fmri.sICA: Spacial ICA for fmri data

In WIAS-BERLIN/fmri: Analysis of fMRI Experiments

Spacial ICA for fmri data

Description

Uses fastICA to perform spatial ICA on fMRI data.

Usage

fmri.sICA(data, mask=NULL, ncomp=20,
  alg.typ=c("parallel","deflation"), fun=c("logcosh","exp"),
  alpha=1, detrend=TRUE, degree=2, nuisance= NULL, ssmooth=TRUE,
  tsmooth=TRUE, bwt=4, bws=8, unit=c("FWHM","SD"))

Arguments

data	fMRI dataset of class ”fmridata”
mask	Brain mask, if NULL then data$mask is used.
ncomp	Number of ICA components to compute.
alg.typ	Alg. to be used in fastICA.
fun	Test functions to be used in fastICA.
alpha	Scale parameter in test functions, see fastICA.
detrend	Trend removal (polynomial)
degree	degree of polynomial trend
nuisance	Matrix of additional nuisance parameters to regress against.
ssmooth	Should spatial smoothing be used for variance reduction
tsmooth	Should temporal smoothing be be applied
bws	Bandwidth for spatial Gaussian kernel
bwt	Bandwidth for temporal Gaussian kernel
unit	Unit of bandwidth, either standard deviation (SD) of Full Width Half Maximum (FWHM).

Details

If specified polynomial trends and effects due to nuisance parameters, e.g., motion parameters, are removed. If smooth==TRUE the resulting residual series is spatially smoothed using a Gaussian kernel with specified bandwidth. ICA components are the estimated using fastICA based on data within brain mask. The components of the result are related as XKW=scomp[mask,] and X=scomp[mask,]*A.

Value

object of class ”fmriICA” list with components

scomp	4D array with ICA component images. Last index varies over components.
X	pre-processed data matrix
K	pre-processed data matrix
W	estimated un-mixing matrix
A	estimated mixing matrix
mask	Brain mask
pixdim	voxelsize
TR	Repetition Time (TR)

Author(s)

Joerg Polzehl [e-mail]

See Also

plot.fmriICA,ICAfingerprint, fastICA

WIAS-BERLIN/fmri documentation built on Sept. 18, 2023, 4:26 a.m.