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