Understanding Functional Network Reorganization During the Information Processing

了解信息处理过程中的功能网络重组

• 批准号: 8670301
• 负责人: MICHAL R ZOCHOWSKI
• 金额: $ 33.35万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-30 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8670301
• 关键词: Animals; Behavioral; Biological Neural Networks; Brain; Brain Pathology; Calcium; Cell Culture Techniques; Characteristics; Cognitive; Cognitive deficits; Communities; Community Networks; Complex; Computer Simulation; Controlled Environment; Coupling; Data; Data Analyses; Data Set; Detection; Development; Diagnostic Procedure; Electric Stimulation; Electroencephalography; Elements; Engineering; Functional Magnetic Resonance Imaging; Goals; Hippocampus (Brain); Human; In Vitro; Interdisciplinary Study; Learning; Measurement; Mediating; Memory; Methods; Metric; Monitor; Mus; Neural Network Simulation; Neurons; Neurosciences; Pathologic Processes; Pathology; Pattern; Physics; Process; Property; Sensory; Simulate; Site; Structure; Synaptic plasticity; Techniques; Testing; Time; Work; analytical tool; base; brain electrical activity; computerized tools; in vivo; information processing; interest; neural patterning; neuronal patterning; novel; optical imaging; public health relevance; relating to nervous system; spatiotemporal; stem; tool; tool development

DESCRIPTION (provided by applicant): It has become clear that spatio-temporal patterning of neuronal activity reflects a complex interaction between dynamical properties of neurons and those of the networks they form. The constant reorganization of these properties underlies most cognitive processes in the brain, and their dysregulation causes brain pathologies. The development of multisite optical imaging and electrophysiological recording techniques has enabled the identification and monitoring of dynamic network organization (so called functional network structure) on different time and spatial scales. To fully understand functional dynamics among neurons in in vitro and in vivo situations, it is imperative to develop analytical and computational tools to detect and characterize these distributed functional network structures from experimental recordings. Within the theoretical community, much interest has been focused on developing tools that allow detection of community structure in networks. These tools are generally optimized to analyze the physical (i.e., anatomical) space of network connections, and to parse the network connectivity structure into communities. Elucidation of functional connectivity in neuronal networks presents a very specific challenge, since anatomical connectivity is only one of the factors that mediate formation of functional interactions. The primary challenge is to construct tools that efficiently detect the formation of dynamic functional communities based on the spatio- temporal patterning of neural electrical activity, and that reliably quantify the properties of the detected communities. We have recently developed a functional community detection method that answers this challenge. We propose to couple it with optimal metrics that permit robust detection of dynamically changing network communities and test it thoroughly in computational, in vitro and in vivo settings. Specifically, w propose to: develop and test, through computer simulations, a set of linear and non-linear metrics tailored for the measurement of dynamic changes in functional network communities (AIM 1); use these tools to investigate formation of functional communities in dissociated, mouse hippocampal cell cultures (AIM 2); and apply the tools to in vivo hippocampal multisite recordings obtained from freely behaving mice undergoing a cognitive task (AIM 3). Tools to detect dynamic formation of functional network structures from temporal activity patterns in a subset of network elements will have a very significant impact on neuroscience, as well as in other biosciences where such information is available. Within neuroscience, such tools will provide a better understanding of brain function during different cognitive tasks. They will also provide a valuable diagnostic method for identifying functional network pathologies.

描述（由申请人提供）：很明显，神经元活动的时空模式反映了神经元的动力学特性与它们形成的网络的动力学特性之间的复杂相互作用。这些特性的持续重组是大脑中大多数认知过程及其失调引起大脑病理的基础。多站点光学成像和电生理记录技术的开发使在不同时间和空间尺度上对动态网络组织（所谓的功能网络结构）的识别和监视。为了充分了解体外和体内情况下神经元之间的功能动力学，必须开发分析和计算工具，以检测和表征从实验记录中的这些分布式功能网络结构。 在理论社区中，非常兴趣集中在开发允许在网络中检测社区结构的工具。通常对这些工具进行优化，以分析网络连接的物理（即解剖学）空间，并将网络连接结构解析为社区。神经元网络中功能连通性的阐明提出了一个非常具体的挑战，因为解剖连通性只是介导功能相互作用形成的因素之一。主要的挑战是构建工具，这些工具可以根据神经电活动的时空模式有效地检测动态功能群落的形成，并可靠地量化检测到的社区的性质。我们最近开发了一种功能性社区检测方法，以应对这一挑战。我们建议将其与最佳指标相结合，以允许对动态变化的网络社区进行强有力的检测，并在计算，体外和体内进行彻底测试。具体而言，W建议：通过计算机模拟开发和测试，这是一组用于测量功能网络社区动态变化的线性和非线性指标（AIM 1）；使用这些工具来研究分解的小鼠海马细胞培养物中功能群落的形成（AIM 2）；并将工具应用于从自由行为经历认知任务的小鼠获得的体内海马多站点记录（AIM 3）。 在网络元素子集中从时间活动模式中检测功能网络结构的动态形成的工具将对神经科学以及其他可用信息的生物科学产生非常重大的影响。在神经科学中，此类工具将在不同的认知任务中更好地了解大脑功能。他们还将提供一种有价值的诊断方法来识别功能网络病理。