解开由网络驱动的流程引发的超大规模组织
Giacomo Barzon1,2, Oriol Artime3,4,5, Samir Suweis1,6,7
1Padova Neuroscience Center, University of Padua, Padova 35131, Italy.
概括
我们介绍了雅可比距离,这是复杂系统的新度量,以揭示隐藏的网络几何. 这种方法有效地将网络结构和动态联系起来,在脑网络分析中表现优于传统方法.
科学领域:
- 复杂系统科学 复杂系统科学
- 网络科学 网络科学
- 动态系统理论 动态系统理论
背景情况:
- 复杂的系统表现出来自动态和网络之间的相互作用的新兴模式.
- 仅凭现有的拓或动态描述符就不足以捕捉这种相互作用.
- 具体的动力学方法限制了对一般原理的理解.
研究的目的:
- 开发一种新的度量,雅可比距离,以揭示网络驱动过程中的潜在几何.
- 分析网络拓与动态过程之间的相互作用.
- 评估该指标在各种应用中的实用性,包括人类大脑网络.
主要方法:
- 在合成和现实世界网络上的非线性动态模型中计算雅可比距离.
- 网络驱动潜伏几何学的分析和计算分析.
- 使用雅科比矩阵光谱解释结构功能不匹配.
主要成果:
- 雅各比亚距离捕捉了时空扰动的传播,揭示了固有的网络几何.
- 过程驱动的潜态几何学取决于动态和网络拓.
- 功能和拓组织之间的不匹配是由雅各比亚矩阵谱解释的.
结论:
- 雅可比式距离为理解复杂系统提供了一种统一的方法.
- 该指标揭示了功能和拓网络组织之间的潜在差异.
- 雅可比式距离在分析人类大脑网络,连接结构和功能的传统方法上提供了显著的优势.
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