Package 'adaptsmoFMRI' reference manual

Title:	Adaptive Smoothing of FMRI Data
Description:	Adaptive smoothing functions for estimating the blood oxygenation level dependent (BOLD) effect by using functional Magnetic Resonance Imaging (fMRI) data, based on adaptive Gauss Markov random fields, for real as well as simulated data. The implemented models make use of efficient Markov Chain Monte Carlo methods. Implemented methods are based on the research developed by A. Brezger, L. Fahrmeir, A. Hennerfeind (2007) <https://www.jstor.org/stable/4626770>.
Authors:	Maximilian Hughes [aut, cre, ctb]
Maintainer:	Maximilian Hughes <[email protected]>
License:	GPL-2
Version:	1.2
Built:	2024-12-06 06:47:50 UTC
Source:	CRAN

Adaptive Smoothing of FMRI Data

Description

Adaptive smoothing functions for estimating the blood oxygenation level dependent (BOLD) effect by using functional Magnetic Resonance Imaging (fMRI) data, based on adaptive Gauss Markov random fields, for real as well as simulated data. The implemented models make use of efficient Markov Chain Monte Carlo methods.

Details

Package:	adaptsmoFMRI
Type:	Package
Version:	1.1
Date:	2022-08-16
License:	GPL-2

Author(s)

Maximilian Hughes Maintainer: [email protected]

References

A. Brezger, L. Fahrmeir, A. Hennerfeind Adaptive Gaussian Markov random fields with applications in human brain mapping, Applied Statistics, 56, Part 3, pp. 327-345, 2007

V. J. Schmid, A. Thornton, B. Witcher Working with the DICOM and NIfTI Data Standards in R, Journal of Statistical Software, Volume 44, Issue 6. 2011

Adaptive GMRF Model (Real Data)

Description

This function estimates the effects of functional MR Images (fMRI), with the method of efficient Markov Chain Monte Carlo (MCMC) simulation. The Metropolis Hastings (MH) algorithm is used for the non-approximate case and the Gibbs sampler for the approximate case.

Usage

  adaptiveGMRF(data, hrf, approximate = FALSE, K = 500, a =
    0.001, b = 0.001, c = 0.001, d = 0.001, nu = 1, filter
    = NULL, block = 1, burnin = 1, thin = 1)
adaptiveGMRF(data, hrf, approximate = FALSE, K = 500, a =
    0.001, b = 0.001, c = 0.001, d = 0.001, nu = 1, filter
    = NULL, block = 1, burnin = 1, thin = 1)

Arguments

`data`	fMRI-data, needs to be an array of dimension `(dx x dy x T)`.
`hrf`	haemodynamic response function, needs to be a vector of length `T`.
`approximate`	logical, if `TRUE` then the approximate case is choosen. Default is `FALSE`.
`K`	scalar, length of the MCMC path, hence iteration steps.
`a`	scalar, shape hyperparameter of the inverse-gamma distribution of the variance parameter ( $\sigma_i^2$ ).
`b`	scalar, scale hyperparameter of the inverse gamma distribution of the variance parameter ( $\sigma_i^2$ ).
`c`	scalar, shape hyperparameter of the inverse gamma distribution of the precision parameter ( $\tau$ ).
`d`	scalar, scale hyperparameter of the inverse gamma distribution of the precision parameter ( $\tau$ ).
`filter`	scalar, a value between 0 and 1 defining to which extent the fMRI-data should be filtered. The corresponding formular is `max(fmri)*filter`.
`nu`	scalar, shape and scale hyperparameter of the gamma distribution of the interaction weights ( $w_{ij}$ ).
`block`	scalar, when `approximate==TRUE` then a block of weights is updated at a time.
`burnin`	scalar, defining the first iteration steps which should be omitted from MCMC path.
`thin`	scalar, only every `thin` step of MCMC path is saved to output.

