Autism Research - 2020 - Audras‐Torrent - WISC‐V Profiles and Their Correlates in Children with Autism Spectrum Disorder

RESEARCH ARTICLE
WISC-V Profiles and Their Correlates in Children with Autism Spectrum
Disorder without Intellectual Developmental Disorder: Report from the
ELENA Cohort
Lee Audras-Torrent, Ela Miniarikova, Flore Couty, Florine Dellapiazza, Mathilde Berard, Cécile Michelon,
Marie-Christine Picot, and Amaria Baghdadli
The intellectual functioning of people with autism spectrum disorder (ASD) without intellectual developmental disorder
(IDD) has not been widely studied. However, marked heterogeneity in assessment measures, samples, and results has
been an obstacle for the generalization of findings. We aimed to (a) describe WISC-V intellectual functioning in a sample
of children with autism spectrum disorder without intellectual developmental disorder, (b) identify WISC-V profiles, and
(c) explore whether WISC-V intellectual functioning is related to ASD symptom severity and adaptive skills. Our sample
consisted of 121 children from 6 to 16 years of age with ascertained ASD without an intellectual developmental disorder
(IDD). The intellectual functioning of the participants was within the average range. Intra-individual analysis showed
that children with ASD performed better on visual than auditory working-memory tasks. Moreover, the intellectual functioning of the participants correlated negatively with ASD symptom severity but positively with adaptive communication
skills. Overall, we identified six intellectual profiles according to verbal and reasoning skills. These findings highlight the
relevance of WISC-V assessment for children with ASD without an IDD to individualize intervention, especially remediation. Autism Res 2021, 14: 997–1006. © 2020 International Society for Autism Research, Wiley Periodicals, LLC
Lay Summary: This study examined WISC-V intellectual functioning in 121 children with autism spectrum disorder
(ASD) without an intellectual developmental disorder (IDD). We found their intellectual functioning to be within the
average, as was that of their peers with typical development (TD), and their verbal and reasoning skills were the most discriminant. In addition, the better their intellectual functioning was, the better their adaptive communication skills and
the less severe their ASD symptoms. These findings highlight the relevance of WISC-V assessment in ASD to individualize
early psychological remediation.
Keywords: autism spectrum disorder; children; intellectual functioning; WISC-V; profiles; adaptive functioning
Introduction
Autism spectrum disorder (ASD) is a neurodevelopmental
disorder characterized by social communication impairment, associated with stereotyped and repetitive patterns
in behavior, interests, or activities [American Psychiatric
Association, 2013]. Intellectual developmental disorders
(IDDs) are frequently associated with ASD, its estimated
prevalence in ASD being 50% in France [Mottron, 2010].
It is well known that IDDs have a negative impact on
overall functioning in ASD [American Psychiatric
Association, 2013; Matson & Shoemaker, 2009].
Intellectual functioning refers to complex, higher-order
forms of cognition, such as reasoning, problem solving,
and decision making [Dai & Sternberg, 2004]. It has been
widely studied in ASD, but many studies have excluded
participants with IDD from their sample to reduce clinical heterogeneity [Black, Wallace, Sokoloff, &
Kenworthy, 2009; Lai et al., 2017; Mayes &
Calhoun, 2008; Oliveras-Rentas, Kenworthy, Roberson
3rd, Martin, & Wallace, 2012]. Overall, available studies
often report strengths and weaknesses in intellectual
functioning of children with ASD. Indeed, they found
that individuals with ASD commonly have higher performance reasoning (i.e., nonverbal performances) than verbal scores [Girardot et al., 2012; Hedvall et al., 2013;
Oliveras-Rentas et al., 2012]. In addition, studies using
various Wechsler Intelligence Scales (WIS; WPPSI-III,
From the Centre de Ressource Autisme Languedoc-Roussillon, Centre d’Excellence sur l’Autisme et les Troubles Neurodéveloppementaux (CeAND),
Montpellier, France (L.A.-T., E.M., F.C., F.D., M.B., C.M., A.B.); Université Paris-Saclay, UVSQ, Inserm, CESP, Team DevPsy, Villejuif, France (M.-C.P.,
A.B.); Department of Medical Information, University Hospital, Montpellier, France (M.-C.P.); Faculté de Médecine, Université de Montpellier, Montpellier, France (A.B.)
