-R:L ADHD-
summary scales (Conners 1997; Conners et al. 1998).
All children (TDC and ADHD) in this study were par-
ticipating in ongoing studies at the NYU Child Study
Center. All families received compensation of $60 for
participating in the study. Written informed consent was
obtained from parents and assent from children, as
approved by the NYU School of Medicine institutional
Primary Measure of Autistic Traits
Social Responsiveness Scale (SRS)
To identify autistic traits, we used the SRS parent version
(Constantino et al. 2003). The SRS is composed of 65
items, 53 of which focus on social-communicative abilities.
These items examine the ability to interpret social cues, to
maintain social conversation, as well as to initiate social
interaction. The 12 remaining items probe repetitive
behaviors or restricted patterns of interest. The 65 SRS
items have been found to form a single factor underlying
ASD that is continuously distributed in the general popu-
lation (Constantino et al. 2003, 2004).
A total T score on the SRS C 60 (1 SD above the mean;
Constantino and Gruber 2005) was used as a cut-off to
identify children with autistic traits (designated as
ADHD?). Some SRS items were missing for six partici-
pants with ADHD and five with TDC (one, two, and three
responses were missing for seven, three, and one child,
respectively; the number of missing items did not differ
between children with ADHD and TDC). To account for
these missing items on the SRS we used a prorated raw
total score for each participant in which the sum of the
response scores was divided by the number of answered
items and multiplied by 65, the total number of items.
Using the prorated raw score, a total T score was obtained.
Consensus Categories for SRS Items–Classification Based
on Symptom Domains
We sought to identify the SRS items directly associated
with the DSM-IV-TR criteria for ASD (American Psychi-
atric Association 1994) and to distinguish them from those
describing behaviors present broadly in other psychiatric
disorders, including ADHD. Specifically, each SRS item
was classified by eight independent raters (co-authors CL,
FXC, ADM, MM, RG, and three anonymous coders from
the University of Michigan Autism and Communication
Disorders Center) into one of four categories. Three cate-
gories were based on the three DSM-IV autism domains:
Reciprocal Social Interaction (S), Communication (C), or
Behavior/Interests (R). A fourth category (Non-SCR)
identified items not exclusively related to the DSM-IV
ASD diagnostic criteria. This allowed us to examine
whether non-SCR items were over-represented in contrib-
uting to the elevated total T scores in ADHD?rather than
the ASD related categories (S, C, or R). Following the
classification of the 65 SRS items into four categories, we
computed the percentage of agreement among the eight
raters for each item. Agreement between the eight raters
ranged from 100% (for 19 items) to 38% (for two items).
with C 75% agreement. For the five items on which no
more than four raters agreed, RG and ADM reached a
consensus on category assignment (see Appendix for item-
by-item consensus and item domain classification in
Table 3). As a result of this process, the S category con-
tained 24 items, the C category contained eight items, and
the R category contained 10 items. The remaining 23 items
were coded as Non-SCR, i.e., not specifically associated
with DSM-IV ASD criteria. For each participant, we
computed mean summary scores S, C, R and non-SCR by
calculating the sum of raw scores (0–3) of all items in the
category and dividing it by the number of items within each
Stereotyped Patterns of
SRS itemswere coded
Other Clinical Measures Used to Characterize ADHD?
We used the Children’s Communication Checklist-2 (CCC-
2) as an additional measure of autistic traits to further
characterize the ADHD?subgroup in comparison to the
ADHD-(SRS Total T score\60). The CCC-2 is a 70-item
parent-based questionnaire that examines aspects of com-
municative functioning, including impairment in pragmatic
aspects of language (Bishop 1998, 2003). Ten domains,
each probed by seven items, are included: speech, syntax,
semantics, coherence, inappropriate initiation, stereotyped
language, use of context, non-verbal communication,
social relations, and interests. The sum of scores on the
1180 J Autism Dev Disord (2011) 41:1178–1191
first seven domains forms the General Communication
Composite (GCC). A summary measure of social interests
and pragmatic aspects of language, the Social Interaction
Deviance Composite (SIDC), is computed by summing the
score for the domains inappropriate initiation, nonverbal
communication, social relation, and interests and sub-
tracting the sum of the remaining domains (Bishop 2003;
Norbury et al. 2004). A GCC score below 55, in combi-
nation with a negative SIDC score, or a SIDC of -15 or
below (regardless of the GCC score) suggests a commu-
nicative profile characteristic of ASD.
