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Testing Sensory and Multisensory Function in Children with Autism Spectrum Disorder
Published on: April 22, 2015
Interpersonal Coordination Deficits in Joint Action in Pre-school Children With Autism Spectrum Disorder: Evidence
Kaiyun Li1, Yue Yang2,3, Caiyan Zheng2,4
1School of Education and Psychology, University of Jinan, 250022, Jinan, China. sep_liky@ujn.edu.cn.
Insights
Children with autism spectrum disorder (ASD) show interpersonal coordination deficits in joint actions. Functional near-infrared spectroscopy (fNIRS) hyperscanning revealed lower behavioral and neural synchrony in ASD, with machine learning effectively identifying these differences.
Area of Science:
- Neuroscience
- Developmental Psychology
- Human-Computer Interaction
Background:
- Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by social interaction and communication challenges.
- Interpersonal coordination, crucial for joint actions, is often impaired in individuals with ASD, though empirical evidence remains debated.
Purpose of the Study:
- To investigate interpersonal coordination deficits in children with ASD during cooperative and competitive tasks.
- To utilize functional near-infrared spectroscopy (fNIRS) hyperscanning to examine neural synchrony during dyadic interactions.
Main Methods:
- 42 children (21 with ASD, 21 without) performed dyadic block-building tasks under cooperative and competitive intentions.
- fNIRS hyperscanning recorded brain activation and interpersonal neural synchronization (INS).
- Machine learning algorithms (Random Forest, AdaBoost, XGBoost) analyzed INS for group discrimination.
Main Results:
- Children with ASD exhibited lower behavioral accuracy and synchrony in both cooperative and competitive tasks.
- Reduced INS was observed in ASD children within specific brain regions (rTPJ-rIFG, rTPJ-rIPL, bilateral TPJ) during cooperation.
- Lower activation in the right inferior parietal lobule (rIPL) was noted in ASD children during competition.
Conclusions:
- The study confirms interpersonal coordination deficits in children with ASD during joint actions, encompassing both cooperative and competitive contexts.
- Interpersonal neural synchronization (INS) serves as a valid neural indicator of these deficits.
- Machine learning offers a promising approach for analyzing and validating INS in ASD research.
Purpose:
Whether children with autism spectrum disorder (ASD) exhibit interpersonal coordination deficits remains controversy. The study employed functional near-infrared spectroscopy (fNIRS) hyperscanning technology to investigate the performance of children with ASD in interpersonal cooperation and competition coordination tasks under dyadic gamed interaction situation.
Methods:
42 children (21 with ASD, 21 without ASD) performed a dyadic block-building task with collaborative intention (leading condition, following condition, turn-taking condition) (Experiment 1) and a competitive intention (Experiment 2) with an experimenter. Signal brain activation and interpersonal neural synchronization (INS) were analyzed and INS values further classified by machine-learning method and computed by using the SHAP toolkit.
Results:
Children with ASD showed lower behavioral accuracy and behavioral synchrony in the tasks of the both two experiments; lower interpersonal neural synchronization(INS) in right temporoparietal junction (rTPJ)-right inferior frontal gyrus (rIFG), rTPJ-right inferior parietal lobule, and bilateral TPJ pairs in the three conditions under collaboration intention in experiment 1; and lower single-brain activation level in the right inferior parietal lobule (rIPL) under competition intention task in experiment 2. Finally, the children with and without ASD in Ex1 could be successfully discriminated against based on INS value by using Random Forest, AdaBoost, and XGBoost machine learning methods.
Conclusions:
From the interpersonal behavioral and neural synchronization, the current study demonstrated the interpersonal coordination deficits in joint action for children with ASD from cooperative and competitive intention; notably, INS is an effective neural indicator and the machine learning method provides a novel strategy for analyzing and validating INS for interpersonal coordination deficits in joint action for the population of ASD.
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