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Multisite schizophrenia classification by integrating structural magnetic resonance imaging data with polygenic risk score

文献类型：期刊论文

作者Hu, Ke1,18,19; Wang, Meng1,18,19; Liu, Yong1,17,18,19; Yan, Hao15,16; Song, Ming1,18,19; Chen, Jun14; Chen, Yunchun13; Wang, Huaning13; Guo, Hua12; Wan, Ping12
刊名NEUROIMAGE-CLINICAL
出版日期2021
卷号32页码:9
关键词Schizophrenia Classification Structural magnetic resonance imaging Gray matter volume Polygenic risk score Machine learning
ISSN号2213-1582
DOI10.1016/j.nicl.2021.102860
通讯作者Jiang, Tianzi(jiangtz@nlpr.ia.ac.cn) ; Liu, Bing(bing.liu@bnu.edu.cn)
英文摘要Previous brain structural magnetic resonance imaging studies reported that patients with schizophrenia have brain structural abnormalities, which have been used to discriminate schizophrenia patients from normal controls. However, most existing studies identified schizophrenia patients at a single site, and the genetic features closely associated with highly heritable schizophrenia were not considered. In this study, we performed standardized feature extraction on brain structural magnetic resonance images and on genetic data to separate schizophrenia patients from normal controls. A total of 1010 participants, 508 schizophrenia patients and 502 normal controls, were recruited from 8 independent sites across China. Classification experiments were carried out using different machine learning methods and input features. We tested a support vector machine, logistic regression, and an ensemble learning strategy using 3 feature sets of interest: (1) imaging features: gray matter volume, (2) genetic features: polygenic risk scores, and (3) a fusion of imaging features and genetic features. The performance was assessed by leave-one-site-out cross-validation. Finally, some important brain and genetic features were identified. We found that the models with both imaging and genetic features as input performed better than models with either alone. The average accuracy of the classification models with the best performance in the cross-validation was 71.6%. The genetic feature that measured the cumulative risk of the genetic variants most associated with schizophrenia contributed the most to the classification. Our work took the first step toward considering both structural brain alterations and genome-wide genetic factors in a large-scale
WOS关键词SUPERIOR TEMPORAL GYRUS ; 1ST-EPISODE SCHIZOPHRENIA ; LIKELIHOOD ESTIMATION ; VOLUME ABNORMALITIES ; OBJECT RECOGNITION ; BIPOLAR DISORDER ; THOUGHT-DISORDER ; BRAIN VOLUME ; MRI ; METAANALYSIS
资助项目National Key Basic Research and Development Program (973)[2011CB707800] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32020200] ; Natural Science Foundation of China[81771451]
WOS研究方向Neurosciences & Neurology
语种英语
WOS记录号WOS:000717669700001
出版者ELSEVIER SCI LTD
资助机构National Key Basic Research and Development Program (973) ; Strategic Priority Research Program of Chinese Academy of Science ; Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/46479]  
专题自动化研究所_脑网络组研究中心
通讯作者Jiang, Tianzi; Liu, Bing
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
2.Chinese Inst Brain Res, Beijing, Peoples R China
3.Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China
4.Univ Queensland, Queensland Brain Inst, Brisbane, Qld, Australia
5.Univ Elect Sci & Technol China, Sch Life Sci & Technol, Minist Educ, Key Lab NeuroInformat, Chengdu, Sichuan, Peoples R China
6.Guangzhou Med Univ, Guangzhou Huiai Hosp, Affiliated Brain Hosp, Guangzhou, Guangdong, Peoples R China
7.Peking Univ, McGovern Inst Brain Res, PKU IDG, Ctr Life Sci, Beijing, Peoples R China
8.Xinxiang Med Univ, Dept Psychol, Xinxiang, Henan, Peoples R China
9.Wuhan Univ, Renmin Hosp, Dept Psychiat, Wuhan, Hubei, Peoples R China
10.Xinxiang Med Univ, Henan Key Lab Biol Psychiat, Xinxiang, Henan, Peoples R China
推荐引用方式
GB/T 7714		 Hu, Ke,Wang, Meng,Liu, Yong,et al. Multisite schizophrenia classification by integrating structural magnetic resonance imaging data with polygenic risk score[J]. NEUROIMAGE-CLINICAL,2021,32:9.
APA Hu, Ke.,Wang, Meng.,Liu, Yong.,Yan, Hao.,Song, Ming.,...&Liu, Bing.(2021).Multisite schizophrenia classification by integrating structural magnetic resonance imaging data with polygenic risk score.NEUROIMAGE-CLINICAL,32,9.
MLA Hu, Ke,et al."Multisite schizophrenia classification by integrating structural magnetic resonance imaging data with polygenic risk score".NEUROIMAGE-CLINICAL 32(2021):9.

入库方式： OAI收割

来源：自动化研究所

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