a basic framework for network analysis that produces graph metrics

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
)

Arguments

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

Value

list of outputs

Author

BB Avants

Examples

# none yet - this is not very well tested with recent ANTsR
if (FALSE) {
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 )

}