论文标题
静止的人脑网络的持续同源状态空间估计
Persistent Homological State-Space Estimation of Functional Human Brain Networks at Rest
论文作者
论文摘要
我们介绍了一种创新的，数据驱动的拓扑数据分析（TDA）技术，用于估计静止时动态变化的功能性人脑网络的状态。我们的方法利用Wasserstein距离来衡量拓扑差异，从而使大脑网络聚集到不同的拓扑状态。该技术的表现优于常用的K-均值聚类在识别大脑网络状态空间方面，无需明确的模型规范而无需有效地纳入大脑网络状态空间。我们进一步研究了这些拓扑特征的遗传基础，并研究了这种状态变化的遗传力。我们的发现表明，大脑网络的拓扑，尤其是在动态状态的变化中，可能会拥有重要的隐藏遗传信息。该方法的MATLAB代码可在https://github.com/laplcebeltrami/ph-stat上获得。
We introduce an innovative, data-driven topological data analysis (TDA) technique for estimating the state spaces of dynamically changing functional human brain networks at rest. Our method utilizes the Wasserstein distance to measure topological differences, enabling the clustering of brain networks into distinct topological states. This technique outperforms the commonly used k-means clustering in identifying brain network state spaces by effectively incorporating the temporal dynamics of the data without the need for explicit model specification. We further investigate the genetic underpinnings of these topological features using a twin study design, examining the heritability of such state changes. Our findings suggest that the topology of brain networks, particularly in their dynamic state changes, may hold significant hidden genetic information. MATLAB code for the method is available at https://github.com/laplcebeltrami/PH-STAT.
