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.

Author

Tustison NJ, Avants BB

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
)