Unmixing functional magnetic resonance imaging data using matrix factorization
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 met...
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| Veröffentlicht in: | International journal of imaging systems and technology 2012-12, Vol.22 (4), p.195-199 |
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| container_title | International journal of imaging systems and technology |
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| creator | Khaliq, Amir A. Qureshi, Ijaz M. Shah, Jawad A. |
| description | 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. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 195–199, 2012 |
| doi_str_mv | 10.1002/ima.22022 |
| format | Article |
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J. Imaging Syst. Technol</addtitle><description>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. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 195–199, 2012</description><subject>fMRI data analysis</subject><subject>fMRI source separation</subject><subject>matrix factorization</subject><issn>0899-9457</issn><issn>1098-1098</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><recordid>eNp1kE1PwkAQhjdGExE9-A-aePJQ2I9uu3skRJEEMCESj5thuyWLtMXdEsFf79aqNy_z-byTmUHoluABwZgObQkDSjGlZ6hHsBRxa85RDwspY5nw7BJdeb_FmBCOeQ8tVlVpj7baRMWh0o2tK9hFJWwq01gdOeNDodImCnM3LZVDA9HBt2EJjbPHqADd1M5-Qiu-RhcF7Ly5-fF9tHp8eBk_xbPnyXQ8msWaMU5jYoRhQkhGC0goFxr42miep4SxIl3nWhR5kmXSgJY4tHKdiDTDkEICkhnM-uium7t39fvB-EZt64MLu3tFCE2yJMWEB-q-o7SrvXemUHsXDnEnRbBq36VCpr7fFdhhx37YnTn9D6rpfPSriDuF9Y05_inAvak0YxlXr4uJWs5pNksnY7VkX3WBe-U</recordid><startdate>201212</startdate><enddate>201212</enddate><creator>Khaliq, Amir A.</creator><creator>Qureshi, Ijaz M.</creator><creator>Shah, Jawad A.</creator><general>Wiley Subscription Services, Inc., A Wiley Company</general><general>Wiley Subscription Services, Inc</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>201212</creationdate><title>Unmixing functional magnetic resonance imaging data using matrix factorization</title><author>Khaliq, Amir A. ; Qureshi, Ijaz M. ; Shah, Jawad A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3352-1e8e388932fa4258ca5bec5d6133f6bdc8fd4779eac905bedc48670a6a4a93e03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>fMRI data analysis</topic><topic>fMRI source separation</topic><topic>matrix factorization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Khaliq, Amir A.</creatorcontrib><creatorcontrib>Qureshi, Ijaz M.</creatorcontrib><creatorcontrib>Shah, Jawad A.</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><jtitle>International journal of imaging systems and technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Khaliq, Amir A.</au><au>Qureshi, Ijaz M.</au><au>Shah, Jawad A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Unmixing functional magnetic resonance imaging data using matrix factorization</atitle><jtitle>International journal of imaging systems and technology</jtitle><addtitle>Int. 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| subjects | fMRI data analysis fMRI source separation matrix factorization |
| title | Unmixing functional magnetic resonance imaging data using matrix factorization |
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