东南大学学报（自然科学版）2011，Vol.41Issue(1)：67-71,5.DOI:10.3969/j.issn.1001-0505.2011.01.014
基于任务态和静息态功能核磁共振信号的抑郁症识别
Recognition of depression using event-related and resting-state functional magnetic resonance signals
摘要
Abstract
In order to improve the recognition accuracy of depression, event-related data and restingstate 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.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Key words
depression/ classification/ functional magnetic resonance imaging (fMRI)/ functional activation related components/ correlation analysis/ spectrum analysis分类
生物科学引用本文复制引用
刘刚,江海腾,刘海燕,王丽,姚志剑,卢青..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):67-71,5.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). （863计划）
