Thank you Martin for your reply. For my shapeAnalysisMANCOVA results, I am referring to multiple comparison corrected results (with FDR fixed at 0.05).
Based on my MFSDA primary results, I looked at the different p-values (guessing that pvalue_Group Center Sex Age Education = p-values for group comparison corrected with the four covariates; pvalue_1 = interaction test (maybe as interaction test in shapeAnalysisMANCOVA) between shape and covariate #1 corrected with the 3 other covariates; etc.) and found that Sex was the covariate which interacted the lesser with shape. So, I launched MFSDA and MANCOVA analyses with only this covariate (I tried without any covariate, but MFSDA led to no result if no covariate is given; I think it is normal as long as MFSDA is called “Covariate significance testing” in SlicerSALT, implying at least one covariate should be given. However, it may be useful to other users to know it). The results (attached files) are quite different… Of course, Sex was the covariate which interacted the lesser with shape among my covariates, it doesn’t mean it interacted weakly with shape. However, it seems that MANCOVA leads to more frequent and extensive deformation clusters. I read that SPHARM-PDM seems to be quite inconsistent. Maybe, using more powerful statistical analysis like MFSDA could provide more reliable results, but it is still to be understood and MFSDA seems quite complicated.
Is there a statistical correction applied in the MFSDA? Or maybe it is not needed with this method? Having 4 different groups, it leads to 6 comparisons for each structure (8 structures in total). If there is no statistical correction in MFSDA, maybe I should at least apply a correction for these 48 comparisons. What do you think? And, if needed, how to compute it?
Finally, how to compute partial correlation between shape results and a cognitive z-score when we use MFSDA analyses? Do I have to launch Covariate significance test with the Z-score as a new covariate and look at the p-value associated with it?
Thank you very much for your help.
Wish you a pleasant day,
Quentin