Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin, United States of America.
PPG/ECG Signal, Taipei, Taiwan.
PLoS Comput Biol. 2024 May 13;20(5):e1011869. doi: 10.1371/journal.pcbi.1011869. eCollection 2024 May.
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.
我们介绍了一种创新的数据驱动拓扑数据分析（TDA）技术，用于估计静息状态下功能人脑网络的状态空间。我们的方法利用 Wasserstein 距离来测量拓扑差异，从而将大脑网络聚类为不同的拓扑状态。与常用的 k-means 聚类相比，该技术通过有效地整合数据的时间动态，而无需明确的模型指定，从而更有效地识别大脑网络状态空间。我们进一步使用双胞胎研究设计研究了这些拓扑特征的遗传基础，研究了这些状态变化的遗传性。我们的研究结果表明，大脑网络的拓扑结构，特别是在其动态状态变化中，可能包含重要的隐藏遗传信息。