Finally, measures of psychopathology were obtained
from parent ratings of the Child Behavior Checklist
(CBCL; Achenbach and Edelbrock 1983) and the CPRS-
R:L (Conners 1997). The CBCL is a questionnaire used to
measure symptoms often observed in children with varying
psychiatric conditions, including externalizing problems
such as aggressiveness, hyperactivity, and conduct prob-
lems, and internalizing problems, such as mood and anxi-
ety symptoms. The CPRS-R:L assesses the behaviors
associated with a diagnosis of ADHD in addition to a broad
range of problematic behaviors, such as conduct, cognitive,
anxiety, and social problems. Parents also provided
demographic information and socio-economic status (SES)
was estimated using the Hollingshead Index of Social
Position (Hollingshead 1975). The Wechsler Abbreviated
Scale of Intelligence (WASI; Wechsler 1999) provided
estimates of IQ.
To assess whether the ADHD?and ADHD-subgroups
differed with respect to any of the SRS consensus cate-
gories (S, C, R, and non-SCR), we modeled the four values
as a function of subgroup, category, and their interaction,
adjusting for age and sex. The S, C, R and non-SCR scores
were modeled simultaneously using MANOVA type mixed
effects models to account for the correlation between the
four measures on the same individual (Song 2007). To test
whether the difference between ADHD?and ADHD-was
the same on all four categories we used a likelihood ratio
test for the interaction between subgroups (a factor with 2
levels: ADHD?and ADHD-) and category (a factor with 4
levels: S, C, R, and non-SCR); i.e., a chi-square test with 3
degrees of freedom. Significance was judged at level
a = 0.05, two-sided. To eliminate the potential con-
founding effect of psychopathology, analyses were also
conducted adjusting for several covariates: CPRS-R:L
DSM-IV-Inattentive, DSM-IV-HI, DSM-IV-Total, CBCL
Internalizing behavior problems, CBCL Externalizing
behavior problems, and CBCL Total problems. This anal-
ysis has 80% power of a two-sided test with a = 0.05 to
detect differences of magnitude of approximately (Cohen’s
d) d = 0.50 using the mixed effects models without
adjusting for covariates.
We compared ADHD?and ADHD-with respect to
categorical factors such as (1) ADHD subtype, (2) medi-
cation (yes/no), (3) comorbidity (yes/no), (4) ethnicity, and
(5) SES using chi-square tests for independence. Compar-
isons between ADHD?and ADHD-for other continuous
clinical characteristics (estimates of IQ, CCC-2, CBCL and
CPRS-R:L) were based on univariate analyses using
ANCOVA, covarying for age and sex. These last tests were
two-sided, and, to correct for multiple comparisons on the
numerous subscales, significance threshold was set at
a = 0.001. With our sample, we can detect effects of
magnitude d = 1.02 using models without covariate
adjustment with 80% power. For both power estimates,
adjusting for covariates, assuming covariates are associated
with outcomes, increases power.
Identification of the ADHD?Subgroup
Based on the SRS Total adjusted T score C 60, 24 (32%)
of the children with ADHD were identified as ADHD?.
