Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, United States of America.
PLoS One. 2023 Mar 13;18(3):e0276419. doi: 10.1371/journal.pone.0276419. eCollection 2023.
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.
理解人群中大脑网络的常见拓扑特征对于理解大脑功能至关重要。将人类连接组抽象为图在深入了解大脑网络的拓扑性质方面发挥了关键作用。在考虑异质性和随机性的情况下，开发脑图的组级统计推断程序仍然是一项具有挑战性的任务。在这项研究中，我们开发了一个基于持久同调的稳健统计框架，使用有序统计量来分析脑网络。有序统计量的使用大大简化了持久条码的计算。我们使用全面的模拟研究验证了所提出的方法，然后将其应用于静息状态功能磁共振图像。我们发现男性和女性大脑网络之间存在统计学上显著的拓扑差异。