Received July 15, 2020; accepted for publication November 9, 2020
Address for correspondence and reprints: Amaria Baghdadli, Centre de Ressource Autisme Languedoc-Roussillon, Centre d’excellence sur l’autisme et
les troubles neurodéveloppementaux, 191 Avenue du Doyen Gaston Giraud, Montpellier 34000, France. E-mail: rech-clinique-autisme@chumontpellier.fr
Published online 27 November 2020 in Wiley Online Library (wileyonlinelibrary.com)
DOI: 10.1002/aur.2444
© 2020 International Society for Autism Research, Wiley Periodicals, LLC
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Autism Research 14: 997–1006, 2021
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WISC-III, WISC-IV, and WIAT-II) have shown that visual
supports used during psychological examinations of children with ASD improved their overall intellectual skills
[Girardot et al., 2012; Mayes & Calhoun, 2008; Samson,
Mottron, Soulières, & Zeffiro, 2012]. Several authors have
also suggested that discrepancies between verbal and performance reasoning skills found with WIS are markers of
ASD [Black et al., 2009; Oliveras-Rentas et al., 2012].
Processing speed (PS) and working memory (WM) were
found to be poor in people with ASD [Boucher, Bigham,
Mayes, & Muskett, 2008; Habib, Harris, Pollick, &
Melville, 2019; Mayes & Calhoun, 2008; Oliveras-Rentas
et al., 2012; Wang et al., 2017]. A recent meta-analysis
highlighted that individuals with ASD have poorer WM
than those with typical development (TD), with spatial
WM being poorer than verbal WM [Wang et al., 2017].
Finally, studies have highlighted that overall intellectual
functioning is lower in people with ASD than those with
TD [Dawson, Soulières, Gernsbacher, & Mottron, 2007;
Nader, Courchesne, Dawson, & Soulières, 2016;
Scheuffgen, Happé, Anderson, & Frith, 2000] but these
findings were obtained using a wide range of IQ
assessments.
WIS are the most frequently administered instruments
to measure overall intelligence in the general population
[Oliveras-Rentas et al., 2012; Weiss, Saklofske, Holdnack, & Prifitera, 2015]. Various measures of intellectual
functioning, such as Raven Progressive Matrices were
used [Bölte, Dziobek, & Poustka, 2009; Dawson
et al., 2007; Nader et al., 2016]. However, authors have
found that WIS provides reliable measures of intellectual
skills in individuals with ASD without IDD [Bölte
et al., 2009; Oliveras-Rentas et al., 2012]. The latest version of the Wechsler Intelligence Scale for Children, the
WISC-V (2016), has undergone changes that could make
it useful for studying intellectual functioning in ASD.
First, the Perceptual Reasoning Index (PRI) is now split
into two domains: the Visuo-Spatial Index (VSI) and the
Fluid-Reasoning Index (FRI), the FRI being a good marker
of intellectual functioning in ASD [Terriot &
Ozenne, 2015]. Second, the Comprehension Subtest
Score, which often corresponds to a weak point in ASD
[Dawson et al., 2007; Oliveras-Rentas et al., 2012], is no
longer required to calculate the Full-Scale Intelligence
Quotient (FSIQ). This change could be at the origin of
higher scores for individuals with ASD for the Verbal
Comprehension Index (VCI) with the WISC-V than with
the WISC-IV [Kuehnel, Castro, & Furey, 2019]. Third, a
new subtest, Picture Span, assesses visual WM, which can
capture the strengths in visual and perceptive reasoning
often reported in ASD [Girardot et al., 2012]. However,
the intellectual functioning of children with ASD has not
yet been thoroughly studied using the WISC-V, except
for verbal skills [Kuehnel et al., 2019].
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Furthermore, studies using WIS have reported that
intellectual functioning in ASD is linked to several clinical dimensions. Significant associations were found
between intellectual functioning and ASD symptom
severity: verbal skills (VCI) negatively correlated with
ADOS communication and social-interaction scores
[Black et al., 2009; Oliveras-Rentas et al., 2012], and
processing speed (PSI) negatively correlated with the
ADOS
communication
score
[Oliveras-Rentas
et al., 2012]. Black et al. [2009] also found a relationship
between intellectual functioning and adaptive skills, verbal and non-verbal intellectual functioning being
strongly associated with adaptive communication skills.
Oliveras-Rentas et al. [2012] found that the VCI, PRI, WM
index (WMI), and PSI were positively associated with
adaptive communication skills. Hedvall et al. [2013]
highlighted that processing speed influenced adaptive
functioning in communication, motor, and daily living
skills. These relationships between intellectual functioning in ASD without IDD, ASD symptom severity, and
adaptive functioning require further investigation to better characterize the needs of this population.
