Statistical Physics of Brain Networks

脑网络的统计物理

• 批准号: 1305476
• 负责人: Hernan Makse
• 金额: $ 37.94万
• 依托单位: CUNY City College
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1305476&HistoricalAwards=false
• 关键词: Statistical; Physics; Brain; Networks

In this project the PIs will develop a theoretical framework to understand information processing in brain networks. The theoretical developments will be tested with experiments done in the collaborating lab of Canals (Alicante) by observation of the hemodynamic and electrical neural activity in animal with micro-electric stimulation in in-vivo animal experiments. A vast corpus of theoretical analysis and experimental data will serve to analyze the brain as a network of networks. This will involve a novel theoretical framework conceived to robustly determine how modules dynamically form and share information at different scales. The network analysis will reveal the brain nodes that are essential to control brain functionality in terms of super-spreaders and super-inhibitor nodes, cascading effects, robustness and vulnerability to node failure. The mathematical framework of the PIs challenges current thinking regarding the functional structure of the brain as a small-world and scale-free network, which is defined by short paths, large local clustering and a single degree distribution. Small-world networks have been proposed to solve a basic conundrum: the brain needs to form modules which ought to be sufficiently independent to guarantee functional specialization and sufficiently connected to bind multiple processors for efficient information transfer. However, this structure presents an intrinsic tension between shortcuts generating small-worlds and the persistence of modularity; a global property unrelated to local clustering. In this project the PIs depart from the current thinking in brain functional structure, replacing the concept of small-world by that of hierarchical Networks of Networks that describes the brain as a set of hierarchical modules made of weak/strong links. The broader significance of the proposed theory for the large-scale organization of the brain extends the mathematical theory of networks to radically novel information processing systems. The findings from this research will have implications, not only for systems neuroscience, but also for a number of complex systems ranging from technological, social to biological networks. This proposal represents a symbiosis between the labs of two physicists with expertise in statistical physics and complex networks and a neuroscientist. Such a setting will provide interdisciplinary and international opportunities to students involved in this project. Further broader educational impacts include involvement of underrepresented minority students from CCNY and curriculum development.

在这个项目中，PI将开发一个理论框架，以了解大脑网络中的信息处理。理论发展将通过在动物中使用微电动刺激在体内动物实验中观察到动物的血流动力学和电神经活性的结合实验室（Alicante）进行的实验。大量的理论分析和实验数据将有助于将大脑作为网络网络分析。这将涉及一个新颖的理论框架，以鲁棒性地确定模块在不同尺度上的动态形成和共享信息。网络分析将揭示大脑节点对于控制大脑功能的大脑节点，而超级抑制剂和超抑制剂节点，级联效应，稳健性和易受节点衰竭的脆弱性。 PI的数学框架挑战了当前关于大脑功能结构作为小世界和无尺度网络的思维，该网络由短路，较大的本地聚类和单度分布定义。已经提出了小世界网络来解决基本的难题：大脑需要形成模块，这些模块应该足够独立以确保功能专业化并足够连接以结合多个处理器以进行有效的信息传输。但是，这种结构呈现出产生小世界的快捷方式与模块化的持久性之间的内在张力。与本地聚类无关的全球属性。在这个项目中，PI偏离了当前在大脑功能结构方面的思维，将小世界的概念取代了网络的层次网络，该网络将大脑描述为由弱/强链接组成的一组分层模块。提出的理论对大脑大规模组织的更广泛的意义将网络的数学理论扩展到了根本新颖的信息处理系统。这项研究的发现将不仅对系统神经科学有影响，而且对从技术，社会到生物网络的许多复杂系统。该建议代表了两个物理学家的实验室之间的共生，这些物理学家在统计物理学和复杂网络方面具有专业知识与神经科学家。这样的环境将为参与该项目的学生提供跨学科和国际机会。更广泛的教育影响包括来自CCNY和课程发展的代表性不足的少数学生的参与。