rsfDenoise: WIP: data-driven denoising for resting state fMRI

In neuroconductor-devel/ANTsR: ANTs in R: Quantification Tools for Biomedical Images

Description

Uses a target function to denoise resting bold data

Usage

rsfDenoise(
  boldmatrix,
  targety,
  motionparams = NA,
  selectionthresh = 0.1,
  maxnoisepreds = 1:12,
  debug = FALSE,
  polydegree = 4,
  crossvalidationgroups = 4,
  tr = 1,
  scalemat = F,
  noisepoolfun = max
)

Arguments

boldmatrix	input bold matrix
targety	target to predict
motionparams	motion parameters / nuisance variables
selectionthresh	e.g. 0.1 take 10 percent worst variables for noise estimation
maxnoisepreds	integer search range e.g 1:10
debug	boolean
polydegree	eg 4 for polynomial nuisance variables
crossvalidationgroups	prior defined or integer valued
tr	bold tr
scalemat	boolean
noisepoolfun	function to help select noise pool e.g. max

Value

matrix is output

Author(s)

Avants BB

Examples

## Not run: 
# if (!exists("fn") ) fn<-getANTsRData("pcasl")
# bold <- antsImageRead( fn )
# avgbold<-getAverageOfTimeSeries(bold)
# boldmask<-getMask( avgbold )
# roimask<-antsImageRead("roi.nii.gz")
# timeselect<-10:(dim(bold)[4]-10)
# # can do this if you like its approach
# cleanfMRI <- preprocessfMRI( bold, maskImage=boldmask,
#   frequencyLowThreshold = 0.01, frequencyHighThreshold = 0.1,
#   spatialSmoothingType = "gaussian", spatialSmoothingParameters = 2,
#   residualizeMatrix=TRUE, numberOfCompCorComponents=2 )
# boldmat<-timeseries2matrix( cleanfMRI$cleanBoldImage, cleanfMRI$maskImage )
# roimat<-timeseries2matrix( cleanfMRI$cleanBoldImage, roimask )
# roimean<-rowMeans( roimat ) # svd instead?
# roimat<-matrix( roimean, ncol=1)
# dnz<-rsfDenoise( boldmat[timeselect,] ,
#   roimat[timeselect,1], motionparams=NA,
#   polydegree=1, crossvalidationgroups = 8, maxnoisepreds=c(2:4), debug=F )
# # might iterate over above to further refine noise variables
# mdl<-bigLMStats( lm( boldmat[timeselect,] ~ roimean[timeselect] +
#    dnz$polys + dnz$noiseu  ), 0.001 )
# betas<-mdl$beta.t[1,]
# sum(betas[betas > 3])
# betaimg<-antsImageClone( boldmask )
# betaimg[ boldmask == 1 ]<-betas
# antsImageWrite( betaimg, "betas2.nii.gz" )
#
# # more complex
# bold<-antsImageRead("bold.nii.gz")
# boldmask<-antsImageRead("meanboldmask.nii.gz")
# aalimg<-antsImageRead("meanboldAALmask.nii.gz")
# data("aal",package="ANTsR")
# dmnlabels<-aal$label_num[aal$isdmn>0]
# aalvec<-aalimg > 0
# whichregion<-3
# for ( i in 1:max(aalimg) )
#   {
#   if ( ! ( i %in% dmnlabels[whichregion] ) )
#     {
#     aalimg[ aalimg == as.numeric(i) ]<-0
#     }
#   }
# maskvec<-boldmask > 0 & aalimg == whichregion
# boldmask[ maskvec ]<-0
# timeselect<-10:dim(bold)[4]
# if ( ! exists("moco") ) {
#   moco<-.motion_correction(bold,moreaccurate=1)
#   moco<-as.matrix( moco$moco_params )[timeselect,3:ncol(moco$moco_params)]
# }
# boldmat<-timeseries2matrix(bold,boldmask)
# boldmat<-boldmat[timeselect,]
# aalimg[aalimg > 0 ]<-1
# dmnvec<-rowMeans(timeseries2matrix(bold,aalimg))[timeselect]
# dmnvec<-(stl(ts(dmnvec, frequency = 4),"per")$time.series)[,2]
# dmnvec2<-(stl(ts(dmnvec, frequency = 100),"per")$time.series)[,2]
# dmnvec<-ts(as.numeric(dmnvec)-as.numeric(dmnvec2))
# dmnmat<-matrix( dmnvec, ncol=1)
# dnz<-rsfDenoise( boldmat , dmnmat[,1], motionparams=moco, polydegree=4,
#                 crossvalidationgroups = 8, maxnoisepreds=1:4, debug=F )
# print(paste("Best number of noise regressors",dnz$n))
# # now recompute the matrix using the full mask
# boldmask<-antsImageRead("meanboldmask.nii.gz")
# boldmat<-timeseries2matrix(bold,boldmask)
# boldmat<-boldmat[timeselect,]
# mdl<-bigLMStats( lm( boldmat ~ dmnmat[,1] + dnz$polys + dnz$noiseu ), 0.001 )
# betas<-mdl$beta.t[1,]

## End(Not run)

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