标题:Application of euler elastica regularized logistic regression on resting-state fMRI for identification of Alzheimer's disease
作者:Guo, Weiping ;Yao, Li ;Long, Zhiying
通讯作者:Long, Zhiying
作者机构:[Guo, Weiping ;Long, Zhiying ] State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing; 100875, China;[Yao, Li 更多
会议名称:4th International Conference on Biomedical Signal and Image Processing, ICBIP 2019
会议日期:August 13, 2019 - August 15, 2019
来源:ACM International Conference Proceeding Series
出版年:2019
页码:51-55
DOI:10.1145/3354031.3354036
摘要:Many machine-learning methods have been widely applied to predict Alzheimer's disease based on functional magnetic resonance imaging (fMRI) data. In our previous study, we proposed the Euler Elastica Regularized Logistic Regression (EELR) method and demonstrated its advantages over the other classifiers. In this study, we applied EELR to resting-state fMRI (RS-fMRI) data of 24 healthy aged subjects and 22 Alzheimer's disease (AD) patients for the identification of Alzheimer's disease. Moreover, in order to reveal the neural discriminative pattern, permutation test was performed to test the differences of EELR weight between AD and healthy aged subject. The results showed that EELR classifier could successfully classify AD and healthy aged subject. Moreover, EELR revealed that the amplitude of low-frequency fluctuations (ALFF) of posterior cingulate cortex, prefrontal cortex and hippocampus are the important biomarkers for distinguishing AD and healthy aged subject.
© 2019 Association for Computing Machinery.
收录类别:EI
资源类型:会议论文
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