点击次数：

所属单位：计算机科学与技术学院/人工智能学院/软件学院

发表刊物：IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS

关键字：Schizophrenia fMRI low-frequency fluctuations tree-guided group lasso multi-kernel learning

摘要：The fractional amplitude of low-frequency fluctuations (fALFF) has been widely used as potential clinical biomarkers for resting-state functional-magneticresonance-imaging-based schizophrenia diagnosis. However, previous studies usually measure the fALFF with specific bands from 0.01 to 0.08 Hz, which cannot fully delineate the complex variations of spontaneous fluctuations in the resting-state brain. In addition, fALFF data are intrinsically constrained by the brain structure, but most of the traditional methods have not consider it in feature selection. For addressing these problems, we propose a model to classify schizophrenia in multifrequency bands with tree-guided group sparse learning. In detail, we first acquire the fALFF data in multifrequency bands (i.e., slow-5: 0.01-0.027 Hz, slow-4: 0.027-0.073 Hz, slow-3: 0.073-0.198 Hz, and slow-2: 0.198-0.25 Hz). Then, we divide the whole brain into different candidate patches and select those significant patches related to schizophrenia using random forest-based important score. Moreover, we use tree-structured sparse learning method for feature selection with the above patch spatial constraint. Finally, considering biomarkers from multifrequency bands can reflect complementary information among multiple-frequency bands, we adopt the multikernel learning method to combine features of multifrequency bands for classification. Our experimental results show that these biomarkers from multifrequency bands can achieve a classification accuracy of 91.1% on 17 schizophrenia patients and 17 healthy controls, further demonstrating that the multifrequency bands analysis can better account for classification of schizophrenia.

ISSN号：2168-2194

是否译文：否

发表时间：2019-01-01

合写作者：Huang, Jiashuang,Zhu, Qi,Hao, Xiaoke,Shi, Xiaomeng,Gao, Shuzhan,Xu, Xijia,张道强

通讯作者：Xu, Xijia,张道强,朱清华