The mean adjusted SRS T scores for ADHD?was
67.7 ± 7.8 and for ADHD-50.9 ± 5.8. As Fig. 1 shows,
Fig. 1 The Social Responsiveness Scale (SRS) Total T score distri-
bution is displayed in the attention deficit hyperactivity disorder
(ADHD) group (n = 75) as a whole and, for illustration, in the
typically developing children (TDC) (n = 69; open diamonds). Both
groups of children with ADHD and TDC had mean total SRS scores
(displayed as black lines for each group) below the cut-off of 60 (1
SD above the population mean). However, 24 children with ADHD
presented with total scores C 60 and were classified as ADHD?
(filled orange triangles), the remaining 51 were classified as ADHD-
(open blue triangles)
J Autism Dev Disord (2011) 41:1178–11911181
the TDC and the ADHD groups as a whole presented with
mean SRS Total adjusted T scores below 60 (44.0 ± 6.2
and 56.3 ± 10.2, respectively).
SRS Consensus Coding Differences in ADHD?
The first question we addressed was whether the differ-
ences between ADHD?and ADHD-were the same across
all SRS categories (S, C, R, Non-SCR). The average cat-
egory scores for both ADHD?and ADHD-and their dif-
ferences are reported in Table 2 and depicted in Fig. 2. A
likelihood ratio test showed that the difference between
ADHD?and ADHD-did not vary significantly across
= 2.23, p = 0.53). Similar results were
found after planned comparisons adjusting for ADHD
severity (CPRS-R:L DSM-IV-Inattentive, DSM-IV-HI, and
DSM-IV-Total) and for global CBCL measures of psy-
Problems, and Total Problems). Thus, the ADHD?children
had significantly elevated SRS scores (p\0.001) in each
autism-like category as well as in the Non-SCR category,
even after accounting for severity of psychopathology. See
Comparisons Between ADHD?and ADHD-
ADHD?and ADHD-subgroups differed significantly in
all CCC-2 domains except those domains that examine
structural aspects of language: speech, syntax, and
semantic domains. They also did not differ on the summary
SIDC domain (see Table 2; Fig. 3).
The ADHD?subgroup had significantly higher ratings of
oppositional behavior on the CPRS-R:L and greater scores
on withdrawn/depressed and total problems scales on the
CBCL. Mean CPRS-R:L and CBCL ratings of the other
scales did not differ significantly between the ADHD?and
ADHD Subtypes, Medication Status, Co-morbidity,
As shown in Table 2, compared to ADHD-, a higher
proportion of the ADHD?children were diagnosed as
ADHD-C (43 vs. 67%, respectively), and a lower propor-
tion was diagnosed as ADHD-I (43 vs. 21%, respectively).
These proportions differed significantly as tested by a
Fisher exact probability test (p\0.05). The ADHD?and
ADHD-subgroups did not differ significantly on ethnicity
(v2(1, N = 73)= 1.30, p = 0.25), SES (v2(1, N = 71)= 0.63,
p = 0.43), current use of medication, comorbidity rate,
sex, age, or estimates of IQ.
To examine the convergence between SRS and CCC-2 cri-
teria in identifying children with ADHD and autistic traits
(ADHD?), we determined the number of children with
ADHD who met the CCC-2 criteria for a language profile
consistent with ASD (i.e., GCC\55 and negative SIDC
and/or SIDC\-15, Bishop 2003). Thirty-five percent
(n = 25) of the 72 ADHD children with usable CCC-2
parent questionnaires met these criteria; 16 of these children
overlapped with the ADHD?per the SRS C 60 cutoff.
