fmri.sICA: Spacial ICA for fmri data

Description Usage Arguments Details Value Author(s) See Also

View source: R/fmrisICA.R

Description

Uses fastICA to perform spatial ICA on fMRI data.

Usage

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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 polzehl@wias-berlin.de

See Also

plot.fmriICA,ICAfingerprint, fastICA


neuroconductor/fmri documentation built on May 20, 2021, 4:28 p.m.