[Mne_analysis] Difference between mne_make_movie & python apply_inverse?

[acgt2 at cam.ac.uk]

Hi MNE-ers

 

I have just switched from using the mne_make_movie command (version 2.7.3)
to compute-the-inverse-solution-and-morph-to-average, to using the python
'apply inverse' operator and the 'morph_data_precomputed' to do the same
thing. I am pleased to find that the results are now similar (as one would
hope), but noticeably better (my experiments involve auditory data, and
results that were about a centimeter away from Heschls Gyrus, had now moved
to exactly on top of Heschls Gyrus). Obviously I'm delighted, but I just
wanted to check that the python version should be expected to give better
results - as I had assumed the two results would be the same. Should  they
be?

 

As far as I can work out, both my two pieces of codes applied the same
parameters. (although smoothing and bmin/max don't make an appearance in the
python code, the python log says '5 smoothing iterations done', so I assume
this is the default)

 

The command line version (split onto several lines for easier reading):

 

mne_make_movie

--inv /inverse-operators/3L-loose0.2-nodepth-reg-inv.fif

--meas Participant_1_EvokedAuditory.fif

--morph average

--morphgrade 

--subject Participant_1

--stc Participant_1_EvokedAuditory.stc

--smooth 5

--snr 1

--bmin -200

--bmax 0

--picknormalcomp

 

 

Python:

 

snr = 1.0

lambda2 = 1.0 / snr ** 2

 

# Make inverse solution

 

inverse_operator =
read_inverse_operator('/inverse-operators/3L-loose0.2-nodepth-reg-inv.fif')

evoked = Evoked('Participant_1_EvokedAuditory.fif')

stc_from = apply_inverse(evoked, inverse_operator, lambda2, "MNE",
pick_normal=True)

 

# First compute morph matices for participant   

subject_to = 'average'

subject_from = 'Participant_1'

vertices_to = mne.grade_to_vertices(subject_to, grade=4,
subjects_dir=subjects_dir)

morph_mat = mne.compute_morph_matrix(subject_from, subject_to,
stc_from.vertno, vertices_to, subjects_dir=subjects_dir)

 

# Morph to average

stc_morphed = mne.morph_data_precomputed(subject_from, subject_to, stc_from,
vertices_to, morph_mat)

stc_morphed.save('Participant_1_EvokedAuditory.stc')

 

 

Thanks for any help,

 

Andy

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