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Are Executive Control Functions Related to Autism Symptoms in High-Functioning Children?Lauren Kenworthy a; David O. Black b; Bryan Harrison a; Anne della Rosa a; Gregory L. Wallace b

a Children's Nat'l Medical Center, Rockville, Maryland, USA b National Institute of Mental Health, Bethesda,Maryland, USA

First Published on: 27 January 2009

To cite this Article Kenworthy, Lauren, Black, David O., Harrison, Bryan, della Rosa, Anne and Wallace, Gregory L.(2009)'AreExecutive Control Functions Related to Autism Symptoms in High-Functioning Children?',Child Neuropsychology,To link to this Article: DOI: 10.1080/09297040802646983URL: http://dx.doi.org/10.1080/09297040802646983

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Child Neuropsychology, iFirst: 1–16, 2009http://www.psypress.com/childneuropsychISSN: 0929-7049 print / 1744-4136 onlineDOI: 10.1080/09297040802646983

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ARE EXECUTIVE CONTROL FUNCTIONS RELATED TO AUTISM SYMPTOMS IN HIGH-FUNCTIONING CHILDREN?

Lauren Kenworthy,1 David O. Black,2 Bryan Harrison,1 Anne della Rosa,1 and Gregory L. Wallace2

1Children’s Nat’l Medical Center, Rockville, Maryland, USA, 2National Institute ofMental Health, Bethesda, Maryland, USA

Background: Linking autism symptoms to cognitive abilities can expand phenotypicdescriptions and facilitate investigations into the etiology and treatment of this multiplexdisorder. Executive dysfunction is one of several potential cognitive phenotypes in autism.Method: Archival clinical data on 89 children diagnosed with Autism Spectrum Disordersand administered a large neuropsychological battery were evaluated for relationshipsbetween executive functioning and autism symptoms.Results: Significant relationships between both laboratory tasks and behavior ratingscales of executive functions and autism symptoms were identified. Multiple regressionanalyses revealed that measures of semantic fluency, divided auditory attention, andbehavioral regulation were significantly correlated with autism symptoms, even afteraccounting for the variance from correlated “nuisance variables,” such as vocabularyand age. Conclusions: Executive dysfunction is related to all three clusters of behavioral symptomsin Autism Spectrum Disorders.

Keywords: Autism; Executive control; Asperger Syndrome; Communication; Social;Repetitive behavior; Fluency; Attention.

Executive dysfunction is a consistent, well-documented finding in school-agechildren with Autism Spectrum Disorders (ASDs; see reviews: Hill, 2004; Pennington &Ozonoff, 1996; Sergeant, Geurts, & Oosterlaan, 2002). Although it has not been demon-strated as a causal factor in ASDs (Dawson, Meltzhoff, Osterling, & Rinaldi, 1998;Dawson et al., 2002; Griffith, Pennington, Wehner, & Rogers, 1999; Liss et al., 2001;Yerys, Hepburn, Pennington, & Rogers, 2007), it may be related to some of the behaviorsthat define this complex, multi-determined group of disorders (Happé, Ronald, & Plomin,2006). The present study investigates this possibility.

Developmental disorders, such as ASDs, attention deficit/hyperactivity disorder(ADHD), and dyslexia, are behaviorally defined but commonly associated with brain

The authors thank Jennifer A. Silvers, Laura Case, and Jennifer L. Sokoloff for their work compilingdata, Alex Martin and Benjamin Yerys for helpful comments on the manuscript, and the many children withASD and their families who made this research possible.

Address correspondence to Dr. Lauren Kenworthy, Children’s Nat’l Medical Center, 14801 Physicians’Lane, Rockville, MD 20850, USA. E-mail: lkenwort@cnmc.org

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2 L. KENWORTHY ET AL.

abnormalities (for review, see: Giedd, Shaw, Wallace, Gogtay, & Lenroot, 2006). Defini-tion of specific neurodevelopmental disorders at the behavioral and biological levels hasbeen hampered by problems of variability, comorbidity, age-related differences, andwidely ranging severity. Frith (2001) and Pennington (2002) argue for the utility ofidentifying specific cognitive deficits in developmental disorders that may arise frombrain abnormalities and drive behavioral symptoms. Exploring cognitive correlates ofbehavioral symptoms in neurodevelopmental disorders offers several possible advantages.Cognitive profiles may supplement behavioral phenotypes, or even represent intermediatephenotypes, and provide important data in the search for genetic etiologies (Gottesman &Gould, 2003). Cognitive phenotypes can also inform intervention by proposing possibleantecedents to behavior problems (Fisher & Happé, 2005; Riggs, Jahromi, Razza,Dillworth-Bart, & Mueller, 2006).

