Poster #43 - Autism Symptom Severity in Toddlerhood: Predicting Developmental Profiles

• In Event: 3-159 - Poster Session 12
  In Poster Session: PS 12 Section - Developmental Disabilities

Integrative Statement

Introduction: Presence of a language impairment is not required for a diagnosis of autism spectrum disorder (ASD); however, children with ASD demonstrate heterogenous language abilities in overall development. Language abilities in this population range from intact and functional to absent and dysfunctional (Tager-Flusberg, 2004). Within-disorder comparisons of language have previously been utilized to define distinct cognitive and linguistic phenotypes (Wittke et al., 2017). Relatedly, this study aimed to identify patterns and associations between early performance on a gold standard assessment for ASD behavior and later performance on language measures. While prior research identified significant correlations between toddlers’ restricted and repetitive behaviors (RRB) and language skills (Ray-Subramanian & Weismer, 2012), limited corroborative evidence exists. The ability to classify symptom patterns observed in older children with ASD has the potential to better inform intervention targets and anticipated developmental trajectories for children demonstrating specific patterns of ASD behaviors.

Method: This study utilized a subset of secondary data collected through the Autism Phenome Project (APP, N = 33), a longitudinal study completed through the MIND Institute at the University of California-Davis. The APP intends to define clinically meaningful ASD subtypes based on behavioral and biological data, specifically evaluating children immediately following ASD diagnosis at age 24-40 months and follow-up evaluations completed approximately 3 years later. Using R statistical software (version 3.5.0), we performed simple linear regression analyses, using Autism Diagnostic Observation Schedule (ADOS-G; Lord et al., 1999) scores at the age of diagnosis to predict receptive language and expressive language performance approximately 3 years later. See Table 1 for a description of assessment measures included in this analysis.

Results: Lower severity of ASD behaviors measured by the total ADOS-G at the age of diagnosis of ASD was a strong predictor of greater receptive (R2 = 0.4232, p < 0.001) and expressive (R2 = 0.2131, p = 0.006849) vocabulary skills 3 years later. Similarly, less severe social affect scores on the ADOS-G predicted stronger receptive (R2 = 0.524, p < 0.001) and expressive (R2 = 0.1768, p = 0.01484) vocabulary skills. Furthermore, when models included only RRB from the ADOS-G no significance was found. Models specific to RRB alone resulted in higher standard error values, highlighting the variability of this ASD symptom in this age group.

Conclusions: Further investigation is warranted prior to implementing specific intervention practices based on these results. These findings emphasize the variability of developmental profiles and skills in individuals with an ASD diagnosis, highlighting a need to consider a child’s entire functional skill set and the child’s skill specific developmental progress when developing individualized interventions. Further, these current findings support social affect as a driving force in the predictive relationship between ASD behavior severity and performance on developmental measures of language (Delgado et al., 2002), above and beyond RRB. Next steps include utilizing a large sample size from the APP dataset and incorporating analysis of spontaneous speech samples at both time points and identifying unique, clinically meaningful phenotypes based on cognitive abilities, language form, language function, lexical diversity, and ASD-behavior severity.

Authors

• Jessica E Blume, Texas Tech University - Human Development & Family Studies
  Presenting Author

• Kacie Wittke, University of Connecticut - Department of Speech Language and Hearing Science
  Non-Presenting Author

• Letitia Naigles, University of Connecticut
  Non-Presenting Author

• Ann Mastergeorge, Texas Tech University
  Non-Presenting Author