fmri.sICA: Spacial ICA for fmri data
In neuroconductor/fmri: Analysis of fMRI Experiments

Description Usage Arguments Details Value Author(s) See Also

Uses fastICA to perform spatial ICA on fMRI data.

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"))

`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).

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.

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)