Package 'neuRosim' reference manual

Title:	Simulate fMRI Data
Description:	Generates functional Magnetic Resonance Imaging (fMRI) time series or 4D data. Some high-level functions are created for fast data generation with only a few arguments and a diversity of functions to define activation and noise. For more advanced users it is possible to use the low-level functions and manipulate the arguments. See Welvaert et al. (2011) <doi:10.18637/jss.v044.i10>.
Authors:	Marijke Welvaert [aut], Joke Durnez [ctb], Beatrijs Moerkerke [ctb], Yves Rosseel [ctb], Karsten Tabelow [ctb, cre], Geert Verdoolaege [ctb]
Maintainer:	Karsten Tabelow <[email protected]>
License:	GPL (>= 2)
Version:	0.2-14
Built:	2025-01-14 05:22:19 UTC
Source:	https://github.com/cran/neuRosim

Functions to Generate fMRI Data Including Activated Data, Noise Data and Resting State Data

Description

The package allows users to generate fMRI time series or 4D data. Some high-level functions are created for fast data generation with only a few arguments and a diversity of functions to define activation and noise. For more advanced users it is possible to use the low-level functions and manipulate the arguments.

Author(s)

Marijke Welvaert with contributions from Joke Durnez, Beatrijs Moerkerke, Yves Rosseel, Karsten Tabelow, and Geert Verdoolaege

Maintainer: Karsten Tabelow <[email protected]>

References

Welvaert, M., Durnez, J., Moerkerke, B., Verdoolaege, G. and Rosseel, Y. (2011). neuRosim: An R Package for Generating fMRI Data. Journal of Statistical Software, 44(10), 1–18

Examples

## Generate fMRI time series for block design
design <- simprepTemporal(totaltime=200, onsets=seq(1,200,40),
	 durations=20, TR=2, effectsize=1, hrf="double-gamma")
ts <- simTSfmri(design=design, SNR=1, noise="white")
plot(ts, type="l")

## Generate fMRI slice for block design with activation in 2 regions
design <- simprepTemporal(totaltime=200, onsets=seq(1,200,40), 
	durations=20, TR=2, effectsize=1, hrf="double-gamma")
region <- simprepSpatial(regions=2, coord=list(c(32,15),c(57,45)), 
	radius=c(10,7), form="sphere")
out <- simVOLfmri(design=design, image=region, dim=c(64,64), 
	SNR=1, noise="none")
plot(out[32,15,], type="l")

## Generate fMRI time series for block design
design <- simprepTemporal(totaltime=200, onsets=seq(1,200,40),
	 durations=20, TR=2, effectsize=1, hrf="double-gamma")
ts <- simTSfmri(design=design, SNR=1, noise="white")
plot(ts, type="l")

## Generate fMRI slice for block design with activation in 2 regions
design <- simprepTemporal(totaltime=200, onsets=seq(1,200,40), 
	durations=20, TR=2, effectsize=1, hrf="double-gamma")
region <- simprepSpatial(regions=2, coord=list(c(32,15),c(57,45)), 
	radius=c(10,7), form="sphere")
out <- simVOLfmri(design=design, image=region, dim=c(64,64), 
	SNR=1, noise="none")
plot(out[32,15,], type="l")

Balloon model

Description

Generates the BOLD signal based on the Balloon model of Buxton et al. (2004).

Usage

  balloon(stim, totaltime, acc, par=list(), verbose=TRUE)
balloon(stim, totaltime, acc, par=list(), verbose=TRUE)

Arguments

`stim`	Vector representing the presence/absence (1-0 coding) of a stimulus/activation in seconds.
`totaltime`	Total duration of stimulus vector in seconds.
`acc`	Microtime resolution of stimulus vector in seconds.
`par`	List representing the parameters of the Balloon model. The list should contain the following: kappa Inhibitory gain factor tau1 Inhibitory time constant tauf FWHM of CBF impulse response taum FWHM of CMRO2 impulse resonse deltat Delay of CBF relative to CMRO2 response n Steady-state flow metabolism relation f1 Normalized CBF response to sustained neural activation tauMTT Transit time through the balloon tau Viscoelastic time constant alpha Steady-state flow-volume relation E0 baseline O2 extraction fraction V0 baseline blood volume a1 weight for deoxyHb change a2 weight for blood volume change
`verbose`	If `TRUE`, warnings are displayed.

Details

Based on the provided stimulus boxcar function, a neural activation function is generated that enters the Balloon model to generate a BOLD response. The microtime resolution ensures a high-precision generation of the response. More details can be found in Buxton et al. (2004).

Value

Vector representing the values of the BOLD signal for the given stimulus vector and microtime resolution.