Results of a chi-square test confirmed that the CCC-2 and
SRS questionnaires were not independent, v2(1, N = 72)=
20.19, p\0.001. Comparisons between the ADHD?and
ADHD-(identified using the CCC-2) on the CPRS-R:L,
and CBCL ratings yield similar results to the comparisons
based on the SRS-identified subgroups (See Tables 4 and 5
Fig. 2 The mean and standard errors of the scores corresponding to
the four categories resulting from the item by item consensus
classification of the Social Responsiveness Scale (SRS) are depicted
for the ADHD?(cross-hatched orange) and ADHD-(solid blue)
subgroups. Three of these categories include items related to DSM-IV
autism diagnostic criteria: Social (S), Communication (C), Restricted/
Repetitive Behavior/Interests (R), one category includes items not
exclusively associated to autism (Non-SCR). As the graph shows, the
ADHD?group not only showed significantly greater increased scores
in the non-SCR items but also on the three categories specifically
related to autism (S, C, R)
1182J Autism Dev Disord (2011) 41:1178–1191
In this study, a substantial proportion of the children with
ADHD presented with elevated parent ratings of autistic
traits (ADHD?). The proportion varied from about one-
third, when using either the SRS or the CCC-2, to about
one-fifth when both measures were combined to identify
ADHD?. This confirmed previous findings of elevated
ratings of autistic traits in ADHD (Reiersen et al. 2007;
Reiersen et al. 2008b; Santosh et al. 2006; Mulligan et al.
2009). In addition, we examined the extent to which such
increased ratings may have reflected non-ASD symptoms
using three different approaches. First, we categorized the
SRS items into the three cardinal ASD diagnostic domains
(Social, Communication, and Repetitive and Restrictive
Behavior) and a category of items non-specifically related
to ASD (non-SCR), then we, used their scores to compare
ADHD?to ADHD-. Previously, authors have suggested
that social problems in children with ADHD are a result of
the symptoms of this disorder (Greene et al. 1996; Char-
man et al. 2007; Marton et al. 2009). If the elevations on
the SRS were solely a result of the behavioral impairments
often observed in children with ADHD (e. g., impulsivity,
inattention), we would expect greater elevations on the
non-SCR category, than on the S, C, and/or R categories.
The similarly elevated scores in all four categories (S, C, R,
and non-SCR) observed in ADHD?indicated that elevated
parent ratings of autistic traits (S, C, R) per the SRS did not
exclusively reflect behavioral symptoms often observed in
ADHD, in contrast to what was previously suggested
(Greene et al. 1996; Charman et al. 2007; Marton et al.
2009). Second, we obtained similar results when control-
ling for the severity of ADHD symptoms as well as other
measures of psychopathology with the CBCL ratings
(internalizing and externalizing problems), providing fur-
ther evidence that elevated SRS ratings in ADHD?do not
simply reflect greater severity of internalizing, externaliz-
ing or ADHD symptoms. Third, we repeated the analyses
on the SRS consensus categories comparing ADHD chil-
dren with and without autistic traits per CCC-2 criteria
instead of the SRS, and again found that autistic traits did
not simply reflect behavioral impairments (as measured by
the non-SCR items). Further, significant differences
between ADHD?and ADHD-on the CCC-2 pragmatic
and social skills scales, but not on the language structure
scales, provide additional evidence that the social reci-
procity impairment in ADHD?resembles the social reci-
procity impairment seen in children with ASD. These
observations suggest that a substantial number of children
with ADHD present with social difficulties that may be
qualitatively similar to autistic traits and which should be a
focus of assessments and treatment planning.
ADHD?children did not significantly differ from
ADHD-with respect to inattention, hyperactivity or anx-
iety domains, nor did they differ on estimates of IQ.
