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

[Vadim Axel]

Are there packages that do joint transformation at once?
As far as I understand the SPM  uses separate steps.


On Tue, May 6, 2014 at 6:16 PM, Yaroslav Halchenko <debian at onerussian.com>wrote:

>
> On Mon, 05 May 2014, J.A. Etzel wrote:
>
> > On 5/5/2014 2:51 PM, Vadim Axel wrote:
> > >I personally use always normalized and I do not think that this
> > >should matter too much. I think given that normalization introduces
> > >some smoothing, it may probably even increase predictions - as Hans
> > >Op De Beeck showed that smoothing might be helpful for prediction
> > >rate.
>
> > Unfortunately, *should* matter doesn't always mean *does* matter, and
> > I'm very hesitant to draw too many conclusions from experiences with
> > smoothing: some spatial normalization algorithms are far, far
> > different than Gaussian smoothing.
>
> > That doesn't mean to never spatially normalize, but I would
> > certainly never assume that it's a neutral procedure.
>
> moreover, in case of linear "normalization" -- if you "normalize" by
> applying a joined transformation (motion correction + normalization) to
> each volume, instead of two separate steps (motion correction of entire
> 4d series and then normalization of the entire 4d at once), then those
> should be nearly identical and theoretically "MVPA" metrics should be
> the same if resolution is nearly the same as original and subjects
> did move at least a bit since then those volumes would get
> interpolated anyway to correct for the motion.
>
> --
> Yaroslav O. Halchenko, Ph.D.
> http://neuro.debian.net http://www.pymvpa.org http://www.fail2ban.org
> Research Scientist,            Psychological and Brain Sciences Dept.
> Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
> Phone: +1 (603) 646-9834                       Fax: +1 (603) 646-1419
> WWW:   http://www.linkedin.com/in/yarik
>
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