Author(s)

G. Verdoolaege, M. Welvaert

References

Buxton, RB, Uludag, K, Dubowitz, DJ and Liu, TT (2004). Modeling the hemodynamic response to brain activation. NeuroImage, 23, S220-S233.

Examples


s <- rep(rep(0,10), rep(1,10), 5)
T <- 100
it <- 0.1
out <- balloon(s, T, it) 
#takes a couple of seconds due to solving of the differential equations

s <- rep(rep(0,10), rep(1,10), 5)
T <- 100
it <- 0.1
out <- balloon(s, T, it) 
#takes a couple of seconds due to solving of the differential equations

Double-gamma Haemodynamic reponse function

Description

Specifies a double-gamma variate haemodynamic response function for the given time vector and parameters.

Usage

  canonicalHRF(x, param = NULL, verbose = TRUE)
canonicalHRF(x, param = NULL, verbose = TRUE)

Arguments

x

Time vector in seconds.

param

List of parameters of the haemodynamic response function. The list should contain the following:

a1: Delay of response relative to onset (default: 6)
a2: Delay of undershoot relative to onset (default:12)
b1: Dispersion of response (default:0.9)
b2: Dispersion of undershoot (default:0.9)
c: Scale of undershoot (default:0.35)

verbose

If TRUE, warnings are displayed.

Value

Vector representing the values of the function for the given time vector and parameters.

Author(s)

M. Welvaert

References

[1] Friston, KJ, Fletcher, P, Josephs, O, Holmes, AP, Rugg, MD and Turner, R (1998). Event-related fMRI: Characterising differential responses. NeuroImage, 7, 30-40.

[2] Glover, GH (1999). Deconvolution of impulse response in event-related BOLD fMRI. NeuroImage, 9, 416-429.

Examples

t <- 1:100
out <- canonicalHRF(t, verbose=FALSE)

t <- 1:100
out <- canonicalHRF(t, verbose=FALSE)

Single Gamma Haemodynamic response function.

Description

Specifies a Gamma variate haemodynamic response function for the given time vector and FWHM.

Usage

gammaHRF(x, FWHM = 4, verbose = TRUE)
gammaHRF(x, FWHM = 4, verbose = TRUE)

Arguments

`x`	Time vector in seconds.
`FWHM`	Full Width Half Maximum of the Gamma variate function.
`verbose`	If `TRUE`, warnings are displayed.

Value

Vector representing the values of the function for the given time vector and FWHM.

Author(s)

M. Welvaert

References

Buxton, RB, Uludag, K, Dubowitz, DJ and Liu, TT (2004). Modeling the hemodynamic response to brain activation. NeuroImage, 23, S220-S233.

Examples

t <- 1:100
out <- gammaHRF(t, verbose=FALSE)

t <- 1:100
out <- gammaHRF(t, verbose=FALSE)

Calculates a discrete Gaussian smoothing kernel (adopted from AnalyzeFMRI)

Description

Calculates a simple, discrete Gaussian smoothing kernel of a specifice size given the covariance matrix of the Gaussian.

Usage

GaussSmoothKernel(voxdim=c(1,1,1), ksize=5, sigma=diag(3,3))
GaussSmoothKernel(voxdim=c(1,1,1), ksize=5, sigma=diag(3,3))

Arguments

`voxdim`	The dimensions of each voxel.
`ksize`	The size (in voxels) of the kernel with which to filter the independent field.
`sigma`	The covariance matrix of the Gaussian kernel.

Value

An array of dimension (ksize,ksize,ksize) containing the smoothing kernel.

Author(s)

J. L. Marchini

Examples

a <- GaussSmoothKernel(voxdim=c(1,1,1), ksize=5, sigma=diag(1,3))

a <- GaussSmoothKernel(voxdim=c(1,1,1), ksize=5, sigma=diag(1,3))

Generate low frequency drift

Description

Generates a low-frequency drift dataset with specified dimensions and frequency.

Usage

lowfreqdrift(dim, freq = 128, nscan, TR, template, verbose = TRUE)
lowfreqdrift(dim, freq = 128, nscan, TR, template, verbose = TRUE)

Arguments

`dim`	A vector specifying the dimensions of the image.
`freq`	The frequency of the drift in seconds.
`nscan`	The number of scans in the dataset.
`TR`	The repetition time in seconds.
`template`	An array representing the anatomical structure or mask with dimensions equal to dim.
`verbose`	Logical indicating if warnings should be printed.

Details

The function generates low-frequency drift based on a basis set of cosine functions. The result is an array with specified dimensions and frequency.

Value

An array containing the drift with dimensions specified in dim.

Author(s)

Y. Rosseel, M. Welvaert

References

Friston et al. (2007). Statistical Parametric Mapping: The analysis of functional brain images. Academic Press.

