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来理解大脑网络中的信息处理。理论进展将通过 Canals 合作实验室（阿利坎特）进行的实验进行测试，通过体内动物实验中微电刺激观察动物的血流动力学和电神经活动。大量的理论分析和实验数据将有助于将大脑作为网络的网络进行分析。这将涉及一个新颖的理论框架，旨在稳健地确定模块如何在不同规模下动态形成和共享信息。网络分析将揭示控制大脑功能所必需的大脑节点，包括超级传播者和超级抑制者节点、级联效应、鲁棒性和节点故障的脆弱性。 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标度网络的想法，该网络由短路径、大局部聚类和单度分布定义。小世界网络被提出来解决一个基本难题：大脑需要形成模块，这些模块应该足够独立以保证功能专业化，并充分连接以绑定多个处理器以实现有效的信息传输。然而，这种结构在生成小世界的捷径与模块化的持久性之间呈现出内在的张力。与局部聚类无关的全局属性。在这个项目中，PI 背离了当前大脑功能结构的思维，用分层网络网络的概念取代了小世界的概念，将大脑描述为一组由弱/强链接组成的分层模块。所提出的大脑大规模组织理论的更广泛意义是将网络数学理论扩展到全新的信息处理系统。这项研究的结果不仅对系统神经科学产生影响，而且对从技术、社会到生物网络等许多复杂系统也产生影响。该提案代表了两位拥有统计物理学和复杂网络专业知识的物理学家与一位神经科学家的实验室之间的共生。这样的环境将为参与该项目的学生提供跨学科和国际化的机会。更广泛的教育影响包括 CCNY 代表性不足的少数族裔学生的参与和课程开发。