Description Usage Arguments Value Author(s) Examples
View source: R/antsSpatialICAfMRI.R
Perform spatial ICA on group or individual fMRI data. Preprocessing should be performed prior to calling this function (cf preprocessfMRI.R).
1 2 3 4 5 6 7 | 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
1 2 3 4 5 6 7 8 9 10 11 | 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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