Heterogeneity in resting-state functional connectivity (FC) are one of the characteristics of autism spectrum disorder (ASD). Traditional resting-state FC primarily focuses on linear correlations, ignoring the nonlinear properties involved in synchronization between networks or brain regions. In the present study, the cross-recurrence quantification analysis, a nonlinear method based on dynamical systems, was utilized to quantify the synchronization stability between brain regions within the salience network (SN) of ASD. Using the resting-state functional magnetic resonance imaging data of 207 children (ASD/typically-developing controls (TC): 105/102) in Autism Brain Imaging Data Exchange database, we analyzed the laminarity and trapping time differences of the synchronization stability between the ASD subtype derived by a K-means clustering analysis and the TC group, and examined the relationship between synchronization stability and the severity of clinical symptoms of the ASD subtypes. Based on the synchronization stability within the SN of ASD, we identified two subtypes that showed opposite changes in synchronization stability relative to the TC group. In addition, the synchronization stability of ASD subtypes 1 and 2 can predict the social interaction and communication impairments, respectively. These findings reveal that ASD subgroups with different patterns of synchronization stability within the SN appear distinct clinical symptoms, and highlight the importance of exploring the potential neural mechanism of ASD from a nonlinear perspective.

中文翻译：

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解决自闭症谱系障碍中显着网络内同步稳定性动态的异质性

静息态功能连接（FC）的异质性是自闭症谱系障碍（ASD）的特征之一。传统的静息态 FC 主要关注线性相关性，忽略网络或大脑区域之间同步所涉及的非线性特性。在本研究中，交叉递归量化分析是一种基于动力系统的非线性方法，用于量化自闭症谱系障碍的显着网络（SN）内大脑区域之间的同步稳定性。利用自闭症脑成像数据交换数据库中 207 名儿童（ASD/典型发育对照（TC）：105/102）的静息态功能磁共振成像数据，我们分析了通过K-means聚类分析和TC组得出ASD亚型，并检查ASD亚型的同步稳定性和临床症状严重程度之间的关系。根据 ASD SN 内的同步稳定性，我们确定了两种亚型，它们相对于 TC 组在同步稳定性方面表现出相反的变化。此外，ASD 亚型 1 和 2 的同步稳定性可以分别预测社交互动和沟通障碍。这些发现揭示了 SN 内具有不同同步稳定性模式的 ASD 亚群表现出不同的临床症状，并强调了从非线性角度探索 ASD 潜在神经机制的重要性。