So, the stc has indeed, and by mistake, non-zero values only in the
occipital pole. I created it like that:
fsave_vertices = [np.arange(10242),np.arange(10242)]
stc_PSD = mne.SourceEstimate(np.mean(PSD_zs,0), fsave_vertices, 0,
1,subject) # PSD_zs being the zscore PSD computed from stc for each epoch
stc_PSD.times = np.array(freqs) # freqs= [7.5, 15, 22.5]
When stc_PSD is morphed into fsaverage, it gives the a correct
reconstruction with smooth=1 but not with smooth=None, as shown in my
precedent emails.
Btw, the source of my mistake comes from I should have used the vertices of
the original stc to get correct sources (which are less than the usual
20484 because of the forward model):
stc_PSD = mne.SourceEstimate(np.mean(PSD_zs,0), stc.vertices, 0, 1,subject)
with:
In [15]: stc.vertices
Out[15]:
[array([ 59, 83, 127, ..., 145777, 145783, 145795]),
array([ 19, 48, 79, ..., 148294, 148319, 148336])]
When I reconstruct the stc with these vertices, the fsaverage results look
on average more or less than for the native brain (however it is more
parcelled). I'm wondering if the differences are coming from a difference
in the smooth parameter, but the function apply_inverse_epochs does not
specify it.
Thanks!
2016-11-11 21:45 GMT+01:00 Denis-Alexander Engemann <
denis.engemann at gmail.com>:
Laetitia, it looks worrisome indeed. I never saw something like this.
In the worst case we can see together next week at Neurospin. In the
meantime I'm happy to look at your code, send it to me privately if you
wish.
Denis
hi Laetitia,
this is extremely weird.
did you inspect the values inside the stc? maybe plot an histogram?
plt.hist(stc.data.ravel())
did you reconstruct only in a label?
Alex
>
> 2016-11-10 15:29 GMT+01:00 dgw <dgwakeman at gmail.com>:
>>
>> Hi Laetitia,
>>
>> I think it would be best to take a step back and see how you got these
>> results. I have a number of questions.
>>
>> 1. How did you produce the individual result? This looks very odd to
>> me (it seems to only show activity in very narrow strips, which is
>> unusual).
>
>
> The data are PSD (zscore relative to baseline) at the stimulation
frequency
> of a black and white stimulus and my aim was to create a retinotopic
map so,
> in that respect, the present map does not seem odd (and I use a small
> smoothing parameter in mne_analyze visualization, like 5 i think)
>>
>>
>> 2. Have you checked the FreeSurfer segmentation and labels to
>> fsaverage? (The data seem to be rotated somehow).
>>
>
> I'm not sure I get your point, which data seems rotated? The one for
> smooth=1 or smooth=None? See attached the same data in the native brain
> space, they fit well with the fsaverage data for smooth=1.
>
>>
>> 3. Can you provide an abbreviated version the code you used to produce
>> this affect?
>
>
> For the stc smooth=1, I run the line :
> stc_fs = mne.morph_data(subject, 'fsaverage', stc, fsave_vertices, 1,
> SUBJECTS_DIR, n_jobs=3)
>
> For the stc smooth=None, I run the line :
> stc_fs = mne.morph_data(subject, 'fsaverage', stc, fsave_vertices, None,
> SUBJECTS_DIR, n_jobs=3)
>
>
>>
>>
>> Thanks!
>> d
>>
>> >
>> > Dear MNE-users,
>> >
>> > I would like to draw your attention on the importance of the smooth
>> > parameter used to morph a source estimate from an individual's brain
to
>> > an
>> > average brain (fsaverage). By default, this parameter is set to None
and
>> > the
>> > doc precised that, in this case, the smooth parameter is
automatically
>> > defined to fill the surface with non-zeros value. Yet, it happen to
me
>> > that
>> > the produced source estimates are sometimes unreliable.
>> >
>> > I attached snapshot where you can see data (here PSD) in the visual
>> > cortex
>> > (nativeBrain). These data are correctly morphed into fsaverage brain
>> > when
>> > the smooth parameter is set to 1. However, the source estimate
created
>> > with
>> > smooth=None does not make sense.
>> >
>> > Maybe it would be better than the default parameter is set to 1 and
not
>> > to
>> > None, or at least that a warning informs which smooth parameter was
>> > finally
>> > used.
>> >
>> > Best,
>> > Laetitia
>> >
>> >
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