Unfortunately, I couldn’t find anything to estimate adjacency matrix for this source space… For example, here (mne.spatial_src_adjacency — MNE 1.3.1 documentation) you can estimate adjacency only for either sufrace or volume source space, but not their combination (mixed source space).
And as far as I understand, there’s no possibility to perform cluster permutation test without estimation of adjacency matrix for a mixed source space.
So, I wanted to ask, whether there’s currently a way to estimate adjacency and perform cluster permutation test for a Mixed source estimate in MNE-Python?
I would be very grateful for any help, as I was trying to find something for several days, but did not succeed…
Thanks for your reply!
Am I right that you mean to try to separate Mixed source estimate into Surface and Volume source estimates and perform cluster analysis for each of them individually?
If yes, I think this should be fine, but I’m just not sure how to deal with the multiple comparisons issue, as technically in this case p-values will be lower for Surface and Volume source spaces individually than if I combined them together. Do you think I can somehow additionally adjust p-values for the clusters in this case, or this is not necessary?
Ideally you would do it jointly but it means you would need to hack something e.g. by concatenating the connectivity matrices of the surface and volumes.