Perform spatial ICA on fMRI bold data.

Perform spatial ICA on group or individual fMRI data. Preprocessing should be performed prior to calling this function (cf preprocessfMRI.R).

antsSpatialICAfMRI(
  boldImages,
  maskImage = NULL,
  numberOfICAComponents = 20,
  normalizeComponentImages = TRUE,
  verbose = FALSE
)

Arguments

boldImages	a list of 4-D ANTs image fMRI data.
maskImage	A 3-D ANTs image defining the region of interest. This must be specified.
numberOfICAComponents	Number of estimated observers (components).
normalizeComponentImages	Boolean to specify whether each component vector element is normalized to its z-score.
verbose	boolean setting verbosity level.

Value

Output list includes standard ICA matrices from the fastICA algorithm:

X = pre-processed data matrix

K = pre-whitening matrix that projects data onto the first n.comp principal components

W = estimated un-mixing matrix (see definition in details)

A = estimated mixing matrix

S = estimated source matrix

and the component images.

Examples

set.seed( 2017 )
boldImages <- list()
n=16
nvox <- n*n*n*12
dims <- c(n,n,n,12)
boldImages[[1]] <- makeImage( dims , rnorm( nvox )+500 )
boldImages[[2]] <- makeImage( dims , rnorm( nvox )+500 )
boldImages[[3]] <- makeImage( dims , rnorm( nvox )+500 )
maskImage = getAverageOfTimeSeries( boldImages[[1]] ) * 0 + 1
icaResults <- antsSpatialICAfMRI( boldImages, maskImage,
  numberOfICAComponents = 2 )
#> [1] "Need fastICA package"