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. Author manuscript; available in PMC: 2021 Dec 1.
Published in final edited form as: J Autism Dev Disord. 2020 Dec;50(12):4541–4547. doi: 10.1007/s10803-020-04505-4

Brief Report: Performance-Based Executive Functioning Abilities are Associated with Caregiver Report of Adaptive Functioning in Autism Spectrum Disorder

Manisha D Udhnani 1,*, Lauren Kenworthy 2, Gregory L Wallace 3, Benjamin E Yerys 1,*
PMCID: PMC7584745  NIHMSID: NIHMS1587598  PMID: 32333300

Abstract

Executive functioning is thought to contribute to adaptive behavior skills development in individuals with autism spectrum disorder (ASD). However, supporting data are largely based on caregiver reports of executive functioning. The current study evaluated whether performance-based measures of executive functioning (working memory and inhibition) explained unique variance in parent-reported adaptive functioning among youth with ASD without an intellectual disability. Both spatial and verbal working memory were associated with adaptive functioning, particularly communication and daily living skills. Our findings demonstrate a robust relationship between working memory and adaptive functioning that translates across different measurement modalities. This preliminary study highlights that targeting executive functioning may be a critical component of an adaptive function training program.

Keywords: autism, executive functioning, adaptive functioning, working memory

Introduction

Adaptive functioning represents a major area of challenge among individuals with autism spectrum disorder (ASD). Defined by the ability to meet the demands of one’s environment in order to function independently, it comprises the ability to dress oneself, maintain interpersonal relationships and leisure activities, and communicate one’s needs (Sparrow, Cicchetti, & Saulnier, 2016). As such, adaptive functioning serves as an important target for intervention. In ASD, adaptive functioning is impaired both across age (Pugliese et al., 2015) and levels of intellectual functioning (Alvares et al., 2019). Adaptive functioning skills tend to fall farther behind chronological age expectations throughout childhood in autism (Pugliese et al., 2015; Bertollo & Yerys, 2019). Further, the relationship between IQ and adaptive functioning in ASD weakens as IQ increases (Alvares et al., 2019; Volkmar et al., 1987).

There is emerging evidence that executive functioning (EF), a set of cognitive skills that regulate one’s thoughts, actions, and emotions in order to achieve goals, explains unique variance in adaptive behavior in ASD (Gilotty, Kenworthy, Sirian, Black, & Wagner, 2002; Pugliese et al., 2015; Bertollo & Yerys, 2019; Liss et al., 2001). EF skills may include controlling impulses (inhibition), holding information or goals in mind and updating them as needed (working memory), switching between mental sets or perspectives (shifting), creating an action plan (planning and organizing), and regulating one’s emotions (emotional control; Gioia, Isquith, Guy, & Kenworthy, 2000). To date, this relationship has largely been evaluated with caregiver report of EF skills, specifically the Behavior Rating Inventory of Executive Function (BRIEF; Gioia et al., 2000). The BRIEF’s Monitor (Bertollo & Yerys, 2019), Initiate (Pugliese et al., 2015; Gilotty et al., 2002), Shift (Gardiner & Iarocci, 2018; Pugliese et al., 2015), and Working Memory scales (Pugliese et al., 2015; Gilotty et al., 2002), explain significant variance in adaptive functioning. As these findings characterize relations between parent report of executive and adaptive abilities, it is unclear whether these relations hold when the two constructs are evaluated using different measurement modalities.

Few studies have utilized task125:187-based measures of EF to examine the relationship between executive abilities and adaptive functioning. As research has demonstrated a weak relationship between performance-based measures and informant reports of EF abilities (for a review, see Toplak, West, & Stanovich, 2013), it is important to understand whether such differences could be attributed to the impurity of EF measures in capturing extraneous cognitive abilities beyond the targeted EF variable/domain (i.e., task impurity problem; Miyake et al., 2000). While both informant report and performance-based measures of EF are susceptible to issues of task impurity (i.e., the likelihood that a measure can capture cognitive abilities in addition to what is intended), performance-based measures can isolate individual EF variables more readily than informant report. One study used EF measures of flexibility and planning and both were associated with adaptive behavior skills among autistic children (Liss et al., 2001). Further, there is additional evidence that a composite measure of performance-based EF tasks (flexibility, planning, and inhibition) can predict adaptive behavior 12 years later, from childhood into adolescence (Kenny, Cribb, & Pellicano, 2018). However, neither study included measures of working memory, an aspect of EF significantly impacted in ASD (Demetriou et al., 2017; Lai et al., 2017) and implicated in the literature in its association with adaptive functioning among school-aged typically developing children (Vuontela et al., 2013). Furthermore, neither study accounted for variance attributed to ADHD symptoms, which may be implicated as working memory and inhibition impairments are well-established endophenotypes of ADHD (Mueller, Hong, Shepard, & Moore, 2017). To fill this gap, the current study investigates whether working memory and inhibition task-based performance are associated with parent report of adaptive functioning in children with ASD without intellectual disability. We hypothesize that greater EF impairments will be associated with lower adaptive behavior skills, particularly Socialization and Daily Living Skills, as difficulties in these adaptive functioning domains are most elevated in ASD without intellectual disability (Liss et al., 2001). We will also explore whether the relationship between EF and adaptive behavior in ASD can be explained by co-occurring ADHD symptoms. Finally, we examine the potential influence of IQ, age, and sex on the EF-adaptive behavior relationship.

