The classification of schizophrenia based on brain structural features: A machine learning approach

doi:10.3724/SP.J.1042.2020.00252

Abstract

Abstract:

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.

Key words: structural Magnetic Resonance Imaging, Diffusion Tensor Imaging, machine learning, schizophrenia, high risk population

CLC Number:

R395

ZHENG Hong, PU Cheng-cheng, WANG Yi, Raymond C. K. CHAN. The classification of schizophrenia based on brain structural features: A machine learning approach[J]. Advances in Psychological Science, 2020, 28(2): 252-265.

Figures/Tables 3

References 54

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