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

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  • Bordier, Cécile
  • Dojat, Michel
  • Micheaux, Pierre Lafaye de

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.

Suggested Citation

  • Bordier, Cécile & Dojat, Michel & Micheaux, Pierre Lafaye de, 2011. "Temporal and Spatial Independent Component Analysis for fMRI Data Sets Embedded in the AnalyzeFMRI R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 44(i09).
    • Handle: RePEc:jss:jstsof:v:044:i09
    DOI: http://hdl.handle.net/10.18637/jss.v044.i09
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    Cited by:

    1. Elizabeth M Sweeney & Joshua T Vogelstein & Jennifer L Cuzzocreo & Peter A Calabresi & Daniel S Reich & Ciprian M Crainiceanu & Russell T Shinohara, 2014. "A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-14, April.

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