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Background <p>Social deficit in autism spectrum disorder (ASD) varies substantially across individuals, yet the neural mechanisms underlying this variability remain poorly understood. Resting state electrophysiological measures may under-engage social information processing and may be less sensitive to ASD-related neural differences. Here we combined EEG with eye tracking during a low demand viewing paradigm to probe neural dynamics and to identify data-driven neurodynamic modes associated with variability in social orienting.</p> Methods <p>We recruited 88 autistic and 71 typically developing (TD) participants for eyes-open resting-state EEG. A subset of these participants, including 58 autistic and 61 TD participants, additionally completed a Social vs. Geometric paradigm with simultaneous EEG and eye tracking. Alpha-band resting-state and task-state EEG were segmented into five microstate (MS) classes (A–E). We compared MS temporal and complexity features between conditions and used support vector machine classification to test whether resting-state or task-state MS features better differentiated ASD from TD participants. For the more discriminative condition, MS-based alpha activity was further characterized by amplitude and phase-locking value (PLV). Participant-level MS-based PLV features were then used for k-means clustering, and moderation models examined whether PLV shaped the association between autistic traits and social orienting.</p> Results <p>Task-state MS features differentiated ASD from TD more accurately than resting-state features. Group differences were primarily expressed in MS-based alpha PLV across the five MS classes, whereas alpha amplitude showed no significant group differences. Clustering identified two PLV-based synchronization modes that were present in both ASD and TD participants. Within ASD, these modes differed in social orienting, and MS A PLV moderated the association between autistic traits and social scene preference ratio.</p> Limitations <p>Given the cross-sectional design, tracing the developmental trajectories of these distinct neurodynamic modes will require future multi-center, longitudinal tracking.</p> Conclusions <p>These findings suggest that social orienting variability within ASD is associated with heterogeneous neurodynamic modes that become most visible under naturalistic social input and are more strongly associated with phase synchronization.</p>

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Phase synchronization modes are associated with heterogeneous social orienting in children and adolescents with autism

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月9日

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  • Xing-Ke Wang,
  • Sheng Yang,
  • He-Li Lu,
  • Chun-Yan Yue,
  • Li-Fang Dai,
  • Shao-Di Wang,
  • Shuang Li,
  • Zhou Zhang,
  • Li-Rui Hao,
  • Rui-Xuan Ren,
  • Yue Cao,
  • Gaoxiang Ouyang

摘要

Background

Social deficit in autism spectrum disorder (ASD) varies substantially across individuals, yet the neural mechanisms underlying this variability remain poorly understood. Resting state electrophysiological measures may under-engage social information processing and may be less sensitive to ASD-related neural differences. Here we combined EEG with eye tracking during a low demand viewing paradigm to probe neural dynamics and to identify data-driven neurodynamic modes associated with variability in social orienting.

Methods

We recruited 88 autistic and 71 typically developing (TD) participants for eyes-open resting-state EEG. A subset of these participants, including 58 autistic and 61 TD participants, additionally completed a Social vs. Geometric paradigm with simultaneous EEG and eye tracking. Alpha-band resting-state and task-state EEG were segmented into five microstate (MS) classes (A–E). We compared MS temporal and complexity features between conditions and used support vector machine classification to test whether resting-state or task-state MS features better differentiated ASD from TD participants. For the more discriminative condition, MS-based alpha activity was further characterized by amplitude and phase-locking value (PLV). Participant-level MS-based PLV features were then used for k-means clustering, and moderation models examined whether PLV shaped the association between autistic traits and social orienting.

Results

Task-state MS features differentiated ASD from TD more accurately than resting-state features. Group differences were primarily expressed in MS-based alpha PLV across the five MS classes, whereas alpha amplitude showed no significant group differences. Clustering identified two PLV-based synchronization modes that were present in both ASD and TD participants. Within ASD, these modes differed in social orienting, and MS A PLV moderated the association between autistic traits and social scene preference ratio.

Limitations

Given the cross-sectional design, tracing the developmental trajectories of these distinct neurodynamic modes will require future multi-center, longitudinal tracking.

Conclusions

These findings suggest that social orienting variability within ASD is associated with heterogeneous neurodynamic modes that become most visible under naturalistic social input and are more strongly associated with phase synchronization.