计算机应用 ›› 2017, Vol. 37 ›› Issue (11): 3339-3344.DOI: 10.11772/j.issn.1001-9081.2017.11.3339

• 应用前沿、交叉与综合 • 上一篇    

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

郭浩, 刘磊, 陈俊杰   

  1. 太原理工大学 计算机科学与技术学院, 太原 030024
  • 收稿日期:2017-05-16 修回日期:2017-07-17 出版日期:2017-11-10 发布日期:2017-11-11
  • 通讯作者: 陈俊杰
  • 作者简介:郭浩(1981-),男,山西祁县人,副教授,博士,CCF会员,主要研究方向:人工智能、智能信息处理、脑信息学;刘磊(1992-),男,山西太原人,硕士研究生,主要研究方向:人工智能、智能信息处理、脑信息学;陈俊杰(1956-),男,河北定州人,教授,博士,主要研究方向:人工智能、智能信息处理、脑信息学。
  • 基金资助:
    国家自然科学基金资助项目(61373101,61472270,61402318,61672374);山西省科技厅应用基础研究项目青年面上项目(201601D021073);山西省教育厅高等学校科技创新研究项目(2016139)。

Brain network analysis and classification for patients of Alzheimer's disease based on high-order minimum spanning tree

GUO Hao, LIU Lei, CHEN Junjie   

  1. College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan Shanxi 030024, China
  • Received:2017-05-16 Revised:2017-07-17 Online:2017-11-10 Published:2017-11-11
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61373101, 61472270, 61402318,61672374), the Natural Science Foundation of Shanxi Province (201601D021073), the Scientific and Technological Innovation Program of Higher Education Institutions in Shanxi (2016139).

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。

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 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 high-order minimum spanning tree greatly improves the accuracy of Alzheimer's disease detection.

Key words: brain network, Minimum Spanning Tree (MST), machine learning, Alzheimer's Disease (AD), functional magnetic resonance imaging

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