A recent study suggested the existence of distinct intellectual profiles among children with ASD [Silleresi
et al., 2020]. The authors studied language abilities and
non-verbal intellectual functioning using Raven’s Progressive Matrices (RPM) and WISC-IV Matrix Reasoning
and Block Design in a sample of 51 verbal children with
ASD without IDD. Their results showed five intellectual
profiles according to the participants’ language skills:
(a) ASD with normal language (LN) and low non-verbal
intellectual quotient (NVIQ), (b) ASD with impaired language (LI) and low NVIQ, (c), ASD with LN and average
NVIQ, (d), ASD with LI and average NVIQ, and (e) ASD
with LN and high NVIQ. The strengths of this study were
that it provided an accurate description of the intellectual
functioning of children with ASD without IDD using
WISC-V and that it investigated its links with language
functioning. The study of the multiple dimensions of
intellectual functioning, such as working memory, visuospatial treatment, processing speed, and fluid reasoning,
in children with ASD without IDD could help to identify
specific characteristics of their intellectual profile. The literature shows that it is possible to identify distinct intellectual profiles in children with ASD [Black et al., 2009]
using various measurement instruments. WISC-V appears
to be a relevant and efficient tool to measure, in particular, verbal intellectual functioning in ASD [Kuehnel
et al., 2019], but its use has not yet been extended to all
intellectual domains of interest in clinical practice. One
of the important benefits would be the tailoring of intervention and education to individual functioning [Oliveras-Rentas et al., 2012]. We aimed to assess WISC-V
intellectual functioning in children with ASD without
IDD and to explore the associations between WISC-V
Audras-Torrent et al./WISC-V in children with ASD without IDD
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profiles, ASD symptom severity, and adaptive functioning. Indeed, only a limited number of studies have investigated the intellectual profiles of children with ASD
without IDD [Baghdadli et al., 2019; Joseph, Steele,
Meyer, & Tager-Flusberg, 2005]. Moreover, there are currently few studies that used WISC-V to study the intellectual functioning of children with ASD without IDD. The
challenge of using WISC-V is to contribute to a detailed
description of the intellectual profiles of children with
ASD and study their relationship with ASD symptom
severity and adaptive functioning to improve our understanding of their individual functioning and tailor treatment approaches. Based on the literature, we
hypothesized that there would be considerable heterogeneity in intellectual functioning, characterized by
strengths in the VSI and FSI and weaknesses in the PSI
and WMI and that, within the WMI, participants would
perform better on the visual Picture Span subtest than on
the verbal Digit Span subtest. We also hypothesized that
ASD symptom severity would be negatively associated
with WISC-V indexes, particularly the VCI and PSI.
Finally, we predicted that there would be positive associations between adaptive communication skills and
WISC-V indices, especially the VCI. Exploring this link
may help to better understand the impact of language
difficulties in ASD without IDD.
Methods
Participants
Participants were recruited from the ELENA cohort, a
large multi-center, longitudinal, prospective cohort of
children with an ascertained ASD [Baghdadli et al., 2019].
Participants were recruited from 15 centers across nine of
the 18 French regions. Overall, 900 children were recruited for this longitudinal study with parental consent.
The inclusion criteria for the ELENA cohort were to be
aged from 2 to 16 years at the moment of the diagnosis
of ASD and to have a diagnosis ascertained by a multidisciplinary, clinical, and standardized process administered by licensed and trained psychologists. Measures
included the Autism Diagnostic Observation Schedule
2 (ADOS-2) [Lord et al., 2012], the Autism Diagnostic
Interview-Revised (ADI-R) [Le Couteur, Lord, &
Rutter, 2003], a direct psychological examination to estimate
the
Intellectual
Quotient
(WISC-V)
[Wechsler, 2014], and a parental interview about the child’s adaptive functioning using the parent/caregiver Vineland Adaptive Behavior Scale (VABS-II) [Sparrow,
Cicchetti, & Balla, 2005]. Parents also completed online
questionnaires and medical and interventional data.
For the present study, we used a subgroup of 121 participants from the ELENA cohort selected according to the
following inclusion criteria: aged from 6 to 16 years,
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having completed all 10 subtests of the WISC-V, and having scores on both the WISC-V and VABS-II ≥70. The children included in our study were only those who were
able to participate in the full WISC-V protocol, implying
that they had adequate verbal skills to understand and
answer verbal instructions. Exclusion criteria were having
an IDD (Full-Scale Intelligence Quotient ≤70 for the
WISC-V, a total adaptive score of the VABS-II ≤70, and
ICM F70, F71, F72), or sensory disabilities, such as blindness or deafness (n = 4), according to clinical examinations and self-completed parental questionnaires
concerning the potential co-occurrence of sensory
disabilities.
Measures
Intellectual functioning of participants was assessed using
the French form of the WISC-V administrated by licensed
and trained psychologists. The WISC-V is composed of
10 primary subtests (represented by scaled scores:
mean = 10, standard deviation = 3) that can be clustered
into composite quotients for five indices (represented by
standard scores: mean = 100, standard deviation = 15)
(described in Table 1). Only the first seven subtests are
used to calculate the Full-Scale Intelligence Quotient
(FSIQ). The internal consistency indicators attest to excellent reliability; alpha indices range from 0.88 to 0.96.
