| Title | Consistent brain structural abnormalities and multisite individualised classification of schizophrenia using deep neural networks |
| Authors | Cui, Yue Li, Chao Liu, Bing Sui, Jing Song, Ming Chen, Jun Chen, Yunchun Guo, Hua Li, Peng Lu, Lin Lv, Luxian Ning, Yuping Wan, Ping Wang, Huaning Wang, Huiling Wu, Huawang Yan, Hao Yan, Jun Yang, Yongfeng Zhang, Hongxing Zhang, Dai Jiang, Tianzi |
| Affiliation | Chinese Acad Sci, Brainnetome Ctr, Inst Automat, Beijing, Peoples R China Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China Univ Chinese Acad Sci, Beijing, Peoples R China Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China Chinese Inst Brain Res, Beijing, Peoples R China Wuhan Univ, Renmin Hosp, Dept Radiol, Wuhan, Hubei, Peoples R China Fourth Mil Med Univ, Dept Psychiat, Xijing Hosp, Xian, Shaanxi, Peoples R China Zhumadian Psychiat Hosp, Zhumadian, Henan, Peoples R China Peking Univ Sixth Hosp, Inst Mental Hlth, Beijing, Peoples R China Peking Univ, Minist Hlth, Key Lab Mental Hlth, Beijing, Peoples R China Peking Univ, Ctr Life Sci, PKU IDG, McGovern Inst Brain Res, Beijing, Peoples R China Xinxiang Med Univ, Affiliated Hosp 2, Henan Mental Hosp, Dept Psychiat, Xinxiang, Henan, Peoples R China Xinxiang Med Univ, Henan Key Lab Biol Psychiat, Xinxiang, Henan, Peoples R China Guanghou Med Univ, Guangzhou Hui Ai Hosp, Guangzhou Brain Hosp, Affiliated Brain Hosp, Guangzhou, Peoples R China Wuhan Univ, Renmin Hosp, Dept Psychiat, Wuhan, Hubei, Peoples R China Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Inst Automat, Beijing, Peoples R China Xinxiang Med Univ, Dept Psychol, Xinxiang, Henan, Peoples R China Univ Elect Sci & Technol China, Sch Life Sci & Technol, Minist Educ, Key Lab NeuroInformat, Beijing, Peoples R China Univ Queensland, Queensland Brain Inst, Brisbane, Qld, Australia |
| Keywords | LIKELIHOOD ESTIMATION VOLUME METAANALYSIS 1ST-EPISODE |
| Issue Date | Feb-2022 |
| Publisher | BRITISH JOURNAL OF PSYCHIATRY |
| 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. |
| URI | http://hdl.handle.net/20.500.11897/637979 |
| ISSN | 0007-1250 |
| DOI | 10.1192/bjp.2022.22 |
| Indexed | SCI(E) SSCI |
| Appears in Collections: | 第六医院 生命科学学院 |
