Classification of Unmedicated Bipolar Disorder Using Whole-Brain Functional Activity and Connectivity: A Radiomics Analysis

doi:10.1093/cercor/bhz152

	Classification of Unmedicated Bipolar Disorder Using Whole-Brain Functional Activity and Connectivity: A Radiomics Analysis
	Wang, Ying 1,2; Sun, Kai 3,4; Liu, Zhenyu4,5 ; Chen, Guanmao 1,2; Jia, Yanbin 6; Zhong, Shuming 5; Pan, Jiyang 6; Huang, Li 1,2; Tian, Jie 3,4,5,7
发表期刊	CEREBRAL CORTEX
ISSN	1047-3211
	2020-03-01
卷号	30 期号:3 页码:1117-1128
通讯作者	Wang, Ying(johneil@vip.sina.com) ; Tian, Jie(jie.tian@ia.ac.cn)
摘要	The 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.
关键词	bipolar disorder machine learning radiomics resting-state functional magnetic resonance imaging
DOI	10.1093/cercor/bhz152
关键词[WOS]	MULTIVARIATE PATTERN-ANALYSIS ; DEFAULT MODE NETWORK ; YOUNG-PEOPLE ; BASE-LINE ; DEPRESSION ; UNIPOLAR ; MRI ; DIAGNOSIS ; REGIONS ; RISK
收录类别	SCI
语种	英语
资助项目	National 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]
项目资助者	National 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研究方向	Neurosciences & Neurology
WOS类目	Neurosciences
WOS记录号	WOS:000535899500020
出版者	OXFORD UNIV PRESS INC
七大方向——子方向分类	医学影像处理与分析
引用统计	被引频次：45[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/173211/39549
专题	中国科学院分子影像重点实验室
通讯作者	Wang, Ying; Tian, Jie
作者单位	1.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
通讯作者单位	中国科学院分子影像重点实验室
推荐引用方式 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.