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Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
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随机效应模型用于理解大脑功能和结构连接之间的变化和关联.

Lingyi Peng, Qiaochu Wang, Yaotian Wang

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    概括

    功能连接 (FC) 和结构连接 (SC) 的网络层面和边缘层面的分析为大脑网络提供了不同的见解. 新的模型揭示了影响这些相关性的不同变异源.

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    科学领域:

    • 神经科学是一个神经科学.
    • 网络科学 网络科学
    • 生物统计学 生物统计学

    背景情况:

    • 人类大脑是一个复杂的网络,具有功能连接 (FC) 和结构连接 (SC).
    • 以前的研究主要集中在FC和SC之间的网络级相关性上,忽视了边缘级分析.
    • 边缘级相关性,检查FC-SC关系在各个主体之间的个别连接,是不充分探索的.

    研究的目的:

    • 系统地分析和比较网络层面和边缘层面的FC-SC相关性.
    • 解释网络层面和边缘层面FC-SC协会优势之间观察到的差异.
    • 引入新的统计模型来分解FC和SC的变化.

    主要方法:

    • 对网络层面和边缘层面的功能连接 (FC) 和结构连接 (SC) 相关性的系统分析.
    • 开发和应用新的随机效应模型.
    • 将FC和SC的变化分解为主体效应,边缘效应及其相互作用.

    主要成果:

    • 网络层面和边缘层面的FC-SC相关性导致关于大脑功能结构关联强度的不同结论.
    • 开发的随机效应模型成功地解开了FC和SC可变性的来源.
    • 不同的效应显著影响网络层面与边缘层面的相关性,独特地对总变异性作出贡献.

    结论:

    • 这项研究强调了考虑网络层面和边缘层面分析对于全面了解大脑连接的重要性.
    • 新的统计建模为评估功能和结构性大脑网络变化源提供了定量框架.
    • 这些发现为功能性和结构性大脑组织之间的复杂关系提供了新的见解.