CASIA OpenIR  > 中国科学院分子影像重点实验室
A two-center radiomic analysis for differentiating major depressive disorder using multi-modality MRI data under different parcellation methods
Sun, Kai1,2; Liu, Zhenyu2,10; Chen, Guanmao3; Zhou, Zhifeng4; Zhong, Shuming5; Tang, Zhenchao2,6; Wang, Shuo2,6; Zhou, Guifei8; Zhou, Xuezhi1,2; Shao, Lizhi2,9; Ye, Xiaoying5; Zhang, Yingli5; Jia, Yanbin5; Pan, Jiyang5; Huang, Li3; Liu, Xia4; Liu, Jiangang6,7; Tian, Jie1,2,6,7,10; Wang, Ying3
Source PublicationJOURNAL OF AFFECTIVE DISORDERS
ISSN0165-0327
2022-03-01
Volume300Pages:1-9
Corresponding AuthorLiu, Xia(liuxia61@gmail.com) ; Liu, Jiangang(jgliu@buaa.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn) ; Wang, Ying(johneil@vip.sina.com)
AbstractBackground: The present study aimed to explore the difference in the brain function and structure between patients with major depressive disorder (MDD) and healthy controls (HCs) using two-center and multi-modal MRI data, which would be helpful to investigate the pathogenesis of MDD. Methods: The subjects were collected from two hospitals. One including 140 patients with MDD and 138 HCs was used as primary cohort. Another one including 29 patients with MDD and 52 HCs was used as validation cohort. Functional and structural magnetic resonance images (MRI) were acquired to extract four types of features: functional connectivity (FC), amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and gray matter volume (GMV). Then classifiers using different combinations among the four types of selected features were respectively built to discriminate patients from HCs. Different templates were applied and the results under different templates were compared. Results: The classifier built with the combination of FC, ALFF, and GMV under the AAL template discriminated patients from HCs with the best performance (AUC=0.916, ACC=84.8%). The regions selected in all the different templates were mainly located in the default mode network, affective network, prefrontal cortex. Limitations: First, the sample size of the validation cohort was limited. Second, diffusion tensor imaging data were not collected. Conclusion: The performance of classifier was improved by using multi-modal MRI imaging. Different templates would be suitable for different types of analysis. The regions selected in all the different templates are possibly the core regions to investigate the pathophysiology of MDD.
KeywordMajor depressive disorder rs-fMRI VBM Radiomics Classification
DOI10.1016/j.jad.2021.12.065
WOS KeywordDEFAULT MODE NETWORK ; BRAIN FUNCTIONAL CONNECTIVITY ; MULTIVARIATE PATTERN-ANALYSIS ; RESTING-STATE ; SPONTANEOUS FLUCTUATIONS ; VOLUME ; CEREBELLUM ; ORGANIZATION ; HOMOGENEITY ; CORTEX
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2017YFA0205200] ; National Natural Science Foun-dation of China[81922040] ; National Natural Science Foun-dation of China[61673051] ; National Natural Science Foun-dation of China[81930053] ; National Natural Science Foun-dation of China[81227901] ; National Natural Science Foun-dation of China[81671670] ; National Natural Science Foun-dation of China[81971597] ; Youth Innovation Promotion Association CAS[2019136]
Funding OrganizationNational Key R&D Program of China ; National Natural Science Foun-dation of China ; Youth Innovation Promotion Association CAS
WOS Research AreaNeurosciences & Neurology ; Psychiatry
WOS SubjectClinical Neurology ; Psychiatry
WOS IDWOS:000740323500001
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/47173
Collection中国科学院分子影像重点实验室
Corresponding AuthorLiu, Xia; Liu, Jiangang; Tian, Jie; Wang, Ying
Affiliation1.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian, Shaanxi, Peoples R China
2.Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
3.Jinan Univ, Med Imaging Ctr, Affiliated Hosp 1, Guangzhou 510630, Peoples R China
4.Shenzhen Kangning Hosp, Shenzhen Inst Mental Hlth, Shenzhen 518003, Peoples R China
5.Jinan Univ, Dept Psychiat, Affiliated Hosp 1, Guangzhou, Peoples R China
6.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med & Engn, Beijing 100191, Peoples R China
7.Beihang Univ, Minist Ind & Informat Technol, Key Lab Big Data Based Precis Med, Beijing, Peoples R China
8.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing, Peoples R China
9.Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China
10.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Sun, Kai,Liu, Zhenyu,Chen, Guanmao,et al. A two-center radiomic analysis for differentiating major depressive disorder using multi-modality MRI data under different parcellation methods[J]. JOURNAL OF AFFECTIVE DISORDERS,2022,300:1-9.
APA Sun, Kai.,Liu, Zhenyu.,Chen, Guanmao.,Zhou, Zhifeng.,Zhong, Shuming.,...&Wang, Ying.(2022).A two-center radiomic analysis for differentiating major depressive disorder using multi-modality MRI data under different parcellation methods.JOURNAL OF AFFECTIVE DISORDERS,300,1-9.
MLA Sun, Kai,et al."A two-center radiomic analysis for differentiating major depressive disorder using multi-modality MRI data under different parcellation methods".JOURNAL OF AFFECTIVE DISORDERS 300(2022):1-9.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Sun, Kai]'s Articles
[Liu, Zhenyu]'s Articles
[Chen, Guanmao]'s Articles
Baidu academic
Similar articles in Baidu academic
[Sun, Kai]'s Articles
[Liu, Zhenyu]'s Articles
[Chen, Guanmao]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Sun, Kai]'s Articles
[Liu, Zhenyu]'s Articles
[Chen, Guanmao]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.