基于脑结构像的精神分裂症机器学习分类

doi:10.3724/SP.J.1042.2020.00252

摘要/Abstract

摘要：

将机器学习应用于精神疾患的临床和基础研究是近年来的趋势。研究者将机器学习应用于精神分裂症患者及高危人群的T1加权像和弥散张量成像的脑影像数据中, 为了解疾病的生理病理学机制提供帮助。回顾以往研究发现额叶及颞叶的脑结构特征具有较高的区分能力, 行为数据和脑影像数据结合的分类效果优于单模态数据。现阶段研究存在样本量不足和泛化能力欠缺的局限, 未来研究应注意扩大样本量、制定标准化的分类方法, 从而进一步探究机器学习在精神疾患中的作用。

关键词: 脑结构像, 弥散张量成像, 机器学习, 精神分裂症, 高危人群

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

中图分类号:

R395

郑泓, 蒲城城, 王毅, 陈楚侨. (2020). 基于脑结构像的精神分裂症机器学习分类. 心理科学进展 , 28(2), 252-265.

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

图/表 3

图1 脑影像数据的机器学习分析步骤

表1 文献总结

图2 精神分裂症患者、高危人群基于脑结构像分类研究的总样本量和分类正确率注：SCZ：精神分裂症; HC：健康对照; FEP：首发精神分裂症; HR：高危人群。实心点代表最高分类正确率; 空心点代表最低分类正确率; 虚线连接同一研究中相同样本量的最高与最低分类正确率。

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