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RJAfinder: An automated tool for quantification of responding to joint attention behaviors in autism spectrum disorder using eye tracking data

Deficits in responding to joint attention (RJA) are early symptoms of autism spectrum disorder (ASD). Currently, no automated tools exist for identifying and quantifying RJA behaviors. A few eye tracking studies have investigated RJA in ASD children but have produced conflicting results. In addition...

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Autores principales: Zhang, Jie, Li, Ziyi, Wu, Yige, Ye, Adam Yongxin, Chen, Lei, Yang, Xiaoxu, Wu, Qixi, Wei, Liping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9714660/
https://www.ncbi.nlm.nih.gov/pubmed/36466175
http://dx.doi.org/10.3389/fnins.2022.915464
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author Zhang, Jie
Li, Ziyi
Wu, Yige
Ye, Adam Yongxin
Chen, Lei
Yang, Xiaoxu
Wu, Qixi
Wei, Liping
author_facet Zhang, Jie
Li, Ziyi
Wu, Yige
Ye, Adam Yongxin
Chen, Lei
Yang, Xiaoxu
Wu, Qixi
Wei, Liping
author_sort Zhang, Jie
collection PubMed
description Deficits in responding to joint attention (RJA) are early symptoms of autism spectrum disorder (ASD). Currently, no automated tools exist for identifying and quantifying RJA behaviors. A few eye tracking studies have investigated RJA in ASD children but have produced conflicting results. In addition, little is known about the trajectory of RJA development through developmental age. Here, a new video was designed including 12 clips of an actor pointing to or looking at an object. Eye tracking technology was used to monitor RJA in three groups: 143 ASD children assessed with the Autism Diagnostic Interview-Revised (ADI-R) and the Autism Diagnostic Observation Schedule (ADOS) (4–7 years old), 113 age- and gender-matched typically developing children (TDC), and 43 typically developing adults (TDA) (19–32 years old). RJAfinder was developed in R and MATLAB to quantify RJA events from the eye tracking data. RJA events were compared among the three groups. Spearman correlation coefficients between total number of RJA events in ASD and the Social Responsiveness Scale (SRS) scores were calculated. A logistic regression model was built using the average valid sampling rate and the total number of RJA events as two predictive variables to classify ASD and TDC groups. ASD children displayed statistically significantly less RJA events than the TDC and TDA groups with medium-to-large-sized effects. ASD and TDC children both displayed more RJA events in response to pointing stimuli than to looking stimuli. Our logistic regression model predicted ASD tendency with 0.76 accuracy in the testing set. RJA ability improved more slowly between the ages of 4–7 years old in the ASD group than in the TDC group. In ASD children, RJA ability showed negative correlation with SRS total T-score as well as the scores of five subdomains. Our study provides an automated tool for quantifying RJA and insights for the study of RJA in ASD children, which may help improve ASD screening, subtyping, and behavior interventions.
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spelling pubmed-97146602022-12-02 RJAfinder: An automated tool for quantification of responding to joint attention behaviors in autism spectrum disorder using eye tracking data Zhang, Jie Li, Ziyi Wu, Yige Ye, Adam Yongxin Chen, Lei Yang, Xiaoxu Wu, Qixi Wei, Liping Front Neurosci Neuroscience Deficits in responding to joint attention (RJA) are early symptoms of autism spectrum disorder (ASD). Currently, no automated tools exist for identifying and quantifying RJA behaviors. A few eye tracking studies have investigated RJA in ASD children but have produced conflicting results. In addition, little is known about the trajectory of RJA development through developmental age. Here, a new video was designed including 12 clips of an actor pointing to or looking at an object. Eye tracking technology was used to monitor RJA in three groups: 143 ASD children assessed with the Autism Diagnostic Interview-Revised (ADI-R) and the Autism Diagnostic Observation Schedule (ADOS) (4–7 years old), 113 age- and gender-matched typically developing children (TDC), and 43 typically developing adults (TDA) (19–32 years old). RJAfinder was developed in R and MATLAB to quantify RJA events from the eye tracking data. RJA events were compared among the three groups. Spearman correlation coefficients between total number of RJA events in ASD and the Social Responsiveness Scale (SRS) scores were calculated. A logistic regression model was built using the average valid sampling rate and the total number of RJA events as two predictive variables to classify ASD and TDC groups. ASD children displayed statistically significantly less RJA events than the TDC and TDA groups with medium-to-large-sized effects. ASD and TDC children both displayed more RJA events in response to pointing stimuli than to looking stimuli. Our logistic regression model predicted ASD tendency with 0.76 accuracy in the testing set. RJA ability improved more slowly between the ages of 4–7 years old in the ASD group than in the TDC group. In ASD children, RJA ability showed negative correlation with SRS total T-score as well as the scores of five subdomains. Our study provides an automated tool for quantifying RJA and insights for the study of RJA in ASD children, which may help improve ASD screening, subtyping, and behavior interventions. Frontiers Media S.A. 2022-11-17 /pmc/articles/PMC9714660/ /pubmed/36466175 http://dx.doi.org/10.3389/fnins.2022.915464 Text en Copyright © 2022 Zhang, Li, Wu, Ye, Chen, Yang, Wu and Wei. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Zhang, Jie
Li, Ziyi
Wu, Yige
Ye, Adam Yongxin
Chen, Lei
Yang, Xiaoxu
Wu, Qixi
Wei, Liping
RJAfinder: An automated tool for quantification of responding to joint attention behaviors in autism spectrum disorder using eye tracking data
title RJAfinder: An automated tool for quantification of responding to joint attention behaviors in autism spectrum disorder using eye tracking data
title_full RJAfinder: An automated tool for quantification of responding to joint attention behaviors in autism spectrum disorder using eye tracking data
title_fullStr RJAfinder: An automated tool for quantification of responding to joint attention behaviors in autism spectrum disorder using eye tracking data
title_full_unstemmed RJAfinder: An automated tool for quantification of responding to joint attention behaviors in autism spectrum disorder using eye tracking data
title_short RJAfinder: An automated tool for quantification of responding to joint attention behaviors in autism spectrum disorder using eye tracking data
title_sort rjafinder: an automated tool for quantification of responding to joint attention behaviors in autism spectrum disorder using eye tracking data
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9714660/
https://www.ncbi.nlm.nih.gov/pubmed/36466175
http://dx.doi.org/10.3389/fnins.2022.915464
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