Joern Diedrichsen played around with this years ago and found four average neighbors iterations to work well with caret-style surfaces:
www.icn.ucl.ac.uk/motorcontrol/download/Caret_surface_statistics.pdf But he probably smoothed the mapped fMRI before doing any sort of t- or f-test. We typically are working with depth, which is already pretty smooth, but decided to do this small amount of smoothing on the resulting t-/f-maps (real and randomized). We recently started doing this stuff on the 164k mesh, and we use 9 iterations for that purpose (4*164k/74k). (Tim Coalson said some nonlinearities more or less cancel each other out, so it's mostly proportional with the number of vertices.) Read what Smith & Nichols say about smoothing in the TFCE paper and form your own conclusions, based on your data. The most important thing is to be consistent (smooth all subjects/sessions the same amount/way). > Hi Donna, > > I have used the TFCE method to generate some statistical meaningful results on cortical asymmetries. Thanks a lot for your help. > Currently, I have a question regarding to step of smoothing t-map with 4 iterations at 0.5 strength using average neighbors method. I found that this step indeed improved the statistical results. What''s > difference between smoothing t-map and original surface attributes such as sulcal depth or surface area? Why we need smooth t-map instead of surface attributes? And also, how the with 4 iterations at 0.5 strength using average neighbors method relate to the Gaussian kernel size or FWHM on my surface mesh with 160K vertices? Thanks. > > Regards, > > Gang > _______________________________________________ > caret-users mailing list > [email protected] > http://brainvis.wustl.edu/mailman/listinfo/caret-users > _______________________________________________ caret-users mailing list [email protected] http://brainvis.wustl.edu/mailman/listinfo/caret-users