Like other neurodevelopmental disorders, ASDs are defined by behaviors, such aspoor eye contact, limited imaginative play, repetitive movements, and restricted interests.Multiple genes are implicated (Folstein & Rosen-Sheidley, 2001) in this highly heritabledisorder. Happé et al. (2006) use evidence from a population-based sample of twins toargue that the behavioral signs of autism do not stem from a unitary deficit. Instead, thetriad of impairments (social, communication, and repetitive behaviors) in autism repre-sents a dissociable set of primarily genetically driven factors. If so, the pursuit of specificcognitive phenotypes that may relate to the discrete impairments in autism could increasepower to detect genetic effects (Payton, 2006). There are a number of candidates for cog-nitive phenotypes in autism, including language (Kjelgaard & Tager-Flusberg, 2001),theory of mind (Baron-Cohen, 1995), executive function (EF), and central coherence(Happé & Frith, 2006). This paper investigates possible linkages between EF and autismsymptoms.

EF is a cognitive domain related to self-regulation and the orchestration of othercognitive functions in order to achieve a goal. Findings of executive dysfunction arerobust in school-age children with ASDs (for reviews see Hill, 2004; Kenworthy, Yerys,Anthony, & Wallace, 2008; Pennington & Ozonoff, 1996; Sergeant et al., 2002), whoshow specific impairment with tasks that require set-shifting and other aspects of mentalflexibility (Gioia, Isquith, Kenworthy, & Barton, 2002; Hughes, Russell, & Robbins, 1994;Rumsey & Hamburger, 1988). In addition, numerous studies have highlighted the inabilityof children with ASD to plan and organize response patterns on tasks such as the Tower ofLondon and the Rey-Osterrieth Complex Figure (Hughes et al., 1994; Kenworthy et al.,2005; Ozonoff, Pennington, & Rogers, 1991).

The extensive literature on EF and theory of mind (TOM), which can be considereda proxy for social abilities, provides evidence for a link between social symptoms and EFin ASD. For example, Pellicano (2007) found correlations between TOM and EF indepen-dent of age and ability level in young children with ASD, supporting the identification ofEF as an important factor in the advancement of TOM. Joseph and Tager-Flusberg (2004)demonstrated a similar relationship among school-age children with ASD. They report apositive relationship between performance on a task requiring working memory and inhib-itory control and TOM, which was independent of language and nonverbal ability. In anintervention study, Fisher and Happé (2005) found that EF training contributed toimprovements in TOM and postulated an indirect “trickle-down” effect of EF training onTOM performance. Taking a much more concrete approach to social abilities, we havepreviously linked parent-reported EF deficits to weak adaptive social skills (Gilotty,Kenworthy, Sirian, Black, & Wagner, 2002).

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EXECUTIVE CONTROL FUNCTIONS AND AUTISM SYMPTOMS 3

Other investigations have explored the link between EF and autism symptomsdirectly. Several previous investigations have targeted the repetitive, inflexible behaviors/interests component of the ASD symptom triad. Lopez, Lincoln, Ozonoff, and Lai (2005)investigated the association between repetitive behaviors and multiple measures of EF in asample of adults with ASD. They documented a significant relationship between cognitiveflexibility and repetitive behaviors as measured by the Autism Diagnostic Interview (ADI;Le Couteur et al., 1989) and the Autism Diagnostic Observation Schedule (ADOS; Lord,Rutter, DiLavore, & Risi, 1999). South, Ozonoff, and McMahon (2007) also found anassociation between cognitive flexibility and repetitive behaviors in a group of childrenbetween 10 and 19 years of age.

Taking a more expansive view of possible EF-autism symptom correlates, Bishopand Norbury (2005a, b) failed to find a relationship between inhibition and any of thethree components of the autism triad but did find a link between fluency performance andcommunication symptoms in autism. Power to reject the null hypothesis may have beenlimited in the inhibition study by small sample size (14 children with ASD). Joseph andTager-Flusberg (2004) investigated the relationship between ADOS symptom scoresand three aspects of EF: working memory, combined working memory and inhibition, andtower task performance. Significant EF-autism symptom correlations were noted. Therelationship between social abilities and EF measures became nonsignificant when verbalability was partialled out of the correlation, however. Many children had language impair-ments (mean naming standard score = 78), which, combined with the limited opportunityto document repetitive behaviors during the ADOS, may have limited power to detectrelationships in this investigation. In summary, conflicting results presented in previousinvestigations of the relationship between EF and autism symptoms may reflect limits insample size, and the scope of symptoms and EFs assessed, as well as the confoundinginfluence of language impairments.

