Independent component analysis for brain FMRI does indeed select for maximal independence.
Where this comes from
- Record sourced from PubMed, PMID 24009746.
- Also identified by DOI 10.1371/journal.pone.0073309 and PMC identifier 3757003.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
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.
Medical subject headings
- Brain
- Brain Mapping
- Magnetic Resonance Imaging
- Principal Component Analysis