[Mne_analysis] Difference between mne_make_movie & python apply_inverse?

Eric Larson larson.eric.d at gmail.com
Thu Jan 24 16:38:50 EST 2013
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The morph precomputed should give equivalent results to morph_data, but it
allows the user to only compute the morph matrix once, which can be more
efficient depending on the use case.

I generally morph to fsaverage for three reasons. One, I've stared at that
brain longest, so I recognize features on it more quickly. Two, when doing
across subjects tests, morphing all subjects to a different brain
(fsaverage) avoids any potential bias (such as not smoothing as much in a
transform) that could otherwise favor the one subject everyone else's data
is morphed to. Three, fsaverage is a grade 5 (20484 vertices total) source
space and thus higher-resolution than my other subjects, which typically
have around 7000 total dipoles. Morphing to a higher resolution should help
minimize loss of spatial information in the transform.

Eric


On Thu, Jan 24, 2013 at 1:30 PM, Alexandre Gramfort <
gramfort at nmr.mgh.harvard.edu> wrote:

> hi denis,
>
> > while we are at it, is there actually one to be preferred way of
> morphing,
> > i.e. morph_precomputed vs morph_data?
>
> morph_precomputed is faster but requires more memory. It gives the same
> output
> as morph_data.
>
> > Also is there any theoretical pro /
> > con for choosing fsaverage vs inter-subject-morphing?
>
> do you mean morphing to the average brain of your study or one random
> subject?
> I am used to morph to fsaverage so I cannot really comment based on prior
> experience.
>
> Alex
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