Examples

d <- c(10,10,10)
freq <- 80
nscan <- 100
TR <- 2
out <- lowfreqdrift(d, freq, nscan, TR, verbose=FALSE)

d <- c(10,10,10)
freq <- 80
nscan <- 100
TR <- 2
out <- lowfreqdrift(d, freq, nscan, TR, verbose=FALSE)

Generate physiological noise

Description

Generates a physiological noise dataset with specified dimensions and standard deviation. The physiological noise is defined as noise caused by heart beat and respiratory rate.

Usage

physnoise(dim, nscan, TR, sigma, freq.heart = 1.17, 
	freq.resp = 0.2, template, verbose = TRUE)
physnoise(dim, nscan, TR, sigma, freq.heart = 1.17, 
	freq.resp = 0.2, template, verbose = TRUE)

Arguments

`dim`	A vector specifying the dimensions of the image.
`nscan`	The number of scans in the dataset.
`TR`	The repetition time in seconds.
`sigma`	The standard deviation of the noise.
`freq.heart`	The frequency in Hz of the heart beat.
`freq.resp`	The frequency in Hz of the respiratory rate.
`template`	An array representing the anatomical structure or mask with dimensions equal to dim.
`verbose`	Logical indicating if warnings should be printed.

Details

The function generates physiological noise. Heart beat and respiratory rate are defined as sine and cosine functions with specified frequencies. Additional Gaussian noise creates variability over voxels. The result is a noise dataset with specified dimensions and desired standard deviation.

Value

An array containing the noise with dimensions specified in dim and nscan.

Author(s)

M. Welvaert

Examples

d <- c(10,10,10)
sigma <- 5
nscan <- 100
TR <- 2
out <- physnoise(d, nscan, TR, sigma, verbose=FALSE)

d <- c(10,10,10)
sigma <- 5
nscan <- 100
TR <- 2
out <- physnoise(d, nscan, TR, sigma, verbose=FALSE)

The Rice Distribution

Description

Density and random generation for the Rician distribution

Usage

rrice(n, vee, sigma) rrice(n, vee, sigma)

Arguments

`n`	number of observations. Must be a positive integer of length 1.
`vee`	non-centrality parameter of the distribution. Must be a positive integer of length 1.
`sigma`	scale parameter of the distribution. Must be a positive integer of length 1.

Details

See VGAM for more details on the parameters and the formula of the probability density function.

Value

Random deviates for the given number of observations.

Author(s)

T.W. Yee

Examples

x <- rrice(n=10,vee=2,sigma=1)
x <- rrice(n=10,vee=2,sigma=1)

Simulate a GRF (adopted from AnalyzeFMRI)

Description

Simulates a Gaussian Random Field with specified dimensions and covariance structure.

Usage

Sim.3D.GRF(d, voxdim, sigma, ksize, mask=NULL, type=c("field","max"))
Sim.3D.GRF(d, voxdim, sigma, ksize, mask=NULL, type=c("field","max"))

Arguments

`d`	A vector specifying the dimensions of a 3D or 4D array.
`voxdim`	The dimensions of each voxel.
`sigma`	The 3D covariance matrix of the field.
`ksize`	The size (in voxels) of the kernel with which to filter the independent field.
`mask`	A 3D mask for the field.
`type`	If `type=="field"` then the simulated field together with the maximum of the field is returned.If `type=="max"` then the maximum of the field is returned.

Details

The function works by simulating a Gaussian r.v at each voxel location and the smoothing the field with a discrete filter to obtain a field with the desired covariance structure.

Value

`mat`	Contains the simulated field if `type=="field"`, else `NULL`
`max`	The maximum value of the simulated field

Author(s)

J. L. Marchini

Examples

     d <- c(64, 64, 21)
     FWHM <- 9
     sigma <- diag(FWHM^2, 3) / (8 * log(2))
     voxdim <- c(2, 2, 4)
     msk <- array(1, dim = d)   
     field <- Sim.3D.GRF(d = d, voxdim = voxdim, sigma = sigma, 
		ksize = 9, mask = msk, type = "max")

d <- c(64, 64, 21)
     FWHM <- 9
     sigma <- diag(FWHM^2, 3) / (8 * log(2))
     voxdim <- c(2, 2, 4)
     msk <- array(1, dim = d)   
     field <- Sim.3D.GRF(d = d, voxdim = voxdim, sigma = sigma, 
		ksize = 9, mask = msk, type = "max")

Prepare spatial structure of the data

Description

Prepare a list defining the necessary parameters to specify the spatial structure of the activation data.

