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 neuroscienti c 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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Article dans une revue
Journal of Statistical Software, University of California, Los Angeles, 2011, 44 (9), pp.1-24
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http://www.hal.inserm.fr/inserm-00659425
Contributeur : Michel Dojat <>
Soumis le : jeudi 12 janvier 2012 - 17:09:28
Dernière modification le : jeudi 1 février 2018 - 01:11:40
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  • HAL Id : inserm-00659425, version 1

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Cécile Bordier, Michel Dojat, Pierre Lafaye de Micheaux. Temporal and Spatial Independent Component Analysis for fMRI Data Sets Embedded in the AnalyzeFMRI R Package. Journal of Statistical Software, University of California, Los Angeles, 2011, 44 (9), pp.1-24. 〈inserm-00659425〉

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