[Mne_analysis] cluster permutation connectivity matrix

[Eric Larson]

Yes that would be useful.

Eric


On Fri, Sep 22, 2017 at 10:45 AM, Chris Holdgraf <choldgraf at berkeley.edu>
wrote:

> I think we should clarify this is the docstring, add a check for the input
> and raise an error, and maybe add an example. Wdyt? I can open a pr if that
> sounds good.
>
>
> On Fri, Sep 22, 2017, 12:43 PM Alexandre Gramfort <
> alexandre.gramfort at inria.fr> wrote:
>
>> hi yvonne,
>>
>> connectivity must be a sparse matrix not a numpy array
>>
>> from scipy import sparse
>> connectivity = sparse.eye(numChan)
>>
>> HTH
>> Alex
>>
>>
>> On Wed, Sep 20, 2017 at 10:00 PM, Yvonne Fonken <yfonken at berkeley.edu>
>> wrote:
>>
>>> Hi Alex,
>>>
>>> Thank you for your reply.
>>>
>>> This is my code, with two versions of a diagonal connectivity matrix
>>> (using version 14.1 of MNE):
>>> # Implement cluster permutation
>>> numChan = Data.shape[1] # Data is shaped: Trials (115) x channels (217)
>>> x timepoints (30) numpy array
>>> #connectivity =  np.diagflat( range(0,numChan), k = 0)
>>> connectivity =  np.diagflat( np.ones((1,numChan)), k = 0) # N x N
>>> diagonal matrix with N being # of electrodes
>>> TestData = np.swapaxes(Data, 1,2) # trials x time x space (electrodes)
>>> T_obs, clusters, p_values, H0 = mne.stats.permutation_cluster_1samp_test(TestData,
>>> n_permutations = 500, tail = 0, connectivity = connectivity)
>>>
>>> Both versions of the connectivity matrix now give me the following
>>> error:
>>> 'numpy.ndarray' object has no attribute 'tocsr'
>>>
>>> Thanks,
>>> Yvonne
>>>
>>> On Tue, Sep 19, 2017 at 1:02 AM, Alexandre Gramfort <
>>> alexandre.gramfort at inria.fr> wrote:
>>>
>>>> Hi Yvonne
>>>>
>>>> I would have said that a sparse diagonal matrix should work.
>>>>
>>>> can you share the code snippet you used?
>>>>
>>>> Alex
>>>>
>>>>
>>>> On Tue, Sep 19, 2017 at 3:17 AM, Yvonne Fonken <yfonken at berkeley.edu>
>>>> wrote:
>>>> > Hi all,
>>>> >
>>>> > I am trying to apply the cluster permutation function in MNE on
>>>> intracranial
>>>> > data (electrodes by time points), either by using
>>>> > mne.stats.permutation_cluster_test, or
>>>> > mne.stats.permutation_cluster_1samp_test. In this case, I do not
>>>> want to
>>>> > cluster across electrodes, but I want to include this dimension in
>>>> order to
>>>> > correct for multiple comparisons.
>>>> >
>>>> > My question is: how do I specify to only cluster over time, and not
>>>> > electrodes? Can I do this using the connectivity matrix, and if yes,
>>>> what
>>>> > would a connectivity matrix look like when all electrodes do not have
>>>> > neighbors? I've tried using diagonal matrices already, but I still get
>>>> > clusters spanning multiple electrodes.
>>>> >
>>>> > Thanks,
>>>> > Yvonne
>>>> >
>>>> > --
>>>> > Yvonne Fonken
>>>> > PhD Candidate
>>>> > Helen Wills Neuroscience Institute
>>>> > University of California, Berkeley
>>>> >
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>>>
>>>
>>>
>>> --
>>> Yvonne Fonken
>>> Graduate student
>>> Helen Wills Neuroscience Institute
>>> University of California, Berkeley
>>>
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