WISC-IV Profiles in Children with Autism Spectrum Disorder. Karen Stack, Dr. Raegan Murphy, Paula Prendeville and Dr.

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1 WISC-IV Profiles in Children with Autism Spectrum Disorder Karen Stack, Dr. Raegan Murphy, Paula Prendeville and Dr. Maria O'Halloran

2 Background to Research Assistant Psychologist working with a specialist Autism Service. Working with multidisciplinary teams (MDT) who provide diagnostic and intervention services to children with ASD and their families Observed patterns of variation in cognitive profiles in some children with ASD. i.e. Gifted profile in Verbal Comprehension but in the Intellectual Disability range in Processing Speed Query regarding atypical cognitive profiles of children with ASD

3 Wechsler Intelligence Scale for Children Fourth Edition UK. (WISC-IV) Fourth Edition (2003) significantly different from previous versions Most widely used assessment tool used test particularly for individuals with ASD (Ozonoff et al,2005;saulnier & Ventola, 2012) Figure taken from (Williams, Weiss, & Rolfhus, 2003)

4 What is a Cognitive Profile Individual s pattern of relative strengths and weaknesses that can be used to develop hypotheses about the child s cognitive functioning (Sattler, 2008). Two approaches used to analyse cognitive profiles: Interindividual Comparing to normative group Intraindividual Comparing to own unique profile Profile Analysis is useful in understanding a child s strengths and weaknesses and in guiding intervention and educational programmes (Hale et al, 2001, Sattler, 2008)

5 Literature ASD WISC IV Cognitive Profiles Significant strengths compared to norm and their own profile in VCI and PRI Significant weakness on the WMI and the PSI. Subtest analysis, Matrix Reasoning was the highest subtest score and Coding the lowest subtest score (Mayes & Calhoun, 2008). Profiles not found in other clinical groups with performances characterised by uneven and scattered scoring (Barbeau, Soulières, Dawson, Zeffiro, & Mottron, 2013; Frith, 2003).

6 Rationale for research Methodological issues in relation to previous research (Flanagan and Kaufman, 2009) Different categories of ASD Diagnostic approach; tools used, checklists, DSM IV modified criteria or ICD 10 The version of the WISC varied across studies. Additionally, it has been suggested that making assumptions based on previous literature is inaccurate since these concepts are based on trends that has been found to exist at group level (Mandy et al, 2015, Towgood et al, 2009)

7 Current Study Aims Investigate whether patterns of specific cognitive profiles emerge in the sample consistent with previous research findings. Series of research questions in relation to the dataset

8 Sample 134 children (112 male, 22 female) Age range 6 years 15 years 8 months (Mean 9:75; SD 2.4) ASD diagnosis based of DSM-IV-TR and ICD-10 Criteria All diagnosed by a MDT according to best practice guidelines (NICE, 2011, PSI 2010) combining clinical observation with standardised Autism assessment tools. IQ > 70

9 Methodology Archival Study Design Normative WISC-IV scores were obtained from the Administration and Scoring Manual (Wechsler, 2004) Critical values from Administration and Scoring Manual where used to establish whether the differences between indices and subtests were statistically significant Kauffman and Flanagan's (2009) cognitive profiling software was used to identify personal and normative strengths/weaknesses in each individual child s profile Group and individual analysis level

10 Results: Verbal v Non Verbal performances Group level analysis For the overall Sample there were no significant differences between the VCI (M = ; SD = 15.70) and the PRI (M = ; SD = 13.13), t (133) =.260, p =.795 (two-tailed) Individual Discrepancy analysis of each individual in the sample s index scores 25% of the individuals in the sample presented with VCI composite scores significantly lower than their PRI composite scores at the 0.05 level of significance. 47% of the individual s in the sample presented with composite scores in the PRI significantly lower than their composite score in the VCI at the 0.05 level of significance.

11 Group Mean Scores Results: Working Memory Index Group level analysis The WMI was significantly lower than the FSIQ (M = 94.36, SD = 13.93) and FSIQ (M = 97.84, SD = 13.93), t (133) = 5.28, p <. 001, moderate effect size. Individual Discrepancy analysis of each individual in the sample s index scores 39% of the sample presented with WMI composite scores significantly lower than VCI composite scores at the 0.05 level of significance WMI scores in relation to FSIQ & VCI Overall FSIQ VCI Index Axis Title Index mean scores WMI

12 Group Mean Scores Results: Processing Speed Index Group level analysis The PSI was significantly lower than FSIQ (M = 97.84, SD = 13.93), t (133) = 3.73, p <. 001 (two-tailed), large effect size. Individual Discrepancy analysis of each individual in the sample s index scores 47% of the sample present with PSI composite scores significantly lower than VCI composite scores at the 0.05 level of significance PSI scores in relation to FSIQ & VCI Overall FSIQ VCI Index Index Index mean scores PSI

