antsSpatialICAfMRI: Perform spatial ICA on fMRI bold data.

In stnava/ANTsR: ANTs in R: Quantification Tools for Biomedical Images

Perform spatial ICA on fMRI bold data.

Description

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

Usage

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(s)

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
)

stnava/ANTsR documentation built on April 16, 2024, 12:17 a.m.