Usage

simprepSpatial(regions, coord, radius = NULL, 
	form = c("cube", "sphere", "manual"), fading = 0)simprepSpatial(regions, coord, radius = NULL, 
	form = c("cube", "sphere", "manual"), fading = 0)

Arguments

`regions`	Number of activated regions.
`coord`	List of coordinates specifying the xyz-coordinates.
`radius`	If form=cube or sphere, the distance between the center and the edge, if form=manual, the number of voxels in each region.
`form`	The form of the activated regions.
`fading`	Decay rate between 0 and 1. 0 means no fading, while 1 results in the fastest decay.

Value

A list with the necessary arguments to be used in simVOLfmri.

Author(s)

M. Welvaert

Examples

coord <- list(c(3,3,3),c(6,6,6))
radius <- c(1,2)
out <- simprepSpatial(2, coord, radius, form="cube", fading=0.2)

coord <- list(c(3,3,3),c(6,6,6))
radius <- c(1,2)
out <- simprepSpatial(2, coord, radius, form="cube", fading=0.2)

Prepare temporal structure of the data

Description

Prepare a list defining the necessary parameters to specify the temporal structure of the activation data.

Usage

simprepTemporal(totaltime, regions = NULL, onsets, durations, 
	TR, effectsize, accuracy=0.1, 
	hrf = c("gamma", "double-gamma", "Balloon"), 
	param = NULL)
simprepTemporal(totaltime, regions = NULL, onsets, durations, 
	TR, effectsize, accuracy=0.1, 
	hrf = c("gamma", "double-gamma", "Balloon"), 
	param = NULL)

Arguments

`totaltime`	Duration of the experiment.
`regions`	Number of regions. If not specified, it is assumed that all regions have the same design matrix.
`onsets`	List or vector representing the onsets of the stimulus in seconds.
`durations`	List or vector representing the durations of the stimulus in seconds.
`TR`	Repetition time in seconds.
`effectsize`	List or number representing the effectsize in each condition.
`accuracy`	Microtime resolution in seconds.
`hrf`	Haemodynamic response function (double-gamma is default)
`param`	Vector, matrix or array representing the parameters of the haemodynamic response function.

Value

A list with the necessary arguments to be used in simVOLfmri or simTSfmri.

Author(s)

M. Welvaert

Examples

ncond <- 2
os <- list(c(20,60),c(15,35))
d <- list(20, 10)
effect <- list(7,10)
total <- 80
TR <- 2
out <- simprepTemporal(total, onsets=os, durations=d, TR=TR, 
	effectsize=effect, hrf="double-gamma")

ncond <- 2
os <- list(c(20,60),c(15,35))
d <- list(20, 10)
effect <- list(7,10)
total <- 80
TR <- 2
out <- simprepTemporal(total, onsets=os, durations=d, TR=TR, 
	effectsize=effect, hrf="double-gamma")

Simulate fMRI time series

Description

Simulates an fMRI time series for the specified design and noise type.

Usage

simTSfmri(design = list(), base=0, nscan = NULL, TR = NULL, SNR=NULL,
	 noise = c("none", "white", "temporal", "low-frequency", 
	"physiological", "task-related", "mixture"), type = c("gaussian", "rician"),
	 weights, verbose = TRUE, rho = 0.2, freq.low = 128, freq.heart = 1.17, 
	freq.resp = 0.2, vee=1)
simTSfmri(design = list(), base=0, nscan = NULL, TR = NULL, SNR=NULL,
	 noise = c("none", "white", "temporal", "low-frequency", 
	"physiological", "task-related", "mixture"), type = c("gaussian", "rician"),
	 weights, verbose = TRUE, rho = 0.2, freq.low = 128, freq.heart = 1.17, 
	freq.resp = 0.2, vee=1)

Arguments

`design`	List generated by `simprepTemporal` specifying the design. If not specified, noise time series are generated.
`base`	Baseline value of the time series.
`nscan`	Number of scans.
`TR`	Repetition time in seconds.
`SNR`	Signal-to-noise ratio of the time series.
`noise`	Type of noise (white is default).
`type`	If `noise==white`, `noise==task-related` or `noise==mixture`, type of system noise (gaussian is default).
`weights`	If `noise==mixture`, vector of weights with 5 elements to specify the fraction of the noise components.
`verbose`	Logical indicating if warnings should be returned.
`rho`	If `noise==temporal` or `noise==mixture`, value of autocorrelation coefficients. The length of the vector indicates the order of the autoregressive model.
`freq.low`	If `noise==low-frequency` or `noise==mixture`, frequency of the low-frequency drift in seconds.
`freq.heart`	If `noise==physiological` or `noise==mixture`, frequency of heart rate in Hz.
`freq.resp`	If `noise==physiological` or `noise==mixture`, frequency of respiratory rate in Hz.
`vee`	If `type=="rician"`, non-centrality parameter of the distribution.

