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Temporal and Spatial Independent Component Analysis for fMRI Data Sets Embedded in the AnalyzeFMRI R Package

Author(s): Cécile Bordier | Michel Dojat | Pierre Lafaye de Micheaux

Journal: Journal of Statistical Software
ISSN 1548-7660

Volume: 44;
Issue: 09;
Date: 2011;
Original page

Keywords: magnetic resonance imaging | diffusion | tractography | image processing | machine learning | R.

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.
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