The present study investigates the relationships between EF and autism symptomsby using data from an archival clinical sample of children with ASD. We explore the rela-tionship between a broad range of EF-related tasks tapping attention, fluency, flexibility,inhibition, planning and working memory, and comprehensive measures of ASD symp-toms in a relatively large sample of high-functioning children with ASD. As argued in Hilland Bird (2006), use of a high-functioning sample reduces the chances of misattributinggeneral cognitive and language impairments to executive dysfunction. Because EF is abroadly defined construct that may be unitary or a set of discrete, fractionable functions(Burgess, Alderman, Evans, Emslie, & Wilson, 1998; Gioia, Isquith, Guy, & Kenworthy,2000), this study samples performance on a wide range of purported tasks of EF, sometapping multiple EF-related abilities (e.g., the Tower of London; Unterrainer & Owen,2006) and others that tap more specific abilities, such as inhibition. Because manystandard laboratory measures of EF lack ecological validity, making EF hard to captureoutside of realistic, “true to life” settings (Bernstein & Waber, 1990; Hill & Bird, 2006), ameasure of everyday EF as reported by parents and teachers was also included. In order tobe inclusive regarding autism symptoms, scores from the ADI and ADOS were combinedto create composite measures of autism symptomatology based on both parent report andclinician observation (Lopez et al., 2005). Although other investigators have used the ADIor ADOS as individual measures of symptoms, we are persuaded by the arguments ofLopez et al. and others that the ADI and ADOS measure different aspects of ASDsymptomatology and that combining them gives a more comprehensive and valid overallmeasure of autism symptoms (see also Risi et al., 2006).

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4 L. KENWORTHY ET AL.

Correlations and regression analyses were used to test the hypothesis that EF per-formance explains a unique portion of variance in autism symptoms independent of ver-bal and nonverbal abilities. Based on previous findings, we hypothesized that acomposite autism symptom measure would be significantly related to parent and teacherreports of executive dysfunction in everyday settings. We also hypothesized that becausethe tower task taps multiple EF domains and has been consistently impaired in ASDgroups in the past, it too would be related to autism symptoms. We further hypothesizedthat category (semantic) fluency would be negatively correlated specifically with com-munication symptoms based on earlier findings (Bishop and Norbury, 2005b) and a pre-sumed relationship between verbal fluency and verbal communication in autism.Although our measure of flexibility, the TEA-Ch Creature Counting test, has not to ourknowledge been previously explored in ASD, we hypothesized that it, like other previ-ously investigated measures of flexibility, would be significantly related to repetitivebehavior/circumscribed interest symptoms. Regarding measures of sustained auditoryand divided auditory attention and autism symptoms, we had no formal hypothesis,because this is a relatively understudied domain of EF in ASD. These tasks offer a pri-mary benefit of increased ecological validity, and, based on anecdotal clinical experi-ence, we considered it important to explore the relationship between social symptomsand the ability to attend to multiple auditory inputs simultaneously.

METHOD

Participants

Participants included a clinically referred sample of 89 children (n = 74 [83%] male)with an autism spectrum diagnosis (autism n = 34; Asperger Syndrome n = 32; pervasivedevelopmental disorder, not otherwise specified n = 23) evaluated by a multidisciplinaryteam at the Center for Autism Spectrum Disorders (CASD) at Children’s National Medi-cal Center in the Washington, D.C., metropolitan area. Each child received a comprehen-sive multidisciplinary evaluation that included a detailed medical and developmentalhistory, an extensive neuropsychological battery, as well as administration of the AutismDiagnostic Interview (ADI; Le Couteur et al., 1989) or the Autism Diagnostic Interview-Revised (ADI-R; Lord, Rutter, & Le Couteur, 1994) and the Autism Diagnostic ObservationSchedule (ADOS; Lord et al., 1999) by a trained, research reliable clinician. All diagnoseswere based on expert clinical impression using Diagnostic and Statistical Manual of MentalDisorders – Fourth Edition (DSM-IV; American Psychiatric Association, 1994) criteria andresults from the ADI/ADI-R, ADOS and developmental history. In addition to meetingDSM diagnostic criteria by clinical impression, all participants also met criteria for an ASDon the ADOS and/or ADI/ADI-R according to criteria established by the National Institutesof Health Collaborative Programs for Excellence in Autism (CPEA; see Lainhart et al.,2006). Because the ADI and ADOS do not have an algorithm for Asperger Syndrome,Lainhart and colleagues developed criteria that include an individual in the broad autismspectrum if they: meet the ADI cutoff for autism in the social domain and at least oneother domain or meet the ADOS cutoff for the combined social and communication score.In order to include the full autism spectrum, these criteria are relatively inclusive and wereused in this study in addition to the criteria that all subjects meet DSM criteria for ASD inthe judgment of a team of clinicians experienced with the assessment and diagnosis ofindividuals with ASD. All participants also had an IQ scaled score of 5 (i.e., standard

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EXECUTIVE CONTROL FUNCTIONS AND AUTISM SYMPTOMS 5

score of 75) or higher on either the Wechsler Vocabulary or Block Design subtests. Demo-graphic and sample characteristics are presented in Table 1.

