| Consistent brain structural abnormalities and multisite individualised classification of schizophrenia using deep neural networks | |
Cui, Yue1,2,3 ; Li, Chao1,2,3; Liu, Bing4,5; Sui, Jing4; Song, Ming1,2,3; Chen, Jun6; Chen, Yunchun7; Guo, Hua8; Li, Peng9,10; Lu, Lin9,10,11; Lv, Luxian12,13; Ning, Yuping14; Wan, Ping8; Wang, Huaning7; Wang, Huiling15; Wu, Huawang14; Yan, Hao9,10; Yan, Jun9,10; Yang, Yongfeng12,13,16; Zhang, Hongxing12,13,17; Zhang, Dai9,10,11; Jiang, Tianzi1,2,3,16,18,19
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| Source Publication | BRITISH JOURNAL OF PSYCHIATRY
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| ISSN | 0007-1250 |
| 2022-02-11 | |
| Pages | 8 |
| Corresponding Author | Jiang, Tianzi(jiangtz@nlpr.ia.ac.cn) |
| Abstract | 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. |
| Keyword | Deep learning grey matter meta-analysis multisite study schizophrenia |
| DOI | 10.1192/bjp.2022.22 |
| WOS Keyword | LIKELIHOOD ESTIMATION ; VOLUME ; METAANALYSIS ; 1ST-EPISODE |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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 |
| Funding Organization | 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 |
| WOS Research Area | Psychiatry |
| WOS Subject | Psychiatry |
| WOS ID | WOS:000754086900001 |
| Publisher | CAMBRIDGE UNIV PRESS |
| Sub direction classification | 人工智能+医疗 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/47618 |
| Collection | 脑网络组研究 |
| Corresponding Author | Jiang, Tianzi |
| Affiliation | 1.Chinese Acad Sci, Brainnetome Ctr, Inst Automat, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China 5.Chinese Inst Brain Res, Beijing, Peoples R China 6.Wuhan Univ, Renmin Hosp, Dept Radiol, Wuhan, Hubei, Peoples R China 7.Fourth Mil Med Univ, Dept Psychiat, Xijing Hosp, Xian, Shaanxi, Peoples R China 8.Zhumadian Psychiat Hosp, Zhumadian, Henan, Peoples R China 9.Peking Univ Sixth Hosp, Inst Mental Hlth, Beijing, Peoples R China 10.Peking Univ, Minist Hlth, Key Lab Mental Hlth, Beijing, Peoples R China 11.Peking Univ, Ctr Life Sci, PKU IDG, McGovern Inst Brain Res, Beijing, Peoples R China 12.Xinxiang Med Univ, Affiliated Hosp 2, Henan Mental Hosp, Dept Psychiat, Xinxiang, Henan, Peoples R China 13.Xinxiang Med Univ, Henan Key Lab Biol Psychiat, Xinxiang, Henan, Peoples R China 14.Guanghou Med Univ, Guangzhou Hui Ai Hosp, Guangzhou Brain Hosp, Affiliated Brain Hosp, Guangzhou, Peoples R China 15.Wuhan Univ, Renmin Hosp, Dept Psychiat, Wuhan, Hubei, Peoples R China 16.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Inst Automat, Beijing, Peoples R China 17.Xinxiang Med Univ, Dept Psychol, Xinxiang, Henan, Peoples R China 18.Univ Elect Sci & Technol China, Sch Life Sci & Technol, Minist Educ, Key Lab NeuroInformat, Beijing, Peoples R China 19.Univ Queensland, Queensland Brain Inst, Brisbane, Qld, Australia |
| First Author Affilication | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Corresponding Author Affilication | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Recommended Citation 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. |
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