ANTsR (version 0.3.3)

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,]
# ## End(Not run)