However, ratings of oppositional behaviors were signifi-
cantly higher for ADHD?children. This observation is
consistent with the established association of increased
social difficulties in children with ADHD and comorbid
ODD (Greene et al. 1996; Mulligan et al. 2009; Matthys
et al. 1999; Jensen et al. 1999). Similarly, ODD symptom
severity has been found to be significantly higher in chil-
dren with an ASD diagnosis and comorbid ADHD-like
symptoms than in children with ASD symptoms alone
(Gadow et al. 2008; Guttmann-Steinmetz et al. 2009). Less
appreciated is the relationship between ODD and ASD
traits in children with ADHD, which emerged from our
data and from a recent large study of 821 children (Mul-
ligan et al. 2009). In that study, elevated ratings on the
Social Communication Questionnaire in ADHD were sig-
nificantly related to increased prevalence of comorbid
ODD and Conduct Disorder (Mulligan et al. 2009). Thus,
the presence of elevated ASD traits in children with ADHD
should stimulate both clinicians and investigators to further
assess for comorbidities including ODD. In contrast to a
recent result in an examination of ASD traits among sib-
lings of children with ASD (Virkud et al. 2009), the
presence of ASD traits in children with ADHD in our
sample was not particularly categorical (i.e., did not
depend on a specific cutoff). In fact, although we
Table 1 SRS consensus categories in ADHD?and ADHD
(n = 51)(n = 24)
Using linear mixed effects, the likelihood ratio test for effect of group was v(1)
The Likelihood Ratio test for the interaction between group and category was v(3)
= 68.98, p\0.001
= 2.23, p = 0.53
J Autism Dev Disord (2011) 41:1178–1191 1183
Table 2 TDC vs. ADHD-vs. ADHD?comparisons in parent ratings of psychopathology
(n = 69)(n = 51)(n = 24) Chi-square
ADHD- combined type n (%)
22 (43) 16 (67)4.021
Males n (%) 30 (43)41 (80) 19 (79)20.52
Current medication n (%)
20 (39)8 (33) 0.21
Comobidity rate n (%)
18 (35) 8 (33) 0.410.54
ANCOVA (age, sex)
Full IQ 112 13110 14107 141.0 2, 1390.37–
Verbal IQ*1141411014 10817 1.62, 1370.21–
Performance IQ* 1081210614104 121.5 2, 1370.24–
Oppositional 434 549 63 1162.1 2, 139
Cognitive/Inattentive464689706238.1 2, 139
Hyperactivity454661370 1196.02, 139
Anxious/Shy45452 1059 1420.9 2, 139
Perfectionism455 497 539 9.12, 139
Social problems4625310611428.3 2, 139
Psychosomatic 4665413561512.72, 139
Restless-impulsive 454 669708191.62, 139
Emotional lability444 519 571524.42, 139
ADHD index443708716 380.5 2, 139
GI total443638688175.4 2, 139
DSM-IV inattentive 443699717307.32, 139
DSM-IV H–I 454 66137310114.02, 139
DSM-IV total443708745186.42, 139
Anxious/Depressed 512 58760923.12, 139
TDC = ADHD-\ADHD?
Withdrawn/Depressed52455561920.8 2, 139
Somatic complaints 524 568 58910.2 2, 139
Social problems501577629 39.82, 139
Thought problems513588 64840.42, 139
Attention problems512657 708144.3 2, 139
Rule breaking behavior51258762736.62, 139
Aggressive behavior512588 63738.02, 139
Internalizing prob44855 1161930.92, 139
Externalizing prob 42856 10 62756.62, 139
Total problems408598666120.6 2, 139
Speech 10310384 3.92, 1320.02–
TDC = ADHD-\ADHD?
TDC = ADHD-\ADHD?
Syntax1039373 11.92, 132
Semantic 1138474 12.42, 132
Inappropriate initiation 123835254.72, 132
Stereotyped112 1036327.5 2, 132
Context113936327.7 2, 132
1184 J Autism Dev Disord (2011) 41:1178–1191
categorized ADHD subgroups with or without autistic traits
based on a T-score cutoff of 60 or above on the SRS, the
distribution of SRS scores was continuous. Further, we
observed a similar pattern with more stringent cutoffs on
the SRS of 65 or 70 (data not shown) and with the CCC-2
scores. The distribution of ASD symptoms in our sample
supports the utility of considering ASD traits in ADHD
from a dimensional perspective, which considers varying
degrees of traits extending in the general population from
healthy individuals to clinical groups (e.g., Constantino
et al. 2004; Skuse et al. 2009; Di Martino et al. 2009).
