Unmixing functional magnetic resonance imaging data using matrix factorization

被引：2

作者：

Khaliq, Amir A. ^{[1
]}

Qureshi, Ijaz M. ^{[2
]}

Shah, Jawad A. ^{[1
]}

机构：

[1] Int Islamic Univ, Dept Elect Engn, Islamabad, Pakistan

[2] Air Univ, ISSS, Dept Elect Engn, Islamabad, Pakistan

来源：

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY | 2012年 / 22卷 / 04期

关键词：

fMRI data analysis; fMRI source separation; matrix factorization; FMRI; ICA;

D O I：

10.1002/ima.22022

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Functional magnetic resonance imaging (fMRI) data is processed by different techniques for detection of activated voxels including principal component analysis (PCA), independent component analysis (ICA), non-negative matrix factorization (NMF), and so on. In this work, a modified version of NMF method is proposed in which data is not supposed to be non-negative. The proposed scheme is applied to synthetic fMRI data along with NMF conventional method. The results of the proposed scheme show that it is not only computationally efficient but also has good quality results as compared to that of NMF in terms of average correlation. Finally, proposed method is applied to monkey's fMRI data, and the results are compared with that of NMF and ICA. (c) 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 195199, 2012; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ima.22022

引用

页码：195 / 199

页数：5

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