A two-center radiomic analysis for differentiating major depressive disorder using multi-modality MRI data under different parcellation methods

doi:10.1016/j.jad.2021.12.065

CORC > 自动化研究所 > 中国科学院自动化研究所 > 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, Kai 5,8; Liu, Zhenyu 7,8; Chen, Guanmao 9; Zhou, Zhifeng 1; Zhong, Shuming 10; Tang, Zhenchao 2,8; Wang, Shuo 2,8; Zhou, Guifei 4; Zhou, Xuezhi 5,8; Shao, Lizhi 6,8
刊名	JOURNAL OF AFFECTIVE DISORDERS
	2022-03-01
卷号	300 页码:1-9
关键词	Major depressive disorder rs-fMRI VBM Radiomics Classification
ISSN号	0165-0327
DOI	10.1016/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.
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关键词	DEFAULT MODE NETWORK ; BRAIN FUNCTIONAL CONNECTIVITY ; MULTIVARIATE PATTERN-ANALYSIS ; RESTING-STATE ; SPONTANEOUS FLUCTUATIONS ; VOLUME ; CEREBELLUM ; ORGANIZATION ; HOMOGENEITY ; CORTEX
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
内容类型	期刊论文
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.