Measures

Socioeconomic status. Socioeconomic status (SES) was based on the four-factor Hollingshead Index of Social Position (Hollingshead, 1975), which utilizes maritalstatus, gender, education, and occupation to compute a household SES score. Lower SESscores reflect greater SES status.

Diagnostic measures. The Autism Diagnostic Interview (ADI or ADI-R; LeCouteur et al., 1989; Lord et al.1994) is a detailed parent or caregiver interview ofdevelopmental history and autism symptoms. Scores are aggregated into symptom clustersthat correspond to DSM-IV criteria for a diagnosis of autism. The Autism DiagnosticObservation Schedule (ADOS; Lord et al. 1999) is a structured play and conversationalinterview that includes a series of social presses and other opportunities to elicit symptomsof an ASD.

ADI and ADOS scores were aggregated into a composite score for each symptomdomain (communication, reciprocal social interaction, and restricted and repetitive behav-iors). This was done by first converting domain raw scores of the ADI and ADOS intostandardized z-scores and then taking the mean of the ADI and ADOS z-scores to createcomposite scores. This approach was taken to make the contribution of ADOS and ADIscores comparable. Using raw scores in this calculation would have overrepresented theADI in the composite because ADI algorithm totals include more items, and thus morepossible points, than the ADOS algorithm totals. Unstandardized raw scores are presentedin Table 1 for the ADOS and ADI/ADI-R.

General cognitive ability. General cognitive ability was assessed using one ofthree Wechsler Intelligence scales, the Wechsler Intelligence Scale for Children – 3rdEdition (Wechsler, 1991), Wechsler Intelligence Scale for Children – 4th Edition(Wechsler, 2003), or the Wechsler Abbreviated Scale of Intelligence (Wechsler, 1999).In order to create comparable cross-test measures, verbal ability was estimated using the

Table 1 Participant Characteristics.

N M SD Range

Chronological age 89 9.6 2.82 6–17Hollingshead Socioeconomic status 69 19.1 8.11 11–44Vocabularya 88 9.6 4.58 1–19Block Designb 89 10.0 3.97 1–19ADI Reciprocal Social 89 19.8 5.11 6–29ADI Communication Verbal 89 15.8 4.13 3–23ADI Repetitive Behavior 89 7.1 2.7 0–12ADOS Communication 81 4.1 1.92 0–8ADOS Social 81 8.1 3.22 2–14ADOS Stereotyped 81 1.9 1.65 0–8

aBased on Wechsler Vocabulary scores reported in scaled format (M = 10, SD = 3).bBased on Wechsler Block Design scores reported in scaled format (M=10, SD = 3).

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6 L. KENWORTHY ET AL.

Vocabulary subtest and nonverbal ability was estimated using the Block Design subtest.Vocabulary and Block Design performance is presented as norm-referenced scaledscores (M = 10; SD = 3).

Executive function measures. All executive functioning measures are pre-sented as age-corrected, norm-referenced standard scores calculated using the referencetables provided for each instrument.

Behavior Rating Inventory of Executive Functions (BRIEF; Gioia et al., 2000). TheBRIEF is an informant report inventory of executive functioning in everyday situations. Ityields eight scales that are collapsed into two broad indices: the Behavioral RegulationIndex (BRI) and the Metacognition Index (MCI). BRI and MCI T-scores (M = 50; SD = 10)from both parent and teacher report were standardized into z-scores and then aggregated tocreate composite scores for the BRI and MCI. Higher scores indicate greater impairment;T-scores ≥ 65 indicate clinically significant symptoms.

Test of Everyday Attention for Children (TEA-Ch; Manly, Robertson, Anderson, &Nimmo-Smith, 1999). The TEA-Ch is a standardized assessment battery of variousaspects of attention and EFs in children including sustained attention, divided attention,working memory/flexibility, and response inhibition. TEA-Ch subtests are reported asscaled scores (M = 10; SD = 3). Four TEA-Ch subtests were used:

1. Score! is a measure of auditory sustained attention. It requires respondents to count thenumber of beeps heard on a tape.

2. Score-DT assesses divided attention and working memory. It combines the sustained-attention demand from Score! with an additional auditory component. Respondentscount the sounds while listening for an animal name spoken during a concurrent audionews broadcast.

3. Creature Counting is a measure of flexibility (or switching) and working memory thatrequires respondents to alternate between counting forward and counting backwardwhile following a path of targets.

4. Walk-Don’t Walk is a measure of sustained attention and prepotent response inhibition.Respondents must attend to two auditory stimuli (i.e., beeps and beeps plus crashingnoise); the former prompts respondents to mark the corresponding tile of a path, whilethe latter cues them to stop marking on the corresponding tile of the path. To receivecredit, the child must correctly mark the number of beeps heard. The temporal intervalbetween the auditory stimuli decreases as the task proceeds.

