大脑网络通信:概念,模型和应用
Caio Seguin1,2, Olaf Sporns3,4,5,6, Andrew Zalesky7,8
1Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia. caio.seguin@unimelb.edu.au.
Nature reviews. Neuroscience
|July 12, 2023
概括
神经科学研究正在探索超越最短路径的新的大脑网络通信模型. 这篇评论整理了这些模型,将图形理论与神经信号联系起来,以更好地了解大脑功能.
科学领域:
- 神经科学是一个神经科学.
- 网络科学 网络科学
- 计算神经科学是一种神经科学.
背景情况:
- 连接学和网络神经科学的进步使研究复杂的大脑网络成为可能.
- 传统模型假设大脑通信只遵循最短的路径.
- 最近的发现挑战了这一假设,需要新的网络通信模型.
研究的目的:
- 为了调查大脑网络通信模型的最新发展.
- 提供网络通信模型和措施的分类.
- 突出应用和指导网络神经科学未来的研究.
主要方法:
- 图形理论数学与生物神经信号 (例如,传输延迟,代谢成本) 之间的概念联系.
- 将关键的网络通信模型和措施组织成一个分类.
- 对基础,认知和临床神经科学中突出的应用进行审查.
主要成果:
- 提出了一个分类系统,以帮助研究人员导航各种网络通信模型.
- 突出了各种连接体信号概念化的优点,缺点和解释.
- 网络通信模型被证明是研究大脑功能的灵活框架.
结论:
- 网络通信模型为神经科学研究提供了一个可处理和可解释的框架.
- 未来的研究应该集中在这些模型的开发,应用和验证上.
- 了解多突触沟通对于推进神经科学至关重要.
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