Functional Connectivity (Cross-Correlation Method)

If you consider two vectors containing t values representing neural activity over t time points, the degree to which the values in these vectors move together, as indexed by the correlation between these vectors, can be taken as a measure of functional connectivity. The underlying assumption is that the activity in two regions should be related if there is some sort of underlying connection between them.

This page assumes you have already obtained time series matrices using the approaches described here. If these data were obtained in FreeSurfer surface space, there will be 2 matrices (lh, rh) times 'r' runs. As I have not yet worked out the process for voxel-space (i.e., using SPM), details are sketchy, but it seems probable that there would only be a single matrix of values per run. In either case, some data cleaning may be required.

These steps will be carried out in MATLAB. The first step then would be to start up MATLAB in your terminal (the ampersand lets the program run in the background so that you can continue to use your terminal window to do other things if you wish):

cd $SUBJECTS_DIR
matlab &

Now that we're in MATLAB, any code snippets can be interpreted as MATLAB code unless otherwise noted.

The next step is to load your data matrices. Assuming the time series are plaintext files containing only structured numerical data (e.g., as produced by the gettimecourses.sh script), you can use the load function to import these data.

cd (['FS_T1_501' filesep 'bold']');
ls('*.wav.txt');

The first of the above two commands will get you into the bold/ directory for subject FS_T1_501, which is where you would expect to find the time series data. The second command will display the names of all files ending in .wav.txt, which is the convention used by the gettimecourses.sh script. Now that you have identified the relevant files, you can import them individually, assigning them meaningful variable names:

run05_left=load('FS_T1_501_lh_lausanne_005.fmcpr.sm6.self.wav.txt');
run05_right=load('FS_T1_501_rh_lausanne_005.fmcpr.sm6.self.wav.txt');
run06_left=load('FS_T1_501_lh_lausanne_006.fmcpr.sm6.self.wav.txt');
run06_right=load('FS_T1_501_rh_lausanne_006.fmcpr.sm6.self.wav.txt');

If you are using the Lausanne 2008 100-region parcellation, you should find that the lh matrices have 500 columns (regions) and the rh matrices have 502.

At this point, it makes sense to merge the left and right hemisphere data into a single matrix, but continue to keep the data for each run separate. If each row represents a time point and each column represents a region, the matrices for a given run will be time x nregions in size. We want to concatenate the matrices together along the column dimension so that the resulting matrix is time x (nregions + nregions) in size (and not (time+time) x nregions!). Horizontal concatenation (i.e., along the columnar dimension, which is what you want to do here) is easily done in MATLAB:

run_05 = [run05_left run05_right];
run_06 = [run06_left run06_right];