Tower of London-Drexel (TOL-DX) (Culbertson & Zillmer, 2000). TOL-DX measuresmultiple EFs such as planning, inhibition, and working memory. It requires the subject towork step-by-step to copy a pattern of beads on pegs using the least number of movespossible. The total-moves score was analyzed as an omnibus measure of EF. Resultsare reported as standard scores (M = 100; SD = 15).

Semantic fluency. Semantic Fluency taps language organization by asking the sub-ject to orally name as many words as possible that belong to a certain category (e.g., foodsor animals). Two versions of this task were given: NEPSY®: A Developmental Neuropsy-chological Evaluation (NEPSY; Korkman, Kirk, & Kemp, 1998) or the Delis-Kaplan

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EXECUTIVE CONTROL FUNCTIONS AND AUTISM SYMPTOMS 7

Executive Functioning System (D-KEFS; Delis, Kaplan, & Kramer, 2001), depending onthe age of the child. Results are reported as scaled scores (M = 10; SD = 3).

Data Analytic Plan

All data were first examined for outliers and to determine that statistical assump-tions of parametric tests were not violated. Data were analyzed initially using bivariatecorrelations between possible nuisance variables (age, Vocabulary, and Block Designscores) and both EF measures and composite autism symptom domains. Statisticallyrelated nuisance variables were included in subsequent analyses. Because this is a clinicaldataset and not all data points were available for all subjects, using statistical significancelevels to identify meaningfully related variables was deemed inappropriate. Instead, amedium effect size or larger, corresponding to an r ≥ .24 by Cohen’s standards (Cohen,1977), was used as entry criterion into subsequent analyses. Scatter plots between relatedpredictor and outcome variables were composed to examine the distribution of scores forproblems that might affect primary results, such as a bimodal distribution and/or outliers.

To understand the unique contribution of each EF measure, multiple regressionmodels were calculated with each autism symptom composite score as the outcome vari-able and EF scores as predictors. Laboratory and informant report measures were analyzedin separate regression models because of the qualitative differences in source and type ofdata. Stepwise regressions, entering predictor variables (EF variables correlated at ≥ .24with autism symptom scores) at step 1, and nuisance variables at step 2 were conducted.This strategy was selected first to examine the relationship between EF and autism symp-toms and then to examine how that relationship changed with the addition of covariates.When there was just one predictor, both the covariates and the predictor were entered inthe same step since entering a single predictor in step 1 would be redundant with bivariatecorrelations.

RESULTS

Preliminary Results and Descriptive Statistics

Inspection of predictor and outcome variables did not reveal any problems withskewness, kurtosis, outliers, or the presence of a bimodal distribution. The frequencydistribution of scores revealed no floor or ceiling effects with one exception: 8 subjects,or 20% of the sample, performed at the floor on the tower task. Inspection of scatterplots of tower scores and autism symptom scores indicated that those subjects with lowscores had autism symptom scores that were evenly distributed above and below themean for this sample, however. In addition, t-tests comparing the 8 subjects at the floorof tower measure to the remaining sample revealed no differences between those at thefloor and the rest of the sample on any of the three symptom domains. As shown inTable 2, despite having scored in the average range on the Wechsler Vocabulary andBlock Design subtests, scores on laboratory measures of EF tended to be in the lowaverage range. The lowest scores were on measures of sustained attention (TEA-ChScore!), response inhibition (TEA-Ch Walk Don’t Walk), and when multiple EFs wereassessed simultaneously (TOL-DX). Similarly, mean scores on both the parent andteacher report of problems in behavior regulation and metacognition were in theclinically significant range (t ≥ 65).

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8 L. KENWORTHY ET AL.

As shown in Table 3, Vocabulary was significantly negatively related to autism commu-nication symptoms and age was positively related to autism reciprocal social interaction symp-toms and negatively related to restricted and repetitive behaviors. With regard to laboratorymeasures of EF, Score DT and Category Fluency were negatively correlated with communica-tion and reciprocal social interaction symptoms and Walk Don’t Walk was negatively corre-lated with restricted and repetitive behaviors. The BRIEF BRI was related to all three autismsymptom domains and BRIEF MCI was related to reciprocal social interaction symptoms.

Primary Analyses

Communication symptoms. Two multiple regressions were conducted withcommunication symptoms as the outcome variable, one with laboratory measures of EF andone with informant report measures of EF. In the first regression, Score DT and Category Flu-ency were entered in step 1 and Vocabulary as a covariate entered in step 2. Results shown inTable 4 indicated that better performance on Category Fluency was associated with fewer

Table 2 Participant Executive Functioning Profile.

