Understanding Alzheimer’s Disease through Graph Theory

• 作者: V. Yegnanarayanan Y. Krithicaa Narayanaa
• 来源期刊: 应用数学与应用物理(英文)
• 学科: 数学
• 关键词: GRAPHS Alzheimer’s Disease Brain Networks Centrality Measures
• 年，卷（期）: 2020,（10）
• 页码范围: 2182-2195
• 页数: 14页
• 分类号: O15

摘要

<div style="text-align:justify;"> We know that functional and structural organization is altered in human brain network due to Alzheimer’s disease. In this paper we highlight how Graph Theory techniques, its structural parameters like connectivity, diameter, vertex centrality, betweenness centrality, clustering coefficient, degree distribution, cluster analysis and graph cores are involved to analyse magnetoencephalography data to explore functional network integrity in Alzheimer’s disease affected patients. We also record that both weighted and unweighted undirected/directed graphs depending on functional connectivity analysis with attention to connectivity of the network and vertex centrality, could model and provide explanation to loss of links, status of the hub in the region of parietal, derailed synchronization in network and centrality loss at the vital left temporal region that is clinically significant were found in cases carrying Alzheimer’s disease. We also notice that graph theory driven measures such as characteristic path length and clustering coefficient could be used to study and report a sudden electroencephalography effect in Alzheimer’s disease through entropy of the cross-sample. We also provide adequate literature survey to demonstrate the latest and advanced graphical tools for both graph layouts and graph visualization to understand the complex brain networks and to unravel the mysteries of Alzheimer’s disease. </div>