Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network

doi:10.1155/2017/4820935

	Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network
	Guo, Hao2,3 ; Qin, Mengna 3; Chen, Junjie 3; Xu, Yong 1; Xiang, Jie 3
刊名	COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
	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%.
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关键词	SMALL-WORLD NETWORKS ; RESTING-STATE ; CONNECTIVITY ; SCHIZOPHRENIA ; ALGORITHM ; CORTEX
WOS研究方向	Mathematical & Computational Biology
语种	英语
出版者	HINDAWI LTD
WOS记录号	WOS:000418826100001
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
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/28232]
专题	中国科学院自动化研究所
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.