Independent component analysis for brain FMRI does indeed select for maximal independence.
A recent paper by Daubechies et al. claims that two independent component analysis (ICA) algorithms, Infomax and FastICA, which are widely used for functional magnetic resonance imaging (fMRI) analysis, select for sparsity rather than independence. The argument was supported by a series of experimen...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2013-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3757003?pdf=render |
| _version_ | 1818324339145048064 |
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| author | Vince D Calhoun Vamsi K Potluru Ronald Phlypo Rogers F Silva Barak A Pearlmutter Arvind Caprihan Sergey M Plis Tülay Adalı |
| author_facet | Vince D Calhoun Vamsi K Potluru Ronald Phlypo Rogers F Silva Barak A Pearlmutter Arvind Caprihan Sergey M Plis Tülay Adalı |
| author_sort | Vince D Calhoun |
| collection | DOAJ |
| description | A recent paper by Daubechies et al. claims that two independent component analysis (ICA) algorithms, Infomax and FastICA, which are widely used for functional magnetic resonance imaging (fMRI) analysis, select for sparsity rather than independence. The argument was supported by a series of experiments on synthetic data. We show that these experiments fall short of proving this claim and that the ICA algorithms are indeed doing what they are designed to do: identify maximally independent sources. |
| first_indexed | 2024-12-13T11:27:01Z |
| format | Article |
| id | doaj.art-8247747505dc4ff28bdeb51588fe1656 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T11:27:01Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-8247747505dc4ff28bdeb51588fe16562022-12-21T23:48:09ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0188e7330910.1371/journal.pone.0073309Independent component analysis for brain FMRI does indeed select for maximal independence.Vince D CalhounVamsi K PotluruRonald PhlypoRogers F SilvaBarak A PearlmutterArvind CaprihanSergey M PlisTülay AdalıA recent paper by Daubechies et al. claims that two independent component analysis (ICA) algorithms, Infomax and FastICA, which are widely used for functional magnetic resonance imaging (fMRI) analysis, select for sparsity rather than independence. The argument was supported by a series of experiments on synthetic data. We show that these experiments fall short of proving this claim and that the ICA algorithms are indeed doing what they are designed to do: identify maximally independent sources.http://europepmc.org/articles/PMC3757003?pdf=render |
| spellingShingle | Vince D Calhoun Vamsi K Potluru Ronald Phlypo Rogers F Silva Barak A Pearlmutter Arvind Caprihan Sergey M Plis Tülay Adalı Independent component analysis for brain FMRI does indeed select for maximal independence. PLoS ONE |
| title | Independent component analysis for brain FMRI does indeed select for maximal independence. |
| title_full | Independent component analysis for brain FMRI does indeed select for maximal independence. |
| title_fullStr | Independent component analysis for brain FMRI does indeed select for maximal independence. |
| title_full_unstemmed | Independent component analysis for brain FMRI does indeed select for maximal independence. |
| title_short | Independent component analysis for brain FMRI does indeed select for maximal independence. |
| title_sort | independent component analysis for brain fmri does indeed select for maximal independence |
| url | http://europepmc.org/articles/PMC3757003?pdf=render |
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