Description Usage Arguments Value Author(s) Examples
View source: R/antsBOLDNetworkAnalysis.R
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.
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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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | # 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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