NCS-FO: Edge-centric maps of functional brain network organization and dynamics

人类大脑由功能和结构连接的神经元组成，这些神经元形成了一个全脑的复杂网络。网络神经科学的一个主要目标是了解这个网络的组织如何帮助支持认知，在人类生命周期中进化，并在疾病和神经精神疾病中受损。然而，几乎所有解决这些问题的进展都依赖于一个特定的网络模型来数学地表示大脑连接的模式，而牺牲了其他可以提供互补或独特见解的模型。该项目旨在扩展和验证另一种以边缘为中心的框架，用于表示和分析大脑连接的模式。该项目将为大脑网络组织与认知/行为表型的关系提供新的见解，并揭示受试者认知状态变化所带来的超快时间尺度下的大脑网络动态。这项研究将支持脑科学、信息学和统计学之间的跨学科合作，并将支持从高中到博士后的各个层次的各种学员。边缘中心框架的主要创新是将功能连接时空分解为框架贡献。这种分解产生了每对大脑区域（网络中的边缘）的共同波动的时间序列。第一个目标是研究边泛函连通性的新构造--估计所有同涨落时间序列对之间的相关模式。将使用在休息时和受试者执行认知要求高的任务时采集的成像数据，为大型受试者队列（N 1000）生成边缘连接。将使用多变量统计方法来发现边缘连接与受试者的行为、人口统计学和临床测量之间的稳健关联。第二个目标是直接分析共同波动时间序列，利用超快的时间尺度来估计它们，以研究自然观看（电影观看）期间大脑网络重构的潜在驱动因素。该项目推进了边缘中心框架作为一个可行的工具，为一般神经科学发现，并将打开大门，为未来的研究，调查脑行为的关系和网络动态应用环境中，而不限于大规模的成像数据。该奖项反映了NSF的法定使命，并已被认为是值得支持的评估使用基金会的智力价值和更广泛的影响审查标准。

The human brain is made up of functionally and structurally connected neural elements that form a brain-wide complex network. A principal goal of network neuroscience is to understand how the organization of this network helps support cognition, evolves over the course of the human lifespan, and becomes compromised in disease and neuropsychiatric disorders. However, virtually all progress made towards addressing these questions has relied upon one particular network model for mathematically representing patterns of brain connectivity, at the expense of other models that could provide complementary or unique insight. This project aims to extend and validate an alternative edge-centric framework for representing and analyzing patterns of brain connectivity. The project will deliver new insights into the relationship of brain network organization with cognitive/behavioral phenotypes and shed light on brain network dynamics at ultra-fast timescales are paralleled by changes in subjects' cognitive states. This research will support cross-disciplinary collaboration among the brain sciences, informatics, and statistics, and will support a diverse set of trainees at all levels, from high school to postdoctoral.This principal innovation of the edge-centric framework is a spatiotemporal decomposition of functional connections into their framewise contributions. This decomposition yields a time series of co-fluctuations for every pair of brain regions (edges in the network). The first aim investigates the novel construct of edge functional connectivity -- the correlation pattern estimated among all pairs of co-fluctuation time series. Edge connectivity will be generated for a large cohort of subjects (N 1000) using imaging data acquired both at rest and while subjects were performing cognitively demanding tasks. Multivariate statistical methods will be used to discover robust associations between edge connectivity and subjects' behavioral, demographic, and clinical measures. The second aim analyzes co-fluctuation time series directly, taking advantage of the ultra-fast timescale at which they are estimated to investigate potential drivers of brain network reconfiguration during naturalistic viewing (movie-watching). This project advances the edge-centric framework as a viable tool for general neuroscientific discovery and will open the door for future studies to investigate brain-behavior relationships and network dynamics in applied contexts and not restricted to large-scale imaging data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.