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基于脑结构特征的精神分裂症机器学习分类研究
Alternative TitleClassification of schizophrenia based on brain structural imaging data: A machine learning approach
郑泓1,2; 王毅1,2; 陈楚侨1,2
2018-10
Conference Name第二十一届全国心理学学术会议
Correspondent Emailwangyi@psych.ac.cn
Source Publication摘要集-第二十一届全国心理学学术会议
Pages1324-1325
Conference Date10.30-11.2
Conference Place北京
Abstract

摘要:将机器学习应用于精神疾患的临床研究中是近年来的研究趋势。机器学习可应用于包括脑成像数据在内的复杂数据,对精神分裂症患者和健康对照进行分类。本研究使用98名精神分裂症患者和89名健康被试的T1加权像和弥散张量成像,经预处理后分别提取116个脑区的灰质体积和18条白质纤维的弥散值,包括各向异性分数(fractional anisotropy, FA),平均扩散系数(mean diffusivity, MD),径向扩散系数(radial diffusivity, RD)和轴向扩散系数(axial diffusivity, AD)。实验使用支持向量机(Support Vector Machine)算法训练数据,采用分类正确率、敏感性、特异性等指标,通过嵌套交叉验证比较不同模态数据(T1加权像和弥散张量成像)的分类效果,探究不同特征选择方法(包括递归特征消除、随机森林、Ridge系数和F值)应用于脑结构数据的优劣,定义判别特征并探究判别特征在新数据中的分类效果。结果表明,同时使用灰质体积和白质纤维弥散值的分类结果(分类正确率范围:45%-65%)优于单模态数据(灰质体积:48%-59%;白质纤维弥散值:39%-63%);四种不同特征选择方法的分类正确率相似,平均正确率在50%-60%。判别特征主要集中在额叶、枕叶、边缘系统、基底神经节、丘脑放射束、弓型束等区域。以判别特征为输入特征的最终模型在新数据中表现良好,可达到97%的正确率。本研究结果提示在多模态数据(灰质体积和白质纤维弥散值)中使用机器学习能很好地分辨精神分裂症患者和健康对照。

Other Abstract

Abstract:Using machine learning for clinical research in patients with mental disorders is popular in recent years. Machine learning is one technique we can use to classify patients with schizophrenia based on the multidimensional imaging dataset. In this study, T1 weighted images and diffusion tensor imaging (DTI) of 98 schizophrenia patients and 89 healthy controls were used to extract grey matter volume of 116 brain regions and summary statistics of 18 white matter fibers, including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD). Data were trained using support vector machine and generated model were evaluated using indices such as accuracy, specificity, sensitivity. We firstly examined the classification performance of different imaging modalities (T1 weighted vs. DTI). We then explored the classification performance of four different feature selection methods (recursive feature elimination, random forest, Ridge coefficient, and F value). The results showed that models generated using both grey matter volume and DTI (accuracy: 45%-65%) is better than models generated using either of them (grey matter volume:48%-59%, white matter fiber: 39%-63%). Classification performances of the four different feature selection methods are similar, and the average accuracy is 50%-60%. Moreover, discriminative features were observed mainly on grey matter volumes of frontal lobe, occipital lobe, basal ganglia, as well as white matter statistics of thalamic radiation and arcuate. The final model established using the discriminative features showed a good performance with a classification accuracy of 97%. These findings suggest machine learning using the multimodal data can provide a good classification of patients with schizophrenia from healthy controls.

Keyword精神分裂症 判别特征 脑结构特征 支持向量机 机器学习
Language中文
Document Type会议论文
Identifierhttp://ir.psych.ac.cn/handle/311026/27346
Collection中国科学院心理健康重点实验室
Affiliation1.中国科学院心理研究所神经心理学与应用认知神经科学实验室, 中国科学院心理健康重点实验室(中国科学院心理研究所)
2.中国科学院大学心理学系
First Author AffilicationKey Laboratory of Mental Health, CAS
Recommended Citation
GB/T 7714
郑泓,王毅,陈楚侨. 基于脑结构特征的精神分裂症机器学习分类研究[C],2018:1324-1325.
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