Value

`dx`	scalar, number of pixels in x-direction.
`dy`	scalar, number of pixels in y-direction.
`I`	scalar, number of pixels.
`coord`	matrix, coordinates of pixels.
`NEI`	scalar, number of weights.
`nei`	matrix, locations of weights in precision matrix.
`mask`	matrix, masked out pixels.
`beta.out`	matrix, MCMC path of covariates.
`w.out`	matrix, MCMC path of weights.
`sigma.out`	matrix, MCMC path of variance parameters.
`tauk.out`	matrix, MCMC path of hyper parameters.

Note

This function is solely for one covariate and real data sets.

Author(s)

Maximilian Hughes

Examples

# See example function for simulated data (one covariate).
# See example function for simulated data (one covariate).

Adaptive GMRF Model (Real Data)

Description

Usage

  adaptiveGMRF2COVAR(data, hrf, approximate = FALSE, K =
    500, a = 0.001, b = 0.001, c = 0.001, d = 0.001, nu =
    1, filter = NULL, block = 1, burnin = 1, thin = 1)
adaptiveGMRF2COVAR(data, hrf, approximate = FALSE, K =
    500, a = 0.001, b = 0.001, c = 0.001, d = 0.001, nu =
    1, filter = NULL, block = 1, burnin = 1, thin = 1)

Arguments

`data`	fMRI-data, needs to be an array of dimension `(dx x dy x T)`.
`hrf`	haemodynamic response function, needs to be a vector of length `T`.
`approximate`	logical, if `TRUE` then the approximate case is choosen. Def#' ault is `FALSE`.
`K`	scalar, length of the MCMC path, hence iteration steps.
`a`	scalar, shape hyperparameter of the inverse-gamma distribution of the variance parameter ( $\sigma_i^2$ ).
`b`	scalar, scale hyperparameter of the inverse gamma distribution of the variance parameter ( $\sigma_i^2$ ).
`c`	scalar, shape hyperparameter of the inverse gamma distribution of the precision parameter ( $\tau$ ).
`d`	scalar, scale hyperparameter of the inverse gamma distribution of the precision parameter ( $\tau$ ).
`filter`	scalar, a value between 0 and 1 defining to which extent the fMRI-data should be filtered. The corresponding formular is `max(fmri)*filter`.
`nu`	scalar, shape and scale hyperparameter of the gamma distribution of the interaction weights ( $w_{ij}$ ).
`block`	scalar, when `approximate==TRUE` then a block of weights is updated at a time.
`burnin`	scalar, defining the first iteration steps which should be omitted from MCMC path.
`thin`	scalar, only every `thin` step of MCMC path is saved to output.

Value

`dx`	scalar, number of pixels in x-direction.
`dy`	scalar, number of pixels in y-direction.
`I`	scalar, number of pixels.
`coord`	matrix, coordinates of pixels.
`NEI`	scalar, number of weights.
`nei`	matrix, locations of weights in precision matrix.
`mask`	matrix, masked out pixels.
`beta.out`	matrix, MCMC path of covariates.
`w.out`	matrix, MCMC path of weights.
`sigma.out`	matrix, MCMC path of variance parameters.
`tauk.out`	matrix, MCMC path of hyper parameters.

Note

This function is solely for two covariates and real data sets.

Author(s)

Maximilian Hughes

Examples

# See example function for simulated data (one covariate).
# See example function for simulated data (one covariate).

Simulated FMRI Data Set (for one covariate)

Description

The data set contains a synthetic spatiotemporal data set, hence a simulated data set. The data set is an array of dimension 20 x 20 x 210.

Value

No return value, called for side effects

Note

This data set was created while inserting one covariate.

Simulated FMRI Data Set (for two covariates)

Description

The data set contains a synthetic spatiotemporal data set, hence a simulated data set. The data set is an array of dimension 20 x 20 x 180.

Value

No return value, called for side effects

Note

This data set was created while inserting two covariates.

Adaptive GMRF Model for Simulated Data

Description

This function estimates the effects of a synthetic spatiotemporal data set resembling functional MR Images (fMRI), with the method of efficient Markov Chain Monte Carlo (MCMC) simulation. The Metropolis Hastings (MH) algorithm is used for the non-approximate case and the Gibbs sampler for the approximate case.

