SCIENCE@home

Intra-topic latency as an automated behavioral marker of treatment response in autism spectrum disorder

E. McKernan,Manoj Kumar,A. di Martino,Lisa H Shulman,A. Kolevzon,C. Lord,Shrikanth S. Narayanan,So Hyun Kim

Published 2022 in Scientific Reports

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 significant group difference in the changes in latency for adult social partners. Results suggest that the automated measure of latency derived from natural social interactions is a scalable and objective method to quantify treatment response in children with ASD.

PUBLICATION RECORD

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-48 of 48 references · Page 1 of 1

CITED BY

Showing 1-12 of 12 citing papers · Page 1 of 1