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Simulating fMRI data: the R package neuRosim

Marijke Welvaert UGent and Yves Rosseel UGent (2011) Workshop on Statistics and Neuroimaging, Abstracts.
abstract
Functional magentic resonance imaging (fMRI) is used as a powerful imaging tool to locate BOLD activation in time and in particular in place. With about 3300 publications in 2010 and heading the same number in 2011, the technique is also very popular in the neuroimaging community. Unfortunately, the ground truth of the data acquired using fMRI is unknown. This is a major problem because the location of activity in the data requires complex analysis processes that need to be validated to ensure that the analysis techniques are working properly. Validation is only possible if the ground truth is known. As a solution, fMRI data are generated artificially. However, simulation studies could be considered as a minority in the fMRI literature (only 100 publications in 2010). Moreover, there is currently no consensus on how to simulate fMRI data, neither is there any attempt made to converge and validate the existing simulation methods. Generally, simulation studies are conducted using ad-hoc methods and in-house software routines. neuRosim is an R package (http://www.r-project.org) that aims to serve as a general standardized software platform to simulate fMRI data. Currently, the package gathers the functionalities of existing simulation studies with the extension to more biophysically plausible models. The main focus lies on the inclusion of several noise sources (for example system noise, temporally and spatially correlated noise, physiological noise, . . . ). neuRosim can be downloaded from CRAN (http://cran.r-project.org) and is released with a GPL licence, meaning that it is completely open-source and can be freely used under almost all platforms (Windows, Mac and Unix). The data generation in neuRosim is fairly fast and, depending on the dimensions of the dataset, can be easily computed on a standard desktop within a few minutes. Therefore, the simulation process can be smoothly incorporated in large simulation studies. During the presentation, we will stress the importance for validated simulation research and demonstrate how neuRosim can contribute by comparing the differences between implemented simulation methods. Further we will show examples that demonstrate the functionalities of the package and discuss briefly our plans for coming updates.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
keyword
fMRI, simulation, physiological noise
in
Workshop on Statistics and Neuroimaging, Abstracts
conference name
Workshop on Statistics and Neuroimaging (WIAS - 2011)
conference location
Berlin, Germany
conference start
2011-11-23
conference end
2011-11-25
language
English
UGent publication?
yes
classification
C3
copyright statement
I have retained and own the full copyright for this publication
id
1996128
handle
http://hdl.handle.net/1854/LU-1996128
date created
2012-01-19 11:22:00
date last changed
2012-01-20 11:14:03
@inproceedings{1996128,
  abstract     = {Functional magentic resonance imaging (fMRI) is used as a powerful imaging tool to locate BOLD activation in time and in particular in place. With about 3300 publications in 2010 and heading the same number in 2011, the technique is also very popular in the neuroimaging community. Unfortunately, the ground truth of the data acquired using fMRI is unknown. This is a major problem because the location of activity in the data requires complex analysis processes that need to be validated to ensure that the analysis techniques are working properly. Validation is only possible if the ground truth is known. As a solution, fMRI data are generated artificially. However, simulation studies could be considered as a minority in the fMRI literature (only 100 publications in 2010). Moreover, there is currently no consensus on how to simulate fMRI data, neither is there any attempt made to converge and validate the existing simulation methods. Generally, simulation studies are conducted using ad-hoc methods and in-house software routines. neuRosim is an R package (http://www.r-project.org) that aims to serve as a general standardized software platform to simulate fMRI data. Currently, the package gathers the functionalities of existing simulation studies with the extension to more biophysically plausible models. The main focus lies on the inclusion of several noise sources (for example system noise, temporally and spatially correlated noise, physiological noise, . . . ). neuRosim can be downloaded from CRAN (http://cran.r-project.org) and is released with a GPL licence, meaning that it is completely open-source and can be freely used under almost all platforms (Windows, Mac and Unix). The data generation in neuRosim is fairly fast and, depending on the dimensions of the dataset, can be easily computed on a standard desktop within a few minutes. Therefore, the simulation process can be smoothly incorporated in large simulation studies. During the presentation, we will stress the importance for validated simulation research and demonstrate how neuRosim can contribute by comparing the differences between implemented simulation methods. Further we will show examples that demonstrate the functionalities of the package and discuss briefly our plans for coming updates.},
  author       = {Welvaert, Marijke and Rosseel, Yves},
  booktitle    = {Workshop on Statistics and Neuroimaging, Abstracts},
  keyword      = {fMRI,simulation,physiological noise},
  language     = {eng},
  location     = {Berlin, Germany},
  title        = {Simulating fMRI data: the R package neuRosim},
  year         = {2011},
}

Chicago
Welvaert, Marijke, and Yves Rosseel. 2011. “Simulating fMRI Data: The R Package neuRosim.” In Workshop on Statistics and Neuroimaging, Abstracts.
APA
Welvaert, M., & Rosseel, Y. (2011). Simulating fMRI data: the R package neuRosim. Workshop on Statistics and Neuroimaging, Abstracts. Presented at the Workshop on Statistics and Neuroimaging (WIAS - 2011).
Vancouver
1.
Welvaert M, Rosseel Y. Simulating fMRI data: the R package neuRosim. Workshop on Statistics and Neuroimaging, Abstracts. 2011.
MLA
Welvaert, Marijke, and Yves Rosseel. “Simulating fMRI Data: The R Package neuRosim.” Workshop on Statistics and Neuroimaging, Abstracts. 2011. Print.