Examining autistic traits dimensionally in ADHD is more
likely to inform our understanding of the underlying
physiopathology, in part because of the greater statistical
power afforded by dimensional analyses.
Results of this study support the growing literature
examining the overlap of ASD and ADHD beyond clinical
measures. Recent studies have found that the relation
between autistic traits and ADHD symptoms is familial
(Mulligan et al. 2009; Nijmeijer et al. 2009) and is mostly
accounted for by genetic influences as shown by the greater
similarity among monozygotic than dizygotic twins (Ron-
ald et al. 2008). Similarly, a substantial proportion of the
genetic influences on self-reported ADHD and autistic
symptoms were found to be shared in a young adult twin
sample (Reiersen et al. 2008b). A clue to one possible
source of such common etiologic relationships was repor-
ted by Smalley et al. (2002) who found that ADHD and
autism share a common and overlapping susceptible locus
in chromosome 16p13. More recently, excessive frequency
Fig. 3 The mean and standard
errors of the scaled scores
corresponding to each sub-scale
of the Children’s
(CCC-2) are depicted for the
and ADHD-(solid blue)
subgroups. Three of these sub-
scales (Speech, Syntax, and
Semantics) are related to
structural aspects of language
(on the right side of the figure),
while the remaining seven sub-
scales are related to pragmatic
aspects of language (on the left
side of the figure). As the graph
depicts, the ADHD?group
shows significantly lower
(worse) scores on the sub-scales
related to pragmatic language
but not on the sub-scales related
to language structure
Table 2 continued
ANCOVA (age, sex)
MeanSDMean SDMean SD
Nonverbal112935245.6 2, 132
Social problems 112835242.0 2, 132
Interests 113835221.5 2, 132
GCC 88 18 7216 4915 37.52, 132
SIDC36-4.649-7.418 18.62, 132
GI global index; H–I hyperactive/impulsive * For 1 child classified as ADHD-(SRS T Score\60) and 1 child classified as ADHD?(SRS
T score C 60) estimates of PIQ and VIQ were unavailable. ** As opposed to the other measures, lower scores on the CCC-2 indicate greater
impairment; 2 ADHD-and 1 ADHD?did not have usable CCC-2 data (1 ADHD-and 1 ADHD?had inconsistent parent scorings, 1 parent of a
child classified as ADHD-had not completed the questionnaire). Additionally, 4 TDC did not have usable CCC-2 scores (2 had inconsistent
parent scorings and 2 had not completed the questionnaire)
J Autism Dev Disord (2011) 41:1178–11911185
of large, rare copy number variations in chromosome
16p13 were reported in a well-characterized ADHD sam-
ple, thus further supporting the potential overlap between
some forms of ADHD and autism (Williams et al. 2010).
Beyond the overlap of core diagnostic symptoms of ADHD
and ASD, several lines of evidence indicate that these two
diagnostic entities share common deficits in other areas
including motor coordination (Reiersen et al. 2008),
attention control and executive functions (Corbett and
Constantine 2006; Reiersen et al. 2008b; Fine et al. 2008;
Schatz et al. 2002; Geurts et al. 2004b), facial affect pro-
cessing (Sinzig et al. 2008b; Yuill and Lyon 2007), and
theory of mind (Buitelaar et al. 1999; Sinzig et al. 2008a).
To date, only one MRI study has examined the neuronal
overlap between ASD and ADHD. Brieber et al. (2007)
found gray matter volume reductions in the parietal lobe
and gray matter density reductions in the temporal lobe in
both groups. Clearly, further examinations of the overlap
between ASD and ADHD are warranted at the neuropsy-
chological, physiological, and genetic domains.
