Reduced multiscale complexity of daily behavioral dynamics in autism spectrum disorder

TL;DR

The study found that children with autism spectrum disorder (ASD) show less complex and more predictable daily behavior patterns compared to typically developing children, which could help in objective diagnosis.

Contribution

The study introduces multiscale entropy analysis as a novel objective metric for characterizing behavioral dynamics in ASD.

Findings

ASD children showed significantly lower multiscale entropy at timescales longer than 6 minutes, indicating reduced behavioral unpredictability.

Multiscale entropy positively correlated with prepulse inhibition levels, suggesting a link to sensorimotor gating.

A negative correlation was found between multiscale entropy and the SDQ total difficulty score, supporting its use as a mental health indicator in ASD.

Abstract

Autism spectrum disorder (ASD) is difficult to diagnose objectively due to its heterogeneous and complex manifestations. This study aimed to objectively characterize the behavioral phenotypes of ASD children by exploring the multiscale behavioral dynamics.

We applied behavioral organization (BO) and multiscale sample entropy (MSE) analyses to physical activity data collected from ASD and typically developing children, using wearable monitors in their daily life. We also examined their correlation with auditory startle response measures and clinical questionnaires, including the Social Responsiveness Scale (SRS) and the Strengths and Difficulties Questionnaire (SDQ).

A significant decrease in MSE at timescales longer than 6 min was observed in ASD children, suggesting decreased irregularity or unpredictability, potentially linked to repetitive behaviors or stereotyped patterns commonly observed in ASD. Additionally, an increase in MSE positively correlated with prepulse inhibition levels, indicating its relationship with sensorimotor gating. Moreover, the observed significant negative correlation with the total difficulty score of SDQ substantiates MSE's potential as an objective metric for assessing general mental health problems associated with ASD.

Multiscale analysis enhances the understanding of ASD's behavioral dynamics, providing valuable metrics for real‐world assessments.

This study offers valuable insights into the multiscale temporal structures of behavioral dynamics in autism spectrum disorder (ASD). Our findings revealed complex interactions between behavioral patterns, neural responses, and clinical symptoms, thereby enhancing our understanding of ASD. Moreover, these results indicate that concentrating on behavioral abnormalities could serve as an objective measure for evaluating ASD in real‐world environments.

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