Value

A vector representing the fMRI time series.

Author(s)

M. Welvaert

Examples

design <- simprepTemporal(totaltime=200, onsets=seq(1,200,40), 
	durations=20, effectsize=1, TR=2, hrf="double-gamma")
ts <- simTSfmri(design=design, SNR=1, noise="white")
plot(ts, type="l")

design <- simprepTemporal(totaltime=200, onsets=seq(1,200,40), 
	durations=20, effectsize=1, TR=2, hrf="double-gamma")
ts <- simTSfmri(design=design, SNR=1, noise="white")
plot(ts, type="l")

Simulate fMRI resting state time series

Description

Synthesizes a single time series x representing resting state activity. The fluctuation frequencies f are limited to a square passband 0.01 Hz <= f <= 0.1 Hz. TR is the repetition time (needed to compute the passband limits), expressed in seconds. N is the required number of samples (needs not be a power of 2).

Usage

simTSrestingstate(nscan, base=0, TR, SNR=NULL, noise = c("none", "white",
	"temporal", "low-frequency", "physiological", "mixture"), 
	type = c("gaussian", "rician"), weights, verbose = TRUE, rho = 0.2,
	freq.low = 128, freq.heart = 1.17, freq.resp = 0.2, vee=1)
simTSrestingstate(nscan, base=0, TR, SNR=NULL, noise = c("none", "white",
	"temporal", "low-frequency", "physiological", "mixture"), 
	type = c("gaussian", "rician"), weights, verbose = TRUE, rho = 0.2,
	freq.low = 128, freq.heart = 1.17, freq.resp = 0.2, vee=1)

Arguments

`nscan`	Number of scans.
`base`	Baseline value of the time series.
`TR`	Repetition time in seconds.
`SNR`	Signal-to-noise ratio of the time series.
`noise`	Type of noise (white is default).
`type`	If `noise==white`, `noise==mixture`, type of system noise (gaussian is default).
`weights`	If `noise==mixture`, vector of weights to specify the fraction of the noise components.
`verbose`	Logical indicating if warnings should be returned.
`rho`	If `noise==temporal` or `noise==mixture`, value of autocorrelation coefficients. The length of the vector corresponds to the order of the autoregressive model.
`freq.low`	If `noise==low-frequency` or `noise==mixture`, frequency of the low-frequency drift in seconds.
`freq.heart`	If `noise==physiological` or `noise==mixture`, frequency of heart rate in Hz.
`freq.resp`	If `noise==physiological` or `noise==mixture`, frequency of respiratory rate in Hz.
`vee`	If `type==rician`, non-centrality parameter of the distribution.

Value

A vector representing the resting state time series

Author(s)

J. Durnez, G. Verdoolaege, M. Welvaert

References

[1] C.G. Fox, Computers & Geoscience, Vol. 13, pp. 369-374, 1987.

[2] M. Fukunaga, Magnetic Resonance Imaging, Vol. 24, pp. 979-992, 2006.

Examples

out <- simTSrestingstate(nscan=50, TR=2, SNR=1, noise="none")
plot(out, type="l")

out <- simTSrestingstate(nscan=50, TR=2, SNR=1, noise="none")
plot(out, type="l")

Simulate 3D or 4D fMRI data

Description

Simulates a 3D or 4D fMRI dataset for the specified design and with activation in the specified regions.

Usage

simVOLfmri(design = list(), image = list(), base=0, dim, nscan = NULL,
	TR = NULL, SNR=NULL, noise = c("none", "white", "temporal", 
	"spatial", "low-frequency", "physiological", "task-related", 
	"mixture"), type = c("gaussian", "rician"), 
	spat = c("corr", "gaussRF", "gammaRF"), weights, verbose = TRUE, 
	rho.temp = 0.2, rho.spat = 0.75, freq.low = 128, 
	freq.heart = 1.17, freq.resp = 0.2, FWHM = 4, gamma.shape = 6, 
	gamma.rate = 1, vee=1, template)
simVOLfmri(design = list(), image = list(), base=0, dim, nscan = NULL,
	TR = NULL, SNR=NULL, noise = c("none", "white", "temporal", 
	"spatial", "low-frequency", "physiological", "task-related", 
	"mixture"), type = c("gaussian", "rician"), 
	spat = c("corr", "gaussRF", "gammaRF"), weights, verbose = TRUE, 
	rho.temp = 0.2, rho.spat = 0.75, freq.low = 128, 
	freq.heart = 1.17, freq.resp = 0.2, FWHM = 4, gamma.shape = 6, 
	gamma.rate = 1, vee=1, template)

