同步和异步功能大脑网络的对比拓
Clayton C McIntyre1,2, Mohsen Bahrami1,3, Heather M Shappell1,4
1Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA.
Network neuroscience (Cambridge, Mass.)
|December 30, 2024
概括
本研究介绍了异步功能网络 (aFN) 并使用fMRI数据将其与同步功能网络 (sFN) 进行比较. aFN揭示了独特的大脑连接模式,为传统的sFN分析提供了补充的见解.
科学领域:
- 神经科学是一个神经科学.
- 网络神经科学 网络神经科学
- 计算神经科学是一种神经科学.
背景情况:
- 同步功能网络 (sFNs) 在网络神经科学中常用.
- 需要新的方法来探索其他功能性大脑连接措施.
研究的目的:
- 介绍和描述使用最佳因果生成的异步功能网络 (aFNs).
- 将aFNs的拓性质与传统的sFNs进行比较.
- 调查年龄对aFN和sFN网络效率和连接概率的影响.
主要方法:
- 分析了212名参与者的功能磁共振成像 (fMRI) 数据.
- 使用最佳因果产生了异步功能网络 (aFN).
- 通过基于关联的方法生成同步功能网络 (sFNs).
- 采用多变量混合效应模型来评估与年龄相关的相互作用与网络属性.
主要成果:
- 在控制网络密度后,aFN与sFN相比,具有更高的全球效率,但局部效率较低.
- 在脑干和轨道前皮层中确定了aFN中外向全球效率的关键节点.
- 在aFNs中,高输入总效率的节点与sFNs中的默认模式网络相关.
- 年龄与aFN中的全局效率和sFN中的局部效率相互作用,影响连接概率.
结论:
- aFN和sFN都提供了关于功能性大脑连接的独特而互补的信息.
- aFN为大脑网络动态提供了一个新的视角,超出了同步相关性.
- 这些发现强调了考虑不同的网络指标对于全面了解大脑功能的重要性.
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