Abstract

Data science advances in behavioral signal processing and machine learning hold the promise to automatically quantify clinically meaningful behaviors that can be applied to a large amount of data. The objective of this study was to identify an automated behavioral marker of treatment response in social communication in children with autism spectrum disorder (ASD). First, using an automated computational method, we successfully derived the amount of time it took for a child with ASD and an adult social partner (N pairs = 210) to respond to each other while they were engaged in conversation bits (“latency”) using recordings of brief, natural social interactions. Then, we measured changes in latency at pre- and post-interventions. Children with ASD who were receiving interventions showed significantly larger reduction in latency compared to those who were not receiving interventions. There was also a …

Date

2022

Authors

Elizabeth P McKernan, Manoj Kumar, Adriana Di Martino, Lisa Shulman, Alexander Kolevzon, Catherine Lord, Shrikanth Narayanan, So Hyun Kim

Journal

Scientific reports

Volume

12

Issue

1

Pages

3255

Publisher

Nature Publishing Group UK