File Download
There are no files associated with this item.
Supplementary
-
Citations:
- Scopus: 0
- Appears in Collections:
Article: A supervised singular value decomposition for independent component analysis of fMRI
Title | A supervised singular value decomposition for independent component analysis of fMRI |
---|---|
Authors | |
Keywords | Basis expansion Spatio-temporal data Singular value decomposition Robustness Functional Magnetic Resonance Imaging |
Issue Date | 2008 |
Citation | Statistica Sinica, 2008, v. 18, n. 4, p. 1233-1252 How to Cite? |
Abstract | Functional 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 Identifier | http://hdl.handle.net/10722/219372 |
ISSN | 2021 Impact Factor: 1.330 2020 SCImago Journal Rankings: 1.240 |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Bai, Ping | - |
dc.contributor.author | Shen, Haipeng | - |
dc.contributor.author | Huang, Xuemei | - |
dc.contributor.author | Truong, Young | - |
dc.date.accessioned | 2015-09-23T02:56:54Z | - |
dc.date.available | 2015-09-23T02:56:54Z | - |
dc.date.issued | 2008 | - |
dc.identifier.citation | Statistica Sinica, 2008, v. 18, n. 4, p. 1233-1252 | - |
dc.identifier.issn | 1017-0405 | - |
dc.identifier.uri | http://hdl.handle.net/10722/219372 | - |
dc.description.abstract | Functional 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.language | eng | - |
dc.relation.ispartof | Statistica Sinica | - |
dc.subject | Basis expansion | - |
dc.subject | Spatio-temporal data | - |
dc.subject | Singular value decomposition | - |
dc.subject | Robustness | - |
dc.subject | Functional Magnetic Resonance Imaging | - |
dc.title | A supervised singular value decomposition for independent component analysis of fMRI | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.scopus | eid_2-s2.0-60149104077 | - |
dc.identifier.volume | 18 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 1233 | - |
dc.identifier.epage | 1252 | - |
dc.identifier.issnl | 1017-0405 | - |