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Simulating fMRI data: the R package neuRosim

Marijke Welvaert (UGent) and Yves Rosseel (UGent)
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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.
Keywords
fMRI, simulation, physiological noise

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Citation

Please use this url to cite or link to this publication:

MLA
Welvaert, Marijke, and Yves Rosseel. “Simulating FMRI Data: The R Package NeuRosim.” Workshop on Statistics and Neuroimaging, Abstracts, 2011.
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), Berlin, Germany.
Chicago author-date
Welvaert, Marijke, and Yves Rosseel. 2011. “Simulating FMRI Data: The R Package NeuRosim.” In Workshop on Statistics and Neuroimaging, Abstracts.
Chicago author-date (all authors)
Welvaert, Marijke, and Yves Rosseel. 2011. “Simulating FMRI Data: The R Package NeuRosim.” In Workshop on Statistics and Neuroimaging, Abstracts.
Vancouver
1.
Welvaert M, Rosseel Y. Simulating fMRI data: the R package neuRosim. In: Workshop on Statistics and Neuroimaging, Abstracts. 2011.
IEEE
[1]
M. Welvaert and Y. Rosseel, “Simulating fMRI data: the R package neuRosim,” in Workshop on Statistics and Neuroimaging, Abstracts, Berlin, Germany, 2011.
@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}},
  keywords     = {{fMRI,simulation,physiological noise}},
  language     = {{eng}},
  location     = {{Berlin, Germany}},
  title        = {{Simulating fMRI data: the R package neuRosim}},
  year         = {{2011}},
}