Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina Grün, Edzer Pebesma & Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Temporal and Spatial Independent Component Analysis for fMRI Data Sets Embedded in the AnalyzeFMRI R Package | Bordier | Journal of Statistical Software
Authors: Cécile Bordier, Michel Dojat, Pierre Lafaye de Micheaux
Title: 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 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.

Page views:: 4868. Submitted: 2010-10-29. Published: 2011-10-31.
Paper: Temporal and Spatial Independent Component Analysis for fMRI Data Sets Embedded in the AnalyzeFMRI R Package     Download PDF (Downloads: 4945)
Supplements:
AnalyzeFMRI_1.1-14.tar.gz: R source package Download (Downloads: 838; 385KB)
Download-replication.txt: Replication materials download instructions Download (Downloads: 919; 176B)
v44i09-replication.tar.bz2: Errata for original paper as it existed between 6-16-2011 and 5-25-2012. Download (Downloads: 745; 141MB)

DOI: 10.18637/jss.v044.i09

by
This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.