fMRINormalization.RdThis function leverages structural image processing based on ANTs cortical thickness to implement standard functional image processing recommendations. The function will crop out the first k time frames, do motion correction and produce a variety of nuisance regressors. It will also do spatial and temporal filtering as well as interpolation between time frames that exceed a given framewise displacement. Finally, we return maps to the common coordinate system. Output may be trimmed in the future but currently provides access at different stages: merging versus filtering and normalized, fused BOLD images in both subject and template space.
fMRINormalization( img, steadyT = 20, fdthresh = Inf, repeatMotionEst = 2, freqLimits = c(0.01, 0.1), nCompCor = 0, polydegree = NA, structuralImage = NULL, structuralSeg = NULL, structuralNodes = NULL, templateMap = NULL, templateImage = NULL, smoothingSigmas = NA, extraRuns = list(), verbose = FALSE )
| img | input time series antsImage. |
|---|---|
| steadyT | number of seconds for steady state (used to remove initial volumes) |
| fdthresh | threshold for framewise displacement. determines what time frames should be interpolated. Set typically between 0.1 and 0.5 or Inf. |
| repeatMotionEst | number of times to repeat motion estimation. We recommend the value 2, in general. The first run improves the template estimate such that the 2nd run gives a more accurate correction. |
| freqLimits | pair defining bandwidth of interest from low to high. |
| nCompCor | number of compcor components to use in CSF plus WM mask. |
| polydegree | eg 4 for polynomial nuisance variables. |
| structuralImage | the structural antsImage of the brain. |
| structuralSeg | a 3 or greater class tissue segmentation of the structural image. |
| structuralNodes | regions of interest for network analysis, in the structural image space. |
| templateMap | antsRegistration output mapping template space (as moving) to struturalImage (fixed). |
| templateImage | template reference space to which we map the BOLD image. |
| smoothingSigmas | 4-vector defining amount of smoothing in FWHM units. |
| extraRuns | a list containing additional BOLD images (runs) to be merged with the first image. |
| verbose | enables visualization as well as commentary. |
outputs a list containing:
fusedImg: runs fused into one image and corrected if polydegree set
fusedImgFilt: runs fused into one image and filtered
seg2bold: strutural segmentation in BOLD space.
nodes2bold: strutural nodes in BOLD space.
boldToTemplate: BOLD fusedImg mapped to template space.
mapsToTemplate: invertible maps from BOLD to template space.
runID: Identifies which run over time series.
boldMat: Matrix of filtered BOLD data.
boldMask: BOLD mask.
motionCorr: Motion corrected data.
polyNuis: Polynomial nuisance variables.
timevals: Temporal variables.
nuisance: Nuisance variables.
FD: mean framewise displacement.
badtimes: time frames that are above FD threshold.
dmnAtBOLDres: Default mode network at BOLD resolution in MNI space.
seg2template: Segmentation in BOLD resolution MNI space.
networkPriors2Bold: WIP: standard network priors in BOLD space.
powersLabels: Powers nodes in BOLD space i.e. fusedImg.
powersPoints: Powers points in BOLD space i.e. fusedImg.
connMatNodes: User provided nodal system connectivity matrix.
connMatNodesPartialCorr: TUser provided nodal system partial correlation matrix.
Avants BB, Duda JT
# this example is long-running ( perhaps 10 minutes on an OSX laptop 2016 ) if (FALSE) { exrun = fMRINormalization( verbose = TRUE ) # will download ex data myid = "BBAvants" # some MRI data pre = paste("~/rsfTest/",sep='') fn = list.files( pre, full.names = TRUE, recursive = TRUE, pattern = glob2rx( paste( myid, "*rsfMRI0.nii.gz", sep='') ) ) img = antsImageRead( fn ) sfn = list.files( pre, full.names = TRUE, recursive = TRUE, pattern = glob2rx( paste( myid, "*BrainSegmentation.nii.gz", sep='') ) ) seg = antsImageRead( sfn ) t1fn = list.files( pre, full.names = TRUE, recursive = TRUE, pattern = glob2rx( paste( myid, "*BrainSegmentation0N4.nii.gz", sep='') ) ) t1 = antsImageRead( t1fn ) tt = fMRINormalization( img, repeatMotionEst=1, structuralImage=t1, structuralSeg=seg, verbose= TRUE ) # bold to template antsApplyTransforms( mni, getAverageOfTimeSeries( img ), transformlist=tt$mapsToTemplate$toTemplate, whichtoinvert=tt$mapsToTemplate$toTemplateInversion ) # template to bold antsApplyTransforms( getAverageOfTimeSeries( img ), mni, transformlist=tt$mapsToTemplate$toBold, whichtoinvert=tt$mapsToTemplate$toBoldInversion ) }