CASIA OpenIR
Classification of Unmedicated Bipolar Disorder Using Whole-Brain Functional Activity and Connectivity: A Radiomics Analysis
Wang, Ying1,2; Sun, Kai3,4; Liu, Zhenyu4,5; Chen, Guanmao1,2; Jia, Yanbin6; Zhong, Shuming5; Pan, Jiyang6; Huang, Li1,2; Tian, Jie3,4,5,7
Source PublicationCEREBRAL CORTEX
ISSN1047-3211
2020-03-01
Volume30Issue:3Pages:1117-1128
Corresponding AuthorWang, Ying(johneil@vip.sina.com) ; Tian, Jie(jie.tian@ia.ac.cn)
AbstractThe aim of this study was to develop and validate a method of disease classification for bipolar disorder (BD) by functional activity and connectivity using radiomics analysis. Ninety patients with unmedicated BD II as well as 117 healthy controls underwent resting-state functional magnetic resonance imaging (rs-fMRI). A total of 4 types of 7018 features were extracted after preprocessing, including mean regional homogeneity (mReHo), mean amplitude of low-frequency fluctuation (mALFF), resting-state functional connectivity (RSFC), and voxel-mirrored homotopic connectivity (VMHC). Then, predictive features were selected by Mann-Whitney U test and removing variables with a high correlation. Least absolute shrinkage and selection operator (LASSO) method was further used to select features. At last, support vector machine (SVM) model was used to estimate the state of each subject based on the selected features after LASSO. Sixty-five features including 54 RSFCs, 7 mALFFs, 1 mReHo, and 3 VMHCs were selected. The accuracy and area under curve (AUC) of the SVM model built based on the 65 features is 87.3% and 0.919 in the training dataset, respectively, and the accuracy and AUC of this model validated in the validation dataset is 80.5% and 0.838, respectively. These findings demonstrate a valid radiomics approach by rs-fMRI can identify BD individuals from healthy controls with a high classification accuracy, providing the potential adjunctive approach to clinical diagnostic systems.
Keywordbipolar disorder machine learning radiomics resting-state functional magnetic resonance imaging
DOI10.1093/cercor/bhz152
WOS KeywordMULTIVARIATE PATTERN-ANALYSIS ; DEFAULT MODE NETWORK ; YOUNG-PEOPLE ; BASE-LINE ; DEPRESSION ; UNIPOLAR ; MRI ; DIAGNOSIS ; REGIONS ; RISK
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[81 671 670] ; National Natural Science Foundation of China[81 501 456] ; National Natural Science Foundation of China[81 772 012] ; Planned Science and Technology Project of Guangdong Province, China[2014B020212022] ; Planned Science and Technology Project of Guangzhou, China[20 160 402 007] ; Planned Science and Technology Project of Guangzhou, China[201 604 020 184] ; National Key Research and Development Plan of China[2017YFA0205200] ; Beijing Natural Science Foundation[7182109]
Funding OrganizationNational Natural Science Foundation of China ; Planned Science and Technology Project of Guangdong Province, China ; Planned Science and Technology Project of Guangzhou, China ; National Key Research and Development Plan of China ; Beijing Natural Science Foundation
WOS Research AreaNeurosciences & Neurology
WOS SubjectNeurosciences
WOS IDWOS:000535899500020
PublisherOXFORD UNIV PRESS INC
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/39549
Collection中国科学院自动化研究所
Corresponding AuthorWang, Ying; Tian, Jie
Affiliation1.Jinan Univ, Affiliated Hosp 1, Med Imaging Ctr, Guangzhou 510630, Peoples R China
2.Jinan Univ, Inst Mol & Funct Imaging, Guangzhou 510630, Peoples R China
3.Xidian Univ, Sch Life Sci & Technol, Minist Educ, Engn Res Ctr Mol & Neuro Imaging, Xian 710071, Peoples R China
4.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
5.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
6.Jinan Univ, Affiliated Hosp 1, Dept Psychiat, Guangzhou 510630, Peoples R China
7.Beihang Univ, Sch Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wang, Ying,Sun, Kai,Liu, Zhenyu,et al. Classification of Unmedicated Bipolar Disorder Using Whole-Brain Functional Activity and Connectivity: A Radiomics Analysis[J]. CEREBRAL CORTEX,2020,30(3):1117-1128.
APA Wang, Ying.,Sun, Kai.,Liu, Zhenyu.,Chen, Guanmao.,Jia, Yanbin.,...&Tian, Jie.(2020).Classification of Unmedicated Bipolar Disorder Using Whole-Brain Functional Activity and Connectivity: A Radiomics Analysis.CEREBRAL CORTEX,30(3),1117-1128.
MLA Wang, Ying,et al."Classification of Unmedicated Bipolar Disorder Using Whole-Brain Functional Activity and Connectivity: A Radiomics Analysis".CEREBRAL CORTEX 30.3(2020):1117-1128.
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