Autism Center of Excellence Network: Neurodevelopmental Biomarkers of Late Diagnosis in Autism

University Of Virginia, Charlottesville VA

Investigators

Linked publications & trials

Abstract

Project Summary: Many people with autism spectrum disorder (ASD) are late- or never diagnosed (LDx). LDx is associated with increased depression, anxiety, and self-harm, and limits access to supports, increasing vulnerability to abuse. Obstacles to timely diagnosis (Dx) may involve individual-level biobehavioral differences, including fewer unusual restricted/repetitive behaviors (RRBs), and strengths in social motivation, executive function, and/or intelligence. Our goal is to integrate qualitative, quantitative, and artificial intelligence methods to identify biobehavioral predictors of LDx, leading to the development of a practicable screening measure for those at LDx risk. To illuminate mechanisms of LDx (1st ASD Dx > 12y) we will build on two legacies of our decade-long longitudinal ACE Network: 1) a deeply phenotyped, longitudinal cohort of autistic youth and young adults; 2) the development of a self-report tool—the Self-Assessment of Autistic Traits (SAAT)—that captures the experience of ASD, including strengths. We will recruit a sample of autistic people (ages 16-30 years) to augment our longitudinal ACE cohort with LDx individuals. Using a mixed-methods approach, we will identify markers of LDx and examine the interplay between Dx timing and well-being outcomes. A stakeholder team of clinicians, self-advocates, autistic people, and parents, all with professional and/or lived experience with LDx ASD, will guide us as we: 1. Identify cognitive, sex, and behavioral differences between timely (TDx) and LDx autistic people. 2. Develop and validate a self-report ASD screening measure as a diagnostic access point for adolescents/adults at risk for LDx. 3. Develop a personalized approach to biobehavioral marker extraction for classification of diagnostic timing (LDx vs. TDx) and prediction of QoL indices, using an innovative artificial intelligence approach to integrate multimodal neuroimaging data with phenotypic information. We will improve research and clinical practice by accelerating the identification of adolescents and adults with ASD who have traditionally been missed or misdirected in the diagnostic process. This work will accelerate ASD Dx, allowing for appropriate supports and improved outcomes.