Executive Functioning Measure N M SD Range

TEA-Ch (scaled scores; M = 10; SD = 3)Score! 60 6.6 3.43 1–15Score DT 41 8.2 3.45 1–15Creature Counting 53 8.9 3.76 3–14Walk Don’t Walk 49 7.0 3.98 1–16

Category Fluency (scaled score) 32 9.5 3.89 1–17Tower of London DX (std. score; M = 100; SD = 15) 41 83.4 20.07 59–122BRIEF (T-scores; M = 50; SD = 10)

Parent Behavior Regulation 80 66.6 12.14 41–87Parent Metacognition 76 69.0 10.39 41–89Teacher Beh. Regulation 64 65.8 14.38 42–99Teacher Metacognition 63 68.4 12.09 42–109

Table 3 Correlations between Covariates, Measures Of Executive Functioning and Autism Symptoms.

NCommunication

SymptomsReciprocal Social

Interaction SymptomsRestricted &

Repetitive Behaviors

CovariatesAge 89 .04 .25* −.24*Vocabulary 88 −.33* −.19 −.01Block Design 89 −.18 −.03 .05

Executive Functioning MeasuresScore DT 41 −.43* −.48* −.10Category Fluency 32 −.44* −.58* .06Walk Don’t Walk 49 −.22 .03 −.34*Score! 60 −.22 −.17 −.12Creature Counting 33 −.12 −.23 −.13Tower of London- DX 41 −.11 −.01 −.20BRIEF Behavior Regulation Index 84 .32* .29* .30*BRIEF Metacognition Index 82 .18 .24* .04

*r ≥ .24 and all statistically significant at p < .05.

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EXECUTIVE CONTROL FUNCTIONS AND AUTISM SYMPTOMS 9

autism communication symptoms after accounting for variance from Score DT and Vocabu-lary. Score DT and Vocabulary did not contribute meaningfully to the model. The secondregression investigated the informant report measure. Both the BRIEF BRI and Vocabularywere entered in the same step. Results indicated that informant report of problems with behav-ior regulation was associated with more communication symptoms (Table 4). Higher Vocabu-lary scores were associated with fewer communication symptoms in this model.

Reciprocal social interaction symptoms. Following the same model asdescribed above, two multiple regressions were conducted with social symptoms as theoutcome variable. In the first regression, Score DT and Category Fluency were entered instep 1 and age at testing was entered in step 2. As shown in Table 5, significant resultsindicated that better performance on both Score DT and Category Fluency was associatedwith fewer social symptoms. Entering age in the model did not influence the results. In thesecond regression both the BRIEF BRI and BRIEF MCI were included in the model andage was entered at step 2. This model was also significant indicating that BRIEF MCI andBRIEF BRI are related to autism social symptoms (Table 5). A review of the T-scoresrelated to each variable indicated that, after accounting for shared variance, neither wassignificant although BRI approached significance. The ∆F score at step 2 was not signifi-cant indicating that age at testing did not contribute to the model.

Restricted and repetitive behaviors. Table 6 displays the results of two multi-ple regressions conducted with restricted and repetitive behaviors as the outcome variable. Inthe first regression, both Walk Don’t Walk and age at testing were entered in the same step.In contrast to the correlations described above, after accounting for variance explained byage, Walk Don’t Walk no longer accounts for significant variance in restricted and repetitivebehaviors. In the second regression, both the BRIEF BRI and age at testing were entered inthe same step. Results indicated that both BRIEF BRI and age were significantly related torestricted and repetitive behaviors such that children with greater behavior regulation prob-lems exhibit more restricted and repetitive behaviors. In both models, results indicate olderchildren exhibit fewer restricted and repetitive behaviors.

Table 4 Laboratory and Parent Report Measures of Executive Functioning Predict Communication Symptoms.

Variables in equation F/∆Fa R2/∆R2b β t pc

Laboratory MeasuresStep 1. Model Summary 6.57 .48 .01

Score DT −.18 −0.88 .39Category Fluency -.63 -3.13 <.01

Step 2. Model Summary .81 .03 .39Score DT −.11 −0.50 .63Category Fluency -.56 -2.60 .02Vocabulary −.21 −0.90 .39

Parent Report MeasureModel Summary 10.43 .21 <.01

BRIEF BRI .30 2.99 <.01Vocabulary -.33 -3.26 <.01

aF indicates F-value at step 1; ∆F indicates change in F from step 1 to step 2.bR indicates R2at step 1; ∆R2indicates change in R2 from step 1 to step 2. cBold indicates p < .05.

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10 L. KENWORTHY ET AL.

