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Multisite schizophrenia classification by integrating structural magnetic resonance imaging data with polygenic risk score | |
Hu, Ke1,2,3![]() ![]() ![]() ![]() ![]() ![]() ![]() | |
发表期刊 | NEUROIMAGE-CLINICAL
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ISSN | 2213-1582 |
2021 | |
卷号 | 32页码:9 |
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 |
关键词 | Schizophrenia Classification Structural magnetic resonance imaging Gray matter volume Polygenic risk score Machine learning |
DOI | 10.1016/j.nicl.2021.102860 |
关键词[WOS] | SUPERIOR TEMPORAL GYRUS ; 1ST-EPISODE SCHIZOPHRENIA ; LIKELIHOOD ESTIMATION ; VOLUME ABNORMALITIES ; OBJECT RECOGNITION ; BIPOLAR DISORDER ; THOUGHT-DISORDER ; BRAIN VOLUME ; MRI ; METAANALYSIS |
收录类别 | SCI |
语种 | 英语 |
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] |
项目资助者 | National Key Basic Research and Development Program (973) ; Strategic Priority Research Program of Chinese Academy of Science ; Natural Science Foundation of China |
WOS研究方向 | Neurosciences & Neurology |
WOS类目 | Neuroimaging |
WOS记录号 | WOS:000717669700001 |
出版者 | ELSEVIER SCI LTD |
是否为代表性论文 | 是 |
七大方向——子方向分类 | 脑网络分析 |
国重实验室规划方向分类 | 多模态智能神经机理解析 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/46480 |
专题 | 脑图谱与类脑智能实验室_脑网络组研究 |
通讯作者 | Jiang, Tianzi; Liu, Bing |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Automat, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing, Peoples R China 5.Peking Univ Sixth Hosp, Inst Mental Hlth, Beijing, Peoples R China 6.Peking Univ, Minist Hlth, Key Lab Mental Hlth, Beijing, Peoples R China 7.Wuhan Univ, Renmin Hosp, Dept Radiol, Wuhan, Hubei, Peoples R China 8.Fourth Mil Med Univ, Xijing Hosp, Dept Psychiat, Xian, Shaanxi, Peoples R China 9.Zhumadian Psychiat Hosp, Zhumadian, Peoples R China 10.Xinxiang Med Univ, Affiliated Hosp 2, Henan Mental Hosp, Dept Psychiat, Xinxiang, Henan, Peoples R China 11.Xinxiang Med Univ, Henan Key Lab Biol Psychiat, Xinxiang, Henan, Peoples R China 12.Wuhan Univ, Renmin Hosp, Dept Psychiat, Wuhan, Hubei, Peoples R China 13.Xinxiang Med Univ, Dept Psychol, Xinxiang, Henan, Peoples R China 14.Peking Univ, McGovern Inst Brain Res, PKU IDG, Ctr Life Sci, Beijing, Peoples R China 15.Guangzhou Med Univ, Guangzhou Huiai Hosp, Affiliated Brain Hosp, Guangzhou, Guangdong, Peoples R China 16.Univ Elect Sci & Technol China, Sch Life Sci & Technol, Minist Educ, Key Lab NeuroInformat, Chengdu, Sichuan, Peoples R China 17.Univ Queensland, Queensland Brain Inst, Brisbane, Qld, Australia 18.Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China 19.Chinese Inst Brain Res, Beijing, Peoples R China |
第一作者单位 | 中国科学院自动化研究所; 模式识别国家重点实验室 |
通讯作者单位 | 中国科学院自动化研究所; 模式识别国家重点实验室; 中国科学院分子影像重点实验室 |
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. |
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