Nonlinear features of gaze behavior during joint attention in children with autism spectrum disorder

Author:

Wang Hongan1,Zhao Xin2,Yu Dongchuan13ORCID

Affiliation:

1. Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering Southeast University Nanjing China

2. Department of Radiology The Third Affiliated Hospital of Zhengzhou University Zhengzhou China

3. Henan Provincial Medical Key Lab of Child Developmental Behavior and Learning The Third Affiliated Hospital of Zhengzhou University Zhengzhou China

Abstract

AbstractSince children with autism spectrum disorder (ASD) might exhibit a variety of aberrant response to joint attention (RJA) behaviors, there is growing interest in identifying robust, reliable and valid eye‐tracking metrics for determining differences in RJA behaviors between typically developing (TD) children and those with ASD. Previous eye‐tracking studies have not been deeply investigated nonlinear features of gaze time‐series during RJA. As a main motivation, this study aimed to extract three nonlinear features (i.e., complexity, long‐range correlation, and local instability) of gaze time‐series during RJA in children with ASD, which can be measured by fractal dimension (FD), Hurst exponent (H), and largest Lyapunov exponent (LLE), respectively. To illustrate our idea, this study adopted a publicly accessible database, including eye‐tracking data collected during RJA from 19 children with ASD (7.74 ± 2.73) and 30 TD children (8.02 ± 2.89), and conducted a battery of nonparametric analysis of covariance (ANCOVA), where gender was used as covariable. Findings showed that gaze time‐series during RJA in autistic children may generally have greater FD but lower H than that in TD controls. This implies that children with ASD possess more complex and unpredictable gaze behaviors during RJA than TD children. Furthermore, nonlinear metrics outperformed traditional eye‐tracking metrics in obtaining higher identification performance with an accuracy of 82% and an AUC value of 0.81, distinguishing the differences between successful and failed RJA trails, and predicting the severity of ASD symptoms. Findings might bring some new insights into the understanding of the impairments in RJA behaviors for children with ASD.

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Genetics (clinical),Neurology (clinical),General Neuroscience

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