fMRI classification method with multiple feature fusion based on minimum spanning tree analysis

doi:10.1016/j.pscychresns.2018.05.001

	fMRI classification method with multiple feature fusion based on minimum spanning tree analysis
	Guo, Hao1,2 ; Yan, Pengpeng 1; Cheng, Chen 1,2; Li, Yao 1; Chen, Junjie 1; Xu, Yong 3; Xiang, Jie 1
发表期刊	PSYCHIATRY RESEARCH-NEUROIMAGING
ISSN	0925-4927
	2018-07-30
卷号	277 页码:14-27
通讯作者	Guo, Hao(feiyu_guo@sina.com)
摘要	Resting state functional brain networks have been widely studied in brain disease research. Conventional network analysis methods are hampered by differences in network size, density and normalization. Minimum spanning tree (MST) analysis has been recently suggested to ameliorate these limitations. Moreover, common MST analysis methods involve calculating quantifiable attributes and selecting these attributes as features in the classification. However, a disadvantage of these methods is that information about the topology of the network is not fully considered, limiting further improvement of classification performance. To address this issue, we propose a novel method combining brain region and subgraph features for classification, utilizing two feature types to quantify two properties of the network. We experimentally validated our proposed method using a major depressive disorder (MDD) patient dataset. The results indicated that MSTs of MDD patients were more similar to random networks and exhibited significant differences in certain regions involved in the limbic-cortical-striatal-pallidal-thalamic (LCSPT) circuit, which is considered to be a major pathological circuit of depression. Moreover, we demonstrated that this novel classification method could effectively improve classification accuracy and provide better interpretability. Overall, the current study demonstrated that different forms of feature representation provide complementary information.
关键词	Functional brain network Minimum spanning tree Classifier Depression Multiple feature fusion
DOI	10.1016/j.pscychresns.2018.05.001
关键词[WOS]	MAJOR DEPRESSIVE DISORDER ; BRAIN NETWORK ANALYSIS ; STATE FUNCTIONAL CONNECTIVITY ; WHITE-MATTER ABNORMALITIES ; CORTICAL THICKNESS ; GERIATRIC DEPRESSION ; ALZHEIMERS-DISEASE ; LONGITUDINAL MEG ; GLOBAL SIGNAL ; GRAPH KERNEL
收录类别	SCI
语种	英语
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] ; National Natural Science Foundation of China[61741212] ; Natural Science Foundation of Shanxi Province[201601D021073] ; Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi[2016139] ; 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] ; National Natural Science Foundation of China[61741212] ; Natural Science Foundation of Shanxi Province[201601D021073] ; Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi[2016139]
项目资助者	National Natural Science Foundation of China ; Natural Science Foundation of Shanxi Province ; Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi
WOS研究方向	Neurosciences & Neurology ; Psychiatry
WOS类目	Clinical Neurology ; Neuroimaging ; Psychiatry
WOS记录号	WOS:000434115100003
出版者	ELSEVIER IRELAND LTD
引用统计	被引频次：11[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/28197
专题	智能制造技术与系统研究中心_智能机器人
通讯作者	Guo, Hao
作者单位	1.Taiyuan Univ Technol, Coll Comp Sci & Technol, 79 Yinze West St, Taiyuan 030024, Shanxi, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 3.Shanxi Med Univ, Hosp 1, Dept Psychiat, Taiyuan, Shanxi, Peoples R China
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Guo, Hao,Yan, Pengpeng,Cheng, Chen,et al. fMRI classification method with multiple feature fusion based on minimum spanning tree analysis[J]. PSYCHIATRY RESEARCH-NEUROIMAGING,2018,277:14-27.
APA	Guo, Hao.,Yan, Pengpeng.,Cheng, Chen.,Li, Yao.,Chen, Junjie.,...&Xiang, Jie.(2018).fMRI classification method with multiple feature fusion based on minimum spanning tree analysis.PSYCHIATRY RESEARCH-NEUROIMAGING,277,14-27.
MLA	Guo, Hao,et al."fMRI classification method with multiple feature fusion based on minimum spanning tree analysis".PSYCHIATRY RESEARCH-NEUROIMAGING 277(2018):14-27.