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Advances in Psychological Science    2020, Vol. 28 Issue (2) : 252-265     DOI: 10.3724/SP.J.1042.2020.00252
Regular Articles |
The classification of schizophrenia based on brain structural features: A machine learning approach
ZHENG Hong1,2,PU Cheng-cheng3,WANG Yi1,2(),Raymond C. K. CHAN1,2
1 Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
2 Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
3 Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
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Machine learning is a promising approach for mental disorders. In recent years, machine learning based on T1 weighted imaging and Diffusion Tensor Imaging (DTI) data has been used to investigate the psychopathology and underlying mechanisms of schizophrenia patients and high-risk population. The findings from the previous literature suggest that structural features of frontal lobe and temporal lobe can improve classification performance. In addition, the combination of behavioural performances and the features of brain structure is superior to the single-modality structural images on classification accuracy. However, the existing empirical studies classifying schizophrenia patients or high-risk population from controls are limited in sample size and generalization ability.

Keywords structural Magnetic Resonance Imaging      Diffusion Tensor Imaging      machine learning      schizophrenia      high risk population     
ZTFLH:  R395  
Corresponding Authors: Yi WANG     E-mail:
Issue Date: 25 December 2019
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Cheng-cheng PU
C. K. CHAN Raymond
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Hong ZHENG,Cheng-cheng PU,Yi WANG, et al. The classification of schizophrenia based on brain structural features: A machine learning approach[J]. Advances in Psychological Science, 2020, 28(2): 252-265.
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