Part of ants registration suite
TimeSCCAN
A tool for sparse statistical analysis on connectivity within a subject :
-h
Print the help menu (short version). --help
Print the help menu (long version). <VALUES>: 1 -o, --output outputImage
Output is a 2D correlation matrix. -l, --number-consecutive-labels 0
Number of consecutive labels in data -R, --minimum-region-size 1
Minimum size of a region: regions below this size are given a 0.0 connectivity value -i, --iterations 20
Number of iterations -s, --sparsity 0.10
Sparsity - a float from (0,1] indicating what fraction of the data to use -n, --n_eigenvectors 2
Number of permutations to use in scca. -r, --robustify 0
rank-based scca -l, --l1 0
use l1 ( > 0 ) or l0 ( < 0 ) penalty, also sets gradient step size e.g. -l 0.5 ( L1 ) , -l -0.5 (L0) will set 0.5 grad descent step for either penalty --ClusterThresh 1
cluster threshold on view P -e, --ridge_cca 0
Number of permutations to use in scca. --partial-scca-option PminusRQ
Choices for pscca: PQ, PminusRQ, PQminusR, PminusRQminusR --timeseriesimage-to-matrix [four_d_image.nii.gz,three_d_mask.nii.gz]
takes a timeseries (4D) image and converts it to a 2D matrix csv format as output.If the mask has multiple labels ( more the one ) then the average time series in each label will be computed and put in the csv. --labelsimage-to-matrix [three_d_mask.nii.gz]
takes a labeled (3D) image and converts it to a 2D matrix csv format as output. --network scca[time-matrix.mhd,label-matrix.mhd]
region-averaging[time-matrix.mhd,label-matrix.mhd]
Build the network connectivity matrix