DISCUSSION

In this investigation we documented several associations between autism symptomsand executive control functions. Multiple regression analyses revealed that measures ofsemantic fluency, divided auditory attention, and behavioral regulation were significantlycorrelated with autism symptoms, even after accounting for the variance from correlated“nuisance variables,” such as vocabulary and age. Our first finding was that, consistentwith our prediction, better performance on semantic fluency was associated with fewerautism communication symptoms, even after accounting for vocabulary, suggesting that,in this sample, as reported previously, verbal fluency is related to the presentation of com-munication symptoms.

Many previous investigators document verbal fluency deficits in individuals withASD and their relatives (Geurts, Verte, Oosterlaan, Roeyers, & Sergeant, 2004; Hughes,Plumet, & Leboyer, 1999; Kleinhans, Akshoomoff, & Delis, 2005; Verte, Geurts, Roeyers,

Table 5 Laboratory and Parent Report Measures of EF Predict Social Symptoms.

Variables in equation F/∆Fa ∆ R2 b β t pc

Laboratory MeasuresStep 1. Model Summary 17.06 .71 <.01

Score DT -.44 -2.96 .01Category Fluency -.60 -4.02 <.01

Step 2. Model Summary .08 .01 .78Score DT -.46 -2.77 .02Category Fluency -.60 -3.82 <.01Age at testing −.05 −0.29 .78

Parent Report MeasuresStep 1. Model Summary 4.07 .09 .02

BRIEF MCI .12 .97 .34BRIEF BRI .22 1.75 .08

Step 2. Model Summary 1.90 .02 .17BRIEF MCI .07 .55 .58BRIEF BRI .21 1.70 .09Age at testing .16 1.38 .17

aF indicates F-value at step 1; ∆F indicates change in F from step 1 to step 2.bR indicates R2 at step 1; ∆R2 indicates change in R2 from step 1 to step 2. cBold indicates p < .05.

Table 6 Age and Parent Report Measures of EF Predict Restricted/Repetitive Symptoms.

Variables in equation F R2 β t pa

Laboratory MeasuresModel Summary 6.17 .21 <.01

Walk Don’t Walk −.23 −1.70 .09Age at testing -.33 -2.41 .02

Parent Report MeasureModel Summary 10.25 .20 <.01

BRIEF BRI .38 3.70 <.01Age at testing -.34 -3.37 <.01

aBold indicates p < .05.

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EXECUTIVE CONTROL FUNCTIONS AND AUTISM SYMPTOMS 11

Oosterlaan, & Sergeant, 2005). Bishop and Norbury (2005a) reported a linkage betweenfluency and autism-related communication symptoms. They argued that pragmatic lan-guage deficits in autism may reflect deficits in generativity as well as social difficulties.Indeed, both on fluency tasks and in conversation, individuals with ASD struggle to gen-erate words that are relevant to a context or topic. Such difficulty could be reflected inADI and ADOS items that assess an individual’s capacity to engage in reciprocal conversa-tions, as well as a tendency to rely on scripted or stereotypic speech, whether it be usedappropriately, such as when using a rote script to ask for help, or inappropriately, such aswhen reciting lines from a favorite book or movie out of context. An alternative, or compli-mentary, hypothesis is that the structural language deficits associated with high-functioningautism and, in some cases, pervasive developmental disorder, not otherwise specifiedadversely affect verbal fluency performance.

We also found that the Behavior Regulation Index (BRI) of the Behavior RatingInventory of Executive Function (BRIEF) predicted ASD-restricted and repetitive behav-ior symptoms. The BRI is comprised of three subdomains that tap behaviors related toinhibition, flexibility, and emotional control. Thus, like South et al. (2007), Lopez et al.(2005), and Turner (1997), we link flexibility to autism-related restricted and repetitivebehavior symptoms. Both our findings and those of Lopez et al. conflict with Turner’shypothesis (1997) that generativity also plays a role in restricted and repetitive symptomsin autism, however, as we found no link between fluency and restricted and repetitivebehavior symptoms.

Our third, and perhaps most significant, finding associates both semantic fluencyand auditory divided attention abilities with ASD social symptoms, a linkage that has notbeen previously described. Indeed, Ozonoff et al. (2004) as well as Joseph and Tager-Flusberg (2004) failed to find an association between EF and ASD social symptoms. Thisdiscrepancy may be explained by the different measures of EF employed in each of thesestudies. Our data are consistent with these investigations in finding no significant associa-tion of performance on a tower or inhibition task with social symptoms. It is also possiblethat differences in age (Ozonoff’s sample included adults) and sample size (Joseph’s samplewas somewhat smaller than ours) contributed to discrepant findings in these studies. One otherstudy (Bishop & Norbury, 2005a) found a trend toward significant correlations betweensocial autism symptoms and ideational fluency in a mixed group that included a subset ofchildren with ASD.

