Temporal and Spatial Independent Component Analysis for fMRI Data Sets Embedded in the AnalyzeFMRI R PackageReport as inadecuate




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* Corresponding author 1 ANTE-INSERM U836, équipe 5 - Neuro-imagerie fonctionnelle et métabolique GIN - Grenoble Institut des Neurosciences 2 Département de Mathématiques et de Statistiques Montréal

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.

keyword : multivariate analysis temporal ICA spatial ICA magnetic resonance imaging neuroimaging





Author: Cécile Bordier - Michel Dojat - Pierre Lafaye de Micheaux -

Source: https://hal.archives-ouvertes.fr/



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