Workbench Command is a set of command-line tools that can be used to perform simple and complex operations within Connectome Workbench.
DO TFCE ON A METRIC FILE wb_command -metric-tfce <surface> - the surface to compute on <metric-in> - the metric to run TFCE on <metric-out> - output - the output metric [-presmooth] - smooth the metric before running TFCE <kernel> - the size of the gaussian smoothing kernel in mm, as sigma by default [-fwhm] - kernel size is FWHM, not sigma [-roi] - select a region of interest to run TFCE on <roi-metric> - the area to run TFCE on, as a metric [-parameters] - set parameters for TFCE integral <E> - exponent for cluster area (default 1.0) <H> - exponent for threshold value (default 2.0) [-column] - select a single column <column> - the column number or name [-corrected-areas] - vertex areas to use instead of computing them from the surface <area-metric> - the corrected vertex areas, as a metric This command does not do any statistical analysis. Please use something like PALM if you are just trying to do statistics on your data. Threshold-free cluster enhancement is a method to increase the relative value of regions that would form clusters in a standard thresholding test. This is accomplished by evaluating the integral of: e(h, p)^E * h^H * dh at each vertex p, where h ranges from 0 to the maximum value in the data, and e(h, p) is the extent of the cluster containing vertex p at threshold h. Negative values are similarly enhanced by negating the data, running the same process, and negating the result. When using -presmooth with -corrected-areas, note that it is an approximate correction within the smoothing algorithm (the TFCE correction is exact). Doing smoothing on individual surfaces before averaging/TFCE is preferred, when possible, in order to better tie the smoothing kernel size to the original feature size. The TFCE method is explained in: Smith SM, Nichols TE., "Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference." Neuroimage. 2009 Jan 1;44(1):83-98. PMID: 18501637