The results of this study should be interpreted in light of
its limitations. The principal limitation is that we relied
exclusively on parent questionnaires as a measure of
autistic traits. Further, we did not use gold-standard
instruments, such as the ADOS, to rule out ASD. However,
we excluded prospective participants with previous diag-
noses of ASD, and children were evaluated by experienced
clinicians using standardized assessments of psychopa-
thology. In ongoing follow-up studies, we are specifically
assessing autistic traits with multiple informants, including
direct observations by clinicians blind to presumptive
diagnosis. Additionally, our referral sample cannot be
considered representative of ADHD; however, we high-
light the consistency in the clinical presentation of children
in our sample with children in other studies (i.e., Geurts
et al. 2004a; Reiersen et al. 2007).
In conclusion, the results of this study have implications
for the assessment and treatment of autistic traits in chil-
dren with ADHD both in regard to the recognition of their
social difficulties and their increased risk of comorbid
ODD. It is likely that the DSM-IV-TR exclusion of the
diagnosis of ASD in individuals with ADHD may prevent
appropriate identification and targeted treatment. Finally,
the appreciation of a specific social impairment associated
with ASD in some children with ADHD may provide a
means to dissect the biological components underlying
NARSAD and NIMH (K23MH087770) awarded to Adriana Di
Martino and to F. Xavier Castellanos by NICHD (R01HD065282),
Autism Speaks, the Stavros Niarchos Foundation, the Leon Levy
Foundation, and the Alicia Koplowitz Foundation. The authors wish
to thank three anonymous clinicians at the University of Michigan
Autism and Communication Disorders Center for graciously provid-
ing their SRS item classification, Dylan Gee, B.A., for helping in data
collection, Sherine Khalil, M.P.A., for her dedicated work on research
administration, and Amy Krain Roy, Ph.D., for helpful suggestions on
an earlier version of this manuscript. Most of all the authors want to
express their sincere gratitude to the parents and their children who
have dedicated their time and commitment to this research.
This work was partly supported by grants from
See Tables 3, 4, 5.
Table 3 SRS item-by-item
Item # Social (S; 24 items)% agreement
2Incongruent facial expressions 75
7 Awareness of others’ state of mind63
10 Very literal75
15*Identifying others’ facial expressions and verbal tone 50
16Poor eye gaze88
18 Trouble forming friendships100
22 Engages with other peers 75
23 Avoids group activities 100
26 Comforts those in distress88
27Does not initiate social interchanges 75
33 Socially awkward88
34Avoids emotional intimacy with others 100
37**Trouble relating to peers 100
38 Modulates his/her response according to the mood of others 100
45Pays attention to what others are interested in 100
46Facial expressions are too serious 88
1186 J Autism Dev Disord (2011) 41:1178–1191
Table 3 continued
* For items 12, 15, 29, 48, and 52, 4
or fewer coders agreed on any one
category. Thus, authors RG and
ADM came to a consensus on these
items. ** Item 37 was consensus
coded by 7 clinicians instead of 8
Items from the SRS copyright ?
2005 by Western Psychological
Services. Item content adapted for
scholarly reference purposes and
reprinted by permission of the
publisher, Western Psychological
Services, 12031 Wilshire Boulevard,
Los Angeles, California, 90025,
Not to be reprinted in whole or in
part for any additional purpose
without the expressed, written
permission of the publisher. All
Item #Social (S; 24 items) % agreement
47 Acts silly or laughs at unsuitable moments75
54 Reacts to people as objects100
55 Is aware of other’s ‘‘personal space’’
Walks in between two people….
