Predicting Brain Regions Related to Alzheimer's Disease Based on Global Feature - Wang, Qi; Chen, Siwei; Wang, He; Chen, Luzeng; Sun, Yongan; Yan, Guiying (Frontiers in Computational Neuroscience, 2021)
Alzheimer's disease (AD) is a common neurodegenerative disease in the elderly, early diagnosis and timely treatment are very important to delay the course of the disease. In the past, most brain regions related to AD were identified based on the imaging methods, and only some atrophic brain regions could be identified. In this work, we used mathematical models to identify the potential brain regions related to AD. In this study, 20 AD patients and 13 healthy controls (non-AD) were recruited by the neurology outpatient department or the neurology ward of Peking University First Hospital from September 2017 to March 2019. First, diffusion tensor imaging (DTI) was used to construct the brain structural network. Next, we set a new local feature index 2hop-connectivity to measure the correlation between different regions. Compared with the traditional graph theory index, 2hop-connectivity exploits the higher-order information of the graph structure. And for this purpose, we proposed a novel algorithm called 2hopRWR to measure 2hop-connectivity. Then, we proposed a new index GFS (Global Feature Score) based on a global feature by combing five local features: degree centrality, betweenness centrality, closeness centrality, the number of maximal cliques, and 2hop-connectivity, to judge which brain regions are related to Alzheimer's disease. As a result, the top ten brain regions in the GFS scoring difference between the AD and the non-AD groups were related to AD by literature verification. The results of the literature validation comparing GFS with local features showed that GFS was superior to individual local features. Finally, the results of the canonical correlation analysis showed that the GFS was significantly correlated with the scores of the Mini-Mental State Examination (MMSE) scale and the Montreal Cognitive Assessment (MoCA) scale. Therefore, we believe the GFS can also be used as a new biomarker to assist in diagnosis and objective monitoring of disease progression. Besides, the method proposed in this paper can be used as a differential network analysis method for network analysis in other domains.