Moreover, the measures are considered to be stable over
time. Concerning the validity of the scale, links between
the subtests within a given index are strong
(on average > 0.70) [Terriot & Ozenne, 2015].
Adaptive functioning was assessed using the parent/caregiver form of the VABS-II [Sparrow et al., 2005]. This standardized caregiver interview of 297 items measures
adaptive behaviors in the domains of communication,
daily living skills and socialization. The reliability of the
VABS-II for each domain was excellent (α = 0.80) and the
intra-class coefficient of the test/retest 0.89.
ASD symptom severity was measured using the ADOS-2
[Lord et al., 2012] a semi-structured behavioral observation protocol. This scale is composed of 25 to 30 items
across social interaction, communication, repetitive and
Table 1.
Subtests and Indices of the WISC-V
Indices
Verbal Comprehension Index (VCI)
Visual–spatial Index (VSI)
Fluid Reasoning Index (FRI)
Working Memory Index (WMI)
Processing Speed Index (PSI)
Audras-Torrent et al./WISC-V in children with ASD without IDD
Subtests
Similarities [SI]
Vocabulary [VC]
Block Design [BD]
Visual Puzzles [VP]
Matrix Reasoning [MR]
Figure Weight [FW]
Digit Span [DG]
Picture Span [PS]
Coding [CD]
Symbol Search [SS]
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stereotyped behaviors, and play domains. The internal
consistency was good (α = 0.50–0.92), the test–retest reliability acceptable (0.64–0.88), and the inter-rater reliability excellent (0.79–0.98). For this study, we used the
Calibrate Severity Score (CSS), ranging from 1 to 10.
A diagnosis of Attention Deficit Hyperactivity Disorder
(ADHD) or Developmental Coordination Disorder (DCD) was
collected at inclusion and systematically completed using
the ICM-10 medical diagnosis (F90 and F82, respectively)
and medical records after being established by a child
psychiatrist.
The parent occupation was recorded for the mother and
father and coded into eight categories: 1 – Agricultural
worker; 2 – Craftsman, sale worker, or entrepreneur; 3 –
Manager, engineer, or doctor; 4 – Intermediate occupation; 5 – Employee; 6 – Worker; 7 – Retired; 8 – No professional occupation.
Procedure
This study was cross-sectional in that it used intellectual
measurements from WISC-V for each participant that
could be entirely collected at one of the timepoints of the
prospective follow-up of the ELENA cohort (at inclusion,
3 years later, or 6 years later). At each timepoint, the
intellectual measurements were collected simultaneously
with other data, including that of the VABS-II [Sparrow
et al., 2005] and ADOS-2 [Lord et al., 2012].
Data Analysis
Descriptive statistics were generated for all measures to
provide an overview of the children’s characteristics in
the entire sample. For the first aim, we performed intraindividual analysis between the 10 WISC-V subtests using
the Wilcoxon signed rank test, for which we calculated
index differences (Δ) for each participant. For the second
aim, we studied intergroup comparisons between sex,
age, VABS-II adaptive scores, ADOS-2 CSS (independent
variables), and the scores for the WISC-V indices and subtests (dependent variables) using Spearman correlation
coefficients, except for the independent variable, sex, for
which ANOVA was used. Finally, hierarchical cluster
analysis (HCA) was performed on the entire sample using
squared Euclidian distance proximity measures [Hair,
Black, Grimm, & Yarnold, 2000] and Ward’s minimumvariance method [Ward, 1963]. This classification into
homogeneous subgroups was based on the 10 subtests of
the WISC-V (SI, VC, BD, MR, FW, DS, CD, VP, PS, and
SS). Cluster analysis aims to maximize between-cluster
variance relative to within-cluster variance. The number
of clusters chosen was based on three graphical methods:
(a) the cubic clustering criterion, (b) semi-partial R2 [Milligan & Cooper, 1985], and (c) a dendrogram that
reflected the hierarchy of the clusters. Analyzes were conducted to assess cluster group differences in WISC-V
scores and clinical characteristics using chi square for
nominal variables (sex) or ANOVA for continuous variables (age, VABS-II scores). The analyzes were conducted
using SAS® version 9.3 statistical software.
Results
Participants
The 121 participants were aged from 6 years to 16 years
and 11 months (mean age 10.7 ± 2.7 years). Their clinical
characteristics and psychiatric comorbidities are presented in Table 2. The parent occupation of mothers covered the following categories: employee (46.4%), worker
(43. 6%), manager (21.4%), intermediate occupation
(12.5%), craftsman (8.9%), and without professional
Table 2. Clinical Characteristic of the Children
n
%
102
19
84.3
15.7
40
81
24
13
33.1
66.9
19.8
10.7
Measures
ADOS-2 Severity score
n
115
Mean
7.5
SD
2.0
VABS II score
Communication
Daily living skills
Socialization
121
80.8
71.1
75.7
10.3
9.7
10.5
Sex
Male
Female
Age
6–11.11 years
12–16.11 years
Psychiatric comorbidities
ADHD (F90)
DCD (F82)
ADHD: attention deficit hyperactivity disorder; DCD: developmental coordination disorder.
