中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
A two-center radiomic analysis for differentiating major depressive disorder using multi-modality MRI data under different parcellation methods

文献类型:期刊论文

作者Sun, Kai5,8; Liu, Zhenyu7,8; Chen, Guanmao9; Zhou, Zhifeng1; Zhong, Shuming10; Tang, Zhenchao2,8; Wang, Shuo2,8; Zhou, Guifei4; Zhou, Xuezhi5,8; Shao, Lizhi6,8
刊名JOURNAL OF AFFECTIVE DISORDERS
出版日期2022-03-01
卷号300页码:1-9
ISSN号0165-0327
关键词Major depressive disorder rs-fMRI VBM Radiomics Classification
DOI10.1016/j.jad.2021.12.065
通讯作者Liu, Xia(liuxia61@gmail.com) ; Liu, Jiangang(jgliu@buaa.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn) ; Wang, Ying(johneil@vip.sina.com)
英文摘要Background: 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.
WOS关键词DEFAULT MODE NETWORK ; BRAIN FUNCTIONAL CONNECTIVITY ; MULTIVARIATE PATTERN-ANALYSIS ; RESTING-STATE ; SPONTANEOUS FLUCTUATIONS ; VOLUME ; CEREBELLUM ; ORGANIZATION ; HOMOGENEITY ; CORTEX
资助项目National 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]
WOS研究方向Neurosciences & Neurology ; Psychiatry
语种英语
出版者ELSEVIER
WOS记录号WOS:000740323500001
资助机构National Key R&D Program of China ; National Natural Science Foun-dation of China ; Youth Innovation Promotion Association CAS
源URL[http://ir.ia.ac.cn/handle/173211/47172]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Liu, Xia; Liu, Jiangang; Tian, Jie; Wang, Ying
作者单位1.Shenzhen Kangning Hosp, Shenzhen Inst Mental Hlth, Shenzhen 518003, Peoples R China
2.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med & Engn, Beijing 100191, Peoples R China
3.Beihang Univ, Minist Ind & Informat Technol, Key Lab Big Data Based Precis Med, Beijing, Peoples R China
4.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing, Peoples R China
5.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian, Shaanxi, Peoples R China
6.Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China
7.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
8.Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
9.Jinan Univ, Med Imaging Ctr, Affiliated Hosp 1, Guangzhou 510630, Peoples R China
10.Jinan Univ, Dept Psychiat, Affiliated Hosp 1, Guangzhou, Peoples R China
推荐引用方式
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.

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来源:自动化研究所

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