Arguments

`design`	List generated by `simprepTemporal` specifying the design. If not specified, noise images are generated.
`image`	List generated by `simprepSpatial` specifying the activated regions. If not specified, noise images are generated
`base`	Baseline of the data. Should be a single number or an array with the same dimensions as in `dim`.
`dim`	Dimensions of the image space.
`nscan`	Number of scans for noise images.
`TR`	Repetition time for noise images.
`SNR`	Signal-to-noise ratio.
`noise`	Type of noise, default is white.
`type`	If `noise==white` or `noise==mixture`, the type of system noise (default is gaussian).
`spat`	If `noise==spatial` or `noise==mixture`, the spatial correlation structure (default is `corr`).
`weights`	If `noise==mixture`, weights vector of six elements.
`verbose`	Logical indicating if warning should be printed.
`rho.temp`	If `noise==temporal` or `noise==mixture`, value of autocorrelation coefficients. The length of the vector indicates the order of the autoregressive model.
`rho.spat`	If `noise==spatial` or `noise==mixture`, and `spat==corr`, value of the correlation coefficient.
`freq.low`	If `noise==low-frequency` or `noise==mixture`, frequency of the low-frequency drift in seconds.
`freq.heart`	If `noise==physiological` or `noise==mixture`, frequency of heart rate in Hz.
`freq.resp`	If `noise==physiological` or `noise==mixture`, frequency of respiratory rate in Hz.
`FWHM`	If `noise==spatial` or `noise==mixture`, and `spat==gaussRF` or `spat==gammaRF`, value of the FWHM of the Gaussian kernel.
`gamma.shape`	If `noise==spatial` or `noise==mixture`, and `spat==gammaRF`, value of the shape parameter of the gamma distribution.
`gamma.rate`	If `noise==spatial` or `noise==mixture`, and `spat==gammaRF`, value of the rate parameter of the gamma distribution.
`vee`	If `type==rician`, non-centrality parameter of the rician distribution.
`template`	An array representing the anatomical structure or mask with dimensions equal to dim.

Value

A 3D or 4D array specifying the values for each voxel in the data.

Author(s)

M. Welvaert

Examples

design <- simprepTemporal(totaltime=200, onsets=seq(1,200,40), 
	durations=20, TR=2, effectsize=1, hrf="double-gamma")
region <- simprepSpatial(regions=2, coord=list(c(32,15),c(57,45)), 
	radius=c(10,7), form="sphere", fading=TRUE)
out <- simVOLfmri(design=design, image=region, dim=c(64,64), 
	SNR=1, noise="none")
plot(out[32,15,], type="l")
image(1:64, 1:64, out[,,10], col = grey(0:255/255))

design <- simprepTemporal(totaltime=200, onsets=seq(1,200,40), 
	durations=20, TR=2, effectsize=1, hrf="double-gamma")
region <- simprepSpatial(regions=2, coord=list(c(32,15),c(57,45)), 
	radius=c(10,7), form="sphere", fading=TRUE)
out <- simVOLfmri(design=design, image=region, dim=c(64,64), 
	SNR=1, noise="none")
plot(out[32,15,], type="l")
image(1:64, 1:64, out[,,10], col = grey(0:255/255))

Generate spatially correlated noise

Description

Generates a spatially correlated noise dataset with specified dimensions and standard deviation.

Usage

spatialnoise(dim, sigma, nscan, method = c("corr", "gammaRF", "gaussRF"), 
	type=c("gaussian","rician"), rho = 0.75, FWHM = 4, gamma.shape = 6, 
	gamma.rate = 1, vee=1, template, verbose = TRUE)
spatialnoise(dim, sigma, nscan, method = c("corr", "gammaRF", "gaussRF"), 
	type=c("gaussian","rician"), rho = 0.75, FWHM = 4, gamma.shape = 6, 
	gamma.rate = 1, vee=1, template, verbose = TRUE)

Arguments

`dim`	A vector specifying the dimensions of the image.
`sigma`	The standard deviation of the noise.
`nscan`	The number of scans in the dataset.
`method`	Method specifying the type of spatial correlation. Default is `"corr"`.
`type`	Type of distribution if `method=="corr"`. Default is `"gaussian"`
`rho`	If `method=="corr"`, the value of the autocorrelation coefficient.
`FWHM`	If `method=="gammaRF"` or `method=="gaussRF"`, the full-width-half-maximum of the Gaussian kernel.
`gamma.shape`	If `method=="gammaRF"`, the shape parameter of the Gamma distribution.
`gamma.rate`	If `method=="gammaRF"`, the shape parameter of the Gamma distribution.
`vee`	If `method=="corr"` and `type=="rician"`, the non-centrality parameter of the rician distribution.
`template`	An array representing the anatomical structure or mask with dimensions equal to dim.
`verbose`	Logical indicating if warnings should be printed.

