解决自闭症谱系障碍中突出网络内的同步稳定性动态中的异质性
Xiaonan Guo1, Xia Zhang1, Junfeng Liu2
1School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China.
Progress in neuro-psychopharmacology & biological psychiatry
|January 31, 2024
概括
自闭症谱系障碍 (ASD) 显示出不同的大脑连接. 非线性分析揭示了两个ASD亚型,在突出网络中具有明显的同步模式,与特定症状严重程度相关.
科学领域:
- 神经科学是一个神经科学.
- 发育神经科学的发展神经科学.
- 计算神经科学是一种神经科学.
背景情况:
- 自闭症谱系障碍 (ASD) 的特点是静止状态功能连接 (FC) 的异质性.
- 传统的FC分析往往忽略了大脑区域之间的非线性同步动态.
- 非线性方法提供了一种更全面的方法来理解ASD中的大脑功能.
研究的目的:
- 在患有自闭症儿童的突出网络 (SN) 中研究非线性同步稳定性.
- 根据同步稳定性模式来识别ASD的潜在亚型.
- 检查同步稳定性与ASD临床症状严重程度之间的关系.
主要方法:
- 采用了非线性动态系统方法的交叉反复量化分析 (CRQA).
- 分析了207名儿童的休息状态功能磁共振成像 (rs-fMRI) 数据 (105名自闭症患者,102名典型的发展对照).
- 应用K-means聚类来识别ASD亚型,并分析了同步稳定性的层状性和捕获时间.
主要成果:
- 在SN中确定了两个不同的ASD亚型,与对照组相比,它们表现出相反的同步稳定性模式.
- 在ASD亚型1中的同步稳定性预测了社会互动障碍.
- 在ASD亚型2中的同步稳定性预测了通信障碍.
结论:
- 具有不同SN同步稳定性模式的ASD亚组与明显的临床症状相关.
- 对大脑连接的非线性分析为ASD神经机制提供了宝贵的见解.
- 这项研究强调了非线性动态在理解自闭症异质性方面的重要性.
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