fmri.sICA.RdUses 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 '' |
|---|---|
| mask | Brain mask, if |
| ncomp | Number of ICA components to compute. |
| alg.typ | Alg. to be used in |
| fun | Test functions to be used in |
| alpha | Scale parameter in test functions, see |
| 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
4D array with ICA component images. Last index varies over components.
pre-processed data matrix
pre-processed data matrix
estimated un-mixing matrix
estimated mixing matrix
Brain mask
voxelsize
Repetition Time (TR)
Joerg Polzehl polzehl@wias-berlin.de