集成和分离之间的平衡推动了网络动态,最大限度地提高了多稳定性和超稳定性
Javier Palma-Espinosa1, Sebastián Orellana-Villota2, Carlos Coronel-Oliveros3,4,5
1Centro Interdisciplinario de Neurociencia de Valparaíso, Valparaíso, Chile. javier.palma@cinv.cl.
Scientific reports
|May 29, 2025
概括
大脑网络结构显著影响其在状态之间切换的能力. 模块化和局部和全球连接的平衡促进了对认知至关重要的丰富的大脑动态.
科学领域:
- 计算神经科学是一种神经科学.
- 网络科学 网络科学
- 系统神经科学 系统神经科学
背景情况:
- 大脑的结构连接塑造了它的动态功能状态,这是认知灵活性和稳定的关键方面.
- 了解整个大脑结构-动力学关系至关重要,但仍然具有挑战性,先前的工作重点是局部机制.
研究的目的:
- 研究网络集成-分离平衡如何影响大脑动态,特别是多稳定性和超稳定性.
- 识别预测神经动态复杂性的关键结构网络属性.
主要方法:
- 使用结构指标 (模块化,效率,小世界性) 分析了一系列网络模型.
- 使用神经质量模型模拟神经活动并分析功能连接动力学 (FCD).
- 使用相互信息 (MI) 量化结构动力学关系.
主要成果:
- 分隔的网络保持动态同步;小世界网络表现出最丰富的动态.
- 在具有中等小世界性的网络中观察到峰值动态丰富性,其特点是高FCD变异性和转移稳定性.
- 网络模块化是动态最强的预测因素,使状态之间的过渡成为可能.
结论:
- 当地专业化,全球整合和模块化之间的平衡对于促进大脑网络的动态复杂性至关重要.
- 这些发现增强了对结构特征如何支配神经动态的理解,支持认知功能.
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