[Mne_analysis] ValueError: connectivity must be of the correct size

Wed Oct 28 03:38:10 EDT 2015

Sorry for getting back to you so late Niki,

I've been traveling lately and this one somehow slipped through my
attention networks unnoticed.

On Thu, Oct 22, 2015 at 5:23 AM, Nicola Jastrzebski <
njasterzebski at swin.edu.au> wrote:

> Hello MNE list I hope you are well,
>
>
>
> I have been using the spatio-temporal cluster analysis of MEG sources
> found in the MNE-Python examples page, however have had no success in
> getting significant clusters for within or between groups differences for
> any of my conditions -- even with a p_threshold of .05. I have tried almost
> everything I can think of to get sig. clusters, but wont bore you with the
> details, it was getting pretty depressing.
>
>
Did you in the first place look at the uncorrected T/F-whatsoever
statistical map? Can you see something that looks like a signal? Have you
tried sensor space analyses?

>
>
> From looking at each of my subjects individually, I think it might have
> had something to do with  inter-subject variability of the all ready
> diffuse source distributions. My MEG experiments involved visual
> stimulation that spanned quite widely across the visual field
> (surround-masking), and now I think I'm paying for that through multiple
> comparisons overkill!
>
>
>
Did you consider analyzing the subejcts individually to see what kind of
effects you get there?
Did you consider a multivariate pattern analysis (e.g. decoding)? It can
help beutralize between-subject variability.
Btw. which kind of inverse solution do you use, what does your general
pipeline look like?
Did you check intermediate steps of your analysis, correctness of
coregistration, artefact rejection, etc/. Have you looked at the whitening
of the cov, e.g. evoked.plot_whit(cov) to detect scaling issues?
Sure you found all bad channels? Etc., etc.

Anyway, I felt hope anew when I recently encountered the hack Denis
> Engemann created to do the same type of cluster analysis, only within
> labels. This will reduce my multiple comparisons problem substantially yeah?
>
>
I am not sure you have multiple comparisons problem (MCP). The clustering
permutation test already reduces your MCP as you have clusterwise
hypotheses, not voxel/vertex-wise. Sounds like you have problem with SNR
and between-subjects variability. Maybe some of your data-processing is
broken. MEG is not so forgiving to early errors in your processing chain ...

>
>
> *https://gist.github.com/dengemann/ea482183be869568412c*
> <https://gist.github.com/dengemann/ea482183be869568412c>
>
> In running this analysis, I have successfully managed to read in each of
> my subjects labels, tack them to each subjects source/stc file, and then
> morph them to the average brain I made in MNE-C. While it reads in all my
> data well, things go awry however, after I run the permutations...
>
>
>
> This is the line of code that my label based cluster analysis hates:
>
>
>
> T_obs, clusters, cluster_p_values, H0 = clu =\
>                 spatio_temporal_cluster_test(X, connectivity=connectivity,
> n_jobs=2,
>                  threshold=f_threshold, n_permutations=n_permutations,
> tail=1)
>
>
> After a minute or 2 of running the permutations without hassle, it spits
> out the following complaint:
>
> 707 if connectivity is not None:
>
>
>
> --> 708 connectivity = _setup_connectivity(connectivity, n_tests, n_times)
> 709
> 710 if (exclude is not None) and not exclude.size == n_tests:
>
>
>
> /mne/stats/cluster_level.pyc in _setup_connectivity(connectivity,
> n_vertices, n_times)
>
>
>
> 520 else: # use temporal adjacency algorithm
> 521 if not round(n_vertices / float(connectivity.shape[0])) == n_times:
> --> 522 raise ValueError('connectivity must be of the correct size')
> 523 # we claim to only use upper triangular part... not true here
> 524 connectivity = (connectivity + connectivity.transpose()).tocsr()
>
>
>
>
>
> ValueError: connectivity must be of the correct size
>
>
This tells you that somewhere you have a your data and the adjacency matix
don't match, number of nodes is not the number of spatial features.

> From my limited understanding, I figure that one or more of my labels
> are broken somehow. I don't understand what is meant by 'connectivity must
> be of the correct size', or what I need to do to my subjects labels in
> order to run the permutations succesfully.
>
> if you have let's say 100 vertices and 100 time points, your connectivity
would be 100 in with the spatial connectivity trick (used in the
spatio_temporal_XXX functions).
What's the shape of your X?

>
> I really wouldn't know where to begin on how to overcome this issue, and
> would be very grateful for any clarification on what is meant by
> 'connectivity must be of the correct size', and what I need to do to fix
> this.
>
>
>
Could you share some code? It would make it easier to help you. Send it
privately if you feel more comfortable like that. It would also be good to
learn more about your protocol / data, e.g. number of conditions and
trials, etc.

Cheers,
Denis

> Thank you in advance,
>
>
>
> Nikki
> ______________________________________________________
> Nicola Jastrzebski
>
> PhD candidate
> Brain and Psychological Sciences Research Centre (BPsyC)
>
> Swinburne University of Technology - Hawthorn campus
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