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Consistent brain structural abnormalities and multisite individualised classification of schizophrenia using deep neural networks

文献类型：期刊论文

作者Cui, Yue17,18,19; Li, Chao17,18,19; Liu, Bing15,16; Sui, Jing16; Song, Ming17,18,19; Chen, Jun14; Chen, Yunchun13; Guo, Hua12; Li, Peng10,11; Lu, Lin9,10,11
刊名BRITISH JOURNAL OF PSYCHIATRY
出版日期2022-02-11
页码8
关键词Deep learning grey matter meta-analysis multisite study schizophrenia
ISSN号0007-1250
DOI10.1192/bjp.2022.22
通讯作者Jiang, Tianzi(jiangtz@nlpr.ia.ac.cn)
英文摘要Background Previous analyses of grey and white matter volumes have reported that schizophrenia is associated with structural changes. Deep learning is a data-driven approach that can capture highly compact hierarchical non-linear relationships among high-dimensional features, and therefore can facilitate the development of clinical tools for making a more accurate and earlier diagnosis of schizophrenia. Aims To identify consistent grey matter abnormalities in patients with schizophrenia, 662 people with schizophrenia and 613 healthy controls were recruited from eight centres across China, and the data from these independent sites were used to validate deep-learning classifiers. Method We used a prospective image-based meta-analysis of whole-brain voxel-based morphometry. We also automatically differentiated patients with schizophrenia from healthy controls using combined grey matter, white matter and cerebrospinal fluid volumetric features, incorporated a deep neural network approach on an individual basis, and tested the generalisability of the classification models using independent validation sites. Results We found that statistically reliable schizophrenia-related grey matter abnormalities primarily occurred in regions that included the superior temporal gyrus extending to the temporal pole, insular cortex, orbital and middle frontal cortices, middle cingulum and thalamus. Evaluated using leave-one-site-out cross-validation, the performance of the classification of schizophrenia achieved by our findings from eight independent research sites were: accuracy, 77.19-85.74%; sensitivity, 75.31-89.29% and area under the receiver operating characteristic curve, 0.797-0.909. Conclusions These results suggest that, by using deep-learning techniques, multidimensional neuroanatomical changes in schizophrenia are capable of robustly discriminating patients with schizophrenia from healthy controls, findings which could facilitate clinical diagnosis and treatment in schizophrenia.
WOS关键词LIKELIHOOD ESTIMATION ; VOLUME ; METAANALYSIS ; 1ST-EPISODE
资助项目National Key Basic Research and Development Program (973)[2011CB707800] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB02030300] ; Natural Science Foundation of China[91132301] ; Natural Science Foundation of China[31771076] ; Natural Science Foundation of China[82151307] ; Youth Innovation Promotion Association, Chinese Academy of Science
WOS研究方向Psychiatry
语种英语
WOS记录号WOS:000754086900001
出版者CAMBRIDGE UNIV PRESS
资助机构National Key Basic Research and Development Program (973) ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Natural Science Foundation of China ; Youth Innovation Promotion Association, Chinese Academy of Science
源URL[http://ir.ia.ac.cn/handle/173211/47617]  
专题自动化研究所_脑网络组研究中心
通讯作者Jiang, Tianzi
作者单位1.Univ Queensland, Queensland Brain Inst, Brisbane, Qld, Australia
2.Univ Elect Sci & Technol China, Sch Life Sci & Technol, Minist Educ, Key Lab NeuroInformat, Beijing, Peoples R China
3.Xinxiang Med Univ, Dept Psychol, Xinxiang, Henan, Peoples R China
4.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Inst Automat, Beijing, Peoples R China
5.Wuhan Univ, Renmin Hosp, Dept Psychiat, Wuhan, Hubei, Peoples R China
6.Guanghou Med Univ, Guangzhou Hui Ai Hosp, Guangzhou Brain Hosp, Affiliated Brain Hosp, Guangzhou, Peoples R China
7.Xinxiang Med Univ, Henan Key Lab Biol Psychiat, Xinxiang, Henan, Peoples R China
8.Xinxiang Med Univ, Affiliated Hosp 2, Henan Mental Hosp, Dept Psychiat, Xinxiang, Henan, Peoples R China
9.Peking Univ, Ctr Life Sci, PKU IDG, McGovern Inst Brain Res, Beijing, Peoples R China
10.Peking Univ, Minist Hlth, Key Lab Mental Hlth, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Cui, Yue,Li, Chao,Liu, Bing,et al. Consistent brain structural abnormalities and multisite individualised classification of schizophrenia using deep neural networks[J]. BRITISH JOURNAL OF PSYCHIATRY,2022:8.
APA Cui, Yue.,Li, Chao.,Liu, Bing.,Sui, Jing.,Song, Ming.,...&Jiang, Tianzi.(2022).Consistent brain structural abnormalities and multisite individualised classification of schizophrenia using deep neural networks.BRITISH JOURNAL OF PSYCHIATRY,8.
MLA Cui, Yue,et al."Consistent brain structural abnormalities and multisite individualised classification of schizophrenia using deep neural networks".BRITISH JOURNAL OF PSYCHIATRY (2022):8.

入库方式： OAI收割

来源：自动化研究所

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