Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network
文献类型：期刊论文
| 作者 | Guo, Hao2,3 ; Qin, Mengna3; Chen, Junjie3; Xu, Yong1; Xiang, Jie3
|
| 刊名 | COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
 |
| 出版日期 | 2017 |
| 页码 | 14 |
| ISSN号 | 1748-670X |
| DOI | 10.1155/2017/4820935 |
| 通讯作者 | Guo, Hao(feiyu_guo@sina.com) |
| 英文摘要 | High-order functional connectivity networks are rich in time information that can reflect dynamic changes in functional connectivity between brain regions. Accordingly, such networks are widely used to classify brain diseases. However, traditional methods for processing high-order functional connectivity networks generally include the clustering method, which reduces data dimensionality. As a result, such networks cannot be effectively interpreted in the context of neurology. Additionally, due to the large scale of high-order functional connectivity networks, it can be computationally very expensive to use complex network or graph theory to calculate certain topological properties. Here, we propose a novel method of generating a high-order minimum spanning tree functional connectivity network. This method increases the neurological significance of the high-order functional connectivity network, reduces network computing consumption, and produces a network scale that is conducive to subsequent network analysis. To ensure the quality of the topological information in the network structure, we used frequent subgraph mining technology to capture the discriminative subnetworks as features and combined this with quantifiable local network features. Then we applied a multikernel learning technique to the corresponding selected features to obtain the final classification results. We evaluated our proposed method using a data set containing 38 patients withmajor depressive disorder and 28 healthy controls. The experimental results showed a classification accuracy of up to 97.54%. |
| WOS关键词 | SMALL-WORLD NETWORKS ; RESTING-STATE ; CONNECTIVITY ; SCHIZOPHRENIA ; ALGORITHM ; CORTEX |
| 资助项目 | National Natural Science Foundation of China[61373101] ; National Natural Science Foundation of China[61472270] ; National Natural Science Foundation of China[61402318] ; National Natural Science Foundation of China[61672374] ; Natural Science Foundation of Shanxi Province[201601D021073] ; Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi[2016139] |
| WOS研究方向 | Mathematical & Computational Biology |
| 语种 | 英语 |
| WOS记录号 | WOS:000418826100001 |
| 出版者 | HINDAWI LTD |
| 资助机构 | National Natural Science Foundation of China ; Natural Science Foundation of Shanxi Province ; Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi |
| 源URL | [http://ir.ia.ac.cn/handle/173211/28232]  |
| 专题 | 自动化研究所_智能制造技术与系统研究中心_智能机器人团队 |
| 通讯作者 | Guo, Hao |
| 作者单位 | 1.Shanxi Med Univ, Hosp 1, Dept Psychiat, Taiyuan, Shanxi, Peoples R China 2.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing, Peoples R China 3.Taiyuan Univ Technol, Coll Comp Sci & Technol, Taiyuan, Shanxi, Peoples R China |
| 推荐引用方式 GB/T 7714 | Guo, Hao,Qin, Mengna,Chen, Junjie,et al. Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network[J]. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE,2017:14. |
| APA | Guo, Hao,Qin, Mengna,Chen, Junjie,Xu, Yong,&Xiang, Jie.(2017).Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network.COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE,14. |
| MLA | Guo, Hao,et al."Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network".COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE (2017):14. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。