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Audras-Torrent et al./WISC-V in children with ASD without IDD
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occupation (7.1%). The parent occupation of fathers covered the following categories: employee (30.7%), manager (28.8%), craftsman (19.2%), worker (15.4%),
intermediate occupation (3.8%), and without professional occupation (2%). The ASD symptom severity score
on the ADOS-2 (CSS) was assessed as high for 54.8%
(scores from 8–10), moderate for 35.7% (scores from 5–7),
and mild for 9.5% (scores <4). For most of these children,
it was possible to administer ADOS-2 modules 3 and
4 (module 3 for 81% and module 4 for 4.2%), with module 2 having been used for the others in spite a correct
language level (verified in the medical report). The mean
communication, socialization, and daily living skills
VABS-II scores ranged from low to moderate.
Intellectual Functioning and Intra-Individual Analysis for
Indices and Subtests
Within the entire sample, all mean index and subtest
scores were in the mean range relative to those of typical
children (Table 3). Intra-individual analysis showed significant differences between indexes scores using the
Wilcoxon signed rank test. VCI scores were higher than
WMI scores (100.8 ± 20.2 vs. 94.0 ± 13.6, Δ = 6.8,
P < 0.001) and PSI scores (100.8 ± 20.2 vs. 93.2 ± 14.1,
Δ = 7.6, P < 0.001). FRI scores were higher than WMI
scores (102.0 ± 14.0 vs. 94.0 ± 13.6, Δ = 8.1, P < 0.001)
and PSI scores (102.0 ± 14.0 vs. 94.0 ± 14.1, Δ = 9.0,
P < 0.001). VSI scores were higher than PSI scores
(102.2 ± 14.6 vs. 93.2 ± 14.1 scores, Δ = 9.0, P < 0.001)
and WMI scores (102.0 ± 14.6 vs. 94.0 ± 13.6 scores,
Δ = 8.2, P < 0.001). We observed no significant differences between VCI and FRI, VSI and FRI, or WMI and PSI.
Intra-individual analysis between FSIQ and index scores
showed the VSI (Δ = −3.4, P = 0.005) and FRI (Δ = −3.2,
P < 0.001) to be higher than the FSIQ and the PSI
Table 3.
Description of WISC-V Scores by Index and Subtest
WISC-V Indexes
Verbal Comprehension Index
Visual Spatial Index
Fluid Reasoning Index
Working Memory Index
Processing Speed Index
Full-Scale IQ
WISC-V Subtests
Similarities
Vocabulary
Block Design
Visual Puzzles
Matrix Reasoning
Figure Weights
Digit Span
Picture Span
Coding
Symbol Search
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Mean
100.8
102.2
102.0
94.0
93.2
98.9
Mean
10.5
9.7
10.3
10.5
10.1
10.5
8.5
9.5
8.6
9.0
SD
20.2
14.6
14
13.6
14.0
15.7
SD
3.8
4
2.8
2.9
2.6
2.8
2.6
2.8
3.0
2.8
Min
45
61
72
65
60
70
Min
1
1
2
4
4
4
1
4
1
3
Max
155
141
127
125
129
147
Max
19
19
18
17
16
16
14
16
17
16
(Δ = 5.7, P < 0.001) and WMI (Δ = 4.9, P < 0.001) to be
lower. However, the VSI scores were similar to the FSIQ
scores (P < 0.08). Intra-individual analysis showed only
one significant difference for the subtest scores (Δ = 0.9,
P < 0.001), participants having a higher score for the Picture Span (9.5 ± 2.9) than Digit Span (8.5 ± 2.6).
Comparisons of Intellectual Functioning across Sex-AgeParent Occupation-ASD Symptom Severity-Adaptive Skills
Intergroup comparisons of index and subtest scores
showed no significant differences due to sex (all
P < 0.05). Intergroup comparisons between children aged
from 6 years to 11 years, 11 months and those aged from
12 years to 16 years, 11 months showed significant differences with higher scores for the older group for the VCI
(106.7 ± 19.6 vs. 97.9 ± 20.0, P < 0.03), Vocabulary subtest (10.8 ± 3.9 vs. 9.2 ± 3.9, P = 0.04), and Similarities
subtest (11.5 ± 3.8 vs. 10.1 ± 3.7, P = 0.04). There were no
significant differences between age groups for the other
indices and subtests (all P > 0.05). We examined the
effect of parents’ occupation on their children’s FSIQ and
found that children whose mothers were employees
(96.5 ± 12.8) had a significantly lower FSIQ than those
whose mothers were managers (114.2 ± 12.5, P = 0.02).
