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<p>Existing facial-expression studies in children with autism spectrum disorder (ASD) rely mainly on discrete, task-driven measures that overlook the sustained emotional fluctuations and ambiguous expressions in naturalistic interactions. This study quantified atypical facial expression patterns in ASD during spontaneous, unscripted interactions. We analyzed 184 naturalistic video sessions from 99 children with ASD and 85 typically developing (TD) peers and extracted three features capturing spontaneous dynamics: emotion variation (temporal stability of emotional states), expression intensity (magnitude of facial muscle activation), and facial coordination (synchrony across facial muscles). These features integrated holistic and processual representations across coarse- and fine-grained levels, enabling detailed quantification of facial patterns. Compared with TD peers, the ASD group exhibited increased prominence of anger, altered emotion transition probabilities, heightened activation in non-core facial muscles, and atypical facial coordination (<i>p</i> &lt; 0.05). These findings reveal subtle facial dynamics inaccessible to traditional approaches and provide a quantitative explanation for the hard-to-describe atypical expressions. Using the fused feature set, ASD classification reached 92.4% accuracy and 0.977 AUC. Regression analyses further predicted symptom severity with mean absolute errors of 13.94 on the ABC scale and 3.84 on the CABS scale. These quantitative and interpretable markers show promise for large-scale ASD screening in naturalistic settings.</p>

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Naturalistic facial dynamics enable quantitative clinical assessment of atypical expression phenotypes in children with autism spectrum disorder

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月21日

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  • Minghao Du,
  • Ping Shi,
  • Zehao Liu,
  • Yunuo Xu,
  • Xiaoya Liu,
  • Wei Liu,
  • Shuang Liu,
  • Dong Ming

摘要

Existing facial-expression studies in children with autism spectrum disorder (ASD) rely mainly on discrete, task-driven measures that overlook the sustained emotional fluctuations and ambiguous expressions in naturalistic interactions. This study quantified atypical facial expression patterns in ASD during spontaneous, unscripted interactions. We analyzed 184 naturalistic video sessions from 99 children with ASD and 85 typically developing (TD) peers and extracted three features capturing spontaneous dynamics: emotion variation (temporal stability of emotional states), expression intensity (magnitude of facial muscle activation), and facial coordination (synchrony across facial muscles). These features integrated holistic and processual representations across coarse- and fine-grained levels, enabling detailed quantification of facial patterns. Compared with TD peers, the ASD group exhibited increased prominence of anger, altered emotion transition probabilities, heightened activation in non-core facial muscles, and atypical facial coordination (p < 0.05). These findings reveal subtle facial dynamics inaccessible to traditional approaches and provide a quantitative explanation for the hard-to-describe atypical expressions. Using the fused feature set, ASD classification reached 92.4% accuracy and 0.977 AUC. Regression analyses further predicted symptom severity with mean absolute errors of 13.94 on the ABC scale and 3.84 on the CABS scale. These quantitative and interpretable markers show promise for large-scale ASD screening in naturalistic settings.