Method for persistent topological features extraction of schizophrenia patients’ electroencephalography signal based on persistent homology - Guo, Guangxing; Zhao, Yanli; Liu, Chenxu; Fu, Yongcan; Xi, Xinhua; Jin, Lizhong; Shi, Dongli; Wang, Lin; Duan, Yonghong; Huang, Jie; Tan, Shuping; Yin, Guimei (Frontiers in Computational Neuroscience, 2022)
With the development of network science and graph theory, brain network research has unique advantages in explaining those mental diseases, the neural mechanism of which is unclear. Additionally, it can provide a new perspective in revealing the pathophysiological mechanism of brain diseases from the system level. The selection of threshold plays an important role in brain networks construction. There are no generally accepted criteria for determining the proper threshold. Therefore, in this study, based on the topological data analysis of persistent homology theory, a multi-scale brain network modeling analysis method is developed, which enables us to quantify various persistent topological features on different scales in a coherent manner. In this method, the Vietoris–Rips filtering algorithm is used to extract dynamic persistent topological features by increasing the threshold in the range of full-scale distances. Subsequently, the persistent topological features are visualized using barcodes and persistence diagrams. Finally, the stability of persistent topological features is analyzed by calculating the Bottleneck distances and Wasserstein distances between the persistence diagrams. Experimental results show that the method extracts the topological features of brain networks more accurately and improves the accuracy of diagnostic classification compared to existing methods. This work lays a foundation for exploring the higher-order topology of brain functional networks in schizophrenia patients, and can also enhance the modeling ability of complex brain systems to better understand, analyze, and predict their dynamic behaviors.