antsSpatialICAfMRI: Perform spatial ICA on fMRI bold data.

Description Usage Arguments Value Author(s) Examples

View source: R/antsSpatialICAfMRI.R

Description

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

Usage

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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

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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 )

neuroconductor-devel/ANTsR documentation built on April 1, 2021, 1:02 p.m.