[1]刘刚,江海腾,刘海燕,等.基于任务态和静息态功能核磁共振信号的抑郁症识别[J].东南大学学报(自然科学版),2011,41(1):67-71.[doi:10.3969/j.issn.1001-0505.2011.01.014]
 Liu Gang,Jiang Haiteng,Liu Haiyan,et al.Recognition of depression using event-related and resting-state functional magnetic resonance signals[J].Journal of Southeast University (Natural Science Edition),2011,41(1):67-71.[doi:10.3969/j.issn.1001-0505.2011.01.014]
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基于任务态和静息态功能核磁共振信号的抑郁症识别()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
41
期数:
2011年第1期
页码:
67-71
栏目:
生物医学工程
出版日期:
2011-01-20

文章信息/Info

Title:
Recognition of depression using event-related and resting-state functional magnetic resonance signals
作者:
刘刚1江海腾1刘海燕2王丽2姚志剑2卢青1
(1东南大学学习科学研究中心,南京 210096)
(2南京医科大学附属南京脑科医院,南京 210029)
Author(s):
Liu Gang1Jiang Haiteng1Liu Haiyan2Wang Li2Yao Zhijian2Lu Qing1
(1Research Center for Learning Science, Southeast University, Nanjing 210096, China)
(2Nanjing Brain Hospital,Nanjing Medical University, Nanjing 210029, China)
关键词:
抑郁症分类功能核磁共振功能信号成分相关分析频谱分析
Keywords:
depression classification functional magnetic resonance imaging (fMRI) functional activation related components correlation analysis spectrum analysis
分类号:
Q42;Q64
DOI:
10.3969/j.issn.1001-0505.2011.01.014
摘要:
为了提高抑郁症识别的准确率,将功能核磁共振成像的任务态数据和静息态数据相结合,建立基于数据驱动的模型以提取识别特征.在没有任何先验知识的条件下,采用独立成分分析法提取任务态数据和静息态数据的独立成分; 然后,利用相关遍历分析法获取功能信号集,利用频谱分析法识别并获取功能信号成分; 最后,将功能信号成分作为贝叶斯分类器的特征输入,完成分类.结果表明,利用该方法提取出的功能信号成分能很好地将抑郁症患者和健康者区分开,整体识别准确率达到77.27%,抑郁症患者识别准确率达到83.33%,健康者识别准确率达到70.00%.实验结果证明了这一方法的有效性及优越性.
Abstract:
In order to improve the recognition accuracy of depression, event-related data and resting-state data in functional magnetic resonance imaging (fMRI) are collaborated and a data-driven model is modeled to extract recognition features. Without any priori knowledge, the component analysis(ICA) is adopted to extract the independent components of the event-related data and resting-state data. Then correlation analysis and spectrum analysis between independent components are used to find the components with major contributions for recognition. Finally, the functional activation related components are taken as the input features of Bayesian classifier to achieve the classification. The results show that the recognition accuracy by this method is 77. 27%; the patient recognition accuracy is 83. 33%; the healthy recognition accuracy is 70. 00%. Thus, the functional signal components extracted can well separate the depressed from the healthy. The experimental results validate the effectiveness and the superiority of this method.

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备注/Memo

备注/Memo:
作者简介:刘刚(1986—),男,硕士生;卢青(联系人),女,博士,副教授,博士生导师,luq@seu.edu.cn.
基金项目:国家高技术研究发展计划(863计划)资助项目(2008AA02Z410)、国家自然科学基金资助项目(30900356)、教育部博士点新进教师基金资助项目(200802861079).
引文格式: 刘刚,江海腾,刘海燕,等.基于任务态和静息态功能核磁共振信号的抑郁症识别[J].东南大学学报:自然科学版,2011,41(1):67-71.[doi:10.3969/j.issn.1001-0505.2011.01.014]
更新日期/Last Update: 2011-01-20