A Spatiotemporal Brain Network Analysis of Alzheimer’s Disease Based on Persistent Homology - Xing, Jiacheng; Jia, Jiaying; Wu, Xin; Kuang, Liqun (Frontiers in Aging Neuroscience, 2022)
Current brain network studies based persistent homology mainly focus on the spatial evolution over multiple spatial scales, and there is little research on the evolution of Alzheimer's disease (AD) spatiotemporal brain network. This paper proposed a persistent homology-based method by combining multiple temporal windows and spatial scales to study the spatiotemporal evolution of brain functional networks. Specifically, a time-sliding window method was performed to establish spatiotemporal network and the persistent homology-based features of such network were obtained. We evaluated our proposed method using resting state functional MRI dataset from Alzheimer’s Disease Neuroimaging Initiative with 31 AD patients and 37 normal controls (NC). In the statistical analysis experiment, most network properties showed better statistical power in spatiotemporal networks than in spatial networks. Moreover, the persistent homology-based features detected more significant differences between groups than standard graph theory properties in spatiotemporal networks. In the clustering experiment, the brain networks on the sliding windows of all subjects were clustered into two highly structured connection states. The AD group showed a longer residence time and a higher window ratio in weak connection state than the NC group, which may be due to the fact that AD patients have not established a firm connection. In summary, the spatiotemporal brain network we constructed contains more detailed information, and the dynamic spatiotemporal brain network analysis method based on persistent homology provides stronger adaptability and robustness in revealing the abnormalities of the functional organization of AD patients.