Connectivity concordance mapping (CCM)

The program vccm computes connectivity concordance maps (CCM). The main idea is to assign a label to each voxel based on the intersubject reproducibility of its whole-brain pattern of connectivity. Specifically, we compute the correlations of time-courses of each voxel with every other voxel for each subject. Voxels whose correlation pattern is consistent across data sets receive high values. Concordance across data sets is measured using either Kendall’s W or the overall concordance measure (OCCC).

Preprocessed functional data sets serve as input to ‘vccm’. It is important that the preprocessing pipeline includes a baseline correction (detrending). The region of interest mask must be geometrically compatible with the functional data and cover the desired portions of the brain (or the entire brain). In particular, the mask must have the same spatial resolution, the same image matrix size and the same orientation as the functional data.

Example:

vccm -in func1.v func2.v -out result.v -mask mask.v -first 20 -len 80 -type kendall

This call uses computes the concordance between the connectivity profiles of the two input files. For example, the two input files might represent measurements acquired of the a subject several weeks apart to monitor recovery after stroke. Here, connectivity is computed using subsequences consisting of 80 time points starting at time point 20 for each voxel covered by the region of interest mask.

Parameters of ‘vccm’

-help

Prints usage information.

-in

Input file.

-out

Output file.

-mask

Region of interest mask.

-minval

Signal threshold. Default: 0

-first

First timestep to use. Default: 0

-length

Length of time series to use, ‘0’ to use full length. Default: 0

-type

Concordance metric [ kendall | occc ]. Default: kendall

Reference: Lohmann et al (2012), “Connectivity Concordance Mapping: A New Tool for Model-Free Analysis of fMRI Data of the Human Brain.” Frontiers in Systems Neuroscience. 2012;6:13. doi:10.3389/fnsys.2012.00013.