Usage

  sim.adaptiveGMRF(data, hrf, approximate = FALSE, K = 500,
    a = 1, b = 1, c = 1, d = 1, nu = 1, block = 1, burnin =
    1, thin = 1)
sim.adaptiveGMRF(data, hrf, approximate = FALSE, K = 500,
    a = 1, b = 1, c = 1, d = 1, nu = 1, block = 1, burnin =
    1, thin = 1)

Arguments

`data`	simulated fMRI-data, needs to be an array of dimension `(20 x 20 x T)`.
`hrf`	haemodynamic response function, needs to be a vector of length `T`.
`approximate`	logical, if `TRUE` then the approximate case is chosen. Default is `FALSE`.
`K`	scalar, length of the MCMC path, hence iteration steps.
`a`	scalar, shape hyperparameter of the inverse-gamma distribution of the variance parameter ( $\sigma_i^2$ ).
`b`	scalar, scale hyperparameter of the inverse gamma distribution of the variance parameter ( $\sigma_i^2$ ).
`c`	scalar, shape hyperparameter of the inverse gamma distribution of the precision parameter ( $\tau$ ).
`d`	scalar, scale hyperparameter of the inverse gamma distribution of the precision parameter ( $\tau$ ).
`nu`	scalar, shape and scale hyperparameter of the gamma distribution of the interaction weights ( $w_{ij}$ ).
`block`	scalar, when `approximate==TRUE` then a block of weights is updated at a time.
`burnin`	scalar, defining the first iteration steps which should be omitted from MCMC path.
`thin`	scalar, only every `thin` step of MCMC path is saved to output.

Value

`dx`	scalar, number of pixels in x-direction.
`dy`	scalar, number of pixels in y-direction.
`I`	scalar, number of pixels.
`iter`	scalar, number of MCMC iterations.
`coord`	matrix, coordinates of pixels.
`nei`	matrix, locations of weights in precision matrix.
`NEI`	scalar, number of weights.
`beta.out`	matrix, MCMC path of covariates.
`w.out`	matrix, MCMC path of weights.
`sigma.out`	matrix, MCMC path of variance parameters.
`tauk.out`	matrix, MCMC path of hyper parameters.

Note

This function is solely for one covariate.

Author(s)

Maximilian Hughes

Examples

# non-transformed hr-function
T <- 210
seq.length <- T*3
index <- seq(3, T*3, by = 3)
hrf <- rep(c(-0.5, 0.5), each=30, times=ceiling(T/30*1.5))
hrf <- as.matrix(hrf[index])
# get simulated data
data("sim_fmri")
data <- data_simfmri
# execute function
set.seed(111222)
K <- 2
a <- b <- c <- d <- nu <- 1
test.sim.adaptive <- sim.adaptiveGMRF(data, hrf, approximate=TRUE, K,
                                      a, b, c, d, nu)
# non-transformed hr-function
T <- 210
seq.length <- T*3
index <- seq(3, T*3, by = 3)
hrf <- rep(c(-0.5, 0.5), each=30, times=ceiling(T/30*1.5))
hrf <- as.matrix(hrf[index])
# get simulated data
data("sim_fmri")
data <- data_simfmri
# execute function
set.seed(111222)
K <- 2
a <- b <- c <- d <- nu <- 1
test.sim.adaptive <- sim.adaptiveGMRF(data, hrf, approximate=TRUE, K,
                                      a, b, c, d, nu)

Adaptive GMRF Model for Simulated Data

Description

Usage

  sim.adaptiveGMRF2COVAR(data, hrf, approximate = FALSE, K
    = 500, a = 1, b = 1, c = 1, d = 1, nu = 1, block = 1,
    burnin = 1, thin = 1)
sim.adaptiveGMRF2COVAR(data, hrf, approximate = FALSE, K
    = 500, a = 1, b = 1, c = 1, d = 1, nu = 1, block = 1,
    burnin = 1, thin = 1)

