I would like to compare/characterize connectivity maps (computed with simple seed-based correlation analyses) from 3 different ROIs. These connectivity maps have the particularity of being very extensive (almost all of the GM), which makes the comparison difficult.
I am a bit lost on how analyze these data?
In my mind, I would like to remove the ‘noise’ in these connectivity maps to extract the specificity of each of these maps. For example, by using the ‘mean connectivity’ of these 3 ROIs merge together as covariates, or by including it in a GLM? But I do not know which function or method would be the most appropriate …
By 3 ROIs I mean 3 separate seeds. To be more precise these ROIs are 3 different parts of a subcortical nucleus. As this nucleus is massively connected with the rest of the brain, I have some difficulties to compare the 3 connectivity maps.
Is it possible that you could define a list of regions based on previous studies or atlas? That way you could solve the specificity issue with an approach discussed here (read the experiment data section only): https://www.biorxiv.org/content/early/2018/02/20/238998
I could not base the analysis on a previous study or atlas
The regions don't have to be all directly related to your current study. For example, you could define 100 potential regions based on an atlas, and it would be fine to have only a small proportion of those 100 regions that might be related to the current data.
Have you performed group analysis with 3dANOVA2 -type 3 or even pairwise comparisons between any two seeds with 3dttest++?
Actually, my dataset comes from HCP resting-state database.
Yes I tried pairwise comparisons with 3dtest++. And yes, It was the best solution that I found from now, but I was looking for a more elegant way.
The
National Institute of Mental Health (NIMH) is part of the National Institutes of
Health (NIH), a component of the U.S. Department of Health and Human
Services.