To our knowledge, ours is the first investigation of the relationship between auditorydivided attention (as measured by the TEA-Ch Score! DT task) and autism symptoms.This task requires a child to simultaneously listen to a news broadcast for an animal nameand to count beeps, thus requiring dual attention and working memory in order to hold theanimal name, number of beeps counted, and rules of the task in mind. It is not unreason-able that this ability to divide attention and working memory between two stimuli hasimplications for social reciprocity and social interaction generally. Interacting withanother person, particularly another child, requires adept “on-line” processing and integra-tion of multiple stimuli, such as facial expression, body language, and intonation (Klin,Jones, Schultz, & Volkmar, 2003).

It is noteworthy that the EF measures on which participants scored most poorly didnot show the strongest relationship to symptoms of autism. We found low EF scores onmeasures of response inhibition, sustained attention, and omnibus EF, but measures ofdivided attention and semantic fluency, both with mean scores in the average range, weresignificantly associated with autism social and communication symptoms. In this sample,

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12 L. KENWORTHY ET AL.

EF performance covaries with autism symptoms most on tasks that are intact for the groupas a whole, indicating the importance of considering the heterogeneity within ASD sam-ples, as well as between ASD and other disorders. Mundy, Henderson, Inge, and Coman(2007) argue for a modifier model in autism that addresses heterogeneity in ASD by con-sidering variables that may not be specific to autism but can alter expression of the disor-der in significant ways, such as increasing social impairment. Our data is more consistentwith identification of divided attention and fluency as possible modifier variables than asintermediate phenotypes in autism.

It is also important to note that, as has been reported elsewhere, many of the EFlaboratory measures used in this investigation were not related to autism symptoms, evenwhen performance on the measure was impaired. For example, performance on the towerwas impaired in this study when compared to normative data (see Table 2), but not relatedto autism symptoms. Poor performance on the tower in ASD may reflect factors that arenot related to symptom expression. On the other hand, failure to find relationships couldreflect limitations in standard laboratory EF tasks such as the tower. Tower tasks arereported to have weak test-retest reliability (Bishop, Aamodt-Leeper, Creswell, McGurk,& Skuse, 2001; Lemay, Bedard, Rouleau, & Tremblay, 2004); although Culbertson andZillmer (2000) report moderate to high test-retest reliability on the Tower of London-DXmove score, which was used in this investigation.

In addition, Hill and Bird (2006) criticize standard measures of EF that fail to cap-ture deficits in high-functioning individuals with ASD for their lack of ecological validity.The BRIEF and the Test of Everyday Attention for Children Score! DT subtest, both usedin this study, are designed for increased ecological validity. By virtue of their reliance oneveryday activities, however, they tap multiple abilities (e.g., language, processing/motoroutput speed) and may fail to isolate discrete EFs. This confound may be unavoidable inthe EF domain. Bernstein and Waber (2007) argue that what we call EF relies not on func-tional modules but on functional neural networks that develop in the context of experi-ence. This observation appears particularly relevant to autism, which has defied modularexplanation at the genetic, neuroanatomical, neurofunctional, and behavioral levels and isincreasingly understood as a disorder of distributed networks in the brain (Muller, 2007).At the same time, it is important to acknowledge that reification of “executive functions”may be premature, or even misguided, as we attempt to capture an illusive construct that isnot easily parsed into discrete components.

Several limitations must be acknowledged in this study. Reliance on archival clin-ical data prevented comprehensive assessment of all subjects using the same metrics,thus reducing power to detect potentially meaningful relationships. Nor were the strictcontrols provided in an experimental setting, such as random task order, imposed.Because there is no control group, we cannot know whether these EF-autism-symptomlinks are specific to ASD or predictive in a wider population. Indeed, future researchmay address this question by attempting to link EF with subclinical traits associatedwith autism in typical controls, and children with other developmental disorders, such asADHD and language impairment. In addition, the EF abilities identified here as predic-tive of autism symptoms shared verbal mediation. Although we limited our sample tohigh-functioning children with ASD and, when appropriate, covaried the influence of anexpressive vocabulary formulation task (a proxy for language ability), we may not havecontrolled adequately for these shared verbal demands. Finally, the restriction of oursample to high-functioning children limits the relevance of these findings to lower func-tioning children with ASD.

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EXECUTIVE CONTROL FUNCTIONS AND AUTISM SYMPTOMS 13

This study contributes to the scientific literature by expanding the question of EF’srole in autism beyond repetitive behaviors to social and communication symptoms.Although EF was originally conceptualized to account for rigidity, perseveration, and othernonsocial deficits in ASD, this study indicates that some aspects of executive dysfunctionmay exacerbate symptoms in all three components of the triad of impairment. In thesearch for intervention targets, ameliorating executive dysfunction may serve to improvenot only nonsocial deficits (as originally conceptualized) but also social and communica-tion difficulties.

Original manuscript received February 8, 2008Revised manuscript accepted November 20, 2008

First published online

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