13 Mean Scores Results: Performances on Individual Subtest Scores Sample and WISC-IV standardisation sample subtest mean scores SI PCn BD VC MR DS SS LNS CO CD Sample Mean Individual Subtests UK Standardisation Sample mean The Similarities subtest (M 11.79; SD 2.87) was significantly higher next highest scoring subtest Picture Concepts (M = 10.83, SD = 2.79), t (101) = 3.37, p =.001 with a moderate effect size. The Coding subtest (M 7.61; SD 2.83) was significantly lower that next lowest scoring subtest Comprehension (M = 8.63, SD = 3.33), t (102) = -2.43, p =.017 with a small effect size. Block Design (M = 10.35; SD = 3.19) subtest were significantly higher than the Matrix Reasoning (M = 9.51; SD = 2.73) subtest, t (101) = 2.79, p =.006 with a moderate effect size.

14 Percentage of Sample Results: Individual Personal Strengths and Weaknesses VCI 33% and PRI 31% were identified as areas of personal strength relative to their own cognitive profiles PSI 36% and WMI 27% were identified as areas of personal weaknesses relative to their own cognitive profiles Only 16 Children out of 134 in the sample did not have a strength or weakness in their profile This profile did not apply unilaterally to the entire sample as over half of the sample did not present with this profile WISC-IV Profiles of Personal Strengths and Weaknesses VCI PRI WMI PSI WISC-IV Index PS PW

15 Discussion No significant difference between VCI and PRI at group level is contrary to previous research (Mayes & Calhoun, 2008; Oliveras-Rentas et al., 2012). individual level Analysis at the revealed that large number of children presented with PRI significantly lower than VCI (i.e. 47% of sample). Contrary to previous research (Charman et al., 2011). Findings in relation to performances on the PRI (i.e. 47% of sample PRI significant lower than VCI) support research suggesting that the PRI appears to function differently for children with ASD (Bardikoff & McGonigle- Chalmers, 2014).

16 Subtest Scaled Scores Discussion No distinct profile emerges that is common to all children with ASD in the sample. Results at group level don t explain some of the considerable individual variability. EG Looking a sample of 5 children selected randomly from my data set Participants A to D in graph. Consistent with research that found that findings at a group level of analysis did not translate at the individual level (Mandy et al, 2015). In their study out of a sample of 104 Children with ASD they found comparable group findings in relation to previous research. However, when this group profile was applied to the sample, only one child met the criteria. This it could be argued there is an Averaging Artifact (Shallice, Burgess, & Frith, 1991) Group Means Misleading Random sample profiles of a selection of children from the sample SI VC CO BD Pcn MR DS LNS CD SS WISC-IV Subtest Child A Child B Child C Child D Child E

17 Implications of research Child - Identification of a profile can help the child better understand and focus on their own cognitive strengths and cognitive weaknesses. Clinicians Clinicians needs to be cognisant of the wide variability in the cognitive profiles of children who present with ASD Best Practice Indicates the value of cognitive profiling for individual children with ASD and the importance of reporting strengths and weaknesses in these profiles Ethics Test selection - Further research required to determine if the WISC is the most suitable assessment tool for children with ASD e.g. Research has shown the Ravens Progressive Matrices to be a better measure of intelligence in Autism than the WISC-IV (Nader, Courchesne, Dawson, & Soulières, 2014)

18 Thank You!!

19 Reference Baron-Cohen, S., Scott, F. J., Allison, C., Williams, J., Bolton, P., Matthews, F. E., & Brayne, C. (2009). Prevalence of autism- spectrum conditions: UK school-based population study. The British Journal of Psychiatry, 194(6), Donders, J., & Janke, K. (2008). Criterion validity of the Wechsler Intelligence Scale for Children Fourth Edition after paediatric traumatic brain injury. Journal of the International Neuropsychological Society, 14(4), Flanagan, D. P., & Kaufman, A. S. (2004). Essentials of WISC-IV assessment (Vol. 46). John Wiley & Sons. Frith, U. (2003). Autism : explaining the enigma (2nd ed.). Malden, MA: Blackwell Pub. Hale, J. B., Fiorello, C. A., Kavanagh, J. A., Hoeppner, J. A. B., & Gaither, R. A. (2001). WISC-III predictors of academic achievement for children with learning disabilities: Are global and factor scores comparable?. School Psychology Quarterly, 16(1), 31. Joseph, R. M., Tager Flusberg, H., & Lord, C. (2002). Cognitive profiles and social communicative functioning in children with autism spectrum disorder. Journal of Child Psychology and Psychiatry, 43(6), Mayes, S. D., & Calhoun, S. L. (2008). WISC-IV and WIAT-II profiles in children with high-functioning autism. Journal of autism and developmental disorders, 38(3), Frith, U. (2004). Emanuel Miller lecture: Confusions and controversies about Asperger syndrome. Journal of Child Psychology and Psychiatry, 45(4), Mandy, W, Murin, M, & Skuse, D. (2015). The Cognitive Profile in Autism Spectrum Disorders. In M. Leboyer & P. Chaste (Eds.), Autism Spectrum Disorders: Phenotypes, Mechanisms and Treatments (Key Issues in Mental Health, Vol. 180) (pp ). Basel: Karger

