View source: R/antsSpatialICAfMRI.R
antsSpatialICAfMRI | R Documentation |
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
)
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. |
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.
Tustison NJ, Avants BB
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
)
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