However, there was no effect of the fathers’ occupation
on their children’s FSIQ (P = 0.5).
We also examined correlations between ASD symptomseverity scores and index and subtest scores. For the indices, the ASD symptom-severity scores negatively correlated with the VCI score (r = −0.20, P = 0.03), VSI score
(r = −0.27, P = 0.004), and FSIQ (r = −0.20, P = 0.03).
Thus, the FSIQ, VCI, and VSI scores decreased with
increasing ASD severity score. For the subtests, the ASD
symptom severity scores negatively correlated with the
Block Design score (r = −0.19, P = 0.04) and Visual Puzzle
score (r = −0.28, P = 0.003). Thus, the Block Design and
Visual Puzzle scores decreased with increasing global ASD
symptom severity score.
We then examined correlations between the indices
and domains of the VABS-II. The VABS-II communication
score positively correlated with all indices. Thus, with an
increasing Vineland communication score, the FSIQ
(r = 0.50; P = 0.0001), VCI (r = 0.49; P = 0.0001), FRI
(r = 0.36; P = 0.001), VSI (r = 0.31; P = 0.0005), WMI
(r = 0.38; P = 0.001), and PSI scores (r = 0.38; P = 0.0001)
also increased. The daily living skills score also positively
correlated with the VSI (r = 0.22; P = 0.01), FRI (r = 0.21;
P = 0.02), and FSIQ scores (r = 0.21; P = 0.02). Finally,
there were no correlations between the Vineland Socialization score and the indices (all P > 0.05). In our sample,
ASD-symptom severity measured by the ADOS-2 negatively correlated with adaptive functioning measured by
the VABS-II for communication (r = −0.34; P = 0.01),
Audras-Torrent et al./WISC-V in children with ASD without IDD
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socialization (r = −0.31; P = 0.01), and daily living skills
(r = −0.23; P = 0.01).
Cluster Analysis of Intellectual Profiles
The 10 WISC-V Subtests scores allowed us to discriminate
between subgroups in the entire sample. The agglomeration coefficients (Pseudo-F statistic, pseudo-T2, Semipartial R2, and cubic clustering criteria) and dendrogram
generated by the cluster analysis suggested a six-cluster
solution (Fig. 1). Based on the Subtests scores of these six
clusters, titles were proposed for each cluster concerning
verbal skills (SI, VC) and reasoning (BD, VP, MR, FW)
Subtests’ differences. Scores equal or below 8 are considered as “low,” scores between 9 to 11 are considered as
“average,” and scores equal or above 12 are considered as
“high.” One subgroup had homogenous scores, with
average scores for all subtests (cluster 2, n = 23), named
the Average Scores (AS) profile. The other five subgroups
had heterogeneous scores. Two subgroups showed low
average scores, the first with average reasoning scores
(cluster 1, n = 33), named the Low Scores with Average
Reasoning (LSAR) subgroup, and the second with average
verbal scores (cluster 3, n = 11), named the Low Scores
with Average Verbal (LSAV) subgroup. Finally, three subgroups showed average scores: high average reasoning
scores for cluster 4 (n = 20), called the Average Scores with
Figure 1.
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High Reasoning (ASHR) subgroup; high average verbal
scores for cluster 5 (n = 20), called the Average Scores with
High Verbal (ASHV) subgroup; and both high verbal and
reasoning scores for cluster 6 (n = 14), called the Average
scores with High Verbal/Reasoning (ASHVR) subgroup.
The cluster results for the ten WISC-V Subtests scores are
presented in Figure 1.
Comparison of the clusters showed no significant difference in age (P = 0.3) or ASD symptom-severity score
(P = 0.08). The percentage of psychiatric comorbidities by
cluster is shown in Table 4.
For the VABS-II scores, comparison of the clusters
showed significant differences in the communication
scores. The LSAR subgroup had a lower score than that of
the AS (73.9 ± 7.9 vs. 83.0 ± 11.0, P < 0.001), ASHR
(73.9 ± 7.9 vs. 83.0 ± 8.5, P < 0.001), ASHV (73.9 ± 7.9
vs. 84.6 ± 10.0, P < 0.001), and ASHVR (73.9 ± 7.9
vs. 87.8 ± 11.4, P < 0.001) subgroups. However, there
were no significant differences between clusters for VABSII socialization and daily living skills (all P > 0.05), as represented in Figure S1.
