ANTsR (version 1.0)

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

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

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,]
# }