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

Published online by Cambridge University Press:  11 February 2022

Yue Cui
Affiliation:
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, China, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China andUniversity of Chinese Academy of Sciences, China
Chao Li
Affiliation:
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, China, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China andUniversity of Chinese Academy of Sciences, China
Bing Liu
Affiliation:
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, China and Chinese Institute for Brain Research, China
Jing Sui
Affiliation:
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, China
Ming Song
Affiliation:
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, China, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China andUniversity of Chinese Academy of Sciences, China
Jun Chen
Affiliation:
Department of Radiology, Renmin Hospital of Wuhan University, China
Yunchun Chen
Affiliation:
Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, China
Hua Guo
Affiliation:
Zhumadian Psychiatric Hospital, China
Peng Li
Affiliation:
Peking University Sixth Hospital/Institute of Mental Health, China and Key Laboratory of Mental Health, Ministry of Health (Peking University), China
Lin Lu
Affiliation:
Peking University Sixth Hospital/Institute of Mental Health, China, Key Laboratory of Mental Health, Ministry of Health (Peking University), China and Center for Life Sciences/PKU-IDG/McGovern Institute for Brain Research, Peking University, China
Luxian Lv
Affiliation:
Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, China and Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, China
Yuping Ning
Affiliation:
Guangzhou Brain Hospital, Guangzhou Hui-Ai Hospital, The Affiliated Brain Hospital of Guangzhou Medical University, China
Ping Wan
Affiliation:
Zhumadian Psychiatric Hospital, China
Huaning Wang
Affiliation:
Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, China
Huiling Wang
Affiliation:
Department of Psychiatry, Renmin Hospital of Wuhan University, China
Huawang Wu
Affiliation:
Guangzhou Brain Hospital, Guangzhou Hui-Ai Hospital, The Affiliated Brain Hospital of Guangzhou Medical University, China
Hao Yan
Affiliation:
Peking University Sixth Hospital/Institute of Mental Health, China and Key Laboratory of Mental Health, Ministry of Health (Peking University), China
Jun Yan
Affiliation:
Peking University Sixth Hospital/Institute of Mental Health, China and Key Laboratory of Mental Health, Ministry of Health (Peking University), China
Yongfeng Yang
Affiliation:
Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, China, Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, China and CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, China
Hongxing Zhang
Affiliation:
Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, China, Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, China and Department of Psychology, Xinxiang Medical University, China
Dai Zhang
Affiliation:
Peking University Sixth Hospital/Institute of Mental Health, China, Key Laboratory of Mental Health, Ministry of Health (Peking University), China and Center for Life Sciences/PKU-IDG/McGovern Institute for Brain Research, Peking University, China
Tianzi Jiang*
Affiliation:
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, China, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China and University of Chinese Academy of Sciences, China, CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, China; Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, China and Queensland Brain Institute, University of Queensland, Australia
*
Correspondence: Tianzi Jiang. Email: 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.

Type
Paper
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of the Royal College of Psychiatrists

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