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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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https://www.hal.inserm.fr/inserm-00659425
Contributor : Michel Dojat <>
Submitted on : Thursday, January 12, 2012 - 5:09:28 PM
Last modification on : Monday, August 31, 2020 - 4:22:04 PM
Long-term archiving on: : Monday, November 19, 2012 - 1:30:16 PM

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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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