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

人脑是由功能上和结构上相连的神经元件组成的，这些神经元件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，在人类的一生中进化，并在疾病和神经精神障碍中受到损害。然而，几乎所有在解决这些问题方面取得的进展都依赖于一个特定的网络模型，该模型以数学方式表示大脑连接的模式，而不是其他可以提供补充或独特洞察力的模型。该项目旨在扩展和验证一个替代的以边缘为中心的框架，以表示和分析大脑连接的模式。该项目将对大脑网络组织与认知/行为表型的关系提供新的见解，并揭示与受试者认知状态变化平行的超快时间尺度上的大脑网络动力学。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种级别的受训人员。边缘中心框架的主要创新是将功能联系的时空分解为其框架贡献。这种分解产生了每一对大脑区域(网络中的边缘)的共同波动的时间序列。第一个目的是研究边缘函数连通性的新结构--估计所有对共波动时间序列之间的相关模式。将使用在静止和当受试者执行认知要求高的任务时获得的成像数据，为大量受试者(N 1000)生成边缘连通性。多元统计方法将被用来发现边缘连通性与受试者的行为、人口统计学和临床测量之间的强大关联。第二个目的是直接分析共波动时间序列，利用它们被估计的超快时间尺度来调查自然观看(看电影)期间大脑网络重新配置的潜在驱动因素。该项目推动了以边缘为中心的框架作为一般神经科学发现的可行工具的发展，并将为未来的研究打开大门，以在应用环境中调查大脑-行为关系和网络动力学，而不限于大规模成像数据。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.