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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/269
DC FieldValueLanguage
dc.contributor.authorNguyen Minh Hienen_US
dc.date.accessioned2020-03-12T16:58:07Z-
dc.date.available2020-03-12T16:58:07Z-
dc.date.issued2017-
dc.identifier.citationhttp://www.sigtelcom.net/2017/en_US
dc.identifier.urihttp://epub.vgu.edu.vn/handle/dlibvgu/269-
dc.description.abstractFunctional Magnetic Resonance Imaging (fMRI) has been valuable to the current understanding of brain function and pre-operative evaluation of patients. In the recent years, the technique has been increasingly applied to the cases when the subject is at rest, also referred to as the resting-state fMRI. Resting-state fMRI measures spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal to investigate the functional topology of the brain. It is possible to identify various anatomically distinct areas of the brain that demonstrate synchronous BOLD fluctuations at rest, also referred to as the brain functional networks. Conventional approach to extract these functional dynamics is the datadriven Independent Component Analysis (ICA) method. In this work, we propose to utilize sparse representations for identifying functional connectivity networks. Specifically, fMRI signals are decomposed into morphological components which have sparse spatial overlap. Allowing sparse spatial overlap between components is a more physically plausible assumption to the statistical independence assumption of the conventional ICA method. The dictionary is learnt from the data using a K-SVD algorithm. Experimental results show that the proposed MCA-KSVD method can be used as an alternative to the conventional ICA method.en_US
dc.language.isoenen_US
dc.subjectResonance imagingen_US
dc.subjectSignal processingen_US
dc.subjectAlgorithm designen_US
dc.titleDictionary learning based on sparse representations for resting-state functional MRI data analysisen_US
dc.typeArticleen_US
dc.relation.conferenceIEEE International Conference on Recent Advances on Signal Processing, Telecommunications and Computing (SIGTELCOM) 2017en_US
dc.relation.duration16-08-2017en_US
dc.relation.conferencevenueDa Nang, Viet Namen_US
item.languageiso639-1other-
item.fulltextNo Fulltext-
item.grantfulltextnone-
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