spatio_temporal_src_connectivity failure about mne-python

[larsoner]

Yeah, it looks like it's only designed to work with Ico source spaces, not Oct -- is there some way to make it work with Oct, though? (I'm not too familiar with this...)

[larsoner]

Okay, I take it back, it seems to work okay. If I add patch info and use:

connectivity = mne.spatio_temporal_src_connectivity(src, n_times=1, dist=5e-3)

I get a connectivity matrix of the same size (8196 x 8196). So the inverse operator is selecting a subset of vertices to use...?

[larsoner]

In any case, we should figure out how to deal with the mismatch between the source estimates that are generated, and the size of these connectivity matrices.

[agramfort]

can it be because some sources are too close to the inner skull and excluded from the source space?

[agramfort]

the --mintdist param in the mne_do_forward_solution

[mluessi]

+1 this would make most sense

[larsoner]

Probably. I'm not sure if there is an easy way to detect this just from the source space instance, though, so I'm not sure how we'd throw an error...

[agramfort]

can you give the lines of code that would fail as a test?

[larsoner]

Sure thing:

import mne
data_path = mne.datasets.sample.data_path('..')
fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'
inverse_operator = mne.minimum_norm.read_inverse_operator(fname_inv)
connectivity = mne.spatio_temporal_src_connectivity(inverse_operator['src'], 
                                                    n_times=1)
a = connectivity.shape[0] 
b = sum([s['nuse'] for s in inverse_operator['src']])
assert a == b

[larsoner]

Basically, a user could assume that the STC they get from using that inverse_operator could be used in spatio-temporal clustering, and it can't very easily since the inverse_operator does not use the whole source space.

[larsoner]

One solution would be to morph the STC from the subject to itself, going from the subset of vertices used by inverse_operator to the full set of source space vertices, but this seems less appealing than somehow being able to generate a connectivity matrix that is compatible with the inverse_operator in the first place.

[agramfort]

Basically, a user could assume that the STC they get from using that inverse_operator could be used in spatio-temporal clustering, and it can't very easily since the inverse_operator does not use the whole source space.

why should the spatio temporal clustering break if sources are ignored
by the inverse operator?
I agree that it can produce artifacts like a clustering not going over
the top of the gyrus but is
that a really issue? just asking.

[larsoner]

The clustering breaks because the STC files generated by that inverse_operator will not have the same number of vertices as the connectivity matrix. They will have b vertices, while the connectivity matrix will have a vertices.

[agramfort]

The clustering breaks because the STC files generated by that inverse_operator will not have the same number of vertices as the connectivity matrix. They will have b vertices, while the connectivity matrix will have a vertices.

how about restricting the connectivity to the used vertices that you
find in the source space structure?

iuse key if I remember.

[larsoner]

That could work. I can give it a shot and see how it goes.

[larsoner]

Alright @agramfort, I think this fixes the issue, and I added a test. I have it raise a warning to the user so they know it's being subsampled.