60 Does not display his/her feelings100
63Inappropriately touches others 75
Communication (C; 8 items)
12*Tells others how he/she feels 38
13Poor turn-taking skills during conversations 88
19 Frustrated in trying to verbally convey ideas63
21 Imitation of others’ actions63
35No typical flow in conversations 100
40 Make-believe games100
51Trouble responding to questions in a direct fashion 75
53Mechanical speech 63
Restricted, Repetitive Behaviors or Interests (R; 10 items)
4 Unusual unyielding patterns of behavior when under stress100
20 Odd sensory interests100
24 Trouble with transitions or changes in schedule
31Cannot stop thinking about a particular subject/topic 88
39 Limited interests100
42 Particularly responsive to sensory input 88
50Hands mannerisms 100
58 Details instead of the big picture 88
61Difficult time modifying his/her views 100
Not Autism Specific (Non-SCR; 23 items)
1More active in social situations75
3Confident when engaging with others 63
5Socially naı ¨ve 63
8 Strange bizarre behaviors 63
9Overly reliant on adults 75
17Knows when something is not just 63
25Unaware about being different from others 63
29*His/her peers think he/she is strange38
30Upset in situations with a lot happening 63
32Appropriate hygiene 100
36Trouble relating to adults63
41Shifts from one task to another88
43Does not display anxiety when parents are gone75
44Poor concept of cause and effect75
49 Does very well with some things but not with others75
52*Aware of being too loud 50
62 Irrational motivations for doing things 88
64Anxious in social interactions 75
J Autism Dev Disord (2011) 41:1178–11911187
Table 4 ADHD-vs. ADHD?comparisons in parent ratings of psychopathology (based on CCC-2)
(n = 47)(n = 25) Chi-square
Males n (%) 40 (85) 18 (72)1.81 0.18
ANCOVA (age, sex)
Full IQ107.13 13.15111.16 15.480.71 1, 680.40
Verbal IQ* 108.26 13.58 110.9217.910.22 1, 67 0.64
Performance IQ* 104.32 13.52107.46 13.40 0.601, 67 0.44
Oppositional 54.13 9.1261.6811.288.851, 68 0.004
Cognitive/inattentive69.118.0168.927.950.24 1, 68 0.62
Hyperactivity 65.0412.7771.36 10.194.161, 680.05
Anxious/Shy 53.1511.37 56.2411.49 1.751, 68 0.19
Perfectionism 48.327.9653.36 8.296.601, 680.01
Social problems 51.268.0263.7614.71 21.061, 68
Psychosomatic52.0011.6260.48 16.085.961, 68 0.02
Restless-impulsive 64.798.5372.20 8.5510.49 1, 680.002
Emotional lability50.79 10.8656.6012.013.441, 680.07
ADHD index 69.436.9271.767.780.80 1, 680.38
GI total61.74 8.3169.008.0010.981, 680.001
DSM-IV inattentive69.11 8.2170.96 8.190.29 1, 680.59
DSM-IV H–I65.8312.62 73.0010.115.481, 680.02
DSM-IV total 69.577.8173.686.543.821, 680.06
Withdrawn/depressed55.06 5.8360.648.9911.611, 680.001
Somatic complaints55.537.0859.929.144.311, 68 0.04
Social problems55.196.1164.407.4332.62 1, 68
Thought problems58.007.5764.727.56 13.741, 68
Attention problems64.917.2870.40 7.198.001, 680.006
Rule breaking behavior58.197.28 61.887.32 4.131, 680.05
Aggressive behavior57.197.8163.087.538.971, 680.004
Internalizing prob 53.4310.7062.407.43 13.381, 68
Externalizing prob55.5710.2062.847.709.621, 680.003
Total problems58.197.9466.64 5.65 22.361, 68
SRS T Total (adjusted
GI global index; H–I hyperactive/impulsive * For 1 child classified as ADHD?estimates of PIQ and VIQ were unavailable. ** Six participants with ADHD and five
TDC had at least one missing SRS question (one, two, and three responses were missing for seven, three, and one child, respectively; ADHD children and TDC did not
differ in the number of missing questions). See text regarding pro-rated scores
Table 5 SRS consensus categories in ADHD?and ADHD-(based on CCC-2)
ADHD?(n = 47) ADHD-(n = 25)
Mean SD MeanSD
Using linear mixed effects, the likelihood ratio test for effect of group was v(1)
= 23.19, p\0.001
The Likelihood Ratio test for the interaction between group and category was v(3)
= 0.43, p = 0.93
1188 J Autism Dev Disord (2011) 41:1178–1191
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