Hi all. I recently conducted a typical GLM on a set of fMRI data. The data is on the surface space (std.141 surface generated by SUMA). I am particularly interested in whether the regression coefficient of a task-related regressor is statistically significant different from zero at the group-level. I first used 3dttest++ to obtained p value at the node level, and clustered contiguous nodes that pass this first-level alpha value into cluster. I then used slow_surf_clustsim.py to estimate the cluster-level size threshold. I had used several online tutorials as my reference and believe that the steps I took are reasonable.
I am aware of the issue about the inflation of false-positive rate related to cluster simulations (e.g., Eklund, Nichols, and Knutsson, 2016 PNAS; Cox et al., 2017 Brain connectivity), and I am aware of the newer methods provided in function 3dClustSim (e.g., the -acf option) which leads to better control of false-positive rate. My question is how this issue apply to the cluster simulations conducted on the surface space, especially those implemented using SurfClust and slow_surf_clustsim.py.
- I had ensured that the targeted smoothness level are set at the same value (fwhm) for my data, and for cluster simulation. Is this good enough? That is, I should not be too worried about the mismatch between the noise spatial extent in the real data, and that of the simulated data.
- Are there any references/papers that provide more detailed descriptions about what's implemented in SurfClust and slow_surf_clustsim.py. Are they the same as the simulations conducted in 3dClustSim, but only that it's on the surface? Or there are critical differences that one should be aware of?