COMMAND:¶

TimeSCCAN

A tool for sparse statistical analysis on connectivity within a subject :

OPTIONS:¶

-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