rsfDenoise.RdUses a target function to denoise resting bold data
rsfDenoise( boldmatrix, targety, motionparams = NA, selectionthresh = 0.1, maxnoisepreds = 1:12, debug = FALSE, polydegree = 4, crossvalidationgroups = 4, tr = 1, scalemat = F, noisepoolfun = max )
| 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 |
matrix is output
Avants BB
if (FALSE) { # 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,] }