论文标题
功能性人脑网络的动态拓扑数据分析
Dynamic Topological Data Analysis of Functional Human Brain Networks
论文作者
论文摘要
开发可靠的方法来区分随着时间的流逝而变化的不同瞬态大脑状态是大脑成像研究中的关键神经科学挑战。拓扑数据分析（TDA）是基于代数拓扑的新型框架，可以应对这一挑战。但是，现有的TDA在某种程度上仅限于捕获动态变化的大脑网络的静态摘要。我们提出了一个新颖的动态-TDA框架，该框架在大脑网络的时间序列中构建了持续的同源性。我们构建了基于Wasserstein距离的推理程序，以区分网络的时间序列。该方法应用于人脑的静止状态功能磁共振图像。我们证明，我们提出的动态-TDA方法可以明显区分男性和女性脑网络的拓扑模式。用于实现此方法的MATLAB代码可在https://github.com/laplcebeltrami/ph-stat上获得。
Developing reliable methods to discriminate different transient brain states that change over time is a key neuroscientific challenge in brain imaging studies. Topological data analysis (TDA), a novel framework based on algebraic topology, can handle such a challenge. However, existing TDA has been somewhat limited to capturing the static summary of dynamically changing brain networks. We propose a novel dynamic-TDA framework that builds persistent homology over a time series of brain networks. We construct a Wasserstein distance based inference procedure to discriminate between time series of networks. The method is applied to the resting-state functional magnetic resonance images of human brain. We demonstrate that our proposed dynamic-TDA approach can distinctly discriminate between the topological patterns of male and female brain networks. MATLAB code for implementing this method is available at https://github.com/laplcebeltrami/PH-STAT.
