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Discriminating Bipolar Disorder From Major Depression Based on SVM-FoBa: Efficient Feature Selection With Multimodal Brain Imaging Data
Jie, Nan-Feng1,2; Zhu, Mao-Hu3; Ma, Xiao-Ying5; Osuch, Elizabeth A.4; Wammes, Michael4; Theberge, Jean4; Li, Huan-Dong1,2; Zhang, Yu1,2; Jiang, Tian-Zi1,2; Sui, Jing1,2,6; Calhoun, Vince D.7,8
Source PublicationIEEE TRANSACTIONS ON AUTONOMOUS MENTAL DEVELOPMENT
2015-12-01
Volume7Issue:4Pages:320-331
SubtypeArticle
AbstractDiscriminating between bipolar disorder (BD) and major depressive disorder (MDD) is a major clinical challenge due to the absence of known biomarkers; hence a better understanding of their pathophysiology and brain alterations is urgently needed. Given the complexity, feature selection is especially important in neuroimaging applications, however, feature dimension and model understanding present serious challenges. In this study, a novel feature selection approach based on linear support vector machine with a forward-backward search strategy (SVM-FoBa) was developed and applied to structural and resting-state functional magnetic resonance imaging data collected from 21 BD, 25 MDD and 23 healthy controls. Discriminative features were drawn from both data modalities, with which the classification of BD and MDD achieved an accuracy of 92.1% (1000 bootstrap resamples). Weight analysis of the selected features further revealed that the inferior frontal gyrus may characterize a central role in BD-MDD differentiation, in addition to the default mode network and the cerebellum. A modality-wise comparison also suggested that functional information outweighs anatomical by a large margin when classifying the two clinical disorders. This work validated the advantages of multimodal joint analysis and the effectiveness of SVM-FoBa, which has potential for use in identifying possible biomarkers for several mental disorders.
KeywordBipolar Disorder Classification Feature Selection Major Depression Multimodal Fusion
WOS HeadingsScience & Technology ; Technology ; Life Sciences & Biomedicine
DOI10.1109/TAMD.2015.2440298
WOS KeywordSUPPORT VECTOR MACHINES ; 1ST EPISODE SCHIZOPHRENIA ; VOXEL-BASED MORPHOMETRY ; UNIPOLAR DEPRESSION ; GRAY-MATTER ; FUNCTIONAL CONNECTIVITY ; PATTERN-ANALYSIS ; MOOD DISORDERS ; FRONTAL-LOBE ; MRI ANALYSIS
Indexed BySCI ; SSCI
Language英语
Funding OrganizationNational High-Tech Development Plan (863)(2015AA020513) ; "100 Talents Plan" of the Chinese Academy of Sciences (CAS) ; Strategic Priority Research Program of the CAS(XDB02060005) ; Chinese National Science Foundation(81471367) ; Ph.D. Research Startup Foundation of Jiangxi Normal University(6247) ; Lawson Health Research Institute(LHR D1374) ; Pfizer Independent Investigator Award(WS2249136) ; Strategic Priority Research Program of the Chinese Academy of Sciences(XDB02060005 ; National Key Basic Research and Development Program (973)(2011CB707800) ; National Institutes of Health(R01EB006841 ; XDB02030300) ; R01EB005846 ; P20GM103472)
WOS Research AreaComputer Science ; Robotics ; Neurosciences & Neurology
WOS SubjectComputer Science, Artificial Intelligence ; Robotics ; Neurosciences
WOS IDWOS:000366614800006
Citation statistics
Cited Times:28[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/10558
Collection脑网络组研究中心
Affiliation1.Chinese Acad Sci, Brainnetome Ctr, Beijing 100083, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100083, Peoples R China
3.Jiangxi Normal Univ, Elementary Educ Coll, Nanchang, Peoples R China
4.Univ Western Ontario, Dept Med Biophys, London, ON N6G 1G9, Canada
5.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China
6.Chinese Acad Sci, Inst Automat, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100083, Peoples R China
7.Univ New Mexico, LBERI, Mind Res Network, Albuquerque, NM 87106 USA
8.Univ New Mexico, Dept Elect & Comp Engn, Albuquerque, NM 87106 USA
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Jie, Nan-Feng,Zhu, Mao-Hu,Ma, Xiao-Ying,et al. Discriminating Bipolar Disorder From Major Depression Based on SVM-FoBa: Efficient Feature Selection With Multimodal Brain Imaging Data[J]. IEEE TRANSACTIONS ON AUTONOMOUS MENTAL DEVELOPMENT,2015,7(4):320-331.
APA Jie, Nan-Feng.,Zhu, Mao-Hu.,Ma, Xiao-Ying.,Osuch, Elizabeth A..,Wammes, Michael.,...&Calhoun, Vince D..(2015).Discriminating Bipolar Disorder From Major Depression Based on SVM-FoBa: Efficient Feature Selection With Multimodal Brain Imaging Data.IEEE TRANSACTIONS ON AUTONOMOUS MENTAL DEVELOPMENT,7(4),320-331.
MLA Jie, Nan-Feng,et al."Discriminating Bipolar Disorder From Major Depression Based on SVM-FoBa: Efficient Feature Selection With Multimodal Brain Imaging Data".IEEE TRANSACTIONS ON AUTONOMOUS MENTAL DEVELOPMENT 7.4(2015):320-331.
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