[test by reto]
|Authors:||Cécile Bordier, Michel Dojat, Pierre Lafaye de Micheaux|
|Title:||Temporal and Spatial Independent Component Analysis for fMRI Data Sets Embedded in the AnalyzeFMRI R Package|
|Abstract:||For statistical analysis of functional magnetic resonance imaging (fMRI) data sets, we propose a data-driven approach based on independent component analysis (ICA) implemented in a new version of the AnalyzeFMRI R package. For fMRI data sets, spatial dimension being much greater than temporal dimension, spatial ICA is the computationally tractable approach generally proposed. However, for some neuroscientific applications, temporal independence of source signals can be assumed and temporal ICA becomes then an attractive exploratory technique. In this work, we use a classical linear algebra result ensuring the tractability of temporal ICA. We report several experiments on synthetic data and real MRI data sets that demonstrate the potential interest of our R package.|
Page views:: 6375. Submitted: 2010-10-29. Published: 2011-10-31.
Temporal and Spatial Independent Component Analysis for fMRI Data Sets Embedded in the AnalyzeFMRI R Package
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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.