Method

Participants

Forty-five children (Mage=9.8 years old) with an ASD diagnosis (without intellectual disability) participated in the current study. See Table 1 for sample characteristics. Twenty-one participants also had elevated ADHD symptoms. Data collection occurred between 2003 to 2008. ASD diagnoses were assigned based on a clinician’s best estimate using DSM-IV-TR criteria (APA, 2000) and informed by scores on the Autism Diagnostic Observation Schedule (Lord et al., 2000) and/or the Autism Diagnostic Interview/Autism Diagnostic Interview-Revised (ADI/ADI-R; LeCouteur et al., 1989; Lord, Rutter, & Le Couteur, 1994). Measurement of ADHD symptoms was based on Home ratings of the ADHD Rating Scale, 4th edition (DuPaul, Power, Anastopoulos, & Reid, 1998). Besides an ASD diagnosis, other inclusion criteria were a Full Scale IQ≥80, as measured by a Wechsler-based instrument (Wechsler Intelligence Scale for Children, 3rd edition [Wechsler, 1991], Wechsler Intelligence Scale for Children, 4th edition [Wechsler, 2003], or Wechsler Abbreviated Scale of Intelligence [Wechsler, 1999]). Exclusion criteria included parent-reported history of genetic or neurological disorders.

Table 1.

Sample characteristics

ASD only Combined ASD+ADHD Total Group
N 19 14 33
  % males 63 79 70
  % ASD+ADHD -- -- 42
M(SD) Range M(SD) Range M(SD) Range
Age 9.9(1.9) 7.3–12.9 9.7(1.4) 7.6–11.8 9.8(1.7) 7.3–12.9
FSIQ 115(19.7) 85–159 111(13.7) 88–134 113(17.3) 85–159
     
VABS Domains
  Communication 93.8(14.5) 67–125 83.7(14.5) 57–116 89.5(17.6) 57–125
  Socialization 82.7(12.8) 60–106 70.5(12.8) 51–105 77.5(17.0) 51–106
  Daily Living Skills 79.1(15.6) 44–105 65.9(15.6) 38–95 73.5(15.5) 38–105
     
EF Measures
  DSB (scaled score) 10.6(3.2) 5–19 8.9(3.2) 5–14 9.9(3.0) 5–19
  SWM Between Errors (raw score) 43.3(16.0) 2–68 49.2(16.0) 12–77 45.8(17.6) 2–77
  WDW Total (scaled score) 6.2(3.0) 1–13 5.6(3.0) 1–16 5.9(3.7) 1–16
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DSB = Digit Span Backwards; SWM = Spatial Working Memory from CANTAB; WDW = Walk Don’t Walk from TEA-Ch

Measures

The ADHD Rating Scale – 4th Edition (Home) - is a caregiver report that assesses the severity of inattention and hyperactivity/impulsivity, based on the diagnostic criteria of ADHD described in the DSM-IV-TR (DuPaul et al., 1998). Symptoms are rated on a scale of 0-3 (Total 0-54), with higher scores indicating greater symptoms. Total Scores were used to measure ADHD symptoms. Children with elevated ADHD symptoms had scores above clinical thresholds for combined or inattentive presentation.

Digit Span Backward from the Wechsler Scales of Intelligence for Children, 4th Edition (Wechsler, 2003) was used as a measure of verbal working memory. Spatial working memory was assessed with the Cambridge Neuropsychological Tests Automated Battery Spatial Working Memory test (Cambridge Cognition, 1996). Both tasks are well-established measures of working memory with robust psychometric properties. Dependent variables included the Digit Span Backward scaled score, and the number of between search errors on the Spatial Working Memory task (a child searching a location where a target ‘token’ was previously found).

