| SmoothEst {AnalyzeFMRI} | R Documentation |
Estimate the variance-covariance matrix of a Gaussian random field
SmoothEst(mat, mask, voxdim, method = "Forman")
mat |
3D array that is the Gaussian Random Field. |
mask |
3D mask array. |
voxdim |
Vector of length 3 containing the voxel dimensions. |
method |
The estimator to use. method = "Forman" (the default) uses the estimator proposed in [1]. method = "Friston" uses the estimator proposed in [2, 3], but tis can be biased when the amount of smoothing is small compared to the size of each voxel (see [1] for more details and example below) |
Calculates the varaince-covariance matrix using the variance covariance matrix of partial derivatives.
A (3x3) diagonal matrix.
J. L. Marchini
[1] Stephen D. Forman et al. (1995) Improved assessment of significant activation in functional magnetic resonance imaging (fMRI): Use of a cluster-size threshold. Magnetic Resonance in Medicine, 33:636-647.
[2] Karl J. Friston et al. (1991) Comparing functional (PET) images: the assessment of significant change. J. Cereb. Blood Flow Metab. 11:690-699.
[3] Stefan J. Kiebel et al. (1999) Robust smoothness estimation in statistical parametric maps using standardized residuals from the general linear model. NeuroImage, 10:756-766.
###############
## EXAMPLE 1 ##
###############
## example that illustrates the bias of the Friston
## method when smoothing is small compared to voxel size
## NB. The presence of bias becomes clearer if the
## simulations below are run about 100 times and
## the results averaged
ksize <- 13
d <- c(64, 64, 64)
voxdim <- c(1, 1, 1)
FWHM <- 2 ## using a small value of FWHM (=2) compared to voxel size (=1)
sigma <- diag(FWHM^2, 3) / (8 * log(2))
mask <- array(1, dim = d)
num.vox <- sum(mask)
grf <- Sim.3D.GRF(d = d, voxdim = voxdim, sigma = sigma, ksize = ksize, mask = mask, type = "field")$mat
sigma
SmoothEst(grf, mask, voxdim, method = "Friston")
SmoothEst(grf, mask, voxdim, method = "Forman") ## compared to sigma
##the Forman estimator is better (on average) than the Friston estimator
###############
## EXAMPLE 2 ##
###############
## increasing the amount of smoothing decreases the bias of the Friston estimator
ksize <- 13
d <- c(64, 64, 64)
voxdim <- c(1, 1, 1)
FWHM <- 5 ## using a large value of FWHM (=5) compared to voxel size (=1)
sigma <- diag(FWHM^2, 3) / (8 * log(2))
mask <- array(1, dim = d)
num.vox <- sum(mask)
grf <- Sim.3D.GRF(d = d, voxdim = voxdim, sigma = sigma, ksize = ksize, mask = mask, type = "field")$mat
SmoothEst(grf, mask, voxdim, method = "Friston")
SmoothEst(grf, mask, voxdim, method = "Forman")
sigma