20 Reference Mayes, S. D., & Calhoun, S. L. (2003). Relationship between Asperger syndrome and high-functioning autism. Learning and behavior problems in Asperger syndrome, Kaufman, A. S., Flanagan, D. P., Alfonso, V. C., & Mascolo, J. T. (2006). Review of Wechsler Intelligence Scale for Children, Fourth Edition (WISC-IV). Journal Of Psychoeducational Assessment, 24(3), Nader, A. M., Courchesne, V., Dawson, M., & Soulières, I. (2014). Does WISC-IV Underestimate the Intelligence of Autistic Children? Journal of autism and developmental disorders. National Indicators Clinical Excellence (NICE)(2011). Recognising and diagnosing autism in children and young people: summary of NICE guidance. Oliveras-Rentas, R. E., Kenworthy, L., Roberson III, R. B., Martin, A., & Wallace, G. L. (2012). WISC-IV profile in high-functioning autism spectrum disorders: impaired processing speed is associated with increased autism communication symptoms and decreased adaptive communication abilities. Journal of autism and developmental disorders, 42(5), Ozonoff, S., Goodlin-Jones, B. L., & Solomon, M. (2005). Evidence-based assessment of autism spectrum disorders in children and adolescents. Journal of Clinical Child and Adolescent Psychology, 34(3),

21 Reference Pellicano, E., Dinsmore, A., & Charman, T. (2014). What should autism research focus upon? Community views and priorities from the United Kingdom. Autism, 18(7), Sattler, J. M. (2008). Assessment of children: Cognitive foundations. JM Sattler Saulnier, C. A., & Ventola, P. E. (2012). Essentials of autism spectrum disorders evaluation and assessment (Vol. 83): John Wiley & Sons. Towgood, Karren J, Meuwese, Julia DI, Gilbert, Sam J, Turner, Martha S, & Burgess, Paul W. (2009). Advantages of the multiple case series approach to the study of cognitive deficits in autism spectrum disorder. Neuropsychologia, 47(13), Wechsler, D. (2004). Wechsler Intelligence Scale for Children-WISC-IV UK Edition. Psychological Corporation. Williams, P. E., Weiss, P. D. L. G., & Rolfhus, P. D. E. (2003). WISC IV Technical Report# 1 Theoretical Model and Test Blueprint. San Antonio, TX: The Psychological Corporation. Wing, L. (1981). Language, social, and cognitive impairments in autism and severe mental retardation. Journal of autism and developmental disorders, 11(1),

22 Descriptives for the Research Sample WISC-IV Index Mean SD Range of Scores Overall IQ Verbal Comprehension Index (VCI) Perceptual Reasoning Index (PRI) Working Memory Index (WMI) Processing Speed Index (PSI)

23 Mean Scores Results: Sample mean scores V s UK Standardisation sample One sample t tests revealed that the mean WMI score for the research sample was significantly lower than the mean scores from the WISC-IV UK standardisation sample, t(133) = , p <.001. This represented a small effect size, d =.32. One sample t tests revealed that the mean PSI research mean score was significantly lower than the mean scores from the WISC-IV UK standardisation sample, t(133) = , p <.001 (two-tailed). This represented a medium effect size, d = Sample and standardisation sample index mean scores Overall FSIQ VCI Index PRI Index WMI Index PSI Index Sample Mean UK Standardisation Sample mean

24 Additional Findings (Subtest Analysis) Mean Std. Deviation Range Min Max p value (Paired t-test) Significant Eta 2 Sub Test Mean Similarities < Picture Concepts < Block Design Vocabulary Matrix Reasoning Digit Span Symbol Search Letter-Number Seq < Comprehension Coding <

25 Percentage of Sample Additional Findings (Ind Subtest Analysis) Strengths and Weaknesses in Individual Subtest Subtest Percententage of sample Personal Weakness Percententage of sample Personal Strength