<b>neuRosim</b>: An<i>R</i>Package for Generating fMRI Data

Welvaert, Marijke; Durnez, Joke; Moerkerke, Beatrijs; Verdoolaege, Geert; Rosseel, Yves

doi:10.18637/jss.v044.i10

Cited by 81 publications

(90 citation statements)

References 37 publications

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“…The simulations were performed in R version 3.3.0 [21] using the packages ggplot2 [22], neuRosim [23] JADE [24] (: and tensorBSS [25], from which the implementation of the tensor regime discussed in Section III-D can be found.…”

Section: Simulationmentioning

confidence: 99%

“…The R-package neuRosim [23] provides a tool for simulating realistic resting-state and task-based !MRI data, and allows one to control the sources of noise and many other effects. In order to compare the methods of interest we use the package to simulate 4-dimensional task-based !MRI data (3 spatial and 1 temporal dimensions) .…”

Section: A Simulation Settingmentioning

confidence: 99%

“…The two HRFs under consideration are gamma HRF and double-gamma HRF. Furthermore, several noise sources are added to the observations, comprising of Rician system noise, temporal noise, low-frequency drift, physiological noise, task related noise and spatial noise, see [23]. The general code for simulating the data is given in the Appendix.…”

Section: A Simulation Settingmentioning

confidence: 99%

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Applying fully tensorial ICA to fMRI data

Virta

Taskinen

Nordhausen

2016

2016 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

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Abstract-There are two aspects in functional magnetic reso nance imaging (fMRI) data that make them awkward to analyse with traditional multivariate methods -high order and high dimension. The first of these refers to the tensorial nature of observations as array-valued elements instead of vectors.Although this can be circumvented by vectorizing the array, doing so simultaneously loses all the structural information in the original observations. The second aspect refers to the high dimensionality along each dimension making the concept of di mension reduction a valuable tool in the processing of fMRI data.Different methods of tensor dimension reduction are currently gaining popUlarity in literature, and in this paper we apply two recently proposed methods of tensorial independent component analysis to simulated task-based fMRI data. Additionally, as a preprocessing step we introduce a novel extension of PCA for tensors. The simulations show that when extracting a sufficiently large number of principal components, the tensor methods find the task signals very reliably, something the standard temporal independent component analysis (tICA) fails in.

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Section: Simulationmentioning

confidence: 99%

Section: A Simulation Settingmentioning

confidence: 99%

Section: A Simulation Settingmentioning

confidence: 99%

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Applying fully tensorial ICA to fMRI data

Virta

Taskinen

Nordhausen

2016

2016 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

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“…For simulation purposes, we used the neuRosim package described in Welvaert et al (2011) to simulate a set of four-dimensional fMRI data. The detailed settings used for data generation are elaborated as follows.…”

Section: Simulation Studiesmentioning

confidence: 99%

Pre-surgical fMRI Data Analysis Using a Spatially Adaptive Conditionally Autoregressive Model

Liu¹,

Berrocal²,

Bartsch³

et al. 2016

Bayesian Anal.

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Spatial smoothing is an essential step in the analysis of functional magnetic resonance imaging (fMRI) data. One standard smoothing method is to convolve the image data with a three-dimensional Gaussian kernel that applies a fixed amount of smoothing to the entire image. In pre-surgical brain image analysis where spatial accuracy is paramount, this method, however, is not reasonable as it can blur the boundaries between activated and deactivated regions of the brain. Moreover, while in a standard fMRI analysis strict false positive control is desired, for pre-surgical planning false negatives are of greater concern. To this end, we propose a novel spatially adaptive conditionally autoregressive model with variances in the full conditional of the means that are proportional to error variances, allowing the degree of smoothing to vary across the brain. Additionally, we present a new loss function that allows for the asymmetric treatment of false positives and false negatives. We compare our proposed model with two existing spatially adaptive conditionally autoregressive models. Simulation studies show that our model outperforms these other models; as a real model application, we apply the proposed model to the pre-surgical fMRI data of two patients to assess peri- and intra-tumoral brain activity.

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“…The activation based on the Bloch equations was generated using POSSUM (Smith et al, 2004). All other data were generated using neuRosim (Welvaert et al, 2011). The simulation study was conducted in R (R Development Core Team, 2010) and each cell of the simulation design was replicated 100 times.…”

Section: Simulation Design and Data Analysismentioning

confidence: 99%