多模式多层网络的中心性与执行职能有关
Lucas C Breedt1, Fernando A N Santos1,2, Arjan Hillebrand3
1Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, The Netherlands.
Network neuroscience (Cambridge, Mass.)
|June 20, 2023
概括
使用多层框架进行分析,在前端-双层网络 (FPN) 内的更高整合与更好的执行功能 (EF) 有关. 这种方法提供了对认知过程的更全面的了解.
科学领域:
- 神经科学是一个神经科学.
- 认知科学 认知科学
- 网络科学 网络科学
背景情况:
- 执行功能 (EF) 依赖于复杂的大脑网络集成.
- 对EF而言,前端平行网络 (FPN) 是至关重要的,但单模数据集成仍然是一个挑战.
研究的目的:
- 使用多层框架整合多式神经成像和神经心理数据.
- 调查FPN整合在执行职能中的作用.
主要方法:
- 利用了33名健康成年人的扩散MRI,休息状态fMRI,MEG和神经心理数据.
- 构建FPN的单层和多层网络模型.
- 计算单层和多层自向量中心性来测量FPN集成.
主要成果:
- 多层FPN的中心性,反映了网络集成,与更好的执行功能有积极的相关性.
- 单层FPN中心性与执行职能没有显著的关联.
- 与单层措施相比,多层方法没有观察到EF解释变异的统计学上显著增加.
结论:
- 对于执行职能来说,FPN集成至关重要.
- 多层网络框架显示了对认知过程的更深入理解的希望.
- 这项研究强调了整合多式联络数据用于网络神经科学研究的好处.
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