基于高阶最小生成树的脑网络分析及对阿兹海默氏症患者的分类

摘要

利用静息态功能磁共振成像技术来研究大脑的功能连接网络是当前脑疾病研究的重要方法之一.这种方法能准确地检测包括阿兹海默氏症在内的多种脑疾病.然而,传统的网络只是研究两个脑区之间相关程度,而且缺乏对大脑区域之间更深层次的交互信息和功能连接之间关联程度的研究.为了解决这些问题,提出了一种构建高阶最小生成树功能连接网络的方法,该方法不仅保证了功能连接网络的生理学意义,而且研究了网络中更复杂的交互信息,提高了分类的准确率.分类结果显示,基于高阶最小生成树功能连接网络的静息态功能磁共振成像分类方法大幅提高了阿兹海默氏症检测的准确率.%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 detect a variety of brain diseases,including Alzheimer's disease.However,the traditional network only studies the correlation between the two brain regions,and lacks a deeper interaction between the brain regions and the association between functional connections.In order to solve these problems,a method was proposed to construct a functional connectivity network of high-order minimum spanning tree,which not only ensured the physiological Significance of functional connectivity network,but also studied 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 functional connectivity network of highorder minimum spanning tree greatly improves the accuracy of Alzheimer's disease detection.