[pymvpa] spatial normalization for MVPA - is it good or bad?

[J.A. Etzel]

I've done both: MVPA in subject space (no spatial normalization), and 
after normalization to an atlas. Even for searchlight analysis both are 
possible: run the searchlights in subject space and normalize for the 
group-level analysis, or normalize first.

Ideally, it wouldn't matter at which stage of analysis spatial 
normalization was done. And it often doesn't seem to matter much in 
practice, particularly when working with fairly large ROIs.

Sometimes working in subject space, with individually-defined ROIs seems 
most sensible, for example when a small, definable anatomic structure is 
of interest (such as the amygdala or hippocampus). Other times you need 
to work with spatially-normalized images, such as when doing a 
multiple-subject analysis.

All spatial normalization algorithms are imperfect, and introduce 
additional blurring, distortions, and dependencies into the images. We 
accept those distortions for mass-univariate GLM analyses; should we 
accept them for MVPA as well? I usually do, hoping that they will be 
relatively minor compared to the distortions already added to the data 
(e.g. movement correction, slice-timing, scanner drift), and in BOLD itself.

Do others have different experiences? Ever seen a dataset in which 
spatial normalization made a meaningful difference?

Jo



On 5/3/2014 9:31 AM, Vadim Axel wrote:
> Hi,
>
> If one uses functional localizer or selects anatomical ROIs individually
> he might run MVPA in a native space (without spatial normalization to
> template of the data). Have you encountered any paper that compared
> normalized vs. non-normlaized prediction rates? Or probably personal
> observation? Obviously, for search-light one needs to normalize.
>
> Thanks,
> Vadim
>
>
>
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-- 
Joset A. Etzel, Ph.D.
Research Analyst
Cognitive Control & Psychopathology Lab
Washington University in St. Louis
http://mvpa.blogspot.com/