Sex and Age Informed Profiles of Executive Function in Autism Across Development

About This Grant

PROJECT SUMMARY Challenges in executive functioning (EF) are highly prevalent in autism; however, profiles of EF vary greatly. Challenges in both “cool,” logical skills, such as working memory and planning, and “hot,” state-dependent skills, such as inhibition and emotional regulation, have been implicated. For both non-autistic and autistic individuals, EF predicts a myriad of key functional outcomes including adaptive behavior, academic success, and mental health. There is burgeoning evidence that EF serves as an underlying mechanism for the development and maintenance of mental health outcomes as well as the heterogeneity inherent to autism. Mental health and EF represent (1) two key drivers of quality of life in autism, (2) are endorsed by autistic people as a high priority for research and intervention, and (3) are tangible and frequent targets of personalized intervention approaches. As such, understanding distinct profiles of EF, the developmental course of EF, and how EF challenges vary as a function of key variables such as sex and age is a promising step towards individualized, actionable intervention across the lifespan. Data driven approaches have been promoted in autism to understand heterogeneity across constellations of traits, yet extant work has not been sufficiently powered to examine the unique and intersecting impact of sex, age of diagnosis, and chronological age on EF. The proposed project will adopt a data driven approach (Latent Profile Analysis–LPA) to parse the heterogeneity of EF across development (4 to 25 years), spanning critical developmental periods where EF development is rapid. In Aim 1, we will use multi-group LPA to identify profiles of EF in a large sample (N = 724) of autistic and non-autistic individuals, equally split on sex and diagnosis. Profile modeling will be based on 5 subscales from a caregiver-reported EF measure (Behavior Rating Inventory of Executive Functioning). In Aim 2, we will determine how profiles of EF vary as a function of age, sex, and age of diagnosis in our autistic cohort. In Aim 3, we will conduct exploratory analysis to examine how EF profiles predict internalizing behaviors in both cohorts. We hypothesize 3 profiles will emerge in each cohort, with the following profiles in the autistic cohort: (A) challenges across all EF domains, (B) challenges in shifting, working memory, and planning, and (C) challenges in emotional regulation and inhibition. We anticipate our variables of interest, including age, sex, and age of diagnosis, will differentially predict profile membership in line with existing literature. Identifying these profiles can (1) inform educational and therapeutic approaches, (2) promote autonomy and well-being for autistic individuals, and (3) elucidate the mechanisms by which EF challenges contribute to mental health across key developmental windows. Training aims include advancing Ms. Russell’s understanding of EF and its relationship to clinical outcomes, enhancing her statistical skills by training in data-driven approaches, and expanding her experience working with data from autistic adults. These aims will support Ms. Russell establishing herself as an expert within the field of autism phenotyping.