Walk Don’t Walk from the Tests of Everyday Attention for Children (Manly, Robertson, Anderson, & Nimmo-Smith, 1999) measured response inhibition. Participants are instructed to follow a tiled path, with every step down that path corresponding to an auditory cue (e.g., beep). The sounds of a beep followed by a crash alert the participant to cease tracking. The number of beeps must correspond with the number of marked tiles in the path. As trials progress, the time between cues decreases (demanding greater effort to inhibit) and the paths become longer (requiring greater sustained attention). Walk Don’t Walk accuracy scores across 20 trials were utilized as the dependent variable.

The Vineland Adaptive Behavior Scales-Interview (VABS; Sparrow, Cicchetti, & Saulnier, 2016) is a structured parent/caregiver interview of adaptive functioning that measures Daily Living Skills, Communication, and Socialization. Standard scores from these three composites are aggregated to generate an Adaptive Behavior Composite (ABC). The current study utilized the three domain standard scores of adaptive functioning.

Procedures & Analyses

The data were collected as part of a larger study examining the neurocognitive profiles of youth with ASD and involved both cognitive assessments and neuroimaging; the current study is a secondary analysis of published data (Yerys et al., 2009). To evaluate the contribution of EF to adaptive functioning, multiple hierarchal regressions were conducted. Specifically, the VABS Communication, Socialization, and Daily Living Skills scores served as the dependent variables, while Digit Span Backward, Spatial Working Memory, and Walk Don’t Walk scores served as the independent EF variables. Significant covariates of no interest (Full Scale IQ, ADHD symptomatology, age, gender) were retained in base regressions, so that when the EF variables were entered we could observe the contribution of EF variables above and beyond the covariates of no interest. We report the ΔF, ΔR2, and Δp-values for each adaptive functioning domain’s regressions.

Results

In this sample of 45 children with ASD (Meanage=9.8, MeanIQ=113), Socialization (Mean=77.5) and Daily Living Skills (Mean=73.5) were, on average, in the moderately low range, while Communication skills (Mean=89.5) were in the adequate range. Full Scale IQ explained variance in Communication, although this finding was marginally significant (ß=0.30, p=0.08). Additionally, ADHD symptoms explained significant variance in both Socialization (ß=−0.52, p<0.01) and Daily Living Skills (ß=−0.48, p<0.01). Thus, as a conservative measure, these variables were retained in further analyses. Because the role of IQ in Communication was marginally significant, we conducted further analyses both with and without IQ in the model. Findings did not differ between these Communication models. Digit Span Backward scores explained significant variance in Communication both with (ß=0.39, p=.04) and without (ß=0.46, p<0.01) IQ entered as a covariate. In contrast, Spatial Working Memory (ß=.33, p=.03) and Digit Span Backward scores (ß=.36, p=.02) both explained unique variance in Daily Living Skills after ADHD symptoms were covaried. With ADHD symptoms entered as a covariate in the model, no EF variables explained significant variance in the Socialization domain. To probe whether our findings were driven by the subset who likely have a co-occurring ADHD diagnosis, we conducted a sensitivity analysis with children in the ASD group who had fewer than six symptoms on both the Inattention and Hyperactivity/Impulsivity scales from the ADHD rating scale. This reduced our sample by slightly less than half; however, the pattern of our primary results did not change (data not shown).

Finally, as a measure of robustness, all covariates were entered in the models along with all EF variables. No EF variables explained significant variance in the Communication (Digit Span Backward: p=.12; Spatial Working Memory: p=.16; Walk Don’t Walk: p=.60), after age, gender, IQ, and ADHD symptomatology were entered into the model. However, Spatial Working Memory did explain significant variance in Daily Living Skills (ß=0.39, ΔR2=0.20, p=.04), while Walk Don’t Walk (ß=0.22, p=.25) and Digit Span Backward (ß=0.33, p=.10) did not. See Table 2.

Table 2.

Results of the multiple hierarchal regressions.

Independent Variables Dependent Variables
df Communication Socialization Daily Living Skills
R2 change F change p R2 change F change p R2 change F change p
Equation 1:
1) IQ 1,31 .08 2.89 .09
2) DSB, SWM, WDW 4,28 .22 2.93 .05
Equation 2:
1) DSB, SWM, WDW 3,29 .29 3.98 .02
Equation 3:
1) ADHD symptomatology 1,31 .25 10.37 <.01 .19 7.42 .01
2) DSB, SWM, WDW 3,28 .09 1.29 .30 .27 4.75 <.01
All variables entered in model
STEPS:
1) Age, Sex, IQ, ADHD symptomatology 4,28 .20 1.71 .18 .27 2.58 .06 .27 2.62 .06
2) DSB, SWM, WDW 7,25 0.15 1.91 .15 .08 1.01 .40 .20 3.24 .04
Total Model R2=.35; F[7,25]=1.89, p=.11 R2=.35; F[7,25]=1.91, p=.11 R2=.48; F[7,25]=3.24, p=.01
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DSB = Digit Span Backwards; SWM = Spatial Working Memory from CANTAB; WDW = Walk Don’t Walk from TEA-Ch

