The Princeton Full Correlation Matrix Analysis (FCMA) Toolbox is a suite of tools for correlating, analyzing, and classifying patterns of correlation in fMRI time-series data.
The key data structure in FCMA is the Full Correlation Matrix (FCM), which contains the correlations between all pairs of voxels and time-intervals. Computing the entire FCM is intractable: the FCM for two one-hour fMRI sessions requires approx. 1.5 x 1019 correlations, nearly 20 years of compute time on a 4000-core computer cluster. The FCMA Toolbox correlates a given set of fixed time-intervals (blocks of TRs you specify) which together with some custom optimizations reduces the computational load to a matter of hours. A modest-sized cluster is still required to do unbiased calculations-- ones that use a wholebrain mask initially rather than depending on a preselected ROI based on activation (such as a GLM or an ANOVA). This is due to the ~40k voxels that will undergo autocorrelation and classification.
Here are the steps for running an FCMA analysis from a user perspective, including how to create an .fcma config file, without discussing the config settings in detail. Please see the config file and reference page for that, and the FCMA Tutorial for a discussion in the context of a real analysis. As far as how to install FCMA and set up a machine or cluster of machines, system administrators should consult the cluster page.
cp [Downloads folder]/template.fcma [Analysis folder]/myparams.fcma
scp myparams.fcma user@server:This will copy the file to your home directory. Once you've logged in you can move it into your analysis directory (which might not be accessible from the login node. On the PNI cluster you must start up an interactive qrsh session first to get access to lab directories where data resides.) If you use the generator you have the option of copying the generated file contents directly into your editor window.
tail -f myparams.oJOBIDwhere JOBID is the job number associated with your job (printed to console; you can also look for the file most recently updated in your current directory.) This example is for an SGE-based cluster like the PNI's. You can check the status of the job via the scheduler command:
qstat -tIf something is horribly wrong you can delete the job (which will also kill all the related subtasks) via: