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

doi:10.1192/bjp.2022.22

	Consistent brain structural abnormalities and multisite individualised classification of schizophrenia using deep neural networks
	Cui, Yue 5,6,7; Li, Chao 5,6,7; Liu, Bing 8,9; Sui, Jing 8; Song, Ming 5,6,7; Chen, Jun 10; Chen, Yunchun 11; Guo, Hua 12; Li, Peng 13,14; Lu, Lin 13,14,15
刊名	BRITISH JOURNAL OF PSYCHIATRY
	2022-02-11
页码	8
关键词	Deep learning grey matter meta-analysis multisite study schizophrenia
ISSN号	0007-1250
DOI	10.1192/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.
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关键词	LIKELIHOOD ESTIMATION ; VOLUME ; METAANALYSIS ; 1ST-EPISODE
WOS研究方向	Psychiatry
语种	英语
出版者	CAMBRIDGE UNIV PRESS
WOS记录号	WOS:000754086900001
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
内容类型	期刊论文
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.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Inst Automat, Beijing, Peoples R China 2.Xinxiang Med Univ, Dept Psychol, Xinxiang, Henan, Peoples R China 3.Univ Elect Sci & Technol China, Sch Life Sci & Technol, Minist Educ, Key Lab NeuroInformat, Beijing, Peoples R China 4.Univ Queensland, Queensland Brain Inst, Brisbane, Qld, Australia 5.Chinese Acad Sci, Brainnetome Ctr, Inst Automat, Beijing, Peoples R China 6.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 7.Univ Chinese Acad Sci, Beijing, Peoples R China 8.Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China 9.Chinese Inst Brain Res, Beijing, Peoples R China 10.Wuhan Univ, Renmin Hosp, Dept Radiol, Wuhan, Hubei, 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.