Discussion

Performance on a working memory task is associated with caregiver ratings of adaptive functioning, particularly Communication and Daily Living Skills, in youth with ASD. Verbal working memory explained significant variance in Communication, both with and without IQ entered in the model as a covariate. Findings also underscored the role of both verbal and spatial working memory in explaining variance in Daily Living Skills. Spatial Working Memory was particularly robust, as it explained significant variance in Daily Living Skills, after age, sex, IQ, and co-occurring ADHD symptoms were considered. Finally, EF performance did not explain variance in Socialization skills.

The current study’s findings extend those of Pugliese et al. (2015), who found that parent-reported working memory was associated with Communication and Daily Living Skills, but not Socialization, in a large sample of children with ASD. Our findings contrast with a prior study showing a relationship between parent-reported working memory and Socialization, but not Daily Living Skills (Gilotty et al., 2002). Moreover, another study did not find parent-reported Working Memory to explain significant variance in a Daily Living Skills screener above variance attributed to age, IQ, and all other seven BRIEF scales (Gardiner & Iarocci, 2018). These discrepancies in findings could be attributed to differences in sample characteristics, as Gilotty et al. (2002) included an age range that spanned through adolescence, or it could be due to differences in model covariates, as Gardiner and Iarocci (2018) covaried for all the BRIEF subscales. The latter may explain why Gilotty et al. (2002) found a relationship between working memory and Socialization without controlling for additional variables (e.g., other BRIEF scales, co-occurring ADHD symptoms), whereas the current study and Gardiner & Iarrocci (2018) included these variables in their models. Moreover, these differences may be driven by the use of parent report, as opposed to performance-based measures of EF. The current study would be one of the first to evaluate this relationship using direct assessment.

Use of performance-based EF measures, as opposed to parent ratings of EF, allowed for the specification of the potential influences of verbal versus spatial working memory on adaptive functioning. Spatial working memory significantly contributed variance to daily living skills in ASD, whereas verbal working memory did not. Both spatial working memory (Williams, Goldstein, Carpenter, & Minshew, 2005) and adaptive functioning (Pugliese et al., 2015) are areas of relative weakness in ASD, while verbal working memory is less impacted (Williams et al., 2005). Spatial skills may be more significantly implicated in items of the VABS and, thus, could explain differential relations.

Identifying these EF-adaptive behavior links, or lack thereof, could inform potential treatment targets in interventions tailored for autistic individuals. This is especially crucial as research suggests increased age-related working memory impairments during adolescence in ASD (Rosenthal et al., 2013). As a cross-sectional study, however, we were unable to ascertain the directionality of the relationship between working memory and daily living skills. Future research with a longitudinal study design can evaluate whether working memory impairments predict future adaptive behavior functioning. Other limitations of the current study include a moderate sample size, which could have contributed to some of the null findings. Finally, due to the nature of our study, secondary analyses of a previously collected dataset restricted the EF variables we were able to analyze. As such, future directions for research include the use of a broader EF battery in order to derive latent EF factors (Miyake & Friedman, 2012) and compare performance to informant report of EF. This investigation further establishes the relationship between EF and adaptive behavior. It also motivates the need to better understand how EF skills support development of adaptive behavior skills, so that adaptive behavior interventions can include EF training modules to support the development of a child’s adaptive behaviour repertoire.

Acknowledgements:

Frederick and Elizabeth Singer Foundation; NIH; National Institute of Mental Health Intramural Research Program; Studies for the Advancement of Autism Research and Treatment; Grant number: NIMH U54 MH066417;

Intellectual and Developmental Disabilities Research Center at Children’s National Medical Center; Grant numbers: NIH IDDRC P30HD40677; NIH T32HD046388;

General Clinic Research Center; Grant number: NIH GCRC M01-RR13297.

Footnotes

Compliance with Ethical Standards

Conflict of interest

This study discusses the literature reported on the Behavior Rating Inventory of Executive Function (BRIEF). Lauren Kenworthy is a co-author of the BRIEF and receives royalty payments from its publisher, Psychological Assessment Resources, Inc.

Ethical Approval

All procedures were performed in accordance with the ethical standards of the institutional research committee regarding use of previously collected data.

Informed Consent

Data were aggregated across individuals who completed informed parental consent and participant assent prior to participation.

Publisher's Disclaimer: This Author Accepted Manuscript is a PDF file of an unedited peer-reviewed manuscript that has been accepted for publication but has not been copyedited or corrected. The official version of record that is published in the journal is kept up to date and so may therefore differ from this version.

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