Emotional and behavioral dysregulation are the primary characteristics youth with autism present in clinical settings and are highly predictive of caregiver stress. But the mechanisms that underlie these phenomena are not well understood. These challenges are compounded for youth who are minimally verbal or have an intellectual disability.
In this lecture, Matthew Siegel will draw upon a new resource, the Autism Inpatient Collection data set, to offer preliminary insights into the relationships between physiologic arousal, emotion dysregulation and the occurrence of challenging behaviors. Such behaviors may represent an attempt to modulate physiologic arousal in minimally verbal individuals with autism spectrum disorders (ASD). Siegel will present pilot data, using machine learning approaches, that identifies physiologic arousal as a biomarker of distress in ASD and that illustrates an opportunity to predict the onset of challenging behavior in real time. This work seeks to address the critical, parent-identified issue of uncertainty regarding when a challenging behavior may occur and may open new avenues for intervention.
