论文标题
通过秩序统计数据对人脑网络进行拓扑数据分析
Topological Data Analysis of Human Brain Networks Through Order Statistics
论文作者
论文摘要
了解人类脑网络的共同拓扑特征是理解大脑功能的核心。人类连接组作为图的抽象在获得大脑网络拓扑特性的见解方面至关重要。在考虑异质性和随机性的同时，在大脑图中的群体级统计推断程序的发展仍然是一项艰巨的任务。在这项研究中，我们使用用于分析大脑网络的顺序统计数据，基于持续的同源性开发了一个强大的统计框架。订单统计信息的使用大大简化了持续条形码的计算。我们使用全面的仿真研究验证了提出的方法，并随后应用于静止状态的功能磁共振图像。我们发现男性和女性脑网络之间具有统计学意义的拓扑差异。
Understanding the common topological characteristics of the human brain network across a population is central to understanding brain functions. The abstraction of human connectome as a graph has been pivotal in gaining insights on the topological properties of the brain network. The development of group-level statistical inference procedures in brain graphs while accounting for the heterogeneity and randomness still remains a difficult task. In this study, we develop a robust statistical framework based on persistent homology using the order statistics for analyzing brain networks. The use of order statistics greatly simplifies the computation of the persistent barcodes. We validate the proposed methods using comprehensive simulation studies and subsequently apply to the resting-state functional magnetic resonance images. We found a statistically significant topological difference between the male and female brain networks.
