中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 智能制造技术与系统研究中心
  5. 智能机器人团队
fMRI classification method with multiple feature fusion based on minimum spanning tree analysis

文献类型：期刊论文

作者Guo, Hao2,3; Yan, Pengpeng3; Cheng, Chen2,3; Li, Yao3; Chen, Junjie3; Xu, Yong1; Xiang, Jie3
刊名PSYCHIATRY RESEARCH-NEUROIMAGING
出版日期2018-07-30
卷号277页码:14-27
关键词Functional brain network Minimum spanning tree Classifier Depression Multiple feature fusion
ISSN号0925-4927
DOI10.1016/j.pscychresns.2018.05.001
通讯作者Guo, Hao(feiyu_guo@sina.com)
英文摘要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.
WOS关键词MAJOR DEPRESSIVE DISORDER ; BRAIN NETWORK ANALYSIS ; STATE FUNCTIONAL CONNECTIVITY ; WHITE-MATTER ABNORMALITIES ; CORTICAL THICKNESS ; GERIATRIC DEPRESSION ; ALZHEIMERS-DISEASE ; LONGITUDINAL MEG ; GLOBAL SIGNAL ; GRAPH KERNEL
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]
WOS研究方向Neurosciences & Neurology ; Psychiatry
语种英语
WOS记录号WOS:000434115100003
出版者ELSEVIER IRELAND 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/173211/28196]  
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, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
3.Taiyuan Univ Technol, Coll Comp Sci & Technol, 79 Yinze West St, Taiyuan 030024, Shanxi, Peoples R China
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.

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。