Autonomic Modulation and Facial Expression in Autism Spectrum Disorder

Background: the autism spectrum disorders (ASD) can be related with abnormalities in cortical structures and cause behavior imbalances. In addition, as soon as the diagnosis is done, the better will be the prognosis. In this context, heart rate variability (HRV) stands out, which is a non-invasive tool representing autonomic modulation, with potential prognostic value. The literature showed there are no changes in HRV at rest using linear methods of analysis, but changes can be identified during tasks. Nonlinear methods of HRV are more sensitive and provide additional information to the linear. Objective: to analyze autonomic modulation using nonlinear and linear indexes of HRV in children with ASD at rest and during tasks in comparison to typical children. In addition, to correlate HRV analysis between them, also between behavior and severity of the disease. Methods: this study involves both typical children and children with ASD. Autonomic modulation will be performed using nonlinear indices (extracted from Poincaré plot, detrended fluctuation of tendency analysis and recurrence plot) and linear indices of HRV in the time (RMSSD e SDNN) and frequency domain (LF, HF, VLF). The tasks consist in games to identify and recognize faces and facial expressions. Behavior and severity of the disease will be evaluated using the Autism Behavior Checklist and the Childhood Autism Rating Scale respectively. Statistical Analysis: to identify differences between moments and protocols two-way analysis of variance will be used along with the Bonferroni post-test or Dunn post-test according to the data distribution. Statistical significance will be set at 5%.