Temporal and Spatial Independent Component Analysis for fMRI data sets embedded in a R package

Cécile Bordier; Michel Dojat; Pierre Lafaye de Micheaux

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arXiv · 1012.0269

Temporal and Spatial Independent Component Analysis for fMRI data sets embedded in a R package

Cécile Bordier·Michel Dojat·Pierre Lafaye de Micheaux

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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 tractable approach generally proposed. However, for some neuroscientific applications, temporal independence of source signals can be assumed and temporal ICA becomes then an attracting 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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BibTeX RIS

Cécile Bordier, Michel Dojat, Pierre Lafaye de Micheaux. 2010-12-01. Temporal and Spatial Independent Component Analysis for fMRI data sets embedded in a R package. https://arxiv.org/abs/1012.0269

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