在休息大脑功能的合振荡器模型中,共同发展的动力学和拓学
Maria Pope1, Caio Seguin2, Thomas F Varley3
1Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States; School of Informatics, Computing & Engineering, Indiana University, Bloomington, IN 47405, United States.
NeuroImage
|July 6, 2023
概括
这项研究引入了一个新的大脑网络模型,该模型可以动态选择活跃的连接,从而改善功能连接性预测. 这种动态方法为大脑沟通和结构功能关系提供了更现实的观点.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 网络科学 网络科学
背景情况:
- 大脑网络结构限制了功能,但动态模型往往忽视了连接如何被选择性地使用.
- 通信模型强调,大脑可能不会同时或均地使用所有连接.
研究的目的:
- 开发一种新的大脑活动动态模型,该模型包含大脑区域之间的选择性通信.
- 研究如何动态限制网络通信影响大脑结构和功能之间的关系.
主要方法:
- 一个相延迟的库拉莫托合振荡器模型被调整为在每个时间步骤中动态限制节点之间的通信.
- 根据当地的动态状态,合网络结构和动态,选择了一个活跃的子图.
- 分析了该模型与实证时间平均功能的连接性相适应.
主要成果:
- 这种新型号的性能明显超过了具有相延迟的标准库拉莫托模型,即使具有额外的参数.
- 分析揭示了一个缓慢演变的拓,在活跃边缘时间序列中出现间歇性集成和分离事件.
结论:
- 在大脑网络模型中动态限制通信提供了更准确的脑功能表现.
- 与节点动力学一起研究网络动力学,可以更好地理解大脑结构-功能关系.
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