1) The smoother the data, the more likely a cluster will be found by chance. When the data are created, they start with some smoothness level. When you smooth them you add more. So you need to match the total level of smoothing when you do the simulations, otherwise your clusters will be way too significant. mri_glmfit creates a fwhm.dat file with an estimate of the total smoothness.
2) Just if they are nearest neighbors. There is no option to tinker with this.
3) Vertices in the maps created by the simulation are thresholded at the level you pass. This is what defines the cluster.
doug
Dankner, Nathan (NIH/NIMH) [F] wrote:
Hello all,
I have a couple of questions regarding the way the cluster correction simulation in freesurfer works. I've read the wiki pages on the subject, but if I've missed something and any of this is answered elsewhere please let me know. My technical knowledge of these things is not great so I am just trying to get some background. First of all, how does smoothing the data prior to running the simulation affect the results? I've run corrections on the same data smoothed with a 10mm FWHM, and also on completely unsmoothed data, and the cluster results were different. Secondly, what determines whether vertices are neighbors or not? Is there an option to tinker with this or is it predetermined? Lastly, how do the p values of individual vertices factor into the simulation? Are they taken into account when the determination of the max cluster size under the null hypothesis is performed? Thanks in advance,
Nate
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