Discussion
We examined overall WISC-V intellectual functioning in
children with ascertained ASD without IDD. As expected,
there was marked heterogeneity in the WISC-V indexes
Mean subtest-scaled scores for each cluster.
Audras-Torrent et al./WISC-V in children with ASD without IDD
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Table 4.
Percentage of Psychiatric Comorbidities by Cluster
ADHD (F90), % (n)
DCD (F82), % (n)
ASHVR (n = 14)
ASHR (n = 20)
ASHV (n = 20)
AS (n = 23)
LSAR (n = 33)
LSAV (n = 11)
Intergroup differences
21.4 (3)
7.1 (1)
20 (4)
10 (2)
15 (3)
15 (3)
13.0 (3)
8.7 (2)
21.1 (7)
6.1 (2)
36.4 (4)
27.3 (3)
ns
ns
AS: average scores; ASHR: high average reasoning; ASHV: average scores with high verbal; ASHVR: average scores with high reasoning; LSAR: low scores
with average reasoning; LSAV: low scores with average verbal; ns: non significant.
and subtests. Intra-individual analyzes showed the VCI,
FRI, and VSI to be significantly higher than the WMI, PSI,
and FSIQ (except VCI, which was quasi equal). Previous
studies have also reported that children with ASD have
strengths in performance reasoning skills [Girardot
et al., 2012; Mayes & Calhoun, 2008; Scheuffgen
et al., 2000], perhaps enhanced by the motor-free design
of the intellectual tasks that were used [Oliveras-Rentas
et al., 2012]. Charman et al. [2011] and Nader
et al. [2016] showed that people with ASD have higher
visual–spatial and abstract reasoning abilities, which may
be specific markers of their intellectual functioning. In
our sample, as well as in previous studies, the mean
VABS-II Communication, Socialization, and Daily Living
Skills scores were lower than the mean FSIQ scores
[Charman et al., 2011; Flanagan et al., 2015; Matthews
et al., 2015; Yang, Paynter, & Gilmore, 2016]. This result
suggests that many individuals with ASD have problems
in converting their intellectual skills into efficient adaptive skills [Charman et al., 2011; Tillmann et al., 2019]. It
is well recognized that people with ASD and average
intellectual functioning are not spared from adaptive difficulties [Charman et al., 2011; Kraper, Kenworthy, Popal,
Martin, & Wallace, 2017]. Consequently, the term “highfunctioning” should be used with caution for the population of individuals with ASD without IDD [Alvares
et al., 2020].
Furthermore, the Working Memory (WMI) of the children in our sample was poorer than their Verbal (VCI),
Fluid Reasoning (FRI), Visuo-Spatial (VSI), and Global
Intellectual Skills (FSIQ), in accordance with the results of
previous studies [Mayes & Calhoun, 2008; Wang
et al., 2017]. A low WMI could be attributed to the attention and language comprehension problems commonly
found in ASD [Hedvall et al., 2013; Mayes &
Calhoun, 2008; Oliveras-Rentas et al., 2012]. The results
in the literature concerning WM have been contradictory, with some studies finding no differences between
spatial and verbal WM in ASD [Joseph et al., 2005],
whereas others found that spatial WM was more highly
impaired in this population [Lai et al., 2017; Wang
et al., 2017]. We found better visual than auditory WM
among our participants. This could be due to the visual
component of the Picture Span subtest, which may have
helped the participants to maintain their attention
[Girardot et al., 2012].
INSAR
In our findings, child’s FSIQ was associated to mother
occupation. Indeed, children whose mothers were
employees had a lower FSIQ than children whose
mothers were managers. This result is concordant with
past literature [Delobel-Ayoub et al., 2015; Piccolo,
Arteche, Fonseca, Grassi-Oliveira, & Salles, 2016], which
highlight the need to focus attention more on family
contextual factors in adapting the child intervention and
family support plan.
Our study also highlighted the presence of links
between WISC-V intellectual functioning and several
clinical dimensions of ASD. We observed a negative correlation between intellectual functioning, particularly
verbal and visual–spatial skills, and the ASD symptomseverity score. These findings replicate those of previous
studies [Black et al., 2009; Oliveras-Rentas et al., 2012]
that showed lower intellectual verbal skills to be related
to greater impairment of communication and socialization, measured by the ADOS [Joseph et al., 2005; Klin
et al., 2007]. The finding of weak associations between
ADOS-2 and WISC-V scores requires further studies on
larger samples. Furthermore, our results show that overall
intellectual functioning is strongly related to adaptive
functioning, especially to VABS-II communication skills,
as previously reported in the literature [Oliveras-Rentas
et al., 2012]. The observation that visual–spatial and fluid
reasoning in children with ASD is related to daily living
skills reinforces the evidence that intellectual functioning
influences daily life functioning [Black et al., 2009].