Details

The function generates spatially correlated noise. When method=="corr", AR(1) voxelwise correlations are introduced. If method=="gaussRF" of method=="gammaRF", respectively a Gaussian Random Field or a Gamma Random Field is created. The result is a noise array with specified dimensions and desired standard deviation. The generation of the random fields is based on the function Sim.3D.GRF from J.L. Marchini in the package AnalyzeFMRI.

Value

An array containing the noise with dimensions specified in dim and nscan.

Author(s)

J. Durnez, B. Moerkerke, M. Welvaert

Examples

d <- c(10,10,10)
sigma <- 5
nscan <- 100
rhospat <- 0.7
out <- spatialnoise(d, sigma, nscan, method="corr", rho=rhospat, verbose=FALSE)

d <- c(10,10,10)
sigma <- 5
nscan <- 100
rhospat <- 0.7
out <- spatialnoise(d, sigma, nscan, method="corr", rho=rhospat, verbose=FALSE)

Generate design matrix.

Description

Generates a design matrix to be used as a model for the simulated activation.

Usage

specifydesign(onsets, durations, totaltime, TR, effectsize, accuracy=0.1, 
	conv = c("none", "gamma", "double-gamma", "Balloon"), 
	cond.names = NULL, param = NULL)
specifydesign(onsets, durations, totaltime, TR, effectsize, accuracy=0.1, 
	conv = c("none", "gamma", "double-gamma", "Balloon"), 
	cond.names = NULL, param = NULL)

Arguments

`onsets`	List or vector representing the onsets in seconds.
`durations`	List or vector representing the durations in seconds.
`totaltime`	Duration of the experiment in seconds.
`TR`	Repetition time in seconds.
`effectsize`	List or number representing the effectsize in each condition.
`accuracy`	Microtime resolution in seconds.
`conv`	Should the design matrix be convoluted, default is none.
`cond.names`	Optional names for the conditions.
`param`	Parameters of the haemodynamic response function. See `gammaHRF` and `canonicalHRF` for more details.

Value

A matrix specifying the design.

Author(s)

M. Welvaert

Examples

os <- list(c(20,60),c(15,35))
d <- list(20, 10)
total <- 80
TR <- 2
out <- specifydesign(os, d, total, TR, effectsize=list(2,5), conv="double-gamma")

os <- list(c(20,60),c(15,35))
d <- list(20, 10)
total <- 80
TR <- 2
out <- specifydesign(os, d, total, TR, effectsize=list(2,5), conv="double-gamma")

Generate activation image

Description

Generates an image with activated regions for specified dimensions. The regions are defined by their center and radius or can be entered manually.

Usage

  specifyregion(dim, coord, radius = NULL, 
	form = c("cube", "sphere", "manual"), 
	fading = 0)
specifyregion(dim, coord, radius = NULL, 
	form = c("cube", "sphere", "manual"), 
	fading = 0)

Arguments

`dim`	Dimensions of the image space.
`coord`	Coordinates of the activated region, if `form=="cube"` or `form=="sphere"`, the coordinates represent the center of the region, if `form=="manual"`, the coordinates should be in matrix form, where the rows represent the voxels and the columns the x-y-z coordinates.
`radius`	If `form=="cube"` or `form=="sphere"`, the distance in voxels from the center of the region to the edge.
`form`	The form of the activated region. Default is `"cube"`.
`fading`	Decay rate between 0 and 1. 0 means no fading, while 1 results in the fastest decay.

Value

An array representing the activation image with specified regions.

Author(s)

M. Welvaert

Examples

d <- c(10,10,10)
coord <- c(3,3,3)
radius <- 1
out <- specifyregion(d, coord, radius, form="sphere")

d <- c(10,10,10)
coord <- c(3,3,3)
radius <- 1
out <- specifyregion(d, coord, radius, form="sphere")

Generate a stimulus boxcar function.

Description

Generates a stimulus boxcar vector for the specified time duration and microtime resolution based on the user-defined onsets and durations.

Usage

stimfunction(totaltime, onsets, durations, accuracy)
stimfunction(totaltime, onsets, durations, accuracy)

Arguments

`totaltime`	Total time of the design in seconds.
`onsets`	Vector representing the onsets of the stimulus in seconds.
`durations`	Vector representing the durations of the stimulus in seconds.
`accuracy`	Microtime resolution in seconds.

Details

If duration is a single number, it is assumed that all stimulus onsets have the same duration.

Value

A vector in microtime resolution specifying the stimulus boxcar function in 1-0 coding.