Arguments

`data`	simulated fMRI-data, needs to be an array of dimension `(20 x 20 x T)`.
`hrf`	haemodynamic response function, needs to be a vector of length `T`.
`approximate`	logical, if `TRUE` then the approximate case is chosen. Default is `FALSE`.
`K`	scalar, length of the MCMC path, hence iteration steps.
`a`	scalar, shape hyperparameter of the inverse-gamma distribution of the variance parameter ( $\sigma_i^2$ ).
`b`	scalar, scale hyperparameter of the inverse gamma distribution of the variance parameter ( $\sigma_i^2$ ).
`c`	scalar, shape hyperparameter of the inverse gamma distribution of the precision parameter ( $\tau$ ).
`d`	scalar, scale hyperparameter of the inverse gamma distribution of the precision parameter ( $\tau$ ).
`nu`	scalar, shape and scale hyperparameter of the gamma distribution of the interaction weights ( $w_{ij}$ ).
`block`	scalar, when `approximate==TRUE` then a block of weights is updated at a time.
`burnin`	scalar, defining the first iteration steps which should be omitted from MCMC path.
`thin`	scalar, only every `thin` step of MCMC path is saved to output.

Value

`dx`	scalar, number of pixels in x-direction.
`dy`	scalar, number of pixels in y-direction.
`I`	scalar, number of pixels.
`iter`	scalar, number of MCMC iterations.
`coord`	matrix, coordinates of pixels.
`nei`	matrix, locations of weights in precision matrix.
`NEI`	scalar, number of weights.
`beta.out`	matrix, MCMC path of covariates.
`w.out`	matrix, MCMC path of weights.
`sigma.out`	matrix, MCMC path of variance parameters.
`tauk.out`	matrix, MCMC path of hyper parameters.

Note

This function is solely for two covariates.

Author(s)

Maximilian Hughes

Examples

# See example function for simulated data (one covariate).
# See example function for simulated data (one covariate).

Simulate FMRI Data

Description

This function returns the synthetic spatiotemporal data set resembling functional MR Images (fMRI) data.

Usage

  sim.fmri(hrf, beta)
sim.fmri(hrf, beta)

Arguments

`hrf`	haemodynamic response function, needs to be a vector of length `T`.
`beta`	scalar, defines the height of the activated area, in form of a cylinder.

Details

The returned data is simulated on a 20 x 20 grid.

Value

fmri

matrix, simulated fmri data.

Note

This function is solely for one covariate.

Author(s)

Maximilian Hughes

Examples

# non-transformed hr-function
T <- 210
seq.length <- T*3
index <- seq(3, T*3, by = 3)
hrf <- rep(c(-0.5, 0.5), each=30, times=ceiling(T/30*1.5))
hrf <- as.matrix(hrf[index])
# define height of activation area
beta <- 3
# use function to obtain fmri data
data <- sim.fmri(hrf, beta)$fmri
# non-transformed hr-function
T <- 210
seq.length <- T*3
index <- seq(3, T*3, by = 3)
hrf <- rep(c(-0.5, 0.5), each=30, times=ceiling(T/30*1.5))
hrf <- as.matrix(hrf[index])
# define height of activation area
beta <- 3
# use function to obtain fmri data
data <- sim.fmri(hrf, beta)$fmri

Simulate FMRI Data

Description

This function returns the synthetic spatiotemporal data set resembling functional MR Images (fMRI) data.

Usage

  sim.fmri2COVAR(hrf, beta.Var1, beta.Var2)
sim.fmri2COVAR(hrf, beta.Var1, beta.Var2)

Arguments

`hrf`	haemodynamic response function, needs to be a vector of length `T`.
`beta.Var1`	scalar, defines the height of the activated area, in form of a cylinder of the first grid.
`beta.Var2`	scalar, defines the height of the activated area, in form of a cylinder of the second grid.

Details

The returned data is simulated on a 20 x 20 grid.

Value

fmri

matrix, simulated fmri data.

Note

This function is solely for two covariates.

