Eigenanatomy with deflation

Simplified, low-parameter eigenanatomy implemented with deflation. The algorithm is able to automatically select hidden sparseness parameters, given the key parameter nvecs. The user should select the cthresh and smoother regularization parameters for a given application and also based on observing algorithm behavior when verbose=TRUE.

eanatDef(
  inmat,
  nvecs = 0,
  mask = NULL,
  smoother = 0,
  cthresh = 0,
  its = 5,
  eps = 0.1,
  positivity = FALSE,
  priors = NA,
  priorWeight = 0,
  sparEpsilon = 0.0001,
  whiten = FALSE,
  verbose = FALSE
)

Arguments

inmat	input matrix
nvecs	number of eigenanatomy vectors to compute. see eanatSelect for a method to compute an optimal nvecs value.
mask	input mask, must match matrix
smoother	regularization parameter, typically 0 or 0.5, in voxels
cthresh	remove isolated voxel islands of size below this value
its	number of iterations
eps	gradient descent parameter
positivity	return unsigned eigenanatomy vectors
priors	external initialization matrix.
priorWeight	weight on priors in range 0 to 1.
sparEpsilon	threshold that controls initial sparseness estimate
whiten	use ICA style whitening.
verbose	controls whether computation is silent or not.

Value

matrix is output, analogous to svd(mat,nu=0,nv=nvecs)

References

Kandel, B. M.; Wang, D. J. J.; Gee, J. C. & Avants, B. B. Eigenanatomy: sparse dimensionality reduction for multi-modal medical image analysis. Methods, 2015, 73, 43-53. PS Dhillon, DA Wolk, SR Das, LH Ungar, JC Gee, BB Avants Subject-specific functional parcellation via Prior Based Eigenanatomy NeuroImage, 2014, 99, 14-27.

See also

eanatSelect https://github.com/stnava/blindSourceSeparationInANTsR

Author

Avants BB, Tustison NJ

Examples

if (FALSE) {
mat <- matrix(rnorm(2000),ncol=50)
nv <- eanatSelect( mat, selectorScale = 1.2 )
esol <- eanatDef( mat, nvecs=nv )
es2 <- sparseDecom( mat, nvecs = nv )
print( paste( "selected", nrow(esol),'pseudo-eigenvectors') )
print( mean( abs( cor( mat %*% t(esol)) ) ) ) # what we use to select nvecs
networkPriors = getANTsRData("fmrinetworks")
ilist = networkPriors$images
mni = antsImageRead( getANTsRData("mni") )
mnireg = antsRegistration( meanbold*mask, mni, typeofTransform = 'Affine')
for ( i in 1:length(ilist) )
  ilist[[i]] = antsApplyTransforms( meanbold,ilist[[i]],mnireg$fwdtransform )
pr = imageListToMatrix( ilist, cortMask )
esol <- eanatDef( boldMat,
  nvecs = length(ilist), cortMask, verbose=FALSE,
  cthresh = 25, smoother = 0, positivity = TRUE, its=10, priors=pr,
  priorWeight=0.15, eps=0.1 )
}