View source: R/antsBOLDNetworkAnalysis.R
antsBOLDNetworkAnalysis | R Documentation |
An implementation of a network analysis framework for BOLD data. We expect that you mapped a label image ( e.g. aal ) to the 3D BOLD space. We build a network and graph metrics from this image and these labels based on the user-defined graph density level.
antsBOLDNetworkAnalysis(
bold = NULL,
mask = NULL,
labels = NULL,
motion,
gdens = 0.2,
threshLo = 1,
threshHi = 90,
freqLo = 0.01,
freqHi = 0.1,
winsortrim = 0.02,
throwaway
)
bold |
input 4D image |
mask |
antsImage defines areas of interest |
labels |
antsImage defines regions of interest ie a parcellation |
motion |
motion parameters - if missing, will estimate from data |
gdens |
graph density applied to network covariance matrix |
threshLo |
lower threshold for the label image |
threshHi |
upper threshold for the label image |
freqLo |
lower frequency cutoff |
freqHi |
upper frequency cutoff |
winsortrim |
winsorize the bold signal by these values eg 0.02 |
throwaway |
this number of initial bold volumes |
list of outputs
BB Avants
# none yet - this is not very well tested with recent ANTsR
## Not run:
myimg <- antsImageRead(getANTsRData("ch2"), 3)
mylab <- antsImageRead(getANTsRData("ch2a"), 3)
boldfn <- getANTsRData("pcasl")
bold <- antsImageRead(boldfn, 4)
avgbold <- getAverageOfTimeSeries(bold)
breg <- antsRegistration(avgbold, myimg, typeofTransform = c("AffineFast"))
warpedParcellation <- antsApplyTransforms(avgbold, mylab,
transformlist = breg$fwdtransforms, interpolator = "NearestNeighbor"
)
mask <- getMask(avgbold)
warpedParcellation <- maskImage(warpedParcellation, img.mask = mask)
old <- NA
labels <- warpedParcellation
gdens <- 0.2
threshLo <- 1
threshHi <- 90
freqLo <- 0.01
freqHi <- 0.1
winsortrim <- 0.02
result <- antsBOLDNetworkAnalysis(bold = bold, mask = mask, warpedParcellation)
## End(Not run)
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