Author(s)

Maximilian Hughes

Examples

# non-transformed hr-function
T <- 180
seq.length <- T*3
index <- seq(3, T*3, by = 3)
vis <- rep(c(-0.5, 0.5), each=30, times=ceiling(T/30*1.5))
vis <- as.matrix(vis[index])
aud <- rep(c(-0.5, 0.5), each=45, times=ceiling(T/30*1.5))
aud <- as.matrix(aud[index])
hrf <- cbind(vis,aud)
# define height of activation area
beta.Var1 <- beta.Var2 <- 3
# use function to obtain fmri data
data <- sim.fmri2COVAR(hrf, beta.Var1, beta.Var2)$fmri
# non-transformed hr-function
T <- 180
seq.length <- T*3
index <- seq(3, T*3, by = 3)
vis <- rep(c(-0.5, 0.5), each=30, times=ceiling(T/30*1.5))
vis <- as.matrix(vis[index])
aud <- rep(c(-0.5, 0.5), each=45, times=ceiling(T/30*1.5))
aud <- as.matrix(aud[index])
hrf <- cbind(vis,aud)
# define height of activation area
beta.Var1 <- beta.Var2 <- 3
# use function to obtain fmri data
data <- sim.fmri2COVAR(hrf, beta.Var1, beta.Var2)$fmri

Plot Function for Weights (Simulated Data)

Description

This function plots the weights interacting between estimated effects for each pixel.

Usage

  sim.weightsplot(weights, nei, nx, ny, thresh = 0.05, ...)
sim.weightsplot(weights, nei, nx, ny, thresh = 0.05, ...)

Arguments

`weights`	matrix, containing MCMC-output the of posterior estimates of weights.
`nei`	matrix, locations of weights in precision matrix.
`nx`	scalar, number of pixels in x-direction.
`ny`	scalar, number of pixels in y-direction.
`thresh`	scalar, defining the threshold to which the median of the weights smaller than this threshold should be plotted.
`...`	graphical parameters for `image` can also be passed on as arguments to this function.

Value

No return value, called for side effects

Note

This function is solely for MCMC-outputs on simulated data.

Author(s)

Maximilian Hughes

Plot Function for Weights (Real Data)

Description

This function plots the weights interacting between estimated effects for each pixel.

Usage

  weightsplot(weights, nei, nx, ny, coord, thresh = 0.1,
    ...)
weightsplot(weights, nei, nx, ny, coord, thresh = 0.1,
    ...)

Arguments

`weights`	matrix, containing MCMC-output the of posterior estimates of weights.
`nei`	matrix, locations of weights in precision matrix.
`nx`	scalar, number of pixels in x-direction.
`ny`	scalar, number of pixels in y-direction.
`coord`	matrix, coordinates of pixels.
`thresh`	scalar, defining the threshold to which the median of the weights smaller than this threshold should be plotted.
`...`	graphical parameters for `image` can also be passed on as arguments to this function.

Value

No return value, called for side effects

Note

This function is solely for MCMC-outputs on real data.

Author(s)

Maximilian Hughes

Package 'adaptsmoFMRI'

Help Index

Adaptive Smoothing of FMRI Data

Description

Details

Author(s)

References

Adaptive GMRF Model (Real Data)

Description

Usage

Arguments

Value

Note

Author(s)

Examples

Adaptive GMRF Model (Real Data)

Description

Usage

Arguments

Value

Note

Author(s)

Examples

Simulated FMRI Data Set (for one covariate)

Description

Value

Note

Simulated FMRI Data Set (for two covariates)

Description

Value

Note

Adaptive GMRF Model for Simulated Data

Description

Usage

Arguments

Value

Note

Author(s)

Examples

Adaptive GMRF Model for Simulated Data

Description

Usage

Arguments

Value

Note

Author(s)

Examples

Simulate FMRI Data

Description

Usage

Arguments

Details

Value

Note

Author(s)

Examples

Simulate FMRI Data

Description

Usage

Arguments

Details

Value

Note

Author(s)

Examples

Plot Function for Weights (Simulated Data)

Description

Usage

Arguments

Value

Note

Author(s)

Plot Function for Weights (Real Data)

Description

Usage

Arguments

Value

Note

Author(s)