Hello all,
I am interested in using the mri_glmfit simulation to control for multiple comparisons in data I have run on the surface in AFNI. Before doing this, I have a few questions:
1. What does the simulation with the mc-z flag do, exactly? It claims to be comparable to AFNI's AlphaSim, but it takes a maximum cluster area for each iteration, which is not exactly what AlphaSim does. Here is my guess:
Given a surface, a given smoothness of the data, and a given per-vertex threshold, for each iteration the simulation populates that surface with random data taken from a normal distribution, thresholds the data, and applies the smoothness of the actual data (supplied as an input parameter). It then computes the maximum cluster size in area for that "image". Doing this n iterations gives a distribution of maximum cluster sizes that occur for random data of a given smoothness, and taking cluster sizes above a certain percentile rank controls for the FWE at a level equal to that percentile rank (e.g., 95th% controls for FWE = .05). AlphaSim does something similar, although instead of taking maximum cluster sizes at each iteration it computes all given cluster sizes. AlphaSim also allows for different cluster connectivity radius, but it seems Freesurfer computes only for neighboring vertices. All in all, if this is correct, it seems like a good implementation.
2. It is my understanding that one could bypass running the glm in Freesurfer and only compute the simulation, as the simulation only needs information about the surface, and the smoothness of the data (which are supplied by the user). To do so, you have to "fake out" Freesurfer to bypass glm, but that turns out to be pretty painless.
3. In a future distribution, is it possible to modify this procedure to also output maximum cluster sizes in terms of number of nodes, rather than area?
Can you please let me know if I am mistaken in any of these assumptions? Thanks in advance.
Anthony