[Mne_analysis] Spatiotemporal cluster permutation on evoked data in a within subjects design

Wed Jan 17 16:29:40 EST 2018

>
> In fact *rms_gard* is the same as *_merge_grad_data* function imported
> from *mne.channels.layout* (*from mne.channels.layout import
> _merge_grad_data as rms_grad*). It merges the data of each pair of grads
> using the RMS and returns an array of shape (n_grads/2, n_times) so for the
> 204 grads I have 102 arrays and the X_temp has this shape (n_subjects,
> n_times, n_grads/2).
>

Ok, that makes sense.

>
> Regarding your suggesting of subtracting the conditions first and then
> using mne.stats.spatio_temporal_cluster_1samp_test instead, could you
> please try to explain a bit more the logic behind using this method instead
> of the way I'm currently using now?
>

IIRC the 1 sample cluster test permutes the sign (of the subtraction
between the two conditions) across subjects. The test thus assesses the
probability that your distribution is distinct from the distribution based
on permuted signs.

Hope that helps,

Jean-Rémi

>
> with best regards
> Rasha
>
>
>
>
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> On 17 January 2018 at 21:07, JR KING <jeanremi.king at gmail.com> wrote:
>
>> Hi Rasha,
>>
>> 1. I'm not sure I follow your script. We don't have an rms_grad function,
>> but I guess you're taking the root mean square across gradiometers. If so,
>> the shape of X_tmp should be 2D (17 x n_times) not 3D (17 * 101 * 102 in
>> your script), shouldn't it?
>>
>> 2. I think the correct way to do the within subject analysis you mention
>> is to use mne.stats.spatio_temporal_cluster_1samp_test: i.e. first you
>> subtract the two condition within subject, second you run the permutations
>> spatio_temporal_cluster_1samp_test(X[:17]-X[17:])
>>
>> Hope that helps,
>>
>> Jean-Rémi
>>
>> On 17 January 2018 at 11:34, Rasha Hyder <rasha.hyder12 at gmail.com> wrote:
>>
>>> Hi all,
>>>
>>> I'm trying to run spatiotemporal cluster permutation on evoked responses
>>> in a within subjects design where each subject is observed in two
>>> conditions: cond1 and cond2:
>>>
>>> to do so I'm using the following:
>>>
>>> # for 17 subjects and 100 ms time window
>>>
>>> conditions= [cond1, cond2]
>>> for j, cond in enumerate(conditions):
>>>     X_tmp = np.zeros((17, 101, 102))
>>>
>>>     for i, subject in enumerate(subjects):
>>>         evoked = mne.read_evokeds(fname)
>>>         grads= evoked.pick_types(meg="grad")
>>>         rms = rms_grad(grads.data)
>>>         X_tmp[i, :] = np.transpose(rms)
>>>
>>>     if j == 0:
>>>         X = X_tmp
>>>     else:
>>>         X = np.vstack((X, X_tmp))
>>>
>>> connectivity, ch_names = find_ch_connectivity(evoked.info,
>>> ch_type='mag')
>>>
>>> T_obs, clusters, p_values, _ = spatio_temporal_cluster_test([X[:17],
>>> X[17:]],
>>>                                                     n_permutations=5000,
>>>                                                     tail=0,
>>>                                                     n_jobs=2,
>>>
>>> connectivity=connectivity)
>>>
>>> After around 20 mints of running the script, the returned p_values look
>>> like this:
>>>
>>> p_values
>>> Out[68]:
>>> array([ 0.9476,  1.    ,  1.    ,  0.8854,  1.    ,  1.    ,  0.9966,
>>>         1.    ,  1.    ,  1.    ,  1.    ,  1.    ,  0.9974,  1.    ,
>>>         1.    ,  1.    ,  1.    ,  1.    ])
>>>
>>> My question is, given the input data: [X[:17], X[17:]] where:
>>> X[17:] contains the data observed in cond1 from all 17 subjects and
>>> similarly X[17:] is the data observed in cond2.
>>>
>>> are the permutations in this case done between cond1 and cond2 within
>>> the same subject (as should be) or randomly between subjects?
>>>
>>> Many thanks in advance
>>>
>>> Rasha
>>>
>>>
>>>
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