Author(s)

M. Welvaert

Examples

total <- 100
os <- c(1, 21, 41, 61, 81)
d <- 10
out <- stimfunction(total, os, d, 0.1)

total <- 100
os <- c(1, 21, 41, 61, 81)
d <- 10
out <- stimfunction(total, os, d, 0.1)

Generate system noise

Description

Generates a system noise dataset with specified dimensions and standard deviation. The noise can be either Gaussian or Rician distributed.

Usage

systemnoise(dim, nscan, type=c("gaussian","rician"), sigma, vee, template, 
		verbose = TRUE)
systemnoise(dim, nscan, type=c("gaussian","rician"), sigma, vee, template, 
		verbose = TRUE)

Arguments

`dim`	A vector specifying the dimensions of the image.
`nscan`	The number of scans in the dataset.
`type`	Distribution of system noise. Default is gaussian.
`sigma`	The standard deviation of the noise.
`vee`	If `type=="rician"`, the non-centrality parameter of the distribution .
`template`	An array representing the anatomical structure or mask with dimensions equal to dim.
`verbose`	Logical indicating if warnings should be printed.

Value

An array containing the noise with dimensions specified in dim and nscan.

Author(s)

M. Welvaert

Examples

d <- c(10,10,10)
sigma <- 5
nscan <- 100
out <- systemnoise(d, nscan, type="rician", sigma, verbose=FALSE)

d <- c(10,10,10)
sigma <- 5
nscan <- 100
out <- systemnoise(d, nscan, type="rician", sigma, verbose=FALSE)

Generate task-related noise

Description

Generates a Gaussian noise dataset with specified dimensions and standard deviation only when a task is performed or activation is present.

Usage

tasknoise(act.image, sigma, type=c("gaussian","rician"), vee=1)
tasknoise(act.image, sigma, type=c("gaussian","rician"), vee=1)

Arguments

`act.image`	Array defining where and when activation is present.
`sigma`	Standard deviation of the noise.
`type`	Distribution of task-related noise. Default is gaussian.
`vee`	If `type=="rician"`, the non-centrality parameter of the distribution.

Details

The function generates random Gaussian noise for those voxels in the dataset that show activation. The result is a noise array with specified dimensions and desired standard deviation.

Value

An array containing the noise.

Author(s)

M. Welvaert

Examples

d <- c(10,10,10)
sigma <- 5
nscan <- 100
act <- array(rep(0, prod(d)*nscan), dim=c(d,nscan))
act[2:4,2:4,2:4,c(20:30,40:50,60:70)] <- 1
out <- tasknoise(act, sigma)

d <- c(10,10,10)
sigma <- 5
nscan <- 100
act <- array(rep(0, prod(d)*nscan), dim=c(d,nscan))
act[2:4,2:4,2:4,c(20:30,40:50,60:70)] <- 1
out <- tasknoise(act, sigma)

Generate temporally correlated noise

Description

Generates an autoregressive noise dataset with specified dimensions and standard deviation.

Usage

temporalnoise(dim, nscan, sigma, rho = 0.2, template, verbose = TRUE)
temporalnoise(dim, nscan, sigma, rho = 0.2, template, verbose = TRUE)

Arguments

`dim`	A vector specifying the dimensions of a 2D or 3D array.
`nscan`	The number of scans in the dataset.
`sigma`	The standard deviation of the noise.
`rho`	The autocorrelation coefficients. The length of the vector determines the order of the autoregressive model.
`template`	An array representing the anatomical structure or mask with dimensions equal to dim.
`verbose`	Logical indicating if warnings should be printed.

Value

An array containing the noise with dimensions specified in dim.

Author(s)

J. Durnez, B. Moerkerke, M. Welvaert

Examples

d <- c(10,10,10)
sigma <- 5
nscan <- 100
rho <- c(0.3,-0.7)
out <- temporalnoise(d, nscan, sigma, rho, verbose=FALSE)

d <- c(10,10,10)
sigma <- 5
nscan <- 100
rho <- c(0.3,-0.7)
out <- temporalnoise(d, nscan, sigma, rho, verbose=FALSE)

Package 'neuRosim'

Help Index

Functions to Generate fMRI Data Including Activated Data, Noise Data and Resting State Data

Description

Author(s)

References

Examples

Balloon model

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Double-gamma Haemodynamic reponse function

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Single Gamma Haemodynamic response function.

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Calculates a discrete Gaussian smoothing kernel (adopted from AnalyzeFMRI)

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Generate low frequency drift

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Generate physiological noise

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

The Rice Distribution

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Simulate a GRF (adopted from AnalyzeFMRI)

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Prepare spatial structure of the data