We then performed a cluster analysis on our sample
and identified six WISC-V profiles, the cluster-based differences mainly covering verbal and reasoning skills, previously reported to be discriminant in ASD [Black
et al., 2009; Girardot et al., 2012; Mayes &
Calhoun, 2008; Mottron, 2004; Oliveras-Rentas
et al., 2012; Silleresi et al., 2020]. The subgroup with the
highest intellectual scores in all domains and strengths in
both verbal and reasoning skills (ASHVR) was that with
the fewest children with associated psychiatric conditions
(28.6%). At the opposite extreme, the subgroup with the
lowest scores in all domains, except verbal (LSAV), was
that with the most children with associated conditions
(63.6%). The VABS-II scores did not differ between the
groups, except in the domain of communication, for
which the LSAR group had lower scores than the others,
possibly related to lower verbal skills.
Audras-Torrent et al./WISC-V in children with ASD without IDD
1003
Our study had several strengths, including a large sample of children with an ascertained ASD who underwent
a full clinical assessment, including administration of the
entire WISC-V scale. Our results, however, need to be
interpreted in the light of certain limitations. First, we
excluded children with IDD from our sample to identify
specific patterns in intellectual functioning, implying
that our results cannot be generalized. Second, we did
not use a control group of children with TD to verify the
specificity of our results.
As weaknesses in verbal working memory and
processing speed in ASD may be related to attention,
graphomotricity, and language difficulties, it is necessary to promote early and targeted developmental intervention. Indeed, early language training improves the
overall outcome in ASD [Warren et al., 2011]. Also, several authors have suggested that the motor skills
involved in processing speed could be improved by
innovative training programs, such as a mechatronic
door training kit [Moorthy, Iyer, Krishnan, &
Pugazhenthi, 2019]. Moreover, cognitive remediation
used in ASD could positively influence a range of cognitive dimensions, learning, and daily living skills, and
improve the quality of life [Doyen et al., 2020;
McConnell, 2002].
Although our results suggest that the verbal skills of
children improved as they got older, further prospective
studies are needed to better describe the course of intellectual functioning in ASD over time. Moreover, as children with ASD and IDD were excluded from our sample,
future studies should also include individuals with IDD
to examine their psychological profile with appropriate
psychological assessments. There is also a need to study
the influence of associated neurodevelopmental disorders
in ASD, especially ADHD, frequently found in ASD, on
psychological functioning.
In conclusion, intellectual functioning assessed by
the WISC-V in children with ASD without IDD shows
common patterns. In addition, children with ASD
without IDD had a poor auditory WM relative to their
visual WM that could not be previously explored with
previous versions of the WIS. Intellectual functioning
measured by the WISC-V positively correlated with
adaptive functioning measured by the VABS-II, but
both intellectual and adaptive functioning negatively
correlated with ASD-symptom severity measured by
the ADOS-2. Intellectual functioning of the children,
based on the WISC-V scores, could be divided into six
profiles according to their verbal and reasoning skills.
These findings highlight the relevance of WISC-V
assessment in ASD to individualize early psychological
remediation. Future studies are needed to describe the
evolution of these intellectual functioning profiles in
ASD during childhood and to assess effects of
remediation.
1004
Acknowledgments
The authors thank the contributing families and the
ELENA cohort staff (Myriam Soussana, Julie Loubersac,
Laetitia Ferrando, Philippe Antoine, and Colette Boy).
The authors express gratitude to the CNSA and DGOS for
funding to conduct this research and prepare the results
for publication. Grant sponsor 1: French Health Ministry
(DGOS) PHRCN 2013, grant number 1: 13–0232; and
Grant sponsor 2: Caisse Nationale de Solidarité pour
l’Autonomie (CNSA), grand number 2: 030319.
Conflict of Interest
The authors have no conflicts of interest to declare.
Ethical Approval
This study and informed consent procedure have been
approved by the Ethics Committee on the Research of
Human Subjects at Marseille Mediterranean (CNIL number DR-2015-393).
Informed Consent
Signed informed consent is obtained from all participating families included in the ELENA cohort.
Author Contributions
L.A.T., E.M., F.C., F.D., and M.B. conceived the study;
contributed to the collection, analysis, and interpretation
of the data; and drafted the manuscript. A.B. is the PI of
the ELENA cohort and also participated in the design of
the current study, drafted the manuscript, and critically
revised it for main intellectual content. C.M. and M.-C.
P. analyzed and interpreted the data. All authors read and
approved the final version to be published.
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Supporting Information
Additional supporting information may be found online
in the Supporting Information section at the end of the
article.
Figure S1 Adaptive functioning and ASD severity scores
for each cluster.
Audras-Torrent et al./WISC-V in children with ASD without IDD
INSAR