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Article: A supervised singular value decomposition for independent component analysis of fMRI

TitleA supervised singular value decomposition for independent component analysis of fMRI
Authors
KeywordsBasis expansion
Spatio-temporal data
Singular value decomposition
Robustness
Functional Magnetic Resonance Imaging
Issue Date2008
Citation
Statistica Sinica, 2008, v. 18, n. 4, p. 1233-1252 How to Cite?
AbstractFunctional Magnetic Resonance Imaging (fMRI) is a non-invasive technique for studying the brain activity. The data acquisition process results a temporal sequence of 3D brain images. Due to the high sensitivity of MR scanners, spikes are commonly observed in the data. Along with the temporal and spatial features of fMRI data, this artifact raises a challenging problem in the statistical analysis. In this paper, we introduce a supervised singular value decomposition technique as a data reduction step of independent component analysis (ICA), which is an effective tool for exploring spatio-temporal features in fMRI data. Two major advantages are discussed: first, the proposed method improves the robustness of ICA against spikes; second, the method uses the fMRI experimental designs to guide the fully data-driven ICA, yielding a more computationally efficient procedure and highly interpretable results. The advantages are demonstrated using spatio-temporal simulation studies as well as a data analysis.
Persistent Identifierhttp://hdl.handle.net/10722/219372
ISSN
2015 Impact Factor: 0.838
2015 SCImago Journal Rankings: 2.292

 

DC FieldValueLanguage
dc.contributor.authorBai, Ping-
dc.contributor.authorShen, Haipeng-
dc.contributor.authorHuang, Xuemei-
dc.contributor.authorTruong, Young-
dc.date.accessioned2015-09-23T02:56:54Z-
dc.date.available2015-09-23T02:56:54Z-
dc.date.issued2008-
dc.identifier.citationStatistica Sinica, 2008, v. 18, n. 4, p. 1233-1252-
dc.identifier.issn1017-0405-
dc.identifier.urihttp://hdl.handle.net/10722/219372-
dc.description.abstractFunctional Magnetic Resonance Imaging (fMRI) is a non-invasive technique for studying the brain activity. The data acquisition process results a temporal sequence of 3D brain images. Due to the high sensitivity of MR scanners, spikes are commonly observed in the data. Along with the temporal and spatial features of fMRI data, this artifact raises a challenging problem in the statistical analysis. In this paper, we introduce a supervised singular value decomposition technique as a data reduction step of independent component analysis (ICA), which is an effective tool for exploring spatio-temporal features in fMRI data. Two major advantages are discussed: first, the proposed method improves the robustness of ICA against spikes; second, the method uses the fMRI experimental designs to guide the fully data-driven ICA, yielding a more computationally efficient procedure and highly interpretable results. The advantages are demonstrated using spatio-temporal simulation studies as well as a data analysis.-
dc.languageeng-
dc.relation.ispartofStatistica Sinica-
dc.subjectBasis expansion-
dc.subjectSpatio-temporal data-
dc.subjectSingular value decomposition-
dc.subjectRobustness-
dc.subjectFunctional Magnetic Resonance Imaging-
dc.titleA supervised singular value decomposition for independent component analysis of fMRI-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-60149104077-
dc.identifier.volume18-
dc.identifier.issue4-
dc.identifier.spage1233-
dc.identifier.epage1252-

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