关键词: 关键词: 脑网络, 最小生成树, 机器学习, 阿兹海默氏症, 功能磁共振成像

Abstract: The use of resting-state functional magnetic resonance imaging to study the functional connectivity network of the brain is one of the important methods of current brain disease research. This method can accurately diagnose a variety of brain diseases, including Alzheimer's disease. However, the traditional network only studies the degree of correlation between the two brain regions, and lacks a deeper degree of interaction between the brain regions and the degree of association between functional connections. In order to solve these problems, this paper proposes a method to construct high-order minimum spanning tree functional connectivity network, which not only ensures the physiological significance of functional connectivity network, but also studies the more complex interactive information in the network and improves the accuracy of classification. The classification results show that the resting-state functional magnetic resonance imaging classification method based on the high-order minimum spanning tree functional connectivity network greatly improves the accuracy of diagnosis of Alzheimer's disease.

Key words: Keywords: Brain Network, Minimum Spanning Tree, Machine Learing, Alzheimer's Disease, Functional Magnetic Resonance Imaging