Guo Xiaonan, Zhang Xia, Liu Junfeng, Zhai Guangjin, Zhang Tao, Zhou Rongjuan, Lu Huibin, Gao Le
School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China.
School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China.
Prog Neuropsychopharmacol Biol Psychiatry. 2024 Apr 20;131:110956. doi: 10.1016/j.pnpbp.2024.110956. Epub 2024 Feb 1.
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.
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，忽略了网络或脑区之间同步所涉及的非线性特性。
在本研究中，基于动力系统的非线性方法交叉递归量化分析被用于量化ASD显著网络（SN）内脑区之间的同步稳定性。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均值聚类分析得出的ASD亚型与TC组之间同步稳定性的分层和捕获时间差异，并研究了同步稳定性与ASD亚型临床症状严重程度之间的关系。
基于ASD的SN内的同步稳定性，我们识别出两种亚型，它们相对于TC组在同步稳定性上表现出相反的变化。此外，ASD亚型1和2的同步稳定性分别可以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亚组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。
