Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
[test by reto]
Authors: Wouter D. Weeda, Frank de Vos, Lourens J. Waldorp, Raoul Grasman, Hilde M. Huizenga
Title: arf3DS4: An Integrated Framework for Localization and Connectivity Analysis of fMRI Data
Abstract: In standard fMRI analysis all voxels are tested in a massive univariate approach, that is, each voxel is tested independently. This requires stringent corrections for multiple comparisons to control the number of false positive tests (i.e., marking voxels as active while they are actually not). As a result, fMRI analyses may suffer from low power to detect activation, especially in studies with high levels of noise in the data, for example developmental or single-subject studies. Activated region fitting (ARF) yields a solution by modeling fMRI data by multiple Gaussian shaped regions. ARF only requires a small number of parameters and therefore has increased power to detect activation. If required, the estimated regions can be directly used as regions of interest in a functional connectivity analysis. ARF is implemented in the R package arf3DS4. In this paper ARF and its implementation are described and illustrated with an example.

Page views:: 3661. Submitted: 2010-11-14. Published: 2011-10-31.
Paper: arf3DS4: An Integrated Framework for Localization and Connectivity Analysis of fMRI Data     Download PDF (Downloads: 3082)
arf3DS4_2.5-3.tar.gz: R source package Download (Downloads: 847; 1MB)
v44i14.R: R example code from the paper Download (Downloads: 852; 469B) Example data Download (Downloads: